MAFT-SO: A novel multi-atlas fusion template based on spatial overlap for ASD diagnosis

Abstract

Autism spectrum disorder (ASD) is a common neurological condition. Early diagnosis and treatment are essential for enhancing the life quality of individuals with ASD. However, most existing studies either focus solely on the brain networks of subjects within a single atlas or merely employ simple matrix concatenation to represent the fusion of multi-atlas. These approaches neglected the natural spatial overlap that exists between brain regions across multi-atlas and did not fully capture the comprehensive information of brain regions under different atlases. To tackle this weakness, in this paper, we propose a novel multi-atlas fusion template based on spatial overlap degree of brain regions, which aims to obtain a comprehensive representation of brain networks. Specifically, we formally define a measurement of the spatial overlap among brain regions across different atlases, named spatial overlap degree. Then, we fuse the multi-atlas to obtain brain networks of each subject based on spatial overlap. Finally, the GCN is used to perform the final classification. The experimental results on Autism Brain Imaging Data Exchange (ABIDE) demonstrate that our proposed method achieved an accuracy of 0.757. Overall, our method outperforms SOTA methods in ASD/TC classification.