Journal Article

Graph convolutional network for fMRI analysis based on connectivity neighborhood

Hu, Xiaoping P., Li, Kaiming, Wang, Lebo

2021

Overview

There have been successful applications of deep learning to functional magnetic resonance imaging (fMRI), where fMRI data were mostly considered to be structured grids, and spatial features from Euclidean neighbors were usually extracted by the convolutional neural networks (CNNs) in the computer vision field. Recently, CNN has been extended to graph data and demonstrated superior performance. Here, we define graphs based on functional connectivity and present a connectivity-based graph convolutional network (cGCN) architecture for fMRI analysis. Such an approach allows us to extract spatial features from connectomic neighborhoods rather than from Euclidean ones, consistent with the functional organization of the brain. To evaluate the performance of cGCN, we applied it to two scenarios with resting-state fMRI data. One is individual identification of healthy participants and the other is classification of autistic patients from normal controls. Our results indicate that cGCN can effectively capture functional connectivity features in fMRI analysis for relevant applications.

• Publisher: MIT Press,MIT Press Journals, The,The MIT Press
• Subject: Artificial neural networks / Autism / Brain architecture / Brain mapping / Computer vision / Connectivity-based neighborhood / Deep learning / Functional connectivity / Functional magnetic resonance imaging / Functional morphology / Graph convolutional network / Graph theory / Machine learning / Magnetic resonance imaging / Methods / Neural networks / Neuroimaging / Performance evaluation / Structured grids (mathematics)