Atypical brain function hierarchy in autism spectrum disorder: insights from a novel analytical approach based on neuronal oscillation pattern.

Hierarchy is the basic character of the human brain. Neuronal oscillation is one of the fundamental features of brain function, revealing abnormal hierarchical structures in psychiatric disorders from a system-level perspective. However, to date, no research has yet quantified the normal and abnormal brain functional hierarchy based on oscillation patterns. Therefore, this study aimed to quantify brain hierarchy based on neuronal oscillation patterns using the wide-scale information across multiple frequency bands of functional magnetic resonance imaging (fMRI) data and further investigate atypical oscillation patterns in autism spectrum disorder (ASD) at the system level. We analyzed resting-state fMRI data from the Autism Brain Imaging Data Exchange II, including 132 participants with ASD and 132 healthy controls. The energy distribution patterns (EDPs) across frequency bands were calculated for different brain networks using multivariate empirical mode decomposition and Hilbert Transform to represent oscillation patterns. The gradient analysis was applied to quantify the EDP segregation among networks, and the network median distance of gradients was compared between the two groups. The k-means clustering was applied to intuitively verify the atypical EDP in ASD. Across all participants, we observed that the EDPs of different brain regions were spatially coupled to the brain hierarchy. Compared to healthy controls, the ASD exhibited reduced segregation between unimodal and transmodal regions on both energy gradient and clustering analyses, correlating with social deficits. Our results quantitatively confirm that oscillation patterns can reflect the functional segregation among networks and provide novel evidence of the system-level imbalances in neuronal oscillations in ASD.