articles on autism research

Publications

Prenatal ultrasound use and risk of autism spectrum disorder: Findings from the case‐control Study to Explore Early Development (SEED)

Prior studies have found no connection between prenatal ultrasound use and ASD. Through an updated study approach, findings from a 2023 SEED study confirm the previous research, showing no association between prenatal ultrasound use and increased risk for ASD. CDC remains committed to exploring potential risk factors for ASD by using world-class data and analytics.

Key Findings

ADDM Network Expands Surveillance to Identify Healthcare Needs and Transition Planning for Youth Five of CDC’s ADDM Network sites (Arkansas, Georgia, Maryland, Utah, and Wisconsin) began monitoring autism spectrum disorder (ASD) in 2018 among 16-year-old adolescents who were initially identified as having characteristics of ASD in 2010. (Published: February 25, 2023)

Study Shows Linking Statewide Data for ASD Prevalence is Effective Linking statewide health and education data is an effective way for states to have actionable local autism spectrum disorder (ASD) prevalence estimates when resources are limited. (Published: January 18, 2023)

Transitioning from Pediatric to Adult Health Care is Often Difficult for Adolescents with ASD Only 1 in 13 adolescents with Autism Spectrum Disorder (ASD) received the recommended guidance to move from pediatric to adult health care. Greater coordination among healthcare programs and interdisciplinary training for providers could expand access to services and increase provider comfort in treating the unique healthcare needs of adolescents with ASD, and support healthcare planning as they transition from pediatric to adult health care. (Published: April 29, 2021)

CDC Releases First Estimates of the Number of Adults Living with Autism Spectrum Disorder in the United States This study fills a gap in data on adults living with ASD in the United States because there is not an existing surveillance system to collect this information. (Published May 10, 2020)

SEED Research

Many additional studies are underway.  We will provide summaries of those studies in the future.

Community-based service use in preschool children with autism spectrum disorder and associations with insurance status

Rubenstein E, Croen L, Lee LC, Moody, E, Schieve LA, Soke GN, Thomas K, Wiggins L, Daniels J

Research in Autism Spectrum Disorders, 2019

This study examined the association between insurance status and community-based services received outside of school among preschool-aged children with a prior autism spectrum disorder (ASD) diagnosis. Children eligible for autism-related special education services are required by law to receive individualized ASD services in school (“Individuals with Disabilities Education Improvement Act of 2004,” 2004). However, additional community-based services such as behavioral therapy, speech therapy, and occupational therapy are often needed. The Study to Explore Early Development (SEED) provides important information, not available in previous studies, on the use of community-based services by insurance status in preschool-aged children. In this report, insurance status was categorized as private insurance alone, public insurance alone, both private and public insurance, or uninsured. The results showed that about 35% of the children with a prior ASD diagnosis had public insurance alone and 51% had private insurance alone. In addition, 13% had both types of insurance, while few children (1%) were uninsured. The most commonly received services were speech therapy and occupational therapy. Nearly 40% of children received no community-based services at all. After adjusting for sociodemographic variables, insurance status was not associated with the number of different types of community-based services received. However, children with public insurance alone were the least likely to receive behavioral therapy and the most likely to receive psychotropic medication. These findings suggest that many preschool-aged children do not receive community-based services, and the receipt of certain important services varies by insurance type. Increasing access and availability for evidence-based services, especially for children with public insurance only, may improve service use and outcomes for children with ASD.

Assessment of demographic and perinatal predictors of non-response and impact of non-response on measures of association in a population-based case control study: findings from the Georgia Study to Explore Early Development

Schieve LA, Harris S, Maenner MJ, Alexander A, Dowling NF

Emerg Themes Epidemiol., 2018

This report describes characteristics of those who did or did not participate in and complete Georgia SEED between 2007 and 2012. The population (POP) sample was recruited from birth certificates. The autism spectrum disorder (ASD) and developmental disability (DD) groups were recruited from health and special education sources servicing children with developmental disabilities. Children who enrolled in the study were given a comprehensive evaluation to confirm case status. Using birth certificate data, demographics of mothers and children who completed SEED were compared with those who were invited but did not complete the study. Those who did not complete the study included those not located, those located but who declined to participate, or those enrolled in SEED but who dropped out before completing most study steps. Researchers found that all mothers who completed the study tended to be older and had more education than mothers who did not complete the study; yet, they were similar on other demographic factors, such as race/ethnicity and marital status.  Mothers in the ASD group, in particular, were more like to have more education than potential ASD cases invited but who did not complete the study. In two risk factor analyses, associations between the ASD and POP groups were not influenced by the differences in the characteristics of those who participated and completed the study. Assessment of non-response has not yet been done for the other SEED sites. For GA SEED, these findings suggest that differences in participation and completion rates do not appear to affect the study results examined. The information in this report is useful to other researchers conducting epidemiologic studies, especially those seeking to enroll large, diverse population-based samples.

Demographic and Operational Factors Predicting Study Completion in a Multisite Case-Control Study of Preschool Children

Bradley CB, Browne EN, Alexander AA, Collins J, Dahm JL, DiGuiseppi CG, Levy SE, Moody EJ, Schieve LA, Windham GC, Young L, Daniels JL

American Journal of Epidemiology , 2018

This report describes study completion among 3,769 families who enrolled in the first phase of SEED between 2007 and 2011. Families were asked to complete multiple steps for SEED, including phone interviews, filling out forms, participating in an in-person visit to check a child’s development, and providing biological specimens (such as cheek swabs and blood). Researchers found that completion was generally 70% or higher for each study step and 58% of participants completed all key study steps. Researchers found that completion rates varied by families’ demographic characteristics and also the distance they had to travel to the study clinic.  This information is important in helping researchers understand the SEED data already collected and in planning future SEED phases. These study findings also inform researchers on possible ways to improve participation in other future studies.

Demographic Profile of Families and Children in the Study to Explore Early Development (SEED): Case-control Study of Autism Spectrum Disorder.

DiGuiseppi CG, Daniels JL, Fallin DM, Rosenberg SA, Schieve LA, Thomas KC, Windham GC, Goss CW, Soke GN, Currie DW, Singer AB, Lee LC, Bernal P, Croen LA, Miller LA, Pinto-Martin JA, Young LM, Schendel DE.

Disability and Health Journal, 2016

This is one of two reports that describe the characteristics of children enrolled in SEED. This report focuses on sociodemographic characteristics. SEED successfully enrolled a highly diverse sample of participants, including minorities and low socioeconomic status families. The SEED population sample represents racial, ethnic, and demographic diversity in the United States. SEED improves upon other ASD risk factor studies in that it does not rely on administrative data sources, which lack many important details of both child development and maternal risk factors. Nor does it rely on small samples from only a few clinics or schools. SEED collects detailed data in a large and diverse sample.  This provides unique opportunities for researchers to learn more about how socioeconomic characteristics relate to risk factors for ASD and health outcomes in children with ASD.

Autism Spectrum Disorder Symptoms among Children Enrolled in the Study to Explore Early Development (SEED).

Wiggins LD, Levy SE, Daniels J, Schieve L, Croen LA, DiGuiseppi C, Blaskey L, Giarelli E, Lee LC, Pinto-Martin J, Reynolds A, Rice C, Rosenberg CR, Thompson P, Yeargin-Allsopp M, Young L, Schendel D.

Journal of Autism and Developmental Disorders, 2015

This is one of two reports that describe the characteristics of children enrolled in SEED. This report focuses on developmental characteristics. Children enrolled in SEED are divided into four groups: three with children who have varying types of developmental delays and disabilities, including ASD, and one with children from the general population. The report describes how various facets of children’s development vary across these four groups and highlights the many needs of children with ASD and other developmental disabilities.

Using standardized diagnostic instruments to classify children with autism in the Study to Explore Early Development.

Wiggins LD, Reynolds A, Rice CE, Moody EJ, Bernal P, Blaskey L, Rosenberg SA, Lee LC, Levy SE.

This report describes the SEED process for determining whether a child enrolled in the study will be classified as an ASD case. This classification is based on an in-person assessment given by trained SEED clinicians. Children enrolled in the study are screened for autism symptoms by asking their mothers to respond to a brief questionnaire.  Children with an indication of possible autism symptoms are assessed further during an in-person visit.  Clinicians give these children a more in-depth developmental evaluation known as Autism Diagnostic Observation Schedule and ask their mothers or other caregivers to participate in an interview known as the Autism Diagnostic Interview – Revised. Besides providing clinicians with information to determine a child’s ASD classification, these assessments provide valuable information on ASD-specific behaviors and traits, allowing researchers to better understand the different characteristics among children with ASD.

The Study to Explore Early Development (SEED): a multisite epidemiologic study of autism by the Centers for Autism and Developmental Disabilities Research and Epidemiology (CADDRE) network.

Schendel DE, Diguiseppi C, Croen LA, Fallin MD, Reed PL, Schieve LA, Wiggins LD, Daniels J, Grether J, Levy SE, Miller L, Newschaffer C, Pinto-Martin J, Robinson C, Windham GC, Alexander A, Aylsworth AS, Bernal P, Bonner JD, Blaskey L, Bradley C, Collins J, Ferretti CJ, Farzadegan H, Giarelli E, Harvey M, Hepburn S, Herr M, Kaparich K, Landa R, Lee LC, Levenseller B, Meyerer S, Rahbar MH, Ratchford A, Reynolds A, Rosenberg S, Rusyniak J, Shapira SK, Smith K, Souders M, Thompson PA, Young L, Yeargin-Allsopp M.

Journal of Autism and Developmental Disorders, 2012

This report describes SEED methods. SEED is one of the largest studies investigating genetic and environmental risk factors for autism spectrum disorder (ASD) and child health and behavioral traits associated with ASD. SEED enrolls preschool-aged children with ASD and other developmental disabilities and children from the general population in six sites across the United States. SEED methods focus on enrolling families from diverse populations in each area. A key strength of SEED includes the collection of in-depth information on child development, which allows researchers to more rigorously classify children into various study groups (ASD, other developmental disabilities, or population controls) than what is done in many other ASD research studies.  In SEED, researchers use standardized assessment tools to determine a children’s final study group and to assess specific behavioral traits among children with ASD. Another key strength is the collection of comprehensive data on child health and potential risk factors for ASD. SEED’s large and diverse sample of study participants allows researchers to analyze data in greater detail than most other ASD studies and answer many important questions about ASD.   Top of Page

Maternal Psychiatric Conditions, Treatment with SSRIs, and Neurodevelopmental Disorders

Ames JL, Ladd-Acosta C, Fallin MD, Qian Y, Schieve LA, DiGuiseppi, C, Lee LC, Kasten EP, Zhou G, MPH, MD, PhD, Pinto-Martin J, Howerton E, Eaton, CL, Croen LA, PhD

Biological Psychiatry, 2021

A study published online in Biological Psychiatry looked at whether psychiatric conditions during pregnancy, like depression, and the use of selective serotonin reuptake inhibitors (SSRIs) were associated with autism spectrum disorder (ASD) among the children of mothers who were treated. The study found ASD was more common among children of mothers who had psychiatric conditions during pregnancy. However, among the subset of children whose mothers had psychiatric conditions, ASD was not more common among those treated with SSRIs. The authors conclude that this study provides evidence that maternal psychiatric conditions during pregnancy, but not the use of SSRIs, are associated with increased risk of ASD. These findings have implications for clinical decision-making regarding the continuation of SSRI treatment during pregnancy.

Maternal Pre-Pregnancy Weight and Gestational Weight Gain in Association with Autism and Developmental Disorders in Offspring

Susana L. Matias, Michelle Pearl, Kristen Lyall, Lisa A. Croen, Tanja V. E. Kral, Daniele Fallin, Li-Ching Lee, Chyrise B. Bradley, Laura A. Schieve, Gayle C. Windham

Obesity, 2021

A study published online explored whether obesity in mothers prior to pregnancy or weight gain during pregnancy was associated with autism spectrum disorder (ASD) or other developmental disorders in their children.  Mothers classified as having severe obesity (body mass index ≥35 kg/m) prior to pregnancy had a significantly higher risk of having children with ASD and other developmental disorders. The largest amounts of weight gain during pregnancy were associated with ASD, particularly among male children. Since pre-pregnancy weight and weight gain during pregnancy are regularly measured and potentially modifiable, these findings could assist targeting high-risk mothers for early interventions.

Infection and Fever in Pregnancy and Autism Spectrum Disorders: Findings from the Study to Explore Early Development

Croen LA, Qian Y, Ashwood P, Ousseny Z, Schendel D, Pinto-Martin J, Fallin D, Levy S, Schieve LA, Yeargin-Allsopp M, Sabourin KR

Autism Research, 2019

This study evaluated the associations between a child having autism spectrum disorder (ASD) or other developmental disabilities (DD), and whether the child’s mother had an infection during her pregnancy. The Study to Explore Early Development’s (SEED’s) detailed data on type and timing of a mother’s infection and whether the mother also had a fever allowed researchers to conduct a more in-depth analysis on this topic than had been done previously. Study findings showed that overall maternal infections during pregnancy were common, occurring in approximately 60% of women in this study, and were not associated with having a child with ASD or DD. Certain infections – those that occurred in the second trimester and were accompanied by fever (7% of mothers) – were associated with ASD in children. These study findings add to other studies of risk factors that highlight the potential association between maternal health during pregnancy and ASD.

Neonatal jaundice in association with autism spectrum disorder and developmental disorder

Cordero C, Schieve LA, Croen LA, Engel SM, Siega-Riz AM, Herring AH, Vladutiu CJ, Seashore CJ, Daniels JL

Journal of Perinatology, 2019

This study examines the association between a child having jaundice just after birth and autism spectrum disorder (ASD) and other developmental disorders (DDs). Jaundice is a yellow discoloration of the skin and eyes that occurs in some newborns because of a build-up of bilirubin, a substance that forms when blood cells are broken down. While most jaundice lasts only a short time, high levels of bilirubin can affect the developing brain. The Study to Explore Early Development’s (SEED’s) detailed data on the health of mothers and their children allowed researchers to conduct a more in-depth analysis on this topic than had been done previously. Study findings showed that among children who had been born too early (or preterm), newborn jaundice was associated with both ASD and other DDs. However, among children born on time, newborn jaundice was not associated with either ASD or other DDs. This study highlights the association between newborn health and ASD and other DDs.

Early Life Exposure to Air Pollution and Autism Spectrum Disorder: Findings from a Multisite Case-Control Study

McGuinn LA, Windham GC, Messer LC, Di Q, Schwartz J, Croen LA, Moody EJ, Rappold AG, Richardson DB, Neas LM, Gammon MD, Schieve LA, Daniels JL

Epidemiology, 2020

This study used Study to Explore Early Development (SEED) data to examine the association between autism spectrum disorder (ASD) and exposure to air pollutants during key periods of brain development. Particulate matter (PM), or tiny particles of air pollution, and ozone are common air pollutants. Previous studies have found an association between ASD and exposure to these air pollutants; however, previous studies have been unable to look at exposure to these air pollutants in relation to key periods of brain development or account for potential differences in pollutants in regions of the United States. This study looked at air pollutant exposure among participants living in six different areas of the United States (located in California, Colorado, Georgia, Maryland, North Carolina, and Pennsylvania) during several critical periods: 3 months before pregnancy, each trimester of pregnancy, the entire pregnancy, and the first year of life. Study findings showed an association between air pollution and ASD by period of exposure; ASD was associated with ozone exposure during the third trimester and with PM exposure during the first year of life. These findings support previous studies of a positive association between ASD and potential exposure to air pollution during the late prenatal period and early postnatal period. Further investigation into these findings may be helpful in increasing our understanding of these association

Air pollution, neighborhood deprivation, and autism spectrum disorder in the Study to Explore Early Development

McGuinn LA, Windham GC, Messer LC, Di Q; Schwartz J, Croen LA, Moody EJ, Rappold AG, Richardson DB, Neas LM, Gammon MD, Schieve LA, Daniels JL

Environmental Epidemiology, 2019

This study used Study to Explore Early Development (SEED) data to examine whether the association between autism spectrum disorder (ASD) and early exposure to air pollution is modified by neighborhood deprivation.  Previous research, including studies using SEED data, have found an association between ASD and exposure to particulate matter (PM), or tiny particles of air pollution, during the first year of life; however, these studies did not look at different measures of neighborhood deprivation, which may also be associated with ASD and are often geographically correlated with air pollution. This study went beyond prior studies by combining data on pollution, roadway proximity, and neighborhood deprivation at the census tract level in six different areas of the United States. Study findings showed that the association between ASD and PM exposure during the first year of life was stronger for children living in neighborhoods of high deprivation, as compared to neighborhoods of moderate or low deprivation. Confirmation of these preliminary findings may be useful in future studies.

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Association Between Breastfeeding Initiation and Duration and Autism Spectrum Disorder in Preschool Children Enrolled in the Study to Explore Early Development

Soke GN, Maenner M, Windham G, Moody E, Kaczaniuk J, DiGuiseppi C, Schieve LA

Autism Res, 2019

This study compared breastfeeding initiation and duration among children with autism spectrum disorder (ASD) to children from the general population without ASD. SEED’s large sample size and diverse sample allowed researchers to conduct a more thorough assessment than previous studies. Study findings showed mothers of children with ASD were just as likely as mothers of children from the general population to initiate breastfeeding. However, among mothers who initiated breastfeeding, those who had children with ASD were less likely than those who had children without ASD to continue breastfeeding for longer than 6 months. The reasons for shorter breastfeeding duration among children with ASD are unclear. A longer duration of breastfeeding might protect a child from developing ASD, but it is also possible that early discontinuation of breastfeeding is related to underlying developmental conditions in children with ASD, such as child irritability, sensory, or health issues. To better understand why the duration of breastfeeding might be shorter among mothers of children with ASD compared to those without ASD, future studies should consider evaluating the reasons women discontinue breastfeeding.

Maternal diabetes and hypertensive disorders in association with autism spectrum disorder

Cordero C, Windham GC, Schieve LA, Fallin MD, Croen LA, Siega-Rizf AM, Engel SM, Herring AH, Stuebe AM, Vladutiu CJ, Daniels JL

This study examined associations between a child having autism spectrum disorder (ASD) or other developmental disabilities (DDs) and whether the child’s mother had diabetes or hypertension during pregnancy. Diabetes and hypertension are among the most common complications experienced by women during pregnancy. SEED’s large sample size and detailed data about the health of mothers and their children allowed researchers to conduct a more in-depth analysis on this topic than previous studies. Study findings showed that having hypertension during pregnancy was associated with both ASD and other DDs in children compared with not having hypertension during pregnancy. Diabetes during pregnancy was not associated with ASD, but was associated with other DDs. This study highlights the relationship between maternal health during pregnancy and children with ASD and other DDs.

Maternal Pre-pregnancy Body Mass Index (BMI) and Gestational Weight Gain in Relation to Autism Spectrum Disorder (ASD) and Other Developmental Disorders in Offspring

Windham GC, Anderson M, Lyall K, Daniels JL, Kral TV, Croen LA, Levy SE, Bradley CB, Cordero C, Young L, Schieve LA

This study examined the relationship between mother’s body mass index (BMI) before pregnancy, mother’s weight gain during pregnancy, and associations with ASD and other developmental disabilities (DDs). Although previous studies have reported an association between higher maternal BMI and ASD, having this information, along with weight gain during pregnancy in SEED, allowed researchers to conduct a more in-depth analysis on this topic than previous studies. Study findings show an association between higher pregnancy weight gain and having a child with ASD, and this association was even stronger when the mother was overweight or obese before becoming pregnant. On the other hand, while maternal BMI before pregnancy was associated with having a child with a DD, mother’s weight gain during pregnancy was not. This study highlights the possible effects of maternal weight on child having ASD or DDs and the importance of maintaining a heathy weight before and during pregnancy.

Brief Report: Maternal Opioid Prescription from Preconception through Pregnancy and the Odds of Autism Spectrum Disorder and Autism Features in Children

Rubenstein E, Young JC, Croen LA, DiGuiseppi C, Dowling NF, Lee LC, Schieve L, Wiggins LD, Daniels J

Journal of Autism and Developmental Disorders, 2018

This study examined possible associations between prescription of opioid medications just before and during pregnancy and ASD and other developmental disabilities (DDs). Currently, the information available on this topic is very limited. SEED collects detailed information about mothers’ health histories, including prescribed medication, which allowed researchers to conduct this exploratory analysis. Illicit opioid use was not included in this analysis. The study findings show that approximately 8% of mothers reported receiving an opioid prescription just before or during pregnancy; of these mothers, the majority (76%) received only one prescription. The most common reasons for opioid prescriptions were migraine headaches, injury, and back pain. Mothers who were prescribed opioids just before becoming pregnant were more likely to have a child with ASD or a child with DDs and some autism symptoms. Researchers were limited by small sample sizes; thus, they were not able to conduct a detailed assessment of whether the associations found were related to the medication itself, the reason the mother took the medication, or some other unknown factors that may be associated with opioid use. This study is among the first to assess possible associations between prescription of opioids just before or during pregnancy and ASD and other DDs. More research is needed to understand how opioid use before and during early pregnancy may impact a child’s development.

Family History of Immune Conditions and Autism Spectrum and Developmental Disorders: Findings from the Study to Explore Early Development

Croen, LA, Qian Y, Ashwood, P, Daniels JL, Fallin D, Schendel D, Schieve LA, Singer AB, Zerbo O

Autism Research, 2018

This study examined the relationship between autism spectrum disorder (ASD) and other developmental disorders (DDs) and having a family history of conditions related to immune system functioning. Such conditions include asthma, allergies, and autoimmune disorders such as eczema or psoriasis. Previous studies have suggested some association, but the results about specific conditions varied. SEED’s large sample size and detailed data on specific types of immune disorders allowed researchers to conduct an in-depth analysis on this topic and examine the associations with ASD alongside associations with other DDs. The study findings show that maternal history of eczema or psoriasis and asthma are associated with both ASD and other DDs in children. Researchers also found that children with ASD are more likely to have eczema or psoriasis and allergies than children without ASD. Autoimmune disorders were not notably increased among children with other DDs. This study highlights the relationship between maternal health before and during pregnancy and ASD and other DDs, and provides researchers more information about the health of children with ASD.

Case-control meta-analysis of blood DNA methylation and autism spectrum disorder

Andrews SV, Sheppard B, Windham GC, Schieve LA, Schendel DE, Croen LA, Chopra P, Alisch RS, Newschaffer CJ, Warren ST, Feinberg AP, Fallin MD, Ladd-Acosta C

Molecular Autism, 2018

In this study, researchers used SEED data and data from another study of children and adolescents with and without ASD to learn more about how genes are regulated in children with ASD. Many genes are turned on or off by a process called “methylation.”  Although methylation does not change a person’s actual genes (or genetic code), methylation helps different types of cells do their specific jobs by affecting which genes are turned on and which genes are not. The researchers examined children’s DNA to look for differences in the methylation of genes between children with and without ASD. Previous studies of methylation in relation to ASD were limited by small sample sizes. This study is one of the largest so far to look broadly at methylation patterns in children with and without ASD. The study showed several potential differences in methylation between children in the two groups. Some of the differences suggest links to brain function, and they were consistent with results from previous studies. These findings provide clues as to how genes might be related to ASD in children.

Associations Between the 2nd to 4th Digit Ratio and Autism Spectrum Disorder in Population-Based Samples of Boys and Girls: Findings from the Study to Explore Early Development.

Schieve LA, Tian L, Dowling N, Croen L, Hoover-Fong J, Alexander A, Shapira SK.

This study examined associations between ASD and the ratio of children’s index (2nd) finger length to their ring (4th) finger length. The ratio of finger lengths (or digit ratio) has been linked to the level of sex hormones a child was previously exposed to during pregnancy. Researchers study digit ratios because they rarely have direct measurements of fetal exposure to hormones.  Study findings in boys showed that digit ratio was associated with ASD, but only in certain subgroups, such as children who had ASD and also a birth defect or genetic syndrome. This suggests the association might not have been related to hormone levels, but might instead be explained by genetics.  Study findings in girls showed that digit ratio was associated with ASD and that the association was not limited to certain subgroups of children.  There has been little past study of the association between digit ratio and ASD, particularly in girls.  The findings in this report suggest that hormone exposures during pregnancy might be related to ASD in girls, but many gaps remain in our understanding of the underlying reasons for this association and further research is needed.

Autism Spectrum Disorder and Birth Spacing: Findings from the Study to Explore Early Development (SEED).

Schieve LA, Tian LH, Drews-Botsch C, Windham GC, Newschaffer C, Daniels JL, Lee LC, Croen LA, Danielle Fallin M.

Autism Research, 2017

This study examined whether the amount of time between pregnancies was associated with ASD or other developmental disabilities in children. SEED’s detailed data on ASD subgroups and other developmental disabilities allowed researchers to conduct a more in depth analysis on this topic than those that have been done previously. The study findings show that both shorter and longer time periods between births are associated with having a child with ASD. Children conceived less than 18 months after their mother’s previous birth and children conceived 60 or more months after their mother’s previous birth were more likely to have ASD than children conceived between 18 to 59 months after their mother’s previous birth. The relationship was stronger in children with more severe ASD symptoms. Also, the association between birth spacing and ASD appeared to be unique to ASD, as there was no association found between birth spacing and having children with other developmental disabilities. The association between birth spacing and ASD was not explained by unplanned pregnancy, an underlying fertility disorder in the mother, or high blood pressure or diabetes during pregnancy. The findings from this study can help healthcare providers counsel their patients on pregnancy spacing.

Prenatal Alcohol Exposure in Relation to Autism Spectrum Disorder: Findings from the Study to Explore Early Development (SEED).

Singer AB, Aylsworth AS, Cordero C, Croen LA, DiGuiseppi C, Fallin MD, Herring AH, Hooper SR, Pretzel RE, Schieve LA, Windham GC, Daniels JL.

Paediatric and Perinatal Epidemiology, 2017

This study examined associations between alcohol use just before and during pregnancy and ASD or other developmental disabilities (DDs). Previous studies have shown that high levels of alcohol use in pregnancy are associated with child developmental effects, such as decreased intellectual ability, hyperactivity, learning difficulties, and autism-like traits. This study investigated whether lower levels of alcohol use before and during pregnancy were associated with developmental outcomes. Most mothers of children in SEED reported no or low levels of alcohol use before or during their pregnancies.  In fact, nearly all mothers reported no alcohol use in the second month of pregnancy or later (93-98% depending on month). Therefore, a main focus of the study was on alcohol use in the three months prior to pregnancy or the first month of pregnancy. The study findings show that modest alcohol use during these four months was not associated with increased risk for either ASD or other DDs.  Although this study did not find an association between ASD or other DDs and modest alcohol use before or during pregnancy, women who are pregnant or planning to become pregnant should continue to follow recommendations to avoid alcohol use because of other known effects on infant and child health.

Maternal and Paternal Infertility Disorders and Treatments and Autism Spectrum Disorder: Findings from the Study to Explore Early Development.

Schieve LA, Drews-Botsch C, Harris S, Newschaffer C, Daniels J, DiGuiseppi C, Croen LA, Windham GC.

Journal of Autism and Developmental Disorders, 2017

This study examined associations between ASD and whether, prior to becoming pregnant, a child’s mother had a condition that might have affected her ability to get pregnant (i.e., infertility). The study also looked at whether the mother had received any medical treatments to help her become pregnant or to prevent miscarriage during early pregnancy. SEED’s detailed data on specific types of infertility disorders and treatments allowed researchers to conduct a much more in depth analysis on this topic than those that have been done previously. The study findings show that several infertility disorders in the mother — including blocked tubes, uterine conditions such as fibroids, endometriosis, and polycystic ovarian syndrome — are associated with ASD in children. However, treatments for infertility or to prevent miscarriage were not associated with ASD.  While the reasons for the associations with infertility conditions could not be studied, possible explanations include increased inflammation during pregnancy or problems with the mother’s immune system. The findings from this study add to studies of other risk factors highlighting the relationship between maternal health before and during pregnancy and ASD.

Pleiotropic Mechanisms Indicated for Sex Differences in Autism.

Mitra I, Tsang K, Ladd-Acosta C, Croen LA, Aldinger KA, Hendren RL, Traglia M, Lavillaureix A, Zaitlen N, Oldham MC, Levitt P, Nelson S, Amaral DG, Hertz-Picciotto I, Fallin MD, Weiss LA.

PLOS Genetics, 2016

In this study, researchers used SEED data and data from other studies to investigate sex-specific genetic effects for ASD. The findings indicate involvement of genes on the X chromosome. These findings help us better understand how ASD might differ in girls and boys.

Presence of an Epigenetic Signature of Prenatal Cigarette Smoke Exposure in Childhood.

Ladd-Acosta C, Shu C, Lee BK, Gidaya N, Singer A, Schieve LA, Schendel DE, Jones N, Daniels JL, Windham GC, Newschaffer CJ, Croen LA, Feinberg AP, Daniele Fallin M.

Environmental Research, 2016

This study examined how environmental exposures, such as smoking during pregnancy, may impact gene regulation in children. Gene regulation is the process by which genes in a cell are turned on or off, and it is important for child development. Like other studies, researchers found that smoking during pregnancy affected gene regulation in children. However, while other studies have assessed these effects in children at the time of birth, the SEED sample provided an opportunity to look at gene regulation in older children. This study showed that the same pattern of gene effects was present in older children whose mothers had smoked in pregnancy as had been previously observed in newborns. These findings suggest that smoking during pregnancy may have lasting effects on child health and development.

Maternal Exposure to Occupational Asthmagens During Pregnancy and Autism Spectrum Disorder in the Study to Explore Early Development.

Singer AB, Windham GC, Croen LA, Daniels JL, Lee BK, Qian Y, Schendel DE, Fallin MD, Burstyn I.

Journal of Autism and Developmental Disorders, 2016

This study examined whether ASD was associated with the mother’s workplace exposure to certain chemicals or other substances during pregnancy. Because previous studies have shown associations between maternal asthma and allergy and ASD, researchers were particularly interested in exposure to substances that are known to trigger asthma symptoms, called asthmagens.  Examples of asthmagens include latex, certain drugs and chemicals such as dyes, and some cleaning products. The findings show that mothers of children with ASD had been exposed to slightly higher levels of workplace asthmagens than mothers of children in the general population. However, the difference was small and could have been due to chance. Many gaps remain in our understanding of how environmental exposures might impact the risk for ASD, and further research is needed.   Top of Page

Many Young Children with Autism Who Use Psychotropic Medication Do Not Receive Behavior Therapy: A Multisite Case-Control Study

Lisa D. Wiggins, PhD, Cy Nadler, PhD, Steven Rosenberg, PhD, Eric Moody, PhD, Nuri Reyes, PhD, Ann Reynolds, MD, Aimee Alexander, MS, Julie Daniels, PhD, Kathleen Thomas, PhD, Ellen Giarelli, PhD, and Susan E. Levy, MD, MPH

Pediatrics, 2021

A study published online in The Journal of Pediatrics explored the rates of psychotropic medication use among preschool-aged children (ages 2-5 years) with autism spectrum disorder (ASD).  While there are no medications to treat core symptoms of ASD, some medications may treat co-occurring symptoms such as attention problems, anxiety, aggression, and self-injurious behaviors.  However, The American Academy of Pediatrics recommends behavior therapy before medication is tried. In the study sample, 37 of 62 (59.7%) children with ASD who used psychotropic medications did not receive the behavior therapy prior to receiving medications.  Pediatricians are an important resource for children and families and can help facilitate behavioral treatment for children with ASD and other behavioral and developmental disorders.

Gastrointestinal Symptoms in 2- to 5-Year-Old Children in the Study to Explore Early Development

Reynolds AM, Soke GN, Sabourin KR, Croen LA, Daniels JL, Fallin MD, Kral TVE, Lee LC, Newschaffer CJ, Pinto-Martin JA, Schieve LA, Sims A, Wiggins LD, Levy SE

Journal of Autism and Developmental Disorders, 2021

This study compared gastrointestinal (GI) symptoms in 2,461 preschool children aged 30–68 months with autism spectrum disorder (ASD) to children with other developmental disabilities (DDs) and children from the general population (POP). Previous studies have shown that GI symptoms are common among children with ASD, but those studies have been limited by small sample sizes and lack of standard measures and comparison groups. Researchers used information from the Study to Explore Early Development (SEED)—including detailed information on GI symptoms, developmental level, and other problems such as anxiety (worry), aggression, and problems related to sleep and attention—to fill these gaps. Parents were asked to complete a detailed questionnaire on GI symptoms and a stool diary for their child. Based on these two instruments, 50.4% of children with ASD had GI symptoms, compared to 42.6% of children with other DDs and 30.6% of POP children. Among children with ASD, researchers also compared children who had lost skills they had previously developed (developmental regression) with those who had not lost previously developed skills and found that more children with developmental regression had GI symptoms (42.9%) than those without regression (31.8%).  Across all three study groups, GI symptoms were related to problems with sleep, attention, anxiety, and aggression.  These findings suggest that GI issues may be more common among children with ASD and are an important healthcare need to address.

Pica, Autism, and Other Disabilities

Fields VL, Soke GN, Reynolds A, Tian LH, Wiggins L, Maenner M, DiGuiseppi C, Kral TVE, Hightshoe K, Schieve LA

This study examined pica in preschool-aged children with autism spectrum disorder (ASD), other developmental disabilities (DDs), and children from the general population (POP). Pica is when a person eats non-food items with no nutritional value—such as paper, hair, paint, or dirt—which can result in medical problems. Previous research on pica in children with ASD and other DDs has been limited by small, non-representative samples, and has lacked a general POP comparison group. Researchers from the Study to Explore Early Development (SEED) examined pica in 4,739 preschool children aged 30–68 months with ASD, other DDs, and from the general population (POP).  Children with ASD and other DDs were further classified according to whether they had co-occurring intellectual disability (ID), and among children in the DD group, whether they had some ASD characteristics, for a total of 6 subgroups (ASD without ID, ASD with ID, DD with ASD characteristics, DD with ASD characteristics and ID, DD without ASD characteristics and with ID, and DD without ASD characteristics and without ID). Study results found that 23.2%, 8.4%, and 3.5% of children in the ASD, DD, and POP groups, respectively, had pica. Within the ASD group, pica was reported in 28.1% of children with ID and 14.0% of children without ID. Within the DD group, pica was reported in 26.3% of children with both ID and some ASD characteristics, 12.0% with some ASD characteristics but without ID, 9.7% with ID but without ASD characteristics, and 3.2% with neither ID nor ASD characteristics. These results show that pica may be common in young children with ASD, ASD characteristics, and/or ID, and suggest that young children in these groups can benefit from careful monitoring and safety precautions to prevent pica.  Parent prevention measures can include closely monitoring children, keeping items out of reach, using childproof locks, finding activities that occupy children’s attention, and informing other caregivers of concerns.

Mapping the Relationship Between Dysmorphology and Cognitive, Behavioral, and Developmental Outcomes in Children with Autism Spectrum Disorder

Tian LH, Wiggins LD, Schieve LA, 1, Yeargin-Allsopp M, Dietz P, Aylsworth AS, Elias ER, Julie E. Hoover‑Fong JE, Meeks NJL, Souders MC, Tsai ACH, Zackai EH, Alexander AA, Dowling NF, Shapira SK

Autism Research, 2020

This study looked at whether having more unusual physical traits (dysmorphic features (DFs)) was related to developmental problems and focused on children with autism spectrum disorder (ASD) compared to children from the general population (POP). Previous studies only looked at whether children with ASD and developmental problems had DFs; these studies did not always include a group of children without ASD. In this study, researchers used information from 881 preschool-aged children 2–5 years old enrolled in the Study to Explore Early Development (SEED). The study included an in-person physical examination where photographs, measurements, and hand scans were taken; these items were reviewed by clinical geneticists to determine the number of DFs in each child. This enabled researchers to ask whether a greater number of DFs was related to more developmental problems. The study found that children with ASD and ID had more language, movement, and learning issues as the number of DFs increased. Children with ASD but without ID had more movement and learning issues as the number of DFs increased. These relationships were not observed in the POP group. These findings suggest that DFs may be linked to the cognitive (learning and memory) problems of children with ASD. Additional studies on groups of children with ASD who do or do not have ID could help explain the findings.

Expressive Dominant Versus Receptive Dominant Language Patterns in Young Children: Findings from the Study to Explore Early Development

Reinhartsen DB, Tapia AL, Watson L, Crais E, Bradley C, Fairchild J, Herring AH, Daniels J

Journal of Autism and Developmental Disorders, 2019

This study examined language skills in children with autism spectrum disorder (ASD), children with other developmental disabilities (DD), and typically developing children from the general population (POP). Previous research has shown that children typically understand more vocabulary and complex language than they can express. However, some studies on the language patterns of children with ASD suggest they may be better at expressing than understanding language. Researchers used information from the Study to Explore Early Development (SEED) to categorize 2,571 children aged 30–68 months according to whether they understood or expressed language better or had similar language skills in both areas.  Study findings showed that all three groups of children were better able to understand than express language.  However, 23.6% of children in the ASD group were better at expressing language, as compared to 11.5% of children in the DD group and 10.8% of children in the POP group. Children in the ASD group who were better at expressing than understanding language typically had noticeable problems understanding language and were younger, had lower nonverbal cognitive skills, and had more serious social symptoms of ASD. These findings highlight the need to consider the type of language deficits when designing clinical interventions or treatment programs for children with ASD.

Wandering Among Preschool Children With and Without Autism Spectrum Disorder

Wiggins LD, DiGuiseppi C, Schieve L, Moody E, Gnakub Soke, Giarelli E, Levy S

Journal of Developmental and Behavioral Pediatrics, 2020

This study describes wandering in children ages 4–5 years with a confirmed autism spectrum disorder (ASD) diagnosis, children with a previous but unconfirmed ASD diagnosis (DDprevASD), children with other developmental disabilities (DD), and children from the general population (POP). Wandering, or leaving a supervised space or care of a responsible person, is common among toddlers who are exploring their environment and learning to be independent. Wandering typically becomes much less common after 4 years of age; however, some studies suggest that wandering may be more common among children with ASD than children with other DD and could compromise child safety and increase parental stress. In this study, researchers described 3,896 parent reports of wandering among their 4–5-year-old children enrolled in the Study to Explore Early Development (SEED) between 2007 and 2016. The researchers also examined the relationship between a child’s likelihood to wander and certain behavioral, developmental, and other factors. Study findings showed that wandering in children aged 4–5 years was reported in 60.4% of children with ASD, compared with 41.3% of children with DDprevASD, 22.3% of children with DD, and 12.4% of children in the POP group. Findings also showed that mood, anxiety, attention, and oppositional problems were all associated with wandering behavior, independent of ASD status. These results provide important information for parents and providers on the occurrence of wandering among children with and without ASD and associated conditions (such as anxiety and attention problems) that may place children at increased risk for wandering from safe environments. Moreover, these results may facilitate discussions between parents and providers about safety, prevention, and interventions that may improve the lives of children who wander and their families.

Injury-related treatments and outcomes in preschool children with autism spectrum disorder: Study to Explore Early Development (SEED)

DiGuiseppi C, Sabourin KR, Levy SE, Soke GN, Lee LC, Wiggins L, Schieve LA

This study examines the parent-reported treatments and outcomes of medically attended injuries among children with autism spectrum disorder (ASD) living in six different areas (located in California, Colorado, Georgia, Maryland, North Carolina, and Pennsylvania) in the United States in 2003–2006, compared to children with other developmental disabilities (DDs) and children from the general population (POP). The Study to Explore Early Development’s (SEED’s) in-depth data on the health of preschool children aged 2–5 years provided researchers with key information on these injuries. For each reported injury, parents were asked whether the injury resulted in loss of consciousness, an emergency department (ED) visit, hospitalization, surgery, or long-term behavior change. Parents were also asked if their child received any medication or injections for each medically attended injury reported. Study results showed that 30% of children in SEED had at least one medically attended injury. Of those children, 83% had at least one injury-related ED visit or hospitalization. Children with ASD were more likely than children from the POP group to have had a surgical procedure for an injury. Children with ASD were also less likely than those with DDs to receive medication or injections to treat injuries. These differences may be a result of characteristics of the child or injury or may reflect the clinicians’ ability to provide certain treatments or judge the severity of the child’s pain due to challenging behaviors associated with ASD. Further research may aid in understanding the differences in treatments prescribed to children with ASD compared to those prescribed to children with DDs or from the general population.

Early life influences on child weight outcomes in the Study to Explore Early Development

Kral TV, Chittams J, Bradley CB, Daniels JL, DiGuiseppi CG, Johnson SL, Pandey J, Pinto-Martin JA, Rahai N, Ramirez A, Schieve LA, Thompson A, Windham G, York W, Young L, Levy SE

Autism, 2019

This study examined overweight and obesity at age 2–5 years in children with and without autism spectrum disorder (ASD) or other developmental disorders (DDs). Obesity rates among U.S. children have increased markedly in recent decades and children with ASD have previously been shown to be at particularly high risk for obesity. SEED’s large sample and detailed data on children with ASD and other DDs allowed researchers to conduct a more in depth analysis on this topic than done previously. Study findings show that children born to mothers who were overweight/obese before becoming pregnant, or gained more weight than recommended during their pregnancies, were more likely to be overweight or obese between the ages of 2–5 years compared with children born to mothers who were underweight or normal weight prior to pregnancy and gained the recommended amount of weight during their pregnancies. These findings were similar for children with ASD, children with other DDs, and children without DDs. However, children with ASD were more likely than children in the other groups to have rapid weight gain in infancy; rapid weight gain was also associated with increased risk for being overweight or obese between ages 2–5 years. This study highlights the importance of maintaining a heathy weight before and during pregnancy and fostering healthy growth during infancy, among all children, including those with and without ASD.

Sleep Problems in 2- to 5-Year-Olds with Autism and Other Developmental Delays

This study assessed sleep problems, such as difficulties going to sleep or staying asleep through the night, in preschool-aged children with ASD, in comparison to children with other developmental disabilities (DDs) and children in the general population. SEED’s large sample and detailed data on preschoolers allowed researchers to conduct a more in-depth analysis on this topic than in previous studies. Study findings show that children with ASD and children with other DDs who have some ASD symptoms have more sleep problems than children with DDs without ASD symptoms and children in the general population. Even when researchers used a conservative definition to classify children as having sleep problems, 47% of children with ASD and 57% of children with other DDs who had some ASD symptoms were reported to have sleep problems, compared to 29% of children with DDs but no ASD symptoms and 25% of children in the general population. Sleep is important for development in young children. Addressing sleep problems among children with ASD and children with other DDs who have ASD symptoms is an important component of healthcare needs in this population.

A Novel Approach to Dysmorphology to Enhance the Phenotypic Classification of Autism Spectrum Disorder in the Study to Explore Early Development

This study used data from SEED to develop a new method to systematically classify certain physical features in children. The purpose of this system is to evaluate dysmorphology, which is the assessment of physical features that do not follow the typical pattern of growth and development. Children with multiple dysmorphic features often have an underlying genetic condition or had early pregnancy exposures that affected their development during the pregnancy.  The SEED dysmorphology classification method is more comprehensive than that used in previous studies. The findings from this study indicate that approximately 17% of children with ASD have a high number of dysmorphic features, and hence, meet the criteria for classification as dysmorphic. In contrast, approximately 5% of children from the general population control group met the criteria for classification as dysmorphic. Some, but not all, of the dysmorphology differences between children with and without ASD were explained by previously recognized and diagnosed genetic conditions and birth defects, which both occur more commonly in children with ASD. This is the first report of dysmorphology among children with ASD in a diverse U.S. population.

Relationship of Weight Outcomes, Co-occurring Conditions, and Severity of Autism Spectrum Disorder in the Study to Explore Early Development

Levy SE, Pinto-Martin JA, Bradley CB, Chittams J, Johnson SL, Pandey J, Alison Pomykacz A, Ramirez A, Reynolds A, Rubenstein E, Schieve LA, Shapira SK, Thompson A, Young L, Kral TV

Journal of Pediatrics, 2018

This study examined overweight and obesity among children with ASD, other developmental disabilities (DDs), and children from the general population. Study findings show that children with ASD or DDs were more likely to be overweight or obese than children from the general population. The proportion of children who were either overweight or obese was 28% in those with ASD, 25% in children with another DD, and 20% in children in the general population. Children with ASD or DDs were also more likely to have birth defects, medical disorders, seizure disorders, attention-deficit/hyperactivity disorder (ADHD), and psychiatric disorders than children from the general population. After controlling for these co-occurring conditions, the association between ASD and overweight or obesity was not changed, but the association between overweight and obesity and other DDs was reduced. In addition, among children with ASD, those with moderate or severe symptoms of ASD were more likely to be overweight or obese than children with mild ASD symptoms. Addressing overweight and obesity among children with ASD and other DDs is an important component of healthcare needs in this population.

Infections in Children with Autism Spectrum Disorder: Study to Explore Early Development (SEED)

Sabourin KR, Reynolds A,  Schendel D, Rosenberg S, Croen L, Pinto-Martin JA, Schieve LA, Newschaffer C, Lee LC, DiGuiseppi C

This study evaluated the association between early childhood infections and ASD and other developmental disabilities (DDs). SEED’s large sample size allowed researchers to conduct a more in-depth analysis on this topic than previous studies. The study findings show that children with ASD were more likely than children with other DDs and children from the general population to have had an infection in the first 28 days of life (early infection). Overall, 4.9% of children with ASD, 4.2% of children with other DDs, and 2.2% of children in the general population had an early infection recorded in their medical records. Children with ASD were also more likely to have an infection in the first 3 years of life than children in the general population, but children with ASD had similar rates of infection during their first 3 years as children with other DDs. This study highlights that ASD is associated with infections very early in the child’s life.

Brief Report: Self-Injurious Behaviors in Preschool Children with Autism Spectrum Disorder Compared to Other Developmental Delays and Disorders.

Soke GN, Rosenberg SA, Rosenberg CR, Vasa RA, Lee LC, DiGuiseppi C.

This study assessed self-injurious behavior, or SIB, among preschool-aged children with ASD in comparison to children with other developmental disabilities (DDs). The study showed that SIB is common in two groups of preschool-aged children – those with ASD and those for whom some autism-related symptoms are reported by their mother or other caregiver, even though they didn’t meet the criteria to be classified as an ASD case.  SIB was much less common in children with other DDs whose mother or caregiver did not report autism-related symptoms. These findings suggest that clinicians working with young children with DDs consider screening for SIB, even in children who do not have an ASD diagnosis.

Associations between Parental Broader Autism Phenotype and Child Autism Spectrum Disorder Phenotype in the Study to Explore Early Development.

Rubenstein E, Wiggins LD, Schieve LA, Bradley C, DiGuiseppi C, Moody E, Pandey J, Pretzel RE, Howard AG, Olshan AF, Pence BW, Daniels J.

Autism, 2018

This study assessed how the variation in developmental features among children with ASD was related to their parents’ own autism-related traits.  The presence of autism traits in family members of children with ASD is commonly referred to as the “broader autism phenotype” or BAP. The study findings show that if one or both parents have traits consistent with BAP, the child’s ASD is more likely to fall within a certain clinical presentation than if neither parent has traits consistent with BAP.  This clinical presentation in the child is characterized by average nonverbal abilities, mild language and motor delays, and increased frequency of other co-occurring developmental difficulties such as anxiety, depression, aggression, and attention difficulties.  The findings reported in this study could help better our understanding of the genetics of ASD.

The Prevalence of Gluten Free Diet Use among Preschool Children with Autism Spectrum Disorder.

Rubenstein E, Schieve L, Bradley C, DiGuiseppi C, Moody E, Thomas K, Daniels J.

This study estimated the proportion of children with ASD who had been on a gluten free diet. Altogether, 20% of preschool-aged children with ASD were currently or previously using a gluten free diet. In contrast, only 1% of children in the general population control group were using a gluten free diet. Children with ASD who also had gastrointestinal problems or had previously had a developmental regression were more likely to use a gluten free diet. This study demonstrates that gluten free diets are commonly used among children with ASD. More research is needed on the effectiveness of a gluten free diet in managing both gastrointestinal and behavioral symptoms related to ASD.

Injuries in Children with Autism Spectrum Disorder: Study to Explore Early Development (SEED).

DiGuiseppi C, Levy SE, Sabourin KR, Soke GN, Rosenberg S, Lee LC, Moody E, Schieve LA.

This study evaluated injuries in preschool-aged children with and without ASD and other developmental disabilities (DDs). Parents of children were asked whether their child had ever had an injury that required medical attention, and what types of injuries had occurred. The study findings showed that injuries were common in all groups of children and there was little difference between groups. Parents reported injuries for 32% of children with ASD, 28% of children with other DDs, and 30% of children in the general population. The most common injuries were open wounds and fractures and the most common reason for injuries was falls. While there was a slight difference in injuries between children with ASD and other DDs, further study found that this was largely explained by a higher level of attention problems in the children with ASD.

Homogeneous Subgroups of Young Children with Autism Improve Phenotypic Characterization in the Study to Explore Early Development.

Wiggins LD, Tian LH, Levy SE, Rice C, Lee LC, Schieve L, Pandey J, Daniels J, Blaskey L, Hepburn S, Landa R, Edmondson-Pretzel R, Thompson W.

This study used a complex computer program to assess the wide range of developmental characteristics among children with ASD.  Researchers identified four subgroups of children within the ASD group: 1) children with mild language delay and average cognitive functioning, but increased cognitive rigidity (or difficulty changing behaviors); 2) children with significant developmental delay, below average cognitive functioning, and repetitive motor behaviors; 3) children with general developmental delay, below average cognitive functioning, and moderate to highly severe autism symptoms; and 4) children with mild language and motor delays, but increased cognitive rigidity and high rates of problem behaviors. This study shows how information on developmental characteristics can be studied using advanced statistical methods to better understand ASD.  This information might also be useful in understanding children’s future health and development.

Self-injurious Behaviors in Children with Autism Spectrum Disorder Enrolled in the Study to Explore Early Development.

Autism, 2017

This study assessed self-injurious behavior, or SIB, among children with ASD. SIB includes head-banging, hair-pulling, arm-biting, scratching, and hitting oneself. SIB is usually mild, but can be severe in some children and may result in injuries requiring medical care. Children with severe SIB may miss out on educational and social activities. This study showed that in the SEED sample, about 28% of preschool-aged children with ASD displayed SIB currently, and 47% had previously displayed SIB. Researchers found SIB was more common in children with low adaptive behavior scores and gastrointestinal, sleep, and behavioral problems. While its causes are not completely understood, identifying SIB early is helpful because it may reduce the likelihood of more severe SIB later.   Top of Page

Temperament Similarities and Differences: A Comparison of Factor Structures from the Behavioral Style Questionnaire in Children with and Without Autism Spectrum Disorder

Barger B, Moody EJ, Ledbetter C, D’Abreu L, Hepburn S, Rosenberg SA

Journal of Autism and Developmental, 2019

This study assessed the performance of the Behavioral Style Questionnaire (BSQ), a commonly used measure of temperament, in children aged 2–5 years with and without autism spectrum disorder (ASD). The BSQ contains 100 questions designed to measure nine different behavioral tendencies, or temperaments, that affect how well children respond to their environment. Previous research has suggested that the BSQ may function differently for children with ASD compared with typically developing children. As such, researchers used Study to Explore Early Development (SEED) data to compare the behavioral tendencies the BSQ identified among children diagnosed with ASD and among children from the general population. Study findings showed that the BSQ did not identify the behavioral tendencies that it was originally designed to measure. Moreover, while the BSQ measured certain behavioral tendencies similarly among children with ASD and children from the general population, for other behavioral tendencies it did not. One behavioral tendency, “Negative Social Interactions”, was unique among children with ASD, and was not found among children from the general population. These findings suggest that more research may help us better understand how the BSQ performs in different groups of children, including children with ASD.

ASD Screening with the Child Behavior Checklist/1.5-5 in the Study to Explore Early Development

Levy SE, Rescorla LA, Chittams JL, Kral TJ, Moody EJ, Pandey J, Pinto-Martin JA, Pomykacz A, Ramirez A, Reyes N, Rosenberg CR, Schieve LA, Thompson A, Young L, Zhang J, Wiggins L

J Autism Dev Disord., 2019

This study assessed the performance of a general developmental assessment tool, known as the Child Behavior Checklist (CBCL), as a screening tool for autism spectrum disorder (ASD) symptoms in preschool-aged children. The CBCL is a broad-spectrum checklist that includes 99 items completed by a parent or a caregiver. Researchers in this study were interested in a subset of 13 items related to pervasive developmental problems. Previous research on this topic produced inconsistent results. SEED’s large sample of children with and without ASD and other developmental disabilities (DDs) allowed for a more thorough assessment. The study results showed that scores from the 13-item subscale were significantly different for children in the ASD group and the DD with ASD features group, compared to children in the DD without ASD features group and the population control group. These findings suggest that this CBCL subscale was effective at identifying children with ASD features needing further evaluation and supports its use as an ASD screening tool. The findings are particularly noteworthy because the CBCL is already widely used by schools and health professionals to screen for other developmental issues such as attention, anxiety, and depression.

DSM-5 criteria for autism spectrum disorder maximizes diagnostic sensitivity and specificity in preschool children

Wiggins LD, Rice CE, Barger B, Soke GN, Lee LC, Moody E, Edmondson-Pretzel R, Levy SE

Soc Psychiatry Psychiatr Epidemiol, 2019

The Diagnostic and Statistical Manual of Mental Disorders (DSM) specifies standardized criteria for diagnosing individuals with autism spectrum disorder (ASD) and other conditions. Criteria for diagnosing ASD were revised between the fourth (DSM-IV-TR) and the fifth edition of the manual (DSM-5). The purpose of this study was to compare DSM-IV-TR and DSM-5 definitions of ASD using information from comprehensive developmental evaluations completed with preschool children enrolled in the Study to Explore Early Development (SEED). This study was important because it compared DSM-IV-TR and DSM-5 definitions of ASD by evaluating children at a time when they often are first diagnosed, using both criteria in a single clinic visit. Study findings showed that DSM-5 criteria had the best balance between identifying true ASD cases, while ruling out children with other developmental disorders, when compared to DSM-IV-TR criteria. Researchers also found good agreement between DSM-5 and DSM-IV-TR for autistic disorder and moderate agreement for a less stringent definition of ASD known as pervasive developmental disorder not otherwise specified (PDD-NOS). These findings support the DSM-5 criteria for ASD in preschool-aged children.

Bayesian Correction for Exposure Misclassification and Evolution of Evidence in Two Studies of the Association between Maternal Occupational Exposure to Asthmagens and Risk of Autism Spectrum Disorder

Singer AB, Fallin MD, Burstyn I

Current Environmental Health Reports, 2018

In this study, researchers used SEED data and data from another study of children with and without ASD to assess how potential errors in coding the data for certain risk factors might influence the findings of epidemiologic studies. Researchers often want to study the effects of certain exposures during pregnancy but may not have the exact data they need. It is rare to have biologic measurements of the chemicals women were exposed to during pregnancy.  Therefore, studies often rely on related information to classify study participants as “likely exposed” or “not exposed” to certain chemicals. For example, studies often use information on a person’s job — such as type of job and industry where the person worked — to estimate possible chemical exposures from their workplace. In this study, researchers used a statistical method to address the possibility that certain job coding schemes could result in errors when evaluating associations between workplace exposures and ASD. They propose a way researchers might use this method in future studies to assess, and possibly correct, exposure classification errors.

Influence of Family Demographic Factors on Social Communication Questionnaire Scores.

Rosenberg SA, Moody EJ, Lee LC, DiGuiseppi C, Windham GC, Wiggins LD, Schieve LA, Ledbetter CM, Levy SE, Blaskey L, Young L, Bernal P, Rosenberg CR, Fallin MD.

This study assessed how the responses to a standardized questionnaire to screen for autism symptoms varied by family demographic characteristics. The study findings indicate that test performance was different in families with an indication of low versus higher socioeconomic status. These findings are important for both researchers and clinicians using autism screening questionnaires; they should be mindful that these tools might perform differently in various sociodemographic groups of children and their parents.

The Broader Autism Phenotype in Mothers is Associated with Increased Discordance Between Maternal-Reported and Clinician-Observed Instruments that Measure Child Autism Spectrum Disorder.

Rubenstein E, Edmondson Pretzel R, Windham GC, Schieve LA, Wiggins LD, DiGuiseppi C, Olshan AF, Howard AG, Pence BW, Young L, Daniels J.

This study assessed whether parents who have autism traits reported their children’s potential autism symptoms in a similar way as parents without an indication of autism traits. The findings indicate that parents with autism traits report more autism traits in their children compared to parents without autism traits, but parent reports do not always match clinician assessments based on observed behaviors in the child. It is possible that parents with some autism traits are more adept at identifying subtle characteristics of autism in their child. Another possible explanation for the study findings is that questions on various child behaviors could be interpreted differently by parents with and without autism traits. Further study is needed. The findings reported in this study could help better our understanding of developmental assessment results in young children.

Screening for Autism with the SRS and SCQ: Variations across Demographic, Developmental and Behavioral Factors in Preschool Children.

Moody EJ, Reyes N, Ledbetter C, Wiggins L, DiGuiseppi C, Alexander A, Jackson S, Lee LC, Levy SE, Rosenberg SA.

This study assessed and compared the performance of two standardized questionnaires to screen for autism symptoms. The accuracy of each questionnaire varied depending on the child’s level of developmental functioning and family sociodemographic traits. For example, the instruments were less accurate when children had high levels of challenging behaviors or lower levels of developmental functioning. Test performance also varied in families with indication of lower versus higher socioeconomic status. These findings are important for both researchers and clinicians using autism screening questionnaires; they should be mindful that these tools perform differently in various sociodemographic groups of children and their parents.

Brief Report: The ADOS Calibrated Severity Score Best Measures Autism Diagnostic Symptom Severity in Pre-School Children.

Wiggins LD, Barger B, Moody E, Soke GN, Pandey J, Levy S.

This report describes SEED methodology for assessing autism symptom severity among children with ASD. Measuring a child’s autism symptoms is often challenging because many children with ASD also have other developmental conditions. This can make it difficult to separate a child’s social and communication challenges from the child’s other developmental delays or conditions. Researchers evaluated several measures of autism severity and found that the Autism Diagnostic Observation Schedule (ADOS) calibrated severity score best measured the severity of core autism symptoms in a way that did not include symptoms of other developmental conditions. Because of findings from this study, the ADOS calibrated severity score will be used in other SEED research to help scientists better understand how the severity of autism symptoms relates to ASD risk factors and health outcomes.

Cross-tissue Integration of Genetic and Epigenetic Data Offers Insight into Autism Spectrum Disorder.

Andrews SV, Ellis SE, Bakulski KM, Sheppard B, Croen LA, Hertz-Picciotto I, Newschaffer CJ, Feinberg AP, Arking DE, Ladd-Acosta C, Fallin MD.

Nature Communications, 2017

In this study, researchers used SEED data and data from other studies to learn more about genetics and genetic regulation in children with ASD. While it is well-understood that genetics are related to ASD, many unanswered questions remain, such as how certain genes are turned on or off. The information from this study provides insights about how certain genes might be related to ASD.

“Gap Hunting” to Characterize Clustered Probe Signals in Illumina Methylation Array Data.

Andrews SV, Ladd-Acosta C, Feinberg AP, Hansen KD, Fallin MD.

Epigenetics & Chromatin, 2016

This study assessed new laboratory approaches to analyzing information on genetics collected through SEED. The findings contribute to the growing literature on how genes and environmental factors might interact in a way that increases the risk for ASD. While this study does not directly study these interactions, researchers describe and demonstrate how new laboratory approaches could help identify genetic associations.   Top of Page

Feature Articles

Autism Research and Resources from CDC April is Autism Acceptance Month. The recognition raises awareness about autism acceptance and promotes inclusion and connectedness for people with autism.

Higher Autism Prevalence and COVID-19 Disruptions Autism spectrum disorder (ASD) continues to affect many children and families. The COVID-19 pandemic brought disruptions to early ASD identification among young children. These disruptions may have long-lasting effects as a result of delays in identification and initiation of services.

Past, Present, and Future Impact of SEED Since the launch of SEED in 2003, CDC has worked with its partners to learn more about the needs of children with autism spectrum disorder (ASD) and other developmental disabilities so that families, communities, and healthcare providers can deliver the supports and services needed to thrive.

Why Act Early if You’re Concerned about Development? Act early on developmental concerns to make a real difference for your child and you! If you’re concerned about your child’s development, don’t wait. You know your child best.

Early Identification and Prevalence of Autism Among 4-year-old and 8-year-old Children: An Easy Read Summary This is an Easy-Read Summary of two reports. The first report is about identifying autism early among 4-year-old children. The second report is on the number of 8-year-old children with autism. (Published December 2, 2021)

Health Status and Health Care Use Among Adolescents Identified With and Without Autism in Early Childhood: An Easy-Read Summary The is an Easy-Read Summary (Published April 30, 2021)

Identifying Autism Among Children: An Easy-Read Summary This is an Easy-Read Summary of two reports. The first report is about the number of 8-year-old children with autism. The second report is about identifying autism early among 4-year-old children. (Published March 27, 2020)

Articles by Year

Statewide county-level autism spectrum disorder prevalence estimates—seven U.S. states, 2018. Annals of Epidemiology, 2023. Shaw KA, Williams S, Hughes MM, et al. [ Read article ]

The Prevalence and Characteristics of Children With Profound Autism, 15 Sites, United States, 2000-2016. Public Health Reports, 2023. Hughes MM, Shaw KA, DiRienzo M, et al. [ Read article ]

Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020. MMWR Surveillance Summaries, 2023. 72 (2): p. 1. Maenner MJ, Warren Z, Williams AR, et al. [ Read article ] [ Easy Read Summary ]

Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020. MMWR Surveillance Summaries, 2023. 72 (1): p. 1. Shaw KA, Bilder DA, McArthur D, et al. [ Read article ] [ Easy Read Summary ]

Social Vulnerability and Prevalence of Autism, Metropolitan Atlanta Developmental Disabilities Surveillance Program (MADDSP). Annals of Epidemiology, 2023. Patrick ME, Hughes MM, Ali A, et al. [ Read article ]

Individualized Education Programs and Transition Planning for Adolescents With Autism. Pediatrics, 2023. Hughes MM, Kirby AV, Davis J, et al. [ Read article ]

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Teachers and educators’ experiences and perceptions of artificial-powered interventions for autism groups

• Guang Li 1 ,
• Mohammad Amin Zarei 2 ,
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Artificial intelligence-powered interventions have emerged as promising tools to support autistic individuals. However, more research must examine how teachers and educators perceive and experience these AI systems when implemented.

The first objective was to investigate informants’ perceptions and experiences of AI-empowered interventions for children with autism. Mainly, it explores the informants’ perceived benefits and challenges of using AI-empowered interventions and their recommendations for avoiding the perceived challenges.

Methodology

A qualitative phenomenological approach was used. Twenty educators and parents with experience implementing AI interventions for autism were recruited through purposive sampling. Semi-structured and focus group interviews conducted, transcribed verbatim, and analyzed using thematic analysis.

The analysis identified four major themes: perceived benefits of AI interventions, implementation challenges, needed support, and recommendations for improvement. Benefits included increased engagement and personalized learning. Challenges included technology issues, training needs, and data privacy concerns.

Conclusions

AI-powered interventions show potential to improve autism support, but significant challenges must be addressed to ensure effective implementation from an educator’s perspective. The benefits of personalized learning and student engagement demonstrate the potential value of these technologies. However, with adequate training, technical support, and measures to ensure data privacy, many educators will likely find integrating AI systems into their daily practices easier.

Implications

To realize the full benefits of AI for autism, developers must work closely with educators to understand their needs, optimize implementation, and build trust through transparent privacy policies and procedures. With proper support, AI interventions can transform how autistic individuals are educated by tailoring instruction to each student’s unique profile and needs.

Peer Review reports

Introduction

Autism education has become an increasingly important area of focus in recent years due to the rising prevalence of autism spectrum conditions (ASC) among children. The estimated prevalence of ASC has increased from 1 in 10,000 in the 1960s to at least 1 in 100 today [ 1 , 2 , 3 ]. ASC is a neurodevelopmental condition characterized by impaired social interaction and communication abilities and stereotypical or obsessive behavior patterns. These impairments can significantly impact an individual’s social, educational, and employment experiences, leading to poor long-term outcomes and difficulties in social transactions, independent work, and job fulfillment [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ].

The reported prevalence of autism spectrum disorders (ASDs) in developed countries is around 2% [ 11 ]. ASDs typically manifest within the first three years of life. They are characterized by challenges in social interaction, speech and language delays, avoidance of eye contact, difficulty adapting to changes in the environment, display of repetitive behaviors, and differences in learning profiles [ 11 , 12 , 13 ]. Those with ASDs, including children and adults, have a high frequency of anxiety and depression. Neurobiological research has revealed differences in brain development between children with ASDs and neurotypical children [ 14 ]. These excessive connections are thought to be due to reduced pruning of damaged neuronal connections during brain development, resulting in disordered neural patterning across the brain and dysregulation in cognitive function coordination between different brain regions [ 14 , 15 ].

The dominant perspective regarding AI technologies has revolved mainly around understanding these systems as a collection of processes and their corresponding responses, emphasizing autonomy, adaptability, and interactivity [ 16 , 17 , 18 , 19 , 20 , 21 ]. These characteristics are considered fundamental technological focuses that researchers argue should be integral to AI systems. Although autonomy, adaptability, and interactivity are significant, they may only cover some essential criteria for an adequate K-12 education. Specifically, these criteria are about skills taught by human educators, such as B. Self-efficacy, technical skills, and socialization skills. Samuel [ 22 ] emphasizes that AI technologies should replicate human actions and mimic expressions of “human intelligence, cognition, and logic.” This highlights the need to refine features that determine effective AI in education. The recent challenges in education due to the pandemic provide a unique opportunity to examine the demands on stakeholders, including educators, students, and parents [ 23 , 24 , 25 , 26 , 27 ].

The potential of artificial intelligence (AI) to drive developments in education is well-recognized [ 6 , 7 ]. Artificial intelligence is one of the technological advancements which can be used in education. AI encompasses a range of technologies that aim to simulate human intelligence, including machine learning, natural language processing, and computer vision [ 8 ]. These technologies have already been used in various applications, from speech recognition to image classification, and can potentially revolutionize how we think about education. In the context of autism, AI has the potential to provide personalized learning experiences that are tailored to the specific needs of each child [ 8 ]. For example, AI-powered systems can analyze a child’s behavior and responses to stimuli and use this information to adapt the learning materials and activities to suit their needs. Furthermore, AI can also be utilized to support communication and social interaction, which are areas of difficulty for many children with autism [ 9 ].

AI-powered interventions in the context of autism education refer to the utilization of artificial intelligence technologies to create tailored and interactive experiences for individuals on the autism spectrum. These interventions encompass a spectrum of applications, including educational tools, therapeutic programs, and support systems designed to address the unique learning and social communication needs of individuals with autism. AI technologies such as machine learning, natural language processing, and computer vision are employed to analyze and respond to the specific behaviors, preferences, and challenges exhibited by each individual [ 1 , 2 , 3 , 4 , 5 , 6 ]. The goal is to provide personalized and adaptive learning experiences, enhance social interaction skills, and offer targeted support for cognitive and emotional development. Examples of AI-powered interventions include virtual reality scenarios, interactive games, and educational software that can dynamically adjust content based on real-time feedback, creating a more individualized and effective educational approach for children with autism [ 2 , 3 , 4 , 5 ].

Moreover, there is a risk of bias and discrimination in AI-powered interventions for children with autism. For example, if the AI system is trained on data that is not representative of the diverse population of children with autism, it may not be effective for all individuals [ 10 ]. Moreover, there is a risk of perpetuating harmful stereotypes or reinforcing inappropriate behaviors if the AI system is not designed and programmed with ethical considerations (10). Third, there are concerns about data privacy and security when using AI in education for children with autism. For instance, if sensitive personal information is collected and stored by the AI system, there is a risk that it could be misused or accessed by unauthorized parties [ 16 ]. Therefore, it is essential to address these challenges and concerns to fully realize the potential of AI in education for children with autism. By doing so, we can create evidence-based and ethically sound interventions that support personalized learning and social communication skills while mitigating the risks associated with AI-powered education.

The potential of AI in autism education lies in its ability to offer personalized learning experiences, tailoring interventions to the unique needs of each child [ 8 ]. By analyzing a child’s behavior and responses, AI can adapt learning materials, potentially revolutionizing education for children with autism. However, this transformative potential is not without challenges. The risk of bias and discrimination looms large, as AI systems may not be effective if trained on non-representative data, perpetuating harmful stereotypes [ 10 ]. Ethical considerations become paramount, addressing concerns about data privacy and security, which, if overlooked, pose potential risks associated with unauthorized access and misuse of sensitive information [ 16 ]. Bridging the gap between the promise of AI in education and its responsible application is crucial. Therefore, this study aims to explore educators’ experiences and perceptions of AI-powered interventions for autism, shedding light on the nuanced landscape where technological advancements intersect with the delicate realm of autism education.

Research questions

In line with the research gap mentioned in the previous section, the following research questions are raised:

What are the benefits and challenges of using AI-powered interventions to support the learning and social communication skills of children with autism from teachers’ and educators’ perceptions?

How can AI-powered interventions be designed and implemented to ensure that they are culturally and linguistically appropriate for a diverse population of children with autism while also avoiding bias and discrimination in the learning materials and activities?

Review of literature

Theoretical background.

Machine learning is a component of artificial intelligence (AI) wherein models perform tasks autonomously without human intervention. Traditional machine learning models are trained using input data, enabling accurate outcome predictions. Deep learning, a subset of machine learning, employs extensive data to prepare models, achieving similarly high prediction accuracies. Both models are frequently utilized in diagnosing neurological disorders such as autism [ 28 , 29 ], ADHD [ 30 , 31 ], and depression [ 32 , 33 ]. Diagnostic inputs encompass images from computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) scans, or electroencephalogram (EEG) signals.

AI has been instrumental in social skills training for children with autism spectrum disorders (ASDs), aiding in recognizing and responding to social cues. Belpaeme et al. [ 34 ] utilized sensory features (facial expressions, body movements, and voice recordings) as inputs to a machine-learning model implemented in a robot for analyzing autistic children’s behavior and engagement levels during therapy. This study demonstrated the robot’s potential to adapt to interactants, influencing engagement. Another survey by Sanghvi et al. [ 35 ] employed postural expressions, specifically silhouette images of the upper body during chess playing, to analyze the engagement levels of autistic children. The integration of representative data with an affect recognition model suggested the potential for the robot to serve as a game-mate for autistic children in real-world scenarios. Kim et al. [ 36 ] employed audio recordings to assess the emotional states of autistic children, enhancing the robot’s ability to evaluate engagement and modify responses for a more interactive learning environment.

Various studies explored diverse input features such as facial expressions [ 37 ], body movements [ 38 ], and biosignals [ 39 ]. Esteban et al. [ 40 ] investigated facial expressions, direction of look, body posture, and voice tones as input features to a model within the NAO robot for assessing the social engagement of autistic children, showcasing the capability of robots to possess increased autonomy. Rudovic et al. [ 41 ] developed a personalized deep model using coordinated video recordings, audio recordings, and biosignals to assess engagement in autistic children, outperforming non-personalized machine learning solutions. Another study created a hybrid physical education teaching tool using speech recognition and artificial intelligence, achieving a recognition accuracy of over 90% for a voice interactive educational robot. Collectively, these studies affirm that AI holds promise in enhancing social interaction and supportive education for children with mental disorders.

Artificial intelligence and education

The use of AI technology in education has led to increased published studies on the subject, with a reported growing interest and impact of research on AI in education [ 42 ]. AI literacy, which refers to the capacity to comprehend the essential processes and concepts underpinning AI in various products and services, has been discussed in several studies [ 43 , 44 , 45 , 46 , 47 ]. Ng et al. [ 48 ] proposed a four-dimensional AI literacy framework covering knowing and understanding AI, using and applying AI, evaluating and creating AI, and AI ethics.

Recent review papers on AI in education have highlighted several major AI applications, such as intelligent tutoring systems, natural language processing, educational robots, educational data mining, discourse analysis, neural networks, affective computing, and recommender systems [ 22 , 23 , 33 – 34 ]. However, Chen et al. [ 49 ] identified some critical issues in their review paper on AI in education, including a lack of effort in integrating deep learning technologies into educational settings, insufficient use of advanced techniques, and a scarcity of studies that simultaneously employed AI technologies and delved extensively into educational theories. Furthermore, there needs to be more knowledge and discussion on the role of AI in early childhood education (ECE), an area often ignored in cutting-edge research.

Using AI to teach children with ASD

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that affects communication, social interaction, and behavior (1). The disorder is characterized by various symptoms and severity levels, making it challenging to provide effective interventions for affected individuals [12]. Children with ASD often experience difficulties in learning and require specialized educational interventions to help them achieve their full potential [1]. In recent years, there has been growing interest in the potential of AI to improve the learning outcomes of children with autism [8). AI has the potential to provide personalized learning experiences that are tailored to the specific needs of each child with autism [ 9 ]. For example, AI-powered systems can analyze a child’s behavior and responses to stimuli and use this information to adapt the learning materials and activities to suit their needs [8].

AI can also be used to support communication and social interaction, which are areas of difficulty for many children with autism [10]. Chatbots and virtual assistants can provide a non-judgmental and non-threatening environment for children to practice their social skills while providing feedback and guidance [ 23 ]. These interventions can be particularly valuable for children who struggle with face-to-face interactions or feel uncomfortable in social situations [ 24 ]. Despite the potential benefits of using AI in education for children with autism, several challenges and concerns need to be addressed:

First, there is a lack of consensus on the most effective ways to use AI to support learning for autistic children [ 8 ]. While there have been some promising results from initial studies, more research is needed to determine the most effective methods for using AI to personalize learning and support social communication skills in this population [10]. Second, there is a risk of bias and discrimination in AI-powered interventions for children with autism. For example, if the AI system is trained on data that is not representative of the diverse population of children with autism, it may not be effective for all individuals [ 9 ]. Moreover, there is a risk of perpetuating harmful stereotypes or reinforcing inappropriate behaviors if the AI system is not designed and programmed with ethical considerations [ 23 ]. And, third, there are concerns about data privacy and security when using AI in education for children with autism. For instance, if sensitive personal information is collected and stored by the AI system, there is a risk that it could be misused or accessed by unauthorized parties [10].

Several research studies have investigated the use of AI in education for children with autism. For example, Goodwin and Stone [8] developed an AI-powered system called Maki, which uses natural language processing to provide personalized feedback on social communication skills. The system was effective in improving social communication skills in children with autism. Similarly, Alzoubi et al. [ 50 ] developed an AI-powered system that uses virtual reality to provide social skills training for children with autism. The system was found to be effective in improving social skills and reducing anxiety in children with autism.

Other research studies have explored the potential of AI to improve different aspects of learning for children with autism. For example, Zhang et al. [ 10 ] developed an AI-assisted system that uses computer vision and machine learning to provide personalized feedback on handwriting skills. The system was effective in improving handwriting skills in children with autism. Similarly, Wang et al. [ 51 ] developed an AI-powered system that uses game-based learning to enhance math skills in children with autism.

There have also been efforts to develop AI-powered systems that can assist teachers and parents in providing effective interventions for children with autism. The system effectively improved the quality of interventions offered by teachers and parents. However, there are also concerns about the potential negative impacts of AI on children with autism. For example, some studies have suggested that excessive use of AI-powered interventions could reduce face-to-face interactions and social skills development [9]. Additionally, there are concerns about the potential for AI-powered interventions to replace human teachers and therapists, which could have negative implications for the quality of care provided to children with autism [8].

To address these concerns and maximize the potential benefits of AI for children with autism, it is essential to prioritize ethical considerations and involve stakeholders in designing and implementing AI-powered interventions [ 23 ]. This includes ensuring that AI systems are developed and programmed to avoid bias and discrimination, protecting the privacy and security of personal data, and promoting transparency and accountability in using AI in education for children with autism [ 10 ].

Other studies have investigated using chatbots and virtual assistants to support social communication skills in children with autism. For example, Kocaballi et al. [ 52 ] developed a chatbot called Tess that provides social skills training and support for children with autism. The chatbot was effective in improving social communication skills in children with autism. Similarly, Tanaka et al. [ 53 ] developed a virtual assistant called Miko that uses artificial empathy to support social communication skills in children with autism.

Further studies highlighted the importance of ethical consideration while using AL in education for children with autism. For example, there is a risk of perpetuating harmful stereotypes or reinforcing inappropriate behaviors if the AI system is not designed and programmed with ethical considerations [ 23 ]. Moreover, there is a risk of bias and discrimination if the AI system is trained on data that is not representative of the diverse population of children with autism [9]. Therefore, it is essential to carefully consider the ethical implications of using AI in education for children with autism. In conclusion, utilizing AI in education can transform how we think about learning and support children with autism to achieve their full potential.

Research Methodology

The study used purposive sampling to select 20 informants who met specific criteria. These individuals were parents or educators of autistic children and had valuable experience using AI-powered interventions to improve their children’s learning and social communication skills. They were all Iranian living in Tehran, Iran. 30% ( n  = 6) were female and 70% ( n  = 14) were male.

The participants in the study encompassed an age range spanning from 29 to 58 years old. Educators teaching experience was above 8 years. Recruitment efforts were conducted through various channels and social media platforms to ensure a diverse and representative sample. Potential participants were fully informed about the study’s purpose, procedures, and possible benefits throughout the recruitment process. They were also told of their rights as participants and the assurance of confidentiality. To confirm their willingness to participate, informants were asked for written consent before formal inclusion in the study.

Data collection

The study used semi-structured interviews and focus groups to collect data from the informants. The researcher developed the interview questions (Appendix), and a panel of three qualitative researchers reviewed their relevance. Interviews were conducted individually, either in person or virtually, and lasted approximately 45–60 min each. Focus groups with 3–5 participants conducted almost or in person were also organized. The duration of the focus group discussions was between 60 and 90 min. During the data collection process, the interviews and focus group sessions were audio-recorded to capture participants’ responses and insights accurately. These recordings were later transcribed verbatim, allowing a comprehensive analysis of the data collected. Through semi-structured interviews and focus groups, the study aimed to obtain complete and detailed information about participants’ experiences and perspectives regarding using AI-assisted interventions to support the learning and social communication skills of children with autism. The semi-structured nature of the interviews allowed for flexibility in exploring different topics while ensuring a consistent data collection framework for all participants. Additionally, the dynamic and interactive nature of the focus groups encouraged group discussions and allowed participants to share and build on one another.

Data analysis

Following the data collection phase, the study thoroughly analyzed the information collected. The audio recordings of the interviews and focus group sessions were transcribed verbatim, resulting in a comprehensive text dataset that captured participants’ responses and insights. The analysis began with a thorough familiarization process in which researchers immersed themselves in the transcribed data to understand participants’ accounts deeply. This immersion allowed researchers to identify recurring themes, patterns, and noteworthy information in the data set. A systematic analysis approach was used to ensure reliability and validity. Data were coded using a combination of inductive and deductive methods. First, an open coding process was conducted in which researchers generated initial codes by closely examining the data and labeling meaningful segments. As the analysis progressed, these codes were refined, grouped, and organized into categories and subcategories, creating a coding framework. After coding, researchers conducted a thematic analysis by identifying overarching themes from the data. The topics represented vital concepts, ideas, and perspectives shared by participants regarding the use of AI-assisted interventions to support the learning and social communication skills of children with autism. Throughout the analysis, the researchers ensured the accuracy and trustworthiness of the findings by employing techniques such as member checking, where participants were allowed to review and validate the interpretations made from their data.

Ethical considerations

The study adhered to ethical guidelines for conducting research with human subjects. Informed consent was obtained from all participants. Participants’ privacy and confidentiality were protected throughout the research process. The study also obtained ethical clearance from a relevant research ethics committee.

The study’s findings were presented in a report summarizing the themes and sub-themes that emerged from the data analysis. The report also provides recommendations for designing and implementing culturally and linguistically appropriate AI-powered interventions for children with autism while avoiding bias and discrimination in the learning materials and activities. The report also includes direct participant quotes to illustrate their experiences and perceptions. The findings are presented based on the order of research questions,

Benefits and challenges of AI-powered interventions

Informants of the study mentioned three benefits and some challenges of AI-empowered intervention for children with autism. Each is explained and exemplified as follows.

Increased engagement and motivation among children with autism

AI-powered interventions can use technologies like robots, virtual reality, and interactive games to provide personalized and engaging experiences for children with autism. Informants believed that AI-powered interventions can effectively increase engagement and motivation among children with autism. For example, educator 1 stated, “Children with autism who interacted with a humanoid robot showed increased engagement and motivation compared to those who received traditional therapy.” Educator 5 said, “By leveraging AI technologies, interventions for children with autism can be tailored to their needs and preferences, providing a more personalized and engaging learning experience. This can lead to improved outcomes and better quality of life for children with autism and their families. This finding is also supported by parent one, who stated, “My son used to struggle with traditional teaching methods, but with AI-powered interventions, he is more engaged and motivated to learn. The technology provides him with immediate feedback, which helps him understand his mistakes and learn from them.”

Customized and individualized interventions that cater to the unique needs of each child

Informants argued that every child with autism is unique, with their own set of strengths and challenges. Therefore, interventions tailored to each child’s specific needs and preferences can be more effective in promoting their development and well-being. This finding echoes the direct quotation by educator 6 who stated, “One size does not fit all when it comes to autism interventions. Each child is unique and requires a personalized approach that takes into account their individual strengths, challenges, and interests.” (Educator 6). Similarly, parent 6 stated, “As a parent, I have learned that the key to helping my child with autism is to focus on his individual needs. By working with his teachers and therapists to develop a personalized intervention plan, we have seen significant progress in his development and well-being.”

Real-time feedback to both children and educators about progress and areas for improvement

Real-time feedback involves providing immediate and ongoing information about a child’s performance and progress in a given activity or intervention. This feedback can reinforce positive behaviors, correct errors, and identify areas where additional support or instruction may be needed. Real-time feedback can be especially beneficial for children with autism, who may benefit from more frequent and targeted feedback to support their learning and development. By providing timely and specific feedback, children with autism can better understand their strengths and areas for improvement, and educators can adjust their interventions and supports accordingly. As an example, one of the educators stated, “Real-time feedback is crucial in helping children with autism learn and grow. By providing immediate and targeted feedback, we can reinforce positive behaviors and help children build new skills.” (Educator 4). Another educator stated, “Real-time feedback is not just important for children but for educators as well. By receiving ongoing feedback about a child’s progress, we can make more informed decisions about the interventions and supports that are most effective for them.“(Educator 8).

The potential for AI-powered interventions to enhance the work of educators and provide them with additional tools and resources

AI-powered interventions have the potential to enhance the work of educators and provide them with additional tools and resources to support the learning and development of children with autism. AI technologies like machine learning algorithms and natural language processing can analyze and interpret data from various sources, including assessment results, behavioral observations, and social communication interactions. This can provide educators with valuable insights and information about each child’s strengths, challenges, and learning needs. Educator 10 stated, “AI-powered interventions can provide educators with powerful tools and resources for supporting autistic children. By analyzing data and providing real-time feedback, these interventions can help educators tailor their teaching strategies and supports to the unique needs of each child.” Educator 3 also stated,” AI-powered interventions have the potential to transform the way we support children with autism in the classroom. By providing educators with insights and information about each child’s learning needs, these interventions can help us deliver more effective and personalized instruction.”

Challenges of AI-powered interventions

The content of interviews with informants was analyzed, and five main themes were extracted. Each is explained and exemplified as follows.

Lack of personalization

Informants stated that while AI-powered interventions have the potential to be personalized, there is a risk that they may not account for the unique needs and preferences of each child. For example, educator 3 stated, “We need to remember that technology is a tool, not a replacement for human interaction.”

Limited access to technology

Not all families and schools can access the necessary technologies for AI-powered interventions. As a parent of a child with autism notes, “Technology can be expensive, and not all families can afford it.”

Difficulty in interpreting and responding to social cues

Children with autism may have trouble analyzing and reacting to social cues, making it challenging to interact with AI technologies. A clinical psychologist notes: “Children with autism may struggle to understand that a robot or virtual character is not a real person, which can limit the effectiveness of AI-powered interventions.”

Ethical concerns

Ethical concerns surrounding using AI technologies with children include privacy, data security, and the potential for misuse or unintended consequences. The Director of Education at one School for Children with Autism notes: “We need to be mindful of the potential risks and unintended consequences of using AI technologies with children with autism.”

Lack of human interaction

While AI-powered interventions can be engaging and interactive, they cannot replace the importance of human interaction in promoting social and emotional development in children with autism. As a parent of a child with autism notes: “Technology can be helpful, but it is important to balance it with real-life experiences and interactions.”

Concerns about the cost and affordability of these interventions

One concern related to using interventions for children with autism is their cost and affordability. Many interventions, such as behavioral and developmental therapies, assistive technologies, and specialized education programs, can be expensive and may not be covered by insurance or other funding sources. This can create barriers for families, particularly those with limited financial resources, in accessing the interventions their child needs to thrive. As Educator 9 stated, “The cost of interventions for children with autism can be a significant burden for families, particularly those with limited financial resources. We must ensure these interventions are accessible and affordable for all families.” Similarly, parent 5 stated, “As a parent of a child with autism, the cost of interventions has been a major concern for our family. Based on our financial limitations, we have had to decide which interventions to prioritize.”

Suggestions for improving the quality of AL-empowered interventions

Interviews with informants were thematically analyzed, and different themes were extracted. Each theme is explained and exemplified as follows.

Using culturally and linguistically appropriate interventions

Participants emphasized the importance of designing and implementing AI-powered interventions that are culturally and linguistically appropriate for a diverse population of children with autism. Some of the suggestions made by participants include:

Ensuring that the language and content of the interventions are culturally sensitive and relevant to the target population.

Incorporating diverse perspectives and experiences into the design and development process.

Providing interventions in multiple languages to accommodate diverse linguistic backgrounds.

Quotations from educators and parents support these suggestions. For instance, educator 1 stated, “Cultural sensitivity is important when designing interventions for children with autism, particularly for those from diverse backgrounds. We need to ensure that the interventions are culturally relevant and take into account the unique needs and experiences of each child.” Similarly, parent 6 stated, “As a parent of a child with autism who comes from a different cultural background, I appreciate interventions that take into account my child’s unique needs and experiences. It’s important to have interventions that are culturally sensitive and relevant.”

Avoiding bias and discrimination

Participants also emphasized the importance of avoiding bias and discrimination in AI-powered interventions’ learning materials and activities. Some of the suggestions made by participants include:

Conducting regular audits of the interventions to identify and address any potential biases or discriminatory content.

Incorporating diverse perspectives and experiences into the design and development process to avoid perpetuating stereotypes.

Providing training and education to educators and developers to ensure that they are aware of and can address potential biases and discrimination.

Quotations from informants support these strategies. As an example, educator 8 stated,

“We need to be careful to avoid stereotypes and biases in the interventions we design and implement. It’s important to be aware of potential biases and to work to address them.” Similarly, parent 7 stated, “To ensure that AI-powered interventions are effective and inclusive, we need to make sure that they are designed with diversity and inclusivity in mind. This means avoiding discrimination and bias in the materials and activities.”

Training educators

Participants discussed the role of educators in implementing AI-powered interventions to support the learning and social communication skills of children with autism. Some of the key findings include:

The importance of providing training and education to educators to ensure that they can effectively implement these interventions.

The need for educators to work collaboratively with parents and other professionals to ensure that the interventions are tailored to the unique needs of each child.

“Educators play a critical role in implementing AI-powered interventions. They need to be trained and educated on how to use these interventions effectively and how to tailor them to the unique needs of each child.” [Educator 3).

We regularly audit the interventions to identify and address potential biases or discriminatory content

Conducting regular audits of interventions for children with autism is an essential step in ensuring that these interventions are effective, evidence-based, and free from biases or discriminatory content. Regular audits help identify areas for improvement, ensure that interventions are aligned with current best practices and ethical guidelines, and promote accountability and transparency in developing and implementing these interventions. Here are two quotations that address the importance of conducting regular audits of interventions for children with autism. To exemplify this finding, the following quotations are presented:

“As educators and researchers, it is our responsibility to ensure that interventions for children with autism are evidence-based, effective, and free from biases or discriminatory content. Regular audits can help us identify and address any areas of concern and promote the highest standards of quality and ethical practice.” (Educator 4). “Regular audits are essential to ensuring that interventions for children with autism are meeting the needs of all children, regardless of their race, ethnicity, gender, or other factors. We must be vigilant in identifying and addressing any biases or discriminatory content that may be present, and work to create interventions that are inclusive and equitable for all children.” (Educator 9).

Involving families and communities in the design and implementation process ensures their voices and perspectives are heard and valued

Involving families and communities in the design and implementation process of interventions for children with autism is crucial to ensuring that their voices and perspectives are heard and valued. Families and communities can provide valuable insights and feedback on the needs and preferences of children with autism and the effectiveness and cultural relevance of interventions. Here are two quotations that address the importance of involving families and communities in the design and implementation process:

“Families and communities are essential partners in the design and implementation of interventions for children with autism. Their insights and feedback can help us create interventions that are effective, culturally relevant, and responsive to the needs of all children.” (Educator 10). “As a parent of a child with autism, I know firsthand the importance of involving families and communities in the design and implementation of interventions. By listening to our voices and perspectives, researchers and educators can create interventions that are more meaningful and effective for our children.” (Parent 8).

Discussion and implications

The present study aimed at exploring the teachers and educators’ experiences and perceptions of artificial intelligence powered interventions for Autism groups. A qualitative research study was employed and interviews were analyzed thematically and different themes were extracted. Participant believed that AI-powered interventions represent a groundbreaking frontier in reshaping the support systems for the learning and social communication skills of children with autism [ 54 ]. Participants also highlighted several noteworthy benefits, with a critical emphasis on the heightened engagement and motivation witnessed among children with autism when exposed to AI-powered interventions [ 1 , 2 , 54 ]. Recognizing the limitations of traditional teaching methods in meeting the distinctive learning needs of these children, AI interventions emerge as a promising avenue [ 1 , 2 ].

The first advantage underscored by participants is the adaptability of AI-powered interventions to provide personalized and individualized support, furnishing real-time feedback to children and educators regarding progress and areas for improvement [ 3 , 4 , 5 ]. This tailored approach aligns seamlessly with the diverse and unique challenges presented by children with autism. However, embracing AI-powered interventions is full of challenges, and participants articulated various concerns [ 55 , 56 ]. Technical glitches and difficulties were identified as potential disruptors of the learning process, prompting apprehensions about an overreliance on technology [ 55 , 56 ]. Moreover, the limited access to technology and resources in specific communities and regions raises concerns about the equitable distribution of intervention benefits [ 55 , 56 ]. Addressing these challenges is imperative to ensure that all children with autism, irrespective of geographical location or socioeconomic status, have equitable access to effective interventions.

The second theme, cultural and linguistic appropriateness, emerged as a primary consideration, with participants highlighting the importance of interventions tailored to the diverse backgrounds of children with autism [ 55 , 56 ]. This includes ensuring that the language and content of interventions are culturally sensitive and relevant, integrating diverse perspectives into the design process, and providing interventions in multiple languages ​​to accommodate linguistic diversity [ 7 , 8 , 9 ]. This finding is consistent with the findings of the previous research which highlighted that language differences can pose significant barriers to accessing autism interventions, highlighting the urgent need for interventions in the child’s native language [ 66 ].

As the third extracted theme “mitigating bias and discrimination in AI-powered interventions” extracted as another critical aspect, necessitating regular audits to identify and rectify potential biases [ 57 ]. The imperative of incorporating diverse perspectives into the design process and providing training to educators and developers to address biases and discrimination was highlighted as crucial [ 10 , 11 ]. This finding confirms the findings of the study that emphasizes the pivotal role of involving families and communities in designing and developing autism interventions to ensure cultural sensitivity and effectiveness [ 67 ].

Despite the above-mentioned potential of AI-powered interventions, the participants concurrently acknowledged the need for further research to evaluate the effectiveness of remote interventions and ensure their cultural and linguistic appropriateness [ 12 , 13 ]. Simultaneously, there are apprehensions and concerns with the potential for these interventions to exacerbate existing disparities in access to care if not implemented equitably. Moreover, challenges have been discerned alongside these benefits, prompting a comprehensive approach to ensure effectiveness, inclusivity, and accessibility [ 55 , 56 ]. Technical glitches, concerns about overreliance on technology, and limited access to resources pose hurdles that need addressing [ 55 , 56 ]. Policymakers must prioritize equitable access, focusing on both technological infrastructure and training programs for educators [ 55 , 56 ].

In addition, ensuring cultural and linguistic appropriateness emerges as a critical consideration in designing and implementing AI-powered interventions [ 55 , 56 ]. Culturally sensitive content, diverse perspectives in development, and multilingual offerings are underscored as essential [ 7 , 8 , 9 ]. Recognizing potential biases and discrimination, participants advocate for regular audits, diversity in development teams, and education on bias mitigation as integral components of ethical AI intervention deployment [ 10 , 11 , 57 ].

AI-powered interventions have emerged as a promising avenue to revolutionize the support for children with autism, offering transformative benefits while presenting challenges that demand careful consideration [ 54 ]. One pivotal advantage emphasized by participants is the heightened engagement and motivation observed among children with autism undergoing AI-powered interventions [ 54 ]. This is particularly noteworthy as traditional teaching methods often need to catch up in meeting the unique learning needs of these children. AI interventions, utilizing technologies such as robots, virtual reality, and interactive games, create personalized and engaging experiences, as reported by educators and parents.

It can also be concluded that transformative potential of AI-powered interventions underscores the need for collaborative efforts among educators, parents, and developers, ensuring effectiveness, inclusivity, and accessibility for all children [ 60 , 61 , 62 , 63 , 64 , 65 ]. The imperative of providing interventions in multiple languages and incorporating diverse perspectives into the design and development process is underscored [ 63 ]. Additionally, including culturally responsive teaching practices alongside AI interventions emerges as a strategy to enhance engagement and outcomes, particularly for children from diverse cultural backgrounds [ 68 ]. Ongoing research, collaborative endeavors, and an unwavering commitment to addressing challenges are imperative to maximize the benefits of AI-powered interventions for children with autism.

It can also be inferred that the collaborative involvement of families and communities is championed to enhance interventions’ impact and cultural sensitivity [ 12 , 13 , 67 ]. Balancing technology with human interaction is deemed crucial, emphasizing the irreplaceable role of personal connections in social and emotional development [ 39 , 41 ]. Moreover, the potential for AI-powered interventions to address access disparities, especially in remote or underserved areas, highlights the importance of further research and evaluation [ 58 , 59 ]. However, concerns persist about exacerbating existing disparities, demanding meticulous attention to cultural, linguistic, and regional nuances.

As another concluding remark, it can be inferred that AI-powered interventions have the potential to revolutionize the way we support the learning and social communication skills of children with autism. These interventions can provide customized and individualized interventions that cater to the unique needs of each child, providing real-time feedback to both children and educators about progress and areas for improvement. AI-powered interventions can also improve access to care for children with autism, particularly for those in remote or underserved areas. The findings suggest that to ensure that AI-powered interventions are culturally and linguistically appropriate for a diverse population of children with autism while also avoiding bias and discrimination in the learning materials and activities, it is essential to incorporate various perspectives and experiences into the design and development process, provide interventions in multiple languages, ensure that the language and content of the interventions are culturally sensitive and relevant, deliver training and education to educators and developers, conduct regular audits of the interventions, involve families and community members in the design and implementation process, and use culturally responsive teaching practices. These efforts can help to address the challenges and considerations of using AI-powered interventions and ensure that all children with autism have access to practical, inclusive, and culturally appropriate interventions.

However, several challenges and considerations need to be taken into account to ensure that these interventions are effective, inclusive, and accessible to all children with autism. These challenges include technical difficulties, overreliance on technology, limited access to technology and resources in specific communities and regions, and the need to design and implement culturally and linguistically appropriate interventions to avoid bias and discrimination.

Finally, one recurring theme is the importance of professional development for educators, which recognizes their critical role in successfully applying AI-powered interventions. Providing educators with technological expertise, cultural sensitivity, and ethical awareness is essential. Furthermore, legislators, educators, and parents must work together to prioritize the financial accessibility of interventions. The ramifications in this complex environment suggest a comprehensive and collaborative strategy. The key to success is overcoming obstacles, adopting technology responsibly, and giving accessibility and inclusivity top priority in intervention and education initiatives. Because technology constantly changes, we must remain committed to ongoing iteration and improvement. Community, parent, and educator feedback loops help us refine AI-powered interventions.

Limitations and suggestions for further studies

The current body of research on AI-powered interventions for children with autism, while promising, grapples with several limitations that warrant careful consideration. Firstly, the generalization of findings remains a challenge, as many studies tend to focus on specific demographic groups or particular manifestations of autism spectrum disorder (ASD). This limits the broader applicability of the insights gained, as the diversity within the autism spectrum may not be comprehensively represented. Additionally, a notable gap exists in understanding the long-term efficacy of AI interventions. While short-term outcomes are frequently explored, there is a scarcity of research delving into the sustained impact of these interventions on the developmental trajectories of children with autism. Longitudinal studies are crucial to elucidating AI-powered approaches’ durability and lasting benefits.

Moreover, the current literature may lack ethnic and cultural diversity, raising concerns about AI interventions’ universal applicability and artistic sensitivity. This underrepresentation hinders our understanding of how these technologies might function across diverse populations. Ethical considerations, although acknowledged, need to be thoroughly examined. Privacy, data security, and potential biases in algorithmic decision-making demand a more in-depth investigation to ensure responsible and equitable use of AI technologies in educational settings.

To address these limitations, future research should prioritize several vital areas. Long-term impact assessments are imperative to ascertain the sustained efficacy of AI interventions over time. Diverse and inclusive studies encompassing a range of ethnicities and cultural backgrounds are essential to validate the universal applicability of these technologies. Robust ethical frameworks should be developed to guide the implementation of AI interventions, addressing privacy, security, and bias concerns. Comparative studies, pitting AI interventions against traditional methods, will offer nuanced insights into their relative advantages and limitations. Family and community involvement in designing and implementing AI interventions should be explored further, recognizing the unique insights these stakeholders bring. Finally, comprehensive cost-benefit analyses are necessary to evaluate the economic aspects of AI interventions, ensuring their affordability and long-term viability in diverse educational settings. In navigating these avenues, researchers can contribute substantively to the responsible and inclusive integration of AI-powered interventions for children with autism.

Data availability

The data will be made available upon request from the corresponding author (Corresponding author: email: [email protected].

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Acknowledgements

The authors would like to thank all participants who contributed to the study.

This work was supported by The General Project of Beijing Postdoctoral Research Foundation in 2023, “Research on the Representation of the Tacit Knowledge of High School History Teachers Based on Natural language processing”. (Project No.2023-zz-182)

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Li, G., Zarei, M.A., Alibakhshi, G. et al. Teachers and educators’ experiences and perceptions of artificial-powered interventions for autism groups. BMC Psychol 12 , 199 (2024). https://doi.org/10.1186/s40359-024-01664-2

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What is Autism?

Autism is a developmental disorder with symptoms that appear within the first three years of life. Its formal diagnostic name is autism spectrum disorder. The word “spectrum” indicates that autism appears in different forms with varying levels of severity. That means that each individual with autism experiences their own unique strengths, symptoms , and challenges. 

Understanding more about ASD can help you better understand the individuals who are living with it. 

How autism spectrum disorders are described

Psychiatrists and other clinicians rely on the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) to define autism and its symptoms. The  DSM-5 definition  recognizes two main symptom areas:

• Deficits in social communication and interaction
• Restricted, repetitive behaviors, interests, or activities

These symptoms appear early in a child’s development—although diagnosis may occur later. Autism is diagnosed when symptoms cause developmental challenges that are not better explained by other conditions.

The definition of autism has been refined over the years. Between 1995 and 2011, the DSM-IV grouped Asperger’s Syndrome and Pervasive Developmental Disorder Not Otherwise Specified (PDD-NOS) with autism. Asperger’s syndrome was an autism spectrum disorder marked by strong verbal language skills and, often, high intellectual ability. PDD-NOS was a more general diagnosis for people who did not fit clearly into the other two categories. 

However, the DSM-5 no longer recognizes Asperger’s syndrome or PDD-NOS as separate diagnoses. Individuals who would previously have received either of these diagnoses may now receive a diagnosis of autism spectrum disorder instead. 

Autism symptoms and behaviors

Individuals with autism may present a range of symptoms, such as: 

• Reduced eye contact
• Differences in body language
• Lack of facial expressions
• Not engaging in imaginative play
• Repeating gestures or sounds
• Closely focused interests
• Indifference to temperature extremes

These are just a few examples of the symptoms an individual with autism may experience. Any individual could have some, all, or none of these symptoms. Keep in mind that having these symptoms does not necessarily mean a person has autism. Only a qualified medical professional can diagnose autism spectrum disorder. 

Most importantly, an individual with autism is first and foremost an individual. Learning about the symptoms can help you start to understand the behaviors and challenges related to autism, but that’s not the same as getting to know the individual. Each person with autism has their own strengths, likes, dislikes, interests, challenges, and skills, just like you do. 

How autism is diagnosed

There is no known biological marker for autism. That means that no blood or genetic test can diagnose the disorder. Instead, clinicians rely on observation, medical histories, and questionnaires to determine whether an individual has autism. 

Physicians and specialists may use one or several of the following screening tools : 

• Modified Checklist for Autism in Toddlers , Revised (M-CHAT), a 20-question test designed for toddlers between 16 and 30 months old. 
• The Ages and Stages Questionnaire (ASQ) , a general developmental screening tool with sections targeting specific ages used to identify any developmental challenges a child may have. 
• Screening Tool for Autism in Toddlers and Young Children (STAT) , an interactive screening tool, comprising 12 activities that assess play, communication, and imitation. 
• Parents’ Evaluation of Developmental Status (PEDS)  is a general developmental parent-interview form that identifies areas of concern by asking parents questions.  

The American Academy of Pediatrics encourages autism screening for all children at their 18 and 24-month well-child checkups. Parents and caregivers can also ask their pediatrician for an autism screening if they have concerns. In rare cases, individuals with autism reach adulthood before receiving a diagnosis. However, most individuals receive an autism diagnosis before the age of 8.

Prevalence of autism

For many years, a diagnosis of autism was rare, occurring in just one child out of 2,000. One reason for this was the diagnostic criteria. Autism was not clearly defined until 1980 when the disorder was included in the DSM-III. Before that time, some cases of autism spectrum disorder may have been mistaken for other conditions. 

Since the ’80s, the rate of autism has increased dramatically around the world. In March 2020, the US Federal Centers for Disease Control announced that  1 in every 54 children  in the United States is affected by autism. 

Although autism is more likely to affect boys than girls, children of all genders have been diagnosed with ASD. Several recent studies investigate the impact of race, ethnicity, and socioeconomic  disparities on the diagnosis of autism spectrum disorder. 1,2,3,4

A short history of autism

Researchers have been working on autism and autism-like disorders since the 1940s. At that time, autism studies tended to be small in scale and used varying definitions of the disorder. Autism was also sometimes lumped in with other conditions.

Focused research into ASD became more common in the 1980s when the DSM-III established autism as a distinct diagnosis. Since then, researchers have explored the causes, symptoms, comorbidities, efficacy of treatments, and many other issues related to autism. 

Researchers have yet to discover a cause for autism. Many of the ideas put forth thus far have been disproven. Likely a combination of genetic , neurological , and environmental factors are at work, which is the case with many psychiatric disorders and conditions. 

Autism Prognosis

Autism is a lifelong condition, and a wide variety of treatments can help support people with ASD. The symptoms and comorbidities—conditions occurring in the same individual—are treatable. Early intervention delivers the best results. Parents and caregivers should seek out the advice of a qualified medical professional before starting any autism treatment. 

Advances in understanding autism, its symptoms, and comorbidities have improved outcomes for individuals with autism. In recent years, more children with autism have attended school in typical classrooms and gone on to live semi-independently. However, the majority remain affected to some degree throughout their lifetime. 

Co-occurring conditions

When a person has more than two or more disorders, these conditions are known as comorbidities. Several comorbidities are common in people with autism. 

These include: 

• Gastrointestinal and immune function disorders
• Metabolic disorders
• Sleep disorders

Identifying co-occurring conditions can sometimes be a challenge because their symptoms may be mimicked or masked by autism symptoms. However, diagnosing and identifying these conditions can help avoid complications and improve the quality of life for individuals with autism. 

Autism in pop culture

Movies and books featuring characters with autism have helped bring autism spectrum disorder into the public consciousness. Some have ignited controversy; others have increased the public’s general understanding of autism. A few have done both. At ARI, we hope that people will rely on evidence-based research to understand autism spectrum disorder better.   

Learn more about autism spectrum disorder by watching one of our expert-led webinars . They help you learn about ASD from clinicians, researchers, and therapists who research autism and support individuals with ASD. 

• Donohue MR, Childs AW, Richards M, Robins DL. Race influences parent report of concerns about symptoms of autism spectrum disorder. Autism . 2019;23(1):100-111. doi:10.1177/1362361317722030
• Durkin MS, Maenner MJ, Baio J, et al. Autism Spectrum Disorder Among US Children (2002-2010): Socioeconomic, Racial, and Ethnic Disparities. Am J Public Health . 2017;107(11):1818-1826. doi:10.2105/AJPH.2017.304032
• Newschaffer CJ. Trends in Autism Spectrum Disorders: The Interaction of Time, Group-Level Socioeconomic Status, and Individual-Level Race/Ethnicity. Am J Public Health . 2017;107(11):1698-1699. doi:10.2105/AJPH.2017.304085
• Yingling ME, Hock RM, Bell BA. Time-Lag Between Diagnosis of Autism Spectrum Disorder and Onset of Publicly-Funded Early Intensive Behavioral Intervention: Do Race-Ethnicity and Neighborhood Matter?. J Autism Dev Disord . 2018;48(2):561-571. doi:10.1007/s10803-017-3354-3

Motor Skills and Executive Function in Autism

autismAdmin 2024-03-01T12:48:58-05:00 May 8th, 2024 | Back to School , Early Intervention , Educational Therapies , Executive Function , Health , Parenting , Sensory , Social Skills , Webinar |

Free webinar at 1 p.m. Eastern time (US), Wednesday, May 8, 2024 Learn about emerging research on the relationship between the development of motor skills and executive function in autistic children.

Editorial – Addressing delays: proactive parent-led interventions during waiting periods

Melanie Glock 2023-12-06T14:19:00-05:00 December 6th, 2023 | News |

The wait for an autism diagnosis and subsequent intervention can be highly stressful for many families, especially when access to needed health and educational services also hinges on the approval of

Prenatal exposure to cannabis may increase likelihood of autism

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Cannabis use during pregnancy may alter placental and fetal DNA methylation (the process of turning genes “on” and “off”) in ways that increase the likelihood of autism spectrum disorder (ASD) or

New multi-national study adds to evidence linking alterations of the gut microbiome to autism

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Strong new evidence linking alterations of the gut microbiome to autism spectrum disorders (ASD) comes from a new multi-national study by James Morton and colleagues. In the study, researchers in North

Sleep problems in infancy associated with ASD, autism traits, and social attention alterations

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A new study from the United Kingdom indicates that sleep problems in infancy may help to predict later social skills deficits, autism traits, and autism diagnoses in children. Jannath Begum-Ali and

Preemptive therapy prior to autism diagnosis may be highly cost-effective

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Preemptive therapy for infants who display early symptoms of autism may be highly cost-effective, according to a new study from Australia. Leonie Segal and colleagues based their economic analysis on a 2021

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The Importance of Lived Experience Perspectives – Insights From the IACC

Joshua A. Gordon, M.D., Ph.D., and Susan Daniels, Ph.D., HHS National Autism Coordinator and Director of the NIMH Office of National Autism Coordination

April 4, 2024

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During National Autism Acceptance Month, NIMH and the NIMH Office of National Autism Coordination  celebrate the important contributions of autistic people in our families and our society, and we reaffirm our support for their acceptance, inclusion, and full participation in all aspects of community life. This April, we would like to highlight NIMH’s unique role in federal autism coordination efforts and reflect on how the lived experiences of autistic people and their families have shaped federal autism research, services, and policy.

We have the privilege of serving as the Chair and Executive Secretary of the Interagency Autism Coordinating Committee (IACC)  . The IACC is a federal advisory committee established by Congress and currently authorized under the Autism CARES Act of 2019. The committee includes federal officials from agencies that support autism research and vital services for people with disabilities, as well as public members, including autistic adults, family members, advocates, researchers, and service providers from diverse communities around the country.

The IACC serves as a forum for community engagement and provides an important point of convergence and collaboration. Federal agency members and public members work together to develop and provide advice that informs the Secretary of the Department of Health and Human Services, federal agencies, Congress, and the President. This advice guides the activities of federal agencies and helps ensure that federal programs are responsive to the needs of the autism community.

Reflecting community needs

In working with the IACC, we have seen how community voices, reflecting the lived experiences of autistic people and their families, can contribute to important advances in federal autism activities. Public input on the co-occurring mental and physical health conditions often experienced by autistic individuals is one such example. These conditions can include seizure disorders, gastrointestinal problems, and disruptions in sleep. They can also include mental disorders and mental health conditions such as anxiety, depression, attention-deficit/hyperactivity disorder (ADHD), self-injury, and suicidal ideation. Many autistic individuals also have learning disabilities or additional developmental conditions and disabilities.

For many people with autism, co-occurring conditions can contribute to lost opportunities and decreased productivity, poor health outcomes, and, in some cases, premature death. Discussions initiated by public members of the IACC, along with public comments received at IACC meetings and at an IACC-sponsored town hall  , helped to shape the research objectives on co-occurring conditions in the inaugural 2009 IACC Strategic Plan   .

The topic of co-occurring conditions remains an IACC priority today. The 2021-2023 IACC Strategic Plan   includes comprehensive recommendations for research investigating the biology underlying co-occurring conditions and autism, as well as interventions and services to address these conditions across the lifespan. Just last year, the IACC issued a Request for Information  seeking additional community input on the topic and received responses from more than 1,200 people. Themes and priorities from these responses will be included in the forthcoming IACC Strategic Plan Update , which will focus on the impact of co-occurring conditions on the physical and mental health of people on the autism spectrum. The update aims to further identify opportunities for research and services to improve well-being for autistic people.

Representing diverse experiences

Hearing from people with lived experience has shed light on additional issues important to the autism community, including wandering and elopement, the needs of transition-age youth and adults, and autism in girls and women. Autistic people and family members have also emphasized the breadth of experiences and challenges across the spectrum of ability and disability and the need for a range of personalized tools, interventions, services, and supports rather than a one-size-fits-all approach.

Based on input from autistic people and families from diverse and underserved communities, the IACC has prioritized the need to increase equity and reduce disparities experienced by autistic individuals across race, ethnicity, culture, sex and gender, socioeconomic status, and geographic location, including rural and urban communities. This also includes the need for more researchers and service providers who come from diverse communities and have lived experience with autism and disability.

The 2021-2023 IACC Strategic Plan includes two cross-cutting recommendations – one on equity and disparities and one on sex and gender – to intensify focus on addressing gaps in these areas and increase equity for all autistic people. The committee also continues to support priorities to ensure that autism research and services meet the needs of individuals across the whole spectrum, including those with the highest support needs, and across the full lifespan into older adulthood. Importantly, the strategic plan emphasizes inclusion and acceptance of all autistic people and reducing barriers to their participation in every aspect of community life.

Prioritizing collaboration and inclusion

In all of this work, consideration of diverse viewpoints and experiences from across the autism community and a spirit of cooperation, collaboration, and civility have been crucial. As the autism landscape continues to evolve, collaboration between federal agencies and community members will remain a cornerstone of progress in improving the health and well-being of autistic people and their families.

Community engagement plays an important role across the broad portfolio of federal research, services, and policy activities related to disabilities, mental health, and physical health. Federal agencies gather public input through federal advisory committees; solicit public comments through formal requests for information; and engage individuals with lived experience in grant review panels, community engagement programs, and community-based participatory research. Lived experience perspectives strengthen federal programs and help ensure federal research and services address the issues most important to those whom they serve.

During Autism Acceptance Month, let us honor the contributions of autistic individuals and others with lived experience; strive to ensure that their voices, perspectives, and priorities are heard and represented in federal activities for research, services, and policy; and work toward a more inclusive society for all.

ORIGINAL RESEARCH article

Interactions between circulating inflammatory factors and autism spectrum disorder: a bidirectional mendelian randomization study in european population.

• 1 China Rehabilitation Research Center, Capital Medical University, Beijing, China
• 2 Changping Laboratory, Beijing, China
• 3 Shandong University, Jinan, Shandong Province, China

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Background: Extensive observational studies have reported an association between inflammatory factors and autism spectrum disorder (ASD), but their causal relationships remain unclear. This study aims to offer deeper insight into causal relationships between circulating inflammatory factors and ASD.Methods: Two-sample bidirectional Mendelian randomization (MR) analysis method was used in this study. The genetic variation of 91 circulating inflammatory factors was obtained from the genome-wide association study (GWAS) database of European ancestry. The germline GWAS summary data for ASD were also obtained (18,381 ASD cases and 27,969 controls). Single nucleotide polymorphisms robustly associated with the 91 inflammatory factors were used as instrumental variables. The random-effects inverse-variance weighted method was used as the primary analysis, and the Bonferroni correction for multiple comparisons was applied. Sensitivity tests were carried out to assess the validity of the causal relationship.The forward MR analysis results suggest that levels of sulfotransferase 1A1, natural killer cell receptor 2B4, T-cell surface glycoprotein CD5, Fms-related tyrosine kinase 3 ligand, and tumor necrosis factor-related apoptosis-inducing ligand are positively associated with the occurrence of ASD, while levels of interleukin-7, interleukin-2 receptor subunit beta, and interleukin-2 are inversely associated with the occurrence of ASD. In addition, matrix metalloproteinase-10, caspase 8, tumor necrosis factor-related activation-induced cytokine, and C-C motif chemokine 19 were considered downstream consequences of ASD. Conclusion: This MR study identified additional inflammatory factors in patients with ASD relative to previous studies, and raised a possibility of ASD-caused immune abnormalities. These identified inflammatory factors may be potential biomarkers of immunologic dysfunction in ASD.

Keywords: Autism Spectrum Disorder, Inflammatory factors, Inflammation, Mendelian randomization, Single nucleotide polymorphisms, Genome-Wide Association Study

Received: 14 Jan 2024; Accepted: 16 Apr 2024.

Copyright: © 2024 Long, Dang, Su, Moneruzzaman and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Hao Zhang, China Rehabilitation Research Center, Capital Medical University, Beijing, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Frontiers for Young Minds

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Is Autism Different for Girls and Boys?

Autism is a common condition that affects the way people think and interact with the world. Most of our knowledge about autism is from research done with autistic boys. This means that we do not know much about the ways that autistic girls may be different than autistic boys. Now, researchers are including more autistic girls in their studies to find out about these differences. However, not all researchers find the same results: some researchers find that autistic boys are better at some tasks and other researchers find that autistic girls are better at those same tasks. In this article, we review some of the findings about differences between autistic girls and boys and talk about why it is important to understand these differences.

What is Autism?

Autism is a common condition that affects how people think ( Figure 1 )—you may even know someone who is on the autism spectrum or be on the spectrum yourself. Many autistic kids understand things differently than non-autistic kids. For example, autistic and non-autistic kids may express themselves differently, so they may have difficulty communicating with and understanding one another (learn more by reading this Frontiers for Young Minds article ). We should learn about and celebrate neurodiversity , or differences in the ways people think, to help us all understand each other ( Figure 2 )!

• Figure 1 - Everyone’s autism looks different! Some autistic children may have repetitive behaviors or movements such as rocking, spinning, or hand flapping.
• Some might also be sensitive to sights, sounds, smells, tastes, and touch. For instance, autistic children might feel uncomfortable around loud noises. Autistic children may also make less eye contact when talking to others. Finally, some autistic children may communicate and interact with others differently. For example, an autistic child might not respond to their name or appear to not hear you at times. An autistic child might also use a singsong voice or repeat some words.

• Figure 2 - Neurodiversity is a word for differences in the way people’s brains work.
• We all exist on a spectrum and have different skills and strengths. Some kids might have a hard time paying attention or sitting still. Other kids might struggle to read or break down words. Many kids are diagnosed with autism. Some kids might learn differently or more quickly than others. Everyone’s brain works differently, meaning we are all neurodiverse!

Autistic kids may also process information from the senses, like sights and sounds, differently. For example, they may hear a firetruck in the distance that non-autistic kids might not notice, or be bothered by the texture of some clothes or foods. Experiencing senses more intensely can be stressful, so autistic kids might also repeat certain behaviors to stay calm, like flapping their hands or covering their ears.

Autism can also lead to unique skills and strengths. For example, autistic kids may do better at tasks that involve remembering small details and patterns or remembering important dates. Autistic kids may also have interests that they know a lot about, like trains, technology, or history.

Many scientists are doing research to better understand autism, because there is still much that we do not know. Part of what we do not know enough about is how autism is different for boys and girls on the spectrum. These differences often mean that girls are diagnosed with autism less often. If autistic girls do not receive a diagnosis of autism, they are missing crucial information that could help them and their families understand the way they think and behave, and they are also missing out on services that can help them.

Are Autistic Girls and Boys Different From Each Other?

More boys than girls are diagnosed with autism ( Figure 3 ), and there are many possible reasons for this. For instance, it is possible that there are biological differences between boys and girls that make it more likely for boys to be diagnosed with autism. Second, boys tend to have autism traits that are easier for teachers and parents to notice, like having trouble talking. Autistic boys are also more likely than autistic girls to have other disorders at the same time that are more easily seen, like attention deficit hyperactivity disorder (ADHD) . The easier the symptoms are to notice, the more likely a child is to be diagnosed.

• Figure 3 - Autism is more common in boys than girls.
• There are 4 times as many autistic boys as autistic girls. This can be because of biological differences between the sexes or the way that autism is defined and diagnosed. Although autism is diagnosed more often in boys, there may be autistic girls who do not have an autism diagnosis because it is easy for doctors to miss the signs of autism in girls. Some studies find that girls might show their autism symptoms differently than boys.

Another possible reason that more boys are diagnosed with autism is because initial descriptions and research studies about autism focused mainly on boys. This means that autism was defined based on what it looks like in boys, and doctors and scientists continue to look for those same traits when making an autism diagnosis for both boys and girls. This can make diagnosing autism in girls more difficult, since girls’ autism traits often look different than boys’ autism traits. For example, girls tend to repeat behaviors that are less obvious than boys’ repetitive behaviors, like collecting stuffed animals or having a strong interest in drawing. Even though doctors and parents may notice these behaviors and interests, they are seen as socially acceptable behaviors for girls, and so they are not thought of as signs of autism [ 1 ]. Many girls also hide or “mask” some of their autism symptoms to try to fit in. This leads to fewer symptoms being noticed by doctors and parents, and an even lower likelihood of girls receiving an autism diagnosis (see this Frontiers for Young Minds article to learn more about how some autistic kids hide aspects of their autism).

Even though autistic kids have autism for their whole lives, boys are usually diagnosed younger than girls. Many autistic boys get diagnosed when they are just 2–5 years old, while girls are more likely to be diagnosed with autism when they are older—sometimes not even until they are adults! We also know that when girls are diagnosed with autism early, those girls often have symptoms that are more similar to autistic boys, like difficulty learning to talk, and they may repeat more obvious behaviors, like lining up toys.

What Differences Have Scientists Found?

Because there are more boys diagnosed with autism than girls, there are not enough girls in most studies to look at how autism is different for girls and boys. Even when studies do look at these differences, scientists do not consistently find the same results as each other.

For example, scientists have compared social communication in autistic girls and boys. Social communication is what lets you share your thoughts and ideas with other people and listen to and understand others’ thoughts and ideas. Communication includes words, hand gestures, and facial expressions. Some scientists found that there were no differences in the social communication of autistic girls and boys [ 2 ]. Other scientists found that autistic boys scored better on a test of speaking and listening than autistic girls [ 3 ], meaning boys had better social communication. Still other scientists found that autistic girls had better social communication skills [ 4 ].

Scientists have also studied whether executive function abilities are different in autistic girls and boys. Executive functions help you focus and stay on task. You use executive functions to plan what you have to do, like the steps necessary to complete your homework. You also use executive functions to control your emotions and behaviors, like knowing to use words instead of hitting people, even when you are angry. Two groups of scientists studied executive function abilities and found the same thing: autistic girls have better executive function abilities than autistic boys [ 5 , 6 ].

However, these same two groups of scientists studied whether autistic boys and girls process visual information differently. Some scientists found autistic boys are better at seeing visual details, like when they have to make a certain pattern out of blocks, or find a shape hidden in a picture [ 5 ]. However, other scientists found the opposite: autistic girls are better at seeing visual details than autistic boys are [ 6 ].

Why All Scientists Might Not Agree

Scientists may get different results even when studying the same skill because autism is a spectrum. Autism looks different in every person, and each autistic person has unique abilities. This means that even when many scientists agree on findings, those findings may not be true for every autistic person. Different findings between studies could also be because of the IQs of autistic kids who participated in the studies. In many studies that found autistic girls did better on tasks than autistic boys, the girls had higher IQs. But in studies that found autistic girls did worse on tasks than autistic boys, the girls had lower IQs. So, findings may differ between studies because they include kids with different abilities.

Studies may also have different findings because of the ages of kids who are included. One study found that social communication differences change with age [ 7 ]. When autistic kids were 1–2 years old, boys were better at social communication, but when autistic kids were 3–6 years old, girls and boys had similar social communication levels. Since girls diagnosed at older ages often have different autism traits than girls diagnosed at younger ages, the differences found between autistic girls and boys at younger ages may not be the same differences found at older ages, when girls with different autism traits are included in studies.

The fact that scientists have not all found the same results tells us that we should do more research to understand autism in girls, and there is still so much to learn! Today, there are many scientists who include autistic girls in their research so that we can better understand what autism is like for girls. If you or someone in your family is autistic, you can get involved in science by signing up for studies, such as SPARK , to help scientists learn more about autism.

Why Are Differences Between Autistic Girls and Boys Important?

Scientists’ findings on differences between autistic girls and boys could be very useful for doctors, parents, and autistic kids. If scientists understand how autistic girls are different from autistic boys, then doctors may be able to diagnose girls more easily. Getting diagnosed at younger ages means autistic kids have more time to use any services they need, like getting help in school. For example, if a child is autistic and sensitive to noise, taking a test in a noisy classroom may be too distracting. Instead, noise-sensitive kids could take the test in a quiet room where there are no distractions. Many schools require specific diagnoses to give kids special accommodations like a quiet room, getting extra time to complete schoolwork, sitting in a less distracting area of the classroom, or being warned about schedule changes or loud noises like fire drills. Without an autism diagnosis, a child may not get the special accommodations that allow them to do their best in school.

Getting diagnosed with autism at a younger age may also help children make friends and learn to communicate better. Because autistic kids may express themselves differently than non-autistic kids, interactions with classmates and other kids may be more difficult. Difficulty with spoken language can also make it hard to make friends. However, if autistic kids are diagnosed at younger ages, they can get speech therapy that helps them with spoken communication, which can make talking to classmates and making friends easier.

Overall, autistic girls and boys seem to experience autism differently. However, scientists have not all found the same thing, and there is still much to learn about autism and how it may be different for girls and boys. Different findings between scientists could be because of the abilities, IQs, or ages of study participants. Scientists should keep studying differences between autistic girls and boys to find out how autistic girls may think and act differently than autistic boys. Such research will be beneficial for autistic girls, helping them to get diagnosed at a younger age, so they and their families can receive any support they might need to be successful.

Autism : ↑ A condition that affects the way people think, and often involves specific interests, difficulties with communication, and differences in processing sensory information. Symptoms vary greatly between individuals.

Spectrum : ↑ A way to classify something that has a broad range. When talking about autism, spectrum means that that there are many different symptoms, and each person experiences autism differently.

Neurodiversity : ↑ Differences in the way people think and the way brains work.

Attention Deficit Hyperactivity Disorder (ADHD) : ↑ A condition that affects people by making it harder to pay attention and control behaviors.

Social Communication : ↑ Talking and interacting with others to share our thoughts and understand other people. Talking can be verbal (through words) or nonverbal (through hand gestures like pointing).

Executive Function : ↑ A set of abilities that are used to help us stay on task, plan our actions, and control our emotions and behaviors.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

[1] ↑ Hiller, R. M., Young, R. L., and Weber, N. 2014. Sex differences in autism spectrum disorder based on DSM-5 criteria: evidence from clinician and teacher reporting. J. Abnorm. Child Psychol. 42:1381–93. doi: 10.1007/s10802-014-9881-x

[2] ↑ de Giambattista, C., Ventura, P., Trerotoli, P., Margari, F., and Margari, L. 2021. Sex differences in autism spectrum disorder: focus on high functioning children and adolescents. Front. Psychiatry 12:539835. doi: 10.3389/fpsyt.2021.539835

[3] ↑ Mahendiran, T., Dupuis, A., Crosbie, J., Georgiades, S., Kelley, E., Liu, X., et al. 2019. Sex differences in social adaptive function in autism spectrum disorder and attention-deficit hyperactivity disorder. Front. Psychiatry 10:607. doi: 10.3389/fpsyt.2019.00607

[4] ↑ Messinger, D. S., Young, G. S., Webb, S. J., Ozonoff, S., Bryson, S. E., Carter, A., et al. 2015. Early sex differences are not autism-specific: a Baby Siblings Research Consortium (BSRC) study. Mol. Autism 6:32. doi: 10.1186/s13229-015-0027-y

[5] ↑ Bölte, S., Duketis, E., Poustka, F., and Holtmann, M. 2011. Sex differences in cognitive domains and their clinical correlates in higher-functioning autism spectrum disorders. Autism 15:497–511. doi: 10.1177/1362361310391116

[6] ↑ Lai, M.-C., Lombardo, M. V., Ruigrok, A. N. V., Chakrabarti, B., Wheelwright, S. J., Auyeung, B., et al. 2012. Cognition in males and females with autism: similarities and differences. PLoS ONE 7:e47198. doi: 10.1371/journal.pone.0047198

[7] ↑ Ros-Demarize, R., Bradley, C., Kanne, S. M., Warren, Z., Boan, A., Lajonchere, C., et al. 2020. ASD symptoms in toddlers and preschoolers: an examination of sex differences. Autism Res. 13:157–66. doi: 10.1002/aur.2241

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Genetic contributions to autism spectrum disorder

1 Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway

2 Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway

3 Department of Psychology, PROMENTA Research Center, University of Oslo, Oslo, Norway

M. Niarchou

4 Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA

A. Starnawska

5 The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark

6 Department of Biomedicine, Aarhus University, Denmark

7 Center for Genomics for Personalized Medicine, CGPM, and Center for Integrative Sequencing, iSEQ, Aarhus, Denmark

8 College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE

C. van der Merwe

9 Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, MA, USA

10 Department of Psychiatry, Autism Research Centre, University of Cambridge, UK

Autism spectrum disorder (autism) is a heterogeneous group of neurodevelopmental conditions characterized by early childhood-onset impairments in communication and social interaction alongside restricted and repetitive behaviors and interests. This review summarizes recent developments in human genetics research in autism, complemented by epigenetic and transcriptomic findings. The clinical heterogeneity of autism is mirrored by a complex genetic architecture involving several types of common and rare variants, ranging from point mutations to large copy number variants, and either inherited or spontaneous ( de novo ). More than 100 risk genes have been implicated by rare, often de novo , potentially damaging mutations in highly constrained genes. These account for substantial individual risk but a small proportion of the population risk. In contrast, most of the genetic risk is attributable to common inherited variants acting en masse , each individually with small effects. Studies have identified a handful of robustly associated common variants. Different risk genes converge on the same mechanisms, such as gene regulation and synaptic connectivity. These mechanisms are also implicated by genes that are epigenetically and transcriptionally dysregulated in autism. Major challenges to understanding the biological mechanisms include substantial phenotypic heterogeneity, large locus heterogeneity, variable penetrance, and widespread pleiotropy. Considerable increases in sample sizes are needed to better understand the hundreds or thousands of common and rare genetic variants involved. Future research should integrate common and rare variant research, multi-omics data including genomics, epigenomics, and transcriptomics, and refined phenotype assessment with multidimensional and longitudinal measures.

Definition of autism

Kanner defined autism in 1943 with detailed case descriptions of children showing social aloofness, communication impairments, and stereotyped behaviors and interests, often accompanied by intellectual disability (ID) (Kanner, 1943 ). A year later, Asperger independently published an article on children presenting marked difficulties in social communication and unusually circumscribed and intense interests, despite advanced intellectual and language skills (Asperger, 1944 ). Three decades later, Wing and Gould united Asperger and Kanner's descriptions and conceptualized a spectrum of autistic conditions (Wing and Gould, 1978 , 1979 ).

The onset of autism is during the first years of life, although symptoms may not be fully apparent or recognized until later (American Psychiatric Association, 2013 ). Autism is a heterogeneous and complex group of conditions with considerable variation in core symptoms, language level, intellectual functioning, and co-occurring psychiatric and medical difficulties. Subtype diagnoses such as childhood autism and Asperger's syndrome were previously used to specify more homogeneous presentations, but were unstable over time within individuals and used unreliably by clinicians (Lord et al., 2020 ). Current editions of the major diagnostic manuals have replaced the subtypes with an overarching autism spectrum disorder diagnosis and instead require specification of key sources of heterogeneity; language level, intellectual functioning, and co-occurring conditions (APA, 2013 ; World Health Organization, 2018 ).

Epidemiology

Prevalence estimates of autism have steadily increased from less than 0.4% in the 1970s to current estimates of 1–2% (Fombonne, 2018 ; Lyall et al., 2017 ). The increase is largely explained by broadening diagnostic criteria to individuals without ID and with milder impairments, and increased awareness and recognition of autistic traits (Lord et al., 2020 ; Taylor et al., 2020 ). There are marked sex and gender differences in autism (Halladay et al., 2015 ; Warrier et al., 2020 ). The male-to-female ratio is approximately 4:1 in clinical and health registry cohorts but closer to 3:1 in general population studies with active case-finding (Loomes, Hull, & Mandy, 2017 ) and 1–2:1 in individuals with moderate-to-severe ID (Fombonne, 1999 ; Yeargin-Allsopp et al., 2003 ). The mechanisms underlying the sex difference are mostly unknown, and hypotheses include a female protective effect (aspects of the female sex conferring resilience to risk factors for autism), prenatal steroid hormone exposure, and social factors such as underdiagnosis and misdiagnosis in women (Ferri, Abel, & Brodkin, 2018 ; Halladay et al., 2015 ).

Co-occurring conditions are the rule rather than the exception, estimated to affect at least 70% of people with autism from childhood (Lai et al., 2019 ; Simonoff et al., 2008 ). Common co-occurring conditions include attention-deficit hyperactivity disorder (ADHD), anxiety, depression, epilepsy, sleep problems, gastrointestinal and immune conditions (Davignon, Qian, Massolo, & Croen, 2018 ; Warrier et al., 2020 ). There is an elevated risk of premature mortality from various causes, including medical comorbidities, accidental injury, and suicide (Hirvikoski et al., 2016 ).

Autism is also associated with positive traits such as attention to detail and pattern recognition (Baron-Cohen & Lombardo, 2017 ; Bury, Hedley, Uljarević, & Gal, 2020 ). Further, there is wide variability in course and adulthood outcomes with regard to independence, social relationships, employment, quality of life, and happiness (Howlin & Magiati, 2017 ; Mason et al., 2020 ; Pickles, McCauley, Pepa, Huerta, & Lord, 2020 ). Rigorous longitudinal studies and causally informative designs are needed to determine the factors affecting developmental trajectories and outcomes.

Environmental factors

Twin studies suggest that 9–36% of the variance in autism predisposition might be explained by environmental factors (Tick, Bolton, Happé, Rutter, & Rijsdijk, 2016 ). There is observational evidence for association with pre- and perinatal factors such as parental age, asphyxia-related birth complications, preterm birth, maternal obesity, gestational diabetes, short inter-pregnancy interval, and valproate use (Lyall et al., 2017 ; Modabbernia, Velthorst, & Reichenberg, 2017 ). Mixed results are reported for pregnancy-related nutritional factors and exposure to heavy metals, air pollution, and pesticides, while there is strong evidence that autism risk is unrelated to vaccination, maternal smoking, or thimerosal exposure (Modabbernia et al., 2017 ). It is challenging to infer causality from observed associations, given that confounding by lifestyle, socioeconomic, or genetic factors contributes to non-causal associations between exposures and autism. Many putative exposures are associated with parental genotype (e.g. obesity, age at birth) (Gratten et al., 2016 ; Taylor et al., 2019a , Yengo et al., 2018 ), and some are associated both with maternal and fetal genotypes (e.g. preterm birth) (Zhang et al., 2017 ). Studies triangulating genetically informative designs are needed to disentangle these relationships (Davies et al., 2019 ; Leppert et al., 2019 ; Thapar & Rutter, 2019 ).

Twin and pedigree studies

In 1944, Kanner noted that parents shared common traits with their autistic children, introducing the ‘broader autism phenotype’ (i.e. sub-threshold autistic traits) and recognizing the importance of genetics (Harris, 2018 ; Kanner, 1944 ). Thirty years later, twin studies revolutionized the field of autism research (Ronald & Hoekstra, 2011 ).

Twin studies were the first to demonstrate the heritability of autism. In 1977, the first twin-heritability estimate was published, based on a study of 10 dizygotic (DZ) and 11 monozygotic (MZ) pairs (Folstein & Rutter, 1977 ). Four out of the 11 MZ pairs (36%) but none of the DZ pairs were concordant for autism. Subsequently, over 30 twin studies have been published, further supporting the high heritability of autism (Ronald & Hoekstra, 2011 ). A meta-analysis of seven primary twin studies reported that the heritability estimates ranged from 64% to 93% (Tick et al., 2016 ). The correlations for MZ twins were at 0.98 [95% confidence interval (CI) 0.96–0.99], while the correlations for DZ twins were at 0.53 (95% CI 0.44–0.60) when the autism prevalence rate was assumed to be 5% (based on the broader autism phenotype) and increased to 0.67 (95% CI 0.61–0.72) when the prevalence was 1% (based on the stricter definition) (Tick et al., 2016 ). Additionally, family studies have found that the relative risk of a child having autism relates to the amount of shared genome with affected relatives ( Fig. 1 ) (Bai et al., 2019 ; Constantino et al., 2013 ; Georgiades et al., 2013 ; Grønborg, Schendel, & Parner, 2013 ; Risch et al., 2014 ; Sandin et al., 2014 ).

Relative risk of autism by degree of relatedness with a person with autism. Relative risk for full and half siblings, and full cousins was provided in Hansen et al. ( 2019 ). Relative risk for half first cousins was estimated based on Xie et al. ( 2019 ). GS, genome shared.

Early twin and pedigree studies demonstrated that the biological relatives of individuals with autism who did not meet the criteria for an autism diagnosis themselves commonly showed elevated autistic traits such as communication and social interaction difficulties (Le Couteur et al., 1996 ), indicating that the heritability is not restricted to the traditional diagnostic boundaries of autism. Twin studies also indicate that although social communication and repetitive behavior trait dimensions each show strong heritability, there is a limited genetic correlation between them (e.g. for a review, see Ronald & Hoekstra, 2011 ). Further, twin studies have found substantial genetic overlap between autistic traits and symptoms of other psychiatric conditions, including language delay (e.g. Dworzynski et al., 2008 ), ID (e.g. Nishiyama et al., 2009 ), ADHD (e.g. Ronald, Edelson, Asherson, & Saudino, 2010 ), and anxiety (e.g. Lundström et al., 2011 ) (for a review, see Ronald & Hoekstra, 2014 ). Moreover, twin and family studies indicate that the sibling recurrence rate of autism is lower in female than male siblings (Palmer et al., 2017 ; Werling & Geschwind, 2015 ), suggesting the female protective effect hypothesis as a potential explanation for the male preponderance in the diagnosis of autism. The hypothesis was supported by results showing that the siblings of autistic females had a higher likelihood of high autistic trait scores and autism than the siblings of autistic males (Ferri et al., 2018 ; Palmer et al., 2017 ; Robinson, Lichtenstein, Anckarsäter, Happé, & Ronald, 2013 ), consistent with females having a higher liability threshold.

Genetic variants differ in the frequency at which they occur in the population (e.g. rare v. common), the type (i.e. SNPs/CNVs/translocations and inversions/indels), and whether they are inherited or de novo . Here, we summarize the findings on genetic risk for autism from linkage and candidate gene studies, common and rare genetic variation studies, epigenomics, and transcriptomics. A glossary of important terms is in Box 1 .

Candidate gene association study: A study that examines the association between a phenotype and a genetic variant chosen a priori based on knowledge of the gene's biology or functional impact.

Complex trait: A trait that does not follow Mendelian inheritance patterns, but is likely the result of multiple factors including a complex mixture of variation within multiple genes.

Copy number variant (CNV): Deletion or duplication of large genomic regions.

de novo mutation: A mutation that is present in the offspring but is either absent in parents or is present only in parental germ cells.

DNA methylation (DNAm): Epigenetic modification of DNA characterized by the addition of a methyl group (-CH 3 ) to the 5 th position of the pyrimidine ring of cytosine base resulting in 5-methylcytosine (5mC).

Epigenetics: The science of heritable changes in gene regulation and expression that do not involve changes to the underlying DNA sequence.

Epigenome-Wide Association Study (EWAS): A study that investigates associations between DNA methylation levels quantified at tens/hundreds of thousands of sites across the human genome, and the trait of interest.

Genome-Wide Association Study (GWAS): A study scanning genome-wide genetic variants for associations with a given trait.

Genetic correlation: An estimate of the proportion of variance shared between two traits due to shared genetics.

Heritability: An estimate of the proportion of variation in a given trait that is due to differences in genetic variation between individuals in a given population.

Heritability on the liability scale : A heritability estimate adjusted for the population prevalence of a given binary trait, typically disorders.

Genetic linkage studies: A statistical method of mapping genes of heritable traits to their chromosomal locations by using chromosomal co-segregation with the phenotype.

Mendelian inheritance: When the inheritance of traits is passed down from parents to children and is controlled by a single gene for which one allele is dominant and the other recessive.

Methylation Quantitative Trait Locus (mQTL): A SNP at which genotype is correlated with the variation of DNA methylation levels at a nearby ( cis- mQTL) or distal ( trans- mQTL) site.

Phenotype: The observable characteristics of an individual.

Polygenic risk score (PRS): An estimate of an individual's genetic liability for a condition calculated based on the cumulative effect of many common genetic variants.

Single nucleotide polymorphism (SNP): A single base pair change that is common (>1%) in the population.

Single nucleotide variant (SNV): A variation in a single nucleotide without any limitation of frequency.

SNP heritability: The proportion of variance in a given phenotype in a population that is attributable to the additive effects of all SNPs tested. Typically, SNPs included have a minor allele frequency >1%.

Linkage and candidate gene studies

Initial linkage studies were conducted to identify chromosomal regions commonly inherited in affected individuals. Susceptibility loci implicated a range of regions, but only two have been replicated (Ramaswami & Geschwind, 2018 ): at chromosome 20p13 (Weiss, Arking, Daly, & Chakravarti, 2009 ) and chromosome 7q35 (Alarcón, Cantor, Liu, Gilliam, & Geschwind, 2002 ). Lack of replication and inconsistent findings were largely due to low statistical power (Kim & Leventhal, 2015 ). Candidate gene association studies identified over 100 positional and/or functional candidate genes for associations with autism (Bacchelli & Maestrini, 2006 ). However, there was no consistent replication for any of these findings (Warrier, Chee, Smith, Chakrabarti, & Baron-Cohen, 2015 ), likely due to limitations in study design (e.g. low statistical power, population diversity, incomplete coverage of variation within the candidate genes, and false positives arising from publication bias) (Ioannidis, 2005 ; Ioannidis, Ntzani, Trikalinos, & Contopoulos-Ioannidis, 2001 ). The advancement of genome-wide association studies (GWAS) and next-generation sequencing techniques has significantly enhanced gene and variant discovery.

Common genetic variation

The SNP-heritability (proportion of variance attributed to the additive effects of common genetic variants) of autism ranges from 65% in multiplex families (Klei et al., 2012 ) to 12% in the latest Psychiatric Genomics Consortium GWAS ( Fig. 2 a ) (Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium, 2017 ; Grove et al., 2019 ). Variation is largely attributable to sample heterogeneity and differences in methods used to estimate SNP-heritability.

Variance explained by different classes of genetic variants in autism. ( a ) Donut chart of the variance explained by different classes of variants. The narrow-sense heritability (82.7%, Nordic average, shades of green) has been estimated using familial recurrence data from Bai et al. ( 2019 ). The total common inherited heritability (12%) has been estimated using LDSC-based SNP-heritability (additive) from Grove et al. ( 2019 ) and the total rare inherited heritability (3%) has been obtained from Gaugler et al. ( 2014 ). The currently unexplained additive heritability is thus 67.7% (total narrow-sense heritability minus common and rare inherited heritabilities combined). This leaves a total of 17.3% of the variance to shared and unique environmental estimates (Bai et al., 2019 ). The term environmental refers to non-additive and non-inherited factors that contribute to variation in autism liability. Of this, de novo missense and protein-truncating variants (Satterstrom et al., 2020 ) and variation in non-genic regions (An et al., 2018 ) together explain 2.5% of the variance. Whilst de novo variation can be inherited in some cases (germline mutation in the parent) and thus shared between siblings, it is unlikely that this will be shared by other related individuals, and thus unlikely to be included in the narrow-sense heritability in Bai et al. ( 2019 ). This is likely to be a lower-bound of the estimate as we have not included the variance explained by de novo structural variants and tandem repeats. Additionally, non-additive variation accounts for ~4% of the total variance (Autism Sequencing Consortium et al., 2019 ). Thus, ~11% of the total variance is currently unaccounted for, though this is likely to be an upper bound. ( b ) The variance explained is likely to change in phenotypic subgroups. For instance, the risk ratio for de novo protein-truncating variants in highly constrained genes (pLI > 0.9) is higher in autistic individuals with ID compared to those without ID (point estimates and 95% confidence intervals provided; Kosmicki et al., 2017 ). ( c ) Similarly, the proportion of the additive variance explained by common genetic variants is higher in autistic individuals without ID compared to autistic individuals with ID (Grove et al., 2019 ). Point estimates and 95% confidence intervals provided.

Early GWASs of autism were underpowered, partly due to overestimating potential effect sizes. Grove et al. ( 2019 ) conducted a large GWAS of autism combining data from over 18 000 autistic individuals and 27 000 non-autistic controls and an additional replication sample. They identified five independent GWAS loci ( Fig. 3 ). Another recent study (Matoba et al., 2020 ) identified a further novel locus by meta-analyzing the results from Grove et al. ( 2019 ) with over 6000 case-pseudocontrol pairs from the SPARK cohort by employing a massively parallel reporter assay to identify a potential causal variant (rs7001340) at this locus which regulates DDH2 in the fetal brain. The sample sizes are still relatively small compared to other psychiatric conditions (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2020 ; Howard et al., 2019 ), though ongoing work aims to double the sample size and identify additional loci.

Karyogram showing the 102 genes implicated by rare variant findings at a false discovery rate of 0.1 or less (Satterstrom et al., 2020 ) and the five index SNPs identified in GWAS (Grove et al., 2019 ) of autism.

Using genetic correlations and polygenic score analyses, studies have identified modest shared genetics between autism and different definitions of autistic traits in the general population (Askeland et al., 2020 ; Bralten et al., 2018 ; Robinson et al., 2016 ; Taylor et al., 2019 b ). There is some evidence for developmental effects, with greater shared genetics in childhood compared to adolescence (St Pourcain et al., 2018 ). These methods have also identified modest polygenic associations between autism and other neurodevelopmental and mental conditions such as schizophrenia, ADHD, and major depressive disorder, related traits such as age of walking, language delays, neuroticism, tiredness, and self-harm, as well as risk of exposure to childhood maltreatment and other stressful life events (Brainstorm Consortium et al., 2018 ; Bulik-Sullivan et al., 2015 ; Grove et al., 2019 ; Hannigan et al., 2020 ; Lee et al., 2019 , b ; Leppert et al., 2019 ; Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013 ; Warrier & Baron-Cohen, 2019 ). Notably, autism is positively genetically correlated with measures of intelligence and educational attainment (EA) (Bulik-Sullivan et al., 2015 ; Grove et al., 2019 ), an observation supported by polygenic score association (Clarke et al., 2016 ). Polygenic Transmission Disequilibrium Tests have identified an over-transmission of polygenic scores for EA, schizophrenia, and self-harm from parents to autistic children, but an absence of such over-transmission to non-autistic siblings (Warrier & Baron-Cohen, 2019 ; Weiner et al., 2017 ), suggesting that these genetic correlations are not explained by ascertainment biases or population stratification. However, a genetic correlation does not necessarily imply a causal relationship between the two phenotypes and may simply index biological pleiotropy. Causal inference methods such as Mendelian randomization can be used to disentangle such relationships (Davies et al., 2019 ; Pingault et al., 2018 ).

The relatively low SNP-heritability in autism compared to other psychiatric conditions may partly be due to phenotypic heterogeneity. In an attempt to reduce phenotypic heterogeneity, Chaste et al. ( 2015 ) identified 10 phenotypic combinations to subgroup autistic individuals. Family-based association analyses did not identify significant loci, and SNP-heritability for the subgroups was negligent. It is unclear if reducing phenotypic heterogeneity increases genetic homogeneity, and investigating this in larger samples is warranted. Another study identified no robust evidence of genetic correlation between social and non-social (restricted and repetitive behavior patterns) autistic traits (Warrier et al., 2019 ). A few studies have investigated the common variant genetic architecture of social and non-social autistic traits in individuals with autism (Alarcón et al., 2002 ; Cannon et al., 2010 ; Cantor et al., 2018 ; Lowe, Werling, Constantino, Cantor, & Geschwind, 2015 ; Tao et al., 2016 ; Yousaf et al., 2020 ) and in the general population (St Pourcain et al., 2014 ; Warrier et al., 2018 , 2019 ), but replication of the identified loci is needed.

Diagnostic classification is another source of heterogeneity: SNP-heritability of Asperger's syndrome (ICD-10 diagnosis) was twice (0.097 ± 0.001) that of childhood autism and unspecified pervasive developmental disorders (Grove et al., 2019 ) [due to overlap in subtype diagnoses, a hierarchy was used: childhood autism>atypical autism>Asperger's syndrome>unspecified subtypes (Grove et al., 2019 )]. Supporting this, polygenic scores for intelligence and EA had larger loadings in the Asperger's syndrome and childhood autism subgroups compared to other subgroups (Grove et al., 2019 ). Additionally, the SNP-heritability of autism (all subtypes) without co-occurring ID diagnosis (0.09 ± 0.005) was three times that of autism with ID (Grove et al., 2019 ) ( Fig. 2 c ).

Rare genetic variation

Rare genetic variants confer significant risk in the complex etiology of autism. They are typically non-Mendelian, with substantial effect sizes and low population attributable risk. It is estimated that ~10% of autistic individuals have been diagnosed with an identifiable rare genetic syndrome characterized by dysmorphia, metabolic, and/or neurologic features (Carter & Scherer, 2013 ; Tammimies et al., 2015 ). Associated syndromes include the 15q11-q13 duplication of the Prader-Willi/Angelman syndrome, fragile X syndrome, 16p11.2 deletion syndrome, and 22q11 deletion syndrome (Sztainberg & Zoghbi, 2016 ). Prevalence estimates for autism vary widely between genetic syndromes; for example, 11% in 22q11.2 deletion syndrome and 54% in Cohen's syndrome (Richards, Jones, Groves, Moss, & Oliver, 2015 ). Of note, estimating the prevalence of autism in the context of genetic syndromes is complex (Havdahl et al., 2016 ; Richards et al., 2015 ).

The rate of gene discovery in autism is a linear function of increasing sample size (De Rubeis et al., 2014 ). Early studies implicated nine genes in the first 1000 autism cases (Neale et al., 2012 ; Sanders et al., 2012 ), increasing to 27 and 33 associated genes from separate analyses of Simons Simplex Collection and Autism Sequencing Consortium (ASC) samples (De Rubeis et al., 2014 ; Iossifov et al., 2014 ). Integrating these samples using the TADA framework implicated a total of 65 autism genes (Sanders et al., 2015 ).

The MSSNG initiative analyzed whole genomes from 5205 individuals ( N cases  = 2636), and identified 61 autism-risk genes, of which 18 were new candidates (Yuen et al., 2017 ). More recently, the largest whole-exome sequencing analysis to date conducted by the ASC ( N  = 35 584, N cases  = 11 986) identified 102 autism-associated genes ( Fig. 3 ), many of which are expressed during brain development with roles in the regulation of gene expression and neuronal communication (Satterstrom et al., 2020 ). Rare CNVs and SNVs associated with autism have pleiotropic effects, thus increasing the risk for other complex disorders such as schizophrenia, ADHD, ID, and epilepsy (Gudmundsson et al., 2019 ; Satterstrom et al., 2019 , 2020 ).

CNVs can impact one or multiple genes and can occur at common or rare frequencies in a population. All CNVs associated with autism have been rare. Recurrent CNVs are among the most convincing rare inherited risk variations for autism, and have a prevalence of about 3% in affected patients (Bourgeron, 2016 ). In comparison, approximately 4–10% of autistic individuals have de novo deletions or duplications (Bourgeron, 2016 ; Pinto et al., 2010 ; Sebat et al., 2007 ) frequently mapped to established risk loci 1q21.1, 3q29, 7q11.23, 15q11.2-13, and 22q11.2 (Sanders et al., 2015 ). A higher global frequency of de novo CNVs is observed in idiopathic autism cases from simplex families (10%) compared to multiplex families (2%) and controls (1%) (Halladay et al., 2015 ; Itsara et al., 2010 ; Sebat et al., 2007 ). Inherited CNVs can be present in unaffected siblings and parents, suggesting a model of incomplete penetrance dependent on the dosage sensitivity and function of the gene(s) they affect (Vicari et al., 2019 ).

Damaging SNVs include nonsense, frameshift, and splice site mutations (collectively referred to as protein-truncating variants, or PTVs), and missense variants. Rare inherited variants have a smaller average effect size and reduced penetrance compared to de novo pathogenic mutations. Early studies on whole exomes from trios established a key role for de novo germline mutations in autism. Whilst analysis in smaller sample sizes indicated only modest increase in de novo mutation rates in autism cases (Neale et al., 2012 ), the rate rose significantly in excess of expectation as the sample size increased (De Rubeis et al., 2014 ; Iossifov et al., 2014 ). Most recently, the ASC observed a 3.5-fold case enrichment of damaging de novo PTVs and a 2.1-fold enrichment for damaging de novo missense variants (Satterstrom et al., 2020 ), concluding that all exome de novo SNVs explain 1.92% of the variance in autism liability (Satterstrom et al., 2020 ) ( Fig. 2 a ).

Comparatively, the ASC discovered a 1.2-fold enrichment of rare inherited damaging PTVs in cases compared to unaffected siblings (Satterstrom et al., 2020 ). Similarly, recent whole-genome analysis found no excess of rare inherited SNVs, and no difference in the overall rate of these variants in affected subjects compared to unaffected siblings (Ruzzo et al., 2019 ).

New advancements

It is estimated that de novo mutations in protein-coding genes contribute to risk in ~30% of simplex autism cases (Yuen et al., 2017 ; Zhou et al., 2019 ). However, recent work has also shown that de novo mutations in non-coding regions of the genome (particularly gene promoters) contribute to autism (An et al., 2018 ; Zhou et al., 2019 ). Adapting machine learning techniques may be key to providing novel neurobiological insights to the genetic influences on autism in the future (An et al., 2018 ; Ruzzo et al., 2019 ; Zhou et al., 2019 ). Additionally, rare tandem repeat expansions in genic regions are more prevalent among autism cases than their unaffected siblings, with a combined contribution of ~2.6% to the risk of autism (Trost et al., 2020 ).

Common and rare variant interplay

The largest component of genetic risk is derived from common variants of additive effect with a smaller contribution from de novo and rare inherited variation ( Fig. 2 a ) (de la Torre-Ubieta, Won, Stein, & Geschwind, 2016 ; Gaugler et al., 2014 ). Notably, KMT2E was implicated in both the latest GWAS (Grove et al., 2019 ) and exome sequencing (Satterstrom et al., 2020 ) analyses. It is hypothesized that common genetic variation in or near the genes associated with autism influences autism risk, although current sample sizes lack the power to detect the convergence of the two (Satterstrom et al., 2020 ).

Whilst higher SNP-heritability is observed in autistic individuals without ID ( Fig. 2 b ), de novo PTVs in constrained genes are enriched in autistic individuals with ID ( Fig. 2 a ). However, the genetic architecture of autism is complex and diverse. For example, common genetic variants also contribute to risk in autistic individuals with ID and in autistic individuals carrying known large-effect de novo variants in constrained genes (Weiner et al., 2017 ). Furthermore, an excess of disruptive de novo variants is also observed in autistic individuals without co-occurring ID compared to non-autistic individuals (Satterstrom et al., 2020 ).

Epigenetics

DNA methylation (DNAm), an epigenetic modification, allows for both genetic and environmental factors to modulate a phenotype (Martin & Fry, 2018 ; Smith et al., 2014 ). DNAm affects gene expression, regulatory elements, chromatin structure, and alters neuronal development, functioning, as well as survival (Kundaje et al., 2015 ; Lou et al., 2014 ; Peters et al., 2015 ; Sharma, Klein, Barboza, Lohdi, & Toth, 2016 ; Yu et al., 2012 ; Zlatanova, Stancheva, & Caiafa, 2004 ). Additionally, putative prenatal environmental risk factors impact the offspring's methylomic landscape (Anderson, Gillespie, Thiele, Ralph, & Ohm, 2018 ; Cardenas et al., 2018 ; Joubert et al., 2016 ), thus providing a plausible molecular mechanism to modulate the neurodevelopmental origins of autism.

Autism Epigenome-Wide Association Study (EWAS) meta-analysis performed in blood from children and adolescents from SEED and SSC cohorts ( N cases  = 796, N controls  = 858) identified seven differentially methylated positions (DMPs) associated ( p  < 10 × 10 −05 ) with autism, five of them also reported to have brain-based autism associations. The associated DMPs annotated to CENPM , FENDRR , SNRNP200 , PGLYRP4 , EZH1 , DIO3 , and CCDC181 genes, with the last site having the largest effect size and the same direction of association with autism across the prefrontal cortex, temporal cortex, and cerebellum (Andrews et al., 2018 ). The study reported moderate enrichment of methylation Quantitative Trait Loci (mQTLs) among the associated findings, suggesting top autism DMPs to be under genetic control (Andrews et al., 2018 ). These findings were further extended by the MINERvA cohort that added 1263 neonatal blood samples to the meta-analysis. The SEED-SSC-MINERvA meta-EWAS identified 45 DMPs, with the top finding showing the consistent direction of association across all three studies annotated to ITLN1 (Hannon et al., 2018 ). The MINERvA sample was also used for EWAS of autism polygenic score, hypothesizing that the polygenic score-associated DNAm variation is less affected by environmental risk factors, which can confound case–control EWAS. Elevated autism polygenic score was associated with two DMPs ( p  < 10 × 10 −06 ), annotated to FAM167A / C8orf12 and RP1L1 . Further Bayesian co-localization of mQTL results with autism GWAS findings provided evidence that several SNPs on chromosome 20 are associated both with autism risk and DNAm changes in sites annotated to KIZ , XRN2 , and NKX2-4 (Hannon et al., 2018 ). The mQTL effect of autism risk SNPs was corroborated by an independent study not only in blood, but also in fetal and adult brain tissues, providing additional evidence that autism risk variants can act through DNAm to mediate the risk of the condition (Hammerschlag, Byrne, Bartels, Wray, & Middeldorp, 2020 ).

Since autism risk variants impact an individual's methylomic landscape, studies that investigate DNAm in the carriers of autism risk variants are of interest to provide insight into their epigenetic profiles. A small blood EWAS performed in 52 cases of autism of heterogeneous etiology, nine carriers of 16p11.2del, seven carriers of pathogenic variants in CHD8 , and matched controls found that DNAm patterns did not clearly distinguish autism of the heterogeneous etiology from controls. However, the homogeneous genetically-defined 16p11.2del and CHD8 +/− subgroups were characterized by unique DNAm signatures enriched in biological pathways related to the regulation of central nervous system development, inhibition of postsynaptic membrane potential, and immune system (Siu et al., 2019 ). This finding highlights the need to combine genomic and epigenomic information for a better understanding of the molecular pathophysiology of autism.

It must be noted that a very careful interpretation of findings from peripheral tissues is warranted. DNAm is tissue-specific and therefore EWAS findings obtained from peripheral tissues may not reflect biological processes in the brain. Using the mQTL analytical approach may reduce this challenge, as mQTLs are consistently detected across tissues, developmental stages, and populations (Smith et al., 2014 ). However, not all mQTLs will be detected across tissues and will not necessarily have the same direction of effect (Smith et al., 2014 ). Therefore, it is recommended that all epigenetic findings from peripheral tissues are subjected to replication analyses in human brain samples, additional experimental approaches, and/or Mendelian randomization to strengthen causal inference and explore molecular mediation by DNAm (Walton, Relton, & Caramaschi, 2019 ).

EWASs performed in post-mortem brains have typically been conducted using very small sample sizes, due to limited access to brain tissue (Ladd-Acosta et al., 2014 ; Nardone et al., 2014 ). One of the largest autism EWAS performed in post-mortem brains (43 cases and 38 controls) identified multiple DMPs ( p  < 5 × 10 −05 ) associated with autism (31 DMPs in the prefrontal cortex, 52 in the temporal cortex, and two in the cerebellum) (Wong et al., 2019 ), and autism-related co-methylation modules to be significantly enriched for synaptic, neuronal, and immune dysfunction genes (Wong et al., 2019 ). Another post-mortem brain EWAS reported DNAm levels at autism-associated sites to resemble the DNAm states of early fetal brain development (Corley et al., 2019 ). This finding suggests an epigenetic delay in the neurodevelopmental trajectory may be a part of the molecular pathophysiology of autism.

Overall, methylomic studies of autism provide increasing evidence that common genetic risk variants of autism may alter DNAm across tissues, and that the epigenetic dysregulation of neuronal processes can contribute to the development of autism. Stratification of study participants based on their genetic risk variants may provide deeper insight into the role of aberrant epigenetic regulation in subgroups within autism.

Transcriptomics

Transcriptomics of peripheral tissues.

Gene expression plays a key role in determining the functional consequences of genes and identifying genetic networks underlying a disorder. One of the earliest studies on genome-wide transcriptome (Nishimura et al., 2007 ) investigated blood-derived lymphoblastoid cells gene expression from a small set of males with autism ( N  = 15) and controls. Hierarchical clustering on microarray expression data followed by differentially expressed gene (DEG) analysis revealed a set of dysregulated genes in autism compared to controls. This approach was adopted (Luo et al., 2012 ) to investigate DEGs in a cohort of 244 families with autism probands (index autism case in a family) known to carry de novo pathogenic or variants of unknown significance and discordant sibling carriers of non-pathogenic CNVs. From genome-wide microarray transcriptome data, this study identified significant enrichment of outlier genes that are differentially expressed and reside within the proband rare/ de novo CNVs. Pathway enrichment of these outlier genes identified neural-related pathways, including neuropeptide signaling, synaptogenesis, and cell adhesion. Distinct expression changes of these outlier genes were identified in recurrent pathogenic CNVs, i.e. 16p11.2 microdeletions, 16p11.2 microduplications, and 7q11.23 duplications. Recently, multiple independent genome-wide blood-derived transcriptome analysis (Filosi et al., 2020 ; Lombardo et al., 2018 ; Tylee et al., 2017 ) showed the efficiency of detecting dysregulated genes in autism, including aberrant expression patterns of long non-coding RNAs (Sayad, Omrani, Fallah, Taheri, & Ghafouri-Fard, 2019 ).

Transcriptomics of post-mortem brain tissue

Although blood-derived transcriptome can be feasible to study due to easy access to the biological specimen, blood transcriptome results are not necessarily representative of the transcriptional machinery in the brain (GTEx Consortium, 2017 ). Hence, it is extremely hard to establish a causal relationship between blood transcriptional dysregulations and phenotypes in autism. A landmark initiative by Allen Brain Institute to profile human developing brain expression patterns (RNA-seq) from post-mortem tissue enabled neurodevelopmental research to investigate gene expression in the brain (Sunkin et al., 2013 ). Analyzing post-mortem brain tissue, multiple studies identified dysregulation of genes at the level of gene exons impacted by rare/ de novo mutations in autism (Uddin et al., 2014 ; Xiong et al., 2015 ), including high-resolution detection of exon splicing or novel transcript using brain tissue RNA sequencing (RNA-seq). High-resolution RNA-seq enabled autism brain transcriptome analysis on non-coding elements, and independent studies identified an association with long non-coding RNA and enhancer RNA dysregulation (Wang et al., 2015 ; Yao et al., 2015 ; Ziats & Rennert, 2013 ).

Although it is difficult to access post-mortem brain tissue from autistic individuals, studies of whole-genome transcriptome from autism and control brains have revealed significantly disrupted pathways ( Fig. 4 ) related to synaptic connectivity, neurotransmitter, neuron projection and vesicles, and chromatin remodeling pathways (Ayhan & Konopka, 2019 ; Gordon et al., 2019 ; Voineagu et al., 2011 ). Recently, an integrated genomic study also identified from autism brain tissue a component of upregulated immune processes associated with hypomethylation (Ramaswami et al., 2020 ). These reported pathways are in strong accordance with numerous independent autism studies that integrated genetic data with brain transcriptomes (Courchesne, Gazestani, & Lewis, 2020 ; Uddin et al., 2014 ; Yuen et al., 2017 ). A large-scale analysis of brain transcriptome from individuals with autism identified allele-specific expressions of genes that are often found to be impacted by pathogenic de novo mutations (Lee et al., 2019 a ). The majority of the studies are in consensus that genes that are highly active during prenatal brain development are enriched for clinically relevant mutations in autism (Turner et al., 2017 ; Uddin et al., 2014 ; Yuen et al., 2017 ). Recently, a large number (4635) of expression quantitative trait loci were identified that were enriched in prenatal brain-specific regulatory regions comprised of genes with distinct transcriptome modules that are associated with autism (Walker et al., 2019 ).

Most commonly reported three pathways (Ayhan & Konopka, 2019 ; Gordon et al., 2019 ; Voineagu et al., 2011 ) associated with autism. ( a ) The synaptic connectivity and neurotransmitter pathway involves genes (yellow rectangular box) within presynaptic and postsynaptic neurons. Neurotransmitter transport through numerous receptors is an essential function of this pathway; ( b ) the chromatin remodeling pathway involves binding of remodeling complexes that initiate the repositioning (move, eject, or restructure) of nucleosomes that potentially can disrupt gene regulation; and ( c ) the neural projection pathway [adapted from Greig, Woodworth, Galazo, Padmanabhan, & Macklis ( 2013 )] involves the projection of neural dendrite into distant regions and the migration of neuronal cells through ventricular (VZ) and subventricular zones (SVZ) into the different cortical layers (I-VI).

Single-cell transcriptomics

Recent advancement of single-cell transcriptomics enables the detection of cell types that are relevant to disorder etiology. A recent case–control study conducted single-cell transcriptomics analysis on 15 autism and 16 control cortical post-mortem brain tissues generating over 100 000 single-cell transcriptomics data (Velmeshev et al., 2019 ). Cell-type analysis revealed dysregulations of a specific group of genes in cortico-cortical projection neurons that correlate with autism severity (Velmeshev et al., 2019 ). Deciphering cell-type identification has future implications, in particular for the implementation of precision medicine. However, single-cell technology is at very early stages of development and computationally it is still very complex to classify cell-type identity.

The emergence of CRISPR/Cas9 genome editing technology can potentially become an effective tool in future therapeutics of genetic conditions associated with autism. Although introducing and reversing DNA mutation is becoming a mature technology within in vitro systems, much work needs to be done for in vivo use of genome editing. Single-cell OMICs is another emerging field that has the potential to decipher developmental (spatio-temporally) brain cell types that are associated with autism. Identifying cell clusters and defining cell identity is a major computational challenge. Artificial intelligence can significantly improve these computational challenges to identify the molecular associations of autism at the single-cell level.

Clinical and therapeutic implications

In some, but not all, best practice clinical guidelines, genetic tests such as fragile X testing, chromosomal microarray, and karyotype testing are part of the standard medical assessment in a diagnostic evaluation of autism to identify potentially etiologically relevant rare genetic variants (Barton et al., 2018 ). The guidelines vary with respect to whether genetic testing is recommended for all people with autism, or based on particular risk factors, such as ID, seizures, or dysmorphic features. The DSM-5 diagnosis of autism includes a specifier for associated genetic conditions (APA, 2013 ). Although genetic test results may not usually have consequences for treatment changes, the results could inform recurrence risk and provide families with access to information about symptoms and prognosis. In the future, gene therapy, CRISPR/Cas9, and genome editing technologies may lead to the gene-specific design of precision medicine for rare syndromic forms of autism (Benger, Kinali, & Mazarakis, 2018 ; Gori et al., 2015 ).

Given that a substantial proportion of the genetic liability to autism is estimated to be explained by the cumulative effect of a large number of common SNPs, polygenic scores have gained traction as potential biomarkers. However, the predictive ability of polygenic scores from the largest autism GWAS to date is too low to be clinically useful. The odds ratio when comparing the top and bottom polygenic score decile groups is only 2.80 (95% CI 2.53–3.10) (Grove et al., 2019 ). Additionally, polygenic scores based on the samples of European ancestry do not translate well in populations with diverse ancestry (Palk, Dalvie, de Vries, Martin, & Stein, 2019 ).

Genetic testing can in the future become useful for informing screening or triaging for diagnostic assessments or identifying who may be more likely to respond to which type of intervention (Wray et al., 2021 ). Genetics may also help identify individuals with autism who are at a high risk of developing co-occurring physical and mental health conditions or likely to benefit from treatments of such conditions. A top research priority for autistic people and their families is addressing co-occurring mental health problems (Autistica, 2016 ), which may sometimes be the primary treatment need as opposed to autism per se . Genomics may also be helpful to repurpose existing treatments and better identify promising treatments. There are active clinical trials to repurpose drugs in autism (Hong & Erickson, 2019 ). Moreover, genetics can be used to identify social and environmental mediating and moderating factors (Pingault et al., 2018 ), which could inform interventions to improve the lives of autistic people.

Notably, there are important ethical challenges related to clinical translation of advances in genetics, including concerns about discriminatory use, eugenics concerning prenatal genetic testing, and challenges in interpretation and feedback (Palk et al., 2019 ). People with autism and their families are key stakeholders in genetic studies of autism and essential to include in discussions of how genetic testing should be used.

Conclusions and future directions

Recent large-scale and internationally collaborative investigations have led to a better understanding of the genetic contributions to autism. This includes identifying the first robustly associated common genetic variants with small individual effects (Grove et al., 2019 ) and over 100 genes implicated by rare, mostly de novo , variants of large effects (Sanders et al., 2015 ; Satterstrom et al., 2020 ). These and other findings show that the genetic architecture of autism is complex, diverse, and context-dependent, highlighting a need to study the interplay between different types of genetic variants, identify genetic and non-genetic factors influencing their penetrance, and better map the genetic variants to phenotypic heterogeneity within autism.

Immense collaborative efforts are needed to identify converging and distinct biological mechanisms for autism and subgroups within autism, which can in turn inform treatment (Thapar & Rutter, 2020 ). It is crucial to invest in multidimensional and longitudinal measurements of both core defining traits and associated traits such as language, intellectual, emotional, and behavioral functioning, and to collaboratively establish large omics databases including genomics, epigenomics, transcriptomics, proteomics, and brain connectomics (Searles Quick, Wang, & State, 2020 ). Indeed, large-scale multi-omic investigations are becoming possible in the context of large population-based family cohorts with rich prospective and longitudinal information on environmental exposures and developmental trajectories of different neurodevelopmental traits. Finally, novel methods (Neumeyer, Hemani, & Zeggini, 2020 ) can help investigate causal molecular pathways between genetic variants and autism and autistic traits.

Acknowledgements

We thank the Psychiatric Genomics Consortium, Anders Børglum, and Elise Robinson for their support and advice.

Financial support

Alexandra Havdahl was supported by the South-Eastern Norway Regional Health Authority (#2018059, career grant #2020022) and the Norwegian Research Council (#274611 PI Ted Reichborn-Kjennerud and #288083 PI Espen Røysamb). Maria Niarchou was supported by Autism Speaks (#11680). Anna Starnawska was supported by The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark (R155-2014-1724). Varun Warrier is supported by the Bowring Research Fellowship (St. Catharine's College, Cambridge), the Templeton World Charity Foundation, Inc., the Autism Research Trust, and the Wellcome Trust. Celia van der Merwe is supported by the Simons Foundation NeuroDev study (#599648) and the NIH R01MH111813 grant.

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Communication difficulties in autism spectrum disorder

Many individuals with autism encounter challenges in effective communication. Difficulties with both verbal and nonverbal communication can be frustrating. Still, there are a variety of therapies and alternative communication methods that can help people with autism facing language difficulties succeed in neurotypical spaces.

This post will dive into the types of communication barriers experienced by those with autism, how to recognize autism and speech delays early, and the types of therapies and accommodations that are available to aid those facing the communication challenges associated with autism.

Types of communication difficulties

Individuals with autism may approach communication in non-traditional ways and may experience verbal, nonverbal, and social communication challenges.

Language difficulties associated with ASD might include speaking in a flat tone or repeating words or phrases, known as echolalia. People with autism may also have a speech delay, meaning they may use childlike language even as adults, or they may not have developed speech at all. 1 They also may have difficulty understanding sarcasm and figurative language. 2

For individuals with autism, nonverbal communication might develop differently.

Facial expressions

Individuals with autism may face difficulty in understanding and expressing facial expressions, which are crucial for effective social interaction. Without adequate development of these skills, autistic individuals may have difficulty with interpreting social cues and engaging in meaningful social communication.

Body language

Similarly, individuals with autism may encounter challenges in understanding and utilizing body language effectively.

Underdeveloped nonverbal conversation skills can hinder their ability to engage in social interaction and may contribute to social communication difficulties.

Developing pre-language skills, such as oral language skills, may also present challenges for individuals on the autism spectrum. These difficulties can lead to repetitive or rigid language patterns and behaviors, further impacting their social interactions and communication skills.

Individuals who are neurotypical often use gestures like pointing to complement their verbal communication and typically maintain eye contact during conversations, those with autism may not exhibit these behaviors. They might not engage in typical body language or display facial expressions, which can sometimes lead to misunderstandings or difficulties in expressing their needs.

In particular, many social cues that are widely recognized by people who identify as neurotypical may be missed by someone on the autism spectrum. This can lead to awkwardness or confusion when a socially-expected script is not followed, making it hard for some people with autism to develop close relationships. 3

Additional factors contributing to communication difficulties

Some communication difficulties may be connected with other symptoms of autism spectrum disorder. These symptoms may exacerbate the communication issues or may even be their root cause.

For example, many individuals with autism may experience difficulties with sensory sensitivity. Oversensitivity to sound may make it hard for a person with autism to focus solely on their conversation. In young children, this could also make it difficult to acquire language, as other sounds in the environment may cover up speech. Sensitivity to touch, smell, or visual stimuli may also be distracting, dragging a neurodivergent person’s attention away from communicating effectively. 4

People with autism may also have difficulty recognizing others’ emotions or intentions, a skill sometimes referred to as Theory of Mind; this can make the undertone or unspoken implications of a conversation obscured to an individual with autism. 5

Many neurodivergent people also struggle with executive dysfunction. When executive functioning is impaired, it can hinder someone's ability to initiate or maintain a focus on tasks, including communication tasks. Conversely, this can cause hyperfocus, in which a person can become so focused on their task that they lose awareness of what’s going on around them, including when someone tries to get their attention. 6

Assessment and diagnosis

Early detection of the potential communication concerns associated with ASD can help prevent them or make them less severe. Autism can be detected as early as the toddler stage, with some symptoms including:

• Failure to make eye contact or reciprocate body language, like smiling 7
• Not responding to their own name 7
• Echolalia (repeating sounds or phrases over and over) 7
• “Stimming” behaviors such as hand flapping or rocking back and forth 7
• Sensory sensitivity to textures, tastes, smells, or certain noises 7

If a child is showing early signs of autism or delayed speech, a speech and language evaluation by a professional may help. This testing, performed by a qualified specialist in speech and language development, will assess various aspects of the child’s communication, as well as potential alternative causes, such as hearing loss. 8

Communication strategies

Many tools can help those facing language difficulties communicate more effectively. Augmentative and alternative communication uses additional tools to enhance communication. This could range from body language to paper tools such as spelling boards to digital tools such as text-to-speech programs. 9

Individuals with autism can also benefit from speech therapy. This therapy may help them learn both verbal and nonverbal communication strategies, including more expressive speech patterns, body language, or sign language. 10

People with autism can also be trained in social skills. This usually involves face-to-face instruction with a teacher who can offer guidance in relationship building, conversation skills, and other areas. 11

Supporting individuals with ASD

The family members or caregivers of a child with autism are the first experiences they'll have with social and linguistic development. These role models need to spend time with the child, intentionally developing these skills. Patience is key. It may take a child with autism extra time or effort to absorb what a neurotypical child might pick up quickly. 12

Inclusive education and community support can also help a child with autism succeed. Research shows that modifying classroom environments and accommodating learning differences can improve outcomes for neurodivergent students. 13

Research and innovation

Research into autism is ongoing and continually evolving.

Current studies are looking into ways to detect speech delays even earlier in childhood, augmenting communication with technology, and the effect parents have on the outcome of speech therapy and other treatments. 14

New therapies are being explored for autism and other communication challenges as well. Drug trials are making use of both new and existing medications that may reduce the severity of the core symptoms. It’s important to note, however, that the diversity among those with autism makes it difficult to determine what therapies will work. Many medications work for only a subset of those with autism in their community. 15

Success stories

Though autism can cause many difficulties, it is very possible to live a successful and fulfilling life with such a diagnosis.

Many successful performers, athletes, activists, scientists, entrepreneurs, and artists utilize their strengths and unique perspectives associated with autism.

Some famous names you might recognize—including actor Anthony Hopkins, climate activist Greta Thunberg, baseball star Jim Eisenreich, and novelist Helen Hoang—identify themselves as living with autism.

Many historical figures also may have lived with undiagnosed autism, including scientists Albert Einstein and Sir Isaac Newton, artist Leonardo da Vinci, and fairy-tale writer Hans Christian Andersen. 16

Develop the skills you need to help your students communicate

The University of Kansas School of Education and Human Sciences (KU SOEHS) is here to help you attain your educational dreams.

KU’s online master’s programs in special education are ranked #1 Best Online Master's in Special Education Programs in the nation by U.S. News & World Report and are designed to help you build a more inclusive classroom environment for all of your students. 17

If you’re ready to earn a master’s to impact your future and career trajectory, schedule a call to speak with an admissions outreach advisor today .

• Retrieved on March 15, 2024, from nidcd.nih.gov/health/autism-spectrum-disorder-communication-problems-children
• Retrieved on March 15, 2024, from ncbi.nlm.nih.gov/pmc/articles/PMC3991690/
• Retrieved on March 15, 2024, from educationonline.ku.edu/community/social-difficulties-in-autism-spectrum-disorder
• Retrieved on March 15, 2024, from ncbi.nlm.nih.gov/pmc/articles/PMC3086654/
• Retrieved on March 15, 2024, from ncbi.nlm.nih.gov/pmc/articles/PMC5487761/
• Retrieved on March 15, 2024, from books.google.com/books?hl=en&lr=&id=xaafOGu0fSIC&oi=fnd&pg=PA133&dq=executive+dysfunction+and+communication&ots=WvuE27WJve&sig=Zr222n8uDTsd3w9WslJXjcjnyF4#v=onepage&q=executive%20dysfunction%20and%20communication&f=false
• Retrieved on March 15, 2024, from nhs.uk/conditions/autism/signs/children/
• Retrieved on March 15, 2024, from asha.org/public/speech/disorders/autism/#professional
• Retrieved on March 15, 2024, from autism.org.uk/advice-and-guidance/professional-practice/aug-alt-comm
• Retrieved on March 15, 2024, from nichd.nih.gov/health/topics/autism/conditioninfo/treatments/speech-language
• Retrieved on March 15, 2024, from ncbi.nlm.nih.gov/pmc/articles/PMC7670840/
• Retrieved on March 15, 2024, from verywellmind.com/how-to-care-for-someone-with-autism-5213890
• Retrieved on March 15, 2024, from ncbi.nlm.nih.gov/pmc/articles/PMC9620685/
• Retrieved on March 15, 2024, from nidcd.nih.gov/health/autism-spectrum-disorder-communication-problems-children#5
• Retrieved on March 15, 2024, from sparkforautism.org/discover_article/finding-new-treatments-for-autism/
• Retrieved on March 15, 2024, from autismparentingmagazine.com/famous-people-with-autism/#Athletes_on_the_spectrum
• Retrieved on March 15, 2024, from usnews.com/education/online-education/education/online-special-education-rankings

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The sudden rise of AuDHD: what is behind the rocketing rates of this life-changing diagnosis?

Just over a decade ago, autism and ADHD were thought to be mutually exclusive. But in recent years, all that has changed

H e had beaten more than 19,000 applicants for a place at medical school, yet Khurram Sadiq was now bunking off his hospital shifts. The 19-year-old felt inexplicably anxious around strangers on the wards and was hiding from his own patients. During lectures he couldn’t focus on what he was being taught. He deemed himself “a goof, a dunce” in contrast to his peers. Sadiq couldn’t motivate himself to revise for his exams and instead found himself panic reading textbooks in the final days. He passed his undergraduate pre-medical exams by the skin of his teeth. That was 30 years ago. In the decades since, Dr Sadiq has qualified as a consultant psychiatrist, been diagnosed with both autism and attention deficit hyperactivity disorder (ADHD), specialised in autism and ADHD psychiatry and met hundreds of patients with struggles similar to his. He is now trying to spread what was once an unbelievable message: that both autism and ADHD can coexist in the same person simultaneously. Just over a decade ago, the two conditions were considered to be mutually exclusive, with the Diagnostic and Statistical Manual of Mental Disorders, often referred to as “psychiatry’s bible”, stating that the diagnosis of one precluded the existence of the other. This wasn’t revised until 2013. “It led to a fork in the road,” says Dr Jessica Eccles, spokesperson for the Royal College of Psychiatrists. “Not only for clinical practice, but also for research and public understanding of these conditions.” Now some specialists believe that the coexistence of both conditions is not just possible, but frequent. One study by researchers at Duke University found that up to half of people diagnosed as autistic also exhibit ADHD symptoms, and that characteristics of autism are present in two-thirds of people with ADHD. “My clinical experience suggests it’s more than three-quarters in both directions,” adds Dr Eccles. Online, the idea that autism and ADHD can coexist is so widely accepted that it has spawned its own label – “AuDHD” – and a groundswell of people who say they recognise its oxymoronic nature, perpetual internal war and rollercoaster of needs. There are tens of thousands of people in AuDHD self-help forums, and millions more watching AuDHD videos. Some of those videos come from Samantha Stein, a British YouTuber. “The fact that you can have both [autism and ADHD] at the same time is kind of paradoxical in nature,” she admits. “You think: ‘How can you be extremely rigid and need routines and structure, but also be completely incapable of maintaining a routine and structure?’” The 38-year-old started making videos on autism after her diagnosis in 2019, then began covering AuDHD after learning that she also had ADHD. “I realised that autistic adults – especially those who are diagnosed late in life – more often than not seem to have ADHD as well,” says Stein. Her first video on the subject, “ 5 signs you have ADHD and autism ”, has now been viewed more than 2m times.

Some critics like to describe ADHD – and more recently autism – as a “fashionable” diagnosis, a misinformed excuse for life’s struggles. It’s almost inevitable that the new AuDHD label will cause a similar backlash. To see just how misguided this is, we must first understand both autism and ADHD. Both are lifelong neurodevelopmental conditions that affect how people think, perceive the world and interact with others, according to Embracing Complexity , an umbrella group of organisations that research neurodiversity.

Autism and ADHD affect people on a spectrum of severity, both are legally recognised as disabilities, and neither are mental illnesses to be “cured”, although the knock-on effects can lead to mental illness . People who experience ways of thinking that diverge from those experienced by the majority of people are described as “ neurodivergent ”. Autism spectrum disorder (ASD) is caused by multiple genetic factors that aren’t yet fully understood . Contrary to misconception, autism doesn’t equate to impaired intelligence, and only around half of people with autism also have a co-occurring intellectual disability. According to the National Autistic Society, autism is characterised by social challenges, repetitive behaviours, over- or under-sensitivity to surroundings and highly focused interests. Autism is experienced in a multitude of ways. To empathise with the autistic trait of oversensitivity, for example, imagine that all your senses are amplified . The hum of your fridge is louder, the overhead lights are brighter, your itchy jumper is pricklier. It’s distracting while you’re trying to work, it’s draining to pretend it isn’t bothering you and you become increasingly stressed as a result. “For me, eating in a canteen is like eating in a nightclub for a neurotypical person,” says Jill Corbyn, who is autistic and the director of support organisation Neurodiverse Connection . “It’s unpleasantly loud, it’s going to distract you from your food, it’s anxiety-inducing.” Additionally, some autistic people may find social situations exhausting or overwhelming, or feel incompetent when they’re unable to decipher the subtleties of interpersonal communication, 60% of which is non-verbal . Charli Clement, 23, explains that while a non-autistic person may rehearse parts of a conversation before a date or a job interview, her autism leads her to “script significantly” before even ordering a drink at a bar.

“I try to make sure I’m not doing something that will be perceived as ‘wrong’, so focusing on what the person is saying and what I should be replying is overwhelming,” she says.

Compounding the experience is the feeling many autistic people have that it isn’t “normal” to feel this way and that they must camouflage their discomfort to fit in with the pack. This “masking”, as it’s known, is exhausting, invalidating and can lead to burnout . ADHD is also not fully understood. There’s evidence that the condition, involving an imbalance of neurotransmitters – including dopamine, in the brain – has both genetic and environmental causes. These chemical messengers are responsible for motivation, movement, planning, reward, memory, focus, alertness, impulse control and threat response, among others. People with untreated ADHD, whose reward pathways are therefore more dysregulated, can subsequently experience disordered moods, sleep, eating habits and dysfunction in almost every area of life. Some people with ADHD are like pinballs of external chaos – of lost keys, missed appointments and cluttered homes. Others may appear inattentive, distracted by balls of chaotic thoughts into which they frequently retreat from the world to untangle. ADHD affects people to different degrees. But many say their lives are marred by their brain’s misguided attempts to correct its chemical imbalances . They impulsively dopamine-spike with food, sex, drugs, booze, the internet, people, hobbies and novelty of all shades. “I am a slave to my own brain and it’s tiring,” writes one anonymous person on an ADHD Reddit support group. Another asks: “Do you also feel like a slave to your desires?” She gives the examples of “chasing girls, gambling, chasing men, eating, hobby-hopping, extreme budgeting, falling in love [with] the wrong person, spending extravagantly”.

What frequently underpins the external and internal chaos, according to experts and many ADHDers alike, is a pervasive sense of deep shame and the quiet realisation that their potential in life is not being met .

When autism meets ADHD, it’s a curious form of alchemy, according to those who have both. Sometimes the conditions are in conflict; at other times they’re symbiotic. There is no such thing as a perfect 50/50 split, explains Sadiq, and the brain is often “seesawing” between both conditions. This makes the presentation of AuDHD a distinctive condition in its own right, “completely different from pure ADHD or pure ASD”, he adds.

In his Ted Talk, “When Order and Anarchy Live Together”, Sadiq describes the dualities of the condition: “Silence v noise; structure v chaos; repetition v novelty; caution v risk-taking …” Mattia Maurée, a non-binary composer and host of the AuDHD Flourishing podcast, discovered the AuDHD concept after following separate pieces of advice about autism and ADHD that “just weren’t working for me”. “It was like: ‘No, my life is still really, really hard,’” they tell me from Philadelphia. AuDHD is uniquely “cyclical”, says Maurée, with big bursts of energy followed by a crash. “AuDHDers can also be incredibly creative and innovative, maybe because of that brain hyper-connectivity.” Creativity is cited as the most positive AuDHD attribute by everyone I speak to, along with the subtle pairings of traits that “complement each other in a really nice way”, as Stein puts it. “ADHD gives me a love of novelty and a very creative side. And then autism allows me to focus on a topic that I’m really interested in. All of that allows me to be very self-directed.”

The paradoxes of AuDHD can camouflage each other or – on the surface at least – cancel each other out, which is why some AuDHDers experience missed or incorrect diagnoses. In February, Sadiq saw a patient who had been referred to his NHS clinic for an ADHD diagnosis. He realised 15 minutes into the consultation that the patient was autistic. “If I had no lived experience of autism and ADHD I would have missed it completely,” he says. “I would have diagnosed either social anxiety or a personality disorder.” In spite of his expertise, Sadiq is not formally qualified to make an autism diagnosis, and instead he had to refer the patient on to the autism service within the NHS trust. He believes that psychiatrists specialising in autism should also be trained in ADHD and vice versa, because otherwise “they’re going to be missing a lot”.

It’s not just the medical profession that needs more coordination. Charities such as ADHD UK and the National Autistic Society also work independently from one another. Legislation such as the government-backed The Buckland Review of Autism Employment, which recently called for employers to boost support for autistic people , scrutinises autism provisions but not ADHD ones. ADHD UK is one of many advocacy groups calling for the Autism Act , which legally compels the government to support autistic people, to be widened in scope to include other forms of neurodiversity. Once a correct dual diagnosis is obtained, there are still complications. ADHD can be successfully managed with medication and behavioural coaching, but some autistic people react badly to this medication. Research indicates that stimulants are overall less tolerable for AuDHDers than they are for people with ADHD, according to the global research platform Embrace Autism, with one report finding that side-effects doubled in those with both conditions.

Another quirk of AuDHD treatment is that in some cases, it’s only after “quietening” someone’s ADHD symptoms that their autism traits come to the fore. This is often when people realise their autistic side for the first time, and it could explain why rates of self-reported autism closely follow those of ADHD. The medical professionals I interviewed for this article were emphatic that ADHD medication cannot cause autism. Instead, Dr Eccles says: “It has just changed the balance of symptoms. The balance of masking has changed.” The prevalence of autism was widely believed to be 1% until last year, when a first of its kind study published in the Lancet found the true rate to be more than double that, with at least 1.2 million autistic people in the UK. The prevalence of ADHD in UK adults is around 4% , according to ADHD UK, and assessment waiting lists for both conditions are increasing year on year, with waits of a decade in some parts of the country for ADHD assessment. When naysayers argue that we are in the midst of an overdiagnosis epidemic, charities often point them to the statistics on suicide, and the fact that the ripple effects of ADHD and autism often lead to mental ill-health. Autistic adults without a learning disability are far more likely to die by suicide. In 2022, researchers from Cambridge and Nottingham University, analysing coroners’ inquest records, concluded that a significant number of people who had died by suicide were likely autistic but undiagnosed. Adults with ADHD, meanwhile, are  five times more likely to attempt suicide than their neurotypical peers. Yet AuDHDers have been found to be at even greater risk of suicide than either those with only autism or ADHD, according to an academic study of more than 50,000 people.

For people like Clement, criticism about over-labelling is the least of her concerns. As a teenager she spent time in a psychiatric unit before the nature of her AuDHD was fully realised. “I’d already given myself labels,” she says. “I already thought that I was weird and broken. So having a label that actually made sense and encompassed my experience was so liberating.”

She now works part-time advising psychiatric hospitals on how to ensure their sensory environments are adequate for neurodiverse people.

Other AuDHDers give colourful analogies to describe the epiphany of diagnosis. Before the discovery, I’m told, it’s as if you are trying to fit in and be a horse rather than celebrating the fact that you’re a zebra. It’s like being trapped in a maze in the dark, then suddenly the lights are on and now there’s a way to navigate out.

Stein describes her life as “fundamentally walking parallel to, but never quite included in society”. Her diagnosis, however, “allowed me to look at my life through the lens of far more compassion – as a pretty good autistic person rather than a broken neurotypical person”.

“I think in some ways [AuDHD] can be a very beautiful thing,” she says.

“You just need the right support to be able to access those parts of you. And you need the label to know what the hell is going on in your brain.”

• Health & wellbeing
• Neurodiversity
• Attention deficit hyperactivity disorder

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