# Clustering Alzheimer's Disease Subtypes via Similarity Learning and Graph Diffusion

arXiv: https://arxiv.org/abs/2410.03937 · HTML: https://mlsanity.com/paper/2410.03937/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 65 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [A Unified Framework for Structured Graph Learning via Spectral Constraints](https://mlsanity.com/paper/1904.09792/) (2019)
- [Structured Graph Learning Via Laplacian Spectral Constraints](https://mlsanity.com/paper/1909.11594/) (2019)
- [TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data](https://mlsanity.com/paper/2001.09016/) (2020)
- [Estimation of Graphical Models through Structured Norm Minimization](https://mlsanity.com/paper/1609.09010/) (2016)
- [Simultaneous Clustering and Estimation of Heterogeneous Graphical Models](https://mlsanity.com/paper/1611.09391/) (2016)
- [High-Dimensional Inference for Cluster-Based Graphical Models](https://mlsanity.com/paper/1806.05139/) (2018)
- [Multilayer Clustered Graph Learning](https://mlsanity.com/paper/2010.15456/) (2020)
- [SIMLR: Machine Learning inside the SIR model for COVID-19 Forecasting](https://mlsanity.com/paper/2106.01590/) (2021)

## Abstract (arXiv)

Alzheimer's disease (AD) is a complex neurodegenerative disorder that affects millions of people worldwide. Due to the heterogeneous nature of AD, its diagnosis and treatment pose critical challenges. Consequently, there is a growing research interest in identifying homogeneous AD subtypes that can assist in addressing these challenges in recent years. In this study, we aim to identify subtypes of AD that represent distinctive clinical features and underlying pathology by utilizing unsupervised clustering with graph diffusion and similarity learning. We adopted SIMLR, a multi-kernel similarity learning framework, and graph diffusion to perform clustering on a group of 829 patients with AD and mild cognitive impairment (MCI, a prodromal stage of AD) based on their cortical thickness measurements extracted from magnetic resonance imaging (MRI) scans. Although the clustering approach we utilized has not been explored for the task of AD subtyping before, it demonstrated significantly better performance than several commonly used clustering methods. Specifically, we showed the power of graph diffusion in reducing the effects of noise in the subtype detection. Our results revealed five subtypes that differed remarkably in their biomarkers, cognitive status, and some other clinical features. To evaluate the resultant subtypes further, a genetic association study was carried out and successfully identified potential genetic underpinnings of different AD subtypes. Our source code is available at: https://github.com/PennShenLab/AD-SIMLR.