Identification of Discriminative Subnetwork from fMRI-Based Complete Functional Connectivity Networks

Abstract

The comprehensive set of neuronal connections of the human brain, which is known as the human connectomes, has provided valuable insight into neurological and neurodevelopmental disorders. Functional Magnetic Resonance Imaging (fMRI) has facilitated this research by capturing regionally specific brain activity. Resting state fMRI is used to extract the functional connectivity networks, which are edge-weighted complete graphs. In these complete functional connectivity networks, each node represents one brain region or Region of Interest (ROI), and each edge weight represents the strength of functional connectivity of the adjacent ROIs. In order to leverage existing graph mining methodologies, these complete graphs are often made sparse by applying thresholds on weights. This approach can result in loss of discriminative information while addressing the issue of biomarkers detection, i.e. finding discriminative ROIs and connections, given the data of healthy and disabled population. In this work, we demonstrate a novel framework for representing the complete functional connectivity networks in a threshold-free manner and identifying biomarkers by using feature selection algorithms. Additionally, to compute meaningful representations of the discriminative ROIs and connections, we apply tensor decomposition techniques. Experiments on a fMRI dataset of neurodevelopmental reading disabilities show the highly interpretable nature of our approach in finding the biomarkers of the diseases.