Heterogeneity of Degree Centrality Revealed Different Subtypes in Children with Autism Spectrum Disorder.

Scientists studied brain scans from 105 autistic children and 102 non-autistic children. They found three different patterns of brain connectivity in autism, each linked to different brain regions. Two of these patterns could predict specific autism traits - one predicted repetitive behaviors and another predicted social communication challenges. This helps explain why autism looks so different from child to child.

This neuroimaging study analyzed brain connectivity patterns in 105 children with autism spectrum disorder (ASD) and 102 typically developing controls using functional MRI data. Researchers used advanced machine learning techniques to identify three distinct subtypes of ASD based on degree centrality patterns in brain networks. Each subtype showed unique alterations in brain regions including the fusiform gyrus, insula, and inferior frontal gyrus. The study found that brain connectivity patterns in two of the subtypes could predict specific autism symptoms: subtype 1 predicted restricted and repetitive behaviors, while subtype 3 predicted social communication difficulties.

This research demonstrates the neurobiological heterogeneity underlying autism and suggests that different brain connectivity patterns may explain the wide variation in autism presentations and outcomes.

Findings suggest autism heterogeneity has neurobiological basis, potentially supporting subtype-specific interventions. However, clinical application requires validation and development of accessible biomarkers beyond research-grade neuroimaging.

Cross-sectional design limits understanding of developmental changes. Sample limited to children, unclear age range. Validation in independent samples needed. Clinical translation of neuroimaging subtypes requires further research.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that exhibits a wide range of clinical heterogeneity. This study aimed to explore the heterogeneity of ASD based on deviations in brain functional networks.Resting-state functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange database were analyzed in 105 children with ASD and 102 demographically matched typical controls (TC) children. Heterogeneity through discriminative analysis (HYDRA) was utilized to identify subtypes of ASD based on the degree centrality (DC) maps. Voxel-wise group comparisons were then performed between ASD subtypes and the TC group.

The relationship between the altered DC and the symptom severity was finally analyzed for ASD subtypes using the multivariate support vector regression approach.HYDRA identified three subtypes of ASD. Distinct DC alteration patterns were observed in brain regions including the fusiform gyrus, insula, and inferior frontal gyrus in ASD subtypes. Moreover, the altered DC values for ASD subtype 1 and subtype 3 can predict the restricted and repetitive behavior and social communication impairments in ASD, respectively.Our findings demonstrated the heterogeneity of brain functional networks in ASD and provided a promising way to explain the high heterogeneity of clinical symptoms and outcomes.