Shame and guilt are similar yet distinct self-conscious emotions that often facilitate the attainment of social goals and motivate behaviors that promote social acceptance. Recent studies have shown that individuals with autism or high autistic traits may tend to exhibit higher shame-proneness and lower guilt-proneness. This study examined whether this profile of self-conscious emotions can be explained by the functional organization of the brain using resting-state fMRI. Autistic traits, shame- and guilt-proneness and whole-brain resting-state fMRI data were measured in 45 neurotypical individuals. Our results revealed that the positive association between autistic traits and shame-proneness was mediated by resting-state functional connectivity between the right frontal pole and several regions among the cortical midline structures, including the precuneus, anterior cingulate and posterior cingulate. Additionally, functional connectivity between the right frontal pole and precuneus was found to mediate the negative association between autistic traits and guilt-proneness. These findings highlight the role of the cortical midline structures as a key neural substrate underlying differential experiences of negative self-conscious emotions among individuals with high autistic traits.

Traditional psychological research has often treated inter-subject variability as statistical noise (even, nuisance variance), focusing instead on averages rather than individual differences. This approach has limited our understanding of the substantial heterogeneity observed in neuropsychiatric disorders, particularly autism spectrum disorder (ASD). In this introduction to a special issue on this theme, we discuss recent advances in cognitive computational neuroscience that can lead to a more systematic notion of core symptom dimensions that differentiate between ASD subtypes. These advances include large participant databases and data-sharing initiatives to increase sample sizes of autistic individuals across a wider range of cultural and socioeconomic backgrounds. Our perspective helps to build bridges between autism symptomatology and individual differences in autistic traits in the non-autistic population and introduces finer-grained dynamic methods to capture behavioral dynamics at the individual level. We specifically focus on how cognitive computational models have emerged as powerful tools to better characterize autistic traits in the general population and autistic population, particularly with respect to social decision-making. We finally outline how we can combine and harness these recent advances, on the one hand, big data initiatives, and on the other hand, cognitive computational models, to achieve a more systematic and nuanced understanding of autism that can lead to improved diagnostic accuracy and personalized interventions.

Autism Spectrum Disorder (ASD) is defined as a unidimensional condition, and autism traits are measured on a continuum where the high end of the spectrum represents individuals likely to have an ASD diagnosis. However, the large heterogeneity of ASD has thrown this unidimensional conceptualization into question. With the exact underlying cause(s) of autism yet to be identified, there is a pressing need to establish core, underlying dimensions of ASD that can capture heterogeneity within the autism spectrum, thereby better specifying both autistic traits and ASD symptoms. Here we describe one important transdiagnostic dimension, the cognitive rigidity-flexibility dimension, that may impact autistic traits and symptoms across symptom-relevant cognitive domains. We first discuss how diminished cognitive flexibility manifests in core autistic traits and autism symptoms in perception, attention, learning, social cognition, and communication. We then propose to supplement assessments of autistic traits in the general population and autism symptoms in individuals with an ASD diagnosis with a comprehensive batter of cognitive flexibility measures in these symptom-relevant domains. We conjecture that systematic differences in domain-general versus domain-specific cognitive flexibility can distill subgroups within the autism phenotype. While we focus on the cognitive flexibility dimension here, we believe that it is important to extend this framework to other higher order dimensions that can capture core autism symptoms and transdiagnostic symptom severity. This approach can characterize the latent, multi-faceted structure of autism, thereby yielding greater precision in diagnostic classification and the creation of more targeted interventions.

Social rewards (e.g. smiles) powerfully shape human behavior, starting from early childhood. Yet, the neural architecture that enables differential processing of social and nonsocial rewards remains largely unknown. Few previous studies that directly compared social vs nonsocial stimuli have used stimuli that have low ecological validity or are not matched on low-level stimulus parameters – limiting the scope of inference. To address this gap in knowledge, social and nonsocial reward images taken from the real world were matched on valence, arousal, and key low-level stimulus properties and presented to 37 adults in a functional magnetic resonance imaging (fMRI) study. Individual self-reported preference for social images was associated with the functional connectivity between the left anterior insula (LAI) and medial orbitofrontal cortex (mOFC), as well as that between the left Fusiform Gyrus (LFG) and the Anterior Cingulate Cortex (ACC). Autistic traits negatively modulated LAI – mOFC connectivity and LFG – ACC connectivity. Reduced functional connectivity between these regions may contribute to the lower social reward responsivity in individuals with high autistic traits, as also noted from their lower valence ratings to social rewards. This study provides evidence for a new experimental paradigm to test social reward processing at a behavioral and neural level, which can contribute to potential transdiagnostic biomarkers for social cognitive processes.

The surge of online psychological assessments have brought the autism research community both opportunities and challenges: while they enable rapid large-scale data collection and more power to characterize individual differences, they also bring concerns about data quality, generalizability beyond online samples, and whether autistic traits can be reliably characterized with self-report measures administered online. Here we tackle these concerns by providing a systematic characterization of the autistic traits variability across individuals in a large cross-sectional dataset (N = 2826) as well as its temporal reliability within individuals in a test-retest dataset (N = 247), with both online and in-lab samples. We measured autistic traits using the Social Responsiveness Scale, 2nd version, Adult Self Report (SRS-2-ASR) – a tool that quantifies individual differences in autistic traits along a continuum for the general adult population. Across individuals, we found elevated SRS scores in online samples and were able to trace this effect to specific subsets of SRS items. SRS scores also covaried with internalizing symptoms, decreased with age, and were lower in women compared to other genders. Within individuals, we find moderate-to-good test-retest reliability of SRS scores over long intervals, with no difference between online and in-lab samples, suggesting robust temporal stability. We conclude that there are systematic differences in autistic traits between online and in-lab samples that are partly explained by systematic population-level differences in internalizing symptoms, particularly social anxiety. Future studies that sample across different populations should measure, control for, or stratify with respect to these factors.

Autism is marked by substantial heterogeneity in cognitive, affective, behavioral, and neural profiles. This heterogeneity among diagnosed individuals is also reflected in the broad spread of autistic traits in the general population. Moving beyond a narrow focus on social-communication and/or repetitive-behavior differences, contemporary accounts increasingly emphasize multidimensional and transdiagnostic processes and the need to integrate evidence across levels of analysis, from self-report and cognitive measures to neuroimaging and computational models. The contributions in this Special Issue collectively advance this agenda by providing insights into individual differences in autistic traits and autism-relevant phenotypes, clarifying their measurement and stability, and proposing mechanistic frameworks that can help explain why diverse profiles emerge. In this conclusion piece, we summarize the manuscripts included in the special issue, highlight shared themes and discuss remaining issues and future directions, particularly related to naturalistic, interactive neuroscience. Ultimately, we hope this work will contribute to the development of more precise and inclusive models of autism.

Relative to the general population, autistic adults are at elevated risk for depression. Factors related to this risk are poorly understood, yet identifying such factors is important for improving mental health in autistic people. Emotion regulation (ER) challenges may be one such factor. However, few studies have examined ER challenges and depression in autistic adults. We examined ER challenges, depressive symptomatology and their associations in 775 (aged 18–83 years) autistic adults using network analysis, a method that permits identification of key components of ER and depression and their interrelatedness. Three non-regularized weighted undirected networks were estimated: ER challenges, depressive symptomatology, and combined ER-depressive challenges. Community structures revealed in the ER challenges and depressive symptomatology networks align with theoretical/nosological models of ER challenges/depressive symptoms as well as extant research using network analysis to examine these constructs. The combined ER challenges-depressive symptomatology network indicated that ER challenges and depressive symptomatology are interrelated but distinct constructs. These preliminary findings using cross-sectional data provide a first step in understanding associations between a candidate factor in depression vulnerability in autistic adults – ER challenges – and identify important future research directions.