Analysis of the Over-Connectivity in Autistic Brains Using the Maximum Spanning Tree: Application on the Multi-Site and Heterogeneous ABIDE Dataset

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that touches children in an early age and alters the function of the brain. Previous studies put forward theories of under and over-connectivity between regions of the autistic brain. Hence, to understand the disorder and find an early diagnosis corroborating the existing theories is of central importance. In this paper, we propose a framework that takes into account the properties of over-connectivity in the autistic brain using the maximum spanning tree (MaxST), since this latter is known to describe high connectivity values. The novelty of the proposed approach is to adopt elimination of the information related to the overconnectivity theory, i.e elimination of the MaxST. This permits to measure the impact of the suppression and thus to well emerge the aforementioned connectivity alterations. With an overall objective of facilitating the early diagnosis of this disorder. The tested dataset is the large multi-site Autism Brain Imaging Data Exchange (ABIDE). The results show that this approach provides accurate prediction up to 70%. They also highlight the importance of every parameter used in all the steps that lead to the final result.