Graph-Based Deep Learning Approaches for Alzheimer’s Disease: A Systematic Review
Keywords:
Alzheimer’s Disease, Early Diagnosis, Graph Neural Network (GNN), Neuroimaging, Stage ProgressionAbstract
The Alzheimer Disease (AD) is a progressive neurodegenerative disease that involves memory impairment, intellectual degradation and alterations in the brain structure. With the rising trend of AD in the world, early diagnosis and proper staging have become the key towards early intervention and management. Conventional machine learning and imaging- based techniques can be difficult to represent a complicated relationship between brain regions and multimodal biomarkers that play a role in AD progression. In the recent past, Graph Neural Networks (GNNs) are proving useful in analyzing this type of graph-structured data, allowing more effectively modeling patterns of brain connectivity as measured by neuroimaging technologies such as MRI, fMRI, and EEG, and patterns of brain connectivity as measured by non-imaging sources such as electronic health records. This is a systematic review of the current developments in GNN-based methods of AD diagnosis, prediction of its stage progression, and the description of biomarkers. Eight exemplar studies are evaluated regarding the usage of datasets, methodological frameworks and performance metrics. Reported results show very exceptional accuracies which are up to 98.10% and AUC values approaching 1.0, underscoring the robustness of GNN models in both binary and multi-class classification. Despite these advancements, challenges persist regarding generalizability across datasets, integration of multimodal and longitudinal data, and model interpretability. This paper highlights current achievements and research gaps, outlining how graph-based deep learning can drive the next generation of intelligent, interpretable, and clinically applicable AD diagnostic systems.
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