Identifying Alzheimer’s Disease-Induced Topology Alterations in Structural Networks Using Convolutional Neural Networks

Identifying topology alterations in white matter connectivity has emerged as a promising avenue for exploring potential markers of Alzheimer’s disease (AD). However, conventional graph learning methods struggle to accurately represent the subtle and heterogeneous topology alterations caused by AD, leading to marginal classification accuracy. In this study, we address this issue through a two-fold approach. Firstly, to more reliably capture AD-induced alterations, we collect multi-shell high-angular resolution diffusion MRI data and construct a topology tensor to incorporate multiple edge-based attributes. Secondly, we propose a novel CNN framework called REST-Net, utilizing lightweight convolutional kernels to integrate the multiple attributes, enhancing its capacity for topology representation. With extensive experiments, REST-Net outperforms seven state-of-the-art graph learning methods for binary and tertiary classification tasks. Of utmost importance, the white matter connections identified by REST-Net guide the selection of target bundles for further analysis, which can potentially provide valuable insights for clinical and pharmacological investigations.