ArticleIEEE transactions on medical imaging2023
Supervised Phenotype Discovery From Multimodal Brain Imaging.
Article in IEEE transactions on medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.Journal of medical Internet research · 2026Pooled it
- Investigating the Amyloid-Tau-Neurodegeneration Framework in Alzheimer's Disease Using Semi-Supervised Multimodal Imaging Data Fusion.medRxiv : the preprint server for health sciences · 2025Article
- Generating synthetic task-based brain fingerprints for population neuroscience using deep learning.Communications biology · 2025Article
- Confound Controlled Multimodal Neuroimaging Data Fusion and Its Application to Developmental Disorders.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2025Article
- Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers.Bioinformatics (Oxford, England) · 2024Article
- Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes.medRxiv : the preprint server for health sciences · 2024Article
- Biomarker discovery using machine learning in the psychosis spectrum.Biomarkers in neuropsychiatry · 2024Article
- Running in the FAMILY: understanding and predicting the intergenerational transmission of mental illness.European child & adolescent psychiatry · 2024Article
- Individualised prediction of longitudinal change in multimodal brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Principal and independent genomic components of brain structure and function.Genes, brain, and behavior · 2024Article
- TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography.Frontiers in neuroscience · 2024Article
- Data-driven multimodal fusion: approaches and applications in psychiatric research.Psychoradiology · 2023Review
- Three-way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data.Human brain mapping · 2022Article
- White matter association tracts underlying language and theory of mind: An investigation of 809 brains from the Human Connectome Project.NeuroImage · 2022Article
- Investigating the amyloid-tau-neurodegeneration framework in Alzheimer's disease using semi-supervised multimodal imaging data fusion.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Data-driven discovery of image-derived phenotypes (IDPs) from large-scale multimodal brain imaging data has enormous potential for neuroscientific and clinical research by linking IDPs to subjects' demographic, behavioural, clinical and cognitive measures (i.e., non-imaging derived phenotypes or nIDPs). However, current approaches are primarily based on unsupervised approaches, without the use of information in nIDPs. In this paper, we proposed a semi-supervised, multimodal, and multi-task fusion approach, termed SuperBigFLICA, for IDP discovery, which simultaneously integrates information from multiple imaging modalities as well as multiple nIDPs. SuperBigFLICA is computationally efficient and largely avoids the need for parameter tuning. Using the UK Biobank brain imaging dataset with around 40,000 subjects and 47 modalities, along with more than 17,000 nIDPs, we showed that SuperBigFLICA enhances the prediction power of nIDPs, benchmarked against IDPs derived by conventional expert-knowledge and unsupervised-learning approaches (with average nIDP prediction accuracy improvements of up to 46%). It also enables the learning of generic imaging features that can predict new nIDPs. Further empirical analysis of the SuperBigFLICA algorithm demonstrates its robustness in different prediction tasks and the ability to derive biologically meaningful IDPs in predicting health outcomes and cognitive nIDPs, such as fluid intelligence and hypertension.
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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.