Alterations in connectome dynamics in autism spectrum disorder: a harmonized mega-and meta-analysis study using the autism brain imaging data exchange dataset

Yapei Xie

Abstract:

BACKGROUND: 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.

METHODS: To investigate case-control differences in dynamic connectome topology, we conducted mega- and meta-analyses of resting-state functional magnetic resonance imaging (rs-fMRI) data from $N = 939$ participants ($N_{ASD} = 440$, $N_{Control} = 499$, all males), collected from 18 independent sites within the Autism Brain Imaging Data Exchange (ABIDE) dataset. Functional data were preprocessed and analyzed using harmonized protocols, and brain module dynamics were 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.

RESULTS: Compared with healthy control participants, individuals with ASD exhibited a higher global mean and lower standard deviation ($\sigma$) of whole-brain module dynamics, indicating an unstable and less regionally differentiated dynamic pattern. More specifically, individuals with ASD showed higher module switching predominantly in the medial prefrontal cortex (mPFC), posterior cingulate gyrus (PCG), and angular gyrus (AG), and lower switching in visual regions. These alterations in brain dynamics significantly predicted social impairments in individuals with ASD and correlated with expression profiles of genes primarily involved in neurotransmitter transport and secretion regulation, as well as with previously identified autism-related genes.

CONCLUSIONS: This study is the first to identify consistent alterations in brain network dynamics in ASD along with corresponding transcriptomic signatures, furthering insights into the biological mechanisms underlying this disorder.