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Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as ‘spectrum’ or ‘autisms’ reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case–control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being ‘feature-rich’, big data should be both ‘broad’ (i.e., large sample size) and ‘deep’ (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model’s utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with ‘supervised’ models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.

It is unclear whether transgender and gender-diverse individuals have elevated rates of autism diagnosis or traits related to autism compared to cisgender individuals in large non-clinic-based cohorts. To investigate this, we use five independently recruited cross-sectional datasets consisting of 641,860 individuals who completed information on gender, neurodevelopmental and psychiatric diagnoses including autism, and measures of traits related to autism (self-report measures of autistic traits, empathy, systemizing, and sensory sensitivity). Compared to cisgender individuals, transgender and gender-diverse individuals have, on average, higher rates of autism, other neurodevelopmental and psychiatric diagnoses. For both autistic and non-autistic individuals, transgender and gender-diverse individuals score, on average, higher on self-report measures of autistic traits, systemizing, and sensory sensitivity, and, on average, lower on self-report measures of empathy. The results may have clinical implications for improving access to mental health care and tailoring adequate support for transgender and gender-diverse individuals. It is unclear if rates of autism and other neurodevelopmental and psychiatric diagnoses are elevated in transgender and gender-diverse individuals compared to cisgender individuals. Here, the authors use data from five different large-scale datasets to identify elevated rates of autism diagnoses, diagnoses of other neurodevelopmental and psychiatric conditions, and elevated traits related to autism in transgender and gender-diverse individuals, compared to cisgender individuals.

Autism is both a medical condition that gives rise to disability and an example of human variation that is characterised by neurological and cognitive differences. The goal of evidence-based intervention and support is to alleviate distress, improve adaptation, and promote wellbeing. Support should be collaborative, with autistic individuals, families, and service providers taking a shared decision-making approach to maximise the individual's potential, minimise barriers, and optimise the person–environment fit. Comprehensive, naturalistic early intervention with active caregiver involvement can facilitate early social communication, adaptive functioning, and cognitive development; targeted intervention can help to enhance social skills and aspects of cognition. Augmentative and alternative communication interventions show preliminary evidence of benefit in minimising communication barriers. Co-occurring health issues, such as epilepsy and other neurodevelopmental disorders, sleep problems, and mental health challenges, should be treated in a timely fashion. The creation of autism-friendly contexts is best achieved by supporting families, reducing stigma, enhancing peer understanding, promoting inclusion in education, the community, and at work, and through advocacy.

Autism is a set of heterogeneous neurodevelopmental conditions, characterised by early-onset difficulties in social communication and unusually restricted, repetitive behaviour and interests. The worldwide population prevalence is about 1%. Autism affects more male than female individuals, and comorbidity is common (>70% have concurrent conditions). Individuals with autism have atypical cognitive profiles, such as impaired social cognition and social perception, executive dysfunction, and atypical perceptual and information processing. These profiles are underpinned by atypical neural development at the systems level. Genetics has a key role in the aetiology of autism, in conjunction with developmentally early environmental factors. Large-effect rare mutations and small-effect common variants contribute to risk. Assessment needs to be multidisciplinary and developmental, and early detection is essential for early intervention. Early comprehensive and targeted behavioural interventions can improve social communication and reduce anxiety and aggression. Drugs can reduce comorbid symptoms, but do not directly improve social communication. Creation of a supportive environment that accepts and respects that the individual is different is crucial.

There currently exist no self-report measures of social camouflaging behaviours (strategies used to compensate for or mask autistic characteristics during social interactions). The Camouflaging Autistic Traits Questionnaire (CAT-Q) was developed from autistic adults’ experiences of camouflaging, and was administered online to 354 autistic and 478 non-autistic adults. Exploratory factor analysis suggested three factors, comprising of 25 items in total. Good model fit was demonstrated through confirmatory factor analysis, with measurement invariance analyses demonstrating equivalent factor structures across gender and diagnostic group. Internal consistency (α = 0.94) and preliminary test–retest reliability (r = 0.77) were acceptable. Convergent validity was demonstrated through comparison with measures of autistic traits, wellbeing, anxiety, and depression. The present study provides robust psychometric support for the CAT-Q.

The new coronavirus disease (COVID-19) pandemic is changing how society operates. Environmental changes, disrupted routines, and reduced access to services and social networks will have a unique impact on autistic individuals and their families and will contribute to significant deterioration in some. Access to support is crucial to address vulnerability factors, guide adjustments in home environments, and apply mitigation strategies to improve coping. The current crisis highlights that our regular care systems are not sufficient to meet the needs of the autism communities. In many parts of the world, people have shifted to online school and increased use of remote delivery of healthcare and autism supports. Access to these services needs to be increased to mitigate the negative impact of COVID-19 and future epidemics/pandemics. The rapid expansion in the use of telehealth platforms can have a positive impact on both care and research. It can help to address key priorities for the autism communities including long waitlists for assessment and care, access to services in remote locations, and restricted hours of service. However, system-level changes are urgently needed to ensure equitable access and flexible care models, especially for families and individuals who are socioeconomically disadvantaged. COVID-19 mandates the use of technology to support a broader range of care options and better meet the diverse needs of autistic people and their families. It behooves us to use this crisis as an opportunity to foster resilience not only for a given individual or their family, but also the system: to drive enduring and autism-friendly changes in healthcare, social systems, and the broader socio-ecological contexts.

Background There is inconsistent evidence for a clear pattern of association between ‘camouflaging’ (strategies used to mask and/or compensate for autism characteristics during social interactions) and mental health. Methods This study explored the relationship between self-reported camouflaging and generalised anxiety, depression, and social anxiety in a large sample of autistic adults and, for the first time, explored the moderating effect of gender, in an online survey. Results Overall, camouflaging was associated with greater symptoms of generalised anxiety, depression, and social anxiety, although only to a small extent beyond the contribution of autistic traits and age. Camouflaging more strongly predicted generalised and social anxiety than depression. No interaction between camouflaging and gender was found. Limitations These results cannot be generalised to autistic people with intellectual disability, or autistic children and young people. The sample did not include sufficient numbers of non-binary people to run separate analyses; therefore, it is possible that camouflaging impacts mental health differently in this population. Conclusions The findings suggest that camouflaging is a risk factor for mental health problems in autistic adults without intellectual disability, regardless of gender. We also identified levels of camouflaging at which risk of mental health problems is highest, suggesting clinicians should be particularly aware of mental health problems in those who score at or above these levels.

There is limited information on the presentation and characteristics of psychotic illness experienced by people with autism spectrum disorder (ASD). To describe autistic and psychotic phenomenology in a group of individuals with comorbid ASD and psychosis (ASD-P) and compare this group with populations affected by either, alone. We studied 116 individuals with ASD-P. We compared features of their ASD with people with ASD and no comorbid psychosis (ASD-NP), and clinical characteristics of psychosis in ASD-P with people with psychosis only. Individuals with ASD-P had more diagnoses of atypical psychosis and fewer of schizophrenia compared with individuals with psychosis only. People with ASD-P had fewer stereotyped interests/behaviours compared with those with ASD-NP. Our data show there may be a specific subtype of ASD linked to comorbid psychosis. The results support findings that psychosis in people with ASD is often atypical, particularly regarding affective disturbance.

Some autistic people “camouflage” their differences by modeling neurotypical behaviors to survive in a neurotypical-dominant social world. It remains elusive whether camouflaging is unique to autism or if it entails similar experiences across human groups as part of ubiquitous impression management (IM). Here we examined camouflaging engagement and theoretical drivers in the general population, drawing on the transactional IM framework and contextualizing findings within both contemporary autism research and the past IM literature. A large representative U.S. general population sample (N = 972) completed this survey study. We combined exploratory item factor analysis and graph analysis to triangulate the dimensional structure of the Camouflaging Autistic Traits Questionnaire (CAT-Q) and examined its correspondence with prior autism-enriched psychometric findings. We then employed hierarchical regression and elastic-net regression to identify the predictors of camouflaging, including demographic (e.g., age, gender), neurodivergence (i.e., autistic and ADHD traits), socio-motivational, and cognitive factors. We found a three-factor/dimensional structure of the CAT-Q in the general population, nearly identical to that found in previous autism-enriched samples. Significant socio-motivational predictors of camouflaging included greater social comparison, greater public self-consciousness, greater internalized social stigma, and greater social anxiety. These camouflaging drivers overlap with findings in recent autistic camouflaging studies and prior IM research. The novel psychometric and socio-motivational evidence demonstrates camouflaging as a shared social coping experience across the general population, including autistic people. This continuity guides a clearer understanding of camouflaging and has key implications for autism scholars, clinicians, and the broader clinical intersecting with social psychology research. Future research areas are mapped to elucidate how camouflaging/IM manifests and functions within person-environment transactions across social-identity and clinical groups. •It is elusive if camouflaging is part of impression management in the population.•The Camouflaging Autistic Traits Questionnaire (CAT-Q) measures camouflaging.•CAT-Q has a 3-factor/dimension structure in the general population.•CAT-Q shows convergent validity with a measure of impression management.•Social comparison, self-consciousness, stigma, and anxiety predict camouflaging.

Excitation-inhibition (E:I) imbalance is theorized as an important pathophysiological mechanism in autism. Autism affects males more frequently than females and sex-related mechanisms (e.g., X-linked genes, androgen hormones) can influence E:I balance. This suggests that E:I imbalance may affect autism differently in males versus females. With a combination of in-silico modeling and in-vivo chemogenetic manipulations in mice, we first show that a time-series metric estimated from fMRI BOLD signal, the Hurst exponent (H), can be an index for underlying change in the synaptic E:I ratio. In autism we find that H is reduced, indicating increased excitation, in the medial prefrontal cortex (MPFC) of autistic males but not females. Increasingly intact MPFC H is also associated with heightened ability to behaviorally camouflage social-communicative difficulties, but only in autistic females. This work suggests that H in BOLD can index synaptic E:I ratio and that E:I imbalance affects autistic males and females differently. Autism is a condition that is usually diagnosed early in life that affects how a person communicates and socializes, and is often characterized by repetitive behaviors. One key theory of autism is that it reflects an imbalance in levels of excitation and inhibition in the brain. Excitatory signals are those that make other brain cells more likely to become active; inhibitory signals have the opposite effect. In non-autistic individuals, inhibitory activity outweighs excitatory activity. In people with autism, by contrast, an increase in excitatory activity is believed to produce an imbalance in excitation and inhibition. Most of the evidence to support this excitation-inhibition imbalance theory has come from studies of rare mutations that cause autism. Many of these mutations occur on the sex chromosomes or are influenced by androgen hormones (hormones that usually play a role on typically male traits). However, most people with autism do not possess these particular mutations. It was thus unclear whether the theory could apply to everyone with autism or, for example, whether it may better apply to specific groups of individuals based on their sex or gender. This is especially important given that about four times as many men and boys compared to women and girls are diagnosed with autism. Trakoshis, Martínez-Cañada et al. have now found a way to ask whether any imbalance in excitation and inhibition in the brain occurs differently in men and women. Using computer modeling, they identified a signal in brain scans that corresponds to an imbalance of excitation and inhibition. After showing that the technique works to identify real increases in excitation in the brain scans of mice, Trakoshis, Martínez-Cañada et al. looked for this signal, or biomarker, in brain scans of people with and without autism. All the people in the study identified with the gender that matched the sex they were assigned at birth. The results revealed differences between the men and women with autism. Men with autism showed an imbalance in excitation and inhibition in specific ‘social brain' regions including the medial prefrontal cortex, but women with autism did not. Notably, many of these brain regions are strongly affected by androgen hormones. Previous studies have found that women with autism are sometimes better at hiding or ‘camouflaging’ their difficulties when socializing or communicating than men with autism. Trakoshis, Martínez-Cañada et al. showed that the better a woman was at camouflaging her autism, the more her brain activity in this region resembled that of non-autistic women. Excitation-inhibition imbalance may thus affect specific brain regions involved in socializing and communication more in men who have autism than in women with the condition. Balanced excitation and inhibition in these brain areas may enable some women with autism to camouflage their difficulties socializing or communicating. Being able to detect imbalances in activity using standard brain imaging could be useful for clinical trials. Future studies could use this biomarker to monitor responses to drug treatments that aim to adjust the balance between excitation and inhibition.