Knowledge Graph and Large Language Model-Based Analysis of fMRI Brain Functional Neuroimaging Research.
Authors
Affiliations (4)
Affiliations (4)
- School of Management, Fudan University, Shanghai 200433, China.
- School of Pharmacy, Guizhou University, Guiyang 550025, China.
- The First Affiliated Hospital of Naval Medical University, Shanghai 200433, China.
- School of Humanities and Management Sciences, Southwest Medical University, Luzhou 646000, China.
Abstract
The rapid growth of multimodal neuroimaging research has produced fragmented literature that limits systematic characterization of cross-modal relationships and disease-specific knowledge structures. To address this, we constructed a multimodal neuroimaging knowledge graph from 1838 peer-reviewed studies (2016-2026) spanning fMRI, EEG, fNIRS, and PET, using an LLM-based extraction and retrieval-augmented semantic merging pipeline. The resulting graph comprised 4190 nodes and 7007 edges, exhibiting a scale-free topology with a dominant connected component covering 76.6% of nodes. Alzheimer's disease, the hippocampus, and fMRI/PET emerged as the most central hubs linking disease, anatomical, and methodological dimensions. Louvain community detection identified 25 functional modules, with seven major communities-centered on Alzheimer's biomarker integration, molecular/fluid imaging, and psychiatric functional connectivity-forming the field's core structure. Cross-modal analysis revealed the strongest coupling between fMRI and PET, indicating high methodological convergence. At the disease level, Alzheimer's disease displayed a mature, hierarchically organized biomarker system, whereas major depressive disorder and chronic pain showed diffuse, less consolidated knowledge structures. These results reveal pronounced disparities across neuroimaging research domains and demonstrate that LLM-augmented knowledge graphs can systematically uncover latent structural organization relevant to multimodal integration and biomarker discovery.