Multi-modal Integration Analysis of Alzheimer's Disease Using Large Language Models and Knowledge Graphs

TL;DR

This paper introduces a novel framework combining large language models and knowledge graphs to integrate multi-modal Alzheimer's disease data across independent cohorts, uncovering new insights without needing patient ID matching.

Contribution

The study presents a new method for population-level multi-modal data integration in AD research using LLMs and knowledge graphs, enabling hypothesis generation from fragmented datasets.

Findings

Identified significant correlations linking metabolic factors to tau pathology.

Discovered unexpected links between EEG patterns and gene expression.

Validated robustness of findings across multiple independent datasets.

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 multimodal 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 k=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.