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September 27, 2025Frontiers in NeuroscienceOpen Access

Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances

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Authors

SAShahzad AliMPMichele PianaMPMatteo Pardini

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Overview

This review highlights how graph neural networks improve diagnostic performance in Alzheimer's disease, suggesting future advancements in multimodal approaches.

Key Points

  • Graph neural networks significantly enhance diagnosis accuracy in Alzheimer's disease by analyzing multimodal neuroimaging data.
  • Comprehensive evaluation covers GNN frameworks and their diagnostic performance across major datasets like ADNI and OASIS.
  • The analysis includes critical comparisons of GNN architectures to address limitations and opportunities for future research.
  • Advancements in GNN applications could lead to better integration of AI technologies in clinical practices for neurodegenerative diseases.

Cite This Study

Ali et al. (2025) studied this question.

synapsesocial.com/papers/68d7be6ceebfec0fc5238230https://doi.org/10.3389/fnins.2025.1623141
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  4. 4Graph Theory and GNNs to Unravel the Topographical Organization of Brain Lesions in Variants of Alzheimer's Disease Progression2024 · 1 citations
  5. 5Multiomics-based graph convolutional neural network for Alzheimer’s disease diagnosis and MCI progression prediction2025