Multi-Modal Diagnosis of Alzheimer's Disease Using Interpretable Graph Convolutional Networks.

ABSTRACT: The interconnection between brain regions in neurological disease encodes vital information for the advancement of biomarkers and diagnostics. Although graph convolutional networks are widely applied for discovering brain connection patterns that point to disease conditions, the potential of connection patterns that arise from multiple imaging modalities has yet to be fully realized. In this paper, we propose a multi-modal sparse interpretable GCN framework (SGCN) for the detection of Alzheimer's disease (AD) and its prodromal stage, known as mild cognitive impairment (MCI). In our experimentation, SGCN learned the sparse regional importance probability to find signature regions of interest (ROIs), and the connective importance probability to reveal disease-specific brain network connectio

SUBMITTER: Zhou H 

PROVIDER: S-EPMC11754532 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature