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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.

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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 Other children may develop normally for the first few months or years of life, but then suddenly become withdrawn or aggressive or lose language skills they've already acquired.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

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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.


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

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.
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  • 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.
  • Autism spectrum disorder and digestive symptoms

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presentations of autism

Presentation of Autism Spectrum Disorder in Females: Diagnostic Complexities and Implications for Clinicians

  • By: Jessica Scher Lisa, PsyD Harry Voulgarakis, PhD, BCBA St. Joseph’s College
  • April 1st, 2020
  • assessment , behaviors , diagnosis , females , research , Spring 2020 Issue
  • 9098    0

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by pervasive deficits in social communication and patterns of restricted, repetitive, stereotyped behaviors and interests (American Psychiatric Association, 2013). Beyond the […]

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by pervasive deficits in social communication and patterns of restricted, repetitive, stereotyped behaviors and interests (American Psychiatric Association, 2013). Beyond the main diagnostic criteria, however, there is considerable heterogeneity in the symptom presentations that is demonstrated by people with ASD, including severity, language, cognitive skills, and related deficits (Evans et al, 2018). Regarding sex differences, it has been well established that ASD is diagnosed more often in males than in females, with recent estimates suggesting a 3:3:1 ratio (Hull & Mandy, 2017). Despite the fact that this is well known, there is considerable uncertainty about the nature of this sex discrepancy and how it relates to the ASD diagnostic assessment practice (Evans et al, 2018). Additionally, it has been widely accepted that males and females with ASD present differently, which has implications for the sex discrepancy in diagnostic practices, thus females are generally under-identified (Evans et al, 2018).

doctor physician, healthcare professional portrait, smiling sincere with clipboard at hospital clinic

The fact that females with ASD are under-identified and often overlooked can be due to a number of factors. First, they often don’t fit the “classic” presentation that is most often associated with the ASD diagnosis; specifically, there is a distinct ASD female phenotype that looks dissimilar to the typical ASD male presentation. Females with ASD tend to present with less restricted interests and repetitive behaviors (RRBs) (Supekar and Menon, 2015), thus standing out less both in society, as well as on screening and diagnostic measures. Fewer RRBs makes ASD appear in a different way, often more subtle, than what is considered to be the norm. It is also important to note that evidence suggests that even when females with ASD are identified, they receive their diagnosis (and related support) later than equivalent males with ASD (Giarelli et al, 2010). The implications for under- or late-identification are enormous and deserve empirical attention in an effort to improve diagnostic methods for ASD in females.

Harry Voulgarakis, PhD, BCBA

Harry Voulgarakis, PhD, BCBA

Jessica Scher Lisa, PsyD

Jessica Scher Lisa, PsyD

While no consistent, reliable differences have been found between sex and core ASD symptoms (e.g. Bolte et al, 2011; Holzmann et al, 2007; Mandy et al, 2012), it has been well documented that compared to males, females with ASD that are undiagnosed or are diagnosed at a later age generally present with less severe ASD symptoms and more intact language and cognitive skills (Begeer et al, 2013; Giarelli et al, 2010; Rutherford et al, 2016). Research has also noted that females with ASD may be better able to compensate for symptoms despite having core deficits associated with ASD (Livingston & Happe, 2017; Hull et al, 2017). There has been some suggestion that females must exhibit more severe symptoms, impairment, or co-occurring problems in order to receive diagnoses of ASD (Evans et al, 2018). This finding is due to an analysis of previous research that demonstrates the following: females with ASD perform better on measures of nonverbal communication (which may mask other symptoms), females with ASD face more social, friendship, and language demands than males with ASD, and that females with ASD can exhibit patters of restricted interests and repetitive behaviors, as well as social and communicative problems that are deemed more socially acceptable as compared to the patterns seen in males with ASD (Lai et al, 2015; Rynkiewicz et al, 2016; Dean et al, 2014). This theory also accounts for the findings that females with ASD in general present with more severe behavioral, emotional, and cognitive problems compared to males (Frazier, et al, 2014; Holtmann et al, 2007; Horiuchi et al, 2014; Stacy et al, 2014). Further, Hiller and colleagues (2014) found that females were more likely to show an ability to integrate non-verbal and verbal behaviors, and initiate friendships, and exhibited less restricted interests. Teachers reported fewer concerns for females with ASD than for males, including concerns about behaviors and social skills. These data support the idea that that females with ASD may “look” different from the considerable “classic” presentation of ASD and may also present as less impaired in an academic setting.

The vast differences associated with gender presentation in ASD require that clinicians involved in diagnostic work become more cognizant of these broader phenotypes and adjust their assessment practices accordingly to better detect females presenting with atypical symptoms that still fall on the autism spectrum. Notably, many common diagnostic tools lack sensitivity to such a presentation. To that end, it is important to recognize that generally speaking, the evidence base, and hence the diagnostic criteria for ASD in itself comes from research among male-predominant samples (e.g. Edwards et al, 2012; Watkins et al, 2014). Therefore, while the efforts to study this area further are prominent, it is important to be mindful of the fact that existing assessment tools and diagnostic criteria likely contain sex/gender bias (Evans et al, 2018). Without addressing the neurological and diagnostic challenges pertaining to these sex/gender issues, any research in this area will be influenced by the underlying problem of not knowing how ASD should be defined and diagnosed in males as compared to females (Lai et al, 2015).

Currently, the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) is arguably the most commonly relied upon diagnostic instrument for ASD. The ADOS-2 is a semi-structured observational assessment designed to evaluate aspects of communication, social interaction, and stereotyped behaviors and restricted interests (Lord et al, 2000; 2012). In contrast to what has been documented with regard to the strong differences in the prevalence of ASD, differences between the sexes in the phenotypic presentation of ASD have been found to be much smaller in size, with inconsistencies in the findings with regard to severity level of the core symptoms, as well as age and general level of functioning. For example, some studies have found no significant differences between sexes with regard to the behavioral presentation of ASD on the ADOS (e.g. Lord et al., 2000; Lord et al., 2012, Ratto et al, 2017), while others have reported some differences (e.g. Lai et al., 2015).

In order to examine these inconclusive findings further, Tillman et al (2018) looked at data containing 2684 individuals with ASD from over 100 different sites across 37 countries. Children and adults were administered one of four ADOS modules (modules are determined by expressive language level). The Autism Diagnostic Interview, Revised (ADI-R) was also administered as well as a general intellectual ability instrument, such as the Wechsler Intelligence Scale for Children, or a different measure depending on age and verbal capabilities. Effects of sex were determined after excluding non-verbal IQ as a predictor. No main effect of sex was found for ADOS symptom severity, or on the specific ADOS subscales. Females showed lower scores on the RRB scale with increasing age. This result is similar to previous meta-analytic research on small-scale studies as well as large-scale studies (Van Wijngaarden-Cremers et al, 2014; Mandy et al, 2012, Supekar & Menon, 2015; Wilson et al, 2016; Charman et al., 2017). The researchers concluded that this adds to the current body of literature that supports the notion that females with ASD show lower levels of RRBs than males, but exhibit a more similar autistic phenotype to boys in relation to social communication deficits across ages (Tillman et al, 2018). Thus, it is possible to surmise that females with ASD are being under-identified as a result of exhibiting fewer RRBs. Notably, research has found that clinicians are hesitant to diagnose ASD without the presence of RRB (Mandy et al, 2012), as the diagnosis of ASD in the DSM-5 requires at least two types of RRBs. Lai et al. (2015) made the case that females with ASD may simply be exhibiting different RRBs rather than fewer, and it is possible that these less common forms of RRBs are being missed during diagnostic assessments.

Understanding the phenotypic differences in the presentation of autism is critical for diagnosticians for several reasons. It is crucial to understand that aspects of the diagnostic criteria for ASD may present on other ways in females though not be elevated on standard measure scales. As a result, those who do not receive an appropriate diagnosis will subsequently not receive an appropriate intervention. Beyond the obvious concern associated with females on the autism spectrum not receiving intervention associated with their autism symptomatology, there are a range of other mental health concerns that may dually go unaddressed. Higher functioning adolescents with ASD, which is often the presentation consistent with females that get “missed” in the diagnostic process, are at greater risk for developing depression (Greenlee et al, 2016) and anxiety (Steensel, Bogels, & Dirksen, 2012). Adults with high-functioning ASD are also at increased risk for suicidality (Hedley et al, 2017). More recent, emerging research suggests that while those with ASD may be able to mask their symptoms the majority of the day and thus not reach the diagnostic threshold in scandalized measures, doing so causes them significant distress and puts them at increased risks for such co-occurring mental health concerns.

The under-diagnosis of ASD in females with ASD lends itself to a population of women who end up wondering “what is wrong” with them. Females who do not have the opportunity to understand themselves in the context of neurodiversity tend to waste time and efforts on imitating and trying to fit-in (Bargiela et al, 2016). They are at far greater risk of bullying, as well as being taken advantage of socially, with subtle difficulties in perceiving and responding appropriately to social cues rendering them inept in certain situations that require a degree of social assimilation. These females have missed out on the benefits of early intervention, most often in the social realm, and can be plagued with identity issues later in life as they try to play catch-up in light of a new diagnosis. The timely identification of ASD can mitigate some of these risks and problems by improving the quality of life, increasing access to services, reducing self-criticism, and helping to foster a positive sense of identity. As such, diagnostic experts have a responsibility to continue to stay abreast of research developing in this area and adjusting their assessment practices accordingly.

Drs. Scher Lisa and Voulgarakis are Assistant Professors in the Department of Child Study at Saint Joseph’s College, New York. They are both also clinicians in private practice. You can find more information about their respective practices at www.drjessicascherlisa.com and www.drharryv.com .

Bölte, S., Duketis, E., Poustka, F., & Holtmann, M. (2011). Sex differences in cognitive domains and their clinical correlates in higher-functioning autism spectrum disorders. Autism, 15(4), 497–511. doi: 10.1177/1362361310391116

Charman, T., Loth, E., Tillman, J., Crawley, D., Wooldridge, C., Goyard, D. et al (2017). The EU-AIMS Longitudinal European Autism Project (LEAP): Clinical characterization. Molecular Autism, 8(1), 27.

Evans, S. C., Boan, A. D., Bradley, C., & Carpenter, L. A. (2018). Sex/Gender Differences in Screening for Autism Spectrum Disorder: Implications for Evidence-Based Assessment. Journal of Clinical Child & Adolescent Psychology, 48(6), 840–854. doi: 10.1080/15374416.2018.1437734

Giarelli, E., Wiggins, L.D., Rice, C. E., Levy, S. E., Kirby, R. S., Pinto-Martin, J., et al. (2010). Sex differences in the evaluation and diagnosis of autism spectrum disorders among children. Disability and Health Journal , 3 (2), 107-116. doi:10.1016/jdhjo.2009.07.001.

Hiller, R. M., Young, R. L., & Weber, N. (2014). Sex Differences in Autism Spectrum Disorder based on DSM-5 Criteria: Evidence from Clinician and Teacher Reporting. Journal of Abnormal Child Psychology, 42(8), 1381–1393. doi: 10.1007/s10802-014-9881-x

Holtmann, M., Bolte, S., & Poustka, F. (2007). Autism spectrum disorders: Sex differences in autistic behavior domains and coexisting psychopathology. Developmental Medicine & Child Neurology, 49, 361-366. doi: 10.1111/dmcn.2007.49.issue-5

Horiuchi, F., Oka, Y., Uno, H., Kawabe, K., Okada, F., Saito, I., Ueno, S. I. (2014). Age-and sex-related emotional and behavioral problems in children with autism spectrum disorders: Comparison with control children. Psychiatry and Clinical Neurosciences, 68, 542-550. doi:10.1111/psc.12164

Hull, L., Petrides, K.V., Allison, C., Smith, P., Baron-Cohen, S., Lai, M.C., & Mandy, W. (2017). “Putting on my best normal”: Social camouflaging in adults with autism spectrum conditions. Journal of Autism and Developmental Disorders, 47, 2519-2534. doi:10.1007/s10803-017-3166-5

Lai, M.C., Lombardo, M., Auyeung, B., Chakrabarti, B., & Baron-Cohen, S. (2015). Sex/gender differences and autism: Setting the scene for future research. Journal of the American Academy of Child and Adolescent Psychiatry, 54, 11-24.

Livingston, L.A., & Happe, F. (2017). Conceptualizing compensation in neurodevelopmental disorders: Reflections from autism spectrum disorder. Neuroscience & Behavioral Reviews, 80, 729-742. doi: 10.1016/j. neubiorev.2017.06.005

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Lord, C., Rutter, M., DiLavore, P.C., Risi, S., Gotham, K., & Bishop, S. (2012). Autism diagnostic observation schedule, Second edition (ADOS-2) Manual (Part I): Modules 1-4. Torrance: CA: western Psychological Services.

Mandy, W. P., Chilvers, R., Chowdhury, U., Salter, G., Seigal, A., & Skuse, D. (2012). Sex differences in autism spectrum disorder: Evidence from a large sample of children and adolescents. Journal of Autism and Developmental Disorders, 42, 1304-1313. doi: 1007/s10803-011-1356-0

Ratto, A.B., Kenworthy, L. Yerys, B.E., Bascom, J., Wieckowski, A.T., White, S., et al (2017). What about the girls? Sex-based differences in autistic traits and adaptive skills. Journal of Autism and Developmental Disorders, 48, 1698-1711.

Rutherford, M., McKenzie, K., Johnson, T., Catchpole, C., O’Hare, A., McClure, I., Murray, A. (2016). Gender ratio in a clinical population sample, age of diagnosis and duration of assessment in children and adults with autism spectrum disorder. Autism, 20, 628-634. doi10.1177/1362361315617879

Supekar, K., Menon, V. (2015). Sex differences in structural organization of motor systems and their dissociable links with repetitive/restricted behaviors in children with autism. Super and Menon Molecular Autism, 6, 50 doi: 10.1186/s13229-015-0042-z.

Tillman, J., Ashwood, K., Absoud, M., olte, S., Bonnet-Brilhalut, F., Buitelaar, J.K. et al (2018). Evaluation sex and age differences in ADI-R and ADOS scores in a large European Multi-site sample of individuals with autism spectrum disorder. Journal of Autism and Developmental Disorders, 48(7), 2490-2505.

Van Wijngaarden-Cremers, P.J., van Eeten, E., Groen, W.B., Van Deurzen, P.A., Oosterling, I.J., & Van der Gaag, R.J. (2014). Gender and age differences in the core triad of impariments in autism spectrum disorders: A systematic review and meta-analysis. Journal of Autism and Developmental Disorders, 44(3), 627-635.

Wilson, C.E., Murphy, C.M., McAlonan, G., Robertson, D.M., Spain, D., Haywayrd, H. et al (2016) Does sex influence the diagnostic evaluation of autism spectrum disorder in adults? autism, 20(7), 808-819.

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  • v.9(Suppl 1); 2020 Feb

Autism spectrum disorder: definition, epidemiology, causes, and clinical evaluation

Holly hodges.

1 Department of Pediatrics, Baylor College of Medicine and Meyer Center for Developmental Pediatrics, Texas Children’s Hospital, Houston, TX, USA;

Casey Fealko

2 Western Michigan University Homer Stryker MD School of Medicine, Kalamazoo, MI, USA;

Neelkamal Soares

3 Department of Pediatric and Adolescent Medicine, Western Michigan University Homer Stryker MD School of Medicine, Kalamazoo, MI, USA

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and the presence of restricted interests and repetitive behaviors. There have been recent concerns about increased prevalence, and this article seeks to elaborate on factors that may influence prevalence rates, including recent changes to the diagnostic criteria. The authors review evidence that ASD is a neurobiological disorder influenced by both genetic and environmental factors affecting the developing brain, and enumerate factors that correlate with ASD risk. Finally, the article describes how clinical evaluation begins with developmental screening, followed by referral for a definitive diagnosis, and provides guidance on screening for comorbid conditions.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and the presence of restricted interests and repetitive behaviors ( 1 ). In 2013, the Diagnostic and Statistical Manual of Mental Disorders —5 th edition (DSM-5) was published, updating the diagnostic criteria for ASD from the previous 4 th edition (DSM-IV) ( Table 1 ) ( 1 , 2 ).

ASD, autism spectrum disorder; SPCD, social (pragmatic) communication disorder.

In DSM-5, the concept of a “spectrum” ASD diagnosis was created, combining the DSM-IV’s separate pervasive developmental disorder (PDD) diagnoses: autistic disorder, Asperger’s disorder, childhood disintegrative disorder, and pervasive developmental disorder not otherwise specified (PDD-NOS), into one. Rett syndrome is no longer included under ASD in DSM-5 as it is considered a discrete neurological disorder. A separate social (pragmatic) communication disorder (SPCD) was established for those with disabilities in social communication, but lacking repetitive, restricted behaviors. Additionally, severity level descriptors were added to help categorize the level of support needed by an individual with ASD.

This new definition is intended to be more accurate and works toward diagnosing ASD at an earlier age ( 3 ). However, studies estimating the potential impact of moving from the DSM-IV to the DSM-5 have predicted a decrease in ASD prevalence ( 4 , 5 ) and there has been concern that children with a previous PDD-NOS diagnosis would not meet criteria for ASD diagnosis ( 5 - 7 ). There are varying reports estimating the extent of and effects of this change. One study found that with parental report of ASD symptoms alone, the DSM-5 criteria identified 91% of children with clinical DSM-IV PDD diagnoses ( 8 ). However, a systematic review suggests only 50% to 75% of individuals maintain diagnoses ( 9 ) and other studies have also suggested a decreased rate of diagnosis of individuals with ASD under the DSM-5 criteria ( 10 ). Often those who did not meet the requirements were previously classified as high functioning Asperger’s syndrome and PDD-NOS ( 11 , 12 ). Overall, most studies suggest that the DSM-5 provides increased specificity and decreased sensitivity compared to the DSM-IV ( 5 , 13 ); so while those diagnosed with ASD are more likely to have the condition, there is a higher number of children whose ASD diagnosis is missed, particularly older children, adolescents, adults, or those with a former diagnosis of Asperger’s disorder or PDD-NOS ( 14 ). Nevertheless, the number of people who would be diagnosed under the DSM-IV, but not under the new DSM-5 appears to be declining over time, likely due to increased awareness and better documentation of behaviors ( 4 ).

It has yet to be determined how the new diagnosis of SPCD will impact the prevalence of ASD. One study found the new SPCD diagnosis encompasses those individuals who possess subthreshold autistic traits and do not qualify for a diagnosis of ASD, but who still have substantial needs ( 15 ). Furthermore, children who previously met criteria for PDD-NOS under the DSM-IV might now be diagnosed with SPCD.


The World Health Organization (WHO) estimates the international prevalence of ASD at 0.76%; however, this only accounts for approximately 16% of the global child population ( 16 ). The Centers for Disease Control and Prevention (CDC) estimates about 1.68% of United States (US) children aged 8 years (or 1 in 59 children) are diagnosed with ASD ( 6 , 17 ). In the US, parent-reported ASD diagnoses in 2016 averaged slightly higher at 2.5% ( 18 ). The prevalence of ASD in the US more than doubled between 2000–2002 and 2010–2012 according to Autism and Developmental Disabilities Monitoring Network (ADDM) estimates ( 6 ). Although it may be too early to comment on trends, in the US, the prevalence of ASD has appeared to stabilize with no statistically significant increase from 2014 to 2016 ( 19 ). Changing diagnostic criteria may impact prevalence and the full impact of the DSM-5 diagnostic criteria has yet to be seen ( 17 ).

Insurance mandates requiring commercial plans to cover services for ASD along with improved awareness have likely contributed to the increase in ASD prevalence estimates as well as the increased diagnosis of milder cases of ASD in the US ( 6 , 20 , 21 ). While there was only a modest increase in prevalence immediately after the mandates, there have been additional increases later as health care professionals better understood the regulatory and reimbursement process. The increase in prevalence may also be due to changes in reporting practices. One study in Denmark found the majority of increase in ASD prevalence from 1980–1991 was based on changes of diagnostic criteria and inclusion of outpatient data, rather than a true increase in ASD prevalence ( 21 ).

ASD occurs in all racial, ethnic, and socioeconomic groups, but its diagnosis is far from uniform across these groups. Caucasian children are consistently identified with ASD more often than black or Hispanic children ( 6 ). While the differences appear to be decreasing, the continued discrepancy may be due to stigma, lack of access to healthcare services, and a patient’s primary language being one other than English.

ASD is more common in males ( 22 , 23 ) but in a recent meta-analysis ( 24 ), true male-to-female ratio is closer to 3:1 than the previously reported 4:1, though this study was not done using the DSM-5 criteria. This study also suggested that girls who meet criteria for ASD are at higher risk of not receiving a clinical diagnosis. The female autism phenotype may play a role in girls being misdiagnosed, diagnosed later, or overlooked. Not only are females less likely to present with overt symptoms, they are more likely to mask their social deficits through a process called “camouflaging”, further hindering a timely diagnosis ( 25 ). Likewise, gender biases and stereotypes of ASD as a male disorder could also hamper diagnoses in girls ( 26 ).

Several genetic diagnoses have an increased rate of co-occurring ASD compared to the average population, including fragile X, tuberous sclerosis, Down syndrome, Rett syndrome, among others; however, these known genetic disorders account for a very small amount of overall ASD cases ( 27 - 30 ). Studies of children with sex chromosome aneuploidy describe a specific social functioning profile in males that suggests more vulnerability to autism ( 22 , 23 , 31 , 32 ). With the increased use of chromosomal microarray, several sites (chromosome X, 2, 3, 7, 15, 16, 17, and 22 in particular) have proven to be associated with increased ASD risk ( 28 ).

Other risk factors for ASD include increased parental age and prematurity ( 33 - 35 ). This could be due to the theory that older gametes have a higher probability of carrying mutations which could result in additional obstetrical complications, including prematurity ( 36 ).

ASD is a neurobiological disorder influenced by both genetic and environmental factors affecting the developing brain. Ongoing research continues to deepen our understanding of potential etiologic mechanisms in ASD, but currently no single unifying cause has been elucidated.

Neuropathologic studies are limited, but have revealed differences in cerebellar architecture and connectivity, limbic system abnormalities, and frontal and temporal lobe cortical alterations, along with other subtle malformations ( 28 , 37 , 38 ). A small explorative study of neocortical architecture from young children revealed focal disruption of cortical laminar architecture in the majority of subjects, suggesting problems with cortical layer formation and neuronal differentiation ( 39 ). Brain overgrowth both in terms of cortical size and additionally in terms of increased extra-axial fluid have been described in children with ASD and are areas of ongoing study both in terms of furthering our understanding of its etiology, but also as a potential biomarker ( 40 , 41 ).

Genetic factors play a role in ASD susceptibility, with siblings of patients with ASD carrying an increased risk of diagnosis when compared to population norms, and a much higher, although not absolute, concordance of autism diagnosis in monozygotic twins ( 42 - 44 ).

Genome wide association studies and whole exome sequencing methods have broadened our understanding of ASD susceptibility genes, and learning more regarding the function of these genes can shed light on potential biologic mechanisms ( 45 ). For example candidate genes in ASD include those that play a role in brain development or neurotransmitter function, or genes that affect neuronal excitability ( 46 , 47 ). Many of the genetic defects associated with ASD encode proteins that are relevant at the neuronal synapse or that are involved in activity-dependent changes in neurons, including regulatory proteins such as transcription factors ( 42 , 48 ). Potential “networks” of ASD genetic risk convergence include pathways involved in neurotransmission and neuroinflammation ( 49 ). Transcriptional and splicing dysregulation or alterations in epigenetic mechanisms such as DNA methylation or histone acetylation and modification may play a role ( 42 , 49 - 51 ). A recent study describes 16 newly identified genes associated with ASD that raise new potential mechanisms including cellular cytoskeletal structure and ion transport ( 52 ). Ultimately, ASD remains one of the most genetically heterogeneous neuropsychiatric disorders with rarer de novo and inherited variants in over 700 genes ( 53 ).

While genetics clearly play a role in ASD’s etiology, phenotypic expression of genetic susceptibility remains extremely variable within ASD ( 54 ). Genetic risk may be modulated by prenatal, perinatal, and postnatal environmental factors in some patients ( 35 ). Prenatal exposure to thalidomide and valproic acid have been reported to increase risk, while studies suggest that prenatal supplements of folic acid in patients exposed to antiepileptic drugs may reduce risk ( 55 - 57 ). Research has not confirmed if a small positive trial of folinic acid in autism can be used to recommend supplementation more broadly ( 58 ). Advanced maternal and paternal age have both been shown to have an increased risk of having a child with ASD ( 59 ). Maternal history of autoimmune disease, such as diabetes, thyroid disease, or psoriasis has been postulated, but study results remain mixed ( 60 , 61 ). Maternal infection or immune activation during pregnancy is another area of interest and may be a potential risk factor according to recent investigations ( 62 - 65 ). Both shorter and longer inter-pregnancy intervals have also been reported to increase ASD risk ( 66 ). Infants born prematurely have been demonstrated to carry a higher risk for ASD in addition to other neurodevelopmental disorders ( 34 ). In a prior epidemiologic review, obstetric factors including uterine bleeding, caesarian delivery, low birthweight, preterm delivery, and low Apgar scores were reported to be the few factors more consistently associated with autism ( 67 ). A recent meta-analysis reported several pre, peri and postnatal risk factors that resulted in an elevated relative risk of ASD in offspring ( 35 ), but also revealed significant heterogeneity, resulting in an inability to make true determination regarding the importance of these factors.

Despite the hysteria surrounding the now retracted Lancet article first published in 1998, there is no evidence that vaccines, thimerosal, or mercury is associated with ASD ( 68 - 70 ). In the largest single study to date, there was not an increased risk after measles/mumps/rubella (MMR) vaccination in a nationwide cohort study of Danish children ( 70 ).

Ultimately, research continues to reveal factors that correlate with ASD risk, but no causal determinations have been made. This leaves much room for discovery with investigators continuing to elucidate new variants conveying genetic risk, or new environmental correlates that require further study ( 52 ).

Evaluation in ASD begins with screening of the general pediatric population to identify children at-risk or demonstrating signs suggestive of ASD, following which a diagnostic evaluation is recommended. The American Academy of Pediatrics (AAP) guidelines recommend developmental surveillance at 9, 15 and 30 months well child visits and autism specific screening at 18 months and again at 24 or 30 months ( 28 , 71 ). Early red flags for ASD include poor eye contact, poor response to name, lack of showing and sharing, no gesturing by 12 months, and loss of language or social skills. Screening tools for ASD in this population include the Modified Checklist for Autism in Toddlers, Revised, with Follow-up (M-CHAT-R/F) and Survey of Wellbeing of Young Children (SWYC) ( 72 , 73 ). Red flags in preschoolers may include limited pretend play, odd or intensely focused interests, and rigidity. School age children may demonstrate concrete or literal thinking, have trouble understanding emotions, and may even show an interest in peers but lack conversational skills or appropriate social approach. If there is suspicion of ASD in these groups, screening tools available include the Social Communication Questionnaire (SCQ), Social Responsiveness Scale (SRS), and Autism Spectrum Screening Questionnaire (ASSQ) ( 74 - 76 ).

If concerns are raised at screening, primary care clinicians are recommended to refer the child to early intervention if less than 3 years of age or to the public school system for psychoeducational evaluation in order to establish an individual education program (IEP) if the child is three years of age or older. Clinicians should additionally refer the child to a specialist (pediatric neurologist, developmental-behavioral pediatrician, child psychiatrist, licensed child psychologist) for a definitive diagnosis and comprehensive assessment ( 71 ). A comprehensive assessment should include a complete physical exam, including assessment for dysmorphic features, a full neurologic examination with head circumference, and a Wood’s lamp examination of the skin. A parent interview, collection of any outside informant observations, and a direct clinician observation of the child’s current cognitive, language, and adaptive functioning by a clinician experienced with ASD should be components of this comprehensive assessment. ( 28 , 71 , 77 , 78 ).

Additionally, primary care clinicians need to be aware of (and evaluate for) potential co-occurring conditions in children with ASD. According to a surveillance study of over 2,000 children with ASD, 83% had an additional developmental diagnosis, 10% had at least one psychiatric diagnosis, and 16% at least one neurologic diagnosis ( 79 ). In the past, rates of co-morbid intellectual disability (ID) in patients with ASD were reported from 50% to 70%, with the most recent CDC estimate reported at 31.0% (26.7% to 39.4%) with ID defined as intelligence quotient (IQ) ≤70 ( 6 , 80 ). Other common co-occurring medical conditions include gastrointestinal (GI) disorders, including dietary restrictions and food selectivity, sleep disorders, obesity, and seizures ( 81 - 84 ). Studies using electronic health record (EHR) analysis revealed prevalence of epilepsy ~20% and GI disorders [without inflammatory bowel disease (IBD)] at 10–12% ( 82 ). Epilepsy has been shown to have higher prevalence rates in ASD with comorbid ID and medical disorders of increased risk such as tuberous sclerosis complex (TSC) ( 85 - 87 ). GI disorders or GI symptomatology, including diarrhea, constipation, restrictive eating, or reflux, have been shown to be prominent in ASD across multiple studies ( 81 , 82 , 88 , 89 ). Sleep problems have been reported to occur in anywhere from 50% to 73% of patients with ASD with variation in prevalence dependent on the definition of sleep symptoms or the measurement tool used ( 90 - 92 ). Rates of overweight and obesity in ASD are reported to be roughly 33% and 18% respectively, higher than rates in typically developing children ( 81 - 84 , 93 ).

Other behavioral or psychiatric co-occurring conditions in ASD include anxiety, attention deficit/hyperactivity disorder (ADHD), obsessive compulsive disorder, and mood disorders or other disruptive behavior disorders ( 81 ). Rates of co-occurring ADHD are reported anywhere from 25% to 81% ( 81 , 94 ). A recent meta-analysis of 30 studies measuring rates of anxiety and 29 studies measuring rates of depression reported a high degree of heterogeneity from the current literature, but stated pooled lifetime prevalence for adults with ASD to be 42% for any anxiety disorder and 37% for any depressive disorder, though the use of self-report measures and the presence of ID could influence estimates ( 95 ). In children with ASD seeking treatment, the rate of any anxiety disorder was found to be similar at 42% and in addition this study reported co-morbid oppositional defiant disorder at a rate of 46% and mood disorders at 8%, with 66% of the sample of over 600 patients having more than one co-occurring condition ( 94 ).

Currently no clear ASD biomarkers or diagnostic measures exist, and the diagnosis is made based on fulfillment of descriptive criteria. In light of a relatively high yield in patients with ASD, clinical genetic testing is recommended and can provide information regarding medical interventions or work up that might be necessary and help with family planning ( 96 ). The American College of Medical Genetics and Genomics (ACMGG) guidelines currently recommend chromosomal microarray for all children, fragile X testing in males, and additional gene sequencing, including PTEN and MECP2 , in certain patients as first tier genetic testing in the work up of ASD ( 97 ). High resolution G-banded karyotype, once recommended for all patients with ASD, is no longer routinely indicated based on recent consensus recommendations, but might still be performed in patients with a family or reproductive history suggestive of chromosomal rearrangements or specific syndromes such as sex chromosome anomalies or Trisomy 21 ( 96 - 98 ). Several professional societies recommend genetic testing for ASD, including the American Academy of Neurology, the AAP, ACMGG, and the American Academy of Child and Adolescent Psychiatry, and a child may require further referral to a geneticist and/or genetic counselor, depending on results of testing ( 25 , 28 , 97 , 99 ). As the field of genetics continues to advance rapidly, recent publications suggest whole exome sequencing may become the preferred method for clinical genetic testing in individuals with ASD ( 100 , 101 ).

Aside from genetic testing, no other laboratory work up is routinely recommended for every patient with a diagnosis of ASD. However, further evaluation may be appropriate for patients with particular findings or risk factors. Metabolic work-up should be considered in patients with any of the following concerning symptoms or signs: a history of clear developmental regression including loss or plateau of motor skills; hypotonia; recurrent episodes of vomiting, lethargy or hypoglycemia; microcephaly or poor growth; concern for other organ involvement; coarse features; or concern for seizures or ataxia. Based on the patient’s history and presentation, components of a metabolic laboratory evaluation could include complete blood count (CBC), liver and renal function tests, lactate, pyruvate, carnitine, amino acids, an acylcarnitine profile, urine organic acids and/or urine glycosaminoglycans ( 97 , 102 ). Children with a history of pica should have a lead level measured ( 28 , 103 ). In a child with significantly restricted food intake, one should consider a laboratory evaluation of nutritional status. Sleep symptoms may warrant a referral for a possible sleep study, and if restless sleep symptoms are present, an evaluation for iron deficiency is not unreasonable, particularly if dietary rigidity limits iron intake ( 104 ).

Neuroimaging is not routinely recommended for every patient with ASD ( 28 , 99 ), but may be appropriate in patients with a suspicion for TSC or other neurocutaneous disorders, microcephaly, or an abnormal neurologic exam (spasticity, severe hypotonia, unilateral findings). Patients with suspected seizures should have an electroencephalography (EEG) obtained ( 102 ). If accessible, it might be appropriate to immediately refer children with concern for further genetic, metabolic or neurologic conditions to a specialist who can then obtain and interpret the aforementioned testing. At this time there is inadequate evidence to recommend routine testing for celiac disease, immunologic or neurochemical markers, mitochondrial disorders, allergy testing, hair analysis, intestinal permeability studies, erythrocyte glutathione peroxidase studies, stool analysis, urinary peptides or vitamin and mineral deficiencies without a history of severe food selectivity.

ASD is a neurodevelopmental disorder characterized by deficits in social communication and the presence of restricted interests and repetitive behaviors. Recent changes to the diagnostic criteria occurred with the transition to the new diagnostic manual (DSM-5) and will likely impact prevalence, which currently stands at 1 in 59 children in the US. ASD is a neurobiological disorder influenced by both genetic and environmental factors affecting the developing brain. Research continues to reveal factors that correlate with ASD risk and these findings may guide further etiologic investigation, but no final causal pathway has been elucidated. Clinical evaluation begins with developmental screening of the general pediatric population to identify at-risk children, followed by referral to a specialist for a definitive diagnosis and comprehensive neuropsychological assessment. Children with ASD should also be screened for common co-morbid diagnoses. While no clear biomarkers or diagnostic measures exist, clinical genetic testing is recommended as part of the initial medical evaluation. Further medical work up or subspecialist referrals may be pursued based on specific patient characteristics.


Funding: None.

Ethical Statement : The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Conflicts of Interest : The authors have no conflicts of interest to declare.

Jessica Penot LPC

Recognizing Autism in Females

What makes women different..

Posted March 31, 2022 | Reviewed by Gary Drevitch

  • What Is Autism?
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  • Autism presents very differently in females and it is important to recognize these differences
  • Females are very good at camouflaging the symptoms of autism, which can lead to increased anxiety and depression.
  • Early intervention in females can lead to improved outcomes and reduced risk for victimization.

Rose Pesca used with permission (Rose's Healing Arts in Denver, Colorado)

Research shows females receive Autism Spectrum Disorder (ASD) diagnoses later than males and that they are underdiagnosed and often misdiagnosed (Leedham, Thompson, smith, Freeth, 2019). It is believed this can partly be attributed to the fact that our knowledge of autism has largely been based on male samples (Gould & Ashton- Smith, 2011). Females’ ability to mask and hide their symptoms has also made it harder for clinicians and parents to recognize the signs of ASD in females. It can also be argued that it isn’t just our scientific study of ASD that has been based on male samples, but that our pop culture and cultural understandings of ASD are also usually based on male images. When most of us think of people with autism in pop culture, we think of males. I missed the symptoms of autism in myself for years because I couldn't relate to the images of autism I had been shown in the media.

So what does autism look like in women? Although women meet the same diagnostic criteria as their male counterparts, there are many ways in which it is unique in females.

Camouflaging or Masking

Unlike the media representations of autism, females on the spectrum usually report being painfully aware that they are not like their peers, and they attempt to adjust for this by mimicking the behaviors of others and trying to hide behaviors they perceive as abnormal. It is important to note that males mask too, but not as frequently as females. They try to act like the “normal” kids, or what they perceive normal to be. They mask.

This process causes significant anxiety and can cause meltdowns and emotional exhaustion because it is exhausting for those of us with autism. For me, masking feels like you are being asked to perform in a new play with no script and no director to an audience composed entirely of critics.

  • Eating Disorders

Many females with autism are misdiagnosed with eating disorders, according to Spek et al. (2020, Journal of Autism and Developmental Disorders). People with ASD experience a multitude of eating problems and women with ASD are often recognized as having eating disorders. As sensory issues are one of the hallmarks of autism, it isn’t a surprise that people with ASD struggle with eating. In females, this can be mixed with societal pressure to conform to social norms regarding eating and commonly leads to symptoms of anorexia, bulimia, and binge eating disorder .

Restricted Interests That Are Intense but Not as Obvious

Females have restricted interests in different things than are expected. Studies have shown that males tend to be more interested in mechanical topics as females tend to be more interested in relationships, people, animals, fictional characters and worlds, or psychology (Grove et al. 2018). Males and females both have hyper-fixated and restricted interests but many of my female clients get fixated on things like Dungeons and Dragons, animals, world-building, books, bones, autism itself, and television shows.

I have had so many collections and interests over the years, I can hardly keep count. I once became so interested in collecting ghost stories, a publishing house noticed my activity and offered me a publishing deal, which lead to three books. This was not my job; it was a consuming and restricted interest. People definitely thought I was odd and told me to my face that I was odd, but because I didn’t meet the stereotype, no one would have thought I had ASD. I worked with one girl who spent 8-10 hours a day crafting different role-playing game worlds for their friends to play online. I had another who collected bones. The restricted interests are there but they aren't what people expect.

Social Interactions Are Difficult and Draining

According to a survey we conducted here at Tree of Life Behavioral Health, many females with autism have difficulty obtaining and maintaining friendships and acquaintances. They lose friends and they don’t usually understand why. They feel rejected and isolated because the nuanced interactions that lead to deep bonding are a constant mystery.

Although females with ASD may appear normal in social interactions, if you talk to them, they will tell you that those interactions are a constant source of stress and anxiety. Females with ASD describe themselves as aliens and outsiders who can never quite navigate other humans. They struggle constantly with feelings of isolation and alienation.

Most Females Are Misdiagnosed Prior to Their Autism Diagnosis

Most females with autism are diagnosed with anxiety, depression , borderline personality disorder , or bipolar disorder prior to their diagnosis with autism. In general, people with autism do have higher rates of depression and anxiety. They feel like outsiders, aliens, and they struggle with social isolation , all of which can lead to both depression and anxiety. Females are often misdiagnosed with borderline personality disorder because they have difficulty regulating their emotions and they struggle with relationships and social interactions. This inability to regulate their emotions often leads to bipolar diagnoses as well.

presentations of autism

These factors, combined with the fact that many clinicians don’t understand autism well and understand the presentation of autism in females even less well, contribute to a perfect storm in which many clinicians adhere to preconceived notions and give females the diagnoses that are more common to females.

Fantasy Worlds

According to Hendrickx (2015), one of the most common differences between male and female ASD presentation is the presence of imaginary play. One of the things most people who test and screen for ASD look for is a lack of imagination or limited imaginary play. However, girls with autism have described this to be almost the opposite: They may live in an atypically rich world of imagination with multiple imaginary friends.

Most of my clients get lost in role-playing games and books and the fictional characters in their books and movies can be more real to them than their peers. They can relate to fictional characters and understand their motives and backstories as real people are hidden and difficult.

One of my projects over the last year has been looking at females with autism and trauma . According to Buuren et al. (2021), people with autism show a higher risk of adverse events and trauma. Females with autism are even more at risk. In a survey done at my clinic, 90% of the sample of females with ASD also met the diagnostic criteria for PTSD . This makes it very difficult to see the symptoms of autism because often they are mixed with symptoms of PTSD. Many of the females we surveyed felt they were more vulnerable to being victimized because of their undiagnosed and untreated ASD. In a world filled with people they don’t understand, how can they tell which people are dangerous and which are safe?

Buuren, Ella Logregt-van, Hoekert, Marjolijn, &Sizoo, Bram (2021). Autism Adverse Events, and Trauma. Autism Spectrum Disorders. Chapter 3.

Agniezka, Rynkiewicz, Janas-Kozik, Malgorzata, & Slopien, Agniezka (2019), Girls and women with autism. Psychiatry Poland. 53(4):737-752

Gould, J & Ashton Smith, J. (2011). Missed diagnosis or misdiagnosis? Girls and women on th3e autism Spectrum. Good Autism Practice. 12

Grove, R., Hoekstra, R.A., Wierda, M, Begeer, S ( 2018). Special Interests and subjective wellbeing in autistic adults. Autism Research. 11(%), 766-775.

Hendrickx, Sarah. (2015). Women and Girls with Autism Spectrum Disorder: Understanding Life Experiences from Early Childhood to Old Age. Jessica Kinsley Publisher. London

Lai, Meng-Chuan, Baron-Cohen, Simon, & Buxbaum, Joseph D. (2015). Understanding autism in the light of sex/gender. Molecular Autism. 6:24

Leedham, Alexandra, Thompson A. R. Smith, R, Freeth, M (2019). ‘I was exhausted trying to figure it out: The experiences of females receiving an autism diagnosis in middle to late adulthood. Autsim.

Spek, A. Rijnsoever, Wendy. % Kiep, Michele (2020), Eating Problems in Men and Women with an Autism Spectrum Disorder. Journal of Autism and Developmental Disorders.

Jessica Penot LPC

Jessica Penot, LPC, is the founder and director of Tree of Life Behavioral Health in Madison, Alabama and the author of 10 books including the bestselling novel, The Accidental Witch.

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The people around us have a stronger influence on our decisions and actions than we realize. Here’s what research reveals about our networks’ gravitational force.

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Signs and Symptoms of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a developmental disability caused by differences in the brain. People with ASD often have problems with social communication and interaction, and restricted or repetitive behaviors or interests. People with ASD may also have different ways of learning, moving, or paying attention. It is important to note that some people without ASD might also have some of these symptoms. But for people with ASD, these characteristics can make life very challenging.

Learn more about ASD

Social Communication and Interaction Skills

Social communication and interaction skills can be challenging for people with ASD.

Baby - Human Age, Waving - Gesture, Child, Happiness, Looking At Camera

Examples of social communication and social interaction characteristics related to ASD can include

  • Avoids or does not keep eye contact
  • Does not respond to name by 9 months of age
  • Does not show facial expressions like happy, sad, angry, and surprised by 9 months of age
  • Does not play simple interactive games like pat-a-cake by 12 months of age
  • Uses few or no gestures by 12 months of age (for example, does not wave goodbye)
  • Does not share interests with others by 15 months of age (for example, shows you an object that they like)
  • Does not point to show you something interesting by 18 months of age
  • Does not notice when others are hurt or upset by 24 months of age
  • Does not notice other children and join them in play by 36 months of age
  • Does not pretend to be something else, like a teacher or superhero, during play by 48 months of age
  • Does not sing, dance, or act for you by 60 months of age

Restricted or Repetitive Behaviors or Interests

People with ASD have behaviors or interests that can seem unusual. These behaviors or interests set ASD apart from conditions defined by problems with social communication and interaction only.

Examples of restricted or repetitive behaviors and interests related to ASD can include

Close-up of child playing with toy blocks on the carpet.

  • Lines up toys or other objects and gets upset when order is changed
  • Repeats words or phrases over and over (called echolalia)
  • Plays with toys the same way every time
  • Is focused on parts of objects (for example, wheels)
  • Gets upset by minor changes
  • Has obsessive interests
  • Must follow certain routines
  • Flaps hands, rocks body, or spins self in circles
  • Has unusual reactions to the way things sound, smell, taste, look, or feel

Other Characteristics

Most people with ASD have other related characteristics. These might include

  • Delayed language skills
  • Delayed movement skills
  • Delayed cognitive or learning skills
  • Hyperactive, impulsive, and/or inattentive behavior
  • Epilepsy or seizure disorder
  • Unusual eating and sleeping habits
  • Gastrointestinal issues (for example, constipation)
  • Unusual mood or emotional reactions
  • Anxiety, stress, or excessive worry
  • Lack of fear or more fear than expected

It is important to note that children with ASD may not have all or any of the behaviors listed as examples here.

Learn more about screening and diagnosis of ASD

Learn more about treating the symptoms of ASD

2023 Community Report on Autism. The latest ADDM Network Data

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Are there gender-based variations in the presentation of Autism amongst female and male children?

  • Original Paper
  • Open access
  • Published: 13 July 2022
  • volume  53 ,  pages 3627–3635 ( 2023 )

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The Questionnaire for Autism Spectrum Conditions (Q-ASC; Attwood, Garnett & Rynkiewicz, 2011 ) is one of the few screening instruments that includes items designed to assess female-specific ASD-Level 1 traits. This study examined the ability of a modified version of the Q-ASC (Q-ASC-M; Ormond et al., 2018 ) to differentiate children with and without ASD-Level 1. Participants included 111 parents of autistic children and 212 parents of neurotypical children (5–12 years). Results suggested that the gendered behaviour, sensory sensitivity, compliant behaviours, imagination, and imitation subscales differentiated autistic females from neurotypical females. Compared to autistic males, autistic females had higher scores on gendered behaviour, sensory sensitivity, social masking, and imitation. Results are discussed in relation to early detection of autistic female children.

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In this paper we examine the extent to which autistic female children have characteristics that distinguish them from autistic male children and neurotypical females. This has important implications for early detection and evidence-based service provision for this population. At the outset, we note that in this paper we use autism-first language and we use the term autism rather than autism spectrum disorder . We acknowledge that there are bifurcated views on the use of autism-first language versus person-first language (Autistic Self Advocacy Network, 2021 ; Bury et al., 2020 ; Callahan, 2018 ). We retain the use of autism-first language in recognition of the view that autism is integral to a person’s identity and not an additional aspect, and because this is the preference of advocates and autism experts (Autistic Self Advocacy Network, 2021 ; Botha et al., 2021 ; Bury et al., 2020 ; Department of Social Services, 2021 ; National Autistic Society, 2021 ). We understand that views and opinions differ on this point (Botha et al., 2021 ), but note that the terms ‘autistic person’ and ‘autistic’ are the preferred terms with largest agreement amongst Australian adults who reported having a diagnosis of autism (Bury et al., 2020 ). We also understand that there are variations in the preferences of autistic people from neurotypical groups (e.g., Callahan, 2018 ), and we accept that some readers of this paper may disagree with our adoption of autism-first language.

ASD-Level 1 (formally known as Asperger’s syndrome) is a presentation of autism without intellectual or language impairment according to the Diagnostic and Statistical Manual of Mental Disorders – 5th Edition (APA, 2013 ). ASD-Level 1 is typically diagnosed when individuals demonstrate restrictive/repetitive patterns of behaviours and poor social abilities, but do not display language or cognitive deficits (APA, 2013 ; Attwood, 2007 , 2012 ). Traditionally, autism has been recognised more frequently in males than females. However, the gender ratio for autism changes with age from 5.5:1 in elementary school children to 2.3:1 for autistic adolescents and between 1.8:1 to 2.57:1 for autistic adults (Posserud et al., 2021 ; Rutherford et al., 2016 ). Clinical studies have also suggested that autistic females are more likely to display accompanying intellectual impairment (Mandy et al., 2012 ), suggesting that females without accompanying intellectual impairment or language delays present differently and are therefore missed or misdiagnosed (Rivet & Matson, 2011 ; Rynkiewicz & Łucka, 2015 ). Indeed, it has been found that mental health professionals may reflexively attribute the autistic female presentation to other diagnoses such as Personality Disorders, Schizophrenia, Eating Disorders, Borderline Personality Disorder, Selective Mutism, Separation Anxiety Disorder, Depression, or Specific Phobias (Bulhak-Paterson, 2015 ; Christensen, 2016; Rynkiewicz & Łucka, 2015 ). Moreover, common comorbid diagnoses may further complicate accuracy in the assessment protocol for females.

There are several differences in the presentations of autistic males and females, and these differing presentations may be associated with being overlooked in diagnostic interviews (Lai et al., 2015 ). Relative to autistic males, autistic females have been shown to have an increased ability to camouflage their social confusion (Cook et al., 2021 ; Goddard et al., 2014 ; Hull et al., 2019 ; Simone, 2010 ; Willey, 2015), and to mimic or imitate genuine reciprocal interactions after carefully evaluating nuances of people’s actions, emotional atmosphere, and social conventions (Attwood, 2007 ; Bulhak-Paterson, 2015 ). Compared to autistic males, autistic females have been shown to be more likely to apologise and appease during social situations, which may contrast with their emotional experience (Bulhak-Paterson, 2015 ; Garnett et al., 2013 ), and which results in them appearing to have fewer social inadequacies (Bargiela et al., 2016 ). In their systematic review of empirical literature, Cook et al. ( 2021 ) found that autistic camouflaging is associated with significantly poorer mental health outcomes.

There are several differences in the ways in which autistic males and females present when their play with peers is observed, and during a diagnostic assessment. During the elementary school years autistic boys tend to play alone and some distance from peers while autistic girls tend to stay in close proximity to peers and weave in and out of activities, which may camouflage their social challenges to teachers and parents and autistic girls can utilise social opportunities in order to watch the interactions of other girls to mimic social behaviours (Dean et al., 2017 ). By camouflaging their emotional experience through nuanced imitation, the pervasive and disruptive nature of female social problems remains covert, reducing the likelihood that they will be diagnosed with autism (Bargiela et al., 2016 ).

The second way that autistic females and males differ in their presentation of autism relates to their special interests. Autistic females may present with special interests that are considered ‘normal’ for their gender and age, such as intense interest in a popular celebrity, television show, or collecting dolls or riding horses (Attwood, 2007 ; Attwood & Grandin, 2006 ; Kopp & Gillberg, 2011 ). Autistic females have also been shown to differ from autistic males in the intensity as well as the focus of their more socially aligned special interests, and may display an intense interest in learning about the conventions of friendship (Attwood, 2012 ; Supekar & Menon, 2015 ). Importantly, it has been shown that the choice and pursuit of ‘special interests’ in autistic females appear to be interlaced with camouflage and imitating behaviour, suggesting greater difficulty in identifying unusual ‘special interests’, as required in Section B of DSM-5 (APA, 2013 ), which may contribute to higher rates of autism diagnosis in males compared to females (McLennan et al., 1993 ; Rynkiewicz & Łucka, 2015 ).

The third way that autistic males and females may differ with respect to their presentation relates to gender typicality. Autistic females may not present as gender-typical or may actively reject social gender conventions (Faherty, 2006 ). Expression of this characteristic may include specific fashion and clothing choices; rejection of fashion trends, and being a ‘tomboy’, or ‘gender-rebel’ (Attwood, 2007 ; Kopp & Gillberg, 2011 ). Although anecdotal accounts and clinical observations have noted a higher level of gender incongruence (e.g., gender dysphoria, atypical gender identity) for autistic individuals (Attwood, 2007 , 2012 ; Glidden et al., 2016 ), research is yet to systematically investigate the co-occurrence of gender identity issues and autism (Glidden et al., 2016 ; Wood & Halder, 2014 ). Wood and Halder’s (2014) systematic review found that autistic females displayed more masculine traits than nonautistic (neurotypical) females, which may contribute to clinical observations of gender incongruence among autistic individuals.

As male populations have largely informed diagnostic protocols, unique characteristics of autistic females have been overlooked in research and clinical settings (Glidden et al., 2016 ). Also, while there are a range of instruments for measuring autism symptomatology, many of these instruments were not developed using female samples, so it is therefore unsurprising that unique characteristics of autistic females have been overlooked.

The most standardised and empirically supported diagnostic assessment used to identify behavioural characteristics and assist in the diagnosis of ASD-Level 1 is the Autism Diagnostic Observation Schedule - Revised (ADOS-2; DiLavore et al., 1995). Methodologically, the ADOS-2 was not adequately standardised on females, nor does it accommodate or conceptualise the proposed social masking and imitation components of the female presentation of autism (Cheslack-Postava & Jordan-Young, 2012; Kamp-Becker et al., 2018 ; Lai et al., 2015 ; Rynkiewicz et al., 2016). Similarly, research samples comprising the normative data for the ADOS-2 did not include individuals with the milder characteristics of ASD-Level 1 (Lai et al., 2015 ). Thus, the ADOS does not allow clinicians and diagnosticians to sufficiently assess the broader range of presentations outside classic autism traits and is yet to adequately inform diagnosis for autistic females (Bargiela et al., 2016 ; Rivet & Matson, 2011 ). Indeed, it has been found that adolescent females are at higher risk of misdiagnosis from the ADOS-2 procedure, in spite of their clinical presentation and developmental history suggesting autism (Rynkiewicz & Łucka, 2015 ).

A recent review of general screening tools for detecting autism in females noted that sex differences may not be captured on standard screening tools (Lundstrom et al., 2019). There are two instruments developed to assess for the features of autism more commonly seen in females. The first is the Autism Spectrum Screening Questionnaire (ASSQ; Ehlers, Gillberg & Wing, 1999 ) and later versions ( ASSQ-REV; Kopp & Gillberg, 2011 ); ASSQ-GIRLS; Kopp & Gillberg, 2011 ). ASSQ-GIRLS has an 18-item subscale designed to measure characteristics of autism in females (Kopp & Gillberg, 2011 ). The second is the Questionnaire for Autism Spectrum Conditions (Q-ASC; Attwood et al., 2011 ). The Q-ASC is a 61-item screening questionnaire completed by parents/caregivers that includes items measuring characteristics of the female presentation. A recent factor analytic study (Ormond et al., 2018 ) found 8 subscales derived from 31 items, including gendered behaviour, sensory sensitivity, compliant behaviour, friendships and play, social masking, imagination, and imitation (one subscale, talents and interests, was subsequently dropped because of low reliability; Field, 2014 ; Ormond et al., 2018 ).

The present study investigated the extent to which the Q-ASC (Ormond et al., 2018 ) discriminates between 5 and 12 year old autistic females and males compared to neurotypical females and males. The key hypothesis was that the seven Q-ASC subscales of Gendered Behaviour, Sensory Sensitivity, Compliant Behaviour, Friendships and Play, Social Masking, Imagination, and Imitation would discriminate between autistic females and nonautistic (neurotypical) females and would discriminate between autistic females and autistic males. We also explored the extent to which two subscales, Sensory Sensitivity and Compliant Behaviour, discriminated between autistic and neurotypical children.


Participants were parents of 323 children aged 5 to 12 years ( M  = 8.06, SD  = 2.25), 111 of whom had a diagnosis of ASD-Level 1, and 212 of whom did not have a clinical/ASD-Level 1 diagnosis (‘neurotypical’ children). Table  1 provides a gender and age breakdown for both the autistic sample and the neurotypical sample. Table  2 provides data on the primary and comorbid diagnoses of the autistic group.

Archival clinical participant data were obtained from a specialist autism psychology clinic located in Brisbane, Australia. To meet eligibility for inclusion in the study, parents confirmed that their child had received a diagnosis of ASD-Level 1 (autism without language or intellectual impairment) and were in the selected age range (5–12 years). All archival data was de-identified to protect the privacy of all participants. Diagnostic protocol and conferment of the current sample was deemed clinically appropriate at the expert discretion of clinical psychologist diagnosticians. Neurotypical participants were recruited through social media (e.g., Facebook) and school newsletters. To meet eligibility for inclusion in this subgroup, parents confirmed that their child had not received a clinical diagnosis of autism or a neurodevelopmental disorder, did not attend a special school, and were in the selected age range (5–12 years). Furthermore, the non-clinical sample were screened using The Autism Spectrum Screening Questionnaire - Girl (ASSQ-GIRL; Kopp & Gillberg, 2011 ) for features consistent with the autistic female presentation clinically identified in both males and females to ensure integrity of the sample.

The Modified Questionnaire for Autism Spectrum Conditions ( Q-ASC-M: Attwood et al., 2011 ; Ormond et al., 2018 ) is a 36-item questionnaire designed to assess parent/caregiver perceptions of behaviours and abilities associated with autism in children aged 5–19 years, including those pertaining to the female presentation of ASD-Level 1. Respondents are required to rate their level of agreement with each item on a four-point scale ranging from 1 ‘ definitely disagree ’ to 4 ‘ definitely agree’ . The subscales of the modified version have demonstrated adequate internal consistency: Gendered Behaviour (5 Items, α = 0.86; e.g., “Is s/he interested in looking feminine?); Sensory Sensitivity (6 Items, α = 0.72; e.g., “Is s/he bothered by bright lights or certain kind of lights?”); Compliant Behaviour (5 Items, α = 0.72; e.g., “Is s/he well-behaved at home?); Friendships & Play (5 Items, α = 0.76; e.g., “Does s/he enjoy playing with others?”); Social Masking (5 Items, α = 0.61; e.g., “Does s/he have a facial ‘mask’ that hides his/her social confusion?); Imagination (5 Items, α = .67; e.g., “Is s/he interested in fiction?”); and Imitation (5 Items, α = .62; e.g., “Does s/he copy or clone him/herself on others?”). Items on each subscale are summed to produce a total subscale score. With the exception of the sensory sensitivity subscale, subscale scores may range from 5–20, with higher scores indicating greater autism-consistent behaviours in the various areas. Scores on the Sensory Sensitivity subscale may range from 6–24, with higher scores indicating greater problems with sensory sensitivity.

The Autism Spectrum Screening Questionnaire - Girls (ASSQ-GIRL; Kopp & Gillberg, 2011 ) is an 18-item parent or caregiver-rated questionnaire designed to assess behavioural characteristics consistent with an emerging female presentation of autism in child and adolescent females. Parents/caregivers rate their level of agreement with each item on a three-point scale ( 0 ‘No’, 1 ‘ Somewhat’, 2 ‘Yes’) across 18 items (α = 0.94; e.g., “Interacts mostly with younger children”). Total scores may range from 0 to 36, with higher scores indicating greater reported levels of features consistent with the female presentation of autism. A cut-off score of 20 indicates a greater likelihood of ASD-Level 1 (Kopp & Gillberg, 2011 ). Therefore, it was necessary for all non-clinical participants to score below 20 for inclusion in the study. The ASSQ-GIRL has demonstrated high internal consistency (a  = 0.94) and good convergent validity ( r  = .85, n  = 191; p  < .001) for males and females with a clinical presentation.

Prior to commencement of the study, ethics approval was sought and granted by the Griffith University Human Research Ethics Committee. All archival data was de-identified prior to the transfer and safe storage of electronic and paper-based questionnaire responses for analysis. All non-clinical data was collected using secure online survey software. Participants accessed the questionnaire via a weblink and provided tacit informed consent of the research terms stated in the Information and Consent Form prior to completing the online survey. Participants confirmed their understanding of the purpose of the study, inclusion criteria, the expected time to complete the questionnaire (approximately 10 min), details of voluntary, confidential, and anonymous participation, and their ability to withdraw at any time without penalty. Only those who provided informed consent were able to proceed to the questionnaires. Incentive to participate in the current study was offered as inclusion in a prize draw to win an iPad or a range of $50 gift cards. Entering the draw was optional and was not linked to survey responses.

All statistical analyses were performed in IBM SPSS version 26. Power analyses conducted using G*Power (Faul et al., 2007 ), with a power of 0.80, a medium effect size ( OR  = 3.47) for logistic regression, and an alpha of 0.05, suggested that a total sample size of 101 participants were required for analyses. Thus, the current sample of 323 participants was sufficient.

To assess the ability of the Q-ASC to accurately discriminate between autistic and nonautistic (neurotypical) females and males, two hierarchal binary logistic regressions were used, one for females and one for males. For each regression analysis, age was controlled by entering this on Step 1 of modelling, then the 7 Q-ASC subscale scores were entered on Step 2. The Hosmer and Lemeshow Test was used to examine model fit, in addition to the Cox and Snell R 2 to ascertain the amount of variance the subscale variables add over and above the other variables. The Wald test was used to ascertain the relative contribution of each independent variable for predicting the likelihood of group membership.

To examine differences between autistic females and males, seven univariate ANOVAs were conducted to examine the difference between gender, and autistic/neurotypical status. The dependant variables were scores on the seven subscales of the Q-ASC: Gendered Behaviour, Sensory Sensitivity, Compliant Behaviour, Friendships & Play, Social Masking, Imagination, and Imitation. The between groups variables were gender (female and male) and autistic/neurotypical status (autistic and nonautistic diagnosis). Therefore, these models include two main effects (gender and autistic/neurotypical status), and an interaction (gender X autistic/neurotypical status). Follow-up difference tests were conducted to examine any main effects and interactions.

Data Cleaning

Prior to analyses, the data was cleaned to identify any outliers, data entry errors, and missing data (Tabachnick & Fidell, 2013). No univariate outliers were detected through examination of the standardized residuals, and undue influence was checked with Cook’s Distances and DFbeta values. Four female participants were found to have Cook’s Distance values outside acceptable limits (> 1) and were removed to avoid undue influence (Tabachnick & Fidell, 2013). Further, multicollinearity, logit linearity, and independence of errors were examined and found to be within acceptable limits (Field, 2014 ). The means and standard deviations of each of the seven subscales of the Q-ASC-M by age and gender are provided in Table  3 .

The binary logistic regression of autistic/neurotypical status on Q-ASC subscales and age was significant for females, χ 2 (7) = 179.12, p  < .001, Cox and Snell R 2  = 0.62, Nagelkerke R 2  = 0.93, explaining 92.8% of the variance in ASD-Level 1 diagnosis. The Hosmer and Lemeshow test confirmed that the model was a good fit for the data, χ 2 ( df  = 8) = 3.84, p  = .87. Coefficients for the model are presented in Table  4 . As shown in Table 5 5 , age, Gendered behaviour, Sensory Sensitivity, Compliant Behaviour, Imagination, and Imitation were significant predictors in the final model. It was found that for every year increase in age, there was a 3.28 increase in the chance of an ASD-Level 1 diagnosis. Within the subscales, each unit increase in Gendered Behaviour, Sensory Sensitivity, Imagination, and Imitation there was a 2.86, 3.28, 3.18, 2.11, and 3.52 times increase in the chance of an ASD-Level 1 diagnosis, respectively. Additionally, with each unit increase in compliant behaviour, there was a 57.7% reduction in the likelihood of an autism diagnosis.

The binary logistic regression of autistic/neurotypical status on Q-ASC subscales and age was significant for males, χ 2 (7) = 76.58, p  < .001, Cox and Snell R 2  = 0.49, Nagelkerke R 2  = 0.65, explaining 64.7% of the variance in autism diagnosis (present/absent). The Hosmer and Lemeshow test confirmed that the model had good fit to the data (Hosmer-Lemeshow Goodness of fit χ 2 (8) = 12.83, p  = .12. Coefficients for the model are presented in Table  5 . As shown in Table  5 , in the final model, age, sensory sensitivity and compliant behaviour were predictive of autism diagnosis. It was found that for every increase of one year, there was a 1.29 timed increase in the chance of an autism diagnosis, for each unit increase in sensory sensitivity, there was a 1.36 increase in the chance of an ASD-Level diagnosis, and for each unit increase in compliant behaviour, there was a 23.4% reduction in the likelihood of an autism diagnosis.

The main effects and interactions from the ANOVAs conducted on the seven Q-ASC subscales can be seen in Table  6 . The results of the follow-up difference tests can be seen in Table  7 . The results showed statistically significant interactions between gender and autistic/neurotypical status for Gendered Behaviour, Sensory Sensitivity, Social Masking, and Imitation. It was found that females with an autistic diagnosis had statistically significantly higher scores on the Gendered Behaviour (M Diff = 3.08), Sensory Sensitivity (M Diff = 2.18), Social Masking (M Diff = 2.28), and Imitation (M Diff = 3.26), when compared to males with an autistic diagnosis. No differences were detected between males and females in the nonautistic group, with the exception of Sensory Sensitivity, where females scored significantly higher than males (M Diff = 1.57).

There is increasing acceptance and clinical recognition that autism may present differently in female children compared to male children (Attwood, 2012 ; Chawarska et al., 2016 ; Cridland et al., 2014 ; Dworzynski et al., 2012 ; Garnett et al., 2013 ; Wilkinson, 2008 ). Systematic ways to identify the apparently subtle and complex characteristics in autistic females are yet to be established, yet the need for clinicians and diagnosticians to be able to identify autism accurately and sensitively in both genders is paramount. This study aimed to investigate the ability of the Q-ASC-M, to accurately discriminate between autistic and nonautistic (neurotypical) females and males aged 5–12 years.

The results were generally consistent with the key hypothesis. Compared to autistic males, autistic female children had higher scores on gendered behaviour, sensory sensitivity, social masking, and imitation. Gender differences between autistic children on compliant behaviour, friendships and play, and imagination were not significant. Gendered behaviour, sensory sensitivity, compliant behaviour, imagination, and imitation were all found to discriminate between autistic females compared to neurotypical females. The findings that social masking and friendship and play did not discriminate between neurotypical and autistic females were inconsistent with the key hypothesis. Exploratory analyses indicated that the sensory sensitivity and compliant behaviour subscales discriminated between autistic and neurotypical male children.

Both gender differences and differences across diagnostic categories on Q-ASC scales indicate that autistic females present differently from their male counterparts and neurotypical females. In particular, the results for gendered behaviour are novel as there has been little empirical research conducted on gender typicality in autistic females previously, and are consistent with clinical observations that autistic females often present as gender-atypical or may actively defy social gender conventions (Faherty, 2006 ). They also align with the results of Ormond et al. ( 2018 ) who found that parents reported a greater level of observed incongruence in gendered behaviour for autistic females than autistic males. Similarly, the findings for imitation are consistent with research and clinical observation suggesting that autistic females may undertake a cognitive process of imitation as a social-cognitive defence due to social and communication deficits, awareness of identity, and sense of self (Giarelli et al., 2010 ; Goddard et al., 2014 ). This may appear as avidly observing others socially, adopting a different persona, or copying or cloning someone identified as socially successful as a potential mask for social deficits (Glidden et al., 2016 ). Finally, the results for imagination are consistent with Kopp and Gillberg ( 2011 ) who found that females were more likely to engage in fantasy and fiction, which were associated with behavioural characteristics that served as a function to ease social anxiety.

Contrary to the key hypothesis, the friendships and play and social masking subscales did not discriminate between autistic and neurotypical female children, suggesting that these two subscales may not be useful in their current form when screening for autism in females. It may be that these characteristics are more evident in adolescents rather than younger children. Indeed, Ormond et al. ( 2018 ) found that parents of adolescents reported lower levels of friendships and play characteristics than parents of younger children, suggesting a subtler presentation in earlier developmental years. This may also be the case with social masking, with this skill perhaps requiring greater cognitive ability and maturity. Future research should replicate this study with adolescents to assess whether differences in friendships and play and social masking are evident between those with and without autism in an older cohort.

The domains of sensory sensitivity and compliant behaviour significantly discriminated between children with and without autism for both genders, which demonstrate congruence of these two characteristics across genders. Thus, compared to parents of neurotypical children, parents of autistic children report sensory sensitivities and noncompliance (i.e., greater behavioural difficulties, less compliance with requests, and disproportionate reactivity) in their children. It is interesting to note that previous clinical research suggests a variance in presentations across contexts, with greater externalising behaviours seen in autistic females at home, compared to school (Bulhak-Paterson, 2015 ; Willey, 2015). Indeed, autistic female children demonstrate an ability for social learning and present with compliant, helpful and socially acceptable behaviour at school as a learned approach for greater likability, and to camouflage their deficits, with no teacher-reported problems (Cridland et al., 2014 ). However, the resultant emotional suppression may present as a significant shift in behaviour at home, where children may display depressed mood, social confusion, and distress (e.g., externalised behaviour or meltdowns; Attwood, 2007 ; Gould & Ashton-Smith, 2011 ). Thus, noncompliance in autistic female children may well be picked up by parents but not teachers, highlighting the importance of parent report in the diagnosis of autism in females.

Autistic females who remain undiagnosed, or who are incorrectly diagnosed with and treated for alternative psychopathology, may experience significant and detrimental economic and health impacts in later life as a result (Wilkinson, 2008 ). The benefit of early and accurate diagnosis has been shown to align with appropriate and effective treatments that contribute to better management and adaptation to symptoms and provide a sense of meaning and greater levels of wellbeing (Giarelli et al., 2010 ; Gould & Ashton-Smith, 2011 ). The results of this study suggest that the Q-ASC-M screens well for females with AS.

This study is limited by its cross-sectional design, so causality cannot be established. The neurotypical sample was screened for autism using the ASSQ-GIRL and cut-off scores relating to the female presentation of autism (Kopp & Gillberg, 2011 ), but they were not assessed by a trained clinician. Future research should ensure that control groups are assessed by trained professionals to ensure that they are indeed neurotypical. Because limited demographic information was available, it is unclear how generalisable these results are across groups varying in socioeconomic status and other key demographics.


The results of this study suggest that the Q-ASC-M is adequate in its ability to discriminate between autistic males and females and nonautistic (neurotypical) individuals, with greater discriminatory capacity for females compared than males. Only two of the seven subscales discriminated between males with and without AS, whereas five of the seven subscales discriminated between females with and without AS. Q-ASC scores on gendered behaviour, sensory sensitivity, social masking, and imitation may assist health and education professionals in detection of autism in female children.

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Simcoe, S.M., Gilmour, J., Garnett, M.S. et al. Are there gender-based variations in the presentation of Autism amongst female and male children?. J Autism Dev Disord 53 , 3627–3635 (2023). https://doi.org/10.1007/s10803-022-05552-9

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The various types of Autism are considered a “spectrum disorder” – an umbrella, with a range of autistic syndromes at varying degree of severities. It can also be depicted, and this is my preferred version while educating parents, as a rainbow where the colors blend and overlap. Each of the distinct color represents a particular category of autism. Yet, the transition from one color to the next on a rainbow is similar to the transition from mild to severe autism.

Fig 1 : Autism Spectrum – The Rainbow Effect

Each of these categories demonstrates varying degrees of difficulties a person faces with social, verbal, communicative and repetitive behaviors. Just as a shade in rainbow overlaps and blends with the next color, so does autism making it harder to determine where one level or type of autism starts and where it terminates.

Key Types of Autism

What this page contains

The Journey from Classical Autism to Autism Spectrum Disorder

Till about the 1970s, the classical autism studies included all shades of ASD bundled under a generic term ‘Autism’.

Today, however, physicians, therapists, and researchers consider each of these five categories while referring to specific autism symptoms:

  • Autistic Disorder – also known as  Classical Autism
  • Asperser’s Syndrome
  • Pervasive Developmental Disorder , otherwise known as PDD-NOS
  • Rett’s Syndrome  (primarily a common type of  autism in girls )
  • Childhood disintegrative disorder also referred to as CDD

Classical Interpretation of Autism

Fig 2 : The Classic view of Autism – so 1970s…

Modern Interpretation of Types of Autism

Fig 3 : Autism as interpreted today – A Spectrum of Disorders

“Each type of Autism demonstrates a degree of difficulty that a patient faces with verbal, social and communicative interactions. Just as a shade in rainbow overlaps and blends to the next one, so does the autism spectrum; thus turning it into a challenging exercise for physicians to determine where one range in the spectrum starts and where it ends,” comments Mary Alexa, autism therapy specialist.

Autism Severities per Types

Support classification of different autism types.

Each of the above types of Autism mentioned earlier falls into one of the following categories, based on the level of support they require. For more details on the Autism Levels, please check out our page on Autism Spectrum Disorder DSM 5

An Insight into the Various Types of Autism

Let us now get a deeper insight into each of the following forms of Autism.

Fig 3: Overlap between Asperger’s and PPD NOS

As mentioned at the beginning of this article, the various types of autism spectrum disorders present a significant overlap with one another. The following 3 characteristics are carefully evaluated to arrive at the right conclusion:

  • Applied Behavior Analysis  for ASD
  • Social skills within families  coping with Autism  and externally
  • Autism Communication Skills

For example, it is extremely hard to discriminate between mild PDD and moderate Asperger’s symptoms as a patient may demonstrate both characteristics in the autism spectrum quotient.

These classify people who fall under the high functioning autism spectrum. They are often intelligent and excel in academics and work life. However, their impairment lies in the lack of social skills. While they develop communication and language skills in the same way as any other developing child, their deficits become more obvious with age as they struggle to keep up with the expectations of their family and extended community circles.  Read more about Asperger’s

Pervasive Development Disorder

Pervasive Developmental Disorder – Not Otherwise specified is used to classify people who do not fit into any particular category of Autism. They meet some of the criteria for classical autism, but not necessarily all. Their impairments could range from mild to severe requiring support ranging from anywhere between Level 1 to Level 2. Functioning level is usually moderate to high, barring exceptions where they overlap with other disorder syndromes.  Read more about PDD NOS

Childhood Disintegrative Disorder

CDD, also known as Heller’s Syndrome is an interesting one;  typically affecting toddlers and pre-schoolers. In this case, the child grows normally until (at least) the age of 2 and then shows a sudden drop in social, communication and behavioral skills. CDD is often overlooked initially by the parents as they tend to attribute this sudden impairment as a ‘transient and temporary’ phase for their child and would expect it to pass away.  Read more about CDD

Rett’s Syndrome

Rett’s syndrome occurs only in girls – the only form of Autism Spectrum Disorder can be diagnosed and medically confirmed. Girls with Rett’s Syndrome suffer from significant communication impairment. Also, one of the common symptoms of Rett’s Syndrome is the girl’s limited ability to use their hands for regular activity. Typically this syndrome deteriorates with the girl’s age, thus requiring more support and time.  Read more about Rett’s Syndrome

Classical Autism

Among all the various types of Autism, Classical autism is perhaps the broadest and most predominant form of autism. In technical terms, anyone showing autistic tendencies that satisfy the guidelines laid out by “ DSM 5  Autism Spectrum Disorder” is termed  Autistic .

The effects of autism in such people may range from mild to very severe. Research has shown that the brain of autistic children has a fair number of electric impulses that any other normal brain of similar age.  Read more about Classical Autism

To conclude, even though these are the five main types of Autism, the actual list is far more extensive. It is highly likely that any particular individual can exhibit autistic trends from one or more forms of Autism and therefore may require a varying level of support from medical professionals, therapists and (above all) their families.

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Asperger Syndrome no longer exists; it is now simply called “high-functioning autism”, or HFA. This is a result of the May, 2010 exposé by Dr. Herwig Czech of the University of Vienna, exposing Dr. Asperger’s own eugenic beliefs, his own nationalism…and his footsie-ing with the Nazi Party. Also of interest was damning evidence of the Nazis’ endorsement of Asperger…as well as Asperger’s referral of two girls with autism to the T-4 “Am Spiegelgrund” ‘hospital system’ (which was actually a triage for extermination of those the Nazis called “unerwünschen” [unwanted] or “lebensunwertes leben” [life unworthy of life]) and to their deaths. Asperger Syndrome was resultingly removed from the DSM in 2013 and the ICD in 2021. Asperger Syndrome, simply put, no longer exists. Call it HFA.

This information is incredibly inaccurate and perpetuates stigma against all autistic people.

Rett Syndrome hasn’t been recognised as a form of autism in decades and it was officially classified under chromosomal and genetic disorders in the DSM-V in 2015. The only reason it was classified as autism was because there was so little research done on it ‘because it only effected a small number of girls’ and it was the only way medical insurance would cover things for those kids. Doctors would say “yes, there’s something seriously wrong with your daughter but we don’t know what” and because there was no diagnosis, the insurance companies would tell parents they’d have to pay for specialised daycares and wheelchairs and leg braces and home aides and specialists across the country who might have some clue as to how to help their baby by themselves. So those babies were diagnosed as ‘autistic’. Not updating this to educate your readers (who may include parents of Rett babies) is a disservice to them.

Maggie, you might have wanted to check the date of the blog post, which clearly shows it was made in 2014. Besides, the writer of this post hasn’t been active since 2019. When you come with information of 2015, it’s kind of obvious why this post doesn’t include it.

Regardless, if you bash someone for incorrect information, you might want to actually verify your own information first. The actual reason why Rett’s syndrome got diagnosed as a pervasive developmental disorder, not autism to begin with, has to do with the fact that Rett syndrome often manifests with autistiform behavior. The onset of Rett syndrome is also nearly identical as the autistic disorder and Asperger’s. And at younger ages, it’s very similar to what is known as regressive autism. That doesn’t mean there weren’t major differences. Like people with Rett often become socially engaged again as they grow older, are able to use eye movements to communicate their wishes and their movements problems tend to be much more severe than those seen in autistic people. Rett is also known for involving problems with the autonomic nervous system, which is not the case in autism and Asperger’s. However, when the DSM-IV was made, it wasn’t actually even known that Rett syndrome was a genetic disorder, as this discovery was only made in 1999, while the DSM-IV was published back in 1994. Without that knowledge, it is kind of hard not to suppose that Rett syndrome might be a really severe variant of a pervasive developmental disorder.

Also, what you’re saying makes no sense at all. Having a pervasive developmental disorder and autism were always 2 different things. Only since the DSM-V they have become rather similar due to autism, Asperger’s syndrome, PDD-NOS and Heller’s syndrome all being called autism spectrum disorders these days. However, note both the term “spectrum” and “disorders”, ending with an S, meaning multiple. Even these days there’s still recognition in the diagnosis that there are differences, although the ASD diagnosis has caused a huge amount of stigma for especially those with Heller’s syndrome and those who are normal or high-functioning with the regular form of autism. Rett syndrome wasn’t autism ever, it was a pervasive developmental disorder. Also, most people with Rett syndrome nowadays still have an autism spectrum disorder together with their Rett syndrome, so the link is most certainly still there. It’s just more similar to Fragile X syndrome, Klinefelter, Prader Willi and some other condition with autistiform behavior.

You may consider watching this video of a Ted Talk by a woman who has ASD. It really helped me understand “the spectrum” (which I am on) much better. This idea of “higher IQ = lesser impairment” is highly erroneous at best. I believe it’s just a “dumbing down” of the whole concept to make it easier to explain.

In fact, as she notes, the idea of “the spectrum” as a linear concept is extremely flawed because it makes people with “high-functioning” ASD feel that their impairments shouldn’t cause them as much trouble, when they clearly do. The “higher IQ = lesser autism” concept is also, at worst, very damaging to the view that the general public holds of people on the spectrum.

Honestly, this is why more people on the spectrum should be involved in research and understanding of the condition. Current definitions amount to a parallel of “mansplaining” in my view, since the people defining the terms really have no real frame of reference. Unfortunately, many people on the spectrum will not be tapped for this since, regardless of their actual intelligence (and like me), they likely will not complete any higher education (if indeed they even graduate high school, which I did not).

A minor aside: There is some pretty rigorous debate over whether or not IQ can even be accurately measured in people with ASD, which I fully understand having been tested many times with widely varying results. I believe this is due to comprehension of the tests themselves more than ability or inability to take them, but that’s just my own unsupported opinion.


I don’t think they’re claiming that high IQ is equivalent to less impairment, just that those two things seem to correlate.

Nobody is endorsing High IQ and less impairment concept. If a child with Autism has high IQ it simply means that they are much more trainable. If given therapy and proper training they can live their life with little or no support.

I know a specialist in Perth Western Australia AUSTRALIA who treats drug addictions and has a type of autism but was told by a patient that it had a big long name possibly very long (an acronym) and can’t think what it could be? Can you please help me?

PDD-NOS probably

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The Selective Social Attention task in children with autism spectrum disorder: Results from the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) feasibility study


  • 1 Center for Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, Washington, USA.
  • 2 Department of General Pediatrics, University of Washington School of Medicine, Seattle, Washington, USA.
  • 3 Yale Child Study Center, Yale University School of Medicine, New Haven, Connecticut, USA.
  • 4 Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, Washington, USA.
  • 5 Department of Computer Science, University of Colorado - Colorado Springs, Colorado Springs, Colorado, USA.
  • 6 Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China.
  • 7 Department of Psychiatry & Behavioral Science, University of Washington School of Medicine, Seattle, Washington, USA.
  • 8 Duke Center for Autism and Brain Development, Duke University, Durham, North Carolina, USA.
  • 9 Emergency Medicine, Yale University, New Haven, Connecticut, USA.
  • 10 Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.
  • 11 Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA.
  • 12 Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA.
  • 13 Department of Neurology, Keck School of Medicine of USC, Los Angeles, California, USA.
  • 14 Division of Neurology, Children's Hospital Los Angeles, Los Angeles, California, USA.
  • 15 Department of Medical Social Sciences, Northwestern University, Evanston, Illinois, USA.
  • 16 Department of Psychology, University of California Los Angeles, Los Angeles, California, USA.
  • 17 Department of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • 18 Department of Biostatistics, University of California Los Angeles, Los Angeles, California, USA.
  • PMID: 37749934
  • DOI: 10.1002/aur.3026

The Selective Social Attention (SSA) task is a brief eye-tracking task involving experimental conditions varying along socio-communicative axes. Traditionally the SSA has been used to probe socially-specific attentional patterns in infants and toddlers who develop autism spectrum disorder (ASD). This current work extends these findings to preschool and school-age children. Children 4- to 12-years-old with ASD (N = 23) and a typically-developing comparison group (TD; N = 25) completed the SSA task as well as standardized clinical assessments. Linear mixed models examined group and condition effects on two outcome variables: percent of time spent looking at the scene relative to scene presentation time (%Valid), and percent of time looking at the face relative to time spent looking at the scene (%Face). Age and IQ were included as covariates. Outcome variables' relationships to clinical data were assessed via correlation analysis. The ASD group, compared to the TD group, looked less at the scene and focused less on the actress' face during the most socially-engaging experimental conditions. Additionally, within the ASD group, %Face negatively correlated with SRS total T-scores with a particularly strong negative correlation with the Autistic Mannerism subscale T-score. These results highlight the extensibility of the SSA to older children with ASD, including replication of between-group differences previously seen in infants and toddlers, as well as its ability to capture meaningful clinical variation within the autism spectrum across a wide developmental span inclusive of preschool and school-aged children. The properties suggest that the SSA may have broad potential as a biomarker for ASD.

Keywords: autism spectrum disorder; biomarkers; child; eye-tracking technology; social attention.

© 2023 International Society for Autism Research and Wiley Periodicals LLC.

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November 9, 2023

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The autism-linked gene SYNGAP1 could impact early stages of human brain development, study reveals

by Keck School of Medicine of USC


More information: Marcella Birtele et al, The autism-associated gene SYNGAP1 regulates human cortical neurogenesis, Nature Neuroscience (2023). DOI: 10.1038/s41593-023-01477-3 Journal information: Nature Neuroscience

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Frist Center for Autism and Innovation

Frist Center for Autism and Innovation

FCAI Director and Floreo CEO Present at The Tennessee Enabling Technologies Summit

Posted by stasikjs on Friday, November 10, 2023 in FCAI News , News .

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On Thursday November 9th, Frist Center for Autism and Innovation Director, Keivan Stassun, and Floreo CEO Vijay Ravindran, presented at The Tennessee Enabling Technologies Summit, giving a presentation called Advancing Independence and Employment with Virtual Reality and Artificial Intelligence Technologies .

Stassun and Ravindran spoke alongside others from the Tennessee tech sector to discuss various topics in enabling technologies; such as skills building tools and vocational rehabilitation.

Presentations and information on all speakers can be found here .

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  • Open access
  • Published: 26 October 2023

Effect of presentation rate on auditory processing in Rett syndrome: event-related potential study

  • Daria Kostanian   ORCID: orcid.org/0000-0003-1436-8909 1 ,
  • Anna Rebreikina   ORCID: orcid.org/0000-0001-5714-2040 1 , 2 ,
  • Victoria Voinova   ORCID: orcid.org/0000-0001-8491-0228 3 &
  • Olga Sysoeva   ORCID: orcid.org/0000-0002-4005-9512 1 , 2  

Molecular Autism volume  14 , Article number:  40 ( 2023 ) Cite this article

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Rett syndrome (RS) is a rare neurodevelopmental disorder characterized by mutations in the MECP2 gene. Patients with RS have severe motor abnormalities and are often unable to walk, use hands and speak. The preservation of perceptual and cognitive functions is hard to assess, while clinicians and care-givers point out that these patients need more time to process information than typically developing peers. Neurophysiological correlates of auditory processing have been also found to be distorted in RS, but sound presentation rates were relatively quick in these studies (stimulus onset asynchrony, SOA < 1000 ms). As auditory event-related potential (ERP) is typically increased with prolongation of SOA we aim to study if SOA prolongation might compensate for observed abnormalities.

We presented a repetitive stimulus (1000 Hz) at three different SOAs of 900 ms, 1800 ms, and 3600 ms in children with RS ( N  = 24, Mean age = 9.0 ± 3.1) and their typical development (TD) peers ( N  = 27, Mean age = 9.7 ± 3.4) while recording 28-channels electroencephalogram, EEG. Some RS participants ( n  = 10) did not show clear ERP and were excluded from the analysis.

Major ERP components (here assessed as N1P1 and P2N1 peak-to-peak values) were smaller at SOA 900 than at longer SOAs in both groups, pointing out that the basic mechanism of adaptation in the auditory system is preserved in at least in RS patients with evident ERPs. At the same time the latencies of these components were significantly delayed in the RS than in TD. Moreover, late components (P2N1 and N2P2) were drastically reduced in Rett syndrome irrespective of the SOA, suggesting a largely affected mechanism of integration of upcoming sensory input with memory. Moreover, developmental stagnation of auditory ERP characterized patients with RS: absence of typical P2N1 enlargement and P1 and N1 shortening with age at least for shortest SOA.


We could not figure out the cause for the high percentage of no-evident ERP RS participants and our final sample of the RS group was rather small. Also, our study did not include a control clinical group.


Thus, auditory ERPs inform us about abnormalities within auditory processing that cannot be fully overcomed by slowing presentation rate.

Rett syndrome (RS) is a neurodevelopmental disorder associated with mutations in the X-linked gene MECP2 [ 1 ]. This disease is characterized by a variety of physiological, motor, and cognitive deficits that usually follow relatively typical initial development [ 2 ]. After regression that occurs at about 6–18 months of age, most children with RS is non-verbal [ 3 , 4 , 5 ] and have severe problems with goal-oriented motor actions [ 6 ], making it hardly possible to use standard tools to assess their cognitive abilities. Thus, limited data exist on the specification of cognitive function including the ability to perceive and understand speech in RS.

Auditory event-related potentials (ERP) is a convenient tool for assessing the processing of auditory information in the brain, as it does not require participants attention and can be used in challenging populations. It consists of positive and negative components (P1, N1, P2, and N2) whose prominence depends on many parameters including rate of presentation [ 7 , 8 , 9 , 10 ]. In particular, the N1 and P2 components become larger as the inter-stimulus interval increases at least up to 12 s [ 11 , 12 ]. Neuronal networks activated by the sounds do not come to their initial state immediately after sound termination but their activation fades slowly. If during this fading period the similar sound is presented it could not elicit the “full” response as the first stimuli have elicited but only its fraction (that might be also called adaptation). The more neurons return to their initial state the larger the response. That what we believe is captured by ERP modulation by the rate of presentation. These neurophysiological changes were linked to the decay of the stimulus’ memory representation as it corresponds with behavioral results in psychophysical experiments [ 11 , 12 , 13 ]. In general, faster presentation rate is better for integration information over many temporally segregated stimuli, while slower rate allows to process each stimuli thoroughly. However, the optimal limit for each type of analysis is varied across individuals. For example, adult dyslexics have faster memory trace decay than good readers that provide difficulties in integrating the information within several seconds while in autism the memory decay is longer putting less weight into the most recent event and increasing the ability to link together unrelated things [ 14 ].

In the current study we aim to examine modulation of auditory ERPs by the rate of stimulation in RS to dig into the mechanisms underlying Rett symptomatology. Previous studies have shown that auditory ERPs are altered in RS: its late components P2 and N2 almost absent when stimulus is presented at about one per second—typical rate of presentation in neurophysiological experiments [ 15 , 16 ]. No study has examined the auditory ERPs in RS in response to more slowly presented stimuli with stimulus onset asynchrony longer than 2 s. However, as the speed of signal processing is reported to be low in RS as indicated with delayed ERP components [ 17 , 18 , 19 ] these patients might benefit from the slower presentation rate.

Assessing ERP modulation by presentation rates in RS provides information on the basic adaptation mechanism in this group that might be differentially preserved depending on the stage of auditory processing. In particular, P1 is linked to early processing of sensory information [ 20 ] and consistent with previous findings we do not expect any deficits in it with different presentation rates in RS. The N1 component, related to both sensory and cognitive processing, on the contrary, has been demonstrated to be especially sensitive to the SOA duration in neurotypical individuals [ 10 ]. This component has been preserved in RS for short SOAs [ 15 ], thus we believe it might also show typical enlargement with SOA prolongation. At the same time, P2 component, associated with consolidation of auditory memory [ 21 ], has been drastically reduced in RS with short SOA, and may recover to the typical level with slowing presentation rate, following the pattern seen in typically developing children. Meanwhile, N2, which has been associated with categorization [ 22 , 23 , 24 , 25 ], and suppression of irrelevant information [ 26 ], has also been reduced in RS with short SOAs [ 15 ]. While there is no data on N2 modulation by stimulus presentation rate in neurotypical samples, nobody examined this aspect in RS keeping the question of its potential recovery at slow presentation rate open.

Thus, we hypothesize that by increasing the stimulus onset asynchrony from 0.9 to 3.6 s we might see recovery of auditory ERP components in RS that will get more typical. Simultaneously we are assessing if the ERP components are modulated by the rate of tone presentation, getting insight into the mechanism of basic memory function in this group.

As both N1 and P2 components are modulated by the duration of the interstimulus interval in neurotypical individuals, there is a discussion concerning whether common or independent processes are behind this modulation [ 27 ]. From one side, a strong correlation between the amplitudes of N1 and P2 was shown in Pereira work [ 9 ], which might be indicative of the common mechanism underlying their modulations. However, fitting an adaptation model for each of these components demonstrates distinct dynamics of their adaptation [ 11 ]. As N1 and P2 components are differentially affected in the Rett syndrome ERPs, assessing the relationship between their amplitudes and their modulation also appears to be important.


Rett syndrome group: 24 children aged from 3 to 17 (mean age = 9.0 ± 3.1) with Rett syndrome participated in this study. They were recruited during clinical visits to the Research Clinical Institute of Pediatrics in Moscow, Russian Federation. The diagnosis was based on current diagnostic criteria and was confirmed clinically by a medical doctor specializing in this population (V.V.) as well as via genetic testing. All participants had a pathogenic variant in the MECP2 gene. All participants were in the post-regression phase. Severity of RS was measured using the Rett syndrome severity scale (RSSS) [ 28 ]. This scale assesses individual parameters: frequency and severity of seizures, breathing irregularities, scoliosis, ability to walk, use of hands, speech and sleep.

More detailed characteristics of the sample, including the type of mutations, can be found in the Additional file 1 : Table 1. All participants from this group were females as this rare disease affects mostly females.

Typical development group: Twenty-seven children aged from 2.5 to 16 (mean age = 9.7 ± 3.4) years without neurological, psychiatric disorders, mental and speech delays, or hearing problems according to parental reports. 8 out of TD participants were males.

Parents or legal representatives have given written consent to the children’s participation in the study, after the procedure was explained to them. Where capable children have given verbal consent to participate. The research procedure met the standards for research from the Helsinki Declaration of 1975 (Protocol 1 from 01.15.2020) and was approved by the ethical committees of IHNA and Nph RAS (Protocol №2 at April 30th, 2020) and Sirius University of Science and Technology amendment from April 15th, 2021.

The sample size was calculated using the G *power 3.1.3 program (Heinrich-Heine-University, Düsseldorf, Germany) with a statistical power of 80%, and a significance level of 0.05. To assess the correlation with symptom severity (anticipated effect size = 0.62, according to Sysoeva work [ 15 ]), the required sample size was 18 participants. To assess interaction effects between two groups with three levels of within-group factor (anticipated effect size = 0.3, according to approximate medium effect size), minimum total sample size should be 20 people.

Experimental design

Pure tone 1000 Hz (duration: 100 ms, loudness: 65 dB) was presented in three experimental blocks with three different stimulus onset asynchrony (SOAs): 900, 1800, and 3600 ms. Stimuli with each type of SOA were presented in separate blocks. Each tone was presented 150 times for 1800-ms and 3600-ms SOA conditions, and 300 times for 900-ms SOA conditions. The large number of trials for the 900-ms SOA condition was a precaution to get sufficient number of epochs for averaging when epochs with motion and other artifacts are excluded and to be able to run other types of analysis. For the current analysis only the first 150 artifact-free epochs from 900-ms SOA condition were used to equate with other SOA conditions. We used 1000 Hz tone as previous studies [ 15 , 29 ] pointed out that the deficits in ERP are present even at the level of pure tones analysis.

Participants sat in a comfortable chair in a sound-attenuated room. Participants listened to sounds binaurally through earphones and watched a muted video of their or parent’s choice. They were instructed to ignore sounds and avoid moving. In case children were not able to follow instructions, the parent sat nearby and held the child if necessary to limit motor activity. In some cases, the child sat on the parent's lap. In the short breaks between blocks, participants can change their positions.

EEG recordings

Electroencephalographic data were recorded using the NeuroTravel system with 28-scalp electrodes arranged according to the international 10–20 system guidelines ('Fp1', 'Fp2', 'F3', 'Fz', 'F4', 'F7', 'F8', 'Fc3', 'Fcz', 'Fc4', 'C3', 'Cz', 'C4', 'Cp3', 'Cpz', 'Cp4', 'P3', 'Pz', 'P4', 'Tp7', 'Tp8', 'T3', 'T4', 'T5', 'T6', 'O1', 'Oz', 'O2'). Linked earlobe electrodes were used as reference, and AFz as ground, and 0.01–70 Hz online filters were applied. The data were sampled at 500 Hz. The electrode impedances were below 10 kΩ.

Data processing

EEG was filtered with 2–20 Hz offline filters. 2 Hz high-pass filter was used due to high amplitude delta oscillations evident in many participants with Rett syndrome. Application of this filter allows accepting more trials for signal averaging and consequently better signal-to-noise ratios. Using such high-pass filters can be associated with risks of artifactual effects [ 30 ] but mainly for the later components (N400 and P600) and absolute amplitude values. Considering peak-to-peak amplitude can help to avoid negative impact of filtration on the results. Despite the fact that the use of strong high-pass filters may lead to misleading results [ 31 ], using high-pass filters is common practice when considering ERP of participants with Rett syndrome (e.g. 3 Hz high-pass filters were used in Saby’s work [ 16 ]).

Bad channel interpolation was applied when necessary (0–2 channels per participant). Automatic raw data inspection with ± 400 μV thresholds was used for rejecting EEG segments with large artifacts, then for artifact (eye movements, muscle activity) rejection, the independent component analysis (ICA) was performed, in particular the ALICE platform was used [ 32 ]. The data were segmented into epochs starting 200 ms before a stimuli onset and lasting 500 ms after the onset. Automatic rejection of the bed segments with signals more than ± 100 μV was applied. ERPs were baseline corrected to -200 ms prestimulus intervals. Mean number of epochs to average for each participant was 150 (first 150 trials taken from 236 + 23 and 226 + 44 remaining after artifact rejections trials) for 900-ms SOA condition, 127 ± 14 and 123 ± 20 for 1800-ms, 123 + 18 and 118 + 18 for 3600-ms SOA conditions for TD and RS, respectively.

The FCz channel was chosen for the analysis, according to the literature as the auditory cortex response is observed in this area [ 7 ]. Also, for this channel ERP components were more pronounced and less affected by artifacts. Some participants from the RS group did not have a clear peaks’ morphology, so it was hard to estimate absolute peak amplitude and latency. Thus all participants were evaluated on the prominence of ERP’s components by two independent experts that were blind to the diagnosys (Fig.  1 ). All participants from TD group had a clearly identified ERP component, while participants from the RS group were divided into two groups: evident-ERP ( N  = 12) and no-evident-ERP components groups ( N  = 10). Two RS participants' data were excluded from the analysis because of a high number of bad channels. In no-evident ERP RS groups average ERP response showed large variability, making differentiation of the ERP components impossible. Evident and no-evident ERP components group were not significantly different in either age ( F (1,20) = 0.002, p  = 0.968, eta2 < 0.001) or RSSS ( F (1,20) = 0.4545, p  = 0.506, eta2 = 0.022). No significant differences that could be attributed to pre-processing features (e.g., number of trials ( F (1,20) = 0.186, p  = 0.671, eta2 = 0.009) or number of removed ICA components ( F (1,20) = 3.274, p  = 0.084, eta2 = 0.141)) were found between the two RS subgroups. In the current manuscript we present the data only for the Rett syndrome girls with evident ERP components that allow the assessment of the latency of the peaks. Further research is needed to dig into the problem of why such a sufficient number of RS girls do not have evident auditory ERPs, whether it relates to some uncontrolled technical issues or to dynamics/etiology of RS.

figure 1

Participants’ grouping that includes information about detectability of Event-related potential (ERP) (groups with * were excluded from the analysis)

For TD and evident-ERP RS groups amplitude and latency measurements for the peaks were made using the MNE python tool and then verified by authors. The peak amplitude and latency values of the components were examined in wide time windows: P1 (33–99 ms), N1 (96–173 ms), P2 (139–264 ms), N2 (210–365 ms) consistent with previous literature and individual peaks assessments [ 7 , 33 , 34 , 35 , 36 ]. Peak amplitudes were calculated relative to baseline and peak latencies were calculated relative to stimulus onset. Peak to peak amplitudes for N1P1, P2N1 and N2P2 were calculated as the difference in amplitude between two peaks. Considering peak to peak amplitude makes it possible to eliminate the effects caused by the contamination of the components [ 37 , 38 , 39 ].

The effects associated with the stimuli presentation rate and diagnosis were investigated by mixed analysis of variance (ANOVA) for the peak P1 amplitude and N1P1, P2N1 and N2P2 peak-to-peak amplitudes, and for latency of P1, N1, P2, and N2 components. Statistical analysis was performed using R-Studio software. Mixed ANOVA included Group as between-subjects factor (RS vs TD), Presentation rate (SOA—stimulus onset asynchrony) as within-subjects factor (three levels: 900 ms, 1800 and 3600 ms) and Age as covariate as well as their interactions. Estimation stats were performed using the package Dabestr [ 40 ]. Also to estimate the relations between the modulation mechanisms of the N1 and P2 components, Pearson correlation coefficient for the associated peak to peak amplitudes was calculated.

Statistical correlations between ERP components, and severity of Rett syndrome were assessed by Pearson correlation coefficients. Partial Pearson correlation coefficients, adjusting for age, were calculated for RSSS and each measurement of the ERP component that showed a significant group effect. Correlation coefficients were calculated using the ppcor library in R ( www.r-package.org ).

The grand-averaged ERPs in response to tones presented with different SOAs showed the expected pattern of identifiable P1, N1, P2 and N2 components in the TD participants (Fig.  2 , FCz, for the topography and all channels response see Additional file 1 : Fig. 1). The adaptation of the component N1 was clearly observed (its amplitude was much smaller at the shortest SOA). It is also notable that the prolongation of SOA from 1800 to 3600 ms had no impact on the evoked response component configuration. In the RS group, it was also possible to recognize the main components (especially the early ones). Comparing the evoked responses of the two groups, the early components of the ERP (P1, N1) in the RS group are relatively preserved. The later components (P2, N2), which were hardly expressed at the short SOA (900 ms), become more evident with an increase in the presentation rate but still clearly reduced and delayed than in TD. Below we prove these observations statistically. See Table 1 for the reference to the numerical values of ERP components’ amplitude and latency measures.

figure 2

Event-related potentials (ERPs) of Typical development (TD) (blue line), and Rett syndrome (RS) (red line) groups (FCz electrode) in different stimulus onset asynchrony (SOA) conditions: a 900 ms, b 1800 ms, c 3600 ms. Shading corresponds to 95% confidence level

P1 peak amplitude value

Significant Age effect ( F (1;35) = 4.448, p  = 0.042, eta2 = 0.113) was found for P1 peak amplitude: P1 reduction with age. There was also significant SOA*Group interaction ( F (2;70) = 3.264, p  = 0.044, eta2 = 0.085) that points to the differential P1 modulation by SOA for TD and RS group (confirmed by Group effect for difference between 900 and 3600 ms SOA condition ( F (1;38) = 4.752, p  = 0.036, eta2 = 0.114) as P1 decrease for TD and increase in RS with prolongation of SOA (Table 1 ), for the full post-hoc comparison see Additional file 1 : Table S3). SOA*Group*Age ( F (2;70) = 3.758, p  = 0.028, eta2 = 0.097) interaction was also observed for this component. Post-hoc follow-up demonstrated that Age effect was observed only in the TD group and only for 1800-ms SOA condition ( F (1;35) = 8.714 p  = 0.006, eta2 = 0.199) (Fig.  3 a) (For more details see Additional file 1 : Table S3).

figure 3

P1 characteristics: a P1 amplitude attenuation with age but only in typical development (TD) group at 1800-ms stimulus onset asynchrony (SOA) condition; b P1 latency shortening with age in all conditions in typical development (TD) but not in Rett syndrome (RS) group at 900-ms SOA condition. Dots represent individual values (blue—TD group, red—RS group)

P1 latency value

For the P1 latency general SOA effect ( F (2;70) = 6.478, p = 0.002, eta2 = 0.162) was found: at 900-ms SOA latency was shorter, compared to other SOAs. The main effect of Age ( F (1;35) = 23.492, p  < 0.001, eta2 = 0.402) reflected longer P1 latency for younger children. Additionally significant SOA*Group*Age interaction ( F (2;70) = 6.251, p  = 0.003, eta2 = 0.152) was observed with post-hoc indicating P1 latency shortening with age for all conditions and groups except the RS group in 900-ms SOA condition (Fig.  3 b).

N1P1 peak-to-peak amplitude value

Significant SOA effect ( F (2;70) = 24.957, p  < 0.001, eta2 = 0.416) was found for N1P1 amplitude. In the 900-ms SOA condition, the amplitude of this component was smaller than in the slower presentation rate. This effect was expressed in both TD and RS groups (Fig.  4 a). Significant SOA*Age interaction ( F (2;70) = 3.576, p  = 0.033, eta2 = 0.093) represented the age-increasing difference between the 900-ms SOA and longer SOAs conditions (Fig.  4 b).

figure 4

N1P1 amplitude characteristics: a N1P1 amplitude modulation by stimulus onset asynchrony (SOA). Dots represent individual values (blue—typical development (TD) group, red—Rett syndrome (RS) group), lower panel shows effect size (Hedges’ g); b SOA effect becomes more pronounced with age. Dots represent individual values in different SOA condition (red—900-ms SOA, blue—1800-ms SOA, green—3600-ms SOA)

N1 latency value

Significant differences between groups were found for the N1 component latency ( F (1;35) = 6.800, p  = 0.013, eta2 = 0.163). N1 were delayed in the RS group compared to TD (Fig.  5 a). Detected significant SOA*Group*Age interaction ( F (1;70) = 4.612, p  = 0.013, eta2 = 0.116) demonstrated that N1 latency shortened with age, but only in the TD group and at 900-ms SOA condition (Fig.  5 b).

figure 5

N1P1 latency characteristics: a Delayed N1 in Rett syndrome (RS) as compared to typical development (TD); b  Age dynamics in TD and RS groups in different stimulus onset asynchrony conditions. Dots represent individual values (blue—TD group, red—RS group), lower panel in ( a ) shows effect size (Hedges’ g)

P2N1 peak-to-peak amplitude value

A significant general SOA effect was revealed ( F (2;70) = 25.737, p  < 0.001, eta2 = 0.301): similar to the N1P1 effect, P2N1 amplitude also become larger with SOA (Fig.  6 a). Additionally P2N1 amplitude enlarges with age ( F (1;35) = 15.093, p  < 0.001, eta2 = 0.301). SOA*Age interaction ( F (1;70) = 5.712, p  = 0.005, eta2 = 0.140) demonstrates that the SOA effect becomes more pronounced with age (Fig.  6 b).

figure 6

P2N1 amplitude characteristics: a P2N1 amplitude modulation by stimulus onset asynchrony (SOA). Dots represent individual values (blue—typical development (TD) group, red—Rett syndrome (RS) group), lower panel shows effect size (Hedges’ g); b SOA effect becomes more pronounced with age. Dots represent individual values in different SOA conditions (red—900-ms SOA, blue—1800-ms SOA, green—3600-ms SOA); c Lower P2N1 amplitude in the RS group. Dots represent individual values (blue—TD group, red—RS group), lower panel shows effect size (Hedges’ g); d P2N1 amplitude enlargement with age in TD, but not in RS group. Dots represent individual values (blue—TD group, red—RS group)

Significant main effect of Group ( F (1;35) = 5.496, p  = 0.025, eta2 = 0.135) pointed to general amplitude reduction in the RS group as compared to TD irrespective of SOA (Fig.  6 c). Additionally P2N1 amplitude enlargement with age was evident only in the TD group (Group*Age interaction ( F (1;35) = 4.891, p  = 0.034, eta2 = 0.123)) (Fig.  6 d).

P2 latency value

The main effect of the SOA was detected: P2 component was significantly delayed at the slower presentation rate irrespective of group ( F (2;70) = 5.252, p  = 0.007, eta2 = 0.130) (Fig.  7 a).

figure 7

P2N1 latency characteristics: a P2 latency modulation by stimulus onset asynchrony (SOA); b Evidently delayed P2 in Rett syndrome (RS) as compared to typical development (TD). Dots represent individual values (blue—TD group, red—RS group), lower panel shows effect size (Hedges’ g)

Also P2 latency was longer in the RS than in TD group (main effect of Group: F (1;35) = 15.272, p  < 0.001, eta2 = 0.304) (Fig.  7 b).

N2P2 peak-to-peak amplitude value

For the N2P2 component significant Group effect ( F (1;35) = 13.506, p  < 0.001, eta2 = 0.278) was found: N2P2 amplitude was reduced in the RS group (Fig.  8 a).

figure 8

N2 characteristics: a Patients with Rett syndrome (RS) showed lower N2P2 amplitude; b The N2 latency modulation by stimulus onset asynchrony (SOA). Dots represent individual values (blue—typical development (TD) group, red—Rett syndrome (RS) group), lower panel shows effect size (Hedges’ g)

N2 latency value

For N2 latency significant SOA effect ( F (2;70) = 11.569, p  < 0.001, eta2 = 0.248) pointed to the prolongation of the latency with the increase of SOA irrespective of the group (Fig.  8 b).

Severity of symptomatology showed no significant correlations with neurophysiological measures that differentiated RS from TD (For more details see Additional file 1 : Figs. 2 and 3).

As there is no unified view on whether the N1 and P2 components and especially their modulation by the rate of presentation represent independent processes, we examine the correlation between N1P1 and P2N1 amplitudes and their modulation by SOAs (difference of amplitudes between conditions). No significant correlations were found for the TD group, but there was a weak correlation ( R  = 0.62) between N1P1 and P2N1 amplitudes in 900-ms SOA condition in the RS group, generally supporting largely different neuronal underpinnings for these effects (For more details see Additional file 1 : Figs. 4 and 5).

Our main goal was to examine the neurophysiological characteristics of auditory processing in girls with Rett syndrome as well as their modulation by the rate of stimulus presentation. We showed that early stages of auditory processing are relatively preserved at least in the subgroup of RS patients, while the later stages, reflected in the P2 and N2 components of ERP, are largely affected being both delayed and attenuated. At the same time, N1 and P2 components of ERP, demonstrated preserve modulation by the stimulation rate in Rett syndrome showing enlargement with the prolongation of stimulus onset asynchrony. Developmental stagnation of some neurophysiological characteristics was also observed in RS. Below we consider these findings in detail.

Preserved modulation of ERP components by the rate of presentation in RS

In line with the previous studies [ 7 , 8 , 9 ] on TD, we also showed that ERP components (N1P1 and P2N1) become more pronounced with increasing stimulus onset asynchronies. The novel result is that ERPs enlargement was mostly between the SOA 900 ms and SOA 1800 ms conditions, while further prolongation in SOA up to 3600 ms did not influence ERPs. This is a rather surprising result as previous studies reported that ERP continues to increase for the SOA of at least up to 12 s, however, they were all conducted in adults [ 11 , 12 ]. For the children population the studies of the effects of SOA on ERP are quite limited and none of the studies included in their design the SOA longer than 2400 ms [ 36 , 41 ]. The absence of further enlargement in the ERP components after 1800 ms SOA may be related to the fact that in children the response recovery period is faster, and, as a result, sensory memory is shorter. In line with this result, it has been shown that in children, previous experience has less influence on perception [ 42 ]. However, this result needs further investigation.

An important finding is that both N1P1 and P2N1 modulation by SOA, that are largely independent, is preserved in the evident-ERP subgroup of patients with RS, even in spite of the largely altered (delayed and attenuated) P2N1 component. This result points to the typical functional meaning of these components and preservation of basic learning ability, reflected in neurophysiological adaptation in RS. As ERP components typically enlarge with the SOA prolongation, we can differentiate major ERP components in RS much better with longer SOA, suggesting that auditory information processing is less affected when tones are presented at a slow rate (e.g. with SOA 1800 and 3600 ms). However, clear between group differences is evident even with these longer SOAs.

The effect of presentation rate on auditory processing in Rett syndrome has been demonstrated previously in the oddball paradigm by means of mismatch negativity (MMN)—neurophysiological correlate of change detection assessed from ERP difference wave (standard, frequent ERP minus rare deviant ERP). MMN in response to frequency deviation has been registered in girls with RS only with a short (450 ms) interval between stimuli, but not with a longer stimulus onset asynchrony (900 ms and 1800 ms) [ 29 ]. This might indicate that the stimulus representation in Rett syndrome persists for a shorter period of time and vanishes with increasing SOA. In the present study, we have found that as SOA increases from 900 to 1800 ms, N1P2 and P2N1 amplitudes in RS become larger, indicating typical release from adaptation, which at first glance is inconsistent with these MMN data. However, the neuronal mechanisms that underlie MMN and N1/P2 components most likely are different with the MMN linked to predictive error processing and the N1/P2 associated with stimulus-specific adaptation (SSA) [ 43 , 44 , 45 ]. Thus, the results obtained in the present and Brima's work may indicate changes in different subprocesses of auditory processing and features of different neuronal populations’ activation in RS.

Thus, choice of the most appropriate SOA for future auditory ERP studies should be driven mainly by the process of interest, with longer SOA more appropriate for tracing change detection deficits and shorter SOA being more sensitive for alternation of basic auditory components. While speculative, such neurophysiological abnormalities together with clearly prolonged timing of information processing result in a very limited time window within which the RS brain processes information more or less adequately. Clinical field might potentially benefit from further work in collaboration with practitioners aiming to develop the training/information presentation protocols for RS that allow: (1) information that needs to be linked together being demonstrated within a short time frame and at the same time (2) provides enough time for the information processing that is clearly prolonged in RS.

We believe that other SOA effects, revealed in our study, do not represent the meaningful insight into the core deficits in RS, as they can be explained by contamination of ERP components, as enlargement in one component can lead to latency shift and reduction in the neighboring components of opposite polarity. In particular, a heightening of the N1 amplitude with SOA prolongation could lead to (1) delay in the next components (P2), which appeared as an increase in its latency with increasing SOA as well as (2) shortening of the previous component latency, which appears as P1 latency decrease with SOA prolongation. Similarly, an enlargement in the amplitude of P2 leads to a prolongation in the latency of the following N2 component.

Delayed and reduced ERP components in RS

In our study significant reduction in the amplitude was shown for P2 (P2N1) and N2 (N2P2) components in RS. Also, the P2 and N1 components were significantly delayed in the RS group compared to TD.

Previous studies describing auditory evoked potentials in patients with Rett syndrome often highlight their attenuation and delay in comparison to typically developing children [ 16 , 18 , 29 ]. In particular, Sysoeva's study demonstrated a reduction in the amplitude of the P2 and N2 components of auditory ERP in response to simple (tones) and more complex (phonemes) types of stimuli [ 15 ], with the former being confirmed in our current study. An atypical reduction of P2 measured as P2N1 amplitude was also reported in a multisite study of Saby and colleagues, however, unlike our results their RS patients showed also a reduction in N1 amplitude (N1P1 peak-to-peak) [ 16 ]. This discrepancy may be due to the slightly different experimental conditions (e.g. varied SOA from 0.6 to 2.0 s) or the wider age range of their patients (2–37 years old), since SOA variability as well as age was shown to increase N1 amplitude [ 35 ].

At the cognitive level these effects can be linked to a disturbance of information processing in the late stages. The P2 component is associated with the consolidation in the auditory memory [ 21 ]. Meanwhile, N2 is associated with the inhibition of irrelevant information [ 26 ] and categorization [ 22 , 23 , 24 , 25 ]. Thus, a reduction in both of these components in Rett syndrome may be related to the alternation in these abilities.

However, the deficits start at the level of the N1 component, which is significantly delayed in Rett syndrome. Such latency shift suggests the increased processing time needed for auditory stimuli identification. For the first time, changes in the auditory ERPs’ latencies in Rett syndrome have been described by Bader in 1989 who reported some patients with RS demonstrating the delay of Pa, N1 and P2 components at the individual level [ 46 ]. Delay in P2 component has been also confirmed in subsequent studies [ 15 ], including our current work. The MMN, that coincides in latency with the N1 component (circa 120 ms), has been also shown to be delayed in Rett syndrome as compared to the control group [ 18 ]. While ERP’s latencies shift in RS in our and previous studies is only about 20–40 ms, information processing in the auditory system is extremely fast, making even small temporal delays critical, especially for complex stimuli processing such as speech.

Noteworthy, the ERP abnormalities similar to what we found in patients with RS is also observed in RS animal models: Mecp2 -deficient mice and rats demonstrated delayed and reduced analogous auditory cortex response [ 47 , 48 , 49 ]. Moreover, ERP component delay is evident in RS not only in auditory modality, but also observed in response to visual stimuli [ 50 , 51 , 52 ]. So this pattern is quite consistent for Rett syndrome and related Mecp2 damages.

Developmental course of ERP components and their relationship with Rett syndrome severity

We have found atypical age dynamics (stagnation) for several neurophysiological characteristics in patients with RS. TD group in our study characterized by developmental decrease in the amplitude and latency of P1, an increase in the amplitude of N1P1 and a shortening of the latency of N1, as well as an increase in the amplitude of P2N1, which corresponds to the previous literature [ 7 , 33 , 34 , 35 , 36 ]. Some age-effects were more pronounced for particular SOA conditions, generally corresponding to longer SOAs, where components are larger. Modulation by presentation rate of N1P1 and P2N1 amplitudes also show increase with age, corresponding with previous results [ 35 ]. Among these developmental changes only N1P1 amplitude followed typical developmental increase in RS. All other components showed atypical age dynamics, e.g. full developmental stagnation irrespective to SOAs for P2N1, or absence of age-related decrease in P1 latency specific only to 900-ms SOA condition with preserved P1 latency decreased for longer SOAs. Thus, this atypical age dynamics of neurophysiological components corresponds well with the view on the Rett syndrome as a disorder of stagnated development also seen at the behavioral level.

The absence of a significant association with age in Rett syndrome, in contrast to the TD group, has been described previously by Sysoeva for 900-ms SOA condition. This work demonstrated the age-dependent increase in N1 and decrease in P1 and N2 components in the TD group, but not in the RS group, but this result was not adequately powered to detect between-group differences in the developmental trajectory [ 15 ]. Differences in age dynamics between the groups have been described by Saby et al., but in this work Rett syndrome group demonstrated a decline in N1P1 peak to peak amplitude with age which was not observed in the TD group [ 16 ]. It was suggested that this negative dynamic could represent a progressive aspect of the disease process. Despite the fact that we found the typical increase of these components (P1, N1) peak-to-peak values in RS group in our sample, which could be caused by differences in the experimental procedure and a narrow age range, our results also indicate altered auditory ERP development, especially for P2N1, which was reduced in the RS group.

Multiple papers [ 15 , 16 , 52 ] demonstrated correlation of ERP amplitudes or latency to Rett syndrome severity. In Saby’s work Clinical Severity Score (CSS [ 53 , 54 ]) and Motor-Behavioral Assessment (MBA [ 54 ]) were used [ 16 ]. Both of these scales showed significant correlation with N1 amplitude and N1P1 peak-to-peak amplitude. Sysoeva et al. used the same RSSS as in our study and observed significant correlation with P2 amplitude [ 15 ]. In our study, no significant correlations between ERP components and RSSS were found. The absence of significant correlations may be due to the coarseness of the chosen scale or to some difficulties in adapting it for the Russian populations.

Approaches to no-evident ERP group

It is a continuous struggle for researchers, especially those who work with a challenging population, to include as much data as possible preferentially without compromising on signal to noise ratio (SNR). However, quite often a lot of data needed to be excluded from the final sample due to various, sometimes subjective, reasons. For example, if you exclude participants without evident ERP components—that seems reasonable as it might represent some problems with EEG recording—you might exclude the patients that indeed have very small and not very pronounced ERP and this absence of evident ERP might be important characteristics of the population considered. Our sample contained about half of the RS group without evident ERPs, and we selected for the main analysis only those who have evident ERPs with measurable ERP components. Abnormalities that are revealed in the evident-ERP group cannot be attributed to the poor SNR or other technical problems, and considered in our study as the genuine neurophysiological atypicalities that characterize RS. Moreover, in this group we can examine not only the amplitude but also latency of the ERP components providing a more in depth view on the origin of the observed changes.

The causes of the fact that only half of the participants with Rett syndrome had evident components are currently unclear, but the phenomenon itself corresponds with previous studies in RS [ 16 , 18 , 29 , 52 ]. This specific pattern was not due to age and the severity of the symptoms of the disease. One of the possible causes of this could be abnormal background EEG, which has always been considered one of the features of this syndrome [ 55 , 56 ]. The epileptiform activity in Rett syndrome is expressed by the appearance of spikes and sharp waves in the central temporal regions and a slowing of the theta-band background EEG in the frontal-central regions [ 17 , 57 , 58 , 59 ]. These features are unrelated to seizures and occur even in patients without a history of epilepsy [ 57 , 58 , 60 , 61 ]. Thus, against the background of an epileptiform activity or just increased low-frequency oscillations, the detection of evoked potentials could be problematic. Whether this “ERP absence” is a true characteristic of some RS patients or a result of technical problems in detection of ERPs in the presence of atypical background EEG activity is an important direction for future research.

Several limitations should be mentioned when interpreting our findings. As we could not figure out the cause for such a high percentage of no-evident ERP RS participants, this issue has to be further investigated. For example, it could be a result of some uncontrolled parameters, such as time of experiment and tiredness of the participant. Thus, the final RS group was of rather small size that limited the generalizability of the results. Also, we used a high-pass filter that could potentially alter the results. Examination of RS behavioral phenotype can be also improved by usage/developing more detailed tools. It is important to point out the absence of a control clinical group. This contrast would allow us to distinguish whether the findings are actually neuromarkers specifically for Rett syndrome or shared with other neurological or psychiatric conditions.

To sum up, the stimulus onset asynchrony modulates the amplitude and latency of the main auditory ERP components similarly in typical development and in Rett syndrome, suggesting preserved basic neuronal learning mechanisms (adaptation) at least at the subgroup of patients with RS with evident auditory ERP. At the same time, even in this group the characteristics of auditory ERP are clearly disturbed, such as N1 and P2 components are delayed and P2 and N2 amplitudes are attenuated and abnormalities persist even at the slowest presentation rate. Such neurophysiological changes suggest deficits at the speed and quality of auditory processing in RS. Moreover, most characteristics of auditory ERP do not show typical developmental changes with age in RS, corresponding to the view on RS as a disorder of stagnated development.

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.


Analysis of variance


Auditory event related potentials

Independent component analysis

Mismatch negativity

  • Rett syndrome

Rett syndrome severity scale

Signal to noise ratio

Stimulus onset asynchrony

Stimulus specific adaptation

Typical development

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Kostanian, D., Rebreikina, A., Voinova, V. et al. Effect of presentation rate on auditory processing in Rett syndrome: event-related potential study. Molecular Autism 14 , 40 (2023). https://doi.org/10.1186/s13229-023-00566-1

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  13. Autism Spectrum Disorder Clinical Presentation

    Autism spectrum disorder (ASD) manifests in early childhood and is characterized by qualitative abnormalities in social interactions, markedly aberrant communication skills, and restricted repetitive behaviors, interests, and activities (RRBs). Essential update: Study suggests imaging of neural representations of social interactions can iden...

  14. Are there gender-based variations in the presentation of Autism amongst

    The Autism Spectrum Screening Questionnaire - Girls (ASSQ-GIRL; Kopp & Gillberg, 2011) is an 18-item parent or caregiver-rated questionnaire designed to assess behavioural characteristics consistent with an emerging female presentation of autism in child and adolescent females.

  15. PPT Autism Spectrum Disorders

    Autism Spectrum Disorders Isabelle Rapin Seminar on developmental disorders Child neurology January 23, 2013 No conflict of interest ... 2009 PowerPoint Presentation PowerPoint Presentation Differentiate levels of investigation! DSM-5 (2013): Autism Spectrum Disorder (ASD) DSM - 5 Some Standardized Behavioral Diagnostic Tests Physical ...

  16. Non-stereotypical Presentations of Autism

    Moreover, individuals with this presentation of autism are more likely to have superior language skills and average or above-average IQ. By understanding and acknowledging different autistic presentations, we can ensure a more comprehensive and inclusive approach to assessment and therapy. "Non-stereotypical" variations of autistic traits ...

  17. Autism Spectrum Disorder (ASD) Presentation

    The History of Autism: • From the early 1900s, autism has referred to a range of neuro-psychological conditions. 9. The Basis of ASD: 1.Genetic factors: may be the most significant cause for ASD spectrum disorders. Early studies of twins had estimated heritability to be over 90%, meaning that genetics explains over 90% of whether a child will ...

  18. Autism Awareness Google Slides and PowerPoint template

    Autism Awareness Presentation Free Google Slides theme and PowerPoint template Autism is a developmental disorder. It makes communication and social interaction a little bit difficult. As it affects many people, it's important to understand much more about the disease to help your patients! Use our lovely template and raise awareness about autism!

  19. Presentation on Autism

    Presentation on Autism. 2. Autistic Spectrum Disorders (ASD) are a group of disorders usually diagnosed in early childhood. Accurate figures are not currently available for Jamaica, but based on the US Centers for Disease Control's (CDC's) rate of one child in every 110 born with autism, we can expect over 400 children with autism to be born in ...

  20. Autism Spectrum Disorder (ASD) Presentation

    9.Redefining Autism (cont.) New DSM-V criteria: Considered a better reflection of the state of knowledge about autism Single umbrella disorder will improve dx of ASD without limiting sensitivity of criteria or change number of children diagnosed Criteria change encourages earlier diagnosis Individuals must show symptoms from early childhood DSM-IV was geared towards ID-ing school age children ...

  21. Autism powerpoint

    60. Has low senses of sight, hearing, touch, smell or taste. 61. May stay away from physical contact because it is overwhelming or to hard to handle/take in. 62. Rubs surfaces, mouth, or licks objects. 63. Has a high tolerance for pain. 64.

  22. Types of Autism

    Today, however, physicians, therapists, and researchers consider each of these five categories while referring to specific autism symptoms: Autistic Disorder - also known as Classical Autism. Asperser's Syndrome. Pervasive Developmental Disorder, otherwise known as PDD-NOS. Rett's Syndrome (primarily a common type of autism in girls)

  23. The Selective Social Attention task in children with autism spectrum

    The Selective Social Attention (SSA) task is a brief eye-tracking task involving experimental conditions varying along socio-communicative axes. Traditionally the SSA has been used to probe socially-specific attentional patterns in infants and toddlers who develop autism spectrum disorder (ASD). Thi …

  24. The autism-linked gene SYNGAP1 could impact early stages of human brain

    The gene SYNGAP1, the variants of which are top risk factors for Autism Spectrum Disorder (ASD), has previously unappreciated effects on the developing brain, according to a new study published in ...

  25. FCAI Director and Floreo CEO Present at The Tennessee Enabling

    On Thursday November 9th, Frist Center for Autism and Innovation Director, Keivan Stassun, and Floreo CEO Vijay Ravindran, presented at The Tennessee Enabling Technologies Summit, giving a presentation called Advancing Independence and Employment with Virtual Reality and Artificial Intelligence Technologies. Stassun and Ravindran spoke alongside others from the Tennessee tech sector to discuss ...

  26. Effect of presentation rate on auditory processing in Rett syndrome

    Background Rett syndrome (RS) is a rare neurodevelopmental disorder characterized by mutations in the MECP2 gene. Patients with RS have severe motor abnormalities and are often unable to walk, use hands and speak. The preservation of perceptual and cognitive functions is hard to assess, while clinicians and care-givers point out that these patients need more time to process information than ...

  27. 2023 Top PIQ: Jennifer Truelove

    This year Jennifer Truelove was the Thompson Center's Top PIQ! Jennifer received nominations from multiple divisions across the Center, highlighting the impact she has across all teams. Jennifer's enthusiasm for our patients, joy in work, and her drive to make an impact is an incredible example for all of our team members. Exemplifies ...