![[2602.17557] Probability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.17557] Probability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis
Summary
This article presents a novel framework for diagnosing Alzheimer's and Lewy body dementia using probability-invariant random walk learning on gyral folding-based cortical similarity networks.
Why It Matters
The research addresses the challenges of diagnosing Alzheimer's and Lewy body dementia, which often share clinical features but require different diagnostic approaches. By utilizing individualized gyral folding networks, this study offers a more accurate method that could enhance diagnostic precision and improve patient outcomes.
Key Takeaways
- Introduces a probability-invariant framework for dementia diagnosis.
- Utilizes individualized gyral folding networks to enhance accuracy.
- Demonstrates improved performance over traditional atlas-based models.
- Addresses anatomical variability in clinical datasets.
- Potential to significantly impact diagnostic strategies for dementia.
Quantitative Biology > Neurons and Cognition arXiv:2602.17557 (q-bio) [Submitted on 19 Feb 2026] Title:Probability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis Authors:Minheng Chen, Jing Zhang, Tong Chen, Chao Cao, Tianming Liu, Li Su, Dajiang Zhu View a PDF of the paper titled Probability-Invariant Random Walk Learning on Gyral Folding-Based Cortical Similarity Networks for Alzheimer's and Lewy Body Dementia Diagnosis, by Minheng Chen and 6 other authors View PDF HTML (experimental) Abstract:Alzheimer's disease (AD) and Lewy body dementia (LBD) present overlapping clinical features yet require distinct diagnostic strategies. While neuroimaging-based brain network analysis is promising, atlas-based representations may obscure individualized anatomy. Gyral folding-based networks using three-hinge gyri provide a biologically grounded alternative, but inter-individual variability in cortical folding results in inconsistent landmark correspondence and highly irregular network sizes, violating the fixed-topology and node-alignment assumptions of most existing graph learning methods, particularly in clinical datasets where pathological changes further amplify anatomical heterogeneity. We therefore propose a probability-invariant random-walk-based framework that classifies individualized gyral folding networks without explicit node alignment. Cortical similarity networks are built from local mo...
