# Alzheimer's Disease Brain Network Mining

arXiv: https://arxiv.org/abs/2512.17276 · HTML: https://mlsanity.com/paper/2512.17276/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 49 references that are in the map.
- First posted on arXiv 19 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease](https://mlsanity.com/paper/1806.01738/) (2018)
- [BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks](https://mlsanity.com/paper/2204.07054/) (2022)
- [20 years of network community detection](https://mlsanity.com/paper/2208.00111/) (2022)
- [Multi-scale brain networks](https://mlsanity.com/paper/1608.08828/) (2016)
- [Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification](https://mlsanity.com/paper/2107.12838/) (2021)
- [Graph Neural Networks for Brain Graph Learning: A Survey](https://mlsanity.com/paper/2406.02594/) (2024)
- [Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images](https://mlsanity.com/paper/1710.04782/) (2017)
- [Multi-scale detection of hierarchical community architecture in structural and functional brain networks](https://mlsanity.com/paper/1704.05826/) (2017)
- [Community Detection in the Stochastic Block Model by Mixed Integer Programming](https://mlsanity.com/paper/2101.12336/) (2021)

## Abstract (arXiv)

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 than one-third of subjects. The framework substantially outperforms all baseline methods, achieving kappa indicating almost perfect agreement compared to weak agreement for the best baseline, a qualitative transformation in diagnostic reliability. Performance remains clinically useful even under severe label scarcity, and we provide theoretical convergence guarantees with proven bounds on label propagation error and transport stability. These results demonstrate that principled semi-supervised learning can unlock the diagnostic potential of the vast repositories of partially annotated neuroimaging data accumulating worldwide, substantially reducing annotation burden while maintaining accuracy suitable for clinical deployment.