Graph-theoretic characterization on differentiation between normal healthy and autism spectrum disorder (ASD) subjects

Abstract

With the growing exercises of structurofunctional network attributes as potential indicators for disease brains, an effective representation and assessments have become important. Eigenvector centrality characterization of functional MRI (fMRI) networks permits node wise graph theoretical representations as brain diagnostic charts. This article analyses adequacy of node centrality measures to perform group difference studies in neuroimaging data.