3D deep convolutional neural network revealed the value of brain network overlap in differentiating autism spectrum disorder from healthy controls

Abstract

Spatial distribution patterns of functional brain networks derived from resting state fMRI data have been widely examined in the literature. However, the spatial overlap patterns among those brain networks have been rarely investigated, though spatial overlap is a fundamental principle of functional brain network organization. To bridge this gap, this paper presents an effective 3D convolutional neural network (CNN) framework to derive discriminative and meaningful spatial brain network overlap patterns that can characterize and differentiate Autism Spectrum Disorder (ASD) from healthy controls. Our experimental results demonstrated that the spatial distribution patterns of connectome-scale functional network maps per se have little discrimination power in differentiating ASD from controls via the CNN framework. In contrast, the spatial overlap patterns instead of spatial patterns per se among these connectome-scale networks, learned via the same CNN framework, have remarkable differentiation power in separating ASD from controls. Our work suggested the promise of using CNN deep learning methodologies to discover discriminative and meaningful spatial network overlap patterns and their applications in functional connectomics of brain disorders such as ASD.