ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2021
A multimodal IVA fusion approach to identify linked neuroimaging markers.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- A Method for Multimodal IVA Fusion Within a MISA Unified Model Reveals Markers of Age, Sex, Cognition, and Schizophrenia in Large Neuroimaging Studies.Human brain mapping · 2024Article
- Data-driven multimodal fusion: approaches and applications in psychiatric research.Psychoradiology · 2023Review
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
In this study, we introduce a method to perform independent vector analysis (IVA) fusion to estimate linked independent sources and apply to a large multimodal dataset of over 3000 subjects in the UK Biobank study, including structural (gray matter), diffusion (fractional anisotropy), and functional (amplitude of low frequency fluctuations) magnetic resonance imaging data from each subject. The approach reveals a number of linked sources showing significant and meaningful covariation with subject phenotypes. One such mode shows significant linear association with age across all three modalities. Robust age-associated reductions in gray matter density were observed in thalamus, caudate, and insular regions, as well as visual and cingulate regions, with covarying reductions of fractional anisotropy in the periventricular region, in addition to reductions in amplitude of low frequency fluctuations in visual and parietal regions. Another mode identified multimodal patterns that differentiated subjects in their time-to-recall during a prospective memory test. In sum, the proposed IVA-based approach provides a flexible, interpretable, and powerful approach for revealing links between multimodal neuroimaging data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.