Alterations in Connectome Dynamics in Autism Spectrum Disorder: A Harmonized Mega- and Meta-analysis Study Using the Autism Brain Imaging Data Exchange Dataset.
440 patients with ASD, 499 healthy control subjects
resting-state functional magnetic resonance imaging data
healthy control participants
alterations in brain dynamics, transcriptomic signatures related to those alterations
Abstract
Neuroimaging studies have reported functional connectome aberrancies in autism spectrum disorder (ASD). However, the time-varying patterns of connectome topology in individuals with ASD and the connection between these patterns and gene expression profiles remain unknown. To investigate case-control differences in dynamic connectome topology, we conducted mega- and meta-analyses of resting-state functional magnetic resonance imaging data of 939 participants (440 patients with ASD and 499 healthy control subjects, all males) from 18 independent sites, selected from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Functional data were preprocessed and analyzed using harmonized protocols, and brain module dynamics was assessed using a multilayer network model. We further leveraged postmortem brain-wide gene expression data to identify transcriptomic signatures associated with ASD-related alterations in brain dynamics. Compared with healthy control participants, individuals with ASD exhibited a higher global mean and lower standard deviation of whole-brain module dynamics, indicating an unstable and less regionally differentiated pattern. More specifically, individuals with ASD showed higher module switching, primarily in the medial prefrontal cortex, posterior cingulate gyrus, and angular gyrus, and lower switching in the visual regions. These alterations in brain dynamics were predictive of social impairments in individuals with ASD and were linked with expression profiles of genes primarily involved in the regulation of neurotransmitter transport and secretion as well as with previously identified autism-related genes. This study is the first to identify consistent alterations in brain network dynamics in ASD and the transcriptomic signatures related to those alterations, furthering insights into the biological basis behind this disorder.