REVIEW article
Front. Neurosci.
Sec. Neuroscience Methods and Techniques
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1623141
This article is part of the Research TopicIntegrating Multimodal Approaches to Unravel Neural Mechanisms of Learning and CognitionView all 9 articles
Graph Neural Networks in Alzheimer's Disease Diagnosis: A Review of Unimodal and Multimodal Advances
Provisionally accepted
- 1University of Bologna, Bologna, Italy
- 2Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, Genova, Italy
- 3Department of Information Sciences, University of Education, Lahore, Pakistan
- 4Dipartimento di Matematica, Universit\'a degli Studi di Genova, Genova, Italy
- 5Dipartimento di Dipartimento di Neuroscienze, riabilitazione, oftalmologia, genetica e scienze materno-infantili, Università degli Studi di Genova, Genova, Italy
- 6IRCCS Ospedale Policlinico San Martino, Genova, Italy
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Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal neuroimaging data to improve AD diagnosis. This review provides a comprehensive overview of GNN applications in AD diagnosis, focusing on data sources, modalities, sample sizes, classification tasks, and diagnostic performance. Drawing on extensive literature searches across PubMed, IEEE Xplorer, Scopus, and Springer, we analyze key GNN frameworks and critically evaluate their limitations, challenges, and opportunities for improvement. In addition, we present a comparative analysis to evaluate the generalizability and robustness of GNN methods across different datasets, such as ADNI, OASIS, TADPOLE, UK Biobank, in-house, etc. Furthermore, we provide a critical methodological comparison across families of GNN architectures (i.e., GCN, ChebNet, GraphSAGE, GAT, GIN, etc.) in the context of AD. Finally, we outline future research directions to refine GNN-based diagnostic methods and highlight their potential role in advancing AI-driven neuroimaging solutions. Our findings aim to foster the integration of AI technologies in neurodegenerative disease research and clinical practice.
Keywords: Alzheimer's disease, deep learning, diagnosis, Graph neural network, multimodal, Neuroimaging, neurological disorders, review
Received: 07 May 2025; Accepted: 01 Sep 2025.
Copyright: © 2025 Ali, Piana, Pardini and Garbarino. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Shahzad Ali, University of Bologna, Bologna, Italy
Sara Garbarino, Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, Genova, Italy
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