Temporal and spatial variability of large-scale dynamic brain networks in ASD.

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by significant impairments in social-cognitive functioning. Prior studies have identified abnormal brain functional connectivity (FC) patterns in individuals with ASD, which are associated with core symptoms and serve as potential biomarkers for diagnosis. However, the patterns of temporal and spatial variability in dynamic functional connectivity networks (dFCNs) in ASD and their relationship with ASD behaviors remain underexplored. This study uses fuzzy entropy to analyze the temporal variability and spatial variability of dFCNs, aiming to reveal distinctive FC patterns in ASD and identify new biomarkers. We conducted a comparative analysis between ASD and healthy controls (HCs), examining the association with clinical symptoms. Our findings indicate increased FC temporal variability in sensorimotor, subcortical, and cerebellar networks in ASD compared to HCs. Additionally, increased spatial variability was observed primarily in visual, limbic, subcortical, and cerebellar networks. Notably, these variability patterns correlated with symptom severity in ASD. Utilizing these spatiotemporal variability features, we developed multi-site classification models that achieved high accuracy (81.25%) in identifying ASD. These results provide novel insights into the neural mechanisms and clinical characteristics of ASD, suggesting that integrated spatiotemporal dFCN features may enhance diagnostic accuracy.