Multiscale static and dynamic brain functional network analysis reveals aberrant connectivity patterns in preschool children with autism spectrum disorder.

Researchers used brain wave recordings to study how different brain regions communicate in preschool children with autism compared to typical children. They found that children with autism had both weaker and stronger connections between brain areas, depending on the specific brain wave frequency. The brain networks were also less organized and had difficulty switching between different states of activity. These findings help explain the complex brain differences that occur early in autism development.

This EEG study examined brain connectivity patterns in 32 preschool children with ASD compared to 32 typically developing children during rest. Using advanced network analysis across multiple frequency bands, researchers found complex connectivity alterations in ASD. Low-order functional connectivity was decreased in theta, alpha, and beta bands but increased in delta band. High-order connectivity showed increases across delta, theta, and alpha bands.

Children with ASD demonstrated reduced network clustering, efficiency, and increased path lengths, indicating impaired brain integration. Dynamic analysis revealed altered state entropy, suggesting difficulties transitioning between network integration and segregation states. These findings highlight the heterogeneous nature of brain connectivity disruptions in early childhood ASD.

Findings suggest brain connectivity biomarkers could aid early ASD identification and monitoring. The frequency-specific and dynamic nature of connectivity disruptions may inform targeted interventions. Understanding these early brain network alterations could guide development of personalized therapeutic approaches and help predict treatment responses in preschool children with autism.

Single-center study with modest sample size (32 per group). EEG has limited spatial resolution compared to fMRI. Cross-sectional design prevents causal inferences. Heterogeneity of ASD may limit generalizability. Resting-state data may not reflect task-related connectivity patterns.

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.