Multi-Modal Integration Analysis of Alzheimer’s Disease Using Large Language Models and Knowledge Graphs
Year: 2025
Type: article
Abstract: We propose a novel framework for integrating fragmented multi-modal data in Alzheimer’s disease (AD) research using large language models (LLMs) and knowledge graphs. While traditional multi-modal analysis requires matched patient IDs across datasets, our approach demonstrates population-level integration of MRI, gene expression, biomarkers, EEG, and clinical indicators from independent cohorts. Statistical analysis identified significant features in each modality, which were connected as nodes in a knowledge graph. LLMs then analyzed the graph to extract potential correlations and generate hypotheses in natural language. This approach revealed several novel relationships, including a potential pathway linking metabolic risk factors to tau protein abnormalities via neuroinflammation (r>0.6, p<0.001), and unexpected correlations between frontal EEG channels and specific gene expression profiles (r=0.42-0.58, p<0.01). Cross-validation with independent datasets confirmed the robustness of major findings, with consistent effect sizes across cohorts (variance <15%). The reproducibility of these findings was further supported by expert review (Cohen’s κ=0.82) and computational validation. Our framework enables cross-modal integration at a conceptual level without requiring patient ID matching, offering new possibilities for understanding AD pathology through fragmented data reuse and generating testable hypotheses for future research.
Source: IEEE Access
Authors: Kanan Kiguchi, Yunhao Tu, Katsuhiro Ajito, Fady Alnajjar, Kazuyuki Murase
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4411599647