research about autism spectrum disorder

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Autism spectrum disorders articles from across Nature Portfolio

Autism spectrum disorders are a group of neurodevelopmental disorders that are characterized by impaired social interaction and communication skills, and are often accompanied by other behavioural symptoms such as repetitive or stereotyped behaviour and abnormal sensory processing. Individual symptoms and cognitive functioning vary across the autism spectrum disorders.

Targeting RNA opens therapeutic avenues for Timothy syndrome

A therapeutic strategy that alters gene expression in a rare and severe neurodevelopmental condition has been tested in stem-cell-based models of the disease, and has been shown to correct genetic and cellular defects.

• Silvia Velasco

Latest Research and Reviews

Anterior cingulate cortex-related functional hyperconnectivity underlies sensory hypersensitivity in Grin2b -mutant mice

• Won Beom Jung
• Eunjoon Kim

Autism patient-derived SHANK2B Y29X mutation affects the development of ALDH1A1 negative dopamine neuron

• Wanjing Lai
• Yingying Zhao
• Lingling Shi

Antisense oligonucleotide therapeutic approach for Timothy syndrome

Antisense oligonucleotides effectively decrease the inclusion of exon  8A of CACNA1C in human cells both in vitro and in rodents transplanted with human brain organoids, and a single intrathecal administration rescued both calcium changes and in vivo dendrite morphology of patient neurons.

• Xiaoyu Chen
• Fikri Birey
• Sergiu P. Pașca

Salience network connectivity is altered in 6-week-old infants at heightened likelihood for developing autism

Infants with heightened vs. lower likelihood of developing autism spectrum disorder exhibit different functional connectivity patterns in the salience network. These patterns predict sensory and social behaviors at 1 year of age.

• Tawny Tsang
• Shulamite A. Green
• Mirella Dapretto

Combined expansion and STED microscopy reveals altered fingerprints of postsynaptic nanostructure across brain regions in ASD-related SHANK3-deficiency

• Jan Philipp Delling
• Helen Friedericke Bauer
• Tobias M. Boeckers

In vivo translocator protein in females with autism spectrum disorder: a pilot study

• Chieh-En Jane Tseng
• Camila Canales
• Nicole R. Zürcher

News and Comment

Vasopressin deficiency: a hypothesized driver of both social impairment and fluid imbalance in autism spectrum disorder.

• Lauren Clarke
• Neil Gesundheit
• Karen J. Parker

Oscillatory index of speech reception deficits

• Jean Mary Zarate

Redefining deficits in autistic emotion recognition

• Connor T. Keating

Autism: don’t negate the value of applied behaviour analysis

• Russell Lang
• Jason Travers

Autism research: GATFAR coordinator responds

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Research on Autism Spectrum Disorder

Study to explore early development (seed).

CDC’s Study to Explore Early Development (SEED) helps us learn more about autism spectrum disorder (ASD) in preschool-aged children. SEED is one of the largest studies of young children, ages 2-5 years, with ASD in the United States. It looks at their risk factors for ASD and developmental characteristics. In 2021, SEED was expanded to learn more about the health, functioning, and needs of people with ASD and other developmental disabilities as they mature.

A study on children’s health and development

A study on adolescent and young adult health and development

A study on teen health and development

CDC releases newsletters twice a year with the latest updates from SEED

A study on COVID-19 pandemic’s impacts on services, health, and behaviors

Highlights from published studies

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Research Studies

Currently Recruiting or Active Research Studies

Please download the document below for our current recruiting studies organized by age range. 

 Study Title

Study description, spark (simons powering autism research) study.

Available in English and Spanish.

If you or your child has a professional diagnosis of autism, Stanford University invites you to learn more about SPARK, a new online research study sponsored by the Simons Foundation Autism Research Initiative. The mission of SPARK is clear: speed up research and advance understanding of autism by creating the nation’s largest autism study. Joining SPARK is simple – register online and provide a DNA sample via a saliva collection kit in the comfort of your own home. Together, we can help spark a better future for all individuals and families affected by autism.

Register  by contacting us at [email protected] or online at www.sparkforautism.org/stanford .

SPARK está trabajando para fomentar la investigación y mejorar nuestra comprensión del autismo. Stanford y más de 30 de las principales escuelas de medicina y centros de investigación del autismo del país forman parte de este esfuerzo.

• Participar en SPARK es gratis y se puede hacer completamente desde casa.
• Muchas de las encuestas de SPARK aportan informes personalizados.
• Los participantes serán notificados en caso de haber otras oportunidades de investigación.
• Los individuos con autismo podrán recibir códigos de regalo de Amazon por un valor de hasta 50 dólares (uno por familia) después de la recepción de sus muestras de saliva.

Para inscribirse en SPARK:  https://sparkforautism.org/Stanford/ES

La inscripción suele llevar unos 20 minutos y puede empezar y parar si lo necesita. Una vez que se registre y complete unos cuestionarios en línea, le enviaremos un kit para recolectar saliva a su domicilio. Para obtener más información, envíe un correo electrónico a [email protected]

Language Treatment Trial for Children with Autism

Researchers at Stanford University are currently recruiting children with autism spectrum disorder to identify MRI-based markers of response to treatment with Pivotal Response Treatment (PRT) targeting language abilities. Children with autism spectrum disorder between the ages of 2 and 4 years 11 months are invited to participate. This study involves up to a 5 month time commitment. The participant must be willing to complete cognitive and behavioral assessments (such as IQ and language testing) and be able to either sleep (young children) or lie still in the scanner during an MRI. After a successful MRI, the participant will be randomized into the PRT trial or DTG (Delayed Treatment Group). PRT will consist of 16 weekly, 60-90 minute sessions of parent training in PRT over a 16 week time period. DTG will consist of your child’s treatments as usual in the community and measurements and questionnaires will need to be filled out on three study visits over the course of the 16 weeks. After completion of the DTG, the participant will be offered PRT parent training sessions similar to the PRT group. There is no cost to participate in the study. If you would like to participate or if you have any questions please call (650) 736-1235 or email:  [email protected]  to discuss the study in more detail. 

2 and 4 years,11 months

Targeting the Neurobiology of Restricted and Repetitive Behaviors in Children with Autism Using N-acetylcysteine Randomized Control Trial

We are recruiting children autism to participate in a study examining the treatment effects of an over-the-counter dietary supplement on the brain.   

Eligibility:  Children with autism spectrum disorder who -

·    are aged between 3 and 12 years old

·    exhibit restricted and repetitive behaviors

·    will drink N-acetyl cysteine dissolved in water

·    will undergo brain scanning (asleep or awake) with magnetic resonance  imaging (MRI)

·    will undergo brain scanning with electroencephalography (EEG)

The study will take place over 3 to 6 visits (some remotely over Zoom) and the approximate time required is about 10 to 12 hours. Individuals that are able to complete both of the MRI/EEG sessions will be compensated $50.

You can find more information about our NAC studies at   https://redcap.link/NACforAutism .

If you have any questions  please call 650-736-1235 or email:  [email protected] .

3 to 12 years

Autism Center of Excellence Sleep Study

Dear Parents,

We are excited to tell you about a new research study for children. We are looking to partner with parents who have children that are between the ages of 4 and 17 years old,  with and without  an Autism Spectrum Disorder (ASD) diagnosis.

What is involved?

• In-person cognitive and behavioral assessments
• Day-time Electroencephalogram (EEG)
• In-home, 2 night sleep monitoring session
• Collection of saliva to measure cortisol and melatonin levels
• Wearing a watch device that tracks sleep and daily activity

What will I receive if I participate?

• Research sleep report and behavioral testing summary upon request
• $50 for each in-person visit to Stanford and $100 for the 2 night in-home sleep assessment

Treatment extension study:

• If your child has ASD, sleep difficulties, and ages 8-17, they may also qualify for sleep medication trials

Interested in participating or want to learn more?  Click Here!

If you would like to reach out to our team directly with any questions, please contact our team below!

Email:  [email protected]

650-498-7215

4 to 17 years

Pregnenolone Randomized Controlled Trial

Neurosteroid Pregnenolone Treatment for Irritability in Adolescents with Autism

Medication treatments for core symptoms of autism spectrum disorder (ASD) continue to be unmet medical needs. The only medications approved by the U.S. Food and Drug Administration (FDA) for the treatment of individuals with ASD are effective in treating irritability and associated aggressive behaviors, but these medications can also cause severe long-term side effects such as diabetes and involuntary motor movements. Therefore, effective medications with more tolerable side effect profiles are highly desirable. This profile is consistent with pregnenolone (PREG). PREG belongs to a new class of hormones known as neurosteroids, which have been shown to be effective in treating various psychiatric conditions including bipolar depression and schizophrenia. As compared to currently FDA-approved medications, our preliminary data suggested that PREG may represent a potentially effective and well-tolerated agent for treating irritability in individuals with ASD. In addition, our experience suggests that PREG might be helpful in improving selected core symptoms such as social deficits and sensory abnormalities of ASD. This study provides the opportunity to further explore the usefulness of PREG in the treatment of irritability and some core symptoms of ASD. We are performing a 12-week randomized double-blind controlled pilot trial to examine the effectiveness of orally administered PREG in reducing irritability and associated behaviors in adolescents with ASD. In this study, we also aim to examine the usefulness of biomarkers (blood levels of neurosteroids, eyetracking and brain wave recording) in predicting treatment response and assessing biologic changes with PREG treatment.

Link to study in Stanford's Clinical Trials Directory

14 to 25 years

Trial of Center-Based vs. In-Home Pivotal Response Treatment (PRT) in Autism (PRT-HvC)

Do you have a child (2-5 years old) with autism and want an intensive center-based or in-home intervention?

Stanford University researchers are recruiting children with autism and their parents to participate in a study examining the effectiveness of a center-based vs. in-home Pivotal Response Treatment (PRT) program in targeting social communication abilities in young children with autism.

Participants must:

• Be diagnosed with Autism Spectrum Disorder
• Be between the ages of 2 years and 5 years 11 months
• Be able to attend 3-hour research treatment sessions 4 days per week and participate in parent training

Based on behavioral screening assessments, children who are eligible will be randomly assigned to either center-based intervention, in-home intervention, or treatment as usual. Those assigned to the treatment-as-usual group will receive treatment after the 16–week period is completed.

Call 650-736-1235 or email [email protected] to learn more.

https://clinicaltrials.gov/ct2/show/NCT04899544 

2 to 5 years

Improving Access to Pivotal Response Treatment (PRT) via Telehealth Parent Training

There is an urgent need for improved access to effective autism treatments. With advances in technology, distance learning models have particular promise for families who cannot access evidence-based parent training locally or may be on long wait-lists for behavioral treatments. Pivotal Response Treatment (PRT) is an established treatment for autism spectrum disorder (ASD); however, a telehealth PRT model has not yet been evaluated in a controlled trial. This study will examine the effects of training parents in PRT via secure video conferencing and investigate 1) whether parents can learn via telehealth to deliver PRT in the home setting (PRT-T) and 2) whether their children will show greater improvement in functional communication skills compared to children in a waitlist control group. Participants will include 40 children age 2 to 5 years with ASD and significant language delay. Eligible children will be randomly assigned to either PRT-T or waiting list. Weekly 60-minute parent training sessions will be delivered for 12 weeks via secure video conferencing software by a PRT-trained study therapist. Link:  https://clinicaltrials.gov/ct2/show/NCT04042337

Note: Participants must live at least 200 miles away from Stanford University (i.e., this study is geared towards out-of-state families or families living at a distance)

A Center Based Randomized Controlled Trial of Pivotal Response Treatment for Preschoolers With Autism

Researchers at Stanford University are currently recruiting children with autism and their parents to participate in a study examining the effectiveness of a center-based Pivotal Response Treatment (PRT) program in targeting social communication abilities in young children with autism. We are currently recruiting children diagnosed with ASD and social communication deficits, aged 2:0 to 3:11 years. Children who are eligible based on behavioral screening assessments will be randomly assigned to either an immediate treatment (PRT) group or a delayed treatment group (DTG). If randomized into the PRT group, the 12-week treatment will consist of a combination of one weekly 60-minute individual parent training session and 12 weekly hours (approximately 3 hours per day for 4 days per week) with your child in a center-based group preschool environment at Stanford University. If randomized into the delayed treatment group, the children will wait 12 weeks to receive the PRT treatment and continue any treatment they are receiving as usual in the community. The cost of clinic-based services varies based on individual family health insurance plans.

For more information, please call (650) 736-1235 or email  [email protected]  to discuss the study in more detail. 

2 and 3 years,11 months

Natural History Study of Individuals with Autism and Germline Heterozygous PTEN Mutations

The goal of this study is to gain a better understanding of PTEN mutation syndromes to identify early markers and ultimately effective interventions for autism spectrum disorder. Individuals 18 months or older are eligible to participate if they have been diagnosed with PTEN hamartoma tumor syndrome. The study involves five visits over a two year period. Three of the visits occur on-site at a study location. The other two visits occur as phone calls. The on-site visits include a blood draw, physical/neurological exams and behavioral testing.

Study Webpage    

18 months and older

Active Studies, not Recruiting

An open-label pilot study of esomeprazole in children with autism.

Researchers at Stanford University are currently examining the effectiveness of esomeprazole in improving social communication deficits in children with Autism Spectrum Disorder (ASD). Esomeprazole is currently FDA-approved for children ages 1 and up for gastroesophageal reflux disease (GERD) and has been identified as a potential treatment for improving social communication in children with ASD. Children with ASD ages 2 through 6 years are invited to participate. The child must be willing to take esomeprazole orally for at least 8 weeks, complete diagnostic and behavioral assessments, and be free of serious medical problems. There is also an optional research blood draw. The study will require visits to Stanford University and the parent/caregiver will be required to complete questionnaires for each visit.

For more information, please go to  https://is.gd/ASDstudy ,  call (650) 736-1235, or email  [email protected] .

2 to 6 years

Vasopressin Treatment Trial for Children with Autism

The purpose of this clinical trial is to investigate the effectiveness of vasopressin nasal spray for treating symptoms associated with autism. Vasopressin is a hormone that is produced naturally within the body and has been implicated in regulating social behaviors. It has been proposed that administration of the hormone may also help improve social functioning in individuals with autism.

Link to study at clinicaltrials.gov

6 to 17 years

• Patient Care & Health Information
• Diseases & Conditions
• Autism spectrum disorder

Autism spectrum disorder is a condition related to brain development that impacts how a person perceives and socializes with others, causing problems in social interaction and communication. The disorder also includes limited and repetitive patterns of behavior. The term "spectrum" in autism spectrum disorder refers to the wide range of symptoms and severity.

Autism spectrum disorder includes conditions that were previously considered separate — autism, Asperger's syndrome, childhood disintegrative disorder and an unspecified form of pervasive developmental disorder. Some people still use the term "Asperger's syndrome," which is generally thought to be at the mild end of autism spectrum disorder.

Autism spectrum disorder begins in early childhood and eventually causes problems functioning in society — socially, in school and at work, for example. Often children show symptoms of autism within the first year. A small number of children appear to develop normally in the first year, and then go through a period of regression between 18 and 24 months of age when they develop autism symptoms.

While there is no cure for autism spectrum disorder, intensive, early treatment can make a big difference in the lives of many children.

Products & Services

• Children’s Book: My Life Beyond Autism

Some children show signs of autism spectrum disorder in early infancy, such as reduced eye contact, lack of response to their name or indifference to caregivers. Other children may develop normally for the first few months or years of life, but then suddenly become withdrawn or aggressive or lose language skills they've already acquired. Signs usually are seen by age 2 years.

Each child with autism spectrum disorder is likely to have a unique pattern of behavior and level of severity — from low functioning to high functioning.

Some children with autism spectrum disorder have difficulty learning, and some have signs of lower than normal intelligence. Other children with the disorder have normal to high intelligence — they learn quickly, yet have trouble communicating and applying what they know in everyday life and adjusting to social situations.

Because of the unique mixture of symptoms in each child, severity can sometimes be difficult to determine. It's generally based on the level of impairments and how they impact the ability to function.

Below are some common signs shown by people who have autism spectrum disorder.

Social communication and interaction

A child or adult with autism spectrum disorder may have problems with social interaction and communication skills, including any of these signs:

• Fails to respond to his or her name or appears not to hear you at times
• Resists cuddling and holding, and seems to prefer playing alone, retreating into his or her own world
• Has poor eye contact and lacks facial expression
• Doesn't speak or has delayed speech, or loses previous ability to say words or sentences
• Can't start a conversation or keep one going, or only starts one to make requests or label items
• Speaks with an abnormal tone or rhythm and may use a singsong voice or robot-like speech
• Repeats words or phrases verbatim, but doesn't understand how to use them
• Doesn't appear to understand simple questions or directions
• Doesn't express emotions or feelings and appears unaware of others' feelings
• Doesn't point at or bring objects to share interest
• Inappropriately approaches a social interaction by being passive, aggressive or disruptive
• Has difficulty recognizing nonverbal cues, such as interpreting other people's facial expressions, body postures or tone of voice

Patterns of behavior

A child or adult with autism spectrum disorder may have limited, repetitive patterns of behavior, interests or activities, including any of these signs:

• Performs repetitive movements, such as rocking, spinning or hand flapping
• Performs activities that could cause self-harm, such as biting or head-banging
• Develops specific routines or rituals and becomes disturbed at the slightest change
• Has problems with coordination or has odd movement patterns, such as clumsiness or walking on toes, and has odd, stiff or exaggerated body language
• Is fascinated by details of an object, such as the spinning wheels of a toy car, but doesn't understand the overall purpose or function of the object
• Is unusually sensitive to light, sound or touch, yet may be indifferent to pain or temperature
• Doesn't engage in imitative or make-believe play
• Fixates on an object or activity with abnormal intensity or focus
• Has specific food preferences, such as eating only a few foods, or refusing foods with a certain texture

As they mature, some children with autism spectrum disorder become more engaged with others and show fewer disturbances in behavior. Some, usually those with the least severe problems, eventually may lead normal or near-normal lives. Others, however, continue to have difficulty with language or social skills, and the teen years can bring worse behavioral and emotional problems.

When to see a doctor

Babies develop at their own pace, and many don't follow exact timelines found in some parenting books. But children with autism spectrum disorder usually show some signs of delayed development before age 2 years.

If you're concerned about your child's development or you suspect that your child may have autism spectrum disorder, discuss your concerns with your doctor. The symptoms associated with the disorder can also be linked with other developmental disorders.

Signs of autism spectrum disorder often appear early in development when there are obvious delays in language skills and social interactions. Your doctor may recommend developmental tests to identify if your child has delays in cognitive, language and social skills, if your child:

• Doesn't respond with a smile or happy expression by 6 months
• Doesn't mimic sounds or facial expressions by 9 months
• Doesn't babble or coo by 12 months
• Doesn't gesture — such as point or wave — by 14 months
• Doesn't say single words by 16 months
• Doesn't play "make-believe" or pretend by 18 months
• Doesn't say two-word phrases by 24 months
• Loses language skills or social skills at any age

There is a problem with information submitted for this request. Review/update the information highlighted below and resubmit the form.

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Autism spectrum disorder has no single known cause. Given the complexity of the disorder, and the fact that symptoms and severity vary, there are probably many causes. Both genetics and environment may play a role.

• Genetics. Several different genes appear to be involved in autism spectrum disorder. For some children, autism spectrum disorder can be associated with a genetic disorder, such as Rett syndrome or fragile X syndrome. For other children, genetic changes (mutations) may increase the risk of autism spectrum disorder. Still other genes may affect brain development or the way that brain cells communicate, or they may determine the severity of symptoms. Some genetic mutations seem to be inherited, while others occur spontaneously.
• Environmental factors. Researchers are currently exploring whether factors such as viral infections, medications or complications during pregnancy, or air pollutants play a role in triggering autism spectrum disorder.

No link between vaccines and autism spectrum disorder

One of the greatest controversies in autism spectrum disorder centers on whether a link exists between the disorder and childhood vaccines. Despite extensive research, no reliable study has shown a link between autism spectrum disorder and any vaccines. In fact, the original study that ignited the debate years ago has been retracted due to poor design and questionable research methods.

Avoiding childhood vaccinations can place your child and others in danger of catching and spreading serious diseases, including whooping cough (pertussis), measles or mumps.

Risk factors

The number of children diagnosed with autism spectrum disorder is rising. It's not clear whether this is due to better detection and reporting or a real increase in the number of cases, or both.

Autism spectrum disorder affects children of all races and nationalities, but certain factors increase a child's risk. These may include:

• Your child's sex. Boys are about four times more likely to develop autism spectrum disorder than girls are.
• Family history. Families who have one child with autism spectrum disorder have an increased risk of having another child with the disorder. It's also not uncommon for parents or relatives of a child with autism spectrum disorder to have minor problems with social or communication skills themselves or to engage in certain behaviors typical of the disorder.
• Other disorders. Children with certain medical conditions have a higher than normal risk of autism spectrum disorder or autism-like symptoms. Examples include fragile X syndrome, an inherited disorder that causes intellectual problems; tuberous sclerosis, a condition in which benign tumors develop in the brain; and Rett syndrome, a genetic condition occurring almost exclusively in girls, which causes slowing of head growth, intellectual disability and loss of purposeful hand use.
• Extremely preterm babies. Babies born before 26 weeks of gestation may have a greater risk of autism spectrum disorder.
• Parents' ages. There may be a connection between children born to older parents and autism spectrum disorder, but more research is necessary to establish this link.

Complications

Problems with social interactions, communication and behavior can lead to:

• Problems in school and with successful learning
• Employment problems
• Inability to live independently
• Social isolation
• Stress within the family
• Victimization and being bullied

More Information

• Autism spectrum disorder and digestive symptoms

There's no way to prevent autism spectrum disorder, but there are treatment options. Early diagnosis and intervention is most helpful and can improve behavior, skills and language development. However, intervention is helpful at any age. Though children usually don't outgrow autism spectrum disorder symptoms, they may learn to function well.

• Autism spectrum disorder (ASD). Centers for Disease Control and Prevention. https://www.cdc.gov/ncbddd/autism/facts.html. Accessed April 4, 2017.
• Uno Y, et al. Early exposure to the combined measles-mumps-rubella vaccine and thimerosal-containing vaccines and risk of autism spectrum disorder. Vaccine. 2015;33:2511.
• Taylor LE, et al. Vaccines are not associated with autism: An evidence-based meta-analysis of case-control and cohort studies. Vaccine. 2014;32:3623.
• Weissman L, et al. Autism spectrum disorder in children and adolescents: Overview of management. https://www.uptodate.com/home. Accessed April 4, 2017.
• Autism spectrum disorder. In: Diagnostic and Statistical Manual of Mental Disorders DSM-5. 5th ed. Arlington, Va.: American Psychiatric Association; 2013. http://dsm.psychiatryonline.org. Accessed April 4, 2017.
• Weissman L, et al. Autism spectrum disorder in children and adolescents: Complementary and alternative therapies. https://www.uptodate.com/home. Accessed April 4, 2017.
• Augustyn M. Autism spectrum disorder: Terminology, epidemiology, and pathogenesis. https://www.uptodate.com/home. Accessed April 4, 2017.
• Bridgemohan C. Autism spectrum disorder: Surveillance and screening in primary care. https://www.uptodate.com/home. Accessed April 4, 2017.
• Levy SE, et al. Complementary and alternative medicine treatments for children with autism spectrum disorder. Child and Adolescent Psychiatric Clinics of North America. 2015;24:117.
• Brondino N, et al. Complementary and alternative therapies for autism spectrum disorder. Evidence-Based Complementary and Alternative Medicine. http://dx.doi.org/10.1155/2015/258589. Accessed April 4, 2017.
• Volkmar F, et al. Practice parameter for the assessment and treatment of children and adolescents with autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry. 2014;53:237.
• Autism spectrum disorder (ASD). Eunice Kennedy Shriver National Institute of Child Health and Human Development. https://www.nichd.nih.gov/health/topics/autism/Pages/default.aspx. Accessed April 4, 2017.
• American Academy of Pediatrics policy statement: Sensory integration therapies for children with developmental and behavioral disorders. Pediatrics. 2012;129:1186.
• James S, et al. Chelation for autism spectrum disorder (ASD). Cochrane Database of Systematic Reviews. http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD010766.pub2/abstract;jsessionid=9467860F2028507DFC5B69615F622F78.f04t02. Accessed April 4, 2017.
• Van Schalkwyk GI, et al. Autism spectrum disorders: Challenges and opportunities for transition to adulthood. Child and Adolescent Psychiatric Clinics of North America. 2017;26:329.
• Autism. Natural Medicines. https://naturalmedicines.therapeuticresearch.com. Accessed April 4, 2017.
• Autism: Beware of potentially dangerous therapies and products. U.S. Food and Drug Administration. https://www.fda.gov/ForConsumers/ConsumerUpdates/ucm394757.htm?source=govdelivery&utm_medium=email&utm_source=govdelivery. Accessed May 19, 2017.
• Drutz JE. Autism spectrum disorder and chronic disease: No evidence for vaccines or thimerosal as a contributing factor. https://www.uptodate.com/home. Accessed May 19, 2017.
• Weissman L, et al. Autism spectrum disorder in children and adolescents: Behavioral and educational interventions. https://www.uptodate.com/home. Accessed May 19, 2017.
• Huebner AR (expert opinion). Mayo Clinic, Rochester, Minn. June 7, 2017.

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Collected Research on Autism Spectrum Disorder

• Clinical Psychological Science
• Current Directions in Psychological Science
• Language Development
• Perspectives on Psychological Science
• Psychological Science
• Social Interaction
• Social Psychology

Autism spectrum disorder (ASD) refers to a range of varying conditions that affect individuals’ abilities to communicate and interact with others. Common characteristics of ASD include challenges with speech and nonverbal communication, and repetitive behaviors and interests. According to a 2017 study reported by the U.S. Centers for Disease Control and Prevention, nearly 5.5 million adults in the United States are diagnosed with a subtype of ASD, but each person who is diagnosed experiences it in unique ways—and has their own strengths and challenges in how they learn, think and problem-solve. 

To celebrate 2022’s Autism Acceptance Month and contribute to the continued education about ASD, we have collected some research on ASD published in the APS journals Clinical Psychological Science , Perspectives on Psychological Science , Current Directions in Psychological Science , and Psychological Science between 2017 and 2021. Complete journal articles are available to logged-in APS members. 

Read more about Autism Spectrum Disorder in the APS archive. 

Pupillary Contagion in Autism  

Martyna A. Galazka, Jakob Åsberg Johnels, Nicole R. Zürcher, Loyse Hippolyte, Eric Lemonnier, Eva Billstedt, Christopher Gillberg, Nouchine Hadjikhani (2018)  

In this study, individuals with and without ASD viewed photographs of women’s and men’s sad and happy faces while an eye tracker measured changes in their pupil sizes and fixation durations. Both groups of individuals showed similar changes in pupil size. Interestingly, participants with ASD fixated on the area around the eyes in the photographs for less time than participants without ASD, and longer fixation times corresponded with less pupillary contagion. Thus, even though they spent less time looking at the eyes, participants with ASD still showed pupillary contagion. Taken together, these results support the overarousal hypothesis of ASD, which suggests that individuals with ASD reduce eye fixation as a way to decrease arousal from processing social and affective stimuli. 

Link Between Facial Identity and Expression Abilities Suggestive of Origins of Face Impairments in Autism: Support for the Social-Motivation Hypothesis    

Ipek Oruc, Fakhri Shafai, Grace Iarocci (2018)  

Oruc and colleagues examined the relationship between face and facial-expression identification in ASD by testing adults with and without ASD in tasks that involved identifying faces and expressions. Results indicated that adults with ASD performed more poorly on both tasks than adults without ASD. Moreover, there was a positive relationship between face- and expression-identification abilities for adults with ASD, but not adults without ASD. Oruc and colleagues also assessed social motivation, finding it to be lower for adults with ASD than for those without ASD. Among adults with ASD who had low social-motivation scores, those with the lowest scores had the lowest face- and expression-identification abilities. These results suggest that impairments in both face and expression processing in ASD might derive from a lack of experience with faces, as the social-motivation hypothesis of ASD proposes. 

Gaze Following Is Related to the Broader Autism Phenotype in a Sex-Specific Way: Building the Case for Distinct Male and Female Autism Phenotypes    

Elisabeth M. Whyte and K. Suzanne Scherf (2017)  

Whyte and Scherf investigated whether sex differences might emerge in eye-gaze processing, thought to be a core deficit in ASD. The authors recruited a nonclinical sample of adult men and women who exhibited either high or low levels of autistic-like traits (ALTs) and showed them a series of images depicting a person among multiple objects. After viewing each image, participants indicated what the person was looking at by choosing one of four options (a target object, a plausible nontarget object, or one of two implausible nontarget objects). Men who had high levels of ALTs showed poorer eye-gaze following than men with low ALTs and women with high ALTs. Women’s performance on the eye-gaze task did not vary according to ALTs. The authors suggest that abnormal eye-gaze processing may be part of the broader male autism phenotype but not the female autism phenotype. 

Atypical Visual Motion-Prediction Abilities in Autism Spectrum Disorder    

Woon Ju Park, Kimberly B. Schauder, Oh-Sang Kwon, Loisa Bennetto, Duje Tadin (2021)  

People with ASD appear to show atypical visual prediction of motion trajectories, this research suggests. Children and adolescents with and without ASD performed a computerized task in which they saw a bird whose movement had been occluded and predicted when it arrived at a target location. Participants without ASD developed a central-tendency bias throughout the experiment—an adaptive behavior indicating accumulation of knowledge about the stimulus statistics—whereas participants with ASD did not show this bias. Smooth-pursuit eye movements for the moving bird were also associated with better performance in participants without ASD and with a bias for responding early among participants with ASD. 

The Use of Prior Knowledge for Perceptual Inference Is Preserved in ASD    

Sander Van de Cruys, Steven Vanmarcke, Ines Van de Put, Johan Wagemans (2017)  

Van de Cruys and colleagues presented participants with Mooney images—simplified black-and-white representations of source images that can be perceived differently before and after exposure to the natural source images—and asked participants to guess what each image showed. After participants saw the source images, they tried to identify each Mooney image again. Participants also completed the Autism-Spectrum Quotient (AQ) questionnaire, a measure of autism-like traits. Regardless of their scores on the AQ questionnaire, all participants showed improvements in recognition accuracy for the Mooney images after exposure to the source images. When the researchers compared adolescents with and without ASD, they found no differences in performance improvements. These findings suggest that the fast formation and application of specific priors, and therefore the ability to apply top-down processing, is preserved in ASD. 

An Electrocortical Measure Associated With Metarepresentation Mediates the Relationship Between Autism Symptoms and Theory of Mind    

Erin J. Libsack, Elizabeth Trimber, Kathryn M. Hauschild, Greg Hajcak, James C. McPartland, Matthew D. Lerner (2021)  

Libsack and colleagues’ study suggests that ASD symptom severity and impairments in theory of mind (ToM; ability to make inferences about others’ state of mind) might be associated with distinct brain activity. Participants with and without ASD viewed vignettes and made mental-state inferences about the characters’ behavior while the researchers used electroencephalography to measure their brain activity. Participants with more accurate ToM and less severe ASD symptoms tended to show a late positive complex (LPC) event-related potential. The LPC, thought to indicate cognitive metarepresentation, may help to explain the heterogeneity in ToM performance in individuals with ASD. 

Adaptation to Vocal Expressions and Phonemes Is Intact in Autism Spectrum Disorder    

Patricia E. G. Bestelmeyer, Bethan Williams, Jennifer J. Lawton, Maria-Elena Stefanou, Kami Koldewyn, Christoph Klein, Monica Biscaldi (2018)  

Prior research using visual paradigms has shown that children with ASD exhibit reduced visual aftereffects compared with typically developing children. The authors extended this work, investigating whether the emotional salience of auditory stimuli would affect sensory adaptation in children with and without ASD. Participants listened to a series of stimuli designed to induce auditory aftereffects, categorizing each recording according to its emotional content or phoneme (a single, irreducible sound in speech). Although children with ASD were worse at categorizing emotional expressions than they were at categorizing basic phonemes, auditory aftereffect sizes were similar for children with and without ASD. These findings suggest that individuals with ASD do not show general impairments in sensory-adaptation mechanisms. 

What Do New Findings About Social Interaction in Autistic Adults Mean for Neurodevelopmental Research?    

Rachael Davis and Catherine J. Crompton (2021)  

New findings suggest that social and communication difficulties among autistic adults can be influenced by mismatches in communication styles that also reflect nonautistic difficulties. Thus, deficit-based accounts of autistic social difficulties may be overly simplistic, because they do not account for the bidirectional nature of interactions between individuals with and without autism. Shifting from a deficit-based view to a difference-based view of autistic social difficulties could increase public understanding of autism, bridge the gap between different interaction styles, and provide opportunities for the inclusion of autistic individuals. 

It Takes All Kinds (of Information) to Learn a Language: Investigating the Language Comprehension of Typical Children and Children With Autism    

Letitia R. Naigles (2020)  

The Longitudinal Study of Early Language (LSEL) has been following the speech, understanding, and interactions of typically developing children and children with ASD. Naigles summarizes the findings of the LSEL: Both groups of children show similar syntactic understanding and word-learning strategies, including within-group variability associated with other aspects of individual behavior. In both groups, early linguistic knowledge and social abilities influence later speech and understanding. These findings suggest that language development might have both social and linguistic foundations. 

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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 2024-05-08T16:09:01-05:00 May 7th, 2024 | Back to School , Early Intervention , Educational Therapies , Executive Function , Health , Parenting , Sensory , Social Skills , Webinar |

Learn about emerging research on the relationship between the development of motor skills and executive function in autistic children. Handouts are online HERE The

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

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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 insurance

Prenatal exposure to cannabis may increase likelihood of autism

Melanie Glock 2024-04-28T15:45:52-05:00 August 29th, 2023 | News |

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 other

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 America,

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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 colleagues

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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Understanding autism: The path to diagnosis, awareness and support

Mayo Clinic Staff

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Diagnosing a person with autism spectrum disorder can be challenging. It's a medical condition that no blood test, brain scan or objective test can pinpoint. And because of each person's distinctive pattern of symptoms, it can be hard to determine its severity.

As people gain familiarity with autism, however, they are becoming more open to discussing the diagnosis and seeking treatment. Society is also becoming more motivated to learn about neurodivergent conditions, including autism.

What is autism spectrum disorder?

Autism spectrum disorder  is a condition related to brain development that affects how a person perceives and socializes with others, causing problems in social interaction and communication. It includes conditions that previously were considered separate, including autism, Asperger's syndrome, childhood disintegrative disorder and an unspecified form of pervasive developmental disorder.

Autism affects children and adults in three areas: communication, social interaction and behaviors.  Children with autism spectrum disorder  may struggle with recognizing their emotions and may feel them more intensely. Regulating their anger and frustration can be difficult and lead to intense bursts of emotions. Children with autism also have higher rates of anxiety and depression.

Each child with autism spectrum disorder is likely to have a distinctive pattern of behavior and level of severity. A healthcare professional will generally describe the severity of the condition based on the person's level of impairments and how those affect their ability to function.

A child or adult with autism spectrum disorder may have problems with social interaction and communication skills, including any of these signs:

• Can't start a conversation, keep one going or can only start one to make requests or label items.
• Doesn't appear to understand simple questions or directions.
• Doesn't express emotions or feelings and appears unaware of others' feelings.
• Doesn't speak or has delayed speech.
• Fails to respond to their name or appears not to hear you sometimes.
• Has difficulty recognizing nonverbal cues, such as interpreting other people's facial expressions, body postures or tone of voice.
• Has poor eye contact and lacks facial expression.
• Inappropriately approaches a social interaction by being passive, aggressive or disruptive.
• Prefers playing alone.
• Repeats words or phrases verbatim but doesn't understand how to use them.

Awareness of autism behaviors

According to the  Centers for Disease Control and Prevention (CDC) , the latest research from 2023 shows that 1 in 36 children was diagnosed with autism. This is an increase from 1 in 44 children just  two years ago .

Children tend to become more aware of their diagnosis around puberty. Kids recognize their differences from their peers and notice their struggle to fit in. They might notice they're not being invited to participate in certain activities or being accepted in the same way as many of their peers. Social interactions become more crucial for young people in middle and high school, which can be stressful for someone on the autism spectrum.

Parents may notice symptoms early on when they see how their child's behaviors, communication and social interactions differ from their peer group. It can be challenging for parents to accept that their child is different from other children. Parents may feel guilty and responsible, even though this developmental condition has no known cause.

Living with autism spectrum disorder

As the number of people living with autism spectrum disorder increases, it's critical to seek out educational opportunities that can help with understanding autism spectrum disorder. What are the strengths and disadvantages of the child? How can that knowledge be used to strengthen the skills of a child with autism? Answering these questions can help identify specific interventions to teach skills relevant to the child.

For example, if a child struggles with regulating emotions, this can be addressed through  treatment  to help them gain more control over their emotions and behaviors.

No cure exists for autism spectrum disorder, and there's no one-size-fits-all treatment. The goal of treatment is to maximize your child's ability to function by reducing their autism spectrum disorder symptoms and  supporting their development and learning . Early intervention during the preschool years is key.

Treatment options may include:

• Behavior and communication therapies
• Educational therapies
• Family therapies
• Medications

When you have a child or loved one with autism, the chance of them having anxiety or depression is increased. Evaluating and treating these symptoms can improve their level of functioning and their overall mental health.

One of the most critical things parents, friends or classmates of someone diagnosed with autism spectrum disorder can do is educate yourself about it while recognizing their strengths.

You can develop increased compassion for your loved ones, classmates, friends and colleagues by recognizing and understanding more about the condition. While you can't eliminate a child's autism or wait for them to outgrow it, you can minimize some of its symptoms and improve quality of life.

Janice Schreier  is a child and adolescent clinical therapist in  Psychiatry & Psychology  in  La Crosse , Wisconsin.

This article first appeared on the Mayo Clinic Health System blog .

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ORIGINAL RESEARCH article

Autism spectrum disorder research: knowledge mapping of progress and focus between 2011 and 2022.

• 1 National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), NHC Key Laboratory of Mental Health (Peking University), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China
• 2 Translational Medicine Center of Chinese Institute for Brain Research, Beijing, China
• 3 Guangdong Key Laboratory of Mental Health and Cognitive Science, Institute for Brain Research and Rehabilitation (IBRR), South China Normal University, Guangzhou, China

Background: In recent years, a large number of studies have focused on autism spectrum disorder (ASD). The present study used bibliometric analysis to describe the state of ASD research over the past decade and identify its trends and research fronts.

Methods: Studies on ASD published from 2011 to 2022 were obtained from the Web of Science Core Collection (WoSCC). Bibliometrix, CiteSpace, and VOSviewer were used for bibliometric analysis.

Results: A total of 57,108 studies were included in the systematic search, and articles were published in more than 6,000 journals. The number of publications increased by 181.7% (2,623 in 2011 and 7,390 in 2021). The articles in the field of genetics are widely cited in immunology, clinical research, and psychological research. Keywords co-occurrence analysis revealed that “causative mechanisms,” “clinical features,” and “intervention features” were the three main clusters of ASD research. Over the past decade, genetic variants associated with ASD have gained increasing attention, and immune dysbiosis and gut microbiota are the new development frontiers after 2015.

Conclusion: This study uses a bibliometric approach to visualize and quantitatively describe autism research over the last decade. Neuroscience, genetics, brain imaging studies, and gut microbiome studies improve our understanding of autism. In addition, the microbe-gut-brain axis may be an exciting research direction for ASD in the future. Therefore, through visual analysis of autism literature, this paper shows the development process, research hotspots, and cutting-edge trends in this field to provide theoretical reference for the development of autism in the future.

Introduction

Autism spectrum disorder (ASD) refers to a group of early-onset, lifelong, heterogeneous neurodevelopmental conditions with complex mechanisms of emergence ( 1 ). The prevalence of ASD has increased from 1 in 69 by 2012 to 1 in 44 by 2018, as reported by the Centers for Disease Control and Prevention for 2012–2018 ( 2 , 3 ). Recent research estimates the male-to-female ratio is closer to 2:1 or 3:1, indicating a higher diagnostic prevalence of autism in males compared to females ( 4 – 6 ). Some studies have shown a high heritability of 80–93% in ASD and reported hundreds of risk gene loci ( 7 ).

Specific autistic characteristics usually appear before the age of 3 years, and some children on the spectrum may have limited nonverbal and verbal communication by the age of 18–24 months ( 8 , 9 ). The diagnosis of ASD is based on the core features of social communication impairment and unusual and repetitive sensory-motor behavior ( 10 ). Some autistic individuals can be definitively diagnosed with autism as early as 2–3 years of age and the mean age of diagnosis for autistic children is still 4–5 years ( 1 , 11 ). It is important to stress that more adults are getting assessed for possible autism ( 5 ). As autism is increasingly diagnosed, multidisciplinary involvement can help have a positive impact on the well-being and quality of life for both children and adults on the spectrum ( 12 ). Several mental diseases also affect autistic individuals, increasing the diagnosis complexity ( 13 ).

Over the past decade, researchers have struggled to explain the neurological etiology, and great progress has been made in the genetics, epigenetics, neuropathology, and neuroimaging of ASD ( 9 ). However, there is a lack of systematic review of field research and discussion of future research hotspots. Bibliometrics ( 14 ) belongs to interdisciplinary research, which has been widely used in science by analyzing highly cited papers, field keyword clustering, and the internal cooperation links of countries, thus providing a comprehensive interpretation of the development process of autism research field ( 15 ).

In some of the previous bibliometrics studies on ASD, a single software was used to focus on a specific field or research aspect of the autism ( 16 – 18 ), and the trend in the past decade has not yet been displayed. The present study comprehensively combines Bibliometrix package, CiteSpace, and VOSviewer to (1) dynamically assess quantitative indicators of ASD research publications and use different index indicators to measure the quality of research; (2) further identify the most contributing countries, institutions, journals, and authors; (3) analyze the citation network architecture; (4) determine the top 100 most cited papers; (5) conduct keyword analysis. Subsequently, bibliometrics was used to understand the current hotspots and trends in the field of ASD research for further in-depth investigation.

Materials and methods

Data collection and search strategies.

We comprehensively searched the Web of Science Core Collection (WoSCC) database from 2011 to 2022. WoSCC is a daily updated database covering an abstract index of multidisciplinary literature that exports complete citation data, maintained by Thomson Reuters (New York, NY, USA) ( 19 ). The articles’ data were independently searched by two researchers on May 29, 2022, to avoid bias caused by database updates. The scientometric retrieval process is illustrated in Figure 1 . A total of 68,769 original articles in English language were retrieved, excluding 11,661 irrelevant articles, such as meeting abstracts, editorial materials, corrections, and letters. A total of 57,108 documents were exported, and the retrieved documents would be exported in the form of all records and references.

Figure 1 . Flowchart of the screening process.

Grey prediction model

Grey models (GM) are used to construct differential prediction models with limited and incomplete data ( 20 ). The GM (1,1) model, with high accuracy and convenient calculations, is extensively utilized in the energy and medical industries ( 21 ). We used the standard GM (1,1) model to forecast the annual publication volume over the next 5 years. The operation of GM (1,1) model was done by using Python software.

Bibliometric analysis and visualization

The records of the retrieved publications were exported to Bibliometrix, CiteSpace, and VOSviewer for further bibliometric analysis.

Bibliometrix package (running on R4.0.3) was utilized to capture and extract the bibliographic information on selected publications, including topic, author, keywords, and country distribution ( 22 ). The productivity of authors/journals in the field was measured by the number of publications (Np) and assessing metrics, such as the number of citations, publication h-index value, and m-index value. The h-index is used to quantify the scientific output and measure the citation impact, and two people with similar h-index may have a similar impact in the scientific field, even if the total number of papers or total citations are different ( 23 ). The m-index can be used to compare the influence of scholars with different academic career years. The number of citations of a document is a measure of its scientific impact to a certain extent ( 24 ). Bibliometrix package was also used to screen the top 100 articles and explore research trends and hotspots.

VOSviewer is a free computer program to visualize bibliometric maps ( 25 ). The keyword co-occurrence network was constructed using VOSviewer. CiteSpace is based on the Java environment and uses methods, such as co-occurrence analysis and cluster analysis, for the visualization of scientific literature research data in specific disciplines. The visual knowledge maps were constructed using the procedural steps of CiteSpace ( 26 ), including time slicing, threshold, pruning, merging, and mapping; then, the contribution of countries and institutions of ASD over the past decade was assessed based on centrality scores. The co-citation network and dual-map of references were constructed by CiteSpace. A dual-map ( 27 ) overlay is a bipartite overlay analysis method by CiteSspace, which uses the distribution map cited journals in the WoS database as the base map, and the map generated by the cited literature data as the overlay map.

Annual publications

A total of 57,108 articles were included in this study, consisting of 46,574 articles, 2,643 conference papers, and 7,891 reviews. From 2011 to 2022, the number of publications maintained a steady growth rate ( Figure 2A ), and the grey prediction model predicted the trend of increasing publication volume in the next 5 years ( Figure 2B ). The main information for all publications is shown in Supplementary Table S1 .

Figure 2 . Global trends in publications of ASD research. (A) Single-year publication output over the past decade. (B) Model forecast curves for publication growth trends.

Distribution of countries and institutions

Autism-related research has been conducted by researchers from a variety of countries and institutions, and articles in this field have been cited 1,231,588 times ( Tables 1 , 2 ). CiteSpace visualizes collaborative networks between institutions and countries ( Figures 3A , B ). As shown in the international collaborations network of autism research ( Figure 3C ), the USA and UK are the leading countries working closely with other countries.

Table 1 . Publications in top 10 most productive countries.

Table 2 . Publications in top 10 most productive Institutions.

Figure 3 . The distribution of countries and institutions. Map of countries (A) and institutions (B) contributed to publications related to ASD research. (C) Network diagram showing international collaborations involved in ASD research. The nodes represent the countries and institutions; the color depth and size of the circle are positively correlated to the number of posts. The thickness of the curved connecting lines represents the strength of collaboration in the countries and institutions.

Analysis of journals

The h-index combines productivity and impact; typically, a high h-index means a high recognition. As presented in Table 3 , the Journal of Autism and Developmental Disorders, PLOS One, and Molecular Psychiatry were among the top three of the 20 journals with the highest h-index. The Journal of Autism and Developmental Disorders has the highest number of articles (3478) and cited number of publications (90308). Among the top 20, four journals with impact factors >10 include Molecular Psychiatry (IF: 13.437), Biological Psychiatry (IF: 12.810), Proceedings of the National Academy of Sciences of the United States of America (IF: 12.779), Journal of the American Academy of Child and Adolescent Psychiatry (IF: 13.113), which have been cited more than 10,000 times. In addition, 75% of journals belong to Q1 ( Table 3 ). The cited journals provided the knowledge base of the citing journals. The yellow paths illustrate that studies published in “molecular, biology, immunology” journals tended to cite journals primarily in the domains of “molecular, biology, genetics,” and “psychology, education, social.” The paths colored with grass-green paths illustrate that studies published in “medicine, medical, clinical” journals tended to cite journals primarily in the domains of “molecular, biology, and genetics.” The pale blue paths showcase that research published in “psychology, education, health” journals preferred to quote journals mostly in the domains of “molecular, biology, genetics,” “health, nursing, medicine,” and “psychology, education, social ( Figure 4 ).”

Table 3 . Top 20 journals ranked by h_index.

Figure 4 . A dual-map overlay of journals that published work related to ASD. A presentation of citation paths at a disciplinary level on a dual-map overlay. The width of the paths is proportional to the z-score-scale citation frequency. The labels on the map represent the research subjects covered by the journals, and the wavy curve connects the citing articles on the left side of the map and the cited articles on the right side of the map.

Analysis of authors

The top 10 most effective authors who have contributed to autism research are listed in Table 4 . The g-index and m-index are derivatives of the h-index, and if scientists publish at least 10 articles, of which 7 papers have been cited cumulatively 51 (>49), the g-index is 7; the m-index is related to the academic age of the scientists. The large g-index, h-index, and m-index indicate a great influence on the scholar’s academic influence and high academic achievement. Professor Catherine Lord from the USA is ranked first and has made outstanding contributions to autism research over the past 10 years. In terms of the number of publications, Simon Baron-Cohen was the most productive author ( n  = 278), followed by Tony Charman ( n  = 212) and Christopher Gillberg ( n  = 206). In terms of citations in this field, Daniel H. Geschwind was ranked first (18,127 citations), followed by Catherine Lord (14,830 citations) and Joseph D. Buxbaum (14,528 citations).

Table 4 . Top 10 most effective authors contributing to autism research.

Analysis of reference

The co-citation analysis network of 1,056,125 references ( Figure 5A ) showed that two articles appear simultaneously in the bibliography of the third cited document. The top 20 co-cited references (over the past decade) summarized in ASD studies are listed in Supplementary Table S2 . Most of this highly cited literature focuses on the genetic field, discovering genetic risk loci and associated mutations, constructing mutation networks highly associated with autism, and identifying genes associated with autism synaptic destruction. Some studies indicated that de novo mutations in ASD might partially explain the etiology. Multiple studies have revealed genetic variants associated with ASD, such as rare copy number variants (CNVs), de novo likely gene-disrupting (LGD) mutations, missense or nonsense de novo variants, and de novo duplications. In the cluster network graph, different colors represent varied clusters, and each node represents a cited paper, displaying the distribution of topics in the field ( Figure 5B ). The network is divided into 25 co-citation clusters ( Figure 5B ), primarily related to the diagnosis, etiology, and intervention of autism. The etiological studies include five clusters, de novo mutation, inflammation, gut microbiota, mitochondrial dysfunction, and mouse model. Intervention literature focuses on early intensive behavioral intervention, intranasal oxytocin, video modeling, and multisensory integration. The diagnostic aspects of ASD include neuroimaging functional connectivity and Diagnostic and Statistical Manual of Mental Disorders (DSM-5). In addition, some of the references focus on gender/sex differences and sleep problems. Coronavirus disease 2019 (COVID-19) is a new cluster for autism research.

Figure 5 . Mapping on co-cited references. (A) A network map showing the co-cited references. (B) Co-cited clusters with cluster labels.

Co-occurrence analysis of keywords

The co-occurrence analysis of keywords in ASD research articles was performed using VOSviewer software; the keywords that occurred ≥200 times were analyzed after being grouped into four clusters of different colors ( Figure 6A ); the temporal distribution of keywords is summarized in Figure 6B . This map identifies various categories of research: Etiological mechanisms (red), Clinical features (green), Intervention features (blue), and the Asperger cluster (yellow). In the “Etiological mechanisms” cluster, the research includes brain structure and function, genetics, and neuropathology. In the “Clinical features” cluster, the common keywords were “symptoms,” “diagnosis,” “prevalence,” and its comorbidities, including “anxiety” and “sleep.” In the “Intervention features” cluster, the research population of ASD is concentrated in “young children,” “intervention,” and “communication.” These interventions improve the learning and social skills through the involvement of parents and schools.

Figure 6 . Keywords co-occurrence network. (A) Cluster analysis of keywords. There are four clusters of keywords: red indicates Cluster 1 ( n  = 145), green indicates Cluster 2 ( n  = 104), blue indicates Cluster 3 ( n  = 78), yellow indicates Cluster 4 ( n  = 80). (B) Evolution of keyword frequency. A minimum number of occurrences of a keyword = 200. Overall, 407 keywords met the threshold criteria. The yellow keywords appear later than purple keywords.

The 100 top-cited publications

The screening of the 100 most cited publications on ASD between 2011 and 2022 by Bibliometrix software package, each with >500 citations. The detailed evaluation index information for countries, institutions, journals, and authors ( Supplementary Tables S3 – S6 ).

Taken together, the results indicated that the United States is the country that publishes the most highly cited articles ( n  = 64), including single-country publications ( n  = 37) and multiple-country publications ( n  = 27); most articles are from academic institutions within the USA ( Figures 7A , B ).

Figure 7 . Analysis of the 100 top-cited publications Characteristics of 100 top-cited publications. The most relevant countries (A) , affiliations (B) , journals (C) and authors (D) . Trend topics (E) and thematic evolution (F) of 100 top-cited publication. Coupling Map (G) : the coupled analysis of the article, references and keywords is carried out, the centrality of the x -axis is displayed, the y -axis is the impact, and the confidence (conf%) is calculated.

The 100 top-cited ASD publications were published in 48 journals; 17 articles were published in Nature ( n  = 17), making it the highest h-index journal in this list ( Supplementary Table S5 ). In addition, 10 articles were published in Cell, and 7 articles were published in Nature Genetics ( Figure 7C ). When considering the individual authors’ academic contributions, Bernie Devlin provided 13 publications, followed by Kathryn Roeder and Stephan J Sanders, with 11 publications each ( Figure 7D ). The details of the top 10 top-cited papers are summarized in Table 5 . An article titled “A general framework for estimating the relative pathogenicity of human genetic variants” published by Martin Kircher in Nature Genetics, received the highest number of citations ( n  = 3,353).

Table 5 . Detail of top 10 citation paper.

The 100 top-cited ASD articles encompassed a range of keywords ( Figure 7E ) and displayed the main cluster of themes through specific periods (2011–2022) by analyzing those in the selected literature. The Sankey diagrams of thematic evolution explain the topics that evolved throughout the years ( Figure 7F ). In summary, the core topics of the ASD field in 2011–2014 consisted of the risk of childhood ASD and further developed into the field of human genetic variants, such as CNV and de novo mutations. In the subperiod 2015–2020, the further expansion of studies in this field leads to new clusters, such as “immune system,” “brain development,” and “fecal microbiota.” Genome research in the upper right quadrant, including mutations and risk, is a major and evolving theme. The coupled map showing the brain-gut axis field, including intestinal microbiota and chain fatty acids, located in the lower right corner is crucial for autism research but is not yet well-developed ( Figure 7G ). The research on autism, including animal models, schizophrenia, is a well-developed field, but that on high-functioning autism and diagnosis is a marginal field.

This study used various bibliometric tools and software to analyze the published articles on ASD based on the WoSCC database from 2011 to 2022. By 2022, the annual number of publications and citations of ASD-related research showed an overall upward trend, reflecting the sustained interest and the diversity of areas.

General information

In terms of regional distribution, researchers from different countries and regions have participated in autism research, and international cooperation has been relatively close over the past decade. The scientific research is supported by several countries and institutions, as well as by large-scale international cooperation ( 28 , 29 ). The USA has the highest collaboration performance, especially with UK, Canada, Australia and China. In addition to the limitations of financial aid, ethical, cultural, and racial issues are complex constraints that should be overcome for more diversity in autism research ( 30 , 31 ). We speculated that further collaboration between institutions and countries could promote autism research.

Among the top 20 academic journals, most of the papers were in the Journal of Autism and Developmental Disorders. The frequent publishing of ASD-related papers indicates the interest of readers and journal editors in Autism. Also, substantial studies have been carried out on ASDs, autism, and molecular autism. These journals are ascribed to the field of ASD, focusing on autism research and communication ASD science. However, the analysis of the 10 most cited publications revealed that they were published in such as Nature, Cell, Lancet; these ASD studies were all from high-impact journals.

From the perspective of authors, some of them have made outstanding contributions to global ASD research. Professor Catherine Lord, the top rank for h-index, m-index analysis conducted by the author, and who developed the two gold standards for autism diagnosis ( 32 , 33 ), are the most influencing factors in the field. ASD is a disease with complex genetic roots. Dr. Catherine Lord has conducted multiple studies using genome-wide association study (GWAS) and gene set analysis to identify variant signatures in autism ( 34 ). A recent meta-analysis showed that 74–93% of ASD risk is heritable, with an analysis of CNVs that highlights the key role of rare and de novo mutations in the etiology of ASD ( 35 ). Variation-affected gene clusters on networks associated with synaptic transmission, neuronal development, and chromatin regulation ( 36 , 37 ). The identification of the cross-disorder genetic risk factors found by assessing SNP heritability in five psychiatric disorders ( 38 ). Five of the top 10 cited papers in Table 5 focus on genetic variation, suggesting that over the past decade, research has shifted from a general concept of genetic risk to the different types of genetic variations associated with autism.

Simon Baron-Cohen of the Autism Research Center at the University of Cambridge was the most published author between 2011 and 2021. He contributed to the mind-blindness hypothesis of autism, developed the autism spectrum quotient (AQ) screening tool for autism, and focused on gender differences in autism ( 39 – 41 ). There are gender/sex differences in the volume and tissue density of brain regions, including the amygdala, hippocampus, and insula, and the heart-blind hypothesis links emotional recognition in individuals with autism to deficits in the amygdala ( 41 – 43 ). Then, Simon et al. backed up the “extreme male brain” theory of autism in a study of 36,000 autistic individuals aged 16–89 ( 44 ). Recently, an increasing number of studies from different perspectives have focused on how sex/gender differences are related to autism ( 4 , 5 , 45 ). In the future, studies of neural dimorphism in brain development in autism need to be conducted across the lifespan to reduce age-induced biases ( 41 ).

Hotspots and Frontiers

Keyword analysis was a major indicator for research trends and hotspot analysis. This study shows that keywords for autism research include etiological mechanism, clinical characteristics, and intervention characteristics. Genetic, environmental, epigenetic, brain structure, neuropathological, and immunological factors have contributed to studying its etiological mechanism ( 46 , 47 ). The studies on the abnormal cortical development in ASD have reported early brain overgrowth ( 48 ), reduced resting cerebral blood flow in the medial PFC and anterior cingulate ( 49 ), focal disruption of neuronal migration ( 50 ), and transcriptomic alterations in the cerebral cortex of autism ( 51 ). Genomics studies have identified several variants and genes that increase susceptibility to autism, affecting biological pathways related to chromatin remodeling, regulation of neuronal function, and synaptic development ( 51 – 54 ). In addition, many autism-related genes are enriched in cortical glutamatergic neurons, and mutations in the genes encoding these proteins result in neuronal excitation-inhibitory balance ( 51 , 55 ). A recent study using single-cell sequencing of the developing human cerebral cortex found strong cell-type-specific enrichment of noncoding mutations in ASD ( 56 ). Interestingly, genes interact with the environment; some studies have shown that environmental exposure during pregnancy is a risk factor for brain development ( 57 ), and there are changes in DNA methylation in the brains of ASD patients, reflecting an underlying epigenetic dysregulation.

Presently, the diagnosis of ASD is mainly based on symptoms and behaviors, but the disease has a high clinical heterogeneity, and the individual differences between patients are obvious ( 58 ). In this study, the keywords of the intervention cluster show the importance of early individualized intervention. Patient data are multidimensional, and individualized diagnoses could be made at multiple levels, such as age, gender, clinical characteristics, and genetic characteristics ( 59 ). Early individual genetic diagnosis aids clinical evaluation, ranging from chromosomal microarray (CMA) to fragile X genetic testing ( 60 ). However, the results of genetic research cannot guide the treatment. Notably, the treatment of autism is dominated by educational practices and behavioral interventions ( 61 ). Medication may address other co-occurring conditions, such as sleep disturbances, epilepsy, and gastrointestinal dysfunction ( 9 ). Professor Catherine Lord pointed out that the future of autism requires coordinated, large-scale research to develop affordable, individualized, staged assessments and interventions for people with ASD ( 62 ). Professor Baron-Cohen noted that increasing the sample size and collecting data from the same individual multiple times could reduce heterogeneity ( 58 ). In addition, screening for objective and valid biomarkers in the future would help to stratify diagnosis and reduce heterogeneity.

According to the keyword trend analysis of 100 highly cited documents, the genetic risk of autism was determined as the hot focus of research, and immune dysregulation and gut microbiome are the new development frontiers after 2015. Patients with ASD have altered immune function, microglia activation was observed in postmortem brain samples, and increased production of inflammatory cytokines and chemokines was observed in cerebrospinal fluid. The microglia are involved in synaptic pruning, and cytokines also affect neuronal migration and axonal projections ( 63 – 65 ). In addition, abnormal peripheral immune responses during pregnancy might affect the developing brain, increasing likelihood of autism ( 66 ). Several studies have pointed to abnormalities in immune-related genes in the brain and peripheral blood of autistic patients ( 51 , 67 , 68 ). Immune dysfunction is involved in the etiology of ASD and mediates the accompanying symptoms of autism. The patients have multiple immune-related diseases, asthma, allergic rhinitis, Crohn’s disease, and gastrointestinal dysfunction ( 69 – 71 ). Children with frequent gastrointestinal symptoms, such as abdominal pain, gas, constipation, or diarrhea, had pronounced social withdrawal and stereotyped behavior ( 70 – 72 ). Several studies suggested that these autism-related gastrointestinal problems might be related to intestinal microbiota composition ( 72 – 74 ). Accumulating evidence suggested that the microbiota-gut-brain axis influences human neurodevelopment, a complex system involving immune, metabolic, and vagal pathways in which bacterial metabolites directly affect the brain by disrupting the gut and blood–brain barrier ( 75 – 78 ). Fecal samples from children with autism contained high Clostridium species and low Bifidobacterium species ( 79 , 80 ). Probiotics can modulate gut microbiota structure and increase the relative abundance of Bifidobacteria , and clinical studies have shown that supplementation with probiotic strains improves attention problems in children with autism ( 81 , 82 ). Recent clinical trials have shown that microbiota transfer therapy improves gastrointestinal symptoms and autism-like behaviors in children with ASD ( 83 , 84 ).

This scientometric study comprehensively analyzes about a decade of global autism research. Research in the field of autism is increasing, with the United States making outstanding contributions, while neuroscience, genetics, brain imaging studies, or studies of the gut microbiome deepen our understanding of the disorder. The study of the brain-gut axis elucidates the mechanism of immunology in autism, and immunological research may be in the renaissance. The current data serve as a valuable resource for studying ASD. However, the future of autism needs further development. In the future, relevant research should be included for a complete representation of the entire autism population, and further collaboration between individuals, institutions, and countries is expected to accelerate the development of autism research.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding authors.

Author contributions

MJ, DZ, JL, and LW conceived and designed the study. MJ, TL, XL, KY, and LZ contributed to data collection and data analysis. MJ wrote the original manuscript. DZ, JL, and LW revised the article and contributed to the final version of the manuscript. All authors contributed to the article and approved the submitted version.

This work was supported by grants from the Key-Area Research and Development Program of Guangdong Province (2019B030335001) and the National Natural Science Foundation of China (grant numbers 82171537, 81971283, 82071541, and 81730037).

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.

Publisher’s note

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.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1096769/full#supplementary-material

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Keywords: autism spectrum disorder, bibliometric study, CiteSpace, VOSviewer, research frontiers

Citation: Jiang M, Lu T, Yang K, Li X, Zhao L, Zhang D, Li J and Wang L (2023) Autism spectrum disorder research: knowledge mapping of progress and focus between 2011 and 2022. Front. Psychiatry . 14:1096769. doi: 10.3389/fpsyt.2023.1096769

Received: 16 November 2022; Accepted: 10 April 2023; Published: 25 April 2023.

Reviewed by:

Copyright © 2023 Jiang, Lu, Yang, Li, Zhao, Zhang, Li and Wang. 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) and the copyright owner(s) 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: Jun Li, [email protected] ; Lifang Wang, [email protected]

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.

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Study identifies primary cause of sensory hypersensitivity in a mouse model of autism spectrum disorder

by Institute for Basic Science

A research team led by Director Kim Eunjoon of the Center for Synaptic Brain Dysfunctions and Director Kim Seong-Gi of the Center for Neuroscience Imaging Research within the Institute for Basic Science (IBS) has identified the primary cause of sensory hypersensitivity related to autism spectrum disorders (ASD).

Autism affects approximately 1 in 36 individuals and is marked by significant challenges in social interaction and communication. Around 90% of autism patients also suffer from abnormal sensory hypersensitivity that deeply affects their daily functioning.

This hypersensitivity results in exaggerated or dampened responses to common sensory stimuli such as sound, light, and touch, which leads to considerable stress and further social withdrawal. The precise brain region responsible for this sensory dysfunction is unknown, which hinders treatment efforts.

The IBS researchers studied an ASD mouse model with a mutation in the Grin2b gene, which encodes the GluN2B subunit of NMDA receptors. NMDA receptors, a type of glutamate receptor in the brain, have garnered attention in the context of autism due to their crucial role in synaptic transmission and neural plasticity.

It was hypothesized that the Grin2b gene mutation in mice would induce ASD-like phenotypes, including sensory abnormalities, and that certain brain mechanisms may play important roles.

The researchers monitored neural activity and functional connectivity in the brains of these mice using activity-dependent markers and functional magnetic resonance imaging (fMRI). In these mice, the researchers discovered increased neuronal activity in the anterior cingulate cortex (ACC).

The ACC is one of the higher-order cortical regions that have been extensively studied for cognitive and emotional brain functions, but have been understudied for brain disease-related sensory abnormalities.

Interestingly, when the hyperactivity of ACC neurons was inhibited using chemogenetic methods, sensory hypersensitivity were normalized, indicating the pivotal role of ACC hyperactivity in sensory hypersensitivity associated with autism.

The work is published in the journal Molecular Psychiatry .

Director Kim Eunjoon states, "This new research demonstrates the involvement of the anterior cingulate cortex (ACC), which has been known for its deep association with cognitive and social functions, in sensory hypersensitivity in autism."

The hyperactivity of the ACC was also associated with the enhanced functional connectivity between the ACC and other brain areas. It is believed both hyperactivity and the hyperconnectivity of the ACC with various other brain regions are involved with sensory hypersensitivity in Grin2b-mutant mice.

Director Kim Seong-Gi states, "Past studies attributed peripheral neurons or primary cortical areas to be important for ASD-related sensory hypersensitivity . These studies often only focused on the activity of a single brain region. In contrast, our study investigates not only the activity of ACC but also the brain-wide hyperconnectivity between the ACC and various cortical/subcortical brain regions, which gives us a more complete picture of the brain."

The researchers plan to study the detailed mechanisms underlying the increased excitatory synaptic activity and neuronal hyperconnectivity. They suspect that the lack of Grin2b expression may inhibit the normal process of weakening and pruning synapses that are less active so that relatively more active synapses can participate in refining neural circuits in an activity-dependent manner. Other areas of research interest is studying the role of ACC in other mouse models of ASD.

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A Longitudinal Analysis of Mothers’ Parenting Stress and Internalizing and Externalizing Behavior of Young Children on the Autism Spectrum

• Original Article
• Open access
• Published: 08 May 2024

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• Jessica Paynter   ORCID: orcid.org/0000-0003-0130-0606 1 , 2 ,
• Vanessa Heng 2 ,
• Madonna Tucker 3 &
• Stephanie Malone 1  

2 Altmetric

We investigated longitudinal relations between internalizing, externalizing, and total behaviors that challenge in young children on the autism spectrum and mothers’ parenting stress. Participants included 93 mothers of children on the autism spectrum aged 27.89–65.84 months, who completed questionnaires on maternal parenting stress, and children’s internalizing (anxiety), externalizing (disruptive), and total behaviors that challenge. Data were collected on early intervention program intake and approximately one year later. Cross-sectional findings indicated small to medium effect size associations between internalizing and externalizing behavior and parenting stress. However, cross-lagged structural equation models found that neither internalizing nor externalizing behavior predicted later parenting stress, nor the reverse. Significant stability effects were found for measures of child internalizing (anxiety), externalizing (disruptive), and total behaviors, and parenting stress. Relations between behaviors that challenge and parenting stress over time were non-significant in our models that controlled for stability of behaviors and parenting stress over time. Implications for research and clinical practice, in understanding and targeting the persistence of behaviors that challenge and parenting stress, are discussed.

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Parenting Stress and Child Behavior Problems in Young Children with Autism Spectrum Disorder: Transactional Relations Across Time

Transactional Relations Between Parenting Stress and Child Autism Symptoms and Behavior Problems

Parenting stress, readiness to change, and child externalizing behaviors in families of clinically referred children.

Avoid common mistakes on your manuscript.

Parents of children on the autism spectrum Footnote 1 report higher levels of parenting stress than other parent groups (Barroso et al., 2018 ; Hayes & Watson, 2013 ). Parenting stress is the sense of distress arising from parenting demands (Hakvoort et al., 2012 ). Children’s behaviors that challenge, including internalizing (e.g., anxiety) and externalizing (e.g., disruptive behaviors) behaviors, are strong, consistent, cross-sectional predictors of parenting stress (Barroso et al., 2018 ; Yorke et al., 2018 ). However, longitudinal findings are mixed (Yorke et al., 2018 ). Mixed longitudinal findings may be due to methodological differences across studies, or relations genuinely varying at different ages and stages of children’s lives. The purpose of the present study is to investigate the longitudinal relationships between mothers’ parenting stress and child behaviors that challenge. Our focus is on the early intervention stage when children are aged 2–5 years, which could inform supports during this key period.

Transactional models of child development predict bidrectional relationsips between parents’ stress and children’s behavior (see Rodriguez et al., 2019 , for an overview). For parents, observing children’s behaviors may be distressing, leading to increased parenting stress over time. Converesly, increased stress in parents may impact how parents respond to their children, including having less resources to respond to desirable behaviors and using parenting practices that inadvertently reinforce behaviors over time. However, in a systematic review, Yorke et al. ( 2018 ) found mixed longitudinal findings into the relations between parents’ stress and children’s behavior over time.

Seven studies were included in Yorke et al.’s review ( 2018 ) that addressed bidirectional relationships longitudinally between parenting stress and child behavior. Results were mixed including positive associations in either or both directions (e.g., Zaidman-Zait et al., 2014 ) and null results (e.g., Peters-Scheffer et al., 2012 ). Studies in the review varied in controls included in analyses, measurement tools, and child ages which may account for mixed findings. For example, autism characteristics were controlled in only one paper (Osborne & Reed, 2009 ) with two cohorts (ages 2:6–4:0 years and 5:0–16:0 years old) over a period of 9–10 months follow-up. Osborne and Reed ( 2009 ) found child behavior was no longer a significant predictor of later parenting stress, after controlling for autism characteristics. However, parenting stress remained a significant longitudinal predictor of child behavior, after controlling for autism characteristics. This study did not control for the potential stability of parenting stress and child behavior over time. The stability of these two variables was examined in two further studies (Peters-Scheffer et al., 2012 ; Zaidman-Zait et al., 2014 ) that yielded mixed results.

Peters-Scheffer et al. ( 2012 ) found in a study with preschool children on the spectrum, null results in assessments completed every six months over two years. That is, earlier parenting stress did not predict later child behavior, nor the reverse. In contrast, Zaidman-Zait et al. ( 2014 ) found, in a sample of children on the spectrum aged 24–47 months, significant pathways for parent-to-child effects at most timepoints 12 months apart, and one significant pathway for child-to-parent effects over four time points. Taken together, at the time of Yorke et al.’s ( 2018 ) review, there were no studies that controlled for stability effects, and included potential confounds (e.g., autism characteristics) which is needed to reconcile the conflicting results. The current study addresses relations between parenting stress and children's behavior over time to address conflicting reports and expand on previous research.

Two additional longitudinal papers have been published recently that begin to address this gap, but again with mixed results. Rodriguez et al. ( 2019 ) investigated longitudinal associations across four time points over three years collected approximately 12 months apart, between child behavior (internalizing and externalizing) and parenting stress (mothers and fathers) in 188 children on the autism spectrum aged 5–12 years. They controlled for stability of each variable in analyses. Mother and father parenting stress each predicted child internalizing behavior at the next time point. In contrast, internalizing behavior did not significantly predict later parenting stress for either parent. Mixed findings for externalizing behaviors were found. In earlier timepoints parenting stress in mothers (T1, T2) significantly predicted later child externalizing behavior (T2, T3). This same pattern was also in seen in fathers for T2 to T3 (T1 to T2 was non-significant). However, externalizing behaviors (T1, T2) did not significantly predict later parenting stress in either parent (T2, T3) in these earlier time points. At the final timepoints (T3 to T4), the reverse was found for fathers only, with externalizing behaviors predicting later parenting stress, but parenting stress for either parent did not significantly predict later externalizing behavior. Thus, this study indicated that different patterns may be seen at different ages or stages of children’s development. A limitation of this study however was that while Rodriguez et al. ( 2019 ) measured autism characteristics, these were not included as controls in their models of parenting stress and child behavior.

Lin et al. ( 2021 ) is the only study to date to the authors’ knowledge that has included both stability effects and autism characteristics to investigate bidirectional longitudinal associations between child behavior and parenting stress. Participants were 75 children on the autism spectrum aged 18–42 months and their parents over two assessments 1.5 years apart in Taiwan. Measures included translated versions of the full Parent Stress Index (PSI; Weng, 2003 ) to evaluate parenting stress, the Child Behavior Checklist (CBCL; Achenbach & Rescorla, 2000 ) to measure child behavior, and the Autism Diagnostic Observation Schedule (Lord et al., 1999 ) module 1 total algorithm raw scores to evaluate autism characteristics. They incorporated three measures of stress using the PSI subscales: parent-related stress (e.g., competence), child-related stress (e.g., child demandingness), and total stress using the total score. Significant stability (i.e., earlier scores strongly predicted later scores) of all three measures of parenting stress and child internalizing and externalizing behaviors were found. Child behavior measures did not show significant pathways in models to later parenting stress measures. In contrast, total parenting stress and child-related stress were significant predictors of later child externalizing behavior, but not internalizing behavior. A limitation however was that the total and child-related stress measures included items about children which may overlap with measures of child externalizing behavior, therefore potentially inflating associations that were found. Evidence for inflation was observed in Yorke et al.’s ( 2018 ) review of cross-sectional studies, whereby higher correlations between total parent stress and child externalizing behavior were observed when compared to parent-specific measures of parenting stress and child externalizing behavior.

While strong cross-sectional associations between parenting stress and child behavior are well established (e.g., Yorke et al., 2018 ), findings for longitudinal relationships are mixed which may be due to significant methodological differences across studies and varying age groups/stages included. Lin et al. ( 2021 ) conducted the only study that controlled for stability of parenting stress and child behavior over time and autism characteristics. However, they used a child behavior measure not designed for children on the spectrum (CBCL; Achenbach & Rescorla, 2000 ) which may not capture the behaviors associated with autism. Furthermore, significant associations between earlier parenting stress and later child externalizing behavior may have been explained at least in part, by overlapping items in total and parent-related stress scales used for analyses that include child-related items. Therefore, in our study we sought to address these limitations. We elected to use a measure designed for children with neurodevelopmental conditions, the Developmental Behavior Checklist (DBC, Einfeld & Tonge, 2002 ) and to include both total scores and a more specific measure of parent-related distress analyzed separately using the PSI short form (Abidin, 1995 ). We deliberately focused on mothers only, as previous research indicated different findings for mothers and fathers (Rodriguez et al., 2019 ) and sources of stress may differ for mothers versus fathers (Davis & Carter, 2008 ; Hastings et al., 2005 ). Our focus was on a specific period, the preschool period (2½–5 years), while children attended an early intervention service, as this may be a particularly important time for parenting stress and child behavior to interact as indicated by previous research (Rodriguez et al., 2019 ). Drawing from Lin et al.’s ( 2021 ) approach, our aim was to investigate longitudinally over an approximate one-year period the relations between internalizing, externalizing, and total behavior that challenges in young children on the autism spectrum and mothers’ parenting stress.

It was hypothesized that higher internalizing and externalizing behaviors in children on the autism spectrum would be associated with increased parenting stress cross-sectionally (as per Barroso et al., 2018 ; Yorke et al., 2018 ). Given mixed results, longitudinal hypotheses were tentative. It was predicted that time 1 (T1) internalizing and externalizing behavior in young children on the spectrum may significantly contribute to increased time 2 (T2) parenting stress one year later, after controlling for T1 parenting stress. Further, it was tentatively hypothesized that T1 parenting stress may predict increased T2 internalizing and externalizing behaviors in children on the autism spectrum, after controlling for T1 internalizing and externalizing behaviors. Autism characteristics and age were variables controlled in the analyses.

Participants

Participants were 93 mother–child dyads drawn from existing data collected as part of usual service delivery at an Australian autism early learning and care program. Data regarding attrition from the program were not available from the service. All children were attending the early learning and care program between assessment periods (time between, M  = 11.74 months, SD  = 2.47). This program is center-based and delivered in an autism-specific group-based early learning context (ages 2½ to 6 years). The program is consistent with a naturalistic developmental behavior intervention model (Schreibman et al., 2015 ). For a description of the program see Paynter et al. ( 2012 ).

Inclusion criteria for the present study were (1) verified autism diagnosis, and (2) maternal completion of a PSI (Abidin, 1995 ) and completion of the DBC (Einfeld & Tonge, 2002 ) at intake and 12-months or exit (whichever came first). To enter the service, all children required a formal diagnosis of autism spectrum disorder from a medical practitioner (e.g., pediatrician) or multidisciplinary team using the Diagnostic and Statistical Manual of Mental Disorders—5th edition criteria (DSM-5, American Psychiatric Association, 2013 ). Existing diagnoses of autism spectrum disorder (DSM-5) were verified for the purpose of this research using the Social Communication Questionnaire (SCQ; Rutter et al., 2003 ) for most ( n  = 88) participants. Where this was not completed by parents, or a score below the cut-off was obtained, the Autism Diagnostic Observation Schedule-2 (ADOS-2) comparison score (≥ 5 +) was used to verify ( n  = 7, SCQ < 11; n  = 1, SCQ missing). Participants were an average age of 45.44 months ( SD  = 9.22, range 27.89- 65.84), and included 77 males and 16 females, see Table  1 for further demographics.

Data were extracted from an existing database from an Australian center-based early intervention service collected between 2014 and 2017. All parents had provided signed informed consent for their data to be included in this database, the study was covered by ethical approval from the Griffith University Human Research Ethics Committee (Approval number 2014/656), and gatekeeper approval (i.e., consent) from the autism early intervention service was granted. Data were collected at intake to the service, after 12 months, and/or on exit, whichever was first, by the service. Where more than two time points were collected the first two were shared for the study ( M  = 11.74 months, SD  = 2.47, range 5.72–17.25 months). Data were shared for the purpose of this study for participants with T1 (intake) and T2 (12-months or exit) results on child behavior and parenting stress, and their T1 autism characteristics, cognitive functioning, and adaptive functioning.

Demographics

Demographics included age at each assessment, diagnosis of child, parental marital status, and primary language spoken at home, see Table  1 .

Autism Characteristics

The Social Communication Questionnaire: Current Form (SCQ, Rutter et al., 2003 ) is a 40-item dichotomous measure of potential autism characteristics which yields a total raw score. The SCQ has good psychometric properties including high sensitivity and specificity (Chandler et al., 2007 ) and convergent validity (Eaves et al., 2006 ). A cut-off of 11 was used to verify diagnosis based on previous research with preschoolers (Eaves et al., 2006 ) and as validated as showing maximum sensitivity and specificity in previous research with younger children (aged 17–45 months; Wiggins et al., 2007 ). The SCQ total score has been used in previous research to verify diagnosis for research including with children of a similar age range (e.g., Paynter et al., 2018b ; Westerveld et al., 2020 ). The total raw score data collected at intake (T1) were used to verify diagnosis.

The Autism Diagnostic Observation Schedule Second Edition (ADOS-2, Lord et al., 2012 ) is a semi-structured observation which examines social functioning, communication, and repetitive behaviors. It was administered by a staff member employed by the early intervention service who was either a research reliable trained assessor (research and assessment manager who was a registered psychologist) or one of their staff (completed or in-progress bachelor degree in a relevant field such as psychology, education, or speech pathology) who had been trained to reliability with them. All children had completed an ADOS-2 and scores from the administration on intake (T1) to the service were used. It was used to confirm diagnosis using the comparison score where an SCQ was not available or the SCQ score was below the cut-off. The ADOS-2 comparison score was used as a measure of autism characteristics in each of the structural equation models as outlined in data analysis and screening.

Adaptive Functioning

The Vineland Adaptive Behavior Scale-2nd Edition (VABS-2, Sparrow et al., 2005 ) is a parent/carer report which assesses children’s adaptive functioning. It measures communication, daily functioning, socialization, and motor skills, and together scores on these domains are used to calculate an adaptive behavior composite. The adaptive behavior composite standard score was used to describe the sample. The VABS-II has been used widely with children on the spectrum (Yang et al., 2016 ) and it shows excellent psychometric properties including split-half reliability and test–retest reliability (Sparrow et al., 2005 ).

Verbal and Non-Verbal Functioning

The Mullen Scales of Early Learning (MSEL, Mullen, 1995 ) is a developmental assessment and includes subscales of receptive and expressive language, visual reception, and fine motor. A fifth gross motor scale (ceiling of three years) was not administered as it was not required to calculate verbal or non-verbal functioning. The manual reports good internal reliability (α = 0.75–0.8; Mullen, 1995 ). Developmental quotients (dividing the age equivalent by chronological age multiplied by 100) were used for analyses as many children in this population do not attain the minimum score to calculate meaningful standard scores (Paynter et al., 2018b ). Consistent with previous research on children on the spectrum (e.g., Paynter et al., 2018b ), developmental quotients were calculated for verbal (averaging receptive and expressive language developmental quotients) and non-verbal (averaging fine motor and visual reception developmental quotients) composites to describe the sample, see Table  1 .

Child Behavior

The Developmental Behavior Checklist (DBC, Einfeld & Tonge, 2002 ) parent form, is a 96-item questionnaire measure of emotional and behavioral challenges in children and adolescents with intellectual and developmental disabilities. It has five subscales, including anxiety, disruptive/antisocial, communication disturbance, social-relating, and self-absorbed which are combined for a total score. The raw scores for the anxiety subscale were used to measure internalizing behaviors. The raw scores for the disruptive/antisocial scale were used to measure externalizing behaviors. The total scores across all five subscales were used to measure total behaviors that challenge. The DBC has high concurrent validity with other challenging behavior measures (Rice et al., 2018 ) and high internal consistency, criterion validity, test-retest reliability, and inter-rater reliability (Einfeld & Tonge, 1995 ). It has been used previously with young children on the autism spectrum (e.g., Adams et al., 2019 ).

Parenting Stress

The Parenting Stress Inventory-Short Form (PSI-SF, Abidin, 1995 ) measures three domains: difficult child, parental distress, and parent–child dysfunctional interaction. Analyses were conducted separately for both the raw score of the parental distress subscale (PSI-PD) and the raw total score (PSI Total, all three domains) to compare results between different ways of operationalizing parenting stress, given the possible confound between child behavior as both dependent and independent variables when the total score is utilized (as noted by Bohadana et al., 2019 ; McStay et al., 2014 ). Previous research has demonstrated excellent internal consistency for the parental distress subscale (e.g., α = 0.86 and 0.90 respectively in Bohadana et al., 2019 ; McStay et al., 2014 ) and for the total score (e.g., α = 0.95, Paynter et al., 2018b ).

Data Analysis and Screening

Descriptive statistics and bivariate correlations between T1 and T2 variables of interest were conducted to describe the initial data and for screening (i.e., to screen for collinearity and to check for data entry errors). Correlations were also conducted controlling for age. Cross-lagged autoregressive models were used to assess the longitudinal relations between parenting stress and child challenging behavior. The autoregressive pathways allow for the growth in the target constructs (i.e., child behaviors that challenge and parenting stress) to be examined, while cross-lagged effects allow for the investigation of (1) the effects of parenting stress levels on later child behaviors that challenge levels, and (2) the effects of child behaviors that challenge levels on later parenting stress levels, while controlling stability effects.

A series of six cross-lagged autoregressive models were developed to explore the inter-relations between parenting stress and child challenging behavior. These models manipulated the measure of child behavior (DBC internalizing/anxiety subscale, DBC externalizing/disruptive behavior subscale, or total score) and parenting stress (PSI-PD or PSI total). For each model, T1 parenting stress and T1 child behavior (DBC internalizing/anxiety subscale, DBC externalizing/disruptive behavior subscale, or total score) were used as direct predictors of both T2 parenting stress and T2 child behavior (DBC internalizing/anxiety subscale, DBC externalizing/disruptive behavior subscale, or total score paired respectively), and therefore included both autoregressive and cross-lagged effects. All manifest variables were regressed on age and ADOS-2 comparison scores (though these regressions are not shown in figures which depict the final parsimonious models). Therefore, any relations between the manifest variables are independent of the shared variance attributable to age and autism characteristics. To obtain the most parsimonious model, any non-significant paths were removed from the initial model iteratively. This did not significantly impact model fit for the final models.

All six models were estimated using MPlus 8.0 (Muthén & Muthén, 1998–2017). Assumption tests indicated that multicollinearity was not a concern for any of the models (all VIFs < 1.38) and all data were normally distributed (Kline, 2010). The resulting models were deemed to be a good fit to the data based on the following fit indices: non-significant chi-square value, comparative fit index (CFI) > 0.95 (> 0.90 is considered acceptable; Brown, 2006), standardized root mean square (SRMR) < 0.08, and root mean square error of approximation (RMSEA) < 0.06 (Hu & Bentler, 1999).

Concurrent Associations

Descriptive statistics for parenting stress and child behavior measures are reported in Table  2 . Correlations, in which missing data were handled by pairwise deletion, are reported in Table  3 . No significant correlations were found between levels of autism characteristics (ADOS-2) and parenting stress (PSI-PD nor PSI total) at either time point, with negative correlations with small effects (< 0.1; Cohen, 1992). However, significant negative correlations of small to medium effect size were observed between autism characteristics (T1) and internalizing (DBC anxiety subscale at T1, r  = − 0.22; and T2, r  = − 0.27), externalizing (DBC disruptive behavior subscale at T1, r  = − 0.26, and T2, r  = − 0.39), and total behaviors (DBC total score, at T1, r  = − 0.23; and T2, r  = − 0.22), see Table  3 . At T1 and T2, all child behavior measures positively correlated significantly with total parenting stress (PSI total) at the same time point (i.e., within T1 and within T2) with medium effects ( rs  = 0.30–0.49). Different results for child behavior and parental distress (PSI-PD) were found within each timepoint, see Table  3 . At T1, internalizing, externalizing, and total child behavior positively correlated with small to medium effects ( rs  = 0.22–0.31) with parental distress, yet at T2 neither form of child behavior (i.e., internalizing/anxiety and externalizing/disruptive), nor the total DBC score, were significantly correlated in zero order correlations with parental distress.

Cross-Lagged Autoregressive Models

Parental distress and child behavior.

The first two models examined the relations between individual subscales of the DBC (i.e., internalizing/anxiety and externalizing/disruptive behavior) and parental distress (PSI-PD), see Fig.  1 . The removal of the non-significant pathways, including the cross-lagged effects, did not result in any appreciable loss in fit for either the internalizing/anxiety (χ 2 difference (2) = 0.37, p  = 0.83) or externalizing/disruptive behavior (χ 2 difference (2) = 0.42, p  = 0.81) models. The final parsimonious models provided a very good fit to the data (anxiety: χ 2 (3) = 0.47, p  = 0.92, RMSEA = 0.00 (90% CI = 0.00–0.06), CFI = 1.00, SRMR = 0.01; disruptive behavior: χ 2 (3) = 1.96, p  = 0.58, RMSEA = 0.00 (90% CI = 0.00–0.15), CFI = 1.00, SRMR = 0.02), with both models revealing significant stability effects for parent distress at T1 and T2, and child behavior at T1 and T2. It is worth noting that, although not reported in the model, autism characteristics did not significantly predict parental distress at T2 in either the anxiety (standardized path coefficient = − 0.04, p  = 0.61) or disruptive behavior model (standardized path coefficient = − 0.04, p  = 0.61).

Manifest cross-lagged autoregressive model predicting parental distress and ( a ) child anxiety or ( b ) disruptive behavior

Parental Stress Total and Child Behavior

The second two models explored the autoregressive and cross-lagged relations between child behavior (i.e., DBC internalizing/anxiety and externalizing/disruptive behavior) and total parental stress score (PSI total) at T1 and T2 (See Fig.  2 ). The removal of non-significant pathways did not impact model fit (anxiety: χ2 difference (2) = 2.51, p  = 0.28; disruptive behavior: χ2 difference (2) = 0.54, p  = 0.76), therefore the parsimonious models were accepted. The final model for anxiety (Fig.  2 a) provided a good fit to the data, χ2 (3) = 6.29, p  = 0.10, RMSEA = 0.11 (90% CI = 0.00–0.23), CFI = 0.97, SRMR = 0.03, demonstrating large significant stability effects between PSI total scores at T1 and T2, and significant stability effects between anxiety at T1 and T2. Much like the internalizing/anxiety path model in Fig.  1 a, autism characteristics were not significant predictors of parental stress at T2 (standardized path coefficient = − 0.09).

Manifest cross-lagged autoregressive model predicting total parental stress score and ( a ) child anxiety or ( b ) disruptive behavior

For externalizing/disruptive behavior, see Fig.  2 b, after the removal of non-significant paths, including the cross-lagged effects, the path model provided an acceptable fit for the data when considering CFI and SRMR, χ2 (3) = 9.85, p  = 0.02, RMSEA = 0.16 (90% CI = 0.06–0.27), CFI = 0.94, SRMR = 0.04. It is clear this model has a poorer fit than the corresponding externalizing/disruptive behavior model presented in Fig.  1 b. Autism characteristics were not considered a significant predictor of parenting stress at T2 (standardized coefficient estimate = − 0.09).

Parenting Stress (Total Score and Parental Distress) and Total Child Behavior

The final two models examined the relations between either parental stress as measured using the PSI-PD subscale alone or the PSI total score and total child behavior. Therefore, behavior in these models included both internalizing (DBC anxiety) and externalizing (DBC disruptive behavior) behaviors. As with the previous models, only the paths representing the autoregressive effects were significant, while all cross-lagged effects were non-significant, and were therefore removed from the model ( p  > 0.05). The iterative removal of the non-significant paths did not cause any appreciable loss of fit for either the PSI-PD model (see Fig.  3 a; χ2 difference (2) = 1.85, p  = 0.40) nor the PSI total score model (see Fig.  3 b; χ2 difference (2) = 0.74. p  = 0.69).

Manifest cross-lagged autoregressive model predicting total behaviors that challenge score and ( a ) parental distress or ( b ) total parental stress score

The final, parsimonious model examining the relations between child behavior and parental distress alone provided a very good fit to the data, χ2 (3) = 3.08, p  = 0.38, RMSEA = 0.02 (90% CI = 0.00–0.18), CFI = 0.999, SRMR = 0.02. In contrast, the model incorporating the total parental stress scale (instead of the PSI-PD subscale) had a poorer fit to the data, χ2 (3) = 9.16, p  = 0.03, RMSEA = 0.15 (90% CI = 0.05–0.27), CFI = 0.94, SRMR = 0.04, although CFI and SRMR were still within the acceptable range. Both models demonstrated large stability estimates between the corresponding T1 and T2 variables. Again, either when entered as the total parental stress score (standardized coefficient estimate = − 0.09) or the PSI-PD subscale (standardized coefficient estimate = − 0.04), autism characteristics were not significantly related to parental stress.

Our aim was to investigate the relations between behaviors that challenge in young children on the autism spectrum and their mothers’ parenting stress. As predicted, the total parent stress score showed significant relations with all measures of child behavior cross-sectionally. However, the more specific measure of parental distress, did not show significant relations at T2. Parenting stress and child behavior measures showed stability when measured approximately one year later. In contrast to expectations, we found no cross-lagged significant pathways within our models that controlled for stability effects (i.e., stability of T1 to T2 parenting stress and child behaviors that challenge). Specifically, in our models neither measure of parent stress (PSI-PD nor PSI total) at T1 predicted any measure of child behavior at T2 and no measure of child behavior (internalizing, externalizing, nor total) at T1 predicted either measure of parenting stress at T2. We discuss these key findings, limitations, future direction, and implications below.

We did not find autism characteristics were significantly associated with concurrent parenting stress, consistent with Lin et al. ( 2021 ). This suggests a child's level of autism characteristics may not lead to higher stress for their parents. Instead, higher stress may be due to factors associated with caregiving for an autistic child such as increased financial outgoings (e.g., costs of accessing additional supports), associated behaviors that challenge (at least cross-sectionally), or how parents cope with caregiving (e.g., Paynter et al., 2013 ). Further, in contrast to some of the previous research (Bader et al., 2015 ; Falk et al., 2014 ), the level of autism characteristics did not influence the association (i.e., did not significantly impact the model fit) between parenting stress and child behaviors over time. This suggests other variables should be considered in understanding the trajectory of parenting stress and child behaviors over time as discussed further below.

Our finding of a negative relationship between autism characteristics and behavior contrast with Lin et al. ( 2021 ). It may be that children with lower autism characteristics have greater insight into their challenges or differences which leads to expressing these challenges more frequently or at a higher intensity in their behavior. Alternatively, differing findings may reflect differences in assessment measures used between studies. We used the DBC to measure child behaviors which may be more sensitive to autism-relevant behaviors than the CBCL as used by Lin et al. given that the CBCL was not designed for this population. Mixed findings suggest future research is needed to evaluate if the measurement used impacts associations between autism characteristics and behavior.

Higher total parenting stress was significantly associated with higher (i.e., amount or intensity) levels of child behaviors that challenge in cross-sectional correlations, which is consistent with previous research (Yorke et al., 2018 ). Given the correlational nature of this analysis, it is not possible to determine causality. It may be that higher parenting stress impacts on parenting practices (e.g., giving in) that inadvertently reinforce behaviors that challenge. Conversely, observing a child’s higher levels of behaviors that challenge may be distressing for the parent, and lead to higher parenting stress. This association may be higher for parenting stress measures (i.e., the PSI difficult child subscale and total score which includes this subscale) that include items about the child’s behavior (e.g., about perceptions of a child’s mood or behavior) as opposed to items about parenting stress more generally (e.g., about the parent’s mood or behaviors). As such, the observed relation between the total parent stress score and child behavior may be driven by the parent stress measure and child behavior measures both including items that consider child behaviors. Consistent with this possibility, when we used the parental distress subscale alone (therefore removing the influence or overlap with child behavior apparent in the total score), a significant concurrent correlation between parental distress and child behavior was only found at T1. This is consistent with previous findings that similarly found lower relations with more specific measures of parenting stress such as the parental distress scale (that reflect on parent mood or behaviors only) and higher relations with broader parenting stress measures that included child behavior items (Bohadana et al., 2019 ; McStay et al., 2014 ; Yorke et al., 2018 ). This means in research and practice it is important to select measures that are distinct (e.g., using the PSI-PD subscale rather than total) to avoid overestimating relations, or capturing for example child behavior rather than parenting stress in measurement. Utilizing distinct measures would enable more accurate identification of specific child behavior and/or parenting stress strengths and challenges for targeted support and monitoring of treatment progress.

We found strong stability shown through large effect sizes, of measures of parenting stress and child behaviors at T1 predicting scores at T2, consistent with previous research (Lin et al., 2021 ; Rodriguez et al., 2019 ). It may be that parenting stress and/or child behaviors are stable over time and/or are less likely to change over an approximately one-year period. Parenting stress may be stable as perhaps the demands arising from caring for an autistic child in a predominantly neurotypical society such as facing stigma and judgement from others (e.g., Rusu et al., 2024 ) may be ongoing, and difficult to change at a societal level, thus maintaining parent reported stress. Stability in child behavior may be explained by a focus in the center-based intervention program on children’s behavior and skills at the center, with a lack of generalization of skills to contexts observed by parents (i.e., for ratings). This may indicate a need for broader supports (e.g., training for parents in how to support or respond to children’s behavior that challenges) in the home setting. This is consistent with the broader literature that has found supports for autistic children show greater effects on outcomes measured at proximal (e.g., behavior with staff at the center) than distal (e.g., behavior at home with parents) levels (see systematic review by Sandbank et al., 2020 ).

Stability effects observed between measures repeated at T1 and T2 may also reflect the timing of assessments at two potentially challenging times for parents and their children. At the intake assessment, families may have just learned about their child’s autism diagnosis, and thus may have been overwhelmed with navigating this information. At the second assessment, approximately 12 months later which was the time of leaving the service for many families, they may have been faced with navigating transitioning from the early learning and care service and into formal education. For parents, this may have led to high levels of parenting stress at each time point. For children, both time points may have coincided with times of transitioning to new routines and environments (e.g., visiting potential schools, see Fontil et al., 2020 ) which could lead to high levels of behaviors that challenge at each time point. More targeted or frequent assessments of child behavior and parenting stress, including use of multiple informants across settings, may be useful to monitor changes in response to specific stressors, in response to targeted supports, and throughout receipt of early learning and care services and specific programming.

We did not find evidence of bidirectional effects of parenting stress and child behavior in our models controlling for stability effects, consistent with Peters-Scheffer et al. ( 2012 ). That is, parenting stress did not significantly predict later child behavior, nor the reverse. Similarly, Lin et al. ( 2021 ), also found non-significant effects between a specific measure of parental distress and later child behavior. Thus, clinicians should not assume that a child’s behavior is causing a parent’s later stress, nor that parenting stress will directly lead to later child behaviors that challenge. This highlights the importance of directly measuring each construct and providing targeted supports for each. It may be that while parenting stress and child behavior are associated concurrently, but over time other factors may impact relations. For example, the parenting practices that parents employ to support or respond to children’s behaviors may be impacted by stress. These parenting practices could increase (e.g., controlling parenting practices such as harsh punishment) or decrease (e.g., mindful parenting, i.e., paying attention to parenting non-judgmentally) child behavior (see systematic review, Suvarna et al., 2024 ). Additionally, avoidant coping strategies (e.g., using alcohol or drugs as a strategy to cope with responding to children’s behavior) may increase parenting stress (Paynter et al., 2013 ; Stuart & McGrew, 2009 ). Further, the interpretation or meaning assigned to their child’s behavior such as forming negative appraisals can negatively impact parenting stress (Paynter al., 2013 ). External supports such as social support, may mitigate parental stress in response to child behavior (Boyd, 2002 ). Coping strategies, appraisals, and supports are captured in theoretical models of adaptation previously applied to autism (such as the Double ABCX Model as used in Paynter et al., 2013 ) and may be useful to include in future supports for decreasing levels of parenting stress.

Three of our key findings include stability effects between like measures at T1 and T2, a lack of relation between autism characteristics and parenting stress, and that earlier parenting stress did not predict later child behaviors. Stability effects and lack of relation between autism characteristics and parenting stress are consistent with Lin et al. ( 2021 ). In contrast, Lin et al. found higher levels of earlier parenting stress predicted higher levels of later child externalizing behaviors. Cultural differences may explain differences between our study conducted in Australia and Lin et al.’s ( 2021 ) findings from Taiwan. That is, there may be different expectations or beliefs regarding autism, behavior, and/or parenting between cultures which may relate to differences in levels of stress, strategies parents use to cope, or supports available (Lin et al., 2011 ). For example, lower knowledge of autism and greater stigma around autism have been reported in studies in Asia compared to the United States (Yu et al., 2020 ). Further, differences in coping strategies of mothers of autistic children cross-culturally have also been found. Taiwanese mothers were reported to use more problem-focused and emotion-focused coping styles than mothers in the United States (Lin et al., 2011 ).

Our sample of children were older compared to children in other studies investigating relations between parenting stress and child behavior over time (Lin et al., 2021 ). Longitudinal relations between parenting stress and child behavior may vary with age as indicated in previous research (Rodriguez et al., 2019 ). Specifically, child age and access to early intervention may have impacted findings across studies. Younger children, as in Lin’s study ( M  = 25.68 months), may have greater contact time with parents as they did not appear to be enrolled in full-time center-based intervention with Lin reporting most (71%) received occupational therapy or speech therapy (63%) services only. In contrast, our sample were older ( M  = 45.44 months), and all were receiving center-based early intervention services. There may be greater opportunity for transactional effects (i.e., parenting stress impacting child behaviors that challenge or child behaviors that challenge impacting parenting stress) in younger age groups due to parents and their child spending more time together at a younger age. We hypothesize, there may also be other changes in the parent–child dynamic as children age, such as parental adjustment to diagnosis, changes in child characteristics (e.g., development of skills or regression may positively or negatively impact stress respectively), increases in parenting skills or increases in coping abilities. Further, we hypothesize that the center-based program may have provided a form of respite to families, so there may have been less opportunity for parenting stress to impact on child behaviors explaining the non-significant effects over time.

Limitations and Future Directions

Our research importantly addressed previous research limitations (i.e., controlled for stability effects, included autism characteristics, used a measure of behavior designed for developmental disabilities, and compared specific vs. broad measures of parenting stress); however, we acknowledge the following limitations. First, we investigated mothers only and there is a need for further research with fathers given sources of stress may differ (Davis & Carter, 2008 ; Hastings et al., 2005 ). Future research with fathers would be of value to inform targeted supports for each parent/caregiver (see Paynter et al., 2018a ). Second, data were not collected on broader caregiver characteristics (e.g., parental mental health, parental autism characteristics, parenting practices, or coping strategies). These additional measures may provide further insights into the interaction between child behavior and parenting stress over time by identifying risk and protective factors, as well as factors potentially amenable to change. Future longitudinal research that draws from theoretical models of adaptation such as the Double ABCX model (McCubbin & Patterson, 1983 ), as used in cross-sectional research (McStay et al., 2014 ; Paynter et al., 2013 ), would be of value in identifying potential risk and protective factors to explore. This research could include variables from these models such as social support, coping strategies, or appraisals to delineate protective and risk factors, and mediators of outcomes over time. This could inform and extend existing parent support research into cognitive behavioral and psychoeducational interventions (see review, Bourke-Taylor et al., 2021).

Finally, we acknowledge potential sampling bias through using data drawn from one specific early intervention service in one Australian state. The content of the specific program may have impacted results, and attrition data were not available. Further, the length of follow-up varied from T1 to T2 between individual participants from 5.72 to 17.25 months and may have impacted results. Future research should include tracking of attrition and whether those who do not complete all assessment measures over time differ from those who do complete all measures, and whether length of time between measures impacts relations. In addition, while access to this service could be partially funded through government financial supports, families contributed financially to their children’s programs potentially biasing towards higher socio-economic status (SES) participants. Parents from higher SES backgrounds may have access to greater resources to support children’s outcomes (such as affording early intervention) while families from lower SES backgrounds may, by necessity, need to prioritize meeting basic needs as per the Family Investment Model (Conger & Donnellan, 2007). SES could subsequently impact on the associations observed between child behavior and parenting stress. Unfortunately, further information on families (e.g., SES) were not available to analyze this possibility and may be of value in future research. Future research investigating relations between child behaviors and parenting stress receiving a range of services would also be of value in exploring other potentially important mediators.

These findings highlight the need to assess rather than to assume that child behaviors that challenge will have a strong direct effect on later parenting stress for mothers over time or vice versa, and to question this potential assumption that may be drawn from cross-sectional findings. Our findings highlight the need for analyses to control for stability of parenting stress and challenging behavior in longitudinal research. We also emphasize the need to consider broader psychological, social, and contextual factors that may impact parenting stress and outcomes for children and both parents. Of concern, is the relative stability of parenting stress and child behaviors which may impact on quality of life for children and their mothers and highlights the need for specific supports to improve outcomes.

The terms “on the autism spectrum” and “on the spectrum” in latter occurrences, are used in line with the preferences of the autistic and autism community (Bury et al., 2020 ; Kenny et al., 2016 ).

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We are grateful to the parents/caregivers for giving their time to support this research study.

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JP designed and conceived the study and led manuscript preparation. VH managed data, conducted initial analyses, and contributed to manuscript preparation. MT collected data and managed the organisational database, extracted data for analysis, informed study conception and design, and contributed to manuscript preparation. SM conducted the final analyses included in the manuscript and contributed to manuscript preparation. All authors read and approved the final manuscript.

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Paynter, J., Heng, V., Tucker, M. et al. A Longitudinal Analysis of Mothers’ Parenting Stress and Internalizing and Externalizing Behavior of Young Children on the Autism Spectrum. J Autism Dev Disord (2024). https://doi.org/10.1007/s10803-024-06362-x

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Brain mechanisms underlying sensory hypersensitivity in a mouse model of autism spectrum disorder

Sensory hypersensitivity in grin2b-mutant mice linked to acc hyperactivity and brain-wide hyperconnectivity.

A research team led by Director KIM Eunjoon of the Center for Synaptic Brain Dysfunctions and Director KIM Seong-Gi of the Center for Neuroscience Imaging Research within the Institute for Basic Science (IBS) has identified the primary cause of sensory hypersensitivity related to autism spectrum disorders (ASD).

Autism affects approximately 1 in 36 individuals and is marked by significant challenges in social interaction and communication. Around 90% of autism patients also suffer from abnormal sensory hypersensitivity that deeply affects their daily functioning. This hypersensitivity results in exaggerated or dampened responses to common sensory stimuli such as sound, light, and touch, which leads to considerable stress and further social withdrawal. The precise brain region responsible for this sensory dysfunction is unknown, which hinders treatment efforts.

The IBS researchers studied an ASD mouse model with a mutation in the Grin2b gene, which encodes the GluN2B subunit of NMDA receptors. NMDA receptors, a type of glutamate receptor in the brain, have garnered attention in the context of autism due to their crucial role in synaptic transmission and neural plasticity. It was hypothesized that the Grin2b gene mutation in mice would induce ASD-like phenotypes, including sensory abnormalities, and that certain brain mechanisms may play important roles.

The researchers monitored neural activity and functional connectivity in the brains of these mice using activity-dependent markers and functional magnetic resonance imaging (fMRI). In these mice, the researchers discovered increased neuronal activity in the anterior cingulate cortex (ACC). The ACC is one of the higher-order cortical regions that have been extensively studied for cognitive and emotional brain functions, but have been understudied for brain disease-related sensory abnormalities.

Interestingly, when the hyperactivity of ACC neurons was inhibited using chemogenetic methods, sensory hypersensitivity were normalized, indicating the pivotal role of ACC hyperactivity in sensory hypersensitivity associated with autism.

Director KIM Eunjoon states, "This new research demonstrates the involvement of the anterior cingulate cortex (ACC), which has been known for its deep association with cognitive and social functions, in sensory hypersensitivity in autism."

The hyperactivity of the ACC was also associated with the enhanced functional connectivity between the ACC and other brain areas. It is believed both hyperactivity and the hyperconnectivity of the ACC with various other brain regions are involved with sensory hypersensitivity in Grin2b-mutant mice.

Director KIM Seong-Gi states, "Past studies attributed peripheral neurons or primary cortical areas to be important for ASD-related sensory hypersensitivity. These studies often only focused on the activity of a single brain region. In contrast, our study investigates not only the activity of ACC but also the brain-wide hyperconnectivity between the ACC and various cortical/subcortical brain regions, which gives us a more complete picture of the brain."

The researchers plan to study the detailed mechanisms underlying the increased excitatory synaptic activity and neuronal hyperconnectivity. They suspect that the lack of Grin2b expression may inhibit the normal process of weakening and pruning synapses that are less active so that relatively more active synapses can participate in refining neural circuits in an activity-dependent manner. Other areas of research interest is studying the role of ACC in other mouse models of ASD.

This study was published in the journal Molecular Psychiatry.

• Nervous System
• Birth Defects
• Brain Tumor
• Neuroscience
• Learning Disorders
• Animal Learning and Intelligence
• Autistic spectrum
• Sensory system
• Asperger syndrome
• House mouse

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Journal Reference :

• Soowon Lee, Won Beom Jung, Heera Moon, Geun Ho Im, Young Woo Noh, Wangyong Shin, Yong Gyu Kim, Jee Hyun Yi, Seok Jun Hong, Yongwhan Jung, Sunjoo Ahn, Seong-Gi Kim, Eunjoon Kim. Anterior cingulate cortex-related functional hyperconnectivity underlies sensory hypersensitivity in Grin2b-mutant mice . Molecular Psychiatry , 2024; DOI: 10.1038/s41380-024-02572-y

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