Graph Fourier transform of fMRI temporal signals based on an averaged structural connectome for the classification of neuroimaging

Abstract

Graph signal processing (GSP) is a framework that enables the generalization of signal processing to multivariate signals described on graphs. In this paper, we present an approach based on Graph Fourier Transform (GFT) and machine learning for the analysis of resting-state functional magnetic resonance imaging (rs-fMRI). For each subject, we use rs-fMRI time series to compute several descriptive statistics in regions of interest (ROI). Next, these measures are considered as signals on an averaged structural graph built using tractography of the white matter of the brain, defined using the same ROI. GFT of these signals is computed using the structural graph as a support, and the obtained feature vectors are subsequently benchmarked in a supervised learning setting. Further analysis suggests that GFT using structural connectivity as a graph and the standard deviation of fMRI time series as signals leads to more accurate supervised classification using a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange) when compared to several other statistical metrics. Moreover, the proposed approach outperforms several approaches, based on using functional connectomes or complex functional network measures as features for classification.

Keywords: Classification; Graph signal processing; Machine learning; Neuroimaging; Resting-state analysis.