Global changes in the connectome in autism spectrum disorders

Abstract

There is an increasing interest in connectomics as means to characterize the brain both in healthy controls and in disease. Connectomics strongly relies on graph theory to derive quantitative network related parameters from data. So far only a limited range of possible parameters have been explored in the literature.In this work, we utilize a broad range of global statistic measures combined with supervised machine learning and apply it to a group of 16 children with autism spectrum disorders (ASD) and 16 typically developed (TD) children, which have been matched for age, gender and IQ. We demonstrate that 86.7% accuracy is achieved in distinguishing between ASD patients and the TD control using highly discriminative graph features in a supervised machine learning setting.