Detection of autism spectrum disorder using multi-scale enhanced graph convolutional network

Abstract

Magnetic Resonance Imaging (MRI) based Autism Spectrum Disorder (ASD) detection approaches face various challenges due to variations in brain connectivity patterns, limited sample sizes, and heterogeneity of available data. These challenges make it hard to find consistent imaging markers. To address these issues, researchers have focused on advanced analysis methods, such as multi-modal imaging techniques and graph-based approaches to gain a comprehensive understanding of ASD neurobiology. However, existing graph-based approaches for ASD detection have primarily focused on pairwise similarities between individuals, neglecting individual characteristics and features. A novel framework to detect ASD using a Multi-Scale Enhanced Graph Convolutional Network (MSE-GCN). The framework combines the functional connectivity of resting-state functional MRI (rs-fMRI) with non-imaging phenotype data from Autism Brain Imaging Data Exchange-I (ABIDE-I). The framework uses MSE-GCN to represent individuals as node in a population graph. Each node corresponds to an individual and connects to feature vectors from imaging data. Edge weights between nodes are assigned to integrate phenotypic information. Then, the multiple parallel GCN layers are designed using random walk embedding. The output of these GCN layers is then combined in the fully connected layer to detect ASD effectively. The performance of the framework is evaluated using the ABIDE-I dataset. In addition, Recursive Feature Elimination and Multilayer Perceptron are utilised for feature selection. The outcome of this approach shows more than 10% advancement in accuracy, achieving an accuracy of 83% by incorporating phenotypic data in conjunction with MRI data within a GCN.