Multiscale static and dynamic brain functional network analysis reveals aberrant connectivity patterns in preschool...

Autism spectrum disorder (ASD) is associated with altered brain functional connectivity, but findings regarding the nature of these abnormalities remain inconsistent, partly due to methodological limitations and the disorder's intrinsic heterogeneity. This study aims to provide a comprehensive characterization of functional network alterations in preschool children with ASD by integrating low- and high-order functional connectivity (LOFC/HOFC), static and dynamic network analysis, and entropy-based state transition assessment.

EEG data were collected from 32 children with ASD and 32 typically developing (TD) children during resting state. Static and dynamic LOFC and HOFC networks were constructed across four frequency bands (delta, theta, alpha, beta). Graph theoretical measures (clustering coefficient, characteristic path length, global and local efficiency) and state entropy were computed to assess network organization and dynamic integration-segregation transitions.

Compared to TD children, those with ASD exhibited decreased LOFC strength in theta, alpha, and beta bands but increased strength in the delta band. In contrast, HOFC analysis revealed higher connectivity in ASD across delta, theta, and alpha bands. Graph metrics showed significantly lower clustering, efficiency, and higher path lengths in the ASD group, indicating reduced integrative capacity. Dynamic network analysis further revealed altered state entropy in ASD, suggesting impaired flexibility in transitioning between network integration and segregation. These alterations varied across frequency bands and time scales, with distinct patterns between LOFC and HOFC.

This multiscale approach demonstrates that ASD in early childhood is characterized by both hypo- and hyper-connectivity, disrupted topological organization, and abnormal temporal dynamics in brain networks. The integration of hierarchical connectivity analysis with dynamic measures provides novel insights into the neurophysiological underpinnings of ASD and may inform future biomarker development.