Autism Spectrum Disorder Screening Using Discriminative Brain Sub-Networks: An Entropic Approach

TL;DR

This study introduces a novel entropic approach using genetic algorithms to identify discriminative brain sub-networks for autism spectrum disorder screening, achieving up to 82.2% accuracy.

Contribution

It presents a new method combining genetic algorithms and entropy features to improve autism screening based on brain network analysis.

Findings

Maximum accuracy of 82.2% in functional networks

Effective discrimination between autism and control groups

Utilizes entropy as a topological descriptor

Abstract

Autism is one of the most important neurological disorders which leads to problems in a person's social interactions. Improvement of brain imaging technologies and techniques help us to build brain structural and functional networks. Finding networks topology pattern in each of the groups (autism and healthy control) can aid us to achieve an autism disorder screening model. In the present study, we have utilized the genetic algorithm to extract a discriminative sub-network that represents differences between two groups better. In the fitness evaluation phase, for each sub-network, a machine learning model was trained using various entropy features of the sub-network and its performance was measured. Proper model performance implies extracting a good discriminative sub-network. Network entropies can be used as network topological descriptors. The evaluation results indicate the acceptable performance of the proposed screening method based on extracted discriminative sub-networks and the machine learning models succeeded in obtaining a maximum accuracy of 73.1% in structural networks of the UCLA dataset, 82.2% in functional networks of the UCLA dataset, and 66.1% in functional networks of ABIDE datasets.