Connectome graph attention network (CGAT): A multimodal approach for enhanced classification of Alzheimer's disease
Year: 2025
Type: article
Abstract: This study proposes a model for Alzheimer's Disease (AD) diagnosis called the Connectome Graph Attention Network (CGAT), designed to improve the classification of AD stages. By integrating multimodal neuroimaging data and clinical diagnosis information, CGAT constructs a connectome graph for each individual, with brain regions as nodes and the connections between them as edges. CGAT enhances classification by employing an attention mechanism that highlights critical brain region connections. A unique feature of the model is its capacity to merge features from diverse imaging modalities effectively, using modality-specific loss weighting to ensure balanced data contribution in the final classification. Developed in PyTorch Geometric (PyG), CGAT was optimized through the NNI AutoML toolkit. The model demonstrated a notable AUROC score of 94.00, outperforming other graph-based models like GCN, GraphSAGE, GGNN, and ChebNet, especially in differentiating EMCI from LMCI. Visualizations using t-SNE revealed CGAT's ability to distinctly separate cognitive states, emphasizing important connections among brain regions. Overall, CGAT efficiently combines multimodal neuroimaging data for improved, multi-class AD stage classification.
Source: Results in Engineering
Authors: Udayakumar P, R. Subhashini
Institution: Vellore Institute of Technology University
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,940
OpenAlex ID: W4410197160