Graph theoretical approach for screening autism on brain complex networks

Abstract

Autism spectrum disorder has been known as a prevalent brain disorder of children in recent years. Numerous studies have indicated differences between functional brain networks of disordered and typically developed brains. Nevertheless, no automatic and effective methodology has been established for the identification of this disorder using functional magnetic resonance images (fMRI). In this research, we investigate differences between Autistic and typically developed brain networks by modeling fMRI data to brain complex networks and propose a method for classification of the aforementioned groups. In this method, applying graphlet counts, as the frequency of predefined non-isomorphic subgraphs, feature vectors have been extracted and using an ensemble classifier, data has been classified into two defined groups. Results, showing 6.5% improvement according to the best baseline method, has reached 69.81% of accuracy for disorder classification.