ArticleMedical image analysis2021
Phenotype discovery from population brain imaging.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- HARMONY: A large-scale harmonized neuroimaging dataset for research on anxious misery disorders.bioRxiv : the preprint server for biology · 2026Article
- Neuro-immune interactions underlying autoimmune diseases: insights from brain imaging data.Molecular brain · 2026Article
- 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
- The Shape of the Brain's Connections Is Predictive of Cognitive Performance: An Explainable Machine Learning Study.Human brain mapping · 2025Review
- Multiscale modes of functional brain connectivity.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- 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
- Casual associations between brain structure and sarcopenia: A large-scale genetic correlation and mendelian randomization study.Aging cell · 2024Article
- Brain imaging traits and epilepsy: Unraveling causal links via mendelian randomization.Brain and behavior · 2024Article
- Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning.Human brain mapping · 2024Article
- Deep learning imaging phenotype can classify metabolic syndrome and is predictive of cardiometabolic disorders.Journal of translational medicine · 2024Article
- TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance.Medical image analysis · 2024Article
- TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography.Frontiers in neuroscience · 2024Article
- Article
- Protocol for Brain Magnetic Resonance Imaging and Extraction of Imaging-Derived Phenotypes from the China Phenobank Project.Phenomics (Cham, Switzerland) · 2023Article
- Statistical Learning Methods for Neuroimaging Data Analysis with Applications.Annual review of biomedical data science · 2023Review
- Supervised Phenotype Discovery From Multimodal Brain Imaging.IEEE transactions on medical imaging · 2023Article
- Data-driven multimodal fusion: approaches and applications in psychiatric research.Psychoradiology · 2023Review
- Deep phenotyping for precision medicine in Parkinson's disease.Disease models & mechanisms · 2022Article
- Three-way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data.Human brain mapping · 2022Article
- The positive-negative mode link between brain connectivity, demographics and behaviour: a pre-registered replication of SmithRoyal Society open science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Neuroimaging allows for the non-invasive study of the brain in rich detail. Data-driven discovery of patterns of population variability in the brain has the potential to be extremely valuable for early disease diagnosis and understanding the brain. The resulting patterns can be used as imaging-derived phenotypes (IDPs), and may complement existing expert-curated IDPs. However, population datasets, comprising many different structural and functional imaging modalities from thousands of subjects, provide a computational challenge not previously addressed. Here, for the first time, a multimodal independent component analysis approach is presented that is scalable for data fusion of voxel-level neuroimaging data in the full UK Biobank (UKB) dataset, that will soon reach 100,000 imaged subjects. This new computational approach can estimate modes of population variability that enhance the ability to predict thousands of phenotypic and behavioural variables using data from UKB and the Human Connectome Project. A high-dimensional decomposition achieved improved predictive power compared with widely-used analysis strategies, single-modality decompositions and existing IDPs. In UKB data (14,503 subjects with 47 different data modalities), many interpretable associations with non-imaging phenotypes were identified, including multimodal spatial maps related to fluid intelligence, handedness and disease, in some cases where IDP-based approaches failed.
Indexed as
Identifiers
What OpenQuestion holds
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.