[2512.17276] Alzheimer's Disease Brain Network Mining

Computer Science > Machine Learning arXiv:2512.17276 (cs) This paper has been withdrawn by Alireza Moayedikia [Submitted on 19 Dec 2025 (v1), last revised 20 Mar 2026 (this version, v2)] Title:Alzheimer's Disease Brain Network Mining Authors:Alireza Moayedikia, Sara Fin View a PDF of the paper titled Alzheimer's Disease Brain Network Mining, by Alireza Moayedikia and Sara Fin No PDF available, click to view other formats Abstract:Machine learning approaches for Alzheimer's disease (AD) diagnosis face a fundamental challenges. Clinical assessments are expensive and invasive, leaving ground truth labels available for only a fraction of neuroimaging datasets. We introduce Multi view Adaptive Transport Clustering for Heterogeneous Alzheimer's Disease (MATCH-AD), a semi supervised framework that integrates deep representation learning, graph-based label propagation, and optimal transport theory to address this limitation. The framework leverages manifold structure in neuroimaging data to propagate diagnostic information from limited labeled samples to larger unlabeled populations, while using Wasserstein distances to quantify disease progression between cognitive states. Evaluated on nearly five thousand subjects from the National Alzheimer's Coordinating Center, encompassing structural MRI measurements from hundreds of brain regions, cerebrospinal fluid biomarkers, and clinical variables MATCHAD achieves near-perfect diagnostic accuracy despite ground truth labels for less tha...