An Explainable Multi-atlas Fusion Model based on Spatial Overlap for ASD Diagnosis

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental condition.Prompt recognition and treatment are vital for enhancing the life quality of individuals affected by ASD.However, current research either focus on a single atlas or a simple matrix concatenation combination, neglecting the complex and spatial relationship among the brain regions in different atlases.To tackle this weakness, in this paper, we propose a novel multi-atlas time-series feature fusion model with three steps based on spatial overlap proportion of brain regions to obtain an explainable representation of brain networks, which aims to achieve excellent diagnosis of ASD/TC.Specifically, we formally introduce the concept of spatial overlap and give its measurement, spatial overlap proportion.Then, we fuse the brain regions of multi-atlas to obtain an explainable brain networks of each subject.Finally, the GCN classifier 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 0.771.Overall, our method outperforms SOTA methods in ASD/TC classification.