ArticleIEEE transactions on medical imaging2022
Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- BriGHT: transcriptome-regularized multimodal neuroimaging for brain disorder prediction.Bioinformatics (Oxford, England) · 2026Article
- MULTI-OBJECT DATA INTEGRATION IN THE STUDY OF PRIMARY PROGRESSIVE APHASIA.The annals of applied statistics · 2025Article
- Alterations in Gray Matter Structure Linked to Frequency-Specific Cortico-Subcortical Connectivity in Schizophrenia via Multimodal Data Fusion.Neuroinformatics · 2025Article
- Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.Schizophrenia bulletin · 2025Review
- Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results.ArXiv · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Recent studies show that multi-modal data fusion techniques combine information from diverse sources for comprehensive diagnosis and prognosis of complex brain disorder, often resulting in improved accuracy compared to single-modality approaches. However, many existing data fusion methods extract features from homogeneous networs, ignoring heterogeneous structural information among multiple modalities. To this end, we propose a Hypergraph-based Multi-modal data Fusion algorithm, namely HMF. Specifically, we first generate a hypergraph similarity matrix to represent the high-order relationships among subjects, and then enforce the regularization term based upon both the inter- and intra-modality relationships of the subjects. Finally, we apply HMF to integrate imaging and genetics datasets. Validation of the proposed method is performed on both synthetic data and real samples from schizophrenia study. Results show that our algorithm outperforms several competing methods, and reveals significant interactions among risk genes, environmental factors and abnormal brain regions.
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