Diagnosing Autism Using T1-W MRI With Multi-Kernel Learning and Hypergraph Neural Network
Mohammad Madine, Islem Rekik, Naoufel Werghi
Abstract
The field of network neuroscience provided unprecedented insights into how brain connectivity gets altered by autism spectrum disorder (ASD) on functional, structural, and morphological levels. However, a few studies have looked to design a framework that captures the complex network structure of the brain and disentangles the heterogeneity of ASD. In this paper, we leverage multi-kernel unsupervised learning in the construction of multiview hypergraph neural networks (HGNN), each capturing a particular view of the brain connectome, to eventually distinguish between ASD and normal control (NC) subjects. Additionally, we tested and measured how our proposed framework compares to other variants based on previous baseline methods. Our classification results outperformed comparison methods and agreed with the literature in the sense that the right hemisphere connectivity was more discriminative in ASD diagnosis than the left hemisphere.