ABSTRACT Extreme/pathological demand avoidance (EDA) is a behavioral profile characterized by the avoidance of everyday demands, with significant impact on the individuals and those around them. Little is understood regarding the correlates and drivers of EDA; understanding these better may lead to targeted interventions and support strategies. Cross‐sectional data from parent‐rated measures were analyzed for 63 children referred to a specialist neurodevelopmental clinic in the United Kingdom. The relationship between EDA traits and a number of psychological (intolerance of uncertainty, anxiety, emotional dysregulation [reactivity and dysphoria], cognitive inflexibility, alexithymia [i.e., difficulty identifying emotions]), and neurodevelopmental (autistic and ADHD) traits was tested. No differences in EDA traits were found by diagnostic group (autistic vs. not‐autistic) or gender (male and female). Across the whole sample, EDA traits were negatively correlated with age and positively correlated with emotional dysregulation reactivity, ADHD traits, cognitive inflexibility, intolerance of uncertainty and alexithymia. EDA was not associated with autistic traits or anxiety. Path models were used to identify which cognitive and behavioral features predicted EDA scores, once inter‐correlations among predictors were controlled for. Only emotional dysregulation, ADHD traits and age predicted EDA‐Q scores. Further research is needed to assess how these variables contribute to EDA in larger more diverse samples, capturing potentially unmeasured variables, and exploring possible mediation pathways.
ABSTRACT Cognitive tests that reflect the complexity of real‐world social interactions are essential for autism spectrum disorder (ASD)‐related clinical assessment and research. This study developed and adapted a Japanese version of the Reading the Mind in Films (RMF) test, which assesses mind‐reading ability through dynamic, multisensory, and context‐rich stimuli. Participants included 23 autistic adults, 39 typically developing adults, and 40 adults with depression as a clinical control. RMF scores in the autistic group were significantly lower than in both the typically developing and depression groups. Hierarchical logistic regression analyses showed that RMF performance provided incremental predictive value beyond Reading the Mind in the Eyes test performance in differentiating autistic individuals from typically developing participants and from participants with depression. Receiver operating characteristic analyses further indicated moderate discriminative accuracy of the RMF for differentiating autistic from non‐autistic individuals. In contrast, the depression group performed comparably to the typically developing group on the RMF, suggesting that the RMF captures ASD‐specific social cognitive difficulties rather than social dysfunction. These findings provide preliminary support for both the construct validity and the potential discriminative utility of the Japanese RMF. This tool has potential applications in future transdiagnostic and cross‐cultural studies.
ABSTRACT This study aimed to develop and validate models built using machine learning methods for the diagnosis of autism spectrum disorder (ASD). A total of 851 children with suspected ASD were enrolled. Related features were acquired using the Autism Behavior Checklist and Auditory Brainstem Response (ABR) outcomes. The Shapley additive explanation (SHAP) values were used to explain the feature impact used in the models with the best performance. In addition, different methods were used to depict group differences. Our study found that significant results in ABR outcomes among children with different phenotypes mainly depend on stimulation intensity. The predominant finding is that signal conduction from the cochlear nerve to the inferior colliculus in children with ASD may be associated with autistic behaviors in a way that displays region‐specific (e.g., cochlear nerve, olivary) and intensity‐dependent (e.g., 45, 65 dB) complexity. The XGBoost models outperformed the other candidates, with the highest prediction accuracy (85.9%) and AUROC (0.779). Wave III and V were identified as the features with largest impact. These findings highlight atypical ABR patterns in ASD and their association with autistic behaviors, contributing to the growing literature on understanding alterations in bioelectrical performance in children with ASD.
ABSTRACT Autism exhibits substantial phenotypic heterogeneity, yet age‐dependent variation in phenotypic profiles and onset timing remains poorly understood. We aimed to characterize stage‐specific phenotypes and temporal onset patterns of autism using longitudinal electronic health records (EHRs). We analyzed EHR data from Columbia University Irving Medical Center. Autism cases were stratified by age at diagnosis. Phenotype enrichment analyses were performed using chi‐square and Fisher's exact tests to identify phenotypes overrepresented in autism cases compared with matched controls, and temporal patterns of phenotype onset were subsequently evaluated to identify stage‐specific clinical signatures and provide insight into differences between early‐ and late‐diagnosed individuals. Among 4818 cases and 1433 phenotypes, 195 and 155 were enriched in toddlerhood and adolescence groups. Early diagnosed individuals exhibited a higher prevalence of sensory‐related conditions, greater exposure to maternal and perinatal complications than their matched controls (all ORs > 1; p < 0.01), and earlier onset of neurologic features, including coordination abnormalities and convulsions, compared with later‐diagnosed individuals. The study provides a publicly accessible resource to enrich autism‐related phenotypic profiles, uncovering notable phenotypic differences and variations in timing of phenotype onset between early‐diagnosed and later‐diagnosed individuals. These findings underscore phenotypic heterogeneity and highlight the need for age‐sensitive screening strategies. To our knowledge, this is among the first studies to jointly characterize phenotypic profiles and their temporal onset patterns across developmental stages in autism using longitudinal EHR data. These findings offer novel insights into age‐dependent clinical signatures, informing earlier and more precise screening strategies.
