Importance:Extant diagnostic and screening tools struggle to accommodate the diverse features of autism spectrum disorder (ASD) while balancing psychometric properties with respondent burden. Bifactor multidimensional item response theory (MIRT)-based computerized adaptive testing (CAT) can increase accuracy and accessibility of diagnostic assessments. Objective:To develop, calibrate, and validate the CAT-Autism tool for children and adolescents. Design, Setting, and Participants:This diagnostic study used National Institute of Mental Health Data Archive data available February 2024 from neurodevelopmental studies including children and adolescents with item-level responses for measures relevant to domains related to autism phenotype features. Based on reported scores from clinical assessments or measures, individuals were grouped by diagnosis as neurotypical and ASD (all levels of severity). Data were analyzed from February 2024 to February 2026. Main Outcomes and Measures:Valid CAT-based estimates in 10 subdomains (restricted to those in sample completing 200 items or more) compared with full item-bank scores; predictive performance was evaluated in discrimination, calibration, and clinical utility analyses against clinician-assigned diagnostic status. Results:A total of 490 questions to parents and caregivers were considered for potential item bank inclusion. The final sample included 10 309 children and adolescents for calibration (mean [SD] age, 9.3 [6.4] years; 7716 male [74.9%]); 2055 children and adolescents diagnosed as neurotypical (19.9%) and 8254 with ASD (80.1%). Of 490 items, 424 fit the bifactor structure with loadings higher than 0.3 on the primary dimension (ASD). Correlation between observed and estimated item-category proportions was r = 0.98, indicating exceptional fit of the model to the data. With age and sex included as external predictors, the CAT-Autism model yielded outstanding diagnostic predictive accuracy for differentiating neurotypical from ASD results with an AUC of 0.95 (95% CI, 0.92-0.98) for 1-to-5-year-olds and 0.94 (95% CI, 0.92-0.97) for 6-to-18-year-olds. Correlation between full item-bank scores and CAT scores (mean, 13 items; range, 6-45 items) was r = 0.95 for 1-to-5-year-olds and r = 0.94 for 6-to-18-year-olds. Conclusions and Relevance:In this development and validation of our prediction model, CAT-Autism surpassed full item-bank scores with an average of 13 questions; these findings demonstrated that adaptively administering a small, statistically optimal subset of items can yield comparable (or better) results while reducing respondent burden. Next steps will be to test implementation parameters and confirm efficacy of CAT-Autism in clinical and community settings.

Precise measurement of individual variation in psychiatric symptoms is essential for developing scalable tools that can ultimately inform treatment development and clinical care. Individuals performing the same cognitive task often adopt qualitatively distinct strategies that may reflect differences in underlying neural processes and psychiatric symptom dimensions. Yet most cognitive paradigms implicitly assume that all individuals rely on the same cognitive strategy, attributing behavioral variability to differences along a shared computational axis. This assumption may obscure meaningful cognitive heterogeneity that cuts across traditional diagnostic categories, which themselves group phenotypically and biologically diverse individuals. Even dimensional approaches may fail to capture distinct computational profiles if they assume a common underlying cognitive process across participants. To test this hypothesis, we combined a computationally grounded Context Generalization task involving different visual stimuli (color, shape, texture, size) with standardized self-report psychiatric questionnaires. We tested how distinct cognitive strategies relate to symptom dimensions commonly observed in autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), depression, and schizotypy. Participants (N=744 in session one; N=584 in session two) were recruited online, enriched for self-reported ASD diagnoses, and matched for sex at birth. We identified qualitatively unique strategies related to goal-directed attention and short-term memory, such as a focused goal-directed strategy and a frequency-based strategy that relied on attending to all the stimulus features. The goal-directed strategy was associated with lower behavioral rigidity, while the frequency-based strategy was associated with elevated behavioral rigidity, despite showing the highest task performance. Strategy membership was substantially stable across sessions. Although transitions from the random-like strategy to the frequency-based strategy occurred less often than expected by chance, the subset of individuals who made this transition showed elevated behavioral rigidity and inattention-related symptoms consistent with the broader frequency-based profile. These findings demonstrate that individuals vary substantially in the cognitive strategies used to solve the same task and highlight the importance of measuring inter-individual variation in computational strategy rather than relying solely on aggregate performance metrics. More broadly, our results support a framework for linking psychiatric phenotypes to interpretable patterns of attention and learning, advancing efforts toward computational phenotyping and precision psychiatry.

Current psychiatric nosology is based on observed and self-reported symptoms. Heterogenous pathophysiological mechanisms may underlie similar symptoms leading to diagnosis not matching up to the neurobiology. Recent research has sought to move away from diagnoses by symptoms, to viewing aberrant mental health in terms of abnormal human neurobehavioral functioning and concurrent deviations in the pathophysiology. Human behavior in a social context is a core neurobehavioral function with large individual variation that may reflect genomic, metabolic or neurobiological variation, whose identification potentially yields more accurate targeting for the development of interventions and biomedical treatments. In this research, we describe an experimental framework that utilizes a zero-sum game of repeated Rock-Paper-Scissors played against an artificial intelligence agent as an assay of social interaction. Human deviation from the Nash Mixed Equilibrium strategy of play, the only guaranteed way to avoid exploitation, can be seen in the sequential dependence of hands. We hypothesize that this deviation represents humans mimicing randomness to avoid exploitation through constant adjustments of behavior, which we analyze in terms of a set of switching heuristic lag-1 conditional response rules. We quantify and interpret the set of rules subjects are able to utilize as mirroring individual traits. Subjects in the study also completed the Autism Quotient Abridged survey, and subscores of the social, imagination and routine factors were found to be predicted by a combination of behavioral features derived from game play.

Prevalence in autism spectrum disorder (ASD) diagnosis has long been strongly male-biased. Yet, consensus has not been reached on mechanisms and clinical features that underlie sex-based discrepancies. Whereas females may be under-diagnosed because of inconsistencies in diagnostic/ascertainment procedures (sex-biased criteria, social camouflaging), diagnosed males may have exhibited more overt behaviors (e.g., hyperactivity, aggression) that prompted clinical evaluation. Applying a novel network-theory-based approach, we extracted data-driven, clinically-relevant insights from a large, well-characterized sample (Simons Simplex Collection) of 2175 autistic males (Ages = 8.9±3.5 years) and 334 autistic females (Ages = 9.2±3.7 years). Exploratory factor analysis (EFA) and expert clinical review reduced data dimensionality to 15 factors of interest. To offset inherent confounds of an imbalanced sample, we identified a subset of males (N=331) matched to females on key variables (Age, IQ) and applied data-driven CDA using Greedy Fast Causal Inference (GFCI) for three groups (All Females, All Males, and Matched Males). Structural equation modeling (SEM) extracted measures of model fit and effect sizes for causal relationships between sex, age, and, IQ on EFA-selected factors capturing phenotypic representations of autism across sensory, social, and restricted and repetitive behavior domains. Our methodology unveiled sex-specific directional relationships to inform developmental outcomes and targeted interventions.

How humans resolve the explore-exploit dilemma in decision making is central to how we flexibly interact with both social and non-social aspects of dynamic environments. However, how individual differences in the cognitive computations underlying exploration relate to social and non-social psychological flexibility traits remains unclear. To test this, we probed decision-making strategies in a cognitive flexibility task, a restless three-armed bandit task, and examined how individual differences in cognitive strategy related to social and non-social traits measured by the Broad Autism Phenotype Questionnaire (BAPQ), a well-validated, clinically-relevant, community instrument, in a large (N = 1001) online sample. In contrast to prior links found between exploratory behavior and cognitive rigidity, we found that differences in choice behavior and exploration were primarily associated with social phenotypes as captured by the BAPQ aloof subscale. Higher scores on the BAPQ aloof subscale, indicative of reduced social interest and engagement, were associated with decreased shift rates, increased win-stay/lose-shift behavior, heightened sensitivity to negative outcomes, and reduced exploration. Reinforcement learning (RL) modeling further revealed that reduced exploration in high aloof individuals was driven by lower decision noise rather than increased cognitive rigidity, suggesting that decreased exploratory behavior may reflect a reduced tendency for stochastic exploration rather than an inflexible learning process. Sparse canonical correlation analysis reveals that the strongest loading for these non-social reward-related measures are in fact socially coded items. These results suggest that differences in motivation to seek information, especially in social contexts, may manifest as decreased exploratory behavior in a non-social decision-making task. Our findings additionally highlight the potential for using computational approaches to reveal general cognitive mechanisms underlying social functioning. High social aloofness was linked to reduced exploration, lower decision noise, and high choice stickiness in a bandit task. These effects reflect habitual and outcome-driven behaviours, linking social disengagement to nonsocial decision flexibility.

IntroductionSupplementary motor area (SMA) hyperactivity is thought to be a key neural mechanism in tics. This study probed SMA’s role in tic expression, voluntary tic control, and premonitory urge experiences using one session of 1 Hz “inhibitory” repetitive transcranial magnetic stimulation (rTMS) targeting SMA in a repeated measures, small-N experimental design.MethodsYouth with Tourette Syndrome (TS) ages 12—17 years (N = 14) completed a clinical assessment and MRI to localize SMA. The video-based Tic Suppression Task (TST) quantified tic frequency and urges during conditions of Free-to-Tic, Suppression, and Suppression+Reward. The TST was followed by randomly assigned active 1 Hz (n = 8) or sham rTMS (n = 6) and TST repetition post-stimulation.ResultsActive rTMS led to greater tic frequency reductions during Free-to-Tic (d = 0.34) and Suppression+Reward (d = 0.24) but not Suppression (d = 0.0). A stronger effect size for active rTMS was observed in both suppression conditions (d = 0.26, d = 0.63) when excluding participants classified as baseline “strong suppressors” (n = 5). Urges did not differ group-wise for Free-to-Tic (d = 0.09) but decreased more following active rTMS in both suppression conditions (d = 0.19, d = 0.52).DiscussionOverall, results suggest that the acute aftereffects of active 1 Hz rTMS to SMA may include reduced natural tic frequency, improved tic controllability, and lower urge intensity, especially while engaged in suppression efforts. Results are consistent with prior literature pointing to SMA hyperactivation in TS and suggests the potential therapeutic value of rTMS.

Objective:Few evidence-based social cognitive interventions are available to autistic youth as they navigate their complex, socially demanding teenage years. Building from pilot research using neuroplasticity-based targeted cognitive training, CICADAS (Care Improving Cognition for ADolescents on the Autism Spectrum), a digital application (app) designed to prime the brain for socio-affective learning, was developed. In a randomized active-controlled trial with 3 comparison arms, CICADAS was evaluated as a stand-alone program and as an augmentation to evidence-based PEERS (Program for the Education and Enrichment of Relationship Skills). Method:Recruiting from clinics providing PEERS, 62 adolescents (11-18 years old) with confirmed autism were enrolled. Adolescents scheduled to start PEERS were assigned using block randomization to PEERS + CICADAS (n = 22) and PEERS + Active Control (n = 21) groups. A third comparison group (n = 19) comprised adolescents who used the app as a stand-alone intervention (CICADAS only). In addition to in-app performance metrics, data were collected from social, behavioral, and cognitive assessments (self-report/parent-report measures) at preintervention (baseline), postintervention (16 weeks), and follow-up (32 weeks) sessions. Results:Significant effects of group, time, and group × time interaction were found on multiple measures collected longitudinally. For example, on the Pediatric Quality of Life Inventory, PEERS + CICADAS participants showed significant psychosocial health improvements (F = 6.862, p = .002) over the study timeline compared with trend level gains in the CICADAS only (F = 2.150, p = .122) and PEERS + Active Control (F = 1.917, p = .153) groups. Whereas all participants improved from baseline on the Social Responsiveness Scale, 2nd Edition (F = 11.038, p < .001), only the PEERS + CICADAS group gained significantly (F = 3.786, p = .026) on the social cognition subscale across all 3 time points. Conclusion:Our data demonstrate the potential of CICADAS as a stand-alone intervention and suggest that engaging with the adaptive app (vs static active control) conferred an additional advantage to autistic teens participating in PEERS. Clinical trial registration information:Care Improving Cognition for ADolescents on the Autism Spectrum (CICADAS); https://www.clinicaltrials.gov/study/NCT04562688.

Autism spectrum disorder (ASD) is most often defined by social communication challenges and behavioral rigidity, but executive function deficits have long been considered potential contributors to autism-related impairments across these domains. Autism is a spectrum, with a broad range of phenotypes presenting below the diagnostic threshold, raising the possibility that variability in executive function may contribute to individual differences in autistic traits in the general population. Value based decision making tasks access aspects of executive function, and critically are amenable to computational approaches to dissect the latent variables that most contribute to individual differences in cognition. We capitalize on this approach to uncover the relationship between autistic traits as measured by the Broad Autism Phenotype Questionnaire (BAPQ) and explore-exploit balance in a three-armed restless bandit decision making task in a large (1001 participants) sample. We find that the BAPQ aloof subscale, which primarily describes social behavior related phenotypes, most strongly explains changes in choice behaviors, including sensitivity to outcomes, changes in choice flexibility, and level of exploration as inferred from a Hidden Markov Model. Canonical correlation analysis reveals that the strongest loading for these non-social reward related measures are in fact socially coded items. These findings suggest that different aspects of executive function challenges may be related to social and nonsocial autism-related behaviors in the general population, and that social components of behavior produce measurable differences in nonsocial tasks.

Abstract Background Exposure with Response Prevention (ERP) is a first-line treatment for OCD, but even when combined with first-line medications it is insufficiently effective for approximately half of patients. Compulsivity in OCD is thought to arise from an imbalance of two distinct neural circuits associated with specific subregions of striatum. Targeted modulation of these circuits via key cortical nodes (dorsolateral prefrontal cortex [dlPFC] or presupplementary motor area [pSMA]) has the potential to improve ERP efficacy by decreasing compulsions during therapy. Methods The NExT (Neuromodulation + Exposure Therapy) trial is a two-phase, multisite early-stage randomized controlled trial designed to examine whether TMS augmentation of ERP alters activity in dlPFC and/or pSMA-associated circuitry and reduces compulsions during therapy in youth with OCD age 12–21 years. Phase 1 (N = 60) will compare two different active TMS regimens with sham: A. continuous theta burst stimulation (cTBS) to pSMA vs. B. intermittent theta burst stimulation (iTBS) to dlPFC. A priori “Go/No-Go” criteria will inform a decision to proceed to Phase 2 and the choice of TMS regimen. Phase 2 (N = 60) will compare the selected TMS regimen vs. sham in a new sample. Discussion This trial is the first to test TMS augmentation of ERP in youth with OCD. Results will inform the potential of TMS to enhance ERP efficacy and enhance knowledge about mechanisms of change. Trial registration ClinicalTrials.gov NCT05931913. Registered prospectively on July 5, 2023.

BackgroundTourette syndrome (TS) tics are typically quantified using "paper and pencil" rating scales that are susceptible to factors that adversely impact validity. Video-based methods to more objectively quantify tics have been developed but are challenged by reliance on human raters and procedures that are resource intensive. Computer vision approaches that automate detection of atypical movements may be useful to apply to tic quantification.ObjectiveThe current proof-of-concept study applied a computer vision approach to train a supervised deep learning algorithm to detect eye tics in video, the most common tic type in patients with TS.MethodsVideos (N = 54) of 11 adolescent patients with TS were rigorously coded by trained human raters to identify 1.5-second clips depicting "eye tic events" (N = 1775) and "non-tic events" (N = 3680). Clips were encoded into three-dimensional facial landmarks. Supervised deep learning was applied to processed data using random split and disjoint split regimens to simulate model validity under different conditions.ResultsArea under receiver operating characteristic curve was 0.89 for the random split regimen, indicating high accuracy in the algorithm's ability to properly classify eye tic vs. non-eye tic movements. Area under receiver operating characteristic curve was 0.74 for the disjoint split regimen, suggesting that algorithm generalizability is more limited when trained on a small patient sample.ConclusionsThe algorithm was successful in detecting eye tics in unseen validation sets. Automated tic detection from video is a promising approach for tic quantification that may have future utility in TS screening, diagnostics, and treatment outcome measurement. (c) 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Scientific inquiry and methodology are based on third person objectivity. Yet, as humans we experience everything through our first-person lens and second-person relational learning. The purpose of this review is to share the journey of discoveries about science and oxytocin from this author's unique and diverse perspective. Hormones are signaling molecules and long distant messengers required to regulate an organism's physiology and behavior. Oxytocin has taken the lead as the most investigated neurohormone that modulates social cognition, influences parenting behaviors, facilitates within or across-species bonding, and even biologically buffers against stressors such as isolation. Our increasing understanding that social connection, community belonging, and trust in others influence both physical and mental health outcomes, has led to numerous intervention and treatment oxytocin studies across a myriad of conditions. No longer just a way to facilitate female reproduction and lactation, oxytocin is now viewed as the “social influencer” that affects not just women but also men along with its closely related neurohormone, vasopressin. This review uses the narrative lens to illustrate how scientific lineage shapes what we study and how investigating oxytocin has been a microcosm to macrocosm metaphor for our collective social learning as a scientific community.

Sensory processing, along with the integration of external inputs into stable representations of the environment, is integral to social cognitive functioning; challenges in these processes have been reported in Autism Spectrum Disorder (ASD) since the earliest descriptions of autism. Recently, neuroplasticity-based targeted cognitive training (TCT) has shown promise as an approach to improve functional impairments in clinical patients. However, few computerized and adaptive brain-based programs have been trialed in ASD. For individuals with sensory processing sensitivities (SPS), the inclusion of some auditory components in TCT protocols may be aversive. Thus, with the goal of developing a web-based, remotely accessible intervention that incorporates SPS concerns in the auditory domain, we assessed auditory SPS in autistic adolescents and young adults (N = 25) who started a novel, computerized auditory-based TCT program designed to improve working memory and information processing speed and accuracy. We found within-subject gains across the training program and between pre/post-intervention assessments. We also identified auditory, clinical, and cognitive characteristics that are associated with TCT outcomes and program engagement. These initial findings may be used to inform therapeutic decisions about which individuals would more likely engage in and benefit from an auditory-based, computerized TCT program.

Quantification of human behavior in a social context may lead to discovery of subtle behavioral variations in a population that can be used to improve classification and screening for psychiatric disorders, and provide more accurate targeting in the development of interventions and biomedical treatments. However, it is difficult to study social interaction in a controlled, reproducible environment, as well as analyze the resulting behavior. In this research, we describe an experimental framework that utilizes a game of iterated Rock-Scissors-Paper played against an artificial intelligence agent, and a behavioral hypothesis of rule-switching to motivate analytical methods, that will extract behavioral features from game data. Subjects in the study also completed the Autism Quotient Abridged survey, and subscores from the survey were found to be predicted these behavioral features. Finding quantifiable, observable behavior that displays a spectrum in a population may be useful to differentiate and diagnose psychiatric illness.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThis study did not receive any funding.### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Institutional Review Board of Worcester Polytechnic Institute gave ethical approval for this work.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors

Human behavior is incredibly complex and the factors that drive decision making-from instinct, to strategy, to biases between individuals-often vary over multiple timescales. In this paper, we design a predictive framework that learns representations to encode an individual's ‘behavioral style’, i.e. long-term behavioral trends, while simultaneously predicting future actions and choices. The model explicitly separates representations into three latent spaces: the recent past space, the short-term space, and the long-term space where we hope to capture individual differences. To simultaneously extract both global and local variables from complex human behavior, our method combines a multi-scale temporal convolutional network with latent prediction tasks, where we encourage embeddings across the entire sequence, as well as subsets of the sequence, to be mapped to similar points in the latent space. We develop and apply our method to a large-scale behavioral dataset from 1,000 humans playing a 3-armed bandit task, and analyze what our model's resulting embeddings reveal about the human decision making process. In addition to predicting future choices, we show that our model can learn rich representations of human behavior over multiple timescales and provide signatures of differences in individuals.