Reconstructing functional brain networks from functional Magnetic Resonance Imaging (fMRI) data typically relies on statistical pruning, where pairwise correlations between brain Regions of Interest (ROIs) are independently thresholded, ignoring potentially relevant collective co-activation patterns within sub-networks. Here we introduce a functional pruning approach that identifies which connections to retain based on their collective importance for distinguishing healthy from affected individuals, rather than on their individual statistical significance. We use geometric deep learning to learn network representations and an Explainable Artificial Intelligence (XAI) tool to identify the most discriminative sub-networks. Using fMRI data from healthy and Autism Spectrum Disorder (ASD) subjects, we build multilayer network representations from a multi-frequency decomposition of the signals and provide robust evidence that the machine-learned sub-systemic co-activation patterns significantly improve the identification of affected individuals. Our results demonstrate how functional pruning — which is based on collective, rather than individual, co-activation patterns — provides mechanistic insights that can be reliably used to characterize brain disorders, at variance with statistical pruning. Our approach is general and can be applied to find biomarkers for other brain diseases and, more broadly, to analyze complex systems where only the activity of individual units can be measured.
Grassia et al. (Thu,) studied this question.