Alterations in Multidimensional Functional Connectivity Architecture in Preschool Children with Autism Spectrum...

Background: Autism Spectrum Disorder (ASD) is a type of neurodevelopmental disorder, and its exact causes are currently unknown. Neuroimaging research suggests that its clinical features are closely linked to alterations in brain functional network connectivity, yet the specific patterns and mechanisms underlying these abnormalities require further clarification. Methods: We recruited 36 children with ASD and 36 age- and sex-matched typically developing (TD) controls. Resting-state EEG data were used to construct static and dynamic low- and high-order functional networks across four frequency bands (δ, θ, α, β). Graph-theoretical metrics (clustering coefficient, characteristic path length, global efficiency, local efficiency) and state entropy were applied to characterize network topology and dynamic transitions between integration and segregation. Additionally, between-frequency networks were built for six band pairs (δ-θ, δ-α, δ-β, θ-α, θ-β, α-β), and network global measures quantified cross-frequency interactions. Results: Low-order networks in ASD showed increased δ and β connectivity but decreased θ and α connectivity. High-order networks demonstrated increased δ connectivity, reduced α connectivity, and mixed alterations in θ and β. Graph-theoretical analysis revealed pronounced α-band topological disruptions in ASD, reflected by a lower clustering coefficient and efficiency and higher characteristic path length in both low- and high-order networks. Dynamic analysis showed no significant entropy changes in low-order networks, while high-order networks exhibited time- and frequency-specific abnormalities, particularly in δ and α (0.5 s window) and δ (6 s window). Between-frequency analysis showed enhanced β-related coupling in low-order networks but widespread reductions across all band pairs in high-order networks. Conclusions: Young children with ASD exhibit coexisting hypo- and hyper-connectivity, disrupted network topology, and abnormal temporal dynamics. Integrating hierarchical, dynamic, and cross-frequency analyses offers new insights into ASD neurophysiology and potential biomarkers.