Multi-Modal Integration Analysis of Alzheimer’s Disease Using Large Language Models and Knowledge Graphs

Multi-Modal Integration Analysis of Alzheimer’s Disease Using Large Language Models and Knowledge Graphs

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-...

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Bibliographic Details
Main Authors: Kanan Kiguchi, Yunhao Tu, Katsuhiro Ajito, Fady Alnajjar, Kazuyuki Murase
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Alzheimer’s disease
biomarker
clinical diagnosis
cross-modal analysis
EEG
gene expression
Online Access:https://ieeexplore.ieee.org/document/11048782/
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Summary:We propose a novel framework for integrating fragmented multi-modal data in Alzheimer&#x2019;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&#x003E;0.6, p&#x003C;0.001), and unexpected correlations between frontal EEG channels and specific gene expression profiles (r=0.42-0.58, p&#x003C;0.01). Cross-validation with independent datasets confirmed the robustness of major findings, with consistent effect sizes across cohorts (variance &#x003C;15%). The reproducibility of these findings was further supported by expert review (Cohen&#x2019;s <inline-formula> <tex-math notation="LaTeX">$\kappa =0.82$ </tex-math></inline-formula>) 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.
ISSN:2169-3536

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