essay about depression and stress

Change Password

Your password must have 6 characters or more:.

• a lower case character, 
• an upper case character, 
• a special character 

Password Changed Successfully

Your password has been changed

Create your account

Forget yout password.

Enter your email address below and we will send you the reset instructions

If the address matches an existing account you will receive an email with instructions to reset your password

Forgot your Username?

Enter your email address below and we will send you your username

If the address matches an existing account you will receive an email with instructions to retrieve your username

• June 01, 2024 | VOL. 181, NO. 6 CURRENT ISSUE pp.461-564
• May 01, 2024 | VOL. 181, NO. 5 pp.347-460
• April 01, 2024 | VOL. 181, NO. 4 pp.255-346
• March 01, 2024 | VOL. 181, NO. 3 pp.171-254
• February 01, 2024 | VOL. 181, NO. 2 pp.83-170
• January 01, 2024 | VOL. 181, NO. 1 pp.1-82

The American Psychiatric Association (APA) has updated its Privacy Policy and Terms of Use , including with new information specifically addressed to individuals in the European Economic Area. As described in the Privacy Policy and Terms of Use, this website utilizes cookies, including for the purpose of offering an optimal online experience and services tailored to your preferences.

Please read the entire Privacy Policy and Terms of Use. By closing this message, browsing this website, continuing the navigation, or otherwise continuing to use the APA's websites, you confirm that you understand and accept the terms of the Privacy Policy and Terms of Use, including the utilization of cookies.

The Critical Relationship Between Anxiety and Depression

• Ned H. Kalin , M.D.

Search for more papers by this author

Anxiety and depressive disorders are among the most common psychiatric illnesses; they are highly comorbid with each other, and together they are considered to belong to the broader category of internalizing disorders. Based on statistics from the Substance Abuse and Mental Health Services Administration, the 12-month prevalence of major depressive disorder in 2017 was estimated to be 7.1% for adults and 13.3% for adolescents ( 1 ). Data for anxiety disorders are less current, but in 2001–2003, their 12-month prevalence was estimated to be 19.1% in adults, and 2001–2004 data estimated that the lifetime prevalence in adolescents was 31.9% ( 2 , 3 ). Both anxiety and depressive disorders are more prevalent in women, with an approximate 2:1 ratio in women compared with men during women’s reproductive years ( 1 , 2 ).

Across all psychiatric disorders, comorbidity is the rule ( 4 ), which is definitely the case for anxiety and depressive disorders, as well as their symptoms. With respect to major depression, a worldwide survey reported that 45.7% of individuals with lifetime major depressive disorder had a lifetime history of one or more anxiety disorder ( 5 ). These disorders also commonly coexist during the same time frame, as 41.6% of individuals with 12-month major depression also had one or more anxiety disorder over the same 12-month period. From the perspective of anxiety disorders, the lifetime comorbidity with depression is estimated to range from 20% to 70% for patients with social anxiety disorder ( 6 ), 50% for patients with panic disorder ( 6 ), 48% for patients with posttraumatic stress disorder (PTSD) ( 7 ), and 43% for patients with generalized anxiety disorder ( 8 ). Data from the well-known Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study demonstrate comorbidity at the symptom level, as 53% of the patients with major depression had significant anxiety and were considered to have an anxious depression ( 9 ).

Anxiety and depressive disorders are moderately heritable (approximately 40%), and evidence suggests shared genetic risk across the internalizing disorders ( 10 ). Among internalizing disorders, the highest level of shared genetic risk appears to be between major depressive disorder and generalized anxiety disorder. Neuroticism is a personality trait or temperamental characteristic that is associated with the development of both anxiety and depression, and the genetic risk for developing neuroticism also appears to be shared with that of the internalizing disorders ( 11 ). Common nongenetic risk factors associated with the development of anxiety and depression include earlier life adversity, such as trauma or neglect, as well as parenting style and current stress exposure. At the level of neural circuits, alterations in prefrontal-limbic pathways that mediate emotion regulatory processes are common to anxiety and depressive disorders ( 12 , 13 ). These findings are consistent with meta-analyses that reveal shared structural and functional brain alterations across various psychiatric illnesses, including anxiety and major depression, in circuits involving emotion regulation ( 13 ), executive function ( 14 ), and cognitive control ( 15 ).

Anxiety disorders and major depression occur during development, with anxiety disorders commonly beginning during preadolescence and early adolescence and major depression tending to emerge during adolescence and early to mid-adulthood ( 16 – 18 ). In relation to the evolution of their comorbidity, studies demonstrate that anxiety disorders generally precede the presentation of major depressive disorder ( 17 ). A European community-based study revealed, beginning at age 15, the developmental relation between comorbid anxiety and major depression by specifically focusing on social phobia (based on DSM-IV criteria) and then asking the question regarding concurrent major depressive disorder ( 18 ). The findings revealed a 19% concurrent comorbidity between these disorders, and in 65% of the cases, social phobia preceded major depressive disorder by at least 2 years. In addition, initial presentation with social phobia was associated with a 5.7-fold increased risk of developing major depressive disorder. These associations between anxiety and depression can be traced back even earlier in life. For example, childhood behavioral inhibition in response to novelty or strangers, or an extreme anxious temperament, is associated with a three- to fourfold increase in the likelihood of developing social anxiety disorder, which in turn is associated with an increased risk to develop major depressive disorder and substance abuse ( 19 ).

It is important to emphasize that the presence of comor‐bid anxiety symptoms and disorders matters in relation to treatment. Across psychiatric disorders, the presence of significant anxiety symptoms generally predicts worse outcomes, and this has been well demonstrated for depression. In the STAR*D study, patients with anxious major depressive disorder were more likely to be severely depressed and to have more suicidal ideation ( 9 ). This is consistent with the study by Kessler and colleagues ( 5 ), in which patients with anxious major depressive disorder, compared with patients with nonanxious major depressive disorder, were found to have more severe role impairment and more suicidal ideation. Data from level 1 of the STAR*D study (citalopram treatment) nicely illustrate the impact of comorbid anxiety symptoms on treatment. Compared with patients with nonanxious major depressive disorder, those 53% of patients with an anxious depression were less likely to remit and also had a greater side effect burden ( 20 ). Other data examining patients with major depressive disorder and comorbid anxiety disorders support the greater difficulty and challenge in treating patients with these comorbidities ( 21 ).

This issue of the Journal presents new findings relevant to the issues discussed above in relation to understanding and treating anxiety and depressive disorders. Drs. Conor Liston and Timothy Spellman, from Weill Cornell Medicine, provide an overview for this issue ( 22 ) that is focused on understanding mechanisms at the neural circuit level that underlie the pathophysiology of depression. Their piece nicely integrates human neuroimaging studies with complementary data from animal models that allow for the manipulation of selective circuits to test hypotheses generated from the human data. Also included in this issue is a review of the data addressing the reemergence of the use of psychedelic drugs in psychiatry, particularly for the treatment of depression, anxiety, and PTSD ( 23 ). This timely piece, authored by Dr. Collin Reiff along with a subgroup from the APA Council of Research, provides the current state of evidence supporting the further exploration of these interventions. Dr. Alan Schatzberg, from Stanford University, contributes an editorial in which he comments on where the field is in relation to clinical trials with psychedelics and to some of the difficulties, such as adequate blinding, in reliably studying the efficacy of these drugs ( 24 ).

In an article by McTeague et al. ( 25 ), the authors use meta-analytic strategies to understand the neural alterations that are related to aberrant emotion processing that are shared across psychiatric disorders. Findings support alterations in the salience, reward, and lateral orbital nonreward networks as common across disorders, including anxiety and depressive disorders. These findings add to the growing body of work that supports the concept that there are common underlying factors across all types of psychopathology that include internalizing, externalizing, and thought disorder dimensions ( 26 ). Dr. Deanna Barch, from Washington University in St. Louis, writes an editorial commenting on these findings and, importantly, discusses criteria that should be met when we consider whether the findings are actually transdiagnostic ( 27 ).

Another article, from Gray and colleagues ( 28 ), addresses whether there is a convergence of findings, specifically in major depression, when examining data from different structural and functional neuroimaging modalities. The authors report that, consistent with what we know about regions involved in emotion processing, the subgenual anterior cingulate cortex, hippocampus, and amygdala were among the regions that showed convergence across multimodal imaging modalities.

In relation to treatment and building on our understanding of neural circuit alterations, Siddiqi et al. ( 29 ) present data suggesting that transcranial magnetic stimulation (TMS) targeting can be linked to symptom-specific treatments. Their findings identify different TMS targets in the left dorsolateral prefrontal cortex that modulate different downstream networks. The modulation of these different networks appears to be associated with a reduction in different types of symptoms. In an editorial, Drs. Sean Nestor and Daniel Blumberger, from the University of Toronto ( 30 ), comment on the novel approach used in this study to link the TMS-related engagement of circuits with symptom improvement. They also provide a perspective on how we can view these and other circuit-based findings in relation to conceptualizing personalized treatment approaches.

Kendler et al. ( 31 ), in this issue, contribute an article that demonstrates the important role of the rearing environment in the risk to develop major depression. Using a unique design from a Swedish sample, the analytic strategy involves comparing outcomes from high-risk full sibships and high-risk half sibships where at least one of the siblings was home reared and one was adopted out of the home. The findings support the importance of the quality of the rearing environment as well as the presence of parental depression in mitigating or enhancing the likelihood of developing major depression. In an accompanying editorial ( 32 ), Dr. Myrna Weissman, from Columbia University, reviews the methods and findings of the Kendler et al. article and also emphasizes the critical significance of the early nurturing environment in relation to general health.

This issue concludes with an intriguing article on anxiety disorders, by Gold and colleagues ( 33 ), that demonstrates neural alterations during extinction recall that differ in children relative to adults. With increasing age, and in relation to fear and safety cues, nonanxious adults demonstrated greater connectivity between the amygdala and the ventromedial prefrontal cortex compared with anxious adults, as the cues were being perceived as safer. In contrast, neural differences between anxious and nonanxious youths were more robust when rating the memory of faces that were associated with threat. Specifically, these differences were observed in the activation of the inferior temporal cortex. In their editorial ( 34 ), Dr. Dylan Gee and Sahana Kribakaran, from Yale University, emphasize the importance of developmental work in relation to understanding anxiety disorders, place these findings into the context of other work, and suggest the possibility that these and other data point to neuroscientifically informed age-specific interventions.

Taken together, the papers in this issue of the Journal present new findings that shed light onto alterations in neural function that underlie major depressive disorder and anxiety disorders. It is important to remember that these disorders are highly comorbid and that their symptoms are frequently not separable. The papers in this issue also provide a developmental perspective emphasizing the importance of early rearing in the risk to develop depression and age-related findings important for understanding threat processing in patients with anxiety disorders. From a treatment perspective, the papers introduce data supporting more selective prefrontal cortical TMS targeting in relation to different symptoms, address the potential and drawbacks for considering the future use of psychedelics in our treatments, and present new ideas supporting age-specific interventions for youths and adults with anxiety disorders.

Disclosures of Editors’ financial relationships appear in the April 2020 issue of the Journal .

1 Substance Abuse and Mental Health Services Administration (SAMHSA): Key substance use and mental health indicators in the United States: results from the 2017 National Survey on Drug Use and Health (HHS Publication No. SMA 18-5068, NSDUH Series H-53). Rockville, Md, Center for Behavioral Health Statistics and Quality, SAMHSA, 2018. https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHFFR2017/NSDUHFFR2017.htm Google Scholar

2 Kessler RC, Chiu WT, Demler O, et al. : Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication . Arch Gen Psychiatry 2005 ; 62:617–627, correction, 62:709 Crossref , Medline ,  Google Scholar

3 Merikangas KR, He JP, Burstein M, et al. : Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication–Adolescent Supplement (NCS-A) . J Am Acad Child Adolesc Psychiatry 2010 ; 49:980–989 Crossref , Medline ,  Google Scholar

4 Kessler RC, McGonagle KA, Zhao S, et al. : Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey . Arch Gen Psychiatry 1994 ; 51:8–19 Crossref , Medline ,  Google Scholar

5 Kessler RC, Sampson NA, Berglund P, et al. : Anxious and non-anxious major depressive disorder in the World Health Organization World Mental Health Surveys . Epidemiol Psychiatr Sci 2015 ; 24:210–226 Crossref , Medline ,  Google Scholar

6 Dunner DL : Management of anxiety disorders: the added challenge of comorbidity . Depress Anxiety 2001 ; 13:57–71 Crossref , Medline ,  Google Scholar

7 Kessler RC, Sonnega A, Bromet E, et al. : Posttraumatic stress disorder in the National Comorbidity Survey . Arch Gen Psychiatry 1995 ; 52:1048–1060 Crossref , Medline ,  Google Scholar

8 Brawman-Mintzer O, Lydiard RB, Emmanuel N, et al. : Psychiatric comorbidity in patients with generalized anxiety disorder . Am J Psychiatry 1993 ; 150:1216–1218 Link ,  Google Scholar

9 Fava M, Alpert JE, Carmin CN, et al. : Clinical correlates and symptom patterns of anxious depression among patients with major depressive disorder in STAR*D . Psychol Med 2004 ; 34:1299–1308 Crossref , Medline ,  Google Scholar

10 Hettema JM : What is the genetic relationship between anxiety and depression? Am J Med Genet C Semin Med Genet 2008 ; 148C:140–146 Crossref , Medline ,  Google Scholar

11 Hettema JM, Neale MC, Myers JM, et al. : A population-based twin study of the relationship between neuroticism and internalizing disorders . Am J Psychiatry 2006 ; 163:857–864 Link ,  Google Scholar

12 Kovner R, Oler JA, Kalin NH : Cortico-limbic interactions mediate adaptive and maladaptive responses relevant to psychopathology . Am J Psychiatry 2019 ; 176:987–999 Link ,  Google Scholar

13 Etkin A, Schatzberg AF : Common abnormalities and disorder-specific compensation during implicit regulation of emotional processing in generalized anxiety and major depressive disorders . Am J Psychiatry 2011 ; 168:968–978 Link ,  Google Scholar

14 Goodkind M, Eickhoff SB, Oathes DJ, et al. : Identification of a common neurobiological substrate for mental illness . JAMA Psychiatry 2015 ; 72:305–315 Crossref , Medline ,  Google Scholar

15 McTeague LM, Huemer J, Carreon DM, et al. : Identification of common neural circuit disruptions in cognitive control across psychiatric disorders . Am J Psychiatry 2017 ; 174:676–685 Link ,  Google Scholar

16 Beesdo K, Knappe S, Pine DS : Anxiety and anxiety disorders in children and adolescents: developmental issues and implications for DSM-V . Psychiatr Clin North Am 2009 ; 32:483–524 Crossref , Medline ,  Google Scholar

17 Kessler RC, Wang PS : The descriptive epidemiology of commonly occurring mental disorders in the United States . Annu Rev Public Health 2008 ; 29:115–129 Crossref , Medline ,  Google Scholar

18 Ohayon MM, Schatzberg AF : Social phobia and depression: prevalence and comorbidity . J Psychosom Res 2010 ; 68:235–243 Crossref , Medline ,  Google Scholar

19 Clauss JA, Blackford JU : Behavioral inhibition and risk for developing social anxiety disorder: a meta-analytic study . J Am Acad Child Adolesc Psychiatry 2012 ; 51:1066–1075 Crossref , Medline ,  Google Scholar

20 Fava M, Rush AJ, Alpert JE, et al. : Difference in treatment outcome in outpatients with anxious versus nonanxious depression: a STAR*D report . Am J Psychiatry 2008 ; 165:342–351 Link ,  Google Scholar

21 Dold M, Bartova L, Souery D, et al. : Clinical characteristics and treatment outcomes of patients with major depressive disorder and comorbid anxiety disorders: results from a European multicenter study . J Psychiatr Res 2017 ; 91:1–13 Crossref , Medline ,  Google Scholar

22 Spellman T, Liston C : Toward circuit mechanisms of pathophysiology in depression . Am J Psychiatry 2020 ; 177:381–390 Link ,  Google Scholar

23 Reiff CM, Richman EE, Nemeroff CB, et al. : Psychedelics and psychedelic-assisted psychotherapy . Am J Psychiatry 2020 ; 177:391–410 Link ,  Google Scholar

24 Schatzberg AF : Some comments on psychedelic research (editorial). Am J Psychiatry 2020 ; 177:368–369 Link ,  Google Scholar

25 McTeague LM, Rosenberg BM, Lopez JW, et al. : Identification of common neural circuit disruptions in emotional processing across psychiatric disorders . Am J Psychiatry 2020 ; 177:411–421 Link ,  Google Scholar

26 Caspi A, Moffitt TE : All for one and one for all: mental disorders in one dimension . Am J Psychiatry 2018 ; 175:831–844 Link ,  Google Scholar

27 Barch DM : What does it mean to be transdiagnostic and how would we know? (editorial). Am J Psychiatry 2020 ; 177:370–372 Abstract ,  Google Scholar

28 Gray JP, Müller VI, Eickhoff SB, et al. : Multimodal abnormalities of brain structure and function in major depressive disorder: a meta-analysis of neuroimaging studies . Am J Psychiatry 2020 ; 177:422–434 Link ,  Google Scholar

29 Siddiqi SH, Taylor SF, Cooke D, et al. : Distinct symptom-specific treatment targets for circuit-based neuromodulation . Am J Psychiatry 2020 ; 177:435–446 Link ,  Google Scholar

30 Nestor SM, Blumberger DM : Mapping symptom clusters to circuits: toward personalizing TMS targets to improve treatment outcomes in depression (editorial). Am J Psychiatry 2020 ; 177:373–375 Abstract ,  Google Scholar

31 Kendler KS, Ohlsson H, Sundquist J, et al. : The rearing environment and risk for major depression: a Swedish national high-risk home-reared and adopted-away co-sibling control study . Am J Psychiatry 2020 ; 177:447–453 Abstract ,  Google Scholar

32 Weissman MM : Is depression nature or nurture? Yes (editorial). Am J Psychiatry 2020 ; 177:376–377 Abstract ,  Google Scholar

33 Gold AL, Abend R, Britton JC, et al. : Age differences in the neural correlates of anxiety disorders: an fMRI study of response to learned threat . Am J Psychiatry 2020 ; 177:454–463 Link ,  Google Scholar

34 Gee DG, Kribakaran S : Developmental differences in neural responding to threat and safety: implications for treating youths with anxiety (editorial). Am J Psychiatry 2020 ; 177:378–380 Abstract ,  Google Scholar

• Cited by None

• Neuroanatomy
• Neurochemistry
• Neuroendocrinology
• Other Research Areas

• Share full article

Advertisement

Supported by

Personal Health

The Devastating Ways Depression and Anxiety Impact the Body

Mind and body form a two-way street.

By Jane E. Brody

It’s no surprise that when a person gets a diagnosis of heart disease, cancer or some other life-limiting or life-threatening physical ailment, they become anxious or depressed. But the reverse can also be true: Undue anxiety or depression can foster the development of a serious physical disease, and even impede the ability to withstand or recover from one. The potential consequences are particularly timely, as the ongoing stress and disruptions of the pandemic continue to take a toll on mental health .

The human organism does not recognize the medical profession’s artificial separation of mental and physical ills. Rather, mind and body form a two-way street. What happens inside a person’s head can have damaging effects throughout the body, as well as the other way around. An untreated mental illness can significantly increase the risk of becoming physically ill, and physical disorders may result in behaviors that make mental conditions worse.

In studies that tracked how patients with breast cancer fared, for example, Dr. David Spiegel and his colleagues at Stanford University School of Medicine showed decades ago that women whose depression was easing lived longer than those whose depression was getting worse. His research and other studies have clearly shown that “the brain is intimately connected to the body and the body to the brain,” Dr. Spiegel said in an interview. “The body tends to react to mental stress as if it was a physical stress.”

Despite such evidence, he and other experts say, chronic emotional distress is too often overlooked by doctors. Commonly, a physician will prescribe a therapy for physical ailments like heart disease or diabetes, only to wonder why some patients get worse instead of better.

Many people are reluctant to seek treatment for emotional ills. Some people with anxiety or depression may fear being stigmatized, even if they recognize they have a serious psychological problem. Many attempt to self-treat their emotional distress by adopting behaviors like drinking too much or abusing drugs, which only adds insult to their pre-existing injury.

And sometimes, family and friends inadvertently reinforce a person’s denial of mental distress by labeling it as “that’s just the way he is” and do nothing to encourage them to seek professional help.

We are having trouble retrieving the article content.

Please enable JavaScript in your browser settings.

Thank you for your patience while we verify access. If you are in Reader mode please exit and  log into  your Times account, or  subscribe  for all of The Times.

Thank you for your patience while we verify access.

Already a subscriber?  Log in .

Want all of The Times?  Subscribe .

Change Password

Your password must have 6 characters or more:.

• a lower case character, 
• an upper case character, 
• a special character 

Password Changed Successfully

Your password has been changed

Create your account

Forget yout password.

Enter your email address below and we will send you the reset instructions

If the address matches an existing account you will receive an email with instructions to reset your password

Forgot your Username?

Enter your email address below and we will send you your username

If the address matches an existing account you will receive an email with instructions to retrieve your username

• Spring 2024 | VOL. 36, NO. 2 CURRENT ISSUE pp.A4-174
• Winter 2024 | VOL. 36, NO. 1 pp.A5-81

The American Psychiatric Association (APA) has updated its Privacy Policy and Terms of Use , including with new information specifically addressed to individuals in the European Economic Area. As described in the Privacy Policy and Terms of Use, this website utilizes cookies, including for the purpose of offering an optimal online experience and services tailored to your preferences.

Please read the entire Privacy Policy and Terms of Use. By closing this message, browsing this website, continuing the navigation, or otherwise continuing to use the APA's websites, you confirm that you understand and accept the terms of the Privacy Policy and Terms of Use, including the utilization of cookies.

The Links Between Stress and Depression: Psychoneuroendocrinological, Genetic, and Environmental Interactions

• Gustavo E. Tafet , M.D., Ph.D. ,
• Charles B. Nemeroff , M.D., Ph.D.

Search for more papers by this author

The role of stress in the origin and development of depression may be conceived as the result of multiple converging factors, including the chronic effect of environmental stressors and the long-lasting effects of stressful experiences during childhood, all of which may induce persistent hyperactivity of the hypothalamic-pituitary-adrenal axis. These changes, including increased availability of corticotropin-releasing factor and cortisol, are also associated with hyperactivity of the amygdala, hypoactivity of the hippocampus, and decreased serotonergic neurotransmission, which together result in increased vulnerability to stress. The role of other monoaminergic neurotransmitters, genetic polymorphisms, epigenetic mechanisms, inflammatory processes, and altered cognitive processing has also been considered in the development of a comprehensive model of the interactions between different factors of vulnerability. Further understanding of the underlying mechanisms that link these factors may contribute significantly to the development of more effective treatments and preventive strategies in the interface between stress and mood disorders.

The link between stressful life events and the origin and development of depression has been widely investigated, providing an increasing body of evidence supporting this association. 1 – 3 Environmental factors likely affect individuals in somewhat different manners, therefore triggering an adaptive response to stress, which depends on both psychological and biological aspects in the interaction between stressors and individual resources. Psychological aspects include all of the cognitive processing related to incoming information; the subjective appraisal of different features related to stressors, such as magnitude and chronicity, predictability, and controllability; and potential resources to cope with them. Biological mediators include the activation of different neural structures underlying information processing, including sensory pathways, which convey environmental input to the CNS, and the resulting activation of neural and neuroendocrine cascades of molecular events, mediated by the subsequent activation of the sympathetic division of the autonomic nervous system and the hypothalamic-pituitary-adrenal (HPA) axis. 4 The efficacy of an adaptive response implies that it may be rapidly activated, to allow reacting in a successful and effective manner during stressful situations, and it should be efficiently controlled and concluded afterward. If it continues in a prolonged and excessive manner (e.g., during chronic stressful situations), it may lead to maladaptive changes, which in turn may contribute to the development of pathological conditions such as anxiety and mood disorders, including depression, particularly in individuals with increased genetic vulnerability. In this regard, various polymorphisms have been investigated as candidate genes, which are known to participate in important molecular pathways involved in the origin of depression. The presence of these genetic variations appears to be involved in the development of depression in response to stressful events, including adverse experiences during childhood and environmental stressors during adulthood. 5 – 10 Moreover, various studies have focused on the role of gene–environment interactions, including the search for these polymorphic variants and the role of transcriptional regulation by epigenetic mechanisms. 6 , 11 – 13 In addition, inflammatory processes associated with adaptive responses to stressful situations, with the consequent synthesis and release of proinflammatory cytokines, may lead to further maladaptive changes of neural and neuroendocrine systems, therefore contributing to the development of depressive symptoms, particularly in chronically stressed individuals.

This article aims to review the evidence for the role played by stress, associated with different converging factors, including a genetic diathesis, a history of adverse early life events, hyperactivity of the HPA axis, decreased monoamines, increased proinflammatory cytokines, and epigenetic mechanisms, such as those observed in response to environmental stressful conditions, and their potential interactions in the etiology of depression. An increased understanding of these factors and their potential interactions may lead to more effective strategies for the treatment of this disorder.

Processing of Environmental Stressors in the Brain

Environmental stressors are perceived and transmitted through sensory pathways to different structures in the CNS, such as the thalamus, which convey projections to the amygdala, and to sensory and association cortices, which in turn also project to different areas of the prefrontal cortex (PFC), including the orbitofrontal cortex, the medial PFC, and the anterior cingulate cortex (ACC). 4 , 7 Direct projections from the thalamus to the amygdala contribute to activate arousal and early alarm reactions, with the subsequent activation of the autonomic nervous system and the HPA axis, whereas indirect projections may reach the amygdala from sensory and association cortices as well as from transition cortices. The latter areas, including the entorhinal, perirhinal, and parahippocampal cortices, in turn project to the hippocampus, where sensory input is integrated with contextual cues, to convey more elaborated information to the amygdala. 14

The amygdala plays a critical role in emotional processing, including the assessment of the emotional relevance of environmental stimuli as well as internal stressors. It plays a key role in the regulation of autonomic and neuroendocrine responses, through projections to the lateral hypothalamus, which mediate the activation of the sympathetic branch of the autonomic nervous system; through direct projections to the paraventricular nucleus of the hypothalamus; or indirectly through the bed nucleus of the stria terminalis, which is involved in activation of the HPA axis. 14 In addition, the amygdala shares important connections with the orbitofrontal cortex and the medial PFC, 15 including Brodmann areas 10 and 32, and the subgenual ACC (Brodmann area 25). 16 The orbitofrontal cortex (Brodmann areas 11–14) has been associated with integration of multimodal sensory stimuli and primary appraisal of their positive or negative value, therefore participating in their affective assessment. 17 The medial PFC overlaps with the ACC, particularly in the subgenual ACC, 17 which regulates emotional responses generated by the amygdala. 18 These structures are in turn connected with the dorsolateral PFC (Brodmann areas 9 and 46) and the ventrolateral PFC (Brodmann areas 45 and 47), which participate in cognitive control and voluntary regulation of emotion. The dorsolateral PFC, which has been associated with executive aspects of cognitive processing 19 (most notably with conscious processing and working memory), receives input from the amygdala through the orbitofrontal cortex and ACC. 15 , 17 The dorsolateral PFC reciprocally projects back to limbic structures, mostly through indirect connections to the ventromedial PFC (Brodmann area 32), which projects to the subgenual ACC. 19 It has been proposed that projections from the ventromedial PFC and the subgenual ACC exert a modulatory effect on the amygdala, 19 , 20 which in turn sends excitatory output to the hypothalamus, 17 – 19 therefore regulating the activity of the HPA axis.

Decreased volume of the subgenual ACC has been described, together with hyperactivity of the amygdala, in individuals with mood disorders, 16 , 21 which has been associated with the role of the subgenual ACC in the top-down regulatory pathway between the dorsolateral PFC and the amygdala, allowing conscious down-regulation of negative emotions. These corticolimbic pathways may be dysfunctional in patients with depression, in which the dorsolateral PFC, dorsomedial PFC, orbitofrontal cortex, and ACC appear to be dysfunctional, particularly during cognitive-emotional tasks, with the consequent disruption of their top-down inhibitory effect expressed in the impaired cognitive modulation of emotions. 20 , 21 Recovery of conscious regulation of negative emotions has been associated with clinical recovery. In addition, decreased hippocampal volume has also been observed, along with increased activity of the amygdala and reduced activity of the dorsolateral PFC. 21 More recently, we documented changes in cortical thickness in patients exposed to child abuse and neglect, with the findings specific to the nature of the abuse. 22

Figure 1 illustrates the network of functional connections among different neural structures involved in adaptive responses to stress, including the processing of environmental stimuli through cortical and subcortical structures, and the activation of the HPA axis.

FIGURE 1. Schematic Representation of Neural Structures Involved in the Stress Response a

a Stressors are perceived by sensory receptors, which convey information to the thalamus, primary sensory cortices, association cortices, transition cortices, the hippocampus, and the amygdala. The amygdala also receives direct input from the thalamus. The orbitofrontal cortex and the medial prefrontal cortex are reciprocally connected and, together with the anterior cingulate cortex, convey information from sensory cortices and association cortices to subcortical structures, including direct connections to the hypothalamus and reciprocal connections with the amygdala. The amygdala participates in the activation of the HPA axis through stimulatory projections to the paraventricular nucleus of the hypothalamus, with consequent synthesis and release of CRF, which stimulates the release of ACTH from the pituitary. In turn, this stimulates the release of glucocorticoids from the adrenals, particularly cortisol. Cortisol exerts negative feedback at the level of the hypothalamus and the pituitary, as well as through the hippocampus, which exerts an inhibitory effect on the HPA axis. Activation of the HPA axis is also regulated by norepinephrine, through projections from the locus coeruleus, and serotonin, and through projections from the raphe nuclei. Both aminergic systems participate in regulation of the stress response through connections with the amygdala and the hippocampus, therefore exerting regulatory effects on both limbic structures. The amygdala is also involved in the activation of the autonomic component of the stress response through CRF inputs to the locus coeruleus. Solid lines indicate stimulatory inputs, whereas dotted lines indicate inhibitory inputs. ACC, anterior cingulate cortex; ACTH, adrenocorticotropin; CRF, corticotropin-releasing factor; DLPFC, dorsolateral prefrontal cortex; HPA, hypothalamic-pituitary-adrenal; MPFC, medial prefrontal cortex; OFC, orbitofrontal cortex.

Role of the HPA Axis

Activation of the HPA axis is initiated in limbic structures, including direct projections from the central nucleus of the amygdala, or indirectly through the bed nucleus of the stria terminalis, which projects to the hypothalamic paraventricular nucleus, where corticotropin-releasing factor (CRF) is synthesized in parvocellular neurons and released to reach the anterior pituitary. There, CRF regulates the transcription of the proopiomelanocortin gene (a common precursor for adrenocorticotropin, β-endorphin, and related peptides) and stimulates the release of adrenocorticotropin into the systemic circulation. Adrenocorticotropin acts upon the adrenal cortex to stimulate the biosynthesis and release of glucocorticoids, particularly cortisol. 23

At the molecular level, cortisol binds to mineralocorticoid receptors (type I) and glucocorticoid receptors (GRs; type II), constituting a hormone-receptor complex, which in turn undergoes conformational changes to allow its recognition and binding to a glucocorticoid response element, in the promoter region of many target genes. 24 Cortisol regulates the activity of the HPA axis through multiple negative feedback loops, which require its binding to GRs located in the paraventricular nucleus and the pituitary, where it down-regulates the synthesis and release of CRF and adrenocorticotropin, respectively, and GRs in the hippocampus, which in turn activates GABAergic projections to the paraventricular nucleus that inhibit HPA axis activity. Hence, many of the effects of cortisol may be understood as a result of transcriptional regulation of various genes, including those involved in the negative feedback loops responsible for the regulation of the HPA axis. 24

In response to short-term exposure to environmental stressors, the amygdala stimulates the HPA axis with the consequent synthesis and release of cortisol, 14 which is self-regulated by negative feedback mechanisms mediated by the glucocorticoid. In addition, the HPA system interacts with CRF neurons in the amygdala, activating a positive feedback loop involved in fear and anger reactions; the HPA also activates catecholaminergic neurons, stimulating arousal and improving cognitive functions. Hence, upon exposure to acute or short-term stressors, cortisol is expected to exert widespread metabolic effects, which is mostly necessary to maintain or restore homeostasis. 25 Cortisol is actively involved in the mobilization of energetic resources, including the stimulation of gluconeogenesis with the resulting increased levels of circulating glucose, and the down-regulation of inflammatory processes, therefore contributing to coping with the stressful situation.

Chronic and persistent activation of the HPA system may disrupt physiological mechanisms, including negative feedback loops, resulting in persistent activation of the system. Circadian rhythms normally characterized by wide variations, with morning zeniths and evening nadirs, are markedly altered during chronic stress, with the consequent increase in plasma cortisol levels and blunted circadian rhythm, mostly due to increased levels of cortisol during the evening and mild changes in the morning. 25 Prolonged exposure to increased levels of cortisol may induce detrimental effects on hippocampal neurons, reducing dendritic branching and inhibiting neurogenesis. 26 Moreover, hypersecretion of CRF and cortisol was also associated with decreased hippocampal volume, particularly in individuals exposed to childhood trauma. 27 Because the hippocampus is involved in the regulation of the HPA axis, it is conceivable that patients with major depression and early life trauma who exhibit reduced hippocampal volume 28 , 29 may also exhibit decreased hippocampal function, therefore resulting in further sensitization of stress responses. 5 These observations support previous reports that associated the origin of depressive symptoms with decreased expression of GRs at the hypothalamic and hippocampal levels, 24 with the resulting hypercortisolism. Hence, an increasing body of evidence supports the association between chronic stress and depression at the molecular level, where hyperactivity of the HPA axis, with the consequent increase of cortisol, represents one of the most consistent findings in both syndromal mood and certain anxiety disorders. 23 , 26

Various studies have focused on genes involved in the regulation of the HPA system, including both the mineralocorticoid receptor and GR genes, resulting in the identification of different single-nucleotide polymorphisms (SNPs). Among these, two different SNPs in the GR gene (BclI and Asp363Ser) have been associated with increased vulnerability for depression in the general population, probably through increased glucocorticoid sensitivity. 30 More recently, various studies have focused on the FK506-binding protein FKBP5, a cochaperone of hsp-90 involved in the regulation of GR sensitivity, 31 which is also involved in HPA axis responsivity. This protein is a component of the GR heterocomplex, which, upon binding of cortisol, is replaced by FKBP4, which in turn facilitates the nuclear translocation of the hormone-receptor complex and its transcriptional activity. 32 Altered GR function may lead to impaired feedback regulation, with the resulting HPA hyperactivation commonly observed in chronic stress and depression. Therefore, various SNPs have been identified in the FKBP5 gene, some of them associated with increased FKBP5 protein expression, which in turn may lead to changes in GR, with the resulting effect on HPA axis regulation. 32 Increased FKBP5 protein expression may reduce hormone-binding affinity and may interfere with the translocation of the hormone-receptor complex. It is noteworthy that glucocorticoids may induce increased expression of this cochaperone, constituting an intracellular negative feedback loop to regulate GR activity. 33 One of the SNPs of the FBPP5 gene, defined as the substitution of a cytosine (C) by a thymine (T) and therefore identified as the high-induction allele T, was associated with increased FKBP5 protein expression and altered HPA response. Upon exposure to stressful stimuli, carriers of the T allele exhibited slower recovery of the cortisol response and homozygous carriers of the allele who experienced severe abuse during childhood presented increased vulnerability for the development of depression during adulthood, 34 which may also be associated with having an increased number of depressive episodes. 32

Role of CRF

CRF-containing circuits in the CNS play a critical role in the coordination of the stress response, both as a neuroendocrine factor regulating the HPA axis and through its function as a neurotransmitter, mediating behavioral, immune, and autonomic responses to stress. 35 CRF neurons are localized throughout different cortical areas, participating in neural pathways involved in cognitive responses, and limbic areas such as the central nucleus of the amygdala and the bed nucleus of the stria terminalis, where it participates in the regulation of emotional responses. 23 CRF projections from the amygdala have been shown to reach the hypothalamic paraventricular nucleus (therefore enhancing the activation of the HPA axis in response to stress) and the monoaminergic nuclei in the brainstem, including the locus coeruleus (LC) and the raphe nuclei (RN). 3 Moreover, CRF stimulates norepinephrine release in the LC, 36 with the consequent noradrenergic activation of the autonomic nervous system and the HPA axis, while mainly inhibiting serotonergic neurons in the RN, 37 which in turn may affect other structures through serotonergic projections to the amygdala, hippocampus, and paraventricular nucleus. 3 Therefore, through the regulation of these monoaminergic systems, CRF participates in neurobiological processes underlying mood and anxiety disorders, producing anxiogenic and depressogenic effects. 35 Increased CSF concentrations of CRF have consistently been reported in depressed and suicidal patients. 38 In addition, CRF may also be involved in anxiety and the encoding of emotional memories, 23 , 35 playing a critical role in the stress response not only during adulthood but also in mediation of the long-lasting effects of trauma and other early life stressful experiences. Moreover, increased levels of CRF may also be involved in neuroplastic changes induced by chronic stress, 39 and this effect may also be enhanced by glucocorticoids as a component of the stress response. 40

Various studies have focused on CRF, CRF-binding protein, and CRF type 1 receptor (CRHR1) genes, resulting in several important findings. 41 Indeed, several SNPs in the CRHR1 and haplotypes formed by certain SNPs involved in mediating the effects of early adverse experiences on the risk for adult depression have been identified. 42 Upon binding to CRF, this receptor participates in the activation of the HPA axis and plays a critical role in emotional and cognitive functions mediated by CRF in extrahypothalamic brain regions, including the amygdala and the LC, 35 therefore influencing arousal, attention, conscious perception of emotional experiences, and memory consolidation. Two haplotypes formed by different SNPs in the CRHR1 gene were associated with reduced symptoms of depression in subjects exposed to early stressful experiences. Because CRHR1 may be critically involved in the consolidation of emotionally charged memories, such as those produced by childhood aversive experiences, it was proposed that carriers of two copies of these haplotypes, which also exhibited overrepresentation of the protective alleles of the studied SNPs, 42 may have altered activation of memory consolidation processes. This may lead to decreased emotional influence in the cognitive processing of these memories, therefore protecting the individual from his or her potentially depressogenic and anxiogenic effects. 43

Role of Serotonin

The serotonergic hypothesis of depression posits deficient serotonergic activity in the CNS with increased vulnerability for the development of depression. The main groups of serotonergic neurons in the CNS are located within the boundaries of the RN, where an array of ascending projections arise from the dorsal RN (B6 and B7) and the medial RN (B8). The dorsal RN–forebrain tract projects to the PFC, amygdala, nucleus accumbens, and ventral hippocampus, among other forebrain structures, 44 and it participates in the state of anticipatory anxiety and thus plays an adaptive role during stressful situations. 45 The dorsal RN–forebrain tract has been associated with activation of the limbic structures (e.g., the amygdala) in the presence of environmental stressors associated with unpleasant experiences, and it is also involved in the regulation of potential emotional reactions. Alterations of this system, particularly involving dorsal RN–amygdala projections, may be associated with symptoms of anxiety. 45 The medial RN–forebrain tract projects to the dorsal hippocampus and hypothalamus, among other neural structures, 44 , 45 and it participates in conferring tolerance to unpleasant, unavoidable, and persistent aversive stimuli such as those perceived during chronic stressful situations. The medial RN–forebrain tract is also associated with adaptive control on negative emotional experiences. Therefore, alterations of this system, particularly involving medial RN–hippocampal projections, may be associated with decreased tolerance to aversive stimuli, learned helplessness, and subsequent depression. 45 , 46 Serotonergic neurons in the RN are also interconnected and are physiologically integrated with other monoaminergic systems in the brainstem, including noradrenergic and dopaminergic circuits. 47 It has been shown that both the dorsal RN and the medial RN receive noradrenergic projections, 48 which appear to be excitatory. The LC receives serotonergic projections from the RN reciprocally, 48 which appear to exert an indirect modulatory effect by inhibiting glutamatergic activation of the LC. The dorsal RN also modulates dopaminergic activity through projections to the ventral tegmental area, which appear to be excitatory, 49 and dopaminergic projections to the dorsal RN reciprocally exert an indirect inhibitory effect by increasing the activity of somatodendritic 5-hydroxytryptamine (serotonin [5-HT]) autoreceptors. 44

Figure 2 illustrates the network of functional connections between different neurotransmitter systems in the CNS, as well as their respective connections with different cortical and limbic structures involved in the stress response.

FIGURE 2. Schematic Representation of Neurotransmitter Systems Involved in the Stress Response and Regulation of Emotional and Cognitive Functions a

a The raphe nuclei send serotonergic projections from their medial component to the hippocampus and from their dorsal component to the amygdala and the DLPFC. The locus coeruleus sends noradrenergic projections to the hippocampus and the amygdala. The ventral tegmental area sends dopaminergic projections to the nucleus accumbens and the DLPFC. The nucleus accumbens is reciprocally connected with the amygdala and the OFC, which in turn is reciprocally connected with the medial prefrontal cortex and the ACC. All of these are reciprocally connected with the amygdala and with the DLPFC. Reciprocal connections between the raphe nuclei, the locus coeruleus, and the ventral tegmental area are also represented. ACC, anterior cingulate cortex; D, dorsal; DLPFC, dorsolateral prefrontal cortex; M, medial; OFC, orbitofrontal cortex.

At the molecular level, 5-HT is released into the synaptic cleft, where it binds to both presynaptic and postsynaptic receptors. A growing number of 5-HT receptors have been identified, including 14 different types, classified in seven families with various subtypes each. Each of the serotonin receptor subtypes exhibits a unique regional neuroanatomic distribution, conferring specificity on the effects of activation of this widespread and diffuse serotonergic innervation. Synaptic concentrations of 5-HT are regulated by the serotonin transporter (5-HTT), which is responsible for its reuptake, therefore regulating its availability to bind and activate specific 5-HT receptors. 47 The 5-HTT is believed to be the primary molecular target of selective serotonin reuptake inhibitors antidepressants. Hence, 5-HTT blockade by selective serotonin reuptake inhibitors is translated into higher 5-HT concentrations in the synaptic cleft, allowing increased activation of 5-HT receptors. 46 , 47 The clinical efficacy of antidepressants is not directly associated with this acute mechanism; instead, it is linked to more adaptive changes. Continuous administration of selective serotonin reuptake inhibitors leads to desensitization or down-regulation of somatodendritic 5-HT 1A autoreceptors in the RN after several days (which are known to moderate the release of 5-HT into the synaptic cleft) and up-regulation of postsynaptic 5-HT 1A and desensitization of 5-HT 2A receptors. 50

In addition to serotonergic projections directly involved in cognitive and emotional functions, projections from the RN have been shown to innervate CRF-containing neurons in the paraventricular nucleus. 51 There is evidence that these projections stimulate the HPA axis and the autonomic nervous system; glucocorticoids and catecholamines may reciprocally affect the serotonergic system during stressful situations. 46 Various studies have shown that postsynaptic 5-HT 1A receptors in different limbic structures may be down-regulated or desensitized by glucocorticoids or exposure to chronic stress. 52 , 53 In addition, it has been shown that cortisol may increase 5-HT uptake in vitro, an effect attributed to increased expression of the 5-HTT gene by the glucocorticoid, 54 therefore providing further support for the reciprocal regulation of the HPA and 5-HT systems and their potential interplay in the interface between stress and depression. 46

Various studies have also focused on the structure of the 5-HTT gene, in which a polymorphism was identified in its promoter region. 55 The promoter activity is regulated by sequence elements located in the upstream regulatory region, known as the 5-HTT gene-linked polymorphic region (5-HTTLPR), where a short (S) and a long (L) allele have been identified. 6 Hence, the short promoter variant (5-HTTLPR-S) was associated with decreased transcriptional efficiency compared with the long allele (5-HTTLPR-L), resulting in decreased expression of the 5-HTT gene, 55 which may affect the modulation of serotonergic activity in response to stress. This notion has been supported by multiple clinical and preclinical studies, 56 including evidence observed in functional brain imaging studies, in which carriers of the S allele (homozygous or heterozygous for the short allele) exhibited increased amygdala reactivity to fearful and threatening stressors compared with those homozygous for the L allele, 57 which suggests that variations in the 5-HTT gene may be involved in psychological responses to stress. 6 Although various studies have shown increasing evidence that this polymorphism moderates the relationship between stress and depression, 56 there are still other studies suggesting certain controversy around this hypothesis.

The amygdala participates in the regulation of emotional reactions to stressful events, and its increased reactivity was associated with anxiety and altered mood regulation. 14 Hence, a potential association between 5-HTT gene polymorphism and increased reactivity of the amygdala in response to negative stressors 58 may contribute to a better understanding of the potential effect of the molecular mechanisms underlying this association. Moreover, the amygdala also plays a critical role in the activation of the HPA axis, and hyperactivation of the amygdala may also lead to increased plasma levels of cortisol. Indeed, carriers of the S allele exhibit increased activation of the amygdala and elevated cortisol levels in response to a laboratory stressor. 11 The association between the 5-HTTLPR-S variation and a potentially decreased expression of the 5-HTT gene may appear paradoxical, considering the potential vulnerability attributed to 5-HTTLPR-S carriers. Therefore, it is conceivable that alterations in 5-HTT gene regulation (and consequent effects on synaptic 5-HT levels) may differ, with the former expressed as a result of constitutive conditions and the latter triggered by environmental factors. It has been proposed that 5-HTTLPR-S carriers may exhibit “essentially” increased concentrations of 5-HT, which may result in down-regulation of postsynaptic 5-HT receptors. This may lead to a relative desensitization of the serotonergic system, 58 providing a potential explanation for the vulnerability exhibited by 5-HTTLPR-S carriers. By contrast, up-regulation of the 5-HTT gene, associated with the effect of environmental stressors and the resulting hyperactivation of the HPA axis and hypercortisolism, may lead to increased 5-HT reuptake and decreased concentrations of 5-HT in the synaptic cleft, 54 which has been widely associated with the development of mood disorders.

Role of Dopamine

Dopamine has also been implicated in the neural mechanisms of stress responses, including stress-related regulation of the HPA axis, as well as in the pathophysiology of depression. 59 , 60 The main groups of dopaminergic neurons in the CNS comprise the retro-rubro field (A8), the substantia nigra pars compacta (A9), and the ventral tegmental area (A10), where the mesolimbic and mesocortical pathways arise. The mesolimbic pathway projects mainly to the nucleus accumbens and other limbic structures, including the amygdala, hippocampus, bed nucleus of the stria terminalis, and septum. This pathway is implicated in the processing and reinforcement of rewarding stimuli, motivation, and the subjective experience of pleasure. 59 The mesocortical pathway projects mainly to the PFC, ACC, and entorhinal cortex and is critically involved in cognitive functions such as concentration and working memory. 59

Environmental stressors provoke increased activity in the amygdala, which in turn may increase the concentrations of dopamine in the mesocortical pathway (particularly in the PFC), therefore conferring exaggerated salience to relatively mild negative stimuli 60 and contributing to the resulting negative bias in cognitive processing. Regarding the mesolimbic pathway, it has been shown that stressful events may induce opposite responses, depending on the potential controllability of the stimuli, 61 and the consequent subjective assessment. Therefore, exposure to acute and controllable stressors was associated with increased dopamine release in the ventral striatum, whereas exposure to chronic and uncontrollable stressful stimuli was associated with decreased dopaminergic activity 61 with resulting anhedonia. Moreover, it has been shown that unavoidable or uncontrollable stressors may lead to decreased dopamine release in the nucleus accumbens and impaired response to environmental stimuli, which may result in the expression and exacerbation of depressive symptoms induced by stress. 62 The inability to experience pleasure, associated with loss of interest and motivation in usual activities, constitutes the pathognomonic anhedonia exhibited by patients with depression, 59 , 60 and it has been shown that impaired dopaminergic function is critically involved in altered reward processing underlying anhedonia. 63 , 64 Moreover, the mesolimbic dopaminergic pathway, particularly the nucleus accumbens, participates in the processing of rewarding and hedonic experiences in association with the orbitofrontal cortex, which may be involved in the subjective assessments of hedonic and rewarding value. 65 The orbitofrontal cortex is connected with the ACC and dorsolateral PFC, where this emotional input participates in cognitive processes; by contrast, the nucleus accumbens receives dopaminergic projections from the ventral tegmental area, which may be enhanced by glutamatergic stimulation from the amygdala, to increase motivation. 65 Substantial interaction has also been described between the ventral tegmental area and the RN, 59 which may be critically involved in emotional processing.

Because increased dopamine release in the mesolimbic pathway has been observed not only in response to rewarding stimuli but also in the presence of aversive situations (particularly when these are perceived as controllable and escapable 61 ), it has been suggested that dopamine plays an adaptive role associated with motivation, increased arousal, and behavioral control in response to stress, including both appetitive and aversive conditions. 66

Role of Norepinephrine

Catecholamines (and more specifically norepinephrine) have long been posited to play a major role in the pathophysiology of affective disorders, forming the catecholamine hypothesis of depression. The main group of norepinephrine-containing neurons in the CNS is located within the LC (A6), where various projections arise to widely innervate cortical and subcortical areas, 48 including the amygdala, the hippocampus, and the paraventricular nucleus of the hypothalamus. 36 Projections from the LC to the ventral tegmental area have been described, in which norepinephrine has been shown to potentiate dopamine release. Projections from the LC to the RN have also been described, in which norepinephrine exerts regulatory effects on 5-HT release. 48 There is also evidence of reciprocal regulation between norepinephrine and 5-HT, not only through connections between both aminergic systems but also through limbic structures such as the hippocampus. 67 In addition, reciprocal connections between norepinephrine - and CRF-containing neurons suggest a critical role of the LC in the regulation of neural and neuroendocrine responses to stress. 36

In response to acute stressors, norepinephrine is released throughout different structures in the CNS, resulting in enhanced arousal and hypervigilance, in the context of adaptive responses to stress. Moreover, activation of the LC has been associated with subsequent stimulation of the lateral hypothalamus, which in turn participates in the activation of the sympathetic branch of the autonomic nervous system, therefore complementing the adaptive response to stress. 36 A potential dysfunction of the LC has been observed during chronic stress (particularly upon exposure to unavoidable or uncontrollable stressors), leading to altered norepinephrine release, which was associated with some features of learned helplessness as well as problems in cognitive functions such as attention and memory, which are frequently observed in depression. In addition, dysregulation of the norepinephrine system has also been described in altered states of arousal, 48 which is commonly observed in anxiety disorders as well as in depression.

Neuroplasticity and Neurogenesis: Role of Neurotrophic Factors

Several studies have focused on the potential role of neurotrophic factors in critical neural processes, with particular attention on the neurotrophin family, which is composed of nerve growth factor, brain-derived neurotrophic factor (BDNF), neurotrophin-3, neurotrophin-4/5, and neurotrophin-6. Among these neurotrophins, a growing body of research has focused on the role of BDNF in the regulation of brain development, neuroplasticity, and neurogenesis. 68 Various studies strongly suggest that decreased levels of BDNF may lead to depressive symptoms, whereas up-regulation of BDNF is associated with clinical recovery. 69 In vitro studies have demonstrated that BDNF may decrease 5-HT uptake, suggesting a potential role of the neurotrophin in regulation of 5-HTT. 70 Chronic stress, with the resulting activation of the HPA axis, may damage neurons in certain CNS structures (particularly in the hippocampus, where high levels of GRs have been found) and these changes have been associated with decreased availability of neurotrophic factors such as BDNF. 71 Moreover, it has been shown that increased levels of glucocorticoids, at least partially, may be involved in down-regulation of BDNF. 72 By contrast, it has been demonstrated that various antidepressants increase the expression of BDNF in the hippocampus 69 in a dose-dependent and time-dependent manner, which is consistent with the time dependency of therapeutic effects of antidepressants, therefore suggesting a role for BDNF in their mechanism of action. 73 The potential association between successful pharmacotherapy and the observed up-regulation of BDNF in the hippocampus suggests that BDNF may be involved in the long-lasting effects of antidepressants through neuroplastic changes in certain neural structures such as the hippocampus, amygdala, and PFC. 69 Moreover, it has been shown that BDNF and 5-HT may induce hippocampal neurogenesis. 74

Most neurons in the CNS are generated during early periods of development, although more recent studies have demonstrated that some neural structures, such as the dentate gyrus of the hippocampus, actually continue generating neurons later in life. 75 Therefore, neurogenesis in the adult CNS may be stimulated by special conditions, particularly those related to enhanced hippocampal activity and increased levels of 5-HT, 76 , 77 but it may be inhibited by stressful situations and increased levels of glucocorticoids. 78 Under chronic stress conditions, with increased activation of the HPA axis, inhibition of hippocampal neurogenesis may interfere with the formation of new cognitions, therefore contributing to provoking and sustaining ongoing depressogenic conditions. According to this hypothesis, successful therapeutic interventions may require recovery of the normal rate of hippocampal neurogenesis. This recovery may be associated with a direct effect of antidepressants through increasing levels of 5-HT 75 or indirectly through modulation of the HPA axis and increasing levels of BDNF, which was associated with up-regulation of neuroplasticity and increasing neurogenesis. This hypothesis remains quite controversial because of failure to confirm the increase in neurogenesis after long-term antidepressant treatment. 79

Various studies have focused on BDNF gene regulation and variations potentially involved in mood disorders, resulting in the identification of different SNPs. Among these, an SNP has been identified at nucleotide position 196 in the coding region of the BDNF gene, where a guanine (G) is replaced by an adenine (A), resulting in the substitution of valine (Val) by methionine (Met) at codon 66, which is thus termed Val66Met. This is where the presence of a Met allele has been associated with a functional alteration (i.e., abnormal intracellular trafficking and decreased secretion of BDNF). 73 , 76 Studies on carriers of the Met-BDNF allele revealed relatively smaller hippocampal volumes compared with those individuals who were homozygous for the Val-BDNF allele. 73 This was also associated with reduced hippocampal activation and deficient cognitive performance, 12 , 73 which have also been associated with lower emotional stability and increased vulnerability for the development of depressive symptoms.

Inflammatory Processes: Role of Cytokines

It has been demonstrated that acute and chronic psychosocial stress may activate inflammatory responses. 80 Increased blood concentrations of proinflammatory cytokines, such as interleukin-1, interleukin-6, and tumor necrosis factor-alpha, have been associated with the effect of diverse environmental stimuli, including psychosocial stress, 81 and this immune activation has also been observed in major depression. 82 Moreover, major depression may induce increased inflammatory responses to stress, and this has been observed mostly in patients exposed to adverse early life events, therefore suggesting a link between these and increased inflammatory responses to stress later in life. 80 To understand the role of proinflammatory cytokines in chronic stress and the subsequent development of depression, various studies have focused on their potential mechanisms of action. Environmental stressors activate the sympathetic branch of the autonomic nervous system, with the resulting release of catecholamines, which in turn activates their receptors on immune cells and thus stimulates the release of proinflammatory cytokines. 83 Chronic inflammatory responses in the CNS may result in excessive release of proinflammatory cytokines, which in turn may lead to decreased concentrations of neurotrophins (including BDNF), leading to impaired neuroplasticity 83 and decreased neurogenesis (particularly in the hippocampus 82 ), which have been associated with the origin of cognitive impairment and mood disorders. Proinflammatory cytokines have also been involved in regulation of the HPA axis, stimulating release of CRF with resulting hypercortisolism, 83 which has been associated with reduced sensitivity of GRs and glucocorticoid resistance. 81 , 83 Increased levels of cortisol, such as those observed during chronic stress, may lead to decreased synthesis of 5-HT due to reduced activity of the rate-limiting enzyme tryptophan hydroxylase. Hypercortisolism has been also associated with increased activity of tryptophan dioxygenase (indoleamine-pyrrole 2,3-dioxygenase), which is responsible for the degradation of tryptophan to kynurenine, with the resulting decreased synthesis and release of 5-HT. 83 Proinflammatory cytokines such as interferon have also been involved in the modulation of this pathway, stimulating indoleamine-pyrrole 2,3-dioxygenase and thus leading to reduced synthesis of 5-HT and increased synthesis of kynurenine. 84 Degradation of kynurenine leads to the formation of 3-hydroxykynurenine, which produces free radical species involved in oxidative stress, and kynurenic acid and quinolinic acid, which activate the glutamatergic system. This leads to neurotoxicity and neuronal apoptosis, which are also involved in the pathophysiology of depression. 83 , 84 In addition, certain proinflammatory cytokines, such as interleukin-1 and tumor necrosis factor, have been shown to affect serotonergic neurotransmission by stimulating the 5-HTT and thus reducing intersynaptic concentrations of 5-HT in the CNS. 83 , 85

Understanding the molecular mechanisms underlying neuroinflammatory processes in the CNS, particularly the role played by proinflammatory cytokines in mood disorders, has inspired various studies aimed at improving depressive symptoms by attenuating these processes. Preclinical studies have demonstrated the efficacy of certain anti-inflammatory cytokines to block the depressive-like state induced by proinflammatory cytokines in rodents. 83 Other studies have also approached the consequences of proinflammatory cytokines, antagonizing the activity of the glutamatergic system, activated by the kynurenine pathway. 81

Stress, Appraisal, and Coping: Role of Psychological Vulnerability

Psychological vulnerability depends on various features related to stressful life events (including strength, intensity, and length of the impact) and the availability of personal resources to cope with them. More remarkably, however, it may depend on cognitive appraisal, particularly the balance between stressors and individual resources, and the resulting coping strategies. 86 Chronic exposure to unavoidable and uncontrollable stressors may lead to decreasing cognitive and behavioral coping strategies to handle environmental events, mostly as a result of cognitive appraisals that personal resources are not enough, which has been associated with increasing feelings of helplessness. 86 According to the cognitive model of depression, 87 early life experiences provide the background to develop cognitive schemas, which in turn represent the basis to transform simple data into cognitions that are learned and stored in long-term memory. Adverse early life events, including childhood sexual or physical abuse 88 and peer victimization 89 (also known as bullying), may contribute to the formation of particular cognitive schemas. These schemas may be inactive during long periods and reactivated by new experiences at a later time, particularly those with strong emotional valence. In response to stressful situations in adulthood, activated dysfunctional schemas may induce negative biases during information processing, with consequent dysfunctional effects, including cognitive processing, emotional reactions, and behavioral responses, constituting the essential core of cognitive vulnerability. 87 Therefore, dysfunctional schemas shaped during childhood, with systematic negative biases, may lead to negatively biased appraisals, with consequent limitations in further processing of the resulting cognitions, therefore leading to feelings of helplessness and subsequent depression.

Epigenetics: Role of Gene–Environment Interactions

The term epigenetics refers to heritable characteristics that are not determined by structural changes in the underlying genetic sequence. At the molecular level, epigenetic mechanisms involve biochemical changes of nucleotides, without altering the DNA sequence, and the associated histone proteins, which constitute chromatin. Changes in the structure of chromatin may affect gene expression by allowing transcription factors to gain access to gene regulatory elements. Hence, environmental factors may induce changes in the chromatin state, which in turn may improve exposure of genes to the impact of different transcription factors, therefore increasing or decreasing gene expression while the original DNA sequence remains unaltered. 90 Potential changes include DNA methylation, which has been associated with down-regulation of gene expression; histone acetylation, which may induce up-regulation of gene expression; and histone methylation and phosphorylation, both of which may lead to activation or repression of transcriptional events. 90 Recent research has contributed to identifying epigenetic mechanisms in the context of stressful situations, which may induce long-lasting changes in gene expression in different neural structures. In turn, such changes have been associated with the development of stress-related conditions such as anxiety disorders and depression. Preclinical studies have revealed that chronic stress may regulate histone acetylation in the hippocampus, inducing transient increases and subsequent decreases; transient increases have also been observed in the amygdala. 91

In addition, preclinical studies also revealed that increased levels of CRF, observed during chronic stress conditions, have been associated with decreased DNA methylation at the promoter region of the CRF gene. 92 Moreover, a history of early adverse experiences has been associated with changes in histone markers and DNA methylation of the GR gene, particularly in the hippocampus, and changes in DNA methylation have also been observed in the GR and BDNF genes. 41 Therefore, chronic stress, including early stressful experiences, may induce diverse epigenetic changes in different neural structures, with a subsequent effect on their respective functions. This, in turn, may predispose individuals to increased vulnerability to stress and to the development of diverse clinical conditions such as depression.

Childhood Trauma: Role of Early Adverse Experiences

Early life stress, defined as adverse conditions and traumatic events experienced during childhood, represents a major factor of vulnerability in the origin and development of depression and bipolar disorder. 3 , 5 , 27 The association between a history of adverse and traumatic experiences during childhood and the development of mood disorders later in life has been observed particularly after additional stressful events during adulthood. 5 It has been shown that adverse early life events (including abuse, neglect, or loss) contribute to the formation of dysfunctional cognitive schemas, which may induce negative biases in response to stressful situations at a later time, therefore contributing to generating cognitive vulnerability. 56 This mechanism was also recently described in victims of bullying. 89 Moreover, it has been proposed that certain early life events, such as neglect, may lead to the formation of dysfunctional attitudes; this has also been associated with long-term hyperactivity of the HPA axis. 93 The effect of adverse early life events has been conclusively demonstrated to induce long-lasting changes in neural and neuroendocrine systems involved in adaptive responses to stress, particularly in CRF neurotransmission. 23 This, in turn, may be translated into persistent sensitization and increased responsiveness to stress. 3 , 5 Increased levels of CRF may lead to hyperactivity of the HPA axis and hypercortisolism, which may induce morphologic changes such as reduced hippocampal volume. 72 In this regard, various studies have focused on the role of hippocampal GRs, and increased levels of cortisol (in a sustained and prolonged manner) have been shown to induce down-regulation of GRs in certain areas of the hippocampus. 94 Moreover, additional research has suggested that the availability and efficacy of hippocampal GRs may be permanently affected as a result of early stressful experiences, 88 therefore contributing to glucocorticoid resistance and the consequent hyperreactivity of the HPA axis observed in response to additional stressful situations. In addition, increased concentrations of cortisol and decreased GR availability, induced by stressful situations during childhood, have been associated with decreased hippocampal volume and neural activity in adulthood as well as increased reactivity of the HPA axis, with the consequent functional alterations observed in adulthood. 88 , 95 A history of early life adverse experiences was also associated with hyperreactivity of neural and neuroendocrine responses to stress, which is reflected through increased CRF activity, hypercortisolism, and glucocorticoid resistance. 27 , 96

Klengel et al. 97 reported that a polymorphism in the FKBP5 gene increases the risk for the development of stress-related psychiatric disorders in adults by an allelic-specific, child abuse/neglect–dependent DNA demethylation in functional glucocorticoid response elements of FKBP5. Thus, activation of a sensitized system in the presence of additional stressful situations later in life may result in an exaggerated and maladaptive activation of the stress response, therefore generating increased vulnerability to the development of depressive symptoms upon exposure to additional stressors in adulthood. 42 , 98

Conclusions

The role of stressful life events in the origin and development of depression may be conceptualized as the result of multiple interactions between the effect of environmental stressors and individual factors of vulnerability. Figure 3 illustrates the role of these factors and their potential interactions at the interface between chronic stress and depression. Genetic factors, including SNPs, may be associated with functional and structural alterations in certain neural structures, including increased reactivity of the amygdala and decreased function of the hippocampus. Adverse early life events have been shown to engender biological changes in the developing CNS, as well as psychological changes reflected in the formation of dysfunctional cognitive schemas, 99 with the resulting biased cognitive processing of environmental stimuli, which may be translated into cognitive and emotional vulnerability. 87 These may be further activated in response to stress in adulthood, contributing to increased vulnerability to depression. 27

FIGURE 3. Schematic Representation of Different Factors Involved in the Stress Response and Their Potential Role in Stress and Depression a

a Genetic polymorphisms (represented as genetic vulnerability) participate in the development of the CNS and, together with the influence of early environmental factors (represented by early life stress) and chronic stress, result in a particular CNS phenotype. Early life stress may also induce certain cognitive vulnerability, which in turn may result in emotional vulnerability. Upon the impact of traumatic events or chronic stress, a predisposed CNS responds with increased levels of CRF, hyperactivation of the HPA axis, and increased levels of cortisol, which may lead to molecular changes in different circuits (represented by molecular vulnerability), as well as altered cognitive and emotional responses (represented by emotional vulnerability). This, in turn, may result in increased vulnerability for the development of symptoms of anxiety and depression. CRF, corticotropin-releasing factor; HPA, hypothalamic-pituitary-adrenal.

The impact of abuse and neglect during childhood clearly leads to persistent changes in neural and neuroendocrine systems involved in the regulation of adaptive responses. Functional or structural alterations in the CNS, particularly in the cerebrocortical regions as well as in the amygdala and the hippocampus, along with cognitive biases, may induce biological changes such as increased levels of CRF. Upon exposure to environmental stressors, this mechanism may be translated into hyperactivity of the HPA system, with increased levels of CRF and cortisol, which in turn may lead to transcriptional events. Such molecular changes affecting different aminergic systems, particularly on the regulation of 5-HT together with altered cognitive processing, may result in emotional changes, thereby predisposing to symptoms of anxiety and depression. Therefore, multiple vulnerability factors (including psychological, biological, cognitive, genetic, and epigenetic factors) converge on different aspects of HPA regulation. This complex set of pathways likely links vulnerability to stress with the pathogenesis of depression. In addition, environmental stress has also been associated with inflammatory responses in the CNS with excessive release of proinflammatory cytokines, which may lead to further stimulation of the HPA axis, with resulting hypercortisolism and impaired 5-HT neurotransmission. Proinflammatory cytokines have also been associated with decreased neurotrophins, with resulting decreases in neuroplasticity and neurogenesis. Therefore, a better understanding of the molecular mechanisms underlying these processes may allow novel strategies aimed at improving depressive symptoms by attenuating neuroinflammation.

The observation that some individuals may exhibit stronger vulnerability to environmental stressors but others may be less sensitive, more resistant, or even resilient to similar experiences highlights the importance of further investigation of the nature of different risk factors. Future research should focus on further understanding the neurobiological background underlying these factors and should identify potential windows of intervention, including neural and molecular mechanisms involved in the interface between cognitive processing of environmental stressors and their potential effects in epigenetic processes. This may lead to the development of more successful treatments aimed at not only restoring altered neural and neuroendocrine mechanisms but also preventing the development of anxiety and mood disorders in vulnerable individuals.

This may be achieved either by identifying different vulnerability factors, which in turn may become targets for novel therapeutic interventions, or by increasing and promoting protective resources in individuals exposed to stressful conditions, particularly those exposed to traumatic events or adverse conditions during childhood.

Dr. Nemeroff has in the last 3 years consulted to Takeda, Xhale, Mitsubishi, Clintara, Taisho, Prismic, and Gerson Lehrman, has received grants/research support from NIH and the Agency for Healthcare Research and Quality, has served on the scientific advisory boards for Xhale, AFSP, the Brain and Behavior Research Foundation, Clintara, and the Anxiety and Depression Association of America, and holds stock in Celgene, Seattle Genetics, Abbvie, Titan, OPKO, and Xhale. Dr. Tafet reports no financial relationships with commercial interests.

1 Gold PW : The organization of the stress system and its dysregulation in depressive illness . Mol Psychiatry 2015 ; 20:32–47 Crossref , Medline ,  Google Scholar

2 Nemeroff CB : The preeminent role of early untoward experience on vulnerability to major psychiatric disorders: the nature-nurture controversy revisited and soon to be resolved . Mol Psychiatry 1999 ; 4:106–108 Crossref , Medline ,  Google Scholar

3 Heim C, Nemeroff CB : The role of childhood trauma in the neurobiology of mood and anxiety disorders: preclinical and clinical studies . Biol Psychiatry 2001 ; 49:1023–1039 Crossref , Medline ,  Google Scholar

4 López JF, Akil H, Watson SJ : Neural circuits mediating stress . Biol Psychiatry 1999 ; 46:1461–1471 Crossref , Medline ,  Google Scholar

5 Heim C, Newport DJ, Mletzko T, et al. : The link between childhood trauma and depression: insights from HPA axis studies in humans . Psychoneuroendocrinology 2008 ; 33:693–710 Crossref , Medline ,  Google Scholar

6 Caspi A, Sugden K, Moffitt TE, et al. : Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene . Science 2003 ; 301:386–389 Crossref , Medline ,  Google Scholar

7 Kaufman J, Plotsky PM, Nemeroff CB, et al. : Effects of early adverse experiences on brain structure and function: clinical implications . Biol Psychiatry 2000 ; 48:778–790 Crossref , Medline ,  Google Scholar

8 Kaufman J, Yang BZ, Douglas-Palumberi H, et al. : Brain-derived neurotrophic factor-5-HTTLPR gene interactions and environmental modifiers of depression in children . Biol Psychiatry 2006 ; 59:673–680 Crossref , Medline ,  Google Scholar

9 Bradley RG, Binder EB, Epstein MP, et al. : Influence of child abuse on adult depression: moderation by the corticotropin-releasing hormone receptor gene . Arch Gen Psychiatry 2008 ; 65:190–200 Crossref , Medline ,  Google Scholar

10 Nemeroff CB, Seligman F : The pervasive and persistent neurobiological and clinical aftermath of child abuse and neglect . J Clin Psychiatry 2013 ; 74:999–1001 Crossref , Medline ,  Google Scholar

11 Gotlib IH, Joormann J, Minor KL, et al. : HPA axis reactivity: a mechanism underlying the associations among 5-HTTLPR, stress, and depression . Biol Psychiatry 2008 ; 63:847–851 Crossref , Medline ,  Google Scholar

12 Gatt JM, Nemeroff CB, Dobson-Stone C, et al. : Interactions between BDNF Val66Met polymorphism and early life stress predict brain and arousal pathways to syndromal depression and anxiety . Mol Psychiatry 2009 ; 14:681–695 Crossref , Medline ,  Google Scholar

13 Binder EB, Owens MJ, Liu W, et al. : Association of polymorphisms in genes regulating the corticotropin-releasing factor system with antidepressant treatment response . Arch Gen Psychiatry 2010 ; 67:369–379 Crossref , Medline ,  Google Scholar

14 LeDoux JE : Brain mechanisms of emotion and emotional learning . Curr Opin Neurobiol 1992 ; 2:191–197 Crossref , Medline ,  Google Scholar

15 Ghashghaei HT, Hilgetag CC, Barbas H : Sequence of information processing for emotions based on the anatomic dialogue between prefrontal cortex and amygdala . Neuroimage 2007 ; 34:905–923 Crossref , Medline ,  Google Scholar

16 Drevets WC, Savitz J, Trimble M : The subgenual anterior cingulate cortex in mood disorders . CNS Spectr 2008 ; 13:663–681 Crossref , Medline ,  Google Scholar

17 Price JL, Drevets WC : Neural circuits underlying the pathophysiology of mood disorders . Trends Cogn Sci 2012 ; 16:61–71 Crossref , Medline ,  Google Scholar

18 Etkin A, Egner T, Kalisch R : Emotional processing in anterior cingulate and medial prefrontal cortex . Trends Cogn Sci 2011 ; 15:85–93 Crossref , Medline ,  Google Scholar

19 Ray RD, Zald DH : Anatomical insights into the interaction of emotion and cognition in the prefrontal cortex . Neurosci Biobehav Rev 2012 ; 36:479–501 Crossref , Medline ,  Google Scholar

20 Motzkin JC, Philippi CL, Wolf RC, et al. : Ventromedial prefrontal cortex is critical for the regulation of amygdala activity in humans . Biol Psychiatry 2015 ; 77:276–284 Crossref , Medline ,  Google Scholar

21 Zhong M, Wang X, Xiao J, et al. : Amygdala hyperactivation and prefrontal hypoactivation in subjects with cognitive vulnerability to depression . Biol Psychol 2011 ; 88:233–242 Crossref , Medline ,  Google Scholar

22 Heim CM, Mayberg HS, Mletzko T, et al. : Decreased cortical representation of genital somatosensory field after childhood sexual abuse . Am J Psychiatry 2013 ; 170:616–623 Crossref , Medline ,  Google Scholar

23 Arborelius L, Owens MJ, Plotsky PM, et al. : The role of corticotropin-releasing factor in depression and anxiety disorders . J Endocrinol 1999 ; 160:1–12 Crossref , Medline ,  Google Scholar

24 Holsboer F : The corticosteroid receptor hypothesis of depression . Neuropsychopharmacology 2000 ; 23:477–501 Crossref , Medline ,  Google Scholar

25 Chrousos GP : Stress and disorders of the stress system . Nat Rev Endocrinol 2009 ; 5:374–381 Crossref , Medline ,  Google Scholar

26 Baumeister D, Lightman SL, Pariante CM : The interface of stress and the HPA axis in behavioural phenotypes of mental illness . Curr Top Behav Neurosci 2014 ; 18:13–24 Crossref , Medline ,  Google Scholar

27 Nemeroff CB, Binder E : The preeminent role of childhood abuse and neglect in vulnerability to major psychiatric disorders: toward elucidating the underlying neurobiological mechanisms . J Am Acad Child Adolesc Psychiatry 2014 ; 53:395–397 Crossref , Medline ,  Google Scholar

28 Bremner JD, Narayan M, Anderson ER, et al. : Hippocampal volume reduction in major depression . Am J Psychiatry 2000 ; 157:115–118 Link ,  Google Scholar

29 Vythilingam M, Heim C, Newport J, et al. : Childhood trauma associated with smaller hippocampal volume in women with major depression . Am J Psychiatry 2002 ; 159:2072–2080 Crossref , Medline ,  Google Scholar

30 DeRijk RH, Schaaf M, de Kloet ER : Glucocorticoid receptor variants: clinical implications . J Steroid Biochem Mol Biol 2002 ; 81:103–122 Crossref , Medline ,  Google Scholar

31 Horstmann S, Lucae S, Menke A, et al. : Polymorphisms in GRIK4, HTR2A, and FKBP5 show interactive effects in predicting remission to antidepressant treatment . Neuropsychopharmacology 2010 ; 35:727–740 Crossref , Medline ,  Google Scholar

32 Binder EB, Salyakina D, Lichtner P, et al. : Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment . Nat Genet 2004 ; 36:1319–1325 Crossref , Medline ,  Google Scholar

33 Vermeer H, Hendriks-Stegeman BI, van der Burg B, et al. : Glucocorticoid-induced increase in lymphocytic FKBP51 messenger ribonucleic acid expression: a potential marker for glucocorticoid sensitivity, potency, and bioavailability . J Clin Endocrinol Metab 2003 ; 88:277–284 Crossref , Medline ,  Google Scholar

34 Appel K, Schwahn C, Mahler J, et al. : Moderation of adult depression by a polymorphism in the FKBP5 gene and childhood physical abuse in the general population . Neuropsychopharmacology 2011 ; 36:1982–1991 Crossref , Medline ,  Google Scholar

35 Nemeroff CB : Neurobiological consequences of childhood trauma . J Clin Psychiatry 2004 ; 65(Suppl 1):18–28 Medline ,  Google Scholar

36 Valentino RJ, Foote SL, Page ME : The locus coeruleus as a site for integrating corticotropin-releasing factor and noradrenergic mediation of stress responses . Ann N Y Acad Sci 1993 ; 697:173–188 Crossref , Medline ,  Google Scholar

37 Kirby LG, Rice KC, Valentino RJ : Effects of corticotropin-releasing factor on neuronal activity in the serotonergic dorsal raphe nucleus . Neuropsychopharmacology 2000 ; 22:148–162 Crossref , Medline ,  Google Scholar

38 Nemeroff CB, Widerlöv E, Bissette G, et al. : Elevated concentrations of CSF corticotropin-releasing factor-like immunoreactivity in depressed patients . Science 1984 ; 226:1342–1344 Crossref , Medline ,  Google Scholar

39 Regev L, Baram TZ : Corticotropin releasing factor in neuroplasticity . Front Neuroendocrinol 2014 ; 35:171–179 Crossref , Medline ,  Google Scholar

40 Timmermans W, Xiong H, Hoogenraad CC, et al. : Stress and excitatory synapses: from health to disease . Neuroscience 2013 ; 248:626–636 Crossref , Medline ,  Google Scholar

41 McEwen BS, Eiland L, Hunter RG, et al. : Stress and anxiety: structural plasticity and epigenetic regulation as a consequence of stress . Neuropharmacology 2012 ; 62:3–12 Crossref , Medline ,  Google Scholar

42 Kendler KS, Kuhn JW, Prescott CA : Childhood sexual abuse, stressful life events and risk for major depression in women . Psychol Med 2004 ; 34:1475–1482 Crossref , Medline ,  Google Scholar

43 Polanczyk G, Caspi A, Williams B, et al. : Protective effect of CRHR1 gene variants on the development of adult depression following childhood maltreatment: replication and extension . Arch Gen Psychiatry 2009 ; 66:978–985 Crossref , Medline ,  Google Scholar

44 Hensler JG : Serotonergic modulation of the limbic system . Neurosci Biobehav Rev 2006 ; 30:203–214 Crossref , Medline ,  Google Scholar

45 Deakin JF, Graeff FG : 5-HT and mechanisms of defence . J Psychopharmacol 1991 ; 5:305–315 Crossref , Medline ,  Google Scholar

46 Tafet GE, Bernardini R : Psychoneuroendocrinological links between chronic stress and depression . Prog Neuropsychopharmacol Biol Psychiatry 2003 ; 27:893–903 Crossref , Medline ,  Google Scholar

47 Hamon M, Blier P : Monoamine neurocircuitry in depression and strategies for new treatments . Prog Neuropsychopharmacol Biol Psychiatry 2013 ; 45:54–63 Crossref , Medline ,  Google Scholar

48 Ressler KJ, Nemeroff CB : Role of norepinephrine in the pathophysiology and treatment of mood disorders . Biol Psychiatry 1999 ; 46:1219–1233 Crossref , Medline ,  Google Scholar

49 Kranz GS, Kasper S, Lanzenberger R : Reward and the serotonergic system . Neuroscience 2010 ; 166:1023–1035 Crossref , Medline ,  Google Scholar

50 Gray NA, Milak MS, DeLorenzo C, et al. : Antidepressant treatment reduces serotonin-1A autoreceptor binding in major depressive disorder . Biol Psychiatry 2013 ; 74:26–31 Crossref , Medline ,  Google Scholar

51 Lesch KP, Gutknecht L : Focus on The 5-HT1A receptor: emerging role of a gene regulatory variant in psychopathology and pharmacogenetics . Int J Neuropsychopharmacol 2004 ; 7:381–385 Crossref , Medline ,  Google Scholar

52 López JF, Chalmers DT, Little KY, et al. : A.E. Bennett Research Award. Regulation of serotonin1A, glucocorticoid, and mineralocorticoid receptor in rat and human hippocampus: implications for the neurobiology of depression . Biol Psychiatry 1998 ; 43:547–573 Crossref , Medline ,  Google Scholar

53 van Riel E, van Gemert NG, Meijer OC, et al. : Effect of early life stress on serotonin responses in the hippocampus of young adult rats . Synapse 2004 ; 53:11–19 Crossref , Medline ,  Google Scholar

54 Tafet GE, Toister-Achituv M, Shinitzky M : Enhancement of serotonin uptake by cortisol: a possible link between stress and depression . Cogn Affect Behav Neurosci 2001 ; 1:96–104 Crossref , Medline ,  Google Scholar

55 Lesch KP, Bengel D, Heils A, et al. : Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region . Science 1996 ; 274:1527–1531 Crossref , Medline ,  Google Scholar

56 Karg K, Burmeister M, Shedden K, et al. : The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited: evidence of genetic moderation . Arch Gen Psychiatry 2011 ; 68:444–454 Crossref , Medline ,  Google Scholar

57 Hariri AR, Mattay VS, Tessitore A, et al. : Serotonin transporter genetic variation and the response of the human amygdala . Science 2002 ; 297:400–403 Crossref , Medline ,  Google Scholar

58 Hariri AR, Drabant EM, Munoz KE, et al. : A susceptibility gene for affective disorders and the response of the human amygdala . Arch Gen Psychiatry 2005 ; 62:146–152 Crossref , Medline ,  Google Scholar

59 Dunlop BW, Nemeroff CB : The role of dopamine in the pathophysiology of depression . Arch Gen Psychiatry 2007 ; 64:327–337 Crossref , Medline ,  Google Scholar

60 Nestler EJ, Carlezon WA Jr : The mesolimbic dopamine reward circuit in depression . Biol Psychiatry 2006 ; 59:1151–1159 Crossref , Medline ,  Google Scholar

61 Suridjan I, Boileau I, Bagby M, et al. : Dopamine response to psychosocial stress in humans and its relationship to individual differences in personality traits . J Psychiatr Res 2012 ; 46:890–897 Crossref , Medline ,  Google Scholar

62 Cabib S, Puglisi-Allegra S : The mesoaccumbens dopamine in coping with stress . Neurosci Biobehav Rev 2012 ; 36:79–89 Crossref , Medline ,  Google Scholar

63 Krishnan V, Nestler EJ : Linking molecules to mood: new insight into the biology of depression . Am J Psychiatry 2010 ; 167:1305–1320 Crossref , Medline ,  Google Scholar

64 Bogdan R, Nikolova YS, Pizzagalli DA : Neurogenetics of depression: a focus on reward processing and stress sensitivity . Neurobiol Dis 2013 ; 52:12–23 Crossref , Medline ,  Google Scholar

65 Der-Avakian A, Markou A : The neurobiology of anhedonia and other reward-related deficits . Trends Neurosci 2012 ; 35:68–77 Crossref , Medline ,  Google Scholar

66 Pruessner JC, Champagne F, Meaney MJ, et al. : Dopamine release in response to a psychological stress in humans and its relationship to early life maternal care: a positron emission tomography study using [11C]raclopride . J Neurosci 2004 ; 24:2825–2831 Crossref , Medline ,  Google Scholar

67 Mongeau R, Blier P, de Montigny C : The serotonergic and noradrenergic systems of the hippocampus: their interactions and the effects of antidepressant treatments . Brain Res Brain Res Rev 1997 ; 23:145–195 Crossref , Medline ,  Google Scholar

68 Mitchelmore C, Gede L : Brain derived neurotrophic factor: epigenetic regulation in psychiatric disorders . Brain Res 2014 ; 1586:162–172 Crossref , Medline ,  Google Scholar

69 Duman RS, Monteggia LM : A neurotrophic model for stress-related mood disorders . Biol Psychiatry 2006 ; 59:1116–1127 Crossref , Medline ,  Google Scholar

70 Mössner R, Daniel S, Albert D, et al. : Serotonin transporter function is modulated by brain-derived neurotrophic factor (BDNF) but not nerve growth factor (NGF) . Neurochem Int 2000 ; 36:197–202 Crossref , Medline ,  Google Scholar

71 Ota KT, Duman RS : Environmental and pharmacological modulations of cellular plasticity: role in the pathophysiology and treatment of depression . Neurobiol Dis 2013 ; 57:28–37 Crossref , Medline ,  Google Scholar

72 Suri D, Vaidya VA : Glucocorticoid regulation of brain-derived neurotrophic factor: relevance to hippocampal structural and functional plasticity . Neuroscience 2013 ; 239:196–213 Crossref , Medline ,  Google Scholar

73 Duman RS : Role of neurotrophic factors in the etiology and treatment of mood disorders . Neuromolecular Med 2004 ; 5:11–25 Crossref , Medline ,  Google Scholar

74 Mahar I, Bambico FR, Mechawar N, et al. : Stress, serotonin, and hippocampal neurogenesis in relation to depression and antidepressant effects . Neurosci Biobehav Rev 2014 ; 38:173–192 Crossref , Medline ,  Google Scholar

75 Jacobs BL, van Praag H, Gage FH : Adult brain neurogenesis and psychiatry: a novel theory of depression . Mol Psychiatry 2000 ; 5:262–269 Crossref , Medline ,  Google Scholar

76 Malberg JE, Eisch AJ, Nestler EJ, et al. : Chronic antidepressant treatment increases neurogenesis in adult rat hippocampus . J Neurosci 2000 ; 20:9104–9110 Crossref , Medline ,  Google Scholar

77 Alenina N, Klempin F : The role of serotonin in adult hippocampal neurogenesis . Behav Brain Res 2015 ; 277:49–57 Crossref , Medline ,  Google Scholar

78 Krugers HJ, Lucassen PJ, Karst H, et al. : Chronic stress effects on hippocampal structure and synaptic function: relevance for depression and normalization by anti-glucocorticoid treatment . Front Synaptic Neurosci 2010 ; 2:24 Medline ,  Google Scholar

79 Hanson ND, Nemeroff CB, Owens MJ : Lithium, but not fluoxetine or the corticotropin-releasing factor receptor 1 receptor antagonist R121919, increases cell proliferation in the adult dentate gyrus . J Pharmacol Exp Ther 2011 ; 337:180–186 Crossref , Medline ,  Google Scholar

80 Haroon E, Raison CL, Miller AH : Psychoneuroimmunology meets neuropsychopharmacology: translational implications of the impact of inflammation on behavior . Neuropsychopharmacology 2012 ; 37:137–162 Crossref , Medline ,  Google Scholar

81 Capuron L, Miller AH : Immune system to brain signaling: neuropsychopharmacological implications . Pharmacol Ther 2011 ; 130:226–238 Crossref , Medline ,  Google Scholar

82 Cattaneo A, Macchi F, Plazzotta G, et al. : Inflammation and neuronal plasticity: a link between childhood trauma and depression pathogenesis . Front Cell Neurosci 2015 ; 9:40 Crossref , Medline ,  Google Scholar

83 Leonard BE : The concept of depression as a dysfunction of the immune system . Curr Immunol Rev 2010 ; 6:205–212 Crossref , Medline ,  Google Scholar

84 Loftis JM, Huckans M, Morasco BJ : Neuroimmune mechanisms of cytokine-induced depression: current theories and novel treatment strategies . Neurobiol Dis 2010 ; 37:519–533 Crossref , Medline ,  Google Scholar

85 Zhu CB, Blakely RD, Hewlett WA : The proinflammatory cytokines interleukin-1beta and tumor necrosis factor-alpha activate serotonin transporters . Neuropsychopharmacology 2006 ; 31:2121–2131 Crossref , Medline ,  Google Scholar

86 Folkman S, Lazarus RS : The relationship between coping and emotion: implications for theory and research . Soc Sci Med 1988 ; 26:309–317 Crossref , Medline ,  Google Scholar

87 Beck AT : The evolution of the cognitive model of depression and its neurobiological correlates . Am J Psychiatry 2008 ; 165:969–977 Crossref , Medline ,  Google Scholar

88 Heim C, Binder EB : Current research trends in early life stress and depression: review of human studies on sensitive periods, gene-environment interactions, and epigenetics . Exp Neurol 2012 ; 233:102–111 Crossref , Medline ,  Google Scholar

89 Gibb BE, Abramson LY, Alloy LB : Emotional maltreatment from parents, verbal peer victimization, and cognitive vulnerability to depression . Cogn Ther Res 2004 ; 28:1–21 Crossref ,  Google Scholar

90 Stankiewicz AM, Swiergiel AH, Lisowski P : Epigenetics of stress adaptations in the brain . Brain Res Bull 2013 ; 98:76–92 Crossref , Medline ,  Google Scholar

91 Peña CJ, Bagot RC, Labonté B, et al. : Epigenetic signaling in psychiatric disorders . J Mol Biol 2014 ; 426:3389–3412 Crossref , Medline ,  Google Scholar

92 Elliott E, Ezra-Nevo G, Regev L, et al. : Resilience to social stress coincides with functional DNA methylation of the Crf gene in adult mice . Nat Neurosci 2010 ; 13:1351–1353 Crossref , Medline ,  Google Scholar

93 Peng H, Long Y, Li J, et al. : Hypothalamic-pituitary-adrenal axis functioning and dysfunctional attitude in depressed patients with and without childhood neglect . BMC Psychiatry 2014 ; 14:45 Crossref , Medline ,  Google Scholar

94 Sapolsky RM, Krey LC, McEwen BS : Stress down-regulates corticosterone receptors in a site-specific manner in the brain . Endocrinology 1984 ; 114:287–292 Crossref , Medline ,  Google Scholar

95 Korosi A, Naninck EF, Oomen CA, et al. : Early-life stress mediated modulation of adult neurogenesis and behavior . Behav Brain Res 2012 ; 227:400–409 Crossref , Medline ,  Google Scholar

96 Meaney MJ, Szyf M : Environmental programming of stress responses through DNA methylation: life at the interface between a dynamic environment and a fixed genome . Dialogues Clin Neurosci 2005 ; 7:103–123 Medline ,  Google Scholar

97 Klengel T, Mehta D, Anacker C, et al. : Allele-specific FKBP5 DNA demethylation mediates gene-childhood trauma interactions . Nat Neurosci 2013 ; 16:33–41 Crossref , Medline ,  Google Scholar

98 Hammen C, Henry R, Daley SE : Depression and sensitization to stressors among young women as a function of childhood adversity . J Consult Clin Psychol 2000 ; 68:782–787 Crossref , Medline ,  Google Scholar

99 Gould F, Clarke J, Heim C, et al. : The effects of child abuse and neglect on cognitive functioning in adulthood . J Psychiatr Res 2012 ; 46:500–506 Crossref , Medline ,  Google Scholar

• Identifying tripartite relationship among cortical thickness, neuroticism, and mood and anxiety disorders 11 April 2024 | Scientific Reports, Vol. 14, No. 1
• Investigating the relationship between hippocampus/dentate gyrus volume and hypothalamus metabolism in participants with major depressive disorder 9 May 2024 | Scientific Reports, Vol. 14, No. 1
• Sodium propionate oral supplementation ameliorates depressive-like behavior through gut microbiome and histone 3 epigenetic regulation The Journal of Nutritional Biochemistry, Vol. 130
• What are blood cortisol levels in anxious, depressive, mixed, and healthy controls in high altitude? Psiquiatría Biológica, Vol. 31, No. 3
• Effects of deep brain stimulation on dopamine D2 receptor binding in patients with treatment-refractory depression Journal of Affective Disorders, Vol. 356
• MUSIC THERAPY GROUP FOR WOMEN OUTPATIENTS WITH MAJOR DEPRESSIVE DISORDER UNDER TREATMENT AS USUAL IN A PSYCHIATRIC INSTITUTION: A NON-RANDOMIZED PILOT TRIAL The Arts in Psychotherapy, Vol. 2017
• Association between sickness presenteeism and depressive symptoms by occupation and employment type during the COVID-19 pandemic Safety and Health at Work, Vol. 64
• Cross-continental environmental and genome-wide association study on children and adolescent anxiety and depression 17 May 2024 | Frontiers in Psychiatry, Vol. 15
• Heterogeneous trajectories of perceived stress and their associations with active leisure: a longitudinal study during the first year of COVID-19 9 May 2024 | Frontiers in Public Health, Vol. 12
• Statistical Genetic Approaches to Investigate Genotype-by-Environment Interaction: Review and Novel Extension of Models 25 April 2024 | Genes, Vol. 15, No. 5
• Chronic corticosterone exposure in rats induces sex-specific alterations in hypothalamic reelin fragments, MeCP2, and DNMT3a protein levels Neuroscience Letters, Vol. 830
• Life Stressors and Mental Well-Being: Experiences of African American Women Navigating Perinatal Loss and Pregnancy Subsequent to Loss 6 February 2024 | Journal of Black Psychology, Vol. 50, No. 3
• The Impact of the COVID-19 Pandemic on Depression, Anxiety, and Stress among Black Women with Depressive Symptoms at a Federally Qualified Health Center 18 April 2024 | Journal of Racial and Ethnic Health Disparities, Vol. 12
• How inflammation influences psychiatric disease 19 March 2024 | World Journal of Psychiatry, Vol. 14, No. 3
• The relationship between neuroticism and the acute psychological stress response: Evidence from the autonomic nervous system 18 March 2024 | Current Psychology, Vol. 92
• Fermented mixed grain ameliorates chronic stress-induced depression-like behavior and memory deficit 27 July 2023 | Food Science and Biotechnology, Vol. 33, No. 4
• Virtual Reality Breathing Interventions for Mental Health: A Systematic Review and Meta-Analysis of Randomized Controlled Trials 18 January 2024 | Applied Psychophysiology and Biofeedback, Vol. 49, No. 1
• Unraveling the associations between unhealthy lifestyle behaviors and mental health in the general adult Chinese population: A cross-sectional study Journal of Affective Disorders, Vol. 349
• Stress exposure, perceived stress severity, and their effects on health 1 April 2024 | Sociology: Theory, Methods, Marketing, Vol. 49, No. 1
• The Role of Lysophosphatidic Acid in Neuropsychiatric and Neurodegenerative Disorders 4 March 2024 | Future Pharmacology, Vol. 4, No. 1
• Value of serum brain-derived neurotrophic factor and glial fibrillary acidic protein for detecting depression in patients with Helicobacter pylori infection Neuroscience Letters, Vol. 825
• Evaluation of the anxiolytic activity of ethanolic extract of Ziziphus mauritiana Lam. in Swiss albino mice Pharmacological Research - Modern Chinese Medicine, Vol. 10
• A soy protein enzymatic digest mitigates Nrf2-related oxidative stress and attenuates depression-like behavior in a mouse model of sub-chronic restraint stress Heliyon, Vol. 10, No. 6
• Alleviating Stress in Parkinson’s Disease: Symptomatic Treatment, Disease Modification, or Both? Journal of Parkinson's Disease, Vol. 9
• Behavioural characteristics and sex differences of a treatment-resistant depression model: Chronic mild stress in the Wistar-Kyoto rat Behavioural Brain Research, Vol. 457
• A mouse model of oral contraceptive exposure: Depression, motivation, and the stress response Hormones and Behavior, Vol. 158
• Effects of relaxation interventions during pregnancy on maternal mental health, and pregnancy and newborn outcomes: A systematic review and meta-analysis 25 January 2024 | PLOS ONE, Vol. 19, No. 1
• Histone 3 Trimethylation Patterns are Associated with Resilience or Stress Susceptibility in a Rat Model of Major Depression Disorder 16 January 2024 | Molecular Neurobiology, Vol. 13
• Diet Quality Mediates the Relationship Between Chronic Stress and Inflammation in Patients With Metabolic Syndrome 9 January 2024 | Journal of Cardiovascular Nursing, Vol. 14
• Socioecological Model of a Military Family’s Health and Well-Being: Inside a Slovenian Military Family 24 August 2022 | Armed Forces & Society, Vol. 50, No. 1
• Association between inflammatory bowel disease and subsequent depression or anxiety disorders - A retrospective cohort study of 31,728 outpatients Journal of Psychiatric Research, Vol. 169
• Clotrimazole ameliorates chronic mild stress-induced depressive-like behavior in rats; crosstalk between the HPA, NLRP3 inflammasome, and Wnt/β-catenin pathways International Immunopharmacology, Vol. 127
• Assessing the Effectiveness of STAPP@Work, a Self-Management Mobile App, in Reducing Work Stress and Preventing Burnout: Single-Case Experimental Design Study 29 February 2024 | Journal of Medical Internet Research, Vol. 26
• Depression in Final-Year Medical Students in Ho Chi Minh City, Vietnam: The Role of Career-Choice Motivation 18 March 2024 | Journal of Medical Education and Curricular Development, Vol. 11
• Interplay of G-proteins and Serotonin in the Neuroimmunoinflammatory Model of Chronic Stress and Depression: A Narrative Review Current Pharmaceutical Design, Vol. 30, No. 3
• A neuronal coping mechanism linking stress-induced anxiety to motivation for reward Science Advances, Vol. 9, No. 49
• Effect of breathwork on stress and mental health: A meta-analysis of randomised-controlled trials 9 January 2023 | Scientific Reports, Vol. 13, No. 1
• Chronic social defeat alters brain vascular-associated cell gene expression patterns leading to vascular dysfunction and immune system activation 28 June 2023 | Journal of Neuroinflammation, Vol. 20, No. 1
• Wistar-Kyoto rats and chronically stressed Wistar rats present similar depression- and anxiety-like behaviors but different corticosterone and endocannabinoid system modulation Progress in Neuro-Psychopharmacology and Biological Psychiatry, Vol. 127
• Investigating the potential effects of α-synuclein aggregation on susceptibility to chronic stress in a mouse Parkinson’s disease model 19 September 2023 | Pharmacological Reports, Vol. 75, No. 6
• A comparative analysis of depressive-like behavior: Exploring sex-related differences and insights 29 November 2023 | PLOS ONE, Vol. 18, No. 11
• Effect of consumption of blue maize tortilla on anxiety-like behaviour, learning, memory and hippocampal BDNF expression in a chronic stress model in rats 29 September 2022 | Nutritional Neuroscience, Vol. 26, No. 11
• Lactobacillus reuteri ATG-F4 Alleviates Chronic Stress-induced Anhedonia by Modulating the Prefrontal Serotonergic System Experimental Neurobiology, Vol. 32, No. 5
• The presentation of anxiety and depression among children and youth diagnosed with hidradenitis suppurativa: A review 10 October 2023 | Pediatric Dermatology, Vol. 63
• Psychological, behavioural and biological factors associated with gastrointestinal symptoms in autistic adults and adults with autistic traits 16 February 2023 | Autism, Vol. 27, No. 7
• Effects of Online Pilates and Face-to-Face Pilates Intervention on Body Composition, Muscle Mechanical Properties, Cardiometabolic Parameters, Mental Health, and Physical Fitness in Middle-Aged Women with Obesity 19 October 2023 | Healthcare, Vol. 11, No. 20
• Antidepressant effects of coumarins and their derivatives: A critical analysis of research advances European Journal of Pharmacology, Vol. 956
• Adverse Childhood Experiences, Intimate Partner Violence, and Mental Well-Being Among Mothers of Toddlers in Tirana, Albania: A Cross-Sectional Mediation Analysis 29 September 2023 | Violence Against Women, Vol. 9
• Emphasizing the Crosstalk Between Inflammatory and Neural Signaling in Post-traumatic Stress Disorder (PTSD) 25 April 2023 | Journal of Neuroimmune Pharmacology, Vol. 18, No. 3
• Stress-mediated dysregulation of the Rap1 small GTPase impairs hippocampal structure and function iScience, Vol. 26, No. 9
• Exploring Novel Therapeutic Approaches for Depressive Disorders: The Role of Allopregnanolone Agonists Cureus, Vol. 291
• Nervous-System-Wise Functional Estimation of Directed Brain–Heart Interplay Through Microstate Occurrences IEEE Transactions on Biomedical Engineering, Vol. 70, No. 8
• Stress in the social context: a behavioural and eco-evolutionary perspective 2 August 2023 | Journal of Experimental Biology, Vol. 226, No. 15
• Pathways to Psychopathology Among Autistic Adults 28 June 2023 | Current Psychiatry Reports, Vol. 25, No. 8
• Stress-Activated Protein Kinase JNK Modulates Depression-like Behaviors in Mice 18 January 2023 | Molecular Neurobiology, Vol. 60, No. 5
• Chronic oral ketamine prevents anhedonia and alters neuronal activation in the lateral habenula and nucleus accumbens in rats under chronic unpredictable mild stress Neuropharmacology, Vol. 228
• Dietary tryptophan-rich protein hydrolysate can acutely impact physiological and psychological measures of mood and stress in healthy adults 28 March 2022 | Nutritional Neuroscience, Vol. 26, No. 4
• Mental health as part of the integrative palliative care in all levels of care in urooncology 23 April 2022 | Aging & Mental Health, Vol. 27, No. 4
• Antidepressant and Neuroprotective Effects of 3-Hydroxy Paroxetine, an Analog of Paroxetine in Rats 26 November 2022 | International Journal of Neuropsychopharmacology, Vol. 26, No. 3
• Cortisol Biosensors: From Sensing Principles to Applications 15 March 2023 | World Scientific Annual Review of Functional Materials
• Elevated Hippocampal CRMP5 Mediates Chronic Stress-Induced Cognitive Deficits by Disrupting Synaptic Plasticity, Hindering AMPAR Trafficking, and Triggering Cytokine Release 3 March 2023 | International Journal of Molecular Sciences, Vol. 24, No. 5
• Can a single session of noninvasive brain stimulation applied over the prefrontal cortex prevent stress-induced cortisol release? Progress in Neuro-Psychopharmacology and Biological Psychiatry, Vol. 121
• Psychoneuro-oncology: How chronic stress grows cancer Clinics in Dermatology, Vol. 54
• Peripheral brain-derived neurotrophic factor (BDNF) and salivary cortisol levels in college students with different levels of academic stress. Study protocol 22 February 2023 | PLOS ONE, Vol. 18, No. 2
• The Role of Genetics in the Development and Pharmacotherapy of Depression and Its Impact on Drug Discovery 2 February 2023 | International Journal of Molecular Sciences, Vol. 24, No. 3
• Role of group II metabotropic glutamate receptors in ketamine's antidepressant actions Pharmacology Biochemistry and Behavior, Vol. 223
• Relating parental stress with sleep disorders in parents and children 25 January 2023 | PLOS ONE, Vol. 18, No. 1
• Anxiety, depression, and academic stress among medical students during the COVID-19 pandemic 11 January 2023 | Frontiers in Psychology, Vol. 13
• Associations Between Demographic, Clinical, and Symptom Characteristics and Stress in Oncology Patients Receiving Chemotherapy 4 March 2022 | Cancer Nursing, Vol. 46, No. 1
• Prevalence of Common Alleles of Some Stress Resilience Genes among Adolescents Born in Different Periods Relative to the Socioeconomic Crisis of the 1990s in Russia 21 December 2022 | Current Issues in Molecular Biology, Vol. 45, No. 1
• Introducing a depression-like syndrome for translational neuropsychiatry: a plea for taxonomical validity and improved comparability between humans and mice 14 September 2022 | Molecular Psychiatry, Vol. 28, No. 1
• Targeting the Arginine Vasopressin V1b Receptor System and Stress Response in Depression and Other Neuropsychiatric Disorders 1 April 2023 | Neuropsychiatric Disease and Treatment, Vol. Volume 19
• Efficacy and Anti-Inflammatory Activity of Ashwagandha Sustained-Release Formulation on Depression and Anxiety Induced by Chronic Unpredictable Stress: in vivo and in vitro Studies 1 July 2023 | Journal of Experimental Pharmacology, Vol. Volume 15
• Gender Differences in Older Adults’ Muscle Strength and Depressive Symptoms: A Relationship Mediated Through Perceived Stress 27 September 2023 | Research in Community and Public Health Nursing, Vol. 34
• Increased academic stress is associated with decreased plasma BDNF in Chilean college students 3 November 2023 | PeerJ, Vol. 11
• Interaction effect between childhood abuse and interleukin-1β levels on suicidality in depressed patients 25 April 2022 | European Archives of Psychiatry and Clinical Neuroscience, Vol. 272, No. 8
• Prevalence, associated factors and perinatal outcomes of antepartum depression in Ibadan Nigeria 18 March 2022 | BMC Pregnancy and Childbirth, Vol. 22, No. 1
• Stress, mental disorder and ketamine as a novel, rapid acting treatment European Neuropsychopharmacology, Vol. 65
• Preschool Depression and Hippocampal Volume: The Moderating Role of Family Income Journal of the American Academy of Child & Adolescent Psychiatry, Vol. 61, No. 11
• HPA Axis Responsiveness Associates with Central Serotonin Transporter Availability in Human Obesity and Non-Obesity Controls 25 October 2022 | Brain Sciences, Vol. 12, No. 11
• Restraint Stress and Repeated Corticosterone Administration Differentially Affect Neuronal Excitability, Synaptic Transmission and 5-HT7 Receptor Reactivity in the Dorsal Raphe Nucleus of Young Adult Male Rats 18 November 2022 | International Journal of Molecular Sciences, Vol. 23, No. 22
• Neighbourhood crime and major depression in Sweden: A national cohort study Health & Place, Vol. 78
• Sleep disorders mediate the link between childhood trauma and depression severity in children and adolescents with depression 28 October 2022 | Frontiers in Psychiatry, Vol. 13
• The cumulative effect of chronic stress and depressive symptoms affects heart rate in a working population 13 October 2022 | Frontiers in Psychiatry, Vol. 13
• Depression among people with dyspepsia and H. pylori infection: A community based cross-sectional study in Ethiopia 6 October 2022 | PLOS ONE, Vol. 17, No. 10
• Cross-Sectional Analysis of Mental Health among University Students: Do Sex and Academic Level Matter? 3 October 2022 | International Journal of Environmental Research and Public Health, Vol. 19, No. 19
• Hormonal contraceptives, stress, and the brain: The critical need for animal models Frontiers in Neuroendocrinology, Vol. 67
• Olfactory loss is a predisposing factor for depression, while olfactory enrichment is an effective treatment for depression 28 September 2022 | Frontiers in Neuroscience, Vol. 16
• Understanding the basis of major depressive disorder in oncological patients: Biological links, clinical management, challenges, and lifestyle medicine 16 September 2022 | Frontiers in Oncology, Vol. 12
• The Lived Experiences of Depression in Huntington’s Disease: A Qualitative Study Journal of Huntington's Disease, Vol. 11, No. 3
• Exaggerated amygdala response to threat and association with immune hyperactivity in depression Brain, Behavior, and Immunity, Vol. 104
• Causal Symbolic Information Transfer for the Assessment of functional Brain-Heart Interplay through EEG Microstates Occurrences: a proof-of-concept study
• Mental Health Conditions According to Stress and Sleep Disorders 29 June 2022 | International Journal of Environmental Research and Public Health, Vol. 19, No. 13
• Different Areas of Chronic Stress and Their Associations with Depression 19 July 2022 | International Journal of Environmental Research and Public Health, Vol. 19, No. 14
• Vestibular Disability/Handicap in Fibromyalgia: A Questionnaire Study 11 July 2022 | Journal of Clinical Medicine, Vol. 11, No. 14
• Perceived stress and E-cigarette use during emerging adulthood: A longitudinal examination of initiation, progression, and continuation Preventive Medicine, Vol. 160
• Efficacy of Withania somnifera supplementation on adult’s cognition and mood Journal of Ayurveda and Integrative Medicine, Vol. 13, No. 2
• CCR2 monocytes repair cerebrovascular damage caused by chronic social defeat stress Brain, Behavior, and Immunity, Vol. 101
• The interplay between self-esteem, expectancy, cognitive control, rumination, and the experience of stress: A network analysis 14 February 2022 | Current Psychology, Vol. 79
• The Cortisol Assessment List (CoAL) A tool to systematically document and evaluate cortisol assessment in blood, urine and saliva Comprehensive Psychoneuroendocrinology, Vol. 9
• Introduction to the Study of Stress 17 May 2022
• Neurobiological Approach to Stress 17 May 2022
• Clinical Approach to Stress 17 May 2022
• Medical Comorbidities of Hispanics with Mental Health Problems 2 December 2022
• Specific and cumulative lifetime stressors in the aetiology of major depression: A longitudinal community-based population study 26 January 2022 | Epidemiology and Psychiatric Sciences, Vol. 31
• Associations of state-level and county-level hate crimes with individual-level cardiovascular risk factors in a prospective cohort study of middle-aged Americans: the National Longitudinal Survey of Youths 1979 19 January 2022 | BMJ Open, Vol. 12, No. 1
• The role of pre-pandemic depression for changes in depression, anxiety, and loneliness during the COVID-19 pandemic: Results from a longitudinal probability sample of adults from Germany 3 November 2022 | European Psychiatry, Vol. 65, No. 1
• Interleukin-6 expression and its modulation by diacerein in a rat model of chronic stress induced cardiac dysfunction Heliyon, Vol. 7, No. 12
• Psychometric properties and correlates of Chinese version of Perceived Stress Scale (CPSS-10) in people with common mental disorders with different employment Statuses 4 August 2021 | Hong Kong Journal of Occupational Therapy, Vol. 34, No. 2
• Association between the expression of lncRNA BASP-AS1 and volume of right hippocampal tail moderated by episode duration in major depressive disorder: a CAN-BIND 1 report 8 September 2021 | Translational Psychiatry, Vol. 11, No. 1
• Psychoneuroimmunology in the context of perinatal depression - Tools for improved clinical practice Brain, Behavior, & Immunity - Health, Vol. 17
• MicroRNAs as Critical Biomarkers of Major Depressive Disorder: A Comprehensive Perspective 10 November 2021 | Biomedicines, Vol. 9, No. 11
• Treadmill exercise sex-dependently alters susceptibility to depression-like behaviour, cytokines and BDNF in the hippocampus and prefrontal cortex of rats with sporadic Alzheimer-like disease Physiology & Behavior, Vol. 241
• Childhood trauma and depression in college students: Mediating and moderating effects of psychological resilience Asian Journal of Psychiatry, Vol. 65
• Back to nature: herbal treatment, environmental enrichment, and social play can protect against unpredictable chronic stress in Long-Evans rats (Rattus norvegicus) 1 August 2021 | Psychopharmacology, Vol. 238, No. 10
• Maternal chewing improves prenatal stress-induced cognitive deficit and anxiety-like behavior associated with alterations of the apoptotic response and serotonin pathway in mouse offspring Archives of Oral Biology, Vol. 130
• HPA Axis in the Pathomechanism of Depression and Schizophrenia: New Therapeutic Strategies Based on Its Participation 30 September 2021 | Brain Sciences, Vol. 11, No. 10
• Roles of fibroblast growth factor 21 in the control of depression‐like behaviours after social defeat stress in male rodents 1 September 2021 | Journal of Neuroendocrinology, Vol. 33, No. 10
• Weak and uneven associations of home, neighborhood, and school environments with stress hormone output across multiple timescales 4 May 2020 | Molecular Psychiatry, Vol. 26, No. 9
• Impact of Depression and Stress on Placental DNA Methylation in Ethnically Diverse Pregnant Women 29 September 2021 | Epigenomics, Vol. 13, No. 18
• Stress and Western diets increase vulnerability to neuropsychiatric disorders: A common mechanism 16 September 2019 | Nutritional Neuroscience, Vol. 24, No. 8
• Associations of job strain and family strain with risk of major depressive episode: A prospective cohort study in U.S. working men and women Journal of Psychosomatic Research, Vol. 147
• Prolonged Maternal Separation Induces the Depression-Like Behavior Susceptibility to Chronic Unpredictable Mild Stress Exposure in Mice BioMed Research International, Vol. 2021
• Endometriosis and related pelvic pain: association with stress, anxiety and depressive symptoms Minerva Obstetrics and Gynecology, Vol. 73, No. 3
• Title: “Labels Matter: Is it stress or is it Trauma?” 10 July 2021 | Translational Psychiatry, Vol. 11, No. 1
• Depression, anxiety, and the COVID-19 pandemic: Severity of symptoms and associated factors among university students after the end of the movement lockdown 27 May 2021 | PLOS ONE, Vol. 16, No. 5
• Traditional Chinese Medicine Body Constitutions and Psychological Determinants of Depression among University Students in Malaysia: A Pilot Study 18 May 2021 | International Journal of Environmental Research and Public Health, Vol. 18, No. 10
• A pioneering FreeSurfer volumetric study of a series of patients with mesial temporal lobe epilepsy and hippocampal sclerosis with comorbid depression Psychiatry Research: Neuroimaging, Vol. 311
• Increased Stress Resistance and Lifespan in Chaenorhabditis elegans Wildtype and Knockout Mutants—Implications for Depression Treatment by Medicinal Herbs 24 March 2021 | Molecules, Vol. 26, No. 7
• Activation of PPG neurons following acute stressors differentially involves hindbrain serotonin in male rats Neuropharmacology, Vol. 187
• Comorbid anxiety-like behavior in a rat model of colitis is mediated by an upregulation of corticolimbic fatty acid amide hydrolase 15 January 2021 | Neuropsychopharmacology, Vol. 46, No. 5
• Prenatal Alcohol Exposure and Hypothalamic-Pituitary-Adrenal Axis Activity of the Offspring in Humans: a Systematic Review 5 January 2021 | Current Addiction Reports, Vol. 8, No. 1
• Depression and anxiety in the Malaysian urban population and their association with demographic characteristics, quality of life, and the emergence of the COVID-19 pandemic 19 February 2021 | Current Psychology, Vol. 16
• Hypothalamic Inflammation as a Potential Pathophysiologic Basis for the Heterogeneity of Clinical, Hormonal, and Metabolic Presentation in PCOS 5 February 2021 | Nutrients, Vol. 13, No. 2
• Potential Interactions Between Environmental and Psychoneurobiological Factors in the Interface Between Stress and Depression: A Road Map to Resilience 24 January 2021
• The 2020 Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders 22 December 2020 | Australian & New Zealand Journal of Psychiatry, Vol. 55, No. 1
• Effects of an African Circle Dance Programme on Internally Displaced Persons with Depressive Symptoms: A Quasi-Experimental Study 19 January 2021 | International Journal of Environmental Research and Public Health, Vol. 18, No. 2
• Yoga and Lifestyle Factors in Heart Disease
• Association between depression, anxiety, stress and perceived quality of life in a Malaysian B40 urban community during the COVID-19 lockdown: A cross-sectional study 29 July 2021 | F1000Research, Vol. 10
• Fluoxetine increases hippocampal neural survival by improving axonal transport in stress-induced model of depression male rats Physiology & Behavior, Vol. 227
• A Systematic Review of the Association Between Psychological Stress and Dementia Risk in Humans Journal of Alzheimer's Disease, Vol. 78, No. 1
• A Gut Feeling: The Importance of the Intestinal Microbiota in Psychiatric Disorders 19 October 2020 | Frontiers in Immunology, Vol. 11
• Efficacy of probiotics on stress in healthy volunteers: A systematic review and meta‐analysis based on randomized controlled trials 14 July 2020 | Brain and Behavior, Vol. 10, No. 9
• The influence of the duration of chronic unpredictable mild stress on the behavioural responses of C57BL/6J mice 18 May 2020 | Behavioural Pharmacology, Vol. 31, No. 6
• Corticosterone Induced the Increase of proBDNF in Primary Hippocampal Neurons Via Endoplasmic Reticulum Stress 6 May 2020 | Neurotoxicity Research, Vol. 38, No. 2
• Monoaminergic balances predict non-depression-like phenotype in Learned Helplessness Paradigm Neuroscience, Vol. 440
• Abnormal functional connectivity as neural biological substrate of trait and state characteristics in major depressive disorder Progress in Neuro-Psychopharmacology and Biological Psychiatry, Vol. 102
• Chronic stress and endothelial dysfunction: mechanisms, experimental challenges, and the way ahead American Journal of Physiology-Heart and Circulatory Physiology, Vol. 319, No. 2
• Heart Rate Variability as an Index of Differential Brain Dynamics at Rest and After Acute Stress Induction 2 July 2020 | Frontiers in Neuroscience, Vol. 14
• Early life stress and glutamate neurotransmission in major depressive disorder European Neuropsychopharmacology, Vol. 35
• Prevalence of and factors associated with depression among hill tribe individuals aged 30 years and over in Thailand Heliyon, Vol. 6, No. 6
• State-of-the-Art: Inflammatory and Metabolic Markers in Mood Disorders 6 June 2020 | Life, Vol. 10, No. 6
• Pharmacological Treatment of Anxiety Disorders: The Role of the HPA Axis 15 May 2020 | Frontiers in Psychiatry, Vol. 11
• Neuroinflammation and depressive disorder: The role of the hypothalamus Journal of Clinical Neuroscience, Vol. 75
• From Work Well-Being to Burnout: A Hypothetical Phase Model 30 April 2020 | Frontiers in Neuroscience, Vol. 14
• Finding intestinal fortitude: Integrating the microbiome into a holistic view of depression mechanisms, treatment, and resilience Neurobiology of Disease, Vol. 135
• Role of glucocorticoid- and monoamine-metabolizing enzymes in stress-related psychopathological processes 19 July 2019 | Stress, Vol. 23, No. 1
• Is Stress a Cause or a Consequence of Endometriosis? 6 January 2020 | Reproductive Sciences, Vol. 27, No. 1
• Hypothalamic–pituitary–adrenal axis and stress
• Immune response to stress induction as a predictor of cognitive-behavioral therapy outcomes in adolescent mood disorders: A pilot study Journal of Psychiatric Research, Vol. 120
• Enduring and Sex-specific Changes in Hippocampal Gene Expression after a Subchronic Immune Challenge Neuroscience, Vol. 428
• Childhood maltreatment and risk for suicide attempts in major depression: a sex-specific approach 7 May 2019 | European Journal of Psychotraumatology, Vol. 10, No. 1
• How Do Couples Cope With Unemployment: Examining Relationships Among Support, Undermining, and Depression 10 March 2019 | The Family Journal, Vol. 27, No. 3
• REM sleep’s unique associations with corticosterone regulation, apoptotic pathways, and behavior in chronic stress in mice 25 January 2019 | Proceedings of the National Academy of Sciences, Vol. 116, No. 7
• Subclinical Lipopolysaccharide from Salmonella Enteritidis Induces Dysregulation of Bioactive Substances from Selected Brain Sections and Glands of Neuroendocrine Axes 2 February 2019 | Toxins, Vol. 11, No. 2
• Advance in Stress for Depressive Disorder 30 November 2019
• Antidepressant and anxiolytic effects of the proprietary Chinese medicine Shexiang Baoxin pill in mice with chronic unpredictable mild stress Journal of Food and Drug Analysis, Vol. 27, No. 1
• Frontiers in Neuroscience, Vol. 13
• A gene–gene interaction between the vascular endothelial growth factor a and brain-derived neurotrophic factor genes is associated with psychological distress in the Taiwanese population Taiwanese Journal of Psychiatry, Vol. 33, No. 3
• An investigation into the stress-relieving and pharmacological actions of an ashwagandha (Withania somnifera) extract 1 September 2019 | Medicine, Vol. 98, No. 37
• Cortisol and alpha-amylase assessment in psychotherapeutic intervention studies: A systematic review Neuroscience & Biobehavioral Reviews, Vol. 95
• Neighborhood disorder, exposure to violence, and perceived discrimination in relation to symptoms in midlife women 19 October 2018 | Women's Midlife Health, Vol. 4, No. 1
• Potassium 2-(1-hydroxypentyl)-benzoate improves depressive-like behaviors in rat model Acta Pharmaceutica Sinica B, Vol. 8, No. 6
• Emotional stress regulation: The role of relative frontal alpha asymmetry in shaping the stress response Biological Psychology, Vol. 138
• Late chronotype is associated with enhanced amygdala reactivity and reduced fronto-limbic functional connectivity to fearful versus happy facial expressions NeuroImage, Vol. 171
• Behavioral and structural adaptations to stress Frontiers in Neuroendocrinology, Vol. 49
• The Neurochemistry of Stress: the Chemistry of the Stress Response and Stress Vulnerability 30 June 2018 | Neurochemical Journal, Vol. 12, No. 2
• Hair cortisol and the relationship with chronic pain and quality of life in endometriosis patients Psychoneuroendocrinology, Vol. 89
• miRNAs As Critical Epigenetic Players in Determining Neurobiological Correlates of Major Depressive Disorder 25 October 2017
• Hepatology International, Vol. 12, No. 4
• Neuropsychopharmacology, Vol. 43, No. 5
• Bibliographie
• Medical Yoga Therapy 10 February 2017 | Children, Vol. 4, No. 2
• Psihijatrija danas, Vol. 49, No. 1
• Hippocampal volume and integrity as predictors of cognitive decline in intact elderly NeuroReport, Vol. 27, No. 11
• Psychosocial stress based on public speech in humans: is there a real life/laboratory setting cross-adaptation? 4 July 2016 | Stress, Vol. 19, No. 4

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

7 Depression Research Paper Topic Ideas

Nancy Schimelpfening, MS is the administrator for the non-profit depression support group Depression Sanctuary. Nancy has a lifetime of experience with depression, experiencing firsthand how devastating this illness can be.

Cara Lustik is a fact-checker and copywriter.

In psychology classes, it's common for students to write a depression research paper. Researching depression may be beneficial if you have a personal interest in this topic and want to learn more, or if you're simply passionate about this mental health issue. However, since depression is a very complex subject, it offers many possible topics to focus on, which may leave you wondering where to begin.

If this is how you feel, here are a few research titles about depression to help inspire your topic choice. You can use these suggestions as actual research titles about depression, or you can use them to lead you to other more in-depth topics that you can look into further for your depression research paper.

What Is Depression?

Everyone experiences times when they feel a little bit blue or sad. This is a normal part of being human. Depression, however, is a medical condition that is quite different from everyday moodiness.

Your depression research paper may explore the basics, or it might delve deeper into the  definition of clinical depression  or the  difference between clinical depression and sadness .

What Research Says About the Psychology of Depression

Studies suggest that there are biological, psychological, and social aspects to depression, giving you many different areas to consider for your research title about depression.

Types of Depression

There are several different types of depression  that are dependent on how an individual's depression symptoms manifest themselves. Depression symptoms may vary in severity or in what is causing them. For instance, major depressive disorder (MDD) may have no identifiable cause, while postpartum depression is typically linked to pregnancy and childbirth.

Depressive symptoms may also be part of an illness called bipolar disorder. This includes fluctuations between depressive episodes and a state of extreme elation called mania. Bipolar disorder is a topic that offers many research opportunities, from its definition and its causes to associated risks, symptoms, and treatment.

Causes of Depression

The possible causes of depression are many and not yet well understood. However, it most likely results from an interplay of genetic vulnerability  and environmental factors. Your depression research paper could explore one or more of these causes and reference the latest research on the topic.

For instance, how does an imbalance in brain chemistry or poor nutrition relate to depression? Is there a relationship between the stressful, busier lives of today's society and the rise of depression? How can grief or a major medical condition lead to overwhelming sadness and depression?

Who Is at Risk for Depression?

This is a good research question about depression as certain risk factors may make a person more prone to developing this mental health condition, such as a family history of depression, adverse childhood experiences, stress , illness, and gender . This is not a complete list of all risk factors, however, it's a good place to start.

The growing rate of depression in children, teenagers, and young adults is an interesting subtopic you can focus on as well. Whether you dive into the reasons behind the increase in rates of depression or discuss the treatment options that are safe for young people, there is a lot of research available in this area and many unanswered questions to consider.

Depression Signs and Symptoms

The signs of depression are those outward manifestations of the illness that a doctor can observe when they examine a patient. For example, a lack of emotional responsiveness is a visible sign. On the other hand, symptoms are subjective things about the illness that only the patient can observe, such as feelings of guilt or sadness.

An illness such as depression is often invisible to the outside observer. That is why it is very important for patients to make an accurate accounting of all of their symptoms so their doctor can diagnose them properly. In your depression research paper, you may explore these "invisible" symptoms of depression in adults or explore how depression symptoms can be different in children .

How Is Depression Diagnosed?

This is another good depression research topic because, in some ways, the diagnosis of depression is more of an art than a science. Doctors must generally rely upon the patient's set of symptoms and what they can observe about them during their examination to make a diagnosis. 

While there are certain  laboratory tests that can be performed to rule out other medical illnesses as a cause of depression, there is not yet a definitive test for depression itself.

If you'd like to pursue this topic, you may want to start with the Diagnostic and Statistical Manual of Mental Disorders (DSM). The fifth edition, known as DSM-5, offers a very detailed explanation that guides doctors to a diagnosis. You can also compare the current model of diagnosing depression to historical methods of diagnosis—how have these updates improved the way depression is treated?

Treatment Options for Depression

The first choice for depression treatment is generally an antidepressant medication. Selective serotonin reuptake inhibitors (SSRIs) are the most popular choice because they can be quite effective and tend to have fewer side effects than other types of antidepressants.

Psychotherapy, or talk therapy, is another effective and common choice. It is especially efficacious when combined with antidepressant therapy. Certain other treatments, such as electroconvulsive therapy (ECT) or vagus nerve stimulation (VNS), are most commonly used for patients who do not respond to more common forms of treatment.

Focusing on one of these treatments is an option for your depression research paper. Comparing and contrasting several different types of treatment can also make a good research title about depression.

A Word From Verywell

The topic of depression really can take you down many different roads. When making your final decision on which to pursue in your depression research paper, it's often helpful to start by listing a few areas that pique your interest.

From there, consider doing a little preliminary research. You may come across something that grabs your attention like a new study, a controversial topic you didn't know about, or something that hits a personal note. This will help you narrow your focus, giving you your final research title about depression.

Remes O, Mendes JF, Templeton P. Biological, psychological, and social determinants of depression: A review of recent literature . Brain Sci . 2021;11(12):1633. doi:10.3390/brainsci11121633

National Institute of Mental Health. Depression .

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition . American Psychiatric Association.

National Institute of Mental Health. Mental health medications .

Ferri, F. F. (2019). Ferri's Clinical Advisor 2020 E-Book: 5 Books in 1 . Netherlands: Elsevier Health Sciences.

By Nancy Schimelpfening Nancy Schimelpfening, MS is the administrator for the non-profit depression support group Depression Sanctuary. Nancy has a lifetime of experience with depression, experiencing firsthand how devastating this illness can be.  

• The Open University
• Guest user / Sign out
• Study with The Open University

My OpenLearn Profile

Personalise your OpenLearn profile, save your favourite content and get recognition for your learning

About this free course

Become an ou student, download this course, share this free course.

Start this free course now. Just create an account and sign in. Enrol and complete the course for a free statement of participation or digital badge if available.

A major aim of this course was to shed some light on the aetiology of depression and anxiety. At the end of it you should have some idea of the complexity of this enterprise. We have focused on one of the best-studied and hence best-understood contributors to psychopathology – stress. This has biological, social and psychological significance, and its operation can be studied and understood at all these levels.

The clear message you should take away is that interaction between these levels is enormously important in aetiology. Biological factors, such as dysregulation of the HPA axis and its consequences, possible abnormalities in brain neurotransmitter systems, the effects of stress on the developing brain at different ages, and the kinds of genes that an individual carries, appear to play an important part in the development and maintenance of emotional disorders such as depression and anxiety. However, these biological factors cannot be divorced from factors that are thought of as psychosocial, such as abuse in childhood, or stressful events and how we perceive them. This is very evident from the most recent developments in genetics, which show how, via epigenetic processes, experiences are translated into the activity (or expression) of genes, which then modify the workings of the brain in ways that affect mood.

Research into epigenetic influences on mental health and ill-health is burgeoning and is likely to make a very significant contribution to our understanding of aetiology in the years to come. If so, it should also help clarify how existing treatments, both pharmacological and psychotherapeutic, for emotional disorders work, or suggest new approaches that would work more effectively.

The HPA axis is overactive in those with depression and anxiety, suggesting a role for chronic stress. Elevated levels of glucocorticoids such as cortisol and corticosterone, resulting from chronic stress, have toxic effects in some areas of the brain and promote neurogenesis in others.

The monoamine hypothesis of mood disorders has been influential in trying to explain the causes of depression. However the picture is now more complex and the view of a simple chemical imbalance as a cause of depression is outdated.

Hypotheses such as the neurotrophic hypothesis and the network hypothesis have been developed to try to account for the complex effects of antidepressant treatments on the brain.

The life-cycle model of stress links brain development with stress effects over the lifetime.

The cognitive approach concentrates on particular ways of thinking and how these cause and sustain depression.

Genetic and other vulnerabilities (also called predispositions or diatheses) can interact with environmental factors, which include psychosocial stressors such as stressful life events and early life stress (including child abuse) to cause emotional disorders such as depression.

Epigenetic processes add another layer of complexity to the interaction between genes and environment. There is increasingly evidence of the importance of epigenetic processes in the aetiology of mood disorders.

Home — Essay Samples — Nursing & Health — Psychiatry & Mental Health — Depression

Essays About Depression

Depression essay topic examples.

Explore topics like the impact of stigma on depression, compare it across age groups or in literature and media, describe the emotional journey of depression, discuss how education can help, and share personal stories related to it. These essay ideas offer a broad perspective on depression, making it easier to understand and engage with this important subject.

Argumentative Essays

Argumentative essays require you to analyze and present arguments related to depression. Here are some topic examples:

• 1. Argue whether mental health stigma contributes to the prevalence of depression in society.
• 2. Analyze the effectiveness of different treatment approaches for depression, such as therapy versus medication.

Example Introduction Paragraph for an Argumentative Essay: Depression is a pervasive mental health issue that affects millions of individuals worldwide. This essay delves into the complex relationship between mental health stigma and the prevalence of depression in society, examining the barriers to seeking help and the consequences of this stigma.

Example Conclusion Paragraph for an Argumentative Essay: In conclusion, the analysis of mental health stigma's impact on depression underscores the urgent need to challenge and dismantle the stereotypes surrounding mental health. As we reflect on the far-reaching consequences of stigma, we are called to create a society that fosters empathy, understanding, and open dialogue about mental health.

Compare and Contrast Essays

Compare and contrast essays enable you to examine similarities and differences within the context of depression. Consider these topics:

• 1. Compare and contrast the symptoms and risk factors of depression in adolescents and adults.
• 2. Analyze the similarities and differences between the portrayal of depression in literature and its depiction in modern media.

Example Introduction Paragraph for a Compare and Contrast Essay: Depression manifests differently in various age groups and mediums of expression. This essay embarks on a journey to compare and contrast the symptoms and risk factors of depression in adolescents and adults, shedding light on the unique challenges faced by each demographic.

Example Conclusion Paragraph for a Compare and Contrast Essay: In conclusion, the comparison and contrast of depression in adolescents and adults highlight the importance of tailored interventions and support systems. As we contemplate the distinct challenges faced by these age groups, we are reminded of the need for age-appropriate mental health resources and strategies.

Descriptive Essays

Descriptive essays allow you to vividly depict aspects of depression, whether it's the experience of the individual or the societal impact. Here are some topic ideas:

• 1. Describe the emotional rollercoaster of living with depression, highlighting the highs and lows of the experience.
• 2. Paint a detailed portrait of the consequences of untreated depression on an individual's personal and professional life.

Example Introduction Paragraph for a Descriptive Essay: Depression is a complex emotional journey that defies easy characterization. This essay embarks on a descriptive exploration of the emotional rollercoaster that individuals with depression experience, delving into the profound impact it has on their daily lives.

Example Conclusion Paragraph for a Descriptive Essay: In conclusion, the descriptive portrayal of the emotional rollercoaster of depression underscores the need for empathy and support for those grappling with this condition. Through this exploration, we are reminded of the resilience of the human spirit and the importance of compassionate understanding.

Persuasive Essays

Persuasive essays involve arguing a point of view related to depression. Consider these persuasive topics:

• 1. Persuade your readers that incorporating mental health education into the school curriculum can reduce the prevalence of depression among students.
• 2. Argue for or against the idea that employers should prioritize the mental well-being of their employees to combat workplace depression.

Example Introduction Paragraph for a Persuasive Essay: The prevalence of depression underscores the urgent need for proactive measures to address mental health. This persuasive essay asserts that integrating mental health education into the school curriculum can significantly reduce the prevalence of depression among students, offering them the tools to navigate emotional challenges.

Example Conclusion Paragraph for a Persuasive Essay: In conclusion, the persuasive argument for mental health education in schools highlights the potential for early intervention and prevention. As we consider the well-being of future generations, we are called to prioritize mental health education as an essential component of a holistic education system.

Narrative Essays

Narrative essays offer you the opportunity to tell a story or share personal experiences related to depression. Explore these narrative essay topics:

• 1. Narrate a personal experience of overcoming depression or supporting a loved one through their journey.
• 2. Imagine yourself in a fictional scenario where you advocate for mental health awareness and destigmatization on a global scale.

Example Introduction Paragraph for a Narrative Essay: Personal experiences with depression can be transformative and enlightening. This narrative essay delves into a personal journey of overcoming depression, highlighting the challenges faced, the support received, and the lessons learned along the way.

Example Conclusion Paragraph for a Narrative Essay: In conclusion, the narrative of my personal journey through depression reminds us of the resilience of the human spirit and the power of compassion and understanding. As we reflect on our own experiences, we are encouraged to share our stories and contribute to the ongoing conversation about mental health.

A Narrative About Depression: Navigating The Abyss

Holden caulfield symptoms, made-to-order essay as fast as you need it.

Each essay is customized to cater to your unique preferences

+ experts online

Depression: Understanding The Silent Epidemic

The epidemic of depression among students and teenagers, the effects of depression in your body and its treatment, the issue of depression and its reality nowadays, let us write you an essay from scratch.

• 450+ experts on 30 subjects ready to help
• Custom essay delivered in as few as 3 hours

Overview of Biological Predispositions and Risk Factors Associated with Depression

How to overcome depression and anxiety, depression: definition, risks, symptoms and treatment, the best way to help someone who is depressed, get a personalized essay in under 3 hours.

Expert-written essays crafted with your exact needs in mind

The Issue of Depression: Mental Battle

What is a depression, living in depression: a firsthand account, teen depression - symptoms and causes, adolescent depression and its contribution to teenage suicides, the issue of depression and its affect in an emerging adulthood, depression: definition and ways of resolving caused problems, depression in teenagers: causes and ways to overcome, depression and its main causes, genetic disorder report: clinical depression, the way teachers can help their students to overcome anxiety and depression, depression and its effects of mind and body, the effectiveness of cognitive behavioural therapy (cbt) for treating individuals with depression and anxiety, how to overcome teenage depression, depression as the reason of serious health problems and suicide, a depressing world with different obstacles, the link between self-esteem and adolescent depression, darwinian psychology and depression: the gender differential hypothesis, prevention of depression, anxiety and burnout in resident doctors – a systematic review, dysregulated processing of negative and positive responses in depression.

Depression, known as major depressive disorder or clinical depression, is a psychological condition characterized by enduring feelings of sadness and a significant loss of interest in activities. It is a mood disorder that affects a person's emotional state, thoughts, behaviors, and overall well-being.

Its origin can be traced back to ancient civilizations, where melancholia was described as a state of sadness and melancholy. In the 19th century, depression began to be studied more systematically, and terms such as "melancholic depression" and "nervous breakdown" emerged. The understanding and classification of depression have evolved over time. In the early 20th century, Sigmund Freud and other psychoanalysts explored the role of unconscious conflicts in the development of depression. In the mid-20th century, the Diagnostic and Statistical Manual of Mental Disorders (DSM) was established, providing a standardized criteria for diagnosing depressive disorders.

Biological Factors: Genetic predisposition plays a role in depression, as individuals with a family history of the disorder are at a higher risk. Psychological Factors: These may include a history of trauma or abuse, low self-esteem, pessimistic thinking patterns, and a tendency to ruminate on negative thoughts. Environmental Factors: Adverse life events, such as the loss of a loved one, financial difficulties, relationship problems, or chronic stress, can increase the risk of depression. Additionally, living in a socioeconomically disadvantaged area or lacking access to social support can be contributing factors. Health-related Factors: Chronic illnesses, such as cardiovascular disease, diabetes, and chronic pain, are associated with a higher risk of depression. Substance abuse and certain medications can also increase vulnerability to depression. Developmental Factors: Certain life stages, including adolescence and the postpartum period, bring about unique challenges and changes that can contribute to the development of depression.

Depression is characterized by a range of symptoms that affect an individual's emotional, cognitive, and physical well-being. These characteristics can vary in intensity and duration but generally include persistent feelings of sadness, hopelessness, and a loss of interest or pleasure in activities once enjoyed. One prominent characteristic of depression is a noticeable change in mood, which can manifest as a constant feeling of sadness or emptiness. Individuals may also experience a significant decrease or increase in appetite, leading to weight loss or gain. Sleep disturbances, such as insomnia or excessive sleepiness, are common as well. Depression can impact cognitive functioning, causing difficulties in concentration, decision-making, and memory recall. Negative thoughts, self-criticism, and feelings of guilt or worthlessness are also common cognitive symptoms. Furthermore, physical symptoms may arise, including fatigue, low energy levels, and a general lack of motivation. Physical aches and pains, without an apparent medical cause, may also be present.

The treatment of depression typically involves a comprehensive approach that addresses both the physical and psychological aspects of the condition. It is important to note that the most effective treatment may vary for each individual, and a personalized approach is often necessary. One common form of treatment is psychotherapy, which involves talking to a mental health professional to explore and address the underlying causes and triggers of depression. Cognitive-behavioral therapy (CBT) is a widely used approach that helps individuals identify and change negative thought patterns and behaviors associated with depression. In some cases, medication may be prescribed to help manage depressive symptoms. Antidepressant medications work by balancing neurotransmitters in the brain that are associated with mood regulation. It is crucial to work closely with a healthcare provider to find the right medication and dosage that suits an individual's needs. Additionally, lifestyle changes can play a significant role in managing depression. Regular exercise, a balanced diet, sufficient sleep, and stress reduction techniques can all contribute to improving mood and overall well-being. In severe cases of depression, when other treatments have not been effective, electroconvulsive therapy (ECT) may be considered. ECT involves administering controlled electric currents to the brain to induce a brief seizure, which can have a positive impact on depressive symptoms.

1. According to the World Health Organization (WHO), over 264 million people worldwide suffer from depression, making it one of the leading causes of disability globally. 2. Depression can affect people of all ages, including children and adolescents. In fact, the prevalence of depression in young people is increasing, with an estimated 3.3 million adolescents in the United States experiencing at least one major depressive episode in a year. 3. Research has shown that there is a strong link between depression and other physical health conditions. People with depression are more likely to experience chronic pain, cardiovascular diseases, and autoimmune disorders, among other medical conditions.

The topic of depression holds immense significance and should be explored through essays due to its widespread impact on individuals and society as a whole. Understanding and raising awareness about depression is crucial for several reasons. Firstly, depression affects a significant portion of the global population, making it a pressing public health issue. Exploring its causes, symptoms, and treatment options can contribute to better mental health outcomes and improved quality of life for individuals affected by this condition. Additionally, writing an essay about depression can help combat the stigma surrounding mental health. By promoting open discussions and providing accurate information, essays can challenge misconceptions and foster empathy and support for those experiencing depression. Furthermore, studying depression allows for a deeper examination of its complex nature, including its psychological, biological, and sociocultural factors. Lastly, essays on depression can highlight the importance of early detection and intervention, promoting timely help-seeking behaviors and reducing the burden of the condition on individuals and healthcare systems. By shedding light on this critical topic, essays have the potential to educate, inspire action, and contribute to the overall well-being of individuals and society.

1. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing. 2. World Health Organization. (2017). Depression and other common mental disorders: Global health estimates. World Health Organization. 3. Kessler, R. C., Bromet, E. J., & Quinlan, J. (2013). The burden of mental disorders: Global perspectives from the WHO World Mental Health Surveys. Cambridge University Press. 4. Beck, A. T., Rush, A. J., Shaw, B. F., & Emery, G. (1979). Cognitive therapy of depression. Guilford Press. 5. Nierenberg, A. A., & DeCecco, L. M. (2001). Definitions and diagnosis of depression. The Journal of Clinical Psychiatry, 62(Suppl 22), 5-9. 6. Greenberg, P. E., Fournier, A. A., Sisitsky, T., Pike, C. T., & Kessler, R. C. (2015). The economic burden of adults with major depressive disorder in the United States (2005 and 2010). Journal of Clinical Psychiatry, 76(2), 155-162. 7. Cuijpers, P., Berking, M., Andersson, G., Quigley, L., Kleiboer, A., & Dobson, K. S. (2013). A meta-analysis of cognitive-behavioural therapy for adult depression, alone and in comparison with other treatments. Canadian Journal of Psychiatry, 58(7), 376-385. 8. Hirschfeld, R. M. A. (2014). The comorbidity of major depression and anxiety disorders: Recognition and management in primary care. Primary Care Companion for CNS Disorders, 16(2), PCC.13r01611. 9. Rush, A. J., Trivedi, M. H., Wisniewski, S. R., Nierenberg, A. A., Stewart, J. W., Warden, D., ... & Fava, M. (2006). Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: A STAR*D report. American Journal of Psychiatry, 163(11), 1905-1917. 10. Kendler, K. S., Kessler, R. C., Walters, E. E., MacLean, C., Neale, M. C., Heath, A. C., & Eaves, L. J. (1995). Stressful life events, genetic liability, and onset of an episode of major depression in women. American Journal of Psychiatry, 152(6), 833-842.

Most Popular

11 days ago

Aithor Review

Now everyone can be a mathematician with the new apple math notes app, how to write a dissertation proposal, how to write a book title in an essay, elon musk criticizes apple’s ai approach and threatens device ban, the causes of depression essay sample, example.

The first—and surprising—risk factor for developing depression is gender. About 20-25% women in the United States develop serious depression, sometimes not just once in their lives; for comparison, only about 12% of male Americans face the same problem—or maybe, they visit a doctor’s office less often (All About Depression.com). This is probably connected to the fact that in today’s America, women often have to deal with a wide range of roles, such as business woman, mother, wife, housekeeper, and so on—and these roles often conflict with each other. Unhappy marriages, hormonal changes, and heredity can be contributing factors.

Another group of factors that lead to depression are different psychological problems. Most often, low self-esteem is the major cause, since it makes a person treat themselves with neglect, prevent them from believing in their own strengths, and see the world pessimistically. Other possible psychological reasons are stress, perfectionism, chronic anxiety, avoidant personality disorders, and so on (PsychCentral).

Personal factors, such as complicated life situations, a tragic family history, childhood traumas, living in stressful environments for a long time, and other similar life circumstances can garner depressive conditions. Genetic proneness is also related to this group of causes. At the same time, it does not mean that a person will automatically develop depression if he or she had cases of depression in their family, or they are in a complicated life situation. This group of factors mostly creates premises, and is commonly combined with other risk factors (Beyond Blue).

Alcohol, by the way, is as strong of a cause of depression as genetic factors or psychological problems. Although it is usually considered that alcohol helps people get rid of stress, and increase their communication, in fact it is a depressant that increases a person’s chances to develop depression (femah.net). These are not the only possible causes of depression, but commonly, this disorder is caused by an aggregate of the factors described above. It is likely that women develop depression more often than men; also, people with psychological problems and complicated personal circumstances are more prone to developing depression. The usage of alcohol not only does not help people get rid of stress, but on the contrary, increases the risks of developing depression. These factors should be taken into account in one’s daily life in order to avoid depression.

“Causes.” All About Depression. N.p., n.d. Web. 11 Feb. 2015.

“What are the Risk Factors for Depression?” Psych Central.com. N.p., n.d. Web. 11 Feb. 2015.

“What Causes Depression.” Beyond Blue. N.p., n.d. Web. 11 Feb. 2015.

“Alcohol as a Depressant.” Femah.net. N.p., n.d. Web. 11 Feb. 2015. .

Follow us on Reddit for more insights and updates.

Comments (0)

Welcome to A*Help comments!

We’re all about debate and discussion at A*Help.

We value the diverse opinions of users, so you may find points of view that you don’t agree with. And that’s cool. However, there are certain things we’re not OK with: attempts to manipulate our data in any way, for example, or the posting of discriminative, offensive, hateful, or disparaging material.

Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

More from Cause and Effect Essay Examples and Samples 2024

Jul 18 2023

How Your Childhood Affects Your Love Style Essay Sample Example

Apr 15 2019

How the Harry Potter Series Became So Popular Essay Sample, Example

Apr 05 2019

Why Our Ancestors Started to Walk on Two Feet Essay Sample, Example

Related writing guides, writing a cause and effect essay.

Remember Me

Is English your native language ? Yes No

What is your profession ? Student Teacher Writer Other

Forgotten Password?

Username or Email

In Sight: The Biological Diagnosis of Depression and Anxiety

The science of psychiatry is gaining on the daunting complexity of the brain..

Updated June 20, 2024 | Reviewed by Hara Estroff Marano

• What Is Depression?
• Find counselling to overcome depression
• Diagnostic models in psychiatry are largely based on clinical experience, with some statistical modeling.
• Scientific biomedical models are necessary to advance understanding of mental illness.
• Understanding the biology of mental illness will allow development of better treatments.
• Personalized analysis of brain networks holds promise for people suffering from depression and anxiety.

According to the World Health Organization (WHO), clinical depression affects nearly 300 million people worldwide. The Centers for Disease Control (CDC) estimates that 20 million or more people in the U.S. have depression at any given time, while more than 18 percent of U.S. adults report depression at some point in their lives and .more than 12 percent of adults report significant feelings of anxiety .

Treatment for depression is of limited effectiveness; only 30-40 percent of those initially treated experience full resolution of symptoms, or remission. What's more, studies show, successive efforts to achieve remission are less and less effective.

Understanding the underlying biology of depression, anxiety, and related conditions such as post- traumatic stress disorder ( PTSD ) is necessary for making correct diagnosis and planning effective treatment. But especially in psychiatry, given the complexities of the brain, diagnosis and treatment are not yet well-grounded in biological understanding.

Medical treatment is ideally based on a number of factors, including knowledge of the disease process, the ability to make accurate diagnoses, and an understanding of how individual factors affect treatment planning and outcome. The National Institutes of Health started the BRAIN Initiative (Brain Research Through Advancing Innovative Neurotechnologies) in 2013, calling for neuroscience -based models of disease and health. Understanding the causal factors of disease suggests the levers clinicians can manipulate to provide the most effective treatment possible.

Toward a More Scientific Psychiatry

Psychiatric diagnosis in the United States is currently based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Although efforts have been made to improve its approach with more specific criteria for mental illness based on statistics and available data, the DSM is not firmly scientifically based. For the vast majority of illnesses described, the diagnostic criteria say little to nothing about the cause of the disease; instead, they primarily reflect long-observed clinical patterns, rendering the DSM a work in progress and of less-than-desirable utility for diagnosis and treatment.

Not all causes of psychiatric disease are strictly biological. For example, inheriting a genetic predisposition to depression—and many genes contribute–does not invariably lead to depression. It’s highly likely that social and family factors can also bring on depression—not being productive or social, for example. Current treatments for depression often address biological and social factors, but there is as yet no standard biological testing for depression nor any clear framework for scientifically based treatment.

DSM 5 identifies several subtypes of depression, all based on clinical observation and statistical analysis. With unipolar depression (as contrasted with bipolar disorder ), subtypes include atypical and melancholic, and specifiers include severity of symptoms as well as presence or absence of psychotic features. Diagnosis is made by reviewing clinical presentation and history, whether informally through clinical interview or formally via structured, guided interview and review of accompanying information. The same approach applies to anxiety disorders, including generalized anxiety, social anxiety , panic disorder, and obsessive-compulsive disorder, as well as to stress-related disorders such as PTSD. The disorders may be divided into subtypes by specifiers, but a true medical-scientific framework is lacking.

Biotyping Depression and Anxiety

A recent study of brain networks in depression and anxiety reported in Nature Medicine (2024) is an important step toward establishing an empirical model of "biotypes",; it echoes prior work on depression 3 and brain-based personality research 4 . Researchers Tozzi and colleagues used functional magnetic resonance imaging (fMRI) to look at brain activity in more than 1,000 people with depression and anxiety, measuring “task-free” brain activity. They repeated imaging in a subset of patients who received psychotherapy or pharmacotherapy or underwent a variety of activities. During the imaging, subjects were shown a variety of stimuli—such as sad, threatening, or happy faces—and were asked to perform a variety of cognitive and attention tasks. Cluster analysis was used to identify underlying biotypes based on brain circuit dysfunction, sometimes referred to as “dysconnectivity” in brain networks. Treatment would, therefore, restore "euconnectivity".

Notably, the study was transdiagnostic: Given how much depression an anxiety overlap, the study didn't assume they are separate disorders. The analysis was unbiased by preconceived diagnostic models. Participants included those diagnosed with various conventional disorders, including major depression, generalized anxiety, panic disorder, social anxiety, PTSD, obsessive-compulsive disorder,. Some patients met criteria for more than one diagnosis.

Six underlying biotypes of depression and anxiety were identified among participants with clinically-significant symptoms. Their labels are complicated, based on activity levels in key brain networks: default mode, or resting state (D); salience, or what stands out as important (S); and attentional (A). In addition to connectivity patterns, research looked at such key factors as negative emotional circuitry in response to sadness and threat (conscious and unconscious ), positive emotion circuits, and cognitive circuits. There were no sex differences in response patterns, and minimal differences in age.

• Biotype D C+ S C+ A C+ . This cluster showed hyperconnectivity among all three networks. This biotype had slow responses identifying sad faces and increased errors in executive function tasks. The response to behavior coaching for wellness was strong.
• Biotype A C− . This cluster had less connectivity in the attention network, less severe stress compared with other biotypes, and relatively little dysfunction in cognitive control, with faster responses on cognitive measures but more errors, as well as faster priming when viewing threatening faces. Response to behavioral coaching was less robust.
• Biotype NS A+ P A+ . This cluster had elevated activity during conscious processing of emotions, with sadness evoking greater negative circuit activity and happiness more positive. This group also experienced more severe anhedonia —inability to enjoy things—and greater ruminative brooding.
• Biotype C A+ . This group showed increased cognitive control under specific conditions. This cluster also showed greater anhedonia, greater anxious arousal, negative bias , and dysregulation in response to threat. Cognitive errors were high, especially with sustained attention. This biotype had a better response to the antidepressant venlafaxine, a serotonin-norepinephrine reuptake inhibitor (SNRI) commonly prescribed.
• Biotype NTC C- C A− . This small group was characterized by loss of functional connectivity in negative emotion circuits during conscious processing of threatening faces and by reduced activity in cognitive control circuits. This cluster had less ruminative brooding and faster reaction times to sad faces.
• Biotype D X S X A X N X P X C X . This small group did not show significant circuit dysfunction. There were slower reaction times to implicit threat.

Implications

While not ready for standard clinical use, the study results build on prior work demonstrating that brain network analysis holds promise for developing biologically based diagnostic testing for depression, anxiety, and stress-related disorders. The study also provides initial proof of concept that psychiatric biotyping could be used in the selection of treatments, with some biotypes responding better to medication and others to psychotherapeutic interventions.

Clearly, more work is needed before such models of illness can underpin diagnosis and treatment. Given the complexity of the human experience, it's important to recognize that many of the causes of mental illness are likely to be social or circumstantial, external to the individual. More debatable is how to distinguish psychology from neuroscience, mind from brain, without becoming neuroreductionistic. Personalized scientific approaches to psychiatry on a par with other medical disciplines remain largely aspirational, but current approaches are likely to move the needle.

1. An important clarification about how the word “causal” is being used here–it is being used to refer to the causes of the problems in the present moment, the precise factors in the complex system which maintain the status quo of health and illness. Notably, we are not necessarily talking about the historical causes–what started the process in motion may not be what is currently causing it to persist. This is a mathematic definition of causality.

2. Causal discovery has been used to look at PTSD among police officers using a process called Protocol for Computation Causal Discovery in Psychiatry (PCCDP). Saxe and colleagues (2020) reviewed a large data set from over 200 police officers. They identified 83 causal pathways with 5 causes: changes (single-nucleotide polymorphisms–SNPs) in histidine decarboxylase and mineralocorticoid receptor genes involved with stress-response, acoustic startle to low perceived threat during training, peritraumatic distress to incident exposure in the first year of service, and general symptom severity during training after one year of service. This study is a proof-of-concept for using causal discovery to identify points for intervention, and clearly could be used preventively–for example, identifying trainees with those features and responding accordingly.

3. Four Biotypes of Depression

4. Brainprint of Basic Mental Activity

WHO Depression Fact Sheet

CDC Depression Prevalence 2020

CDC Anxiety

NIH Brain Initiative

Saxe GN, Bickman L, Ma S, Aliferis C. Mental health progress requires causal diagnostic nosology and scalable causal discovery. Front Psychiatry. 2022 Nov 15;13:898789. doi: 10.3389/fpsyt.2022.898789. PMID: 36458123; PMCID: PMC9705733.

Saxe GN, Ma S, Morales LJ, Galatzer-Levy IR, Aliferis C, Marmar CR. Computational causal discovery for post-traumatic stress in police officers. Transl Psychiatry. 2020 Aug 11;10(1):233. doi: 10.1038/s41398-020-00910-6. PMID: 32778671; PMCID: PMC7417525.

Tozzi, L., Zhang, X., Pines, A. et al. Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety. Nat Med (2024). https://doi.org/10.1038/s41591-024-03057-9

Grant Hilary Brenner, M.D., a psychiatrist and psychoanalyst, helps adults with mood and anxiety conditions, and works on many levels to help unleash their full capacities and live and love well.

• Find a Therapist
• Find a Treatment Center
• Find a Psychiatrist
• Find a Support Group
• Find Online Therapy
• International
• New Zealand
• South Africa
• Switzerland
• Asperger's
• Bipolar Disorder
• Chronic Pain
• Eating Disorders
• Passive Aggression
• Personality
• Goal Setting
• Positive Psychology
• Stopping Smoking
• Low Sexual Desire
• Relationships
• Child Development
• Self Tests NEW
• Therapy Center
• Diagnosis Dictionary
• Types of Therapy

At any moment, someone’s aggravating behavior or our own bad luck can set us off on an emotional spiral that threatens to derail our entire day. Here’s how we can face our triggers with less reactivity so that we can get on with our lives.

• Emotional Intelligence
• Gaslighting
• Affective Forecasting
• Neuroscience

Brain Scans Identify Six Distinct Types of Depression And Anxiety

In the future, getting help for depression might involve a quick brain scan to find the most effective treatment for you.

An analysis of brain activity during rest and while undertaking specific tasks among a large group of people with depression and anxiety has identified six distinct types of brain activity patterns, symptoms, and responses to treatment.

The team from the US and Australia who conducted the study also determined treatments that are more likely to work for some of these categories. This means doctors could potentially match patients with the best therapies based on how their brains function.

"The dominant 'one-size-fits-all' diagnostic approach in psychiatry leads to cycling through treatment options by trial and error," Stanford University neuroscientist Leonardo Tozzi and colleagues write in their published paper, "which is lengthy, expensive and frustrating, with 30–40 percent of patients not achieving remission after trying one treatment."

The researchers studied 801 mostly unmedicated participants who had been diagnosed with either major depressive disorder , generalized anxiety disorder , panic disorder , social anxiety disorder , obsessive-compulsive disorder , or post-traumatic stress disorder , or a combination thereof. They also included 137 people without the conditions as controls.

Functional MRI ( fMRI ) brain scans were used to attain 41 activation and connectivity measures for each participant, focussing on six brain circuits known to play a role in depression. Scans were taken when the participants were at rest and then in response to tasks involving cognition and emotion.

Machine learning was used to cluster those with depression and anxiety into six types based on specific brain pathways that are overactive or underactive, relative to each other and the control participants.

"To our knowledge, this is the first time we've been able to demonstrate that depression can be explained by different disruptions to the functioning of the brain," says senior author Leanne Williams, a psychiatrist and behavioral scientist from Stanford University.

The team then randomly assigned 250 participants to receive one of three antidepressants or engage in talk therapy. The antidepressant venlafaxine worked best on one subtype: people whose cognitive brain regions were overactive.

Talk therapy worked better for people who had more activity in parts of the brain linked to depression and problem-solving. Those with low activity in the brain's attention circuit, on the other hand, benefited less from talk therapy, perhaps suggesting they have more to gain from first treating the lower activity with medication.

"To really move the field toward precision psychiatry , we need to identify treatments most likely to be effective for patients and get them on that treatment as soon as possible," public health scientist Jun Ma from the University of Illinois says .

"Having information on their brain function… would help inform more precise treatment and prescriptions for individuals."

In 2023, some of the same team identified a new cognitive biotype of depression, which affects 27 percent of people with major depressive disorder. The cognitive deficits – in attention, memory, and self-control – are often unaffected by serotonin-targeting antidepressants.

And earlier this year , Williams and a colleague used fMRI to identify those with the cognitive biotype, predicting remission with 63 percent accuracy, compared to 36 percent without fMRI. New treatments for this biotype are being explored.

Depression is complex , as are the factors contributing to it. It can take a long time for those with access to treatment to find one that helps , if they ever do. So every step towards a more effective, personalized approach is useful.

"It's very frustrating to be in the field of depression and not have a better alternative to this one-size-fits-all approach," says Williams. "The goal of our work is figuring out how we can get it right the first time."

The research has been published in Nature Medicine .

Essay on Depression in Youth

Students are often asked to write an essay on Depression in Youth in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Depression in Youth

Understanding depression in youth.

Depression is a serious mental health issue affecting many young people. It’s not just feeling sad, but a constant state of low mood that lasts for weeks or months.

Causes can include school stress, family problems, or bullying. Sometimes, it’s due to chemical imbalances in the brain.

Signs of Depression

Depressed youth may feel hopeless, lose interest in activities they used to enjoy, or have trouble concentrating. They may also sleep too much or too little.

Getting Help

Depression is treatable. If you or a friend are feeling depressed, it’s important to talk to a trusted adult or seek professional help.

250 Words Essay on Depression in Youth

Introduction.

Depression in youth is alarmingly common. According to the World Health Organization, depression is the third leading cause of illness and disability among adolescents. The early onset of depression is concerning, as it can lead to detrimental effects on an individual’s personal development, academic performance, and social relationships.

Depression in youth is multifactorial, with genetics, environmental factors, and personal circumstances playing significant roles. Factors such as family history of depression, traumatic life events, bullying, and physical health problems can contribute to its onset. Additionally, the hormonal changes associated with puberty can make teenagers more susceptible to depression.

Depression in youth can have severe implications. It can lead to poor academic performance, substance abuse, risky sexual behaviors, and even suicide. Furthermore, it can affect a young person’s interpersonal relationships and self-esteem, leading to a vicious cycle of isolation and worsening depressive symptoms.

Depression in youth is a pressing public health issue that requires urgent attention. Early detection and appropriate intervention are crucial to mitigate its devastating effects. As a society, we must foster an environment that encourages open dialogue about mental health, reduces stigma, and promotes access to mental health services for young people.

500 Words Essay on Depression in Youth

Depression, a prevalent mental health disorder, has been increasingly recognized among the youth population. Characterized by persistent feelings of sadness, hopelessness, and a lack of interest in activities, it is not merely a temporary mood swing but a serious condition that interferes with daily life and normal functioning.

The Prevalence of Depression in Youth

Causes and risk factors.

Depression in youth can be attributed to a combination of genetic, biological, environmental, and psychological factors. Genetics play a crucial role, with individuals having a family history of depression being more susceptible. Hormonal changes, particularly during puberty, can also contribute to depression. Environmental factors include traumatic events, family issues, or any form of abuse.

Moreover, the advent of social media has added a new dimension to this issue. The constant comparison, cyberbullying, and the pressure to maintain an ideal online image have been linked to increased levels of anxiety and depression among youth.

Impact of Depression on Youth

Approaches to treatment.

Effective treatment for depression in youth typically involves a combination of psychotherapy, medication, and lifestyle changes. Cognitive Behavioral Therapy (CBT) has been found particularly effective in helping young people manage their symptoms by changing negative thought patterns. Medication, such as antidepressants, may also be prescribed. However, it’s crucial to approach this with caution due to potential side effects.

Lifestyle changes, including regular exercise, a healthy diet, and adequate sleep, can also play a significant role in managing depression. Additionally, support from family, friends, and school can be instrumental in a young person’s recovery.

If you’re looking for more, here are essays on other interesting topics:

Apart from these, you can look at all the essays by clicking here .

Leave a Reply Cancel reply

Save my name, email, and website in this browser for the next time I comment.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 17 June 2024

Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety

• Leonardo Tozzi   ORCID: orcid.org/0000-0002-9429-6476 1 ,
• Xue Zhang   ORCID: orcid.org/0000-0003-4279-899X 1 ,
• Adam Pines 1 ,
• Alisa M. Olmsted 1 , 2 ,
• Emily S. Zhai   ORCID: orcid.org/0000-0001-5341-1178 1 ,
• Esther T. Anene 3 ,
• Megan Chesnut 1 ,
• Bailey Holt-Gosselin 4 ,
• Sarah Chang 5 ,
• Patrick C. Stetz 1 , 2 ,
• Carolina A. Ramirez 6 ,
• Laura M. Hack 1 , 2 ,
• Mayuresh S. Korgaonkar   ORCID: orcid.org/0000-0002-1339-2221 7 , 8 ,
• Max Wintermark 9 ,
• Ian H. Gotlib 10 ,
• Jun Ma 11 &
• Leanne M. Williams   ORCID: orcid.org/0000-0001-9987-7360 1 , 2  

Nature Medicine ( 2024 ) Cite this article

75k Accesses

694 Altmetric

Metrics details

There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or ‘biotypes’ to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free ( n  = 801) and after randomization to pharmacotherapy or behavioral therapy ( n  = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry.

Similar content being viewed by others

Early detection of dementia with default-mode network effective connectivity

Emotions and brain function are altered up to one month after a single high dose of psilocybin

The serotonin theory of depression: a systematic umbrella review of the evidence

Depression and associated anxiety disorders are an important global public health burden 1 , the treatment of which has been hindered by etiological and phenotypic heterogeneity. The current psychiatric diagnostic system assigns a single label to syndromes that may involve the dysfunction of multiple and overlapping neurobiological processes which, in turn, would probably each require a different treatment. This is evident from the fact that more than a third of patients diagnosed with major depressive disorder, and approximately half of patients diagnosed with generalized anxiety disorder, do not respond to first-line treatment 2 , 3 . Unlike the ‘one-size-fits-all’ approach, a precision medicine approach to care requires standardized metrics that are personalized for individual patients and are interpretable to clinicians. However, the promise of this approach is currently limited by a lack of personalized and interpretable measures for quantifying neurobiological dysfunctions in patients with depression and associated anxiety disorders. We believe that such measures should help to elucidate the underlying neurobiological dysfunctions within a neuroscientific theoretical framework, rather than remain an algorithmic black box. Using these measures, patients could be stratified prospectively into subgroups that share similar neurobiological dysfunctions, or ‘biotypes’, each of which would possibly implicate a different set of treatment approaches or a different treatment trajectory.

Efforts to characterize biotypes of depressed and anxious patients with similar brain circuit dysfunctions have typically used task-free functional magnetic resonance imaging (fMRI) 4 , 5 , 6 , 7 . For example, one pioneering study has found biotypes characterized by aberrant connectivity in frontostriatal and limbic networks that respond differently to repetitive transcranial magnetic stimulation (TMS) 4 . Other researchers have found biotypes characterized by hyper- and hypoconnectivity of the default mode network 5 , biotypes that distinguish comorbid anxiety within the context of depression 6 and biotypes that are associated with a poorer response to standard antidepressants 7 .

Nevertheless, we lack evidence about biotypes in depression and anxiety that are based on the participant-level quantification of measures derived from task-evoked imaging modalities. Patients with depression and anxiety exhibit dysfunction in the activity and connectivity of brain circuits in response to specific probes of general and emotional cognition. In other words, in depression and anxiety, the brain continually and flexibly engages different circuits under task-evoked and task-free conditions. Therefore, both sources of information may be useful in delineating biotypes and biotype-guided treatments. This is analogous to cardiac imaging being collected during both rest and task conditions in which the activity of the heart is elicited (for example, stress tests) to enable precise diagnoses and treatment plans, a necessity given the complexity of this organ and its functions 8 . Indeed, clinical trials have found that measures derived from task-based fMRI often predict response in depression treatment (for example, refs. 9 , 10 , 11 , 12 ) and have recently been the biomarker of choice for new pharmacotherapy development (for example, ref. 13 ).

Foundational studies using whole-brain, task-free connectivity biomarkers have often taken an unsupervised whole-brain approach that uses thousands of features for biotyping. However, we posit that clinical translation requires a theoretically informed approach that relies on a well-defined, tractable set of inputs. Such an approach also addresses the potential for obtaining overly optimistic results (overfitting) when thousands of inputs are used in a fully unsupervised manner—an issue that has been raised in the field 14 (but see ref. 15 , which addresses overfitting 11 ).

Finally, previous studies have assessed the ability of biotypes to predict response to a single treatment (for example, TMS 4 or antidepressants 7 ), rather than comparing responses across different classes of treatments. To maximize the translational value of biotypes, the optimal treatment for each biotype should eventually be determined by comparing how different biotypes respond when receiving the same treatment.

In the present study, we demonstrate a new approach to generating biotypes of depression and anxiety based on task-evoked and task-free imaging data, quantified at the individual patient level and evaluated in the context of transdiagnostic symptoms, behaviors and outcomes with multiple types of treatments. Our approach relies on a standardized circuit quantification system that enables us to compute a manageable number of task-evoked and task-free measures of circuit function on an individual participant basis. These measures are firmly grounded in a theoretical synthesis of functional brain imaging studies that implicate dysfunction across large-scale circuits in the clinical features of depression and anxiety 16 , 17 . Thus, our theoretically driven approach provides unique insights that may have been missed by previous studies that either relied only on task-free data or mined large numbers of features using exploratory data analysis techniques. In our sample of 801 participants with depression and anxiety (95% of whom were unmedicated), the use of the same fMRI sequences, symptoms and behavioral measures enabled us to clinically validate theory-driven biotypes and demonstrate that they differ in symptom profiles and performance on general and emotional, cognitive, computerized behavioral tests. Furthermore, a substantial portion of the participants were enrolled into randomized clinical trials of antidepressants or behavioral therapy, which enabled us to demonstrate that our biotypes differ in their outcomes across multiple treatments.

Personalized brain circuit scores define six biotypes

We began by implementing a new standardized image-processing procedure called ‘the Stanford Et Cere Image Processing System’ which quantified task-free and task-evoked brain circuit function at the level of the individual participants ( Methods ). We applied this procedure to a baseline dataset that consisted of brain scans acquired from both task-free and task conditions, utilizing identical scanning protocols, from 801 participants with depression and related anxiety disorders, as well as 137 healthy controls (Table 1 and Supplementary Table 1 ). At the time of baseline scanning, 95% of participants were not receiving any antidepressant treatments and none of the participants was diagnosed with a substance-dependent disorder. We used the same image-processing procedure in a treatment dataset consisting of 250 participants who were reassessed after completing treatment trials. During these trials, the participants were randomly assigned to receive one of three commonly prescribed antidepressant medications (escitalopram, sertraline or venlafaxine extended release (XR) 18 ( n  = 164)) or an established behavioral intervention that integrated problem-solving with behavioral activation, compared with treatment as usual 19 ( n  = 86) (Supplementary Tables 1 and 2 ).

Using our image-processing system, we obtained 41 measures of activation and connectivity of 6 brain circuits of interest for each participant 20 . We have previously shown that these circuit measures satisfy psychometric criteria for construct validation, internal consistency and generalizability 20 . A unique feature of our image-processing system is that quantified circuit measures are expressed in terms of s.d. units from the mean of a healthy reference sample, and thus are interpretable for each individual. We refer to the resulting measures as ‘regional circuit scores’ (Fig. 1 and see Supplementary Methods for details).

a , Measures of task-based activation and functional connectivity and task-free connectivity derived from regions belonging to six circuits for which we have established relevance to depression and anxiety. (i) Default mode (D), salience (S) and attention (A) circuits were derived from the task-free periods of the fMRI. The Negative and Positive (P) circuits were engaged by a facial expressions task. In particular, the Negative circuit was engaged in Threat Conscious (NTC), Threat Non-conscious (NTN) and Sad (NS) conditions. The cognitive control circuit (C) was engaged by a Go–NoGo task. (ii) We defined the regions of interest comprising each circuit from the meta-analytic platform Neurosynth and refined them based on quality control, a set of psychometric criteria and whether they were implicated in depression and anxiety. (iii) We extracted functional connectivity between circuit regions for task-free circuits, and activation and connectivity of regions for task-engaged circuits (regions shown as sphere, connectivity shown as lines). b , We then expressed these measures as s.d. values compared with healthy participants to obtain personalized regional circuit scores for each individual. See Supplementary Table 18 for the full list of scores. c , We computed the distance between each pair of individuals as 1 − the correlation of their regional circuit scores. d , We show the distance matrix between the first 100 participants as a heatmap for illustrative purposes. e , We then used the distances obtained as input for a hierarchical clustering analysis. The individuals depicted have given permission to be included in published facial emotion stimulus sets 36 , 37 . AG, angular gyrus; aI, anterior insula; aIPL, anterior inferior parietal lobule; amPFC, anterior medial prefrontal cortex; Amy, amygdala; dACC, dorsal anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex; LPFC, lateral prefrontal cortex; msPFC, medial superior prefrontal cortex; PCC, posterior cingulate cortex; PCU, precuneus; pgACC, pregenual anterior cingulate cortex; sgACC, subgenual anterior cingulate cortex; vmPFC, venteromedial prefrontal cortex.

To generate biotypes based on regional circuit scores of clinical participants, we used these scores as inputs for a hierarchical clustering algorithm (Fig. 1 and Methods ). We generated solutions for 2–15 clusters and evaluated them as shown in Fig. 2 .

a , We selected candidate biotype solutions selected based on the sum of within-cluster distances. b , We evaluated the silhouette index of our solutions relative to a null multinormal distribution with conserved covariance between individuals 14 . c , We compared the silhouette index of our solutions relative to a solution using permuted participant labels, such that participant–brain correspondence was broken. d , e , We repeated our clustering approach leaving one participant out, 801× ( d ), as well as leaving out 20% of participants, 10,000× ( e ). In each iteration, we subsequently evaluated the overlap between participant biotype assignment in our original solution and each iterative solution by calculating the ARI. f , We evaluated the circuit measurements associated with each biotype across our original dataset and in two random halves of our original dataset separately. Circuit measurements that were consistently >0.5 s.d. from the mean across all these three samples were considered to be stable. g , We referenced the profile of circuit dysfunction to those found in the literature. h , i , To establish the clinical validity of our biotypes, we evaluated the cluster-specific differences in reported symptoms ( h ) and performances in a computerized cognitive battery ( i ). After establishing these differences in the full sample, we evaluated the stability of these symptom and behavioral profiles across two random half-splits of our data, deriving, each time, biotypes from the first half and assigning participants in the second half to a biotype derived from the first. We also followed the same procedure in a leave-study-out framework, leaving one of four of our studies out in each iteration. j , k , We subsequently evaluated the stability of biotype-specific symptom ( j ) and cognitive ( k ) differences relative to out-of-biotype participants in each iteration. We considered a difference to be stable when it was statistically significant in the whole sample and in each of the two random half-splits or in each of the two splits of a leave-study-out iteration. l , To evaluate the clinical utility of our cluster biotypes, we tested for differential symptom severity of each biotype to multiple depression treatments. Plots in this figure are only for illustrating the steps of our analysis.

Biotype validation

We validated our biotypes using six convergent sources of evidence: the elbow method (Fig. 2a ); two procedures proposed by Dinga et al. 14 to evaluate the evidence for biotypes of depression and anxiety (simulation-based significance testing of the silhouette index (Fig. 2b ) and stability using leave-one-out, and leave-20%-out crossvalidation (Fig. 2d,e )); an additional permutation-based significance testing of the silhouette index (Fig. 2c ); split-half reliability of the cluster profiles (Fig. 2f ); and the match of the solution to a theoretical framework of circuit dysfunction in depression and anxiety supported by previous brain imaging research 17 (Fig. 2g ).

The elbow method showed an elbow at five clusters and another, smaller elbow, at nine clusters, which suggested that the optimal solution lay between these two values (Supplementary Fig. 1 ). Simulation-based significance testing of the silhouette index showed that solutions with five or more clusters had a silhouette index that was significantly higher than that obtained by clustering data from a multivariate normal distribution (all P  < 0.05; Supplementary Fig. 2 ) and significantly higher than that obtained by a permutation of the circuit scores across participants ( P  < 0.05; Supplementary Fig. 3 ). Assessment of cluster stability using crossvalidation showed that all solutions had good stability (adjusted Rand index (ARI) > 0.75 for leave-one-out and ARI > 0.28 for leave-20%-out) (Supplementary Fig. 4 ).

Across all validation analyses, six emerged as a viable number of clusters. The silhouette index tests comparing the data with data from a multivariate normal distribution and with a permutation of the circuit scores across participants were significant for this solution (mean silhouette = 0.065, P  = 0.016 and P  < 0.0001, respectively) and crossvalidation showed that it had good stability (leave-study-out ARI = 0.80 and leave-20%-out ARI = 0.35). Also, in the six-cluster solution, a cluster emerged that was characterized by reduced task-evoked activation during cognitive control, which we had specifically hypothesized 16 , 17 .

The six resulting biotypes were distinguished by specific profiles of both task-free and task-evoked activity and/or connectivity, relative both to each other and to our healthy reference sample. To assign a name to these distinctive circuit profiles, we determined which circuit features, activity or connectivity were distinguished by a difference of at least 0.50 s.d. in magnitude away from the healthy reference sample. The distinct activity and connectivity profiles of each biotype are illustrated using a circuit schematic and numerical plot in Fig. 3 with further details illustrated in bar plots in Supplementary Fig. 5 . We named each biotype according to the circuits and circuit features that specifically differentiated them at this threshold relative to each other and to the healthy reference sample. We used the following nomenclature (each circuit is indicated with a letter): D, default mode; S, salience; A, attention; NS, negative affect circuit evoked by sad stimuli; NTC, negative affect circuit evoked by conscious threat stimuli; NTN, negative affect circuit evoked by nonconscious threat stimuli; P, positive affect circuit; C, cognitive circuit. The distinguishing circuit feature is indicated as a subscript: C, connectivity; A, activity, and the direction of dysfunction is indicated by + or −. These distinct profiles were also replicated when conducting the clustering procedure on a random half of the data and assigning participants in the second independent half of the data to each cluster (Supplementary Fig. 6 ).

a – f , Schematic circuit images illustrating the profile of circuit dysfunction defining each biotype (biotypes are labeled a – f ). Circuits are distinguished by colors that correspond to the circuit measure inputs (Fig. 1c ). Spheres represent the regions within each biotype-defining circuit and the size of the spheres represents the magnitude of activation deviation from the healthy reference (small spheres, activation ≤0.5 s.d. below the healthy reference; large spheres, activation ≥0.5 s.d. above the healthy reference). The thickness of lines between the spheres denotes a connectivity deviation (dashed lines, decreased connectivity ≤0.5 s.d. below the healthy reference; thick lines, increased connectivity ≥0.5 s.d. above the healthy reference). Column plots display the average activity across regions that define each circuit or the average connectivity between regions that define each circuit. A visualization of each regional circuit score by biotype is in Supplementary Fig. 5 . In bar plots, we highlight circuits that showed a mean difference of at least 0.50 s.d. below or above the healthy reference. We named each biotype according to the features that differentiated it from the healthy reference. Each circuit is indicated with a letter, the distinguishing circuit feature is indicated as a subscript and the direction of dysfunction is indicated by + or −. The subscript x indicates that the sixth biotype is not differentiated by a prominent circuit dysfunction. Besides this nomenclature, we suggest a short description for each biotype, which connects them with our theoretically synthesized biotypes: D C+ S C+ A C+ , default with salience and attention hyperconnectivity ( n  = 169 participants); A C− , attention hypoconnectivity ( n  = 161 participants); NS A+ P A+ , sad-elicited negative affect with positive affect hyperactivation ( n  = 154 participants); C A+ , cognitive control hyperactivation ( n  = 258 participants); NTC C− C A− , cognitive control hypoactivation with conscious threat-elicited negative affect hypoconnectivity ( n  = 15 participants); and D X S X A X N X P X C X , intact activation and connectivity ( n  = 44 participants).

Biotype D C+ S C+ A C+ ( n  = 169) was distinguished by relative intrinsic hyperconnectivity within the default mode circuit, as well as in the task-free salience and attention circuits (Fig. 3a ). In contrast, biotype A C− ( n  = 161) was distinguished by a relative reduction in intrinsic connectivity specific to the attention circuit (Fig. 3b ). Biotype NS A+ P A+ ( n  = 154) was characterized by heightened activity during conscious emotion processing, specifically within the negative affect circuit evoked by sad stimuli and within the positive affect circuit evoked by happy stimuli (Fig. 3c ). Biotype C A+ ( n  = 258) was distinguished specifically by increased activity within the cognitive control circuit during the inhibition of NoGo stimuli (Fig. 3d ). Biotype NTC C- C A− ( n  = 15) was a smaller cluster differentiated by a relative loss of functional connectivity within the negative affect circuit during the conscious processing of threat faces, as well as by reduced (rather than heightened) activity within the cognitive control circuit during the inhibition of NoGo stimuli (Fig. 3e ). Biotype D X S X A X N X P X C X ( n  = 44) was not differentiated by a substantial circuit dysfunction relative to other biotypes or to the healthy norm; we indicated this by using the subscript x instead of + or − (Fig. 3f ).

These distinct biotype circuit profiles were not explained by differences in scanners, because we removed scanner effects from our data using ComBat ( Methods ) and verified that the distribution of biotypes did not differ across scanners ( χ 2  = 12.773, two-sided P  = 0.237).

Biotypes differ on symptoms, behavior and treatment response

To further characterize the clinical phenotypes distinguished by each circuit biotype, we evaluated the biotype profiles on three different domains of clinically meaningful measures (Fig. 4 ): severity of symptoms, performance on general and emotional cognitive tests and differential treatment response. We highlight that the circuit biotypes derived from clustering were differentiated using only circuit inputs assessed independently from these domains of clinical information such that symptoms, performance and treatment response represented external validation measures.

a – f , Circuit biotypes are visualized using circuit schematics on the left (biotypes are labeled a – f ). We first compared these circuit biotypes on symptoms of depression and related anxiety (column ‘Symptom severity’). Next, we compared biotypes on behavioral performance on general and emotional cognitive tests relevant to social and occupational function (column ‘Behavioral dysfunction’). We compared biotypes on severity after treatment with one of three antidepressant pharmacotherapies (escitalopram, sertraline or venlafaxine XR), a behavioral problem-solving therapy (I-CARE) or usual care (U-CARE) (column ‘Severity after treatment’). To facilitate comparison across units of analysis, all measures were scaled between 0 and 1 so that 0 would represent minimum severity/dysfunction and 1 maximum severity/dysfunction. The column ‘Severity after treatment’ shows differences in symptom severity posttreatment (that is, lower values correspond to better treatment response). Comparisons on severity after treatment were conducted only for biotype/treatment combinations having n  ≥ 5, so only those are shown. We used the biotype nomenclature used previously. The subscript x indicates that the sixth biotype is not differentiated by a prominent circuit dysfunction relative to other biotypes. Besides this nomenclature, we suggest a short plain-English description for each biotype (in quotes), which connects them with our theoretically synthesized biotypes (as shown in Fig. 3 ).

We first asked whether the biotypes were distinguished by the severity of symptoms of depression and anxiety. To address this question, we used Mann–Whitney U -tests to compare the symptom severity of each biotype to the median symptom severity of all clinical participants not in the biotype (Supplementary Fig. 10 and Supplementary Tables 3 and 4 ). For insomnia and suicidality, these comparisons were conducted using χ 2  tests instead (Supplementary Fig. 11 and Supplementary Table 5 ). We considered significant tests for which P  < 0.05. We then replicated significant findings in split-half and leave-study-out analyses (Fig. 2h,j ).

Second, we assessed whether biotypes are distinguished by performance on a computerized battery of general and emotional cognitive tests relevant to daily social and occupational function. We conducted these analyses as described above for symptoms (Supplementary Fig. 12 and Supplementary Tables 6 and 7 ). We then replicated significant findings in split-half and leave-study-out analyses (Fig. 2i,k ).

Third, we assessed whether the biotypes predicted differential treatment response to one of the three pharmacotherapies or to behavioral therapy versus usual care. We conducted these analyses as described above for symptoms and behavior (Fig. 2l , Supplementary Fig. 13 and Supplementary Tables 8 – 10 ).

Biotype D C+ S C+ A C+ , characterized by task-free circuit hyperconnectivity, had slowed behavioral responses in identifying sad faces (effect size (ES) = 0.289, P  = 0.001, confidence interval (CI) = (−0.072, 0.289), replicated in leave-study-out), increased errors in an executive function task (ES = 0.175, P  = 0.044, CI = (9−0.182, 0.166)), fewer commission errors in a cognitive control task (ES = −0.275, P  = 0.002, CI = (−0.505, −0.217), replicated in leave-study-out) slowed responses to target stimuli in a sustained attention task (ES = 0.336, P  = 0.0001, CI = (0.714, 1.099)) (see Fig. 4a and Supplementary Figs. 10 – 12 for detailed visualization and Supplementary Tables 3 – 7 for comparisons). The biotype D C+ S C+ A C+ responded better to I-CARE compared with other biotypes (ES = −0.612, P  = 0.037, CI = (0.137, 0.306), responders = 42%, remitters = 25%) (Fig. 4a , Supplementary Fig. 13 and Supplementary Tables 8 – 10 ).

Biotype A C- , characterized by task-free attention circuit hypoconnectivity, had relatively less severe tension (ES = −0.196, P  = 0.049, CI = (11.5, 15)), but was also differentiated by relatively lower cognitive dyscontrol (ES = −0.305, P  = 0.006, CI = (15.5; 17.5)). In computerized tests, A C− was distinguished by faster responses to target Go stimuli on the Go–NoGo task, (ES = −0.383, P  = 6.20 × 10 −6 , CI = (0.180, 0.510), replicated in split-half), more commission and omission errors on the sustained attention task (ES = 0.300, P  = 0.0004, CI = (−0.302, −0.019); ES = 0.198, P  = 0.020, CI = (−0.308, −0.010)) and faster responses to priming by implicit threat stimuli (ES = −0.256, P  = 0.002, CI = (−0.111, 0.112)) (see Fig. 4b and Supplementary Figs. 10 – 12 for detailed visualization and Supplementary Tables 3 – 7 for comparisons). The A C− biotype had comparatively worse response to I-CARE (ES = 0.593, P  = 0.002, CI = (0.219; 0.350), responders = 26%, remitters = 22%) (Fig. 4a , Supplementary Fig. 13 and Supplementary Tables 8 – 10 ).

Biotype NS A+ P A+ , distinguished by circuit hyperactivation during conscious emotion processing, was distinguished by more severe anhedonia (ES = 0.343, P  = 0.014, CI = (2, 4.5)) and ruminative brooding (ES = 0.294, P  = 0.036, CI = (55.5, 63)) (Fig. 4c ; see Supplementary Figs. 10 – 12 for detailed visualization and Supplementary Tables 3 – 7 for comparisons).

Biotype C A+ , distinguished by heightened activity within the cognitive control circuit, had more severe anhedonia than other biotypes (ES = 0.295, P  = 0.015, CI = (2, 3.5)), more anxious arousal (ES = 0.218, P  = 0.003, CI = (15.5, 17.5)), more negative bias (ES = 0.188, P  = 0.003, CI = (15, 18.5), replicated in split-half) and more threat dysregulation (ES = 0.317, P  = 5.07 × 10 −7 , CI = [7.5, 9], replicated in split-half and leave-study-out). Behaviorally, C A+ had more errors and completion time in the executive function task (ES = 0.164, P  = 0.017, CI = (−0.268, −0.027) and ES = 0.152, P  = 0.027, CI = (−0.164, 0.090)), more commission errors in the Go–NoGo task (ES = 0.158, P  = 0.022, CI = (−0.201, 0.035), replicated in split-half) and more omission errors to target stimuli on the sustained attention task (ES = 0.275, P  = 6.46 × 10 −5 , CI = (−0.045, 0.170), replicated in split-half and leave-study-out) (Fig. 4c ; see Supplementary Figs. 10 – 12 for detailed visualization and Supplementary Tables 3 – 7 for comparisons). This biotype showed a better response to venlafaxine compared with the others (ES = −0.426, P  = 0.034, CI = (0.132, 0.226), responders = 64%, remitters = 40%) (Fig. 4c , Supplementary Fig. 13 and Supplementary Tables 8 – 10 ) .

Biotype NTC C- C A- , differentiated by loss of functional connectivity within the negative affect circuit during the conscious processing of threat faces, as well as reduced activity within the cognitive control circuit, had less ruminative brooding compared with the other biotypes (ES = −0.902, P  = 0.036, CI = (46, 5)), as well as faster reaction times to implicit sad faces (ES = −0.669, P  = 0.024, CI = (−1.316, −0.315)) (Fig. 4d ; see Supplementary Figs. 10 – 12 for detailed visualization and Supplementary Tables 3 – 7 for comparisons).

Biotype D X S X A X N X P X C X was not differentiated by a prominent circuit dysfunction relative to other biotypes or the healthy norm; however, it was distinguished by slower reaction times to implicit threat priming (ES = 0.516, P  = 0.001, CI = (0.254, 0.611)) (Fig. 4e ; see Supplementary Figs. 10 – 12 for detailed visualization and Supplementary Tables 3 – 7 for comparisons).

Finally, we also considered the demographic factors of age and biological sex. The biotypes did not differ in sex distribution ( χ 2  = 12.643, P  = 0.244) and only the A C− biotype was, on average, slightly older than the other biotypes; importantly, however, participants in this biotype were still within the young to mid-adult age range (mean age: 39.69 years, s.d. = 15.739, F  = 8.761, P  = 4.21 × 10 −8 ). Biotypes were also represented differently between datasets, which we expected given the clinical differences between the participants enrolled into each study ( χ 2  = 161.37, P  = 2.2 × 10 −16 ) (Supplementary Table 11 ).

As a context for the above evaluation of how biotypes were distinguished by symptoms, performance and treatment response, we evaluated the correlations between circuit scores and these external measures in the full sample across clusters combined (Supplementary Figs. 7 – 9 ). When thresholded with the false discovery rate correction for all pairwise correlations, we observed significant associations between circuit scores and 21% of the symptom measures, 10% of the performance measures and 31% of the treatment response measures.

Biotypes are transdiagnostic

The distinct clinical and treatment profiles that distinguish the six biotypes indicate that these circuit-derived biotypes dissect the heterogeneity of the traditional diagnostic classification of depression. We next asked whether biotypes transcend diagnostic classifications across the diagnoses that are related to and comorbid with depression. Our sample was composed of participants who met traditional diagnostic criteria for major depressive disorder ( n  = 375), generalized anxiety disorder ( n  = 192), panic disorder ( n  = 75), social anxiety disorder ( n  = 179), obsessive–compulsive disorder ( n  = 47) and post-traumatic stress disorder ( n  = 37). Several participants also met criteria for more than one diagnosis ( n  = 221) (Table 1 ).

The only diagnosis with a different frequency across biotypes was current major depressive disorder ( χ 2  = 24.235, two-sided P  = 0.0002). In particular, the A C− biotype had the highest proportion of participants with current major depressive disorder and the D X S X A X N X P X C X cluster had the lowest proportion (Fig. 5 and Supplementary Table 12 ).

We show the proportion of participants in each biotype who meet diagnostic criteria for major depressive disorder, generalized anxiety disorder, panic disorder, social anxiety disorder, obsessive–compulsive disorder and post-traumatic stress disorder (biotypes are labeled a – f ). χ 2 tests revealed that the frequency of major depressive disorder was significantly different across biotypes (two-sided χ 2  = 24.235, P  = 0.0002). We used the same biotype nomenclature as previously. The subscript x indicates that the sixth biotype is not differentiated by a prominent circuit dysfunction relative to other biotypes. Besides this nomenclature, we suggest a short plain-English description for each biotype (in quotes), which connects them with our theoretically synthesized biotypes, again as expressed in the legend to Fig. 3 .

Brain circuit scores outperform other features for biotyping

To compare prior approaches for biotyping with ours, we repeated our analysis using three competing alternative feature sets, each used in a recent paper reporting the identification of biotypes of depression using resting state fMRI. We then evaluated the results with the same criteria that we used for our own features (Fig. 2 ). Our findings show that our feature set is the only one that outperforms the null hypothesis of no clusters based on simulating data from a multinormal distribution with the same covariance as the original data ( P  = 0.016). In direct statistical comparisons of clustering performance between feature sets used as inputs, our combination of task and task-free regional circuit scores outperformed whole-brain connectomes (silhouette difference = −0.026, P resample  = 0.049, P permute  < 0.0001) and default mode network resting state connectivity (silhouette difference = −0.012, P resample  = 0.256, P permute  < 0.0001), but not connectivity of a network centered on the angular gyrus (silhouette difference = 0.155, P resample  = 1, P permute  = 1). The other feature sets also yielded associations among various metrics of biotypes, symptoms, behavioral performance and treatment response (Supplementary Tables 13 and 14 ).

To assess the impact of including task fMRI measures in addition to task-free brain circuit scores only, we also evaluated, in the same way, the results obtained using only our task-free brain circuit scores as input. To do so we showed that limiting the analysis to task-free brain circuit scores generated results that did not outperform the null hypothesis of no clusters based on simulating data from a multinormal distribution with the same covariance as the original data. Task-based brain circuit scores were also necessary to obtain symptom differences that generalize across random split-halves and behavior differences that generalize across the leave-study-out splits, depending on the number of clusters chosen (Supplementary Table 15 ).

To enable more precise diagnosis and selection of the best treatment for each individual, we need to dissect the heterogeneity of depression and anxiety. The dominant ‘one-size-fits-all’ diagnostic approach in psychiatry leads to cycling through treatment options by trial and error, which is lengthy, expensive and frustrating, with 30–40% of patients not achieving remission after trying one treatment 21 .

In the present study, we focus on the conceptualization of depression and anxiety as disorders of brain circuit function 22 . Using clustering and a new imaging system for the standardized quantification of circuit dysfunction at the level of the individual, we characterized six biotypes of depression and anxiety defined by specific profiles of dysfunction within both task-free and task-evoked brain circuits. These biotypes were validated using several procedures including simulations, crossvalidation and replication in held-out data. We found that the biotypes were distinguished by symptoms and behavioral performance on general and emotional cognitive tests that were not used as inputs in the clustering procedure. Importantly, some of these associations were replicated in split-half and leave-study-out procedures. We also showed that the six biotypes cut across the diagnostic boundaries of depression, anxiety and related comorbid disorders. Importantly for clinical translation, these biotypes predict response to different pharmacological and behavioral interventions.

We believe that this is the first identification of brain-derived biotypes that uses standardized personalized quantification of both task-free and task-evoked brain circuit dysfunctions and assesses response of the biotypes across different types of treatment. Rather than pursuing a fully data-driven approach, we integrated an unsupervised clustering analysis with a theoretical framework suitable for interpretability (Supplementary Table 16 ). We did this to minimize the possibility of overfitting and to generate solutions suited to the prospective selection of patients by biotype for future precision psychiatry trials. In this hybrid approach, each biotype was typified by a specific circuit dysfunction relative to a healthy norm, which mapped on to a unique transdiagnostic clinical phenotype.

Although our identification of six biotypes is one of many possible solutions to disentangling heterogeneity, these biotypes indicate that there may be multiple neural pathways that result in the clinical manifestation of depression and anxiety. By combining imaging data with clinical symptoms and behavior, we delineated clinical patterns that are consistent with the putative function of the circuits underlying each biotype. Importantly, although some biotypes were characterized exclusively by alterations in task-free intrinsic connectivity, others were characterized by alterations in task-evoked changes in activity and connectivity.

In the task-free state, D C+ S C+ A C+ was distinguished by hyperconnectivity of the default mode circuit, coupled with hyperconnectivity of both salience and attention circuits, correlating clinically with slowed emotional and attentional responses, replicated in split-half analyses. Although previous studies have reported circuit alterations in each of these circuits in depression and anxiety, our findings indicate that the D C+ S C+ A C+ biotype exhibits a combination of these alterations. In line with our theoretical taxonomy, the A C+ biotype demonstrated hypoconnectivity rather than hyperconnectivity within the frontoparietal attention circuit. This pattern corresponded to a clinical profile of lapses in concentration and impulsivity, replicated in split-half analyses.

Under task conditions, the NS A+ P A+ biotype displayed heightened activation within subcortical and cortical brain regions associated with processing both sad and positive emotions. Clinically, this biotype also exhibited prominent anhedonia. This profile corresponds with previous findings of heightened activity in the medial prefrontal cortex in response to happy faces, which has been linked to levels of anhedonia 23 , 24 and is consistent with our theoretical taxonomy. Increased activation of the amygdala is a common observation in depression in response to negative emotion 25 , 26 . Notably, biotype NS A+ P A+ exhibits concurrent hyperactivation of the ventral striatum, which may indicate a negative bias alongside anhedonia 17 .

Two additional biotypes displayed contrasting dysfunctions within the cognitive control circuit. Biotype NTC C− C A− exhibited reduced activation during a cognitive control task and decreased connectivity in processing threat consciously. These characteristics suggest impaired cognitive control which is also crucial for regulating emotions. In contrast, C A+ showed increased activation of the cognitive control circuit. This was associated with threat-related symptoms, negative bias and poorer cognitive control, as well as working memory performance, confirmed by both split-half and leave-study-out analyses. The replication of biotype C A+ reinforces its inclusion as an exploratory biotype in our theoretical taxonomy. Although early evidence suggested that heightened cognitive control activity might be compensatory and not necessarily linked to behavioral deficits 27 , our findings indicate that it is associated with specific cognitive–behavioral impairments. These findings highlight the importance of including task fMRI measures in future precision psychiatry studies and the value of using multimodal approaches to achieve more precise diagnoses in depression 28 .

Our approach enabled us to compare the efficacy of different treatments for each biotype to advance neurobiologically informed precision psychiatry. Collecting identical imaging and clinical measures across patients and treatments enabled us to compare the response of each biotype for three antidepressants, a behavioral intervention and treatment as usual. By doing so, we found that the D C+ S C+ A C+ biotype, characterized by hyperconnectivity of the default mode and other task-free circuits, was associated with a better response to behavioral treatment compared with the other biotypes. On the other hand, the biotype characterized by reduced attention circuit connectivity (A C− ), had a worse response to behavioral treatment. Finally, biotype C A+ , characterized by hyperactivation of the cognitive control circuit, had a better response to venlafaxine.

We delineated and validated biotypes using a small number of theoretically motivated features. By integrating theoretically grounded, task-evoked and task-free measures, our analysis provides unique insights that are complementary to those of foundational large studies that have analyzed task-free data using whole-brain techniques 4 , 15 . Nevertheless, as this is the first demonstration, to our knowledge, of a participant-level approach to cluster-derived biotyping using a small number of task-evoked and task-free features, our results should be interpreted with caution. Future studies are needed to investigate these biotypes in new datasets and to prospectively assign participants to treatment based on their biotypes. Also, we acknowledge that obtaining task fMRI measures can be more burdensome than collecting task-free measures only. We compared our results with results obtained using task-free data only and found that including both task and task-free data provided the best validation results, especially in beyond-chance clustering of subjects in feature space. In direct statistical comparisons of clustering performance, our combination of task and task-free regional circuit scores outperformed whole-brain connectomes, default mode network task-free connectivity and task-free regional circuit scores alone, but not connectivity of a network centered on the angular gyrus; however, the last approach did not provide generalizable symptom differences between clusters. Alternative feature sets also yielded several reproducible associations among clusters, symptoms and behavioral performance, consistent with the previous literature. This demonstrates that our approach, although potentially advantageous, does not negate the potential of other feature selection processes for depression biotyping. Future biotyping studies with both task-based and task-free data should consider comparing the performance of each.

Some strengths of our sample are that it represents the entire spectrum of depression and anxiety severity, is almost completely unmedicated (95%) and is recruited from a variety of settings. The sample also features common comorbidities that are often exclusion criteria. However, by including such a diverse population, we potentially reduce our ability to detect additional biotypes that might be more specific to certain clinical settings. It is also possible that some biotypes reflect contributions from comorbidities, which warrants replication in larger transdiagnostic samples. Another possibility is that biotypes are at least partially driven by differences in demographics between datasets. It would not be surprising, for example, if certain age groups belonged more to biotypes characterized by specific brain and clinical dysfunctions, because psychiatric symptoms, treatment response and brain biology all vary with age. We used identical imaging measures to evaluate biotypes across multiple treatments. However, some treatment groups within a biotype were small and could be unduly influenced by comorbidities or treatment design factors; therefore, it is important that the generalizability of our findings be tested by future large treatment studies. We also acknowledge that our imaging measures use a specific set of fMRI tasks that are not widely available. Future replications of our approach will be facilitated by the fact that our tasks are relatively short and easy to implement, as demonstrated by their adoption for large clinical trials such as iSPOT-D, ENGAGE and a recent trial using TMS in treatment-resistant depression 29 . Future studies could also evaluate whether similar clusters can be derived from different tasks that tap into similar domains and compare the results with ours. Our large sample allowed us to evaluate the generalizability of symptom and behavioral differences in split-half and leave-study-out validations. However, the number of participants of clinical trials was too small to perform such analyses for treatment response ( n  < 10 for 90% of comparisons; Supplementary Table 8 ). Future studies should apply our approach to clinical trial data to verify these findings, which should be interpreted prudently until they can be validated in new samples. Finally, the symptom differences between biotypes that we detected were mostly small, with effect sizes ranging from 0.08 to 0.90. The small size of these differences might be a reason why most comparisons did not reach statistical significance when splitting the dataset in two random halves or by study and analyzing each split independently. Small effect sizes in the association between imaging and symptom variables are common 30 , highlighting the need for consistent measures across studies and for finer-grained clinical measures. In the present study, we show the utility of combining four studies using standardized measures. We recommend interpreting the clinical results that did not survive our validation analyses with caution, but the present study is nevertheless a foundation to further test these results.

In conclusion, we leveraged personalized regional dysfunction scores grounded in a theoretical taxonomy of brain dysfunction in mood and anxiety disorders to identify six biotypes in a large transdiagnostic sample of unmedicated individuals with depression and anxiety. These biotypes differed significantly in symptom profiles, performance on behavioral testing and responses to multiple treatments. Our results validate a new theory-driven method for depression biotyping as well as a promising approach to advancing precision clinical care in psychiatry.

Data were obtained from four studies: International Study to Predict Optimized Treatment in Depression (iSPOT-D 18 , https://clinicaltrials.gov/ct2/show/NCT00693849 ), Research on Anxiety and Depression study (RAD 38 ), Human Connectome Project for Disordered Emotional States (HCP-DES 39 ) and Engaging self-regulation targets to understand the mechanisms of behavior change and improve mood and weight outcome (ENGAGE 40 , https://clinicaltrials.gov/ct2/show/NCT02246413 ). Clinical participants from these studies ( n  = 801) represented the full spectrum of severity of depression and anxiety disorders (see Table 1 and Supplementary Table 1 for details). Healthy controls (iSPOT-D, n  = 67; HCP-DES, n  = 70) were used as a reference group for building regional circuit scores from the imaging data (see below). Of the 801 clinical participants, 250 completed randomized controlled trials of either antidepressant pharmacotherapy for major depressive disorder ( n  = 164) 18 or behavioral intervention for clinically substantial depressive symptoms and obesity ( n  = 86) 40 (see Supplementary Table 2 for more details).

All participants provided written informed consent. Procedures were approved by the Stanford University Institutional Review Board (IRB, protocol nos. 27937 and 41837) or the Western Sydney Area Health Service Human Research Ethics Committee.

MRI acquisition and preprocessing

Details of MRI sequences, fMRI tasks, MRI data quantification and quality control are given in Supplementary Methods .

Acquisition

Participants underwent the Stanford Et Cere Image Processing System protocol, which probes six brain circuits: default mode circuit, salience circuit, attention circuit, negative affect circuit, positive affect circuit and cognitive control circuit 17 , 20 . The Facial Expressions of Emotion Tasks probed the positive and negative affect circuits and a Go–NoGo task probed the cognitive control circuit. We derived measures of task-free function of the default mode, attention and salience circuits from the task data 41 , 42 . Task-free measures were independent of those obtained from the task conditions (Supplementary Fig. 14 ).

Preprocessing

The MRI data were preprocessed using fMRIprep 43 . We discarded scans if they contained incidental findings, major artifacts or signal dropouts or had >25% of volumes containing significant frame-wise displacement. An experienced rater (L.T.) also visually checked each scan, leading to the exclusion of 32 participants. Scans removed owing to excessive motion were: Go–NoGo task = 38, Conscious Facial Expressions of Emotion Task = 92, Non-conscious Facial Expressions of Emotion Task = 76 and task free = 51 (see Supplementary Table 17 for the number of scans passing criteria).

Derivation of regional circuit scores

A summary of how regional circuit scores were obtained is given in the following sections (Fig. 1 ; see Supplementary Methods for details). We previously demonstrated that this system produces valid and clinically useful individual circuit clinical scores 20 .

Extraction of imaging features of interest

The regions of interest within six circuits of interest were defined from the meta-analytic platform Neurosynth 44 (see Supplementary Table 18 for search terms and coordinates) and refined by removing regions that did not pass quality control or psychometric criteria. Of the remaining regions, we only retained 29 regions implicated in our theoretical synthesis of dysfunctions in depression and anxiety 20 , 38 . From these regions, we derived 41 features of activation, task-based and task-free connectivity for subsequent analyses 20 (see Supplementary Table 18 and Supplementary Tables S5A and S5B in ref. 20 for details on the regions of interest and features). Our focus on regions defined from theory, meta-analyses and anatomy can lead to reliable and reproducible imaging measures. For example, activations within anatomically defined regions of interest have acceptable-to-high within-participant reliability 45 , as does connectivity within established brain networks 46 .

All following analyses used RStudio 2022.07.2, R v.4.1.3. Code for these analyses and the regions of interest to derive our imaging features are at https://github.com/leotozzi88/cluster_study_2023 .

Imputation of missing values

As a result of missing scans and quality control, some regional circuit scores could not be computed for some participants: 7.57% for the default, salience and attention scores, 9.38% for the negative affect sad scores, 9.38% for the negative affect threat conscious scores, 6.72% for the negative affect threat nonconscious scores, 4.05% for the cognitive control scores and 9.38% for the positive affect scores. We imputed these values separately for each scanner by using multiple imputation by chained equations with random forests (R package miceRanger), using one iteration of a predictive mean matching model with the imaging features as the input.

Correction for scanner effects

We removed the potential confounding effect of between-scanner variability using ComBat 47 , 48 , 49 , an established method that uses an empirical Bayesian framework to remove batch effects.

Referencing to a healthy norm

All imaging features of the clinical participants were expressed in s.d. units relative to the mean and s.d. of healthy controls. These values are henceforth referred to as ‘regional circuit scores’ and represent the amount of dysfunction of each component of each circuit. Subsequent analyses were conducted on the regional circuit scores of the clinical participants only.

Symptom measures

We used self-reported questionnaires to operationalize: ruminative worry (Penn State Worry Questionnaire—Abbreviated total 50 ); ruminative brooding (Ruminative Response Scale total 51 ); anxious arousal (Mood and Anxiety Questionnaire general distress subscale 52 ); negative bias (Depression Anxiety and Stress Scale (DASS) depression subscale); threat dysregulation (DASS anxiety subscale); anhedonia (Snaith–Hamilton Pleasure Scale total 53 ); cognitive dyscontrol (Barratt Impulsiveness Scale attentional impulsiveness subscale 54 ); tension (DASS stress subscale); insomnia (Quick Inventory of Depressive Symptomatology—Self-Report Revised (QIDS-SR) sum of items 1–3 (ref. 55 )); and suicidality (QIDS-SR item 12). In iSPOT-D, we used the Hamilton Depression Rating Scale (HDRS) total score as a measure of depression severity 56 and, in ENGAGE, we used the Symptom Checklist 20 Depression Scale (SCL-20) 57 . See Supplementary Table 19 for the participants in each sample available for each measure.

Clinical diagnoses

DSM-IV-TR (RAD), DSM-5 (HCP-DES) or DSM-IV (iSPOT-D) criteria for major depressive disorder, anxiety disorder, post-traumatic stress disorder or obsessive–compulsive disorder were ascertained by a psychiatrist, general practitioner or researcher using the structured MINI 34 . In ENGAGE, patients were considered eligible if they scored ≥10 on the PHQ-9, a threshold with 88% specificity for major depressive disorder 35 , and had a qualifying body mass index (BMI). Comorbidities were ascertained from electronic health records.

Behavioral performance measures

Cognitive performance was assessed using WebNeuro 37 , 58 , 59 . We focused on the tests for which our regional circuit scores have been shown to predict performance 20 : sustained attention (omission errors, commission errors and reaction times in a continuous performance test); executive function (errors and completion time of a maze test); cognitive control (commission errors and reaction times in a Go–NoGo test); explicit emotion identification (reaction time to identify happy, sad, fearful and angry faces); and implicit priming bias by emotion (difference in reaction time in a face identification task when primed implicitly by happy, sad, fearful and angry faces compared with neutral faces). For analyses, we used the test performance referenced to an age-matched norm generated by WebNeuro ( z -scores). See Supplementary Table 19 for the number of participants in each sample available for each measure.

In iSPOT-D, participants were randomized to one of three treatments: escitalopram (selective serotonin reuptake inhibitor (SSRI)), sertraline (SSRI) or venlafaxine XR (selective serotonin–norepinephrine reuptake inhibitor (SNRI)) 18 . In ENGAGE, participants were randomized to either a behavioral intervention combining problem-solving, behavioral activation and weight loss (Integrated Coaching for Better Mood and Weight, I-CARE) or usual care (U-CARE) 19 , 40 . No treatment was administered in HCP-DES and RAD, so these studies were not considered in the treatment analyses.

Identification of depression biotypes

To identify biotypes within our clinical participants, we used hierarchical clustering of their 41 regional circuit scores. We selected the optimal number of clusters using six convergent sources of evidence: the elbow method; two procedures proposed by Dinga et al. 14 to evaluate biotypes of depression (simulation-based significance testing of the silhouette index and stability using crossvalidation); permutation-based significance testing of the silhouette index; split-half reliability of the cluster profiles; and the match of the solution to a theoretical framework 17 (Fig. 2 ).

Hierarchical clustering

For each pair of clinical participants, we first computed the correlation coefficient of their 41 imaging-derived regional circuit scores (Fig. 1 ). Then, we computed the dissimilarity between each pair of clinical participants as 1 − this correlation (see ref. 60 for a similar approach). We used the between-individual dissimilarity matrix as input to hierarchical clustering using the average as agglomeration method.

Elbow method

The first source of evidence that we used to choose the optimal number of clusters was the elbow method, based on a plot showing the within-cluster sum of distances between participants for solutions between 2 and 15 clusters (Fig. 2a ).

Simulation-based significance testing of silhouette

We tested the probability of our observed average silhouette index occurring under the null hypothesis of no clusters (that is, of the data coming from a multinormal distribution) 14 . For clusters between 2 and 15, we conducted 10,000 simulation runs, in which we drew 801 participants from a multinormal distribution that had the same mean and covariance for each regional circuit score as our data. These simulated participants were then used as input in hierarchical clustering, as described above, and the average silhouette index across participants was calculated. Thus, we obtained null distributions for these average silhouette indices. Finally, we calculated the proportion of average silhouette indices generated under the null that were greater than the one we obtained from our data ( P value). We considered statistically significant solutions with numbers of clusters for which P  < 0.05 (Fig. 2b ).

Permutation-based significance testing of silhouette

For each number of clusters between 2 and 15, we shuffled each brain circuit score across subjects 10,000×, then repeated the hierarchical clustering as described above and calculated the average silhouette index. Thus, we obtained null distributions for these average silhouette indices. Finally, we calculated the proportion of average silhouette indices generated under the null that were greater than the one we obtained from our data ( P value). We considered statistically significant solutions with numbers of clusters for which P  < 0.05 (Fig. 2c ).

Assessment of cluster stability using crossvalidation

To evaluate whether the clustering was stable under small perturbations to the data 14 , we repeated the clustering procedure 801×, each time with one participant left out. For each run and for each solution between 2 and 15 clusters, we calculated the similarity of the new cluster assignments to the original ones using the ARI (Fig. 2d ). We then repeated this procedure while holding out 20% of the sample instead of one participant (Fig. 2e ).

Matching of clusters to a theoretical framework

We identified the primary circuit dysfunction of each cluster by averaging the values of regional circuit scores by circuit and modality (task-based activity, task-based connectivity, task-free connectivity) and identifying the measures that showed a >0.5 s.d. absolute mean difference compared with the healthy norm. We then compared the profile of circuit dysfunction of each cluster with those hypothesized in a theoretical framework of circuit dysfunction in depression and anxiety 16 , 17 .

Split-half replication of cluster profiles

First, we split our dataset into two random samples of equal size. Then, we ran our clustering procedure on the first half-split. Then, we assigned each participant in the second split to one of the clusters obtained in the first half-split. To do so, we computed the mean circuit scores across all participants belonging to each cluster in the first half-split. Then, we calculated Pearson’s correlation coefficient between each participant’s brain circuit scores and these cluster-averaged scores. Each out-of-sample participant was assigned to the cluster for which this correlation was highest. Finally, we identified the primary circuit dysfunctions of each cluster in each split as described above (>0.5 s.d. absolute mean difference compared with the healthy reference data) and examined whether they replicated the circuit profiles found in the whole sample visually and by computing Pearson’s correlation coefficient of the mean profile dysfunction profile of each cluster between splits (Fig. 2f ).

Clinical characterization of biotypes

We characterized our final clustering solution by using external clinical measures independent of cluster inputs: symptoms, clinical diagnoses, performance on behavioral tests and treatment response. Importantly, we also replicated our findings in split-half and leave-study-out analyses (Fig. 2g–l ).

Comparison of symptoms between biotypes

For each symptom, we compared the median severity of participants in each biotype to the median severity of participants who were not in the biotype using Wilcoxon’s tests. As insomnia and suicidality were assessed using only three and one QIDS-SR items, respectively, we used a χ 2 test to compare the fraction of participants in the biotype who endorsed any of the items (total value >0) compared with participants who were not in the biotype. For Wilcoxon’s tests, we calculated the effect size r as the z statistic divided by the square root of the sample size and we considered significant tests for which P  < 0.05 (Fig. 2h,j ).

Comparison of behavioral performance between biotypes

For each of our behavioral performance measures, we compared the median performance of participants in each biotype with the median performance of participants who were not in the biotype using Wilcoxon’s tests. We calculated the effect size r as the z statistic divided by the square root of the sample size and we considered significant tests for which P  < 0.05 (Fig. 2i,k ).

Comparison of treatment response between biotypes

To obtain a comparable measure of symptom severity across our clinical trial datasets, we first scaled the measures of total HDRS scores (collected in iSPOT-D) and SCL-20 scores (collected in ENGAGE) between 0 and 1 based on the minimum and maximum values of each scale. We defined response as a decrease of at least 50% of symptom severity from baseline to follow-up and remission as follow-up HDRS ≤ 7 or SCL-20 ≤ 0.5. Then, for each treatment modality and each biotype, the severity of symptoms after treatment of participants in the biotype was compared with the median symptom severity of clinical participants not in the biotype using Wilcoxon’s tests. For these tests, we excluded biotypes in which only five or fewer participants received a treatment. We calculated the effect size r as the z statistic divided by the square root of the sample size and considered significant tests for which P  < 0.05. (Fig. 2l ).

Split-half replication of clinical associations

We replicated the significant comparisons of behavior and symptoms between biotypes found in the complete sample by splitting the sample into two random halves, repeating the clustering procedure on the first half and then assigning participants in the second half to the clusters obtained in the first half, as described above. We then conducted Wilcoxon’s tests as described above in each split and considered a result replicable if it was significant both in the original sample and in each of the split-half samples (for the second split, we conducted a confirmatory one-sided test).

Leave-study-out replication of clinical associations

For each of the four studies included in our dataset, we replicated the significant comparisons of behavior and symptoms between biotypes by splitting the sample into two subsets: one containing the participants who were not from that study and one containing participants from that study. Then, we repeated the clustering procedure on the first split and assigned participants in the second subset to the clusters obtained in the first split, as described above. We then conducted Wilcoxon’s tests as described above and considered a result replicable if it was significant in each of the two splits when holding out at least one study (for the second split, we conducted a confirmatory one-sided test).

Comparison of diagnoses between biotypes

To evaluate whether the clusters reflected traditional diagnostic categories, we used χ 2 tests to compare the proportion of clinical participants in each biotype who met criteria for major depressive disorder, generalized anxiety disorder, obsessive–compulsive disorder, post-traumatic stress disorder, panic disorder or social phobia.

Comparison of covariates of no interest between biotypes

To verify that biotypes were not driven by scanner effects, we used χ 2 tests to evaluate whether the proportion of participants in each cluster was different across scanners. Similarly, we used χ 2 tests to examine the effects of gender and dataset and a one-way analysis of variance (ANOVA) to test whether different biotypes had different age distributions.

Comparison of brain circuit scores to other biotyping inputs

We selected three alternative feature sets, each used in a recent paper identifying biotypes of depression using resting state fMRI (to our knowledge, no prior publication has used task fMRI): whole-brain functional connectivity from the Power atlas 4 ; functional connectivity in the default mode network 5 ; and a functional connectivity of the angular gyrus 7 . We evaluated these features using the same criteria that we used for our own: (1) solution outperforms null hypothesis of no clusters (simulated data); (2) solution outperforms null hypothesis of no clusters (permuted data); (3) ARI (leave-one-out mean); (4) ARI (leave-20%-out mean); (5) generalizable cluster profiles across random split-half; (6) generalizable symptom differences across random split-half; (7) generalizable behavior differences across random split-half; (8) generalizable symptom differences across leave-study-out; (9) generalizable behavior differences across leave-study-out; and (10) biotypes differ in treatment response. For each of the alternative sets of features, we evaluated the number of clusters reported in the original paper and six clusters (the number that we chose in our analysis). We also conducted two statistical tests comparing clustering performance using our features with other features. First, a resampling test: we sampled 80% of participants, used each set of features to cluster their data and computed the corresponding average silhouette index over 10,000 iterations. For each set of alternative features, we considered as P resample the fraction of samplings in which the silhouette index was higher than the one obtained with our features. Then a permutation test: after clustering each of the imaging feature sets, we randomly permuted the cluster assignments 10,000× and computed a silhouette score for each. This provided us with null distributions of the silhouette index for each feature set. We then calculated the difference between the null distribution of the silhouette index obtained using our features and each of the null distributions obtained from alternative features. We considered as P permute the proportion of permutations in which the difference between the two null distributions was greater than that between the silhouette indices of the real solutions. We considered our features to provide a better clustering when P permute  < 0.05 and P resample  < 0.05.

Finally, we compared our original results to results obtained using only our task-free brain circuit scores, choosing as the number of clusters six (the number we chose in our analysis using all features) and two (the number of clusters with task-free dysfunction identified in our analyses).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The datasets used in this analysis were collected as part of the iSPOT-D, RAD, HCP-DES and ENGAGE studies. These datasets are available upon request from Stanford BrainNet at https://www.stanfordpmhw.com/datasets . The BRAINnet repository meets the requirements for being public but also aligns with the procedures of other official public and scientific repositories such as HCP, ABCD and NDA. This choice is in line with the FAIRness guidelines and it respects the original funding requirements, allowing for appropriate source contributions and citations. Our approach is specifically designed for scientific use, which includes limiting access to for-profit entities to comply with the original funding stipulations and participant consent. Therefore, total open access is not feasible. Our intention is to provide public access that is consistent with the consent agreements and the original funding intentions, similar to the data shared through NIH repositories. On Stanford BRAINnet, we established a data access request form that screens users, similar to other public repositories.

Code availability

Code for the analyses and the regions of interest used to calculate the clinical circuit scores is available at https://github.com/leotozzi88/cluster_study_2023 .

Friedrich, M. J. Depression is the leading cause of disability around the world. JAMA 317 , 1517 (2017).

PubMed   Google Scholar  

Ansara, E. D. Management of treatment-resistant generalized anxiety disorder. Ment. Health Clin. 10 , 326–334 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Ruberto, V. L., Jha, M. K. & Murrough, J. W. Pharmacological treatments for patients with treatment-resistant depression. Pharmaceuticals 13 , 116 (2020).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Drysdale, A. T. et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med 23 , 28–38 (2017).

Article   CAS   PubMed   Google Scholar  

Liang, S. et al. Biotypes of major depressive disorder: neuroimaging evidence from resting-state default mode network patterns. Neuroimage Clin. 28 , 102514 (2020).

Price, R. B., Gates, K., Kraynak, T. E., Thase, M. E. & Siegle, G. J. Data-driven subgroups in depression derived from directed functional connectivity paths at rest. Neuropsychopharmacology 42 , 2623–2632 (2017).

Tokuda, T. et al. Identification of depression subtypes and relevant brain regions using a data-driven approach. Sci. Rep. 8 , 14082 (2018).

Patel, A. R. et al. Stress cardiac magnetic resonance myocardial perfusionimaging: JACC review topic of the week. J. Am. Coll. Cardiol. 78 , 1655–1668 (2021).

Goldstein-Piekarski, A. N. et al. Human amygdala engagement moderated by early life stress exposure is a biobehavioral target for predicting recovery on antidepressants. Proc. Natl Acad. Sci. USA 113 , 11955–11960 (2016).

Nguyen, K. P. et al. Patterns of pretreatment reward task brain activation predict individual antidepressant response: key results from the EMBARC randomized clinical trial. Biol. Psychiatry 91 , 550–560 (2022).

Article   PubMed   Google Scholar  

Pilmeyer, J. et al. Functional MRI in major depressive disorder: a review of findings, limitations, and future prospects. J. Neuroimaging 32 , 582–595 (2022).

Tozzi, L., Goldstein-Piekarski, A. N., Korgaonkar, M. S. & Williams, L. M. Connectivity of the cognitive control network during response inhibition as a predictive and response biomarker in major depression: evidence from a randomized clinical trial. Biol. Psychiatry 87 , 462–472 (2020).

Krystal, A. D. et al. A randomized proof-of-mechanism trial applying the ‘fast-fail’ approach to evaluating κ-opioid antagonism as a treatment for anhedonia. Nat. Med. 26 , 760–768 (2020).

Dinga, R. et al. Evaluating the evidence for biotypes of depression: methodological replication and extension of Drysdale et al. (2017). Neuroimage Clin. 22 , 101796 (2019).

Grosenick, L. et al. Functional and optogenetic approaches to discovering stable subtype-specific circuit mechanisms in depression. Biol. Psychiatry. Cogn. Neurosci. Neuroimaging 4 , 554–566 (2019).

PubMed   PubMed Central   Google Scholar  

Williams, L. M. Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: a theoretical review of the evidence and future directions for clinical translation. Depress. Anxiety 34 , 9–24 (2017).

Williams, L. M. Precision psychiatry: a neural circuit taxonomy for depression and anxiety. Lancet Psychiatry 3 , 472–480 (2016).

Williams, L. M. et al. International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol. Trials 12 , 4 (2011).

Ma, J. et al. Effect of integrated behavioral weight loss treatment and problem-solving therapy on body mass index and depressive symptoms among patients with obesity and depression: the RAINBOW randomized clinical trial. JAMA 321 , 869–879 (2019).

Goldstein-Piekarski, A. N. et al. Mapping neural circuit biotypes to symptoms and behavioral dimensions of depression and anxiety. Biol. Psychiatry 91 , 561–571 (2022).

Gaynes, B. N. et al. What did STAR*D teach us? Results from a large-scale, practical, clinical trial for patients with depression. Pschiatr. Serv. 60 , 1439–1445 (2009).

Article   Google Scholar  

Scangos, K. W., State, M. W., Miller, A. H., Baker, J. T. & Williams, L. M. New and emerging approaches to treat psychiatric disorders. Nat. Med. 29 , 317–333 (2023).

Dichter, G. S., Kozink, R. V., McClernon, F. J. & Smoski, M. J. Remitted major depression is characterized by reward network hyperactivation during reward anticipation and hypoactivation during reward outcomes. J. Affect. Disord. 136 , 1126–1134 (2012).

Keedwell, P. A., Andrew, C., Williams, S. C. R., Brammer, M. J. & Phillips, M. L. The neural correlates of anhedonia in major depressive disorder. Biol. Psychiatry 58 , 843–853 (2005).

Groenewold, N. A., Opmeer, E. M., de Jonge, P., Aleman, A. & Costafreda, S. G. Emotional valence modulates brain functional abnormalities in depression: evidence from a meta-analysis of fMRI studies. Neurosci. Biobehav. Rev. 37 , 152–163 (2013).

Stuhrmann, A., Suslow, T. & Dannlowski, U. Facial emotion processing in major depression: a systematic review of neuroimaging findings. Biol. Mood Anxiety Disord. 1 , 10 (2011).

Matsuo, K. et al. Prefrontal hyperactivation during working memory task in untreated individuals with major depressive disorder. Mol. Psychiatry 12 , 158–166 (2007).

Cuthbert, B. N. & Kozak, M. J. Constructing constructs for psychopathology: the NIMH research domain criteria. J. Abnorm. Psychol. 122 , 928–937 (2013).

Williams, L. M. et al. Identifying response and predictive biomarkers for transcranial magnetic stimulation outcomes: protocol and rationale for a mechanistic study of functional neuroimaging and behavioral biomarkers in veterans with pharmacoresistant depression. BMC Psychiatry 21 , 35 (2021).

Feng, C., Thompson, W. K. & Paulus, M. P. Effect sizes of associations between neuroimaging measures and affective symptoms: a meta-analysis. Depress. Anxiety 39 , 19–25 (2022).

American Psychiatric Association. DSM-IV-TR: Diagnostic and Statistical Manual of Mental Disorders 4th edn (2000).

American Psychiatric Association. DSM-5: Diagnostic and Statistical Manual of Mental Disorders 5th edn (2013).

American Psychiatric Association. DSM-IV: Diagnostic and Statistical Manual of Mental Disorders 4th edn (1994).

Sheehan, D. V. et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J. Clin. Psychiatry 59 , 22–33 (1998).

Kroenke, K., Spitzer, R. L. & Williams, J. B. W. The PHQ-9. J. Gen. Intern. Med 16 , 606–613 (2001).

Gur, R. C. et al. A method for obtaining 3-dimensional facial expressions and its standardization for use in neurocognitive studies. J. Neurosci. Methods 115 , 137–143 (2002).

Mathersul, D. et al. Explicit identification and implicit recognition of facial emotions: II. Core domains and relationships with general cognition. J. Clin. Exp. Neuropsychol. 31 , 278–291 (2009).

Williams, L. M. et al. Developing a clinical translational neuroscience taxonomy for anxiety and mood disorder: protocol for the baseline-follow up Research domain criteria Anxiety and Depression (‘RAD’) project. BMC Psychiatry 16 , 68 (2016).

Tozzi, L. et al. The human connectome project for disordered emotional states: protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression. NeuroImage 214 , 116715 (2020).

Williams, L. M. et al. The ENGAGE study: integrating neuroimaging, virtual reality and smartphone sensing to understand self-regulation for managing depression and obesity in a precision medicine model. Behav. Res. Ther. 101 , 58–70 (2018).

Elliott, M. L. et al. General functional connectivity: shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks. NeuroImage 189 , 516–532 (2019).

Korgaonkar, M. S., Ram, K., Williams, L. M., Gatt, J. M. & Grieve, S. M. Establishing the resting state default mode network derived from functional magnetic resonance imaging tasks as an endophenotype: a twins study. Hum. Brain Mapp. 35 , 3893–3902 (2014).

Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16 , 111–116 (2019).

Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C. & Wager, T. D. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8 , 665–670 (2011).

Holiga, Š. et al. Test-retest reliability of task-based and resting-state blood oxygen level dependence and cerebral blood flow measures. PLoS ONE 13 , e0206583 (2018).

Tozzi, L., Fleming, S. L., Taylor, Z., Raterink, C. & Williams, L. M. Test–retest reliability of the human functional connectome over consecutive days: identifying highly reliable portions and assessing the impact of methodological choices. Netw. Neurosci. https://doi.org/10.1162/netn_a_00148 (2020).

Fortin, J.-P. et al. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167 , 104–120 (2018).

Fortin, J.-P. et al. Harmonization of multi-site diffusion tensor imaging data. NeuroImage 161 , 149–170 (2017).

Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8 , 118–127 (2007).

DeLapp, R. C., Chapman, L. K. & Williams, M. T. Psychometric properties of a brief version of the Penn State Worry Questionnaire in African Americans and European Americans. Psychol. Assess. 28 , 499–508 (2016).

Parola, N. et al. Psychometric properties of the Ruminative Response Scale-short form in a clinical sample of patients with major depressive disorder. Patient Prefer Adherence 11 , 929–937 (2017).

Wardenaar, K. J. et al. Development and validation of a 30-item short adaptation of the Mood and Anxiety Symptoms Questionnaire (MASQ). Psychiatry Res. 179 , 101–106 (2010).

Snaith, R. P. et al. A scale for the assessment of hedonic tone the Snaith–Hamilton pleasure scale. Br. J. Psychiatry 167 , 99–103 (1995).

Patton, J. H., Stanford, M. S. & Barratt, E. S. Factor structure of the Barratt impulsiveness scale. J. Clin. Psychol. 51 , 768–774 (1995).

Rush, A. J. et al. The 16-Item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol. Psychiatry 54 , 573–583 (2003).

Hamilton, M. in Assessment of Depression (eds Sartorius, D. N. & Ban, D. T. A.) 143–152 (Springer, 1986).

Derogatis, L. R., Lipman, R. S. & Covi, L. SCL-90: an outpatient psychiatric rating scale–preliminary report. Psychopharmacol. Bull. 9 , 13–28 (1973).

CAS   PubMed   Google Scholar  

Williams, L. M. et al. Explicit identification and implicit recognition of facial emotions: I. Age effects in males and females across 10 decades. J. Clin. Exp. Neuropsychol. 31 , 257–277 (2009).

Williams, L. M. A platform for standardized, online delivered, clinically applicable neurocognitive assessment. Preprint at bioRxiv https://doi.org/10.1101/2023.08.28.553107 (2023).

Urchs, S. G. et al. Functional connectivity subtypes associate robustly with ASD diagnosis. eLife 11 , e56257 (2022).

Download references

Acknowledgements

We thank M. Schin for her help in data entry. We also thank J. Kilner (Pittsburgh, PA) for his editorial services. This work was supported by the National Institutes of Health (NIH) (grant nos. R01MH101496 (to L.M.W.; NCT02220309 ), UH2HL132368 (to J.M. and L.M.W.; NCT02246413 ), U01MH109985 (to L.M.W.) and U01MH136062 (to L.M.W. and J.M.)) and the Gustavus and Louise Pfeiffer Research Foundation (to L.M.W.). Providing treatment data, iSPOT-D ( NCT00693849 ) was sponsored by Brain Resource Ltd. The funders had no role in study design, data collection, data analysis, data interpretation or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Author information

Authors and affiliations.

Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA

Leonardo Tozzi, Xue Zhang, Adam Pines, Alisa M. Olmsted, Emily S. Zhai, Megan Chesnut, Patrick C. Stetz, Laura M. Hack & Leanne M. Williams

Sierra-Pacific Mental Illness Research, Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA

Alisa M. Olmsted, Patrick C. Stetz, Laura M. Hack & Leanne M. Williams

Department of Counseling and Clinical Psychology, Teacher’s College, Columbia University, New York, NY, USA

Esther T. Anene

Interdepartmental Neuroscience Graduate Program, Yale University School of Medicine, New Haven, CT, USA

Bailey Holt-Gosselin

Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA

Sarah Chang

Center for Intelligent Imaging, University of California San Francisco, San Francisco, CA, USA

Carolina A. Ramirez

Brain Dynamics Centre, Westmead Institute for Medical Research, University of Sydney, Westmead, New South Wales, Australia

Mayuresh S. Korgaonkar

Department of Radiology, Westmead Hospital, Western Sydney Local Health District, Westmead, New South Wales, Australia

Department of Neuroradiology, the University of Texas MD Anderson Center, Houston, TX, USA

Max Wintermark

Department of Psychology, Stanford University, Stanford, CA, USA

Ian H. Gotlib

Department of Medicine, College of Medicine, University of Illinois Chicago, Chicago, IL, USA

You can also search for this author in PubMed   Google Scholar

Contributions

L.T. conceived the study, provided methodology, software, validation, formal analysis and data curation, wrote the original draft, reviewed and edited the manuscript, and provided visualization and project administration. X.Z. provided software and data curation, and reviewed and edited the manuscript. A.P. conceived the study, reviewed and edited the manuscript and provided visualization. A.M.O. reviewed and edited the manuscript. E.S.Z., E.T.A., M.C., B.H.-G. and S.C. did investigations, curated data and reviewed and edited the manuscript. P.C.S. and C.A.R. provided software and reviewed and edited the manuscript. L.M.H. reviewed and edited the manuscript. M.S.K. and J.M. reviewed and edited the manuscript and provided resources. M.W. and I.H.G. reviewed and edited the manuscript and acquired funds. L.M.W. conceived the study, provided resources, reviewed and edited the manuscript, supervised the study, administered the project and acquired funds.

Corresponding author

Correspondence to Leanne M. Williams .

Ethics declarations

Competing interests.

L.M.W. declares US patent application nos. 10/034,645 and 15/820,338: ‘Systems and methods for detecting complex networks in MRI data’. In the past 3 years L.M.H. participated on a Roche Advisory Board. L.T. has been employed by Ceribell Inc. since 30 October 2023. The remaining authors declare no competing interests.

Peer review

Peer review information.

Nature Medicine thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Jerome Staal, in collaboration with the Nature Medicine team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information.

Supplementary methods, figures and tables.

Reporting Summary

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Tozzi, L., Zhang, X., Pines, A. et al. Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety. Nat Med (2024). https://doi.org/10.1038/s41591-024-03057-9

Download citation

Received : 22 May 2023

Accepted : 09 May 2024

Published : 17 June 2024

DOI : https://doi.org/10.1038/s41591-024-03057-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Anxiety and Depression Among College Students Essay

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

Introduction

Methods section.

Education is expected to have appositive importance on the student’s life by enhancing their capability to think and improving their competency. However, it often acts as a source of stress that affects students’ mental health adversely. This causation of academic stress often emanates from the need to have high grades, the requirement to change attitude for success, and even pressures put by various school assignments. These pressures introduced by education can make the student undergo a series of anxiety, depression, and stress trying to conform to the forces. The causes of academic stress are well-researched but there is still no explanation why the rate of strain increases despite some measures being implemented to curb student stress. This research explores this niche by using 100 participants who study at my college.

Nowadays there are many reasons that cause stress among growing number of students who might not know they are going through the condition most of the time. Hence, undiscovered discouragement or uneasiness can cause understudies to feel that they are continually passing up unique open doors. It prompts substance misuse; self-destruction is the second most typical reason for death among undergrads. The main hypothesis of this article is that college and university students have higher depression rates.

Problem Statement

This proposal undercovers how the problem of anxiety and depression is progressing if not addressed. With such countless youngsters experiencing undiscovered tension, it may be challenging for them to appreciate school. Understudies’ emotional well-being is risked when pressure and trouble go unnoticed, which can prompt social and educational issues (Nelson & Liebel, 2018). Educators might battle to perceive uneasiness since these circumstances manifest themselves contrastingly in different people.

Anxiety and depression are complicated disorders with numerous elements that impact people differently. Teachers and staff must be well trained to deal with these unforeseen events. Understudies coming to college come from various financial foundations, which can prompt an assortment of psychological wellness chances (Li et al., 2021). Additionally, current works will be evaluated to differentiate the risk factors associated with stress among university undergraduates worldwide.

There are various reasons which might cause the onset of anxiety and depression. It can be absence of rest, terrible dietary patterns, and lack of activity add to the gloom in undergrads (Ghrouz et al., 2019). Scholarly pressure, which incorporates monetary worries, strain to track down a decent profession after graduation, and bombed connections, is sufficient to drive a few understudies to exit school or more awful.

Numerous parts of school life add to despondency risk factors. For example, understudies today are owing debtors while having fewer work prospects than prior. Discouraged kids are bound to foster the problems like substance misuse (Lattie et al., 2019). For adaptation to close-to-home trouble, discouraged understudies are more inclined than their non-discouraged companions to knock back the firewater, drink pot, and participate in unsafe sexual practices.

Hypothesis on the Topic

The central hypothesis for this study is that college students have a higher rate of anxiety and depression. The study will integrate various methodologies to prove the hypothesis of nullifying it. High rates of anxiety and depression can lead to substance misuse, behavioral challenges, and suicide (Lipson et al., 2018). Anxiety is one of the most critical indicators of academic success, it shows how students’ attitudes change, reflecting on their overall performance.

Participants

The study will use college students who are joining and those already in college. The research period is planned to last six months; college students are between the ages of 18 and 21 and life is changing rapidly at this age (Spillebout et al., 2019). This demography will come from the college where I study. The participants will be chosen randomly, the total number will be 100, both female and male, and from all races.

Apparatus/ Materials/ Instruments

Some of the materials to be used in the study will include pencils, papers, and tests. Paper and pencil are typical supplies that students are familiar with, so using them will not cause additional stress. It will be used during the interview with the students and throughout the study will be in effect (Huang et al., 2018). These have been applied in various studies before, and, hence, they will be instrumental in this study.

The study will follow a step-wise procedure to get the required results. First, the students’ pre-depression testing results would be researched and recorded. Second, the students would undergo standardized testing in the same groups. Scholarly accomplishment is impacted by past intellectual performance and standardized testing (Chang et al., 2020). Third, the students’ levels of depression and anxiety would be monitored along with their test results.

The study will use a descriptive, cross-sectional design with categorical and continuous data. The sample demographic characteristics were described using descriptive statistics. Pearson’s proportion of skewness values and common mistake of skewness was utilized to test the ordinariness of the persistent factors. The distinctions in mean scores between sociodemographic variables and stress will be examined using Tests (Lipson et al., 2018). The independent variable will be essential because it will provide the basis of measurement.

The 100 participants had different anxiety levels, as seen from the Test taken and the various evaluations. Forty-five of the participants had high levels, 23 had medium levels, while the remaining 32 had low levels (Lipson et al., 2018). The correlation and ANOVA, which had a degree of era margin of 0.05, were allowed (Lipson et al., 2018). This finding aligns intending to have clear and comprehensive outcomes.

Significance of the Study

If the results would be not significant, it means that students are not subjected to more pressure on average. If the study results in significant outcomes, this would mean that there is much that needs to be done to reduce student’s anxiety. The idea that scholarly accomplishment is indispensable to progress is built up in higher instructive conditions (Nelson & Liebel, 2018). Many colleges devote money to tutoring, extra instruction, and other support services to help students succeed.

APA Ethical Guidelines

The study will have to follow the APA ethical guidelines because it involves experimenting with humans. Some of the policies include having consent from the participant, debriefing the participant on the study’s nature, and getting IRB permission (Nelson & Liebel, 2018). Ethical guidelines should comply with proficient, institutional, and government rules. They habitually administer understudies whom they likewise instruct to give some examples of obligations.

Limitations

The study also had some limitations, making it hard to get the desired outcomes. It was not easy to detect the population-level connections, but not causality. This case hardened the aspect of confounding and getting the relevant random assignment needed for the study had to access (Nelson & Liebel, 2018). For the right individuals for the investigation to be identified, the sampling was not easy.

This study would be essential as it will create a platform for future studies. The result that was gotten shows that many college students are undergoing the problem of anxiety and depression without knowing that it is happening. Educators will have awareness on what aspects of academics they need to modify to ensure their students are not experiencing mental health challenges. Hence, it makes it possible for future researchers to conduct studies to provide possible solutions.

Chang, J., Yuan, Y., & Wang, D. (2020). Mental health status and its influencing factors among college students during the epidemic of COVID-19. Journal of Southern Medical University , 40(2), 171-176.

Ghrouz, A. K., Noohu, M. M., Manzar, D., Warren Spence, D., BaHammam, A. S., & Pandi-Perumal, S. R. (2019). Physical activity and sleep quality in relation to mental health among college students. Sleep and Breathing Journal , 23(2), 627-634.

Huang, J., Nigatu, Y. T., Smail-Crevier, R., Zhang, X., & Wang, J. (2018). Interventions for common mental health problems among university and college students: A systematic review and meta-analysis of randomized controlled trials. Journal of Psychiatric Research , 107, 1-10.

Lattie, E. G., Adkins, E. C., Winquist, N., Stiles-Shields, C., Wafford, Q. E., & Graham, A. K. (2019). Digital mental health interventions for depression, anxiety, and enhancement of psychological well-being among college students: A systematic review. Journal of Medical Internet Research , 21(7), e12869.

Li, Y., Zhao, J., Ma, Z., McReynolds, L. S., Lin, D., Chen, Z.,… & Liu, X. (2021). Mental health among college students during the COVID-19 pandemic in China: A 2-wave longitudinal survey. Journal of Affective Disorders , 281, 597-604.

Lipson, S. K., Kern, A., Eisenberg, D., & Breland-Noble, A. M. (2018). Mental health disparities among college students of color. Journal of Adolescent Health , 63(3), 348-356.

Nelson, J. M., & Liebel, S. W. (2018). Anxiety and depression among college students with attention-deficit/hyperactivity disorder (ADHD): Cross-informant, sex, and subtype differences. Journal of American College Health , 66(2), 123-132.

Spillebout, A., Dechelotte, P., Ladner, J., & Tavolacci, M. P. (2019). Mental health among university students with eating disorders and irritable bowel syndrome in France. Journal of Affective Disorders , 67(5), 295-301.

The following table shows the significant issues that affect the mental health state of most college students. Based on Huang et al.’s research, the biggest concern for most students included stress about their loved ones. Additionally, the authors found that worrying about one’s academics and schooling was the second depressing experience among most college students.

The following figure shows how on top of the current stressors for students, COVID-19 affects their mental health. Li et al.’s research demonstrates that COVID-19 placed more financial burden than before, especially on students with part-time jobs who often face anxiety and stress due to lack of tuition fees (Li et al., 2021). Generally, the research shows that the financial consequences of coronavirus affect the mental state of most college students.

• Medication and Preventive Systems
• How Cognitive Behavioral Therapy Benefits Children
• The Methods to Reduce Preoperational Anxiety
• Defense Mechanisms and Brain Structure
• Coping with Stress and Physical Health Problems
• Quality of Life With Schizophrenia
• Schizophrenia: The Etiology Analysis
• Schizophrenia as a Chronic Mental Disorder
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2023, April 10). Anxiety and Depression Among College Students. https://ivypanda.com/essays/anxiety-and-depression-among-college-students/

"Anxiety and Depression Among College Students." IvyPanda , 10 Apr. 2023, ivypanda.com/essays/anxiety-and-depression-among-college-students/.

IvyPanda . (2023) 'Anxiety and Depression Among College Students'. 10 April.

IvyPanda . 2023. "Anxiety and Depression Among College Students." April 10, 2023. https://ivypanda.com/essays/anxiety-and-depression-among-college-students/.

1. IvyPanda . "Anxiety and Depression Among College Students." April 10, 2023. https://ivypanda.com/essays/anxiety-and-depression-among-college-students/.

Bibliography

IvyPanda . "Anxiety and Depression Among College Students." April 10, 2023. https://ivypanda.com/essays/anxiety-and-depression-among-college-students/.

• See us on facebook
• See us on twitter
• See us on youtube
• See us on linkedin
• See us on instagram

Six distinct types of depression identified in Stanford Medicine-led study

Brain imaging, known as functional MRI, combined with machine learning can predict a treatment response based on one’s depression “biotype.”

June 17, 2024 - By Rachel Tompa

Researchers have identified six subtypes of depression, paving the way toward personalized treatment. Damerfie -   stock.adobe.com

In the not-too-distant future, a screening assessment for depression could include a quick brain scan to identify the best treatment.

Brain imaging combined with machine learning can reveal subtypes of depression and anxiety, according to a new study led by researchers at Stanford Medicine. The study , published June 17 in the journal Nature Medicine , sorts depression into six biological subtypes, or “biotypes,” and identifies treatments that are more likely or less likely to work for three of these subtypes.

Better methods for matching patients with treatments are desperately needed, said the study’s senior author,  Leanne Williams , PhD, the Vincent V.C. Woo Professor, a professor of psychiatry and behavioral sciences, and the director of Stanford Medicine’s Center for Precision Mental Health and Wellness . Williams, who lost her partner to depression in 2015, has focused her work on pioneering the field of precision psychiatry .

Around 30% of people with depression have what’s known as treatment-resistant depression , meaning multiple kinds of medication or therapy have failed to improve their symptoms. And for up to two-thirds of people with depression, treatment fails to fully reverse their symptoms to healthy levels.  

That’s in part because there’s no good way to know which antidepressant or type of therapy could help a given patient. Medications are prescribed through a trial-and-error method, so it can take months or years to land on a drug that works — if it ever happens. And spending so long trying treatment after treatment, only to experience no relief, can worsen depression symptoms.

“The goal of our work is figuring out how we can get it right the first time,” Williams said. “It’s very frustrating to be in the field of depression and not have a better alternative to this one-size-fits-all approach.”

Biotypes predict treatment response

To better understand the biology underlying depression and anxiety, Williams and her colleagues assessed 801 study participants who were previously diagnosed with depression or anxiety using the imaging technology known as functional MRI, or fMRI, to measure brain activity. They scanned the volunteers’ brains at rest and when they were engaged in different tasks designed to test their cognitive and emotional functioning. The scientists narrowed in on regions of the brain, and the connections between them, that were already known to play a role in depression.

Using a machine learning approach known as cluster analysis to group the patients’ brain images, they identified six distinct patterns of activity in the brain regions they studied.

Leanne Williams

The scientists also randomly assigned 250 of the study participants to receive one of three commonly used antidepressants or behavioral talk therapy. Patients with one subtype, which is characterized by overactivity in cognitive regions of the brain, experienced the best response to the antidepressant venlafaxine (commonly known as Effexor) compared with those who have other biotypes. Those with another subtype, whose brains at rest had higher levels of activity among three regions associated with depression and problem-solving, had better alleviation of symptoms with behavioral talk therapy. And those with a third subtype, who had lower levels of activity at rest in the brain circuit that controls attention, were less likely to see improvement of their symptoms with talk therapy than those with other biotypes.

The biotypes and their response to behavioral therapy make sense based on what they know about these regions of the brain, said Jun Ma, MD, PhD, the Beth and George Vitoux Professor of Medicine at the University of Illinois Chicago and one of the authors of the study. The type of therapy used in their trial teaches patients skills to better address daily problems, so the high levels of activity in these brain regions may allow patients with that biotype to more readily adopt new skills. As for those with lower activity in the region associated with attention and engagement, Ma said it’s possible that pharmaceutical treatment to first address that lower activity could help those patients gain more from talk therapy.

“To our knowledge, this is the first time we’ve been able to demonstrate that depression can be explained by different disruptions to the functioning of the brain,” Williams said. “In essence, it’s a demonstration of a personalized medicine approach for mental health based on objective measures of brain function.”

In another recently published study , Williams and her team showed that using fMRI brain imaging improves their ability to identify individuals likely to respond to antidepressant treatment. In that study, the scientists focused on a subtype they call the cognitive biotype of depression, which affects more than a quarter of those with depression and is less likely to respond to standard antidepressants. By identifying those with the cognitive biotype using fMRI, the researchers accurately predicted the likelihood of remission in 63% of patients, compared with 36% accuracy without using brain imaging. That improved accuracy means that providers may be more likely to get the treatment right the first time. The scientists are now studying novel treatments for this biotype with the hope of finding more options for those who don’t respond to standard antidepressants.

Further explorations of depression

The different biotypes also correlate with differences in symptoms and task performance among the trial participants. Those with overactive cognitive regions of the brain, for example, had higher levels of anhedonia (inability to feel pleasure) than those with other biotypes; they also performed worse on executive function tasks. Those with the subtype that responded best to talk therapy also made errors on executive function tasks but performed well on cognitive tasks.

One of the six biotypes uncovered in the study showed no noticeable brain activity differences in the imaged regions from the activity of people without depression. Williams believes they likely haven’t explored the full range of brain biology underlying this disorder — their study focused on regions known to be involved in depression and anxiety, but there could be other types of dysfunction in this biotype that their imaging didn’t capture.

Williams and her team are expanding the imaging study to include more participants. She also wants to test more kinds of treatments in all six biotypes, including medicines that haven’t traditionally been used for depression.

Her colleague  Laura Hack , MD, PhD, an assistant professor of psychiatry and behavioral sciences, has begun using the imaging technique in her clinical practice at Stanford Medicine through an experimental protocol . The team also wants to establish easy-to-follow standards for the method so that other practicing psychiatrists can begin implementing it.

“To really move the field toward precision psychiatry, we need to identify treatments most likely to be effective for patients and get them on that treatment as soon as possible,” Ma said. “Having information on their brain function, in particular the validated signatures we evaluated in this study, would help inform more precise treatment and prescriptions for individuals.”

Researchers from Columbia University; Yale University School of Medicine; the University of California, Los Angeles; UC San Francisco; the University of Sydney; the University of Texas MD Anderson; and the University of Illinois Chicago also contributed to the study.

Datasets in the study were funded by the National Institutes of Health (grant numbers R01MH101496, UH2HL132368, U01MH109985 and U01MH136062) and by Brain Resource Ltd.

• Rachel Tompa Rachel Tompa is a freelance science writer.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

Hope amid crisis

Psychiatry’s new frontiers

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Financial stress and depression in adults: A systematic review

Naijie guan.

1 Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom

Alessandra Guariglia

2 Department of Economics, University of Birmingham, Edgbaston, Birmingham, United Kingdom

Patrick Moore

Fangzhou xu, hareth al-janabi, associated data.

All relevant data are within the paper and its Supporting Information files.

Financial stress has been proposed as an economic determinant of depression. However, there is little systematic analysis of different dimensions of financial stress and their association with depression. This paper reports a systematic review of 40 observational studies quantifying the relationship between various measures of financial stress and depression outcomes in adults. Most of the reviewed studies show that financial stress is positively associated with depression. A positive association between financial stress and depression is found in both high-income and low-and middle-income countries, but is generally stronger among populations with low income or wealth. In addition to the “social causation” pathway, other pathways such as “psychological stress” and “social selection” can also explain the effects of financial stress on depression. More longitudinal research would be useful to investigate the causal relationship and mechanisms linking different dimensions of financial stress and depression. Furthermore, exploration of effects in subgroups could help target interventions to break the cycle of financial stress and depression.

Introduction

Depression is one of the most common mental health problems and is marked by sadness, loss of interest or pleasure, feelings of guilt or low self-worth, disturbed sleep or appetite, feelings of tiredness, and poor concentration [ 1 ]. Depression is a leading cause of disability and poor health worldwide [ 1 ] and is expected to rank first worldwide by 2030 [ 2 ]. According to a survey from the World Health Organization, more than 322 million people, which accounted for approximately 4.4% of the world population, suffered from depressive disorders in 2015 [ 3 ]. The lifetime risk of developing depression was estimated to be 15%-18% [ 4 ]. Mental health problems including depression have imposed a heavy economic burden on individuals and households who are suffering from mental disorders and even on society [ 5 – 7 ]. Specifically, the global costs of mental health problems are increasing each year in every country. Those costs are estimated to reach approximately 16 trillion dollars by 2030 [ 8 , 9 ]. There is a considerable need to explore the risk factors of mental disorders or the determinants of mental health, which will inform preventive strategies and actions aimed at reducing the risk of getting mental disorders and thereby promoting public mental health.

Many social and economic determinants of depression have been identified. These include proximal factors like unemployment, low socioeconomic status, low education, low income and not being in a relationship and distal factors such as income inequality, structural characteristics of the neighbourhood and so on [ 10 – 12 ]. Research has emerged in the past two decades focusing on the association between the individual or household financial stressors and common mental disorders such as depression and anxiety. However, findings regarding the relationship between different indicators of financial stress and depression are inconclusive in the previous literature. Studies have shown positive associations between depression and various indicators of financial stress such as debt or debt stress, financial hardship, or difficulties [ 13 – 15 ]. Some other studies find no relationships when financial stress was indicated by low income. For example, Zimmerman and Katon [ 16 ] found that when other socioeconomic confounders were considered, no relationship between low income and depression was observed. Besides, there is evidence showing a negative association between low income and major depressive disorder in South Korea [ 17 ]. A 2010 review on poverty and mental disorders also finds that the association between income and mental disorders (including depression) was still unclear [ 18 ].

The social causation theory is one of the theories that has been proposed to explain possible mechanisms underlying the effect of poverty on mental disorders [ 18 , 19 ]. It states that stressful financial circumstances might lead to the occurrence of new depressive symptoms or maintain previous depression. This might be due to exposure to worse living conditions, malnutrition, unhealthy lifestyle, lower social capital, social isolation, or decreased coping ability with negative life events. Individuals or households with limited financial resources are more vulnerable to stressful life events (e.g., economic crises, public-health crises), which might increase the risk of mental health problems [ 18 – 20 ]. However, practically, social causation might not be applicable to situations where individuals are not in poverty or deprivation but still can experience depression due to financial stress.

Reviews to date have examined the relationship between debt specifically and broader mental health outcomes with depression being one of them. For example, two reviews published in 2013 and 2014 reviewed the literature on the relationship between debt and both mental health and physical health [ 21 , 22 ]. They concluded that there was a significant relationship between personal unsecured debt or unpaid debt obligations and the increased risk of common mental disorders, suicidal ideation and so on [ 21 , 22 ]. In terms of depression, they found that there was a strong and consistent positive relationship between debt and depression. Another focus of the literature is on the relationship between poverty and mental health problems including depression in low-and middle-income countries (LMIC). In those reviews, indicators of poverty include low socioeconomic status, low income, unemployment, low levels of education, food insecurity and low social class [ 18 , 23 ]. Both reviews find a positive relationship between poverty and common mental disorders, which exists in many LMIC societies regardless of their levels of development. Being related to low income, factors such as insecurity, low levels of education, unemployment, and poor housing were found to be strongly associated with mental disorders, while the association between income and mental disorders was unclear.

The reviews discussed above focus mainly on the relationship between debt or poverty and mental health outcomes. As sources of financial stress are complex and multidimensional, indicators such as low income or debt are not the only economic risk factor of mental health problems. Other sources of financial stress such as lack of assets, economic hardship or financial difficulties (e.g., whether an individual finds it difficult to meet standard living needs like buying food, clothes, paying bills and so on) might also relate to depression. In addition, various sources of financial stress might be related to mental health problems in different ways. Based on the existing reviews, it is still unknown which domains of financial stress have clearer associations with depression and whether there is heterogeneity in the relationship between financial stress and depression for different populations and contexts. Moreover, the existing reviews do not discuss the possible mechanisms underpinning the association between financial stress and depression. To better understand the association between financial stress and depression and the possible mechanisms underlying it, a systematic review was conducted bringing together a wide range of indicators of financial stress. The eligible economic indicators of financial stress in this review include objective financial variables like income, assets, wealth, indebtedness; as well as measures that capture subjective perceptions of financial stress, such as perceived financial hardship (e.g., subjective feelings of sufficiency regarding food, clothes, medical care, and housing), subjective financial situation (e.g., individuals’ feelings about their overall financial situation), subjective financial stress, subjective financial position, and financial dissatisfaction.

This study aims at providing a comprehensive review of the association between different financial stressors and depression considering the characteristics of the associations of interest and discussing the proposed mechanisms underlying the associations. An understanding of the relationship between financial stress and depression would not only advance our understanding and knowledge of the economic risk factors of mood disorders but also provide policymakers with more understanding of additional public mental health benefits of intervention aimed at alleviating poverty and/or at improving people’s financial conditions.

Search strategies

A systematic review of published literature was conducted using online searches on bibliographic databases. At the first stage, six bibliographic databases including CINAHL, PsycINFO, EMBASE, EconLit, AMED, and Business Source Premier were searched for related peer-reviewed journal articles to April 2019. The search terms are listed in Table 1 . The broad strategy was to combine terms related to finances, with terms related to depression, and terms related to the unit of analysis (individual, household etc). Several key studies that were eligible for inclusion criteria were pre-identified. Before the formal search, pilot searches were performed to make sure the pre-identified key studies can be found by the search. More details of search strategies are displayed in S1 Appendix . All the search results were limited to the English language. No time restriction was added to the search. The reference lists of the eligible studies and several relevant review papers were checked manually to supplement the main electronic searching.

Eligibility criteria

The inclusion and exclusion criteria for study selection were designed to ensure a focus on primary studies and secondary studies conducted on adults, using measures of financial stress (exposure) and depression (outcome). The eligibility criteria were tested on a selection of papers by multiple members of the study team to ensure that studies were categorised accurately. Based on the piloting process, the eligibility criteria were further modified. The following is the list of the final inclusion and exclusion criteria applied in this study.

Studies were included if they met the following criteria: (1) observational and experimental studies on the relationship between the individual or household financial stress and depression or depressive symptoms; (2) original research in peer-reviewed journals; (3) conducted on general population samples aged 18 and over; (4) used indicators which capture different dimensions of an individual or household financial stress, such as income, assets, debt, wealth, economic hardship, financial strain, financial stress, and financial satisfaction; (5) studies that measure depression through both non-clinical and clinical techniques (e.g., Centre for Epidemiologic Studies Depression Scale), were eligible for this review.

Studies were excluded if they were: (1) systematic reviews, dissertations, conference abstracts, or study protocols; (2) studies focusing on a specific population including female-only, male-only, people with a special occupation (e.g., soldiers), migrants, or people with specific illnesses; (3) studies relating to societal (as opposed individual) economic circumstances (e.g., income inequality measured at the community level or the country level) or shocks to the macroeconomy (e.g., stock market crashes, hyperinflation, banking crises, economic depressions, and financial crisis); (4) studies that only reported the joint association between several socioeconomic determinants and depression. These were included only if the association related to individual or household finances were reported and explained individually; (5) studies based on overall mental health, or other types of mental disorders (e.g., anxiety, suicide, self-harm, bipolar disorder, schizophrenia, dementia) where the association between household financial stress and depression was not reported and explained independently.

Study selection

Search strategies were applied to six databases (i.e., CINAHL, PsycINFO, EMBASE, EconLit, AMED, and Business Source Premier EBSCO) to generate a long list of candidate studies. All the search results were exported into Mendeley. Search results, after the removal of duplicates, were screened for relevance using the title and abstract information. The full texts of relevant articles were then checked for eligibility based on the selection criteria. Screening and selection were undertaken by two reviewers independently. All authors were consulted when a disagreement arose. The study selection process and reasons for study exclusions were recorded in a flow chart shown in Fig 1 .

Data analysis

A pre-designed data collection form (listed in S2 Appendix ) was used for the data extraction process. The data extracted from the eligible studies covered the following categories: (1) characteristics of studies: year, author, journal, aim of study, countries, study type, data sources, responsible rate, level of study, eligibility of ethical approval; (2) characteristics of the population: sample size, age group, mean age of the participants, gender; (3) depression measures, definition of depression, validity of the measures; (4) measures of financial stress (exposures) used, measures of the exposure, definition of the exposure, validity of the measures; (5) statistical analysis: econometric methodologies, covariates, whether reverse causality was taken into account, whether there are subgroup analyses and methods (6) main results. The key information being extracted is presented in S1 and S3 Tables, which is a simplified version of the extracted data.

This review focused on the association between each dimension of financial stress and depression and analysed the heterogeneity of the association in different contexts. The eligible studies were reviewed narratively, and the results were stratified by different indicators of financial stress (e.g., low income, low assets, low wealth, debt, financial difficulties and so on). Causal inferences and proposed mechanisms underlying the association between financial stress and depression based on the reviewed evidence were discussed in the discussion section. No meta-analysis was conducted to pool the reviewed evidence due to the substantial heterogeneity in the measurements and definitions of the exposure and outcome variables, study context, and methodologies.

Quality assessment

The quality of the included studies was assessed using an adapted version of the Quality Assessment Tool for Quantitative Studies used in Glonti et al. [ 24 ] (see S3 Appendix ). The original version of this tool is developed by the Effective Public Health Practice Project (EPHPP) [ 25 ]. Seven key domains relating to study design, selection bias, withdrawals, confounders, data collection, data analysis and reporting were considered. Studies can have between six and seven component ratings. The score of each domain equals 1 if the quality is high, 2 if the quality is moderate and 3 if the quality is low. An overall rating for each study was determined based on the ratings for all domains. The overall rating of studies’ quality was classified as high, moderate, or low. Full details of the design and usage of the quality assessment tool can be found in Glonti et al. [ 24 ]. The quality assessment of the included studies was conducted by two reviewers independently. The results of the quality assessment were based on consensus between the two reviewers.

5,134 papers were identified after searching online databases including CINAHL, PsycINFO, EMBASE, EconLit, AMED, and Business Source Premier. The flow chart for the study selection process is displayed in Fig 1 . The total number of papers after removing duplicates was 4,035. Both titles and abstracts of the identified 4,035 papers were screened. 3,763 papers were removed since they did not satisfy the eligibility criteria. The full texts of the remaining 272 papers were accessed and further screened separately by the two reviewers based on the eligible criteria. 235 papers were further excluded leading to 37 studies for consideration. The main reasons for exclusion were that the exposure, the outcome variables of interest, or the targeted population of those studies did not meet the inclusion criteria. Three additional articles were further added after checking the reference lists of all the eligible papers and those of the past relevant review papers [e.g., 18 , 21 ]. 40 articles were finally identified for the data extraction.

Study characteristics

Regarding the number of reviewed studies by years of publication, most of the reviewed studies were published in the past two decades, with a noticeable spike in the last five years. The majority of studies (32 out of 40) reported evidence from high-income European countries and the USA, Australia, Japan, and South Korea. Eight studies were based on low- and middle-countries including China, Chile, and South Africa. In terms of study design, 17 studies were cross-sectional, and 23 studies were longitudinal. The age groups considered in the 40 studies vary: 17 studies focused on the general adult population including young adults, middle-aged adults, and older adults, while 23 studies focused specifically on working-age, young adults, middle-aged, or older adults. Data of study characteristics were displayed in the data extraction form in S1 Table .

Measures of depression

The most commonly used measure for depression was the Centre for Epidemiological Studies Depression Scale (CES-D) [e.g., 26 – 28 ]. Various versions of the CES-D were used in the reviewed studies: six studies used the full version, that is, the 20-item CES-D; 19 studies used the shortened version of the CES-D scale. Other measures were also used to assess the individual’s depressive symptoms such as the Hospital Anxiety and Depression Scale (HADS) [ 29 ], the World Mental Health Composite International Diagnostic Interview (WMH-CIDI) [ 30 , 31 ], a subsection of the General Health Questionnaire (GHQ depression) [ 31 – 33 ], the Alcohol Use Disorder and Associated Disabilities Interview Schedule-IV (AUDADIS-IV) [ 34 ], the 21-item Beck Depression Inventory (BDI) [ 35 ] and the Geriatric Depression Scale (GDS) [ 15 , 36 , 37 ]. Two studies used self-reported depression by asking participants whether or not they had any experience of depression [ 13 , 38 ].

Measures of financial stress

A wide variety of concepts and measures of financial stress were used across the reviewed studies. The financial exposure in the reviewed studies can be divided roughly into two categories. First, personal or household finances, which include income, assets or wealth, debt or hardship were investigated. These economic indicators were measured in different ways. Some studies measured the total amount of assets while other studies measured assets by counting the number of durable items owned by an individual (such as motor vehicles, bicycles, computers, or cameras) or a household (such as fridges, microwaves, TV, cameras). The measures of debt were more diversified: the onset of debt, the amount of debt in general and of different types of debt, the debt-to-asset ratio, debt problems like over-indebtedness, debt arrears, and debt stress. Financial hardship was defined as difficulties in meeting the basic requirements of daily life due to a lack of financial resources. For example, not having enough money for food, clothes, shelter and medical expenses; being unable to pay bills on time or heat the home; having to sell assets; going without meals; or asking for financial help from others were used by these studies as proxies for financial hardship [ 30 , 39 ]. Second, some studies examined the associations between depression and subjective perceptions of financial stress such as perceived financial hardship (e.g., subjective feelings of insufficiency regarding food, clothes, medical care, etc.), subjective financial situation (e.g., individual’s feelings of their overall financial situation), subjective financial stress, subjective financial position, financial dissatisfaction and so on.

Quality of reviewed studies

Full details of the quality assessment of the 40 included studies are displayed in S2 Table and S1 Fig . An observational study design was utilised in all of the included papers. 29 (72.5%), and 11 (27.5%) studies were rated as methodologically strong [ 6 , 13 – 16 , 20 , 26 – 28 , 30 , 31 , 34 , 37 – 53 ] and moderate [ 29 , 31 , 32 , 35 , 36 , 54 – 59 ], respectively. Among the 40 included studies, 34 (85%) had a low risk of selection bias, five (12.5%) had a moderate risk and one had a high risk of selection bias. Eight studies were able to be rated on withdrawals and drop-outs: two of them were rated as “strong”, four achieved a “moderate” rating and one received a “weak” rating. We found that 15 studies (37.5%) had a low risk while 25 (62.5%) had a moderate risk of confounding bias. Regarding the data collection, two studies were rated as ‘strong’, 37 received a ‘strong’ score, and one study was rated as ‘weak’. All the studies received a ‘strong’ rating for data analysis except for one study that was rated as ‘weak’. 35 (87.5%) studies received a ‘strong’ rating for reporting, while five studies had a ‘moderate’ quality of reporting.

Association between income and depression

Eleven studies were identified examining the relationship between individual or household income levels and depression. All controlled for other socioeconomic confounders or/and health status. Seven studies found a statistically significant association between low income and a higher risk of depressive symptoms after adjustment. The positive association between low income and depression was reported in both high-income countries and low- and middle-income countries and found in different age groups (i.e., younger adults, middle-aged adults, and older adults).

The intertemporal relationship between individual or household income and depression was investigated in three longitudinal studies [ 20 , 34 , 58 ]. Osafo et al. found that in the UK, an increase in household relative income (i.e., income rank) was statistically related to a decreased risk of depression at a given time point [ 58 ]. The effect of household income at baseline on the risk of showing depressive symptoms in the following time point was weakened but still statistically significant, controlling for the baseline depression level. Lund and Cois reported similar results: they found that lower household income at baseline could predict a worse depression status during the follow-up period in South Africa [ 20 ]. Based on evidence from the US, Sareen et al. found that individuals with lower levels of household income faced an increased risk of depression compared to those with higher levels of household income [ 34 ]. Furthermore, a reduction in income was also related to an increased risk of depression [ 34 ].

Focusing on pension income, which is one of the main sources of household income for the retired population, Chen et al. found that pension enrolment and pension income were significantly associated with a reduction in CESD scores among Chinese older adults, controlling for other socioeconomic factors and health status [ 43 ].

The strength of the relationship between income and depression varies and can be affected by how income is measured. For example, compared to absolute income, a household’s relative income level within a reference group was found to be a more consistent household financial predictor of depression [ 58 ]. Osafo et al. compared the effect of relative income with that of the absolute value of household income [ 58 ]. They found that a deterioration in the rank of household income was associated with a higher possibility of showing depression at a given time point, as well as the subsequent time point [ 58 ].

The relationship between income and depression holds for all income groups but is more pronounced among lower-income groups. According to Zimmerman and Katon, the association between depression and income is stronger among people with income levels below the median [ 16 ]. Based on a quasi-natural experiment, Reeves et al. also found that the reduction in housing benefits significantly increased the prevalence of depression for low-income UK households [ 38 ]. Additionally, the association between pension income and depressive symptoms in older adults were more pronounced among lower-income groups [ 43 ]. More broadly, the income-depression relationship might be influenced by the economic status of the regions where households live. For example, Jo et al. found that the association between income and depression was significant among participants from low-economic-status regions, while it was insignificant among participants from high-economic-status regions [ 55 ].

Association between material assets and depression

Two studies on the relationship between assets and depression were identified: one cross-sectional study [ 29 ] and one longitudinal study [ 20 ]. Those studies showed that assets were a significant predictor of depression after controlling for demographic and other socioeconomic confounders. Furthermore, the household assets-depression association was found to be stronger for individuals with lower levels of assets at baseline [ 20 , 29 ]. The directions of the assets-depression relationship were investigated in one study. Lund and Cois simultaneously examined both directions of the relationships using a nationally representative survey on South Africa [ 20 ]. They found that low levels of individual and household material assets were significantly related to depression in the follow-up period after controlling for age, gender, race and education. Conversely, having more depression symptoms at baseline was significantly associated with lower levels of individual assets in the follow-up period [ 20 ].

Association between wealth and depression

Three studies explored the relationship between wealth and depression in adults. All of them were based on high-income country contexts including the UK and the US and suggested a positive relationship between individual or household low wealth and depression among middle-aged and older adults. Two longitudinal studies examined the association between wealth and depression. Specifically, Pool et al. found that an increase in household wealth was statistically related to a decrease in the risk of depressive symptoms [ 50 ]. Osafo et al. compared the effect of relative wealth (i.e., wealth rank) and absolute wealth on depressive symptoms [ 58 ]. Their results showed that, instead of the absolute wealth, the wealth rank within a social comparison group was the primary driver of the association between wealth and depressive symptoms [ 58 ]. The strength of the relationship between wealth and depression varies according to the level of wealth at baseline [ 58 ]. For example, Martikainen et al. found the association between household wealth and depression was most pronounced among the lowest wealth group [ 33 ].

Association between debt and depression

Fourteen studies investigated the association between debt and depression and provided empirical evidence based on high-income countries (Europe and the US) and Chile. Three studies were cross-sectional and all of them reported a positive association between debt (assessed by student debt, the occurrence of any debt, or unsecured debt) and depressive symptoms after controlling for demographic and other socioeconomic factors [ 6 , 27 , 53 ]. Eleven longitudinal studies identified by this review investigated the association between debt and depression over time. The definitions and measures of debt vary across studies. Associations between the occurrence and/or amount of financial debt, the occurrence and/or amount of housing debt, excessive mortgage debt, the occurrence of any debt, the debt-to-asset ratio, and the debt-to-income ratio, on the one hand, and depression, on the other, were investigated in the reviewed studies.

The association between changes in debt status and changes in depressive symptoms was investigated in six studies. Specifically, using five waves of data from the Survey of Health, Ageing and Retirement in Europe (SHARE), Hiilamo and Grundy found that both men and women switched from having no financial debt to having substantial financial debt suffered from a deterioration in depressive symptoms [ 28 ]. Also, switching from no mortgage debt to having substantial mortgage debt was positively associated with the deterioration in depressive symptoms among women [ 28 ]. Using a large nationally representative dataset from the Chilean Social Protection Survey (SPS), Hojman et al. also found that individuals who were always over-indebted or switch from having moderate levels of debt to over-indebtedness had more depressive symptoms than those who were never over-indebted [ 46 ]. Additionally, they found that those who were not over-indebted, regardless of the previous debt status, did not experience a worsening in depression, showing that the effect of over-indebtedness on depressive symptoms faded away as the debt levels decreased [ 46 ].

Various measures of debt were used in the reviewed studies such as the occurrence of debt [ 26 , 27 , 53 ], the amount of debt [ 6 , 14 , 26 , 28 , 53 ], and the debt-to-income ratio or debt-to-asset ratio [ 14 , 46 , 48 ]. The debt-depression relationship varies with different operationalisations of debt with the debt to asset ratio being a more reliable predictor of depression than the total debt. Both Sweet et al. and Hojman et al. found that only the debt-to-assets ratio or debt-to-income ratio (rather than the absolute amount of debt) were consistently and positively associated with higher depression scores before and after adjustment (see S1 and S3 Tables for details of the covariates used) [ 14 , 46 ].

Different types of debt such as secured debt (e.g., mortgage debt) and unsecured debt (e.g., consumer debt) might be related to the depression in different ways. The reviewed studies reported a positive association between high levels of mortgage debt and high unsecured consumer debt (regardless of the amount) and depression [ 14 , 48 , 53 ]. For example, Leung and Lau examined the causal relationship between mortgage debt and depressive symptoms and found that a high level of mortgage indebtedness (defined as a mortgage loan to home value ratio over 80%) was associated with more depressive symptoms among mortgagors [ 48 ]. Both Zurlo et al. and Sweet et al. found that unsecured debt (e.g., consumer debt) was a significant predictor of more depressive symptoms [ 14 , 53 ]. Three studies compared the effect of different types of household debts on depression [ 26 , 28 , 46 ]. The results of those three studies suggested that the association between household debt and depressive symptoms was predominantly driven by short-term debt. Specifically, unsecured debt (e.g., financial debt), or short-term debt were associated with a higher risk of experiencing depression, while secured debt itself (e.g., mortgage debt) or long-term debt were not related to depressive symptoms. For example, using longitudinal data from the Survey of Health, Ageing and Retirement in Europe (SHARE), Hiilamo and Grundy found that household financial debt was positively and significantly associated with more depressive symptoms, while the effect of household housing debt on depression was weak or even insignificant [ 28 ]. Berger et al. found a similar result using longitudinal data from the US. Their results (controlling for baseline characteristics and socioeconomic factors) showed that only short-term debt (i.e., unsecured debt) was positively and statistically significantly associated with depressive symptoms, while the effects of mid-term and long-term debt (e.g., mortgage loan) on depressive symptoms were not significant [ 26 ].

However, it is not always the case that the association between debt and depressive symptoms is only driven by consumer debt. As reported in two longitudinal studies by Hiilamo and Grundy and by Gathergood, a secured debt like mortgage might be associated with depression when the secured debt becomes a problem debt [ 28 , 32 ]. Hojman et al. found that mortgage debt had no association with depressive symptoms, while consumer debt was positively and significantly related to more depressive symptoms [ 28 ]. Nevertheless, both Hojman et al. and Alley et al. found that mortgage arrears had a significant effect on more severe depression, even when the effect of consumer debt on depression was controlled [ 40 , 46 ]. In line with their study, Gathergood also found that housing payment problems were strongly associated with a higher depression score [ 32 ].

Association between financial hardship and depression

The association between financial hardship and depression was reported in four studies, all of which were based on high-income countries such as the US and Australia [ 15 , 30 , 39 , 52 ]. They all observed a cross-sectionally positive relationship between financial hardship and depression, which holds after adjustments (see S1 and S3 Tables for details of the covariates used). The intertemporal association between financial hardship and depressive symptoms was reported in two longitudinal studies [ 15 , 39 ]. However, the consistency of the findings is sensitive to the statistical methods applied. Mirowsky and Ross found that current financial hardship was associated with a subsequent increase in depression in the US [ 39 ]. The other study only observed an association between financial hardship at baseline and baseline depression, as well as a weak or even no association between prior financial hardship and current depression [ 15 ]. When the same statistical strategy was applied, the findings from Butterworth et al. were consistent with those were observed in Mirowsky and Ross’s study [ 15 , 39 ].

Furthermore, the reviewed studies showed that the effect of past financial hardship on depressive symptoms decayed with time. In other words, current financial hardship mattered the most for current depressive symptoms. Following Mirowsky and Ross, changes in financial hardship were stratified into four types [ 39 ]. An individual experiencing (not experiencing) current financial hardship and hardship in the past belongs to the always hardship group (no hardship group). An individual experiencing only current (past) financial hardship belongs to the new hardship group (resolved hardship group). Mirowsky and Ross found that the effects of consistent hardship and new financial hardship (3 years later) on depressive symptoms were positive and significant [ 39 ]. Moreover, there was no significant difference in the follow-up depressive symptoms between the consistent hardship group and the new hardship group [ 39 ]. Furthermore, the association between both resolved hardship and no hardship on depressive symptoms was not significant [ 39 ]. Consistent with this, Butterworth et al. also found that the individuals who currently experienced financial hardship were more likely to have depression than those who only experienced financial hardship in the past or never experienced it [ 15 ].

Age was the most analysed moderator of the association between financial hardship and depressive symptoms among the reviewed studies. This review found that there is no consistency in terms of the association between financial hardship and depression across different age groups. Butterworth et al. reported that the effect of financial hardship on depressive symptoms increased with age among Australian adults [ 30 ]. However, Butterworth et al. and Mirowsky and Ross reported different results [ 15 , 39 ]. Specifically, they found that the positive association between financial hardship and depressive symptoms decreased with age in the US. In contrast to the two studies listed above, Butterworth et al. did not find any statistically significant differences regarding this association among different age cohorts in Australia [ 15 ].

Association between subjective financial strain and depression

Eleven studies examined the association between subjective financial indicators (i.e., subjective financial strain, financial dissatisfaction or financial stress) and depression, providing empirical evidence based on high-income countries (Europe, the US, the UK, Japan and Korea) and on China. All of them (including four cross-sectional and seven longitudinal studies) reported a positive relationship between subjective financial strain and depression, holding after adjustments (see S1 and S3 Tables for details of the covariates used)). The intertemporal association between subjective financial strain and depression was reported in two studies [ 44 , 59 ]. For example, Richardson et al. found that increased subjective stress at baseline was associated with greater depression over time [ 59 ]. Similarly, Chi and Chou also found that higher levels of subjective financial strain measured at baseline were associated with more depressive symptoms after three years among Chinese older people [ 44 ]. The association between changes in subjective financial strain and depression was found in one longitudinal study [ 49 ]. Using data from the annual Belgian Household Panel Survey, Lorant et al. found that the worsening subjective financial strain was significantly associated with the increased risk of depressive symptoms and that of caseness of depression [ 49 ].

The positive and significant association between perceived financial strain in childhood and depression in adults was found in both a cross-sectional study and a longitudinal study [ 42 , 47 ]. Using cross-sectional data from 19 European countries in 2014, Boe et al. found that younger adults (25–40) who had experienced financial difficulties as children had higher depression scores in adulthood, while older adults (over 40) did not [ 42 ]. A similar association between adverse childhood financial situation and adults’ depression was also found in a longitudinal study [ 47 ]. Based on a national representative sample of 9,645 South Korean adults without depressive symptoms at baseline, Kim, et al. found that experiencing financial difficulties in childhood was associated with the increased chance of depression in adulthood [ 47 ]. Furthermore, the effect of experiencing financial difficulties in childhood on depression was weaker than that of current financial difficulties [ 47 ].

The gender difference of the association between perceived financial strain and depression was examined in two studies and no statistical difference between females and males was observed, though women tended to report worse depression [ 36 , 44 ].

Summary and discussion of the findings

This systematic review is the most comprehensive synthesis of observational studies quantifying the association between indicators of financial stress and depression in both high- and low- and middle-income countries to date. Findings regarding the relationship between financial stress and depression vary across different indicators of financial stress. Economic indicators such as material assets, unsecured debt, financial hardship, and subjective measures of financial stress are relatively strong and persistent predictors of depressive symptoms, while absolute income and wealth levels have an inconclusive association with depression. The only longitudinal evidence on relative income and relative wealth suggests a stronger relationship between relative income or relative wealth and depressive symptoms than that between absolute income or wealth and depression. Additionally, this review finds that the association between indicators such as income, material assets or wealth and depression is more pronounced in lower socioeconomic groups (i.e., low income or low wealth group). This review is unable to make a conclusion regarding the association between debt and depression across different socioeconomic subgroups. The only evidence is provided in one study showing that there is no difference in the association between debt and depression by assets level. Additionally, there is insufficient evidence to conclude a common pattern regarding the association between financial stressors and depression by gender or age groups, though differences of this relationship across age or gender groups are observed in some of the reviewed studies.

The income-depression association is inconclusive, although income is one of the most commonly used indicators of the individual or household’s economic situation. The reviewed studies consistently reported a positive association between low income and depressive symptoms in univariable analyses. However, this association was largely reduced or even became insignificant when other social and economic factors (such as educational level, employment status and so on) and health status were controlled for [ 31 , 33 ]. The findings are consistent with the results from the previous reviews and empirical research where different mental disorders were considered including depression [ 18 , 23 , 60 ]. It is likely that income has a close correlation with other dimensions of the socioeconomic condition such as educational levels and employment status that affect an individual’s mental health independently from income per se [ 23 ].

Furthermore, this review finds that compared to absolute income (or wealth), relative income (or wealth) in a reference group is a more important risk factor of depression. There is evidence showing a positive association between low-income ranks and current depression scores as well as follow-up depression scores, while no association is found between absolute low income and depression [ 58 ]. The findings here are in line with the previous review, which mainly focused on the association between income inequality and depression [ 61 ]. Patel et al.’s review concluded that a higher level of income inequality at the neighbourhood level was strongly associated with a higher risk of depression [ 61 ]. This review only identified one study investigating the association between relative income or relative wealth and depression. The insufficient evidence on this topic suggests the need for more research to investigate the mental health effects of relative income (or wealth).

Some of the reviewed studies have suggested a positive association between debt and depression despite the substantial heterogeneity in definitions and measurements of debt, study methods, study contexts, and targeted population. The association between debt and depressive symptoms is mainly driven by unsecured debt (e.g., credit card) or late mortgage payments. Secured debt (e.g., mortgage debt) per se is not associated with depressive symptoms. However, depression may still be more likely when individuals or households are no longer able to manage their debt or perform debt obligations. For example, the reviewed evidence shows that mortgage arrears have a significant effect on more severe depression, even when the effect of consumer debt and mortgage debt on depressive symptoms are considered within the same model [ 46 , 48 ]. The findings regarding the relationship between debt and depression are consistent with the findings from the previous reviews on the association between debt and health where depression was one of the outcomes [ 22 , 62 ].

An important consideration regarding the debt-depression relationship is that having personal or household debt does not always lead to depression, as debt is not always a sign of financial problems. Some personal and household loans are taken to finance housing purchases, business, and investments, which are granted based on the borrower’s financial situation and payback abilities. Additionally, except for stress, debt might also bring benefits to mental well-being by generating consumption, feelings of attainment or satisfaction or making investments [ 63 , 64 ]. As a result, the financial stress derived from debt could be partially offset by such positive mental well-being effects. The review suggests that future longitudinal research on the impact of debt on depression should consider mediators to understand the nature of the causal association between debt and mental health.

It should be noted that nearly a half of the reviewed studies are cross-sectional, limiting the ability to draw a conclusion on the directions and the causality of the associations between some indicators of financial stress and depression. A few of the longitudinal studies considered the reverse relationship and/or the unobserved bias using econometric methods. The majority of these longitudinal studies mainly focused on the relationship between debt or subjective measures of financial stress and depression. They provide supportive evidence that, debt and subjective financial stress might lead to subsequent depressive symptoms. Longitudinal evidence remains limited as to the understanding of both directions and causality of the relationships between other indicators of financial stress and depression. For example, only three longitudinal studies provided an exploration of the association between income and depression. The casual relationship between some indicators of financial stress (such as low income, material assets, wealth, financial hardship) and depression should therefore be interpreted with caution.

This review includes a number of studies focusing on the older-aged population. The signs of the relationship between financial stress and depression in different age subgroups do not show a significant difference. Despite this, it should be noted that there might be heterogeneity in the magnitude of the relationship between financial stress and depression across different age subgroups. However, it is difficult to identify if including the studies based on adults aged 50 and over would make the generalisability of the findings towards this population. Because a cross-study comparison is almost impossible as there is a substantial heterogeneity in different studies regarding country contexts, measurements of exposures and outcome variables, study methods and so on.

Based on the reviewed evidence, three possible mechanisms may be behind the relationship between financial stress and depression.

Social causation

Firstly, as highlighted in the introduction, the effects of financial stress on depression can be explained by social causation theory. The reviewed evidence supports the social causation pathway according to which individuals or households who have low income or low wealth are more likely to be exposed to economic uncertainty, unhealthy lifestyle, worse living environment, deprivation, malnutrition, decreased social capital and so on [ 20 , 47 , 65 ]. Those factors might lead to a higher risk of developing depressive symptoms. Individuals or households with limited financial resources are more vulnerable to stressful financial events, which might increase the risk of experiencing depression. This mechanism is applicable to the studies where financial stress is measured by economic indicators related to poverty, such as income poverty, deprivation, and financial hardship.

Psychological stress

The reviewed studies also show that subjective measures of financial stress have adverse effects on depression. Indeed, some studies state that subjective financial stress is more important than objective measures such as the amount of debt [ 13 , 41 , 66 ]. Objective indicators of financial stress might have an indirect effect on depression, which is mediated by the individual’s perception of those objective indicators as resulting in financial stress. Experiencing a similar objective financial situation, people may report different perceptions of the objective financial situation due to the heterogeneity of personal experiences, abilities to manage financial resources, aspirations, and perceived sufficiency of financial resources [ 67 ]. For example, individuals with limited financial resources are more likely to be concerned about the uncertainty of the future financial situation. The expectation of financial stress, not just their occurrence, may also cause depression. Furthermore, people living in poverty face substantial uncertainty and income volatility. The long-run exposure to stress from coping with this volatility may also threaten mental health [ 68 ]. Therefore, it is reasonable to believe that both the respondent’s perception of financial stress and objective measures of financial stress lie at the heart of the relationship between financial stress and depression.

Social selection

Other studies suggest that depression might negatively impact the finances of individuals [ 20 , 69 ]. Social selection theory states that individuals who have mental disorders are more likely to drift into or maintain a worse financial situation [ 20 ]. Evidence shows that mental problems might increase expenditure on healthcare, reduce productivity, and lead to unemployment, as well as be associated with social stigma, all of which are related to lower levels of income [ 18 , 65 , 70 ]. However, some scholars argue that the relative importance of social causation and social selection varies by diagnosis [ 71 ]. Social causation theory is more important to the relationship between financial stress and depression or substance use; while social selection theory is more important in relation to severe mental disorders such as schizophrenia [ 70 , 72 ].

Limitations of the review

This systematic review is the first to comprehensively pool observational studies on the association between individual or household finances and depression or depressive symptoms. However, this review is subject to several limitations. First, since there is substantial heterogeneity in the measurements and definitions of exposure (financial stress) and the outcome variable (depression), targeted populations, and methodologies between studies, a meta-analysis combining the data from the reviewed studies is neither appropriate nor practical. As such, only a narrative approach is used in this review without quantitatively synthesising the data from the studies, which are difficult to compare. Second, the majority of studies reported evidence on high-income countries like the US, the UK, European countries. Therefore, the conclusions of this review are more immediately generalisable to these contexts as opposed to low-and middle-income country contexts. Third, this review undertakes the search on six databases for any related peer-reviewed journal articles without searching for other resources to find grey or unpublished literature and conference abstracts. Excluding the unpublished studies might limit the findings of this review since studies with significant results are more likely to get published [ 73 ]. The published studies may lead this review to overestimate the associations between any financial exposures and depressive symptoms. Fourth, the included exposures in this review are the most direct indicators (i.e., proximal indicators) of financial stress. The findings in this review might not be generalisable to the relationship between a distal factor (e.g., job loss) and depression. Evidence has suggested that a significant life event or experience, for example, job loss, is associated with financial stress and thereby can predict subsequent major depression [ 74 ]. Future review on financial stress and mental health outcomes might benefit from further including the effect of distal factors (such as job loss, changes in working hours, changes in marital status, and so on) on mental health.

Implications

This review has a number of implications for public policy around financial circumstances and depression. Firstly, it highlights the role that measures aimed at alleviating financial poverty and inequality could have in improving public mental health. Secondly, it suggests attention needs to be focused on unsecured debt as a public health measure. For example, providing financial counselling services and financial education to those who have debt stress and depression may help them to effectively deal with individual debt problems and associated depression. Meanwhile, the regulation of unsecured debt markets is crucial to the sustainable development of unsecured lending markets, and thereby supports both financial health and mental health. Thirdly, this review highlights the importance of targeted interventions to break the cycle of financial stress and depression. For example, instead of a one-for-all intervention focusing on the general population, interventions targeted at lower socioeconomic groups might be more effective since the association between financial stress and depression is more pronounced in these groups.

In terms of practical support, the interdisciplinary collaboration of psychologists and financial professionals in the development of interventions aiming to break the vicious cycle of depression and financial stress could be useful. For example, interventions such as poverty alleviation programs, the provision of financial advice or financial education might have a beneficial influence on mental health. The collaboration of policymakers in both mental health areas and financial areas might create win-win situations, having mutual benefits for both areas and saving costs to society in the long run.

Finally, this review highlights the need for further research in certain areas. First, this review suggests that more longitudinal research or randomised control trials (where feasible) are needed to further clarify the directions of the causal relationships and possible mechanisms between different financial stressors and depression or other mental disorders. A better understanding of this can help to design more effective interventions either aimed at alleviating financial stress or at improving mental health. For example, anti-poverty programs such as direct cash transfers might be more helpful for families in poverty or deprivation, where social causation plays the main role in the effect of financial stress on depression. However, if financial stress is due to social comparison, rather than absolute poverty, challenging social attitudes may be more beneficial. This review also calls for more future research to investigate the heterogeneity of the relationship and the difference in the direction of the relationship between financial stress and depression or other mental disorders across different populations. This would provide more precise and solid evidence for developing targeted interventions as noted above.

Second, this review finds that the majority of the existing studies on the financial stress-depression relationship are based on high-income countries. However, low-and middle-income countries have higher levels of poverty and economic inequality, as well as a high economic burden caused by mental disorders and low levels of investment in mental health [ 69 , 75 ]. Therefore, more future research that is based on low-and middle-income country contexts would be important.

In conclusion, this systematic review of the link between financial stress and depression in adults found that financial stress is positively associated with depression, in particular among low socioeconomic groups. The findings suggest directions for policymakers and the need for greater collaboration between psychology and financial professionals, which will be beneficial to developing targeted interventions either to mitigate depression or alleviate financial stress. Further longitudinal research would be useful to investigate the causality and mechanisms of the relationship between different dimensions of financial stress and depression.

Supporting information

S1 checklist, s1 appendix, s2 appendix, s3 appendix, acknowledgments.

We would like to thank two anonymous reviewers for their comments that helped to improve and clarify the manuscript.

Funding Statement

The author(s) received no specific funding for this work.

Data Availability

Your manager is not your therapist

One of the worst things your company can do for your mental health is talk about it too much.

Gen Z wants to talk about mental health . And these days, they want to talk about it at the office.

In a 2023 survey of nearly 3,000 people, Gen Z was almost twice as likely as other generations to say they struggled with their mental health . And nearly half said they're fine talking about it at work — 20% more than other generations. Anecdotally, managers have said their youngest employees confront anxiety and have no qualms about openly discussing it .

This comfort with vulnerability shouldn't be a surprise. Gen Zers grew up amid a movement to destigmatize mental illness and encourage people to get treatment. They witnessed suicide rates tick up, especially among their peers. They watched celebrities like Selena Gomez , Simone Biles , and Demi Lovato speak out about once taboo subjects such as bipolar disorder, depression, and ADHD. And over the past few years, they've watched rates of depression and anxiety climb through the roof. They've felt increasingly empowered to be open about their struggles, support their coworkers, and lobby management for better benefits.

In a recent survey of US businesses conducted by the consultancy group Mercer and published by the US Chamber of Commerce, companies reported an overwhelming increase in demand for mental-health care over the past few years. In response, 94% of companies employing more than 500 people have added mental-health benefits — from expanded access to therapy to in-office programs for mental-health training . Across corporate America, talking about mental health is all the rage.

There's just one problem. While destigmatizing mental illness is important, a workplace overly focused on mental health isn't always a recipe for better mental-health outcomes. Recent articles about " therapy speak " and being " overtherapized " point to a growing sense that all the mental-health talk might be a bit much. In fact, researchers studying the issue think that talking about your psychological struggles too much can make your problems worse.

Related stories

A healthy work environment is one where people feel supported and encouraged to do meaningful work — not one that fixates on their mental health.

Americans are overwhelmingly worried about a mental-health crisis . In a 2022 poll of American adults conducted by the American Psychiatric Association, 79% said they viewed mental health as a public-health emergency in the US. When asked in a December KFF poll about crucial issues for the 2024 presidential candidates to discuss, far more people said access to mental-health care was most important compared with those who listed immigration, gun violence, abortion, or the climate crisis as the top issue.

The concern is well placed. Gallup found that between 2015 and 2023, the share of Americans who said they had been diagnosed with depression increased from about 20% to almost 30%. In just two decades, the number of Americans who received mental-health treatment shot up from 27 million in 2002 to nearly 56 million in 2022. Half of US physicians in a CVS Health/Harris Poll survey last year reported that their patients' mental health was declining.

Among young people, the problem is worse: A 2022 KFF/CNN survey found that adults under 30 were far more likely than those in older age groups to report that they often or always felt depressed or anxious. In a recent survey from the Archbridge Institute's Human Flourishing Lab, where I serve as the director, only 64% of Americans between the ages of 18 and 29 said their mental health was good — less than any other age group and a stark contrast from the roughly 90% of people over 45 who said the same.

These trends have important implications for the workplace. Poor mental health reduces labor-force participation , work engagement, and job performance, costing the economy an estimated $50 billion in lost productivity each year. And companies are noticing the impact: In a 2023 survey of 152 large American employers, 77% of companies reported an increase in mental-health concerns among their employees.

Some psychologists believe that efforts to increase public awareness of mental-health problems in the Western world have actually made the problem worse.

To address this problem, human-resources departments have flooded the workplace with resources and programs: everything from online resources through partnerships with wellness and therapy apps like Calm and BetterHelp to in-house resources such as office peer support groups, mental-health seminars, and spaces specifically for meditation and yoga. Many companies are also facing a push for cultural change. In a recent survey by the National Alliance on Mental Illness, three-quarters of workers polled said it was appropriate to discuss mental health at work, and even more said that supervisors and senior leadership were responsible for helping employees feel comfortable discussing their mental health.

On TikTok, people are recording their on-the-job breakdowns. Across social media, Gen Zers swap tips on avoiding toxic workplaces . And in work-based TV shows like "Severance," "Industry," and "The Bear," mental health is front and center. Everyone seems to agree that companies need to do something.

Breaking through the mental-health stigma is important: Many people struggling with depression or anxiety do not seek help because of their fear that it could harm their reputation, social relationships, and professional aspirations. In that sense, it's a good thing when workplaces become supportive environments where colleagues and supervisors view mental-health issues humanely.

But there's a limit. Too much mental-health talk can be counterproductive. Take concept creep, for example — the idea that the meanings of things like abuse, trauma, anxiety, and depression have expanded over time. Over the years, negative emotional experiences that were once considered a normal part of life have increasingly been viewed as signs of psychological disorders. Trauma , for example, once referred to the severe psychological distress that came from rare, life-threatening experiences. Now, it's used to describe less-severe distress caused by a wider variety of adverse events, such as exposure to offensive speech or violent media.

Some psychologists believe that efforts to increase public awareness of mental-health problems in the Western world have actually made the problem worse — they have encouraged people to fixate on negative psychological experiences and interpret normal levels of emotional discomfort as abnormal. This misinterpretation can lead to a self-fulfilling prophecy, they argue, whereby people begin to think and behave as if they truly have a mental disorder, ultimately increasing their risk of developing one.

Well-intentioned efforts to get people to think and talk more about mental health may inadvertently promote excessive dwelling on negative emotions and personal insecurities — known in psychology as rumination — which can exacerbate psychological distress. Research indicates that rumination can make depression and anxiety disorders worse, which is why helping other people is an especially effective way to reduce symptoms of anxiety and depression — it takes people's minds off their own problems.

The more people view their lives — and work — as meaningful, the lower their risk for depression, anxiety, substance abuse, and suicide is.

So when employers encourage workers to spend time focused on their mental states with "emotional check-ins" or by including more mental-health language in office communications, they may well push staff to ruminate on their problems — and make them worse. And while workplace leaders can lend a sympathetic ear, most are not trained psychologists or psychiatrists and thus lack the expertise required to properly identify and address mental illness.

There's also a professional risk. Sharing your personal health information with colleagues and supervisors can blur professional boundaries and result in discrimination due to an altered perception of your competence that could affect your career advancement. When managers share too much about their psychological struggles, researchers have found, it can undermine how their employees see them.

In other words, the office isn't equipped to treat mental-health issues — but it can help in other ways.

What does have a tangible impact on people's well-being at work is whether they find their work meaningful . The more people view their lives — and work — as meaningful, the lower their risk for depression, anxiety, substance abuse, and suicide is. And when people experience mental-health problems, the things in life they find meaningful can play an important role in their recovery. At work, finding meaning also improves the overall organization. Workers are more likely to report high levels of job satisfaction and low intentions of quitting if they view their work as meaningful.

I've spent two decades of my career as an existential psychologist studying the need for meaning in life. The most important lesson employers can learn is that meaning is about social significance . People feel the most meaningful when they believe that they're making important contributions to the lives of others. Research has found that people are more likely to derive meaning from their work when they focus on how it serves a greater good, rather than how it advances their career. Other research has found that work feels the most meaningful when workers have a strong sense of autonomy at work and believe their efforts significantly and positively influence the lives of others.

Prioritizing positive mental health in the workplace is crucial — most of us spend the majority of our time on the job, after all. But the solution, ultimately, isn't as straightforward as raising awareness and fostering open conversations. Instead, employers should ensure their staff have access to mental-health care while building a positive culture that promotes meaningful work.

Clay Routledge is vice president of research and director of the Human Flourishing Lab at the Archbridge Institute.

About Discourse Stories

Through our Discourse journalism, Business Insider seeks to explore and illuminate the day’s most fascinating issues and ideas. Our writers provide thought-provoking perspectives, informed by analysis, reporting, and expertise. Read more Discourse stories here .

More from Careers

Most popular

• Main content