Topology-aware hybrid graph-transformer network for Alzheimer's disease diagnosis from structural magnetic resonance imaging.

Overview

1. Department of Information Systems, College of Business Administration-Yanbu, Taibah University, Medina, Saudi Arabia
2. School of Computing and Creative Technologies, University of the West of England, Bristol, United Kingdom
3. Applied College, University of Tabuk, Tabuk, Saudi Arabia
4. Department of Computer Science, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

Abstract

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by localized cortical atrophy and large-scale disruption of brain connectivity. Although deep learning (DL) methods have shown promise for neuroimaging-based diagnosis, many approaches fail to jointly capture localized structural changes and global network-level degeneration.

Methods: We propose a topology-aware hybrid DL framework for AD classification from structural MRI. The model integrates (1) a 3D convolutional neural network (CNN) to extract volumetric morphometric features, (2) a dynamic graph attention network (GAT) to infer patient-specific structural connectivity without predefined atlases, and (3) a topology-biased Vision Transformer (Topo-ViT) that incorporates this connectivity into global attention. The framework is trained under strict subject-level data segregation and optimized using focal loss with an AUC-driven strategy.

Results: Evaluated on a structural MRI dataset derived from the OASIS cohort, the proposed model achieved a test ROC-AUC of 0.857, with an overall accuracy of 85% and high sensitivity in detecting demented cases. Ablation studies show that topology-guided attention improves performance over CNN and hybrid baselines. Additional analyses reveal stable connectivity patterns and well-separated latent representations

Discussion: The results demonstrate that integrating topology-aware mechanisms enables more coherent modeling of AD as a network-level disorder. The proposed framework captures both local and global structural patterns, offering improved diagnostic reliability. Further validation on larger datasets is required for clinical deployment.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

Datasets cited

• kaggle.com/datasets/ninadaithal, at Kaggle; found in “Data availability statement”

Data availability statement

The data used in this study are publicly available. The original structural MRI data were obtained from the Open Access Series of Imaging Studies (OASIS-1 dataset, available at https://sites.wustl.edu/oasisbrains/home/oasis-1/). A processed version of the OASIS-1 dataset, derived from the original scans and made available for machine learning research, was accessed through Kaggle at https://www.kaggle.com/datasets/ninadaithal/imagesoasis. The code and Jupyter notebooks developed to implement and evaluate the proposed topology-aware hybrid graph-transformer framework are available from the corresponding author upon reasonable request for academic and research purposes.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 5 keywords, 24 references.

Cite

This paper

Gazem, N. A., Latif, S., Ghaban, W., & Qasem, S. N. (2026). Topology-aware hybrid graph-transformer network for Alzheimer's disease diagnosis from structural magnetic resonance imaging. Frontiers in computational neuroscience, 20, 1834764. https://doi.org/10.3389/fncom.2026.1834764

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