ArticleCommunications biology2024
Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- MCH-Guard: Multimodal machine learning framework for risk stratification of cerebral microhemorrhage risk in the Alzheimer's Disease Neuroimaging Initiative.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Genetic analysis of imaging-derived phenotypes.Nature reviews. Genetics · 2026Review
- Hundreds of cardiac MRI traits derived using 3D diffusion autoencoders share a common genetic architecture.Nature communications · 2026Article
- Image feature embedding with a deep learning framework improves genome-wide association studies on dog endophenotypes.Science advances · 2026Article
- Genetic architecture of white matter microstructure captured by unsupervised deep representation learning of fractional anisotropy maps.Nature communications · 2026Article
- Multi-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traits.Nature cardiovascular research · 2026Article
- Brain-heart-eye axis revealed by multi-organ imaging genetics and proteomics.Nature biomedical engineering · 2026Article
- Replicability of unsupervised deep learning derived image phenotypes.bioRxiv : the preprint server for biology · 2026Article
- HiFiMAP: High-resolution fast identity-by-descent mapping test.medRxiv : the preprint server for health sciences · 2026Article
- Learning dynamics of unsupervised deep learning reveal epoch-specific genetic architectures of brain morphology.bioRxiv : the preprint server for biology · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.Research square · 2026Article
- Computation and resource efficient genome-wide association analysis for large-scale imaging studies.Nature communications · 2026Article
- Learning heritable multimodal brain representation via contrastive learning.bioRxiv : the preprint server for biology · 2026Article
- Genetic and epigenetic analysis of plasma glial fibrillary acidic protein (GFAP) levels in PTSD.Molecular psychiatry · 2026Article
- Genetically inferred effects of brain structure and gene expression on neurodegenerative diseases: a Mendelian randomization study.Archives of medical science : AMS · 2026Article
- Computation and resource efficient genome-wide association analysis for large-scale imaging studies.medRxiv : the preprint server for health sciences · 2025Article
- Imaging-genetics-based dementia risk prediction using deep survival neural networks in the Rotterdam Study.Research square · 2025Article
- Ophthalmic imaging as a measure of cardiovascular and neurological health: a multi-omic analysis of deep-learning derived phenotypes.medRxiv : the preprint server for health sciences · 2025Article
- Unveiling genetic architecture of white matter microstructure through unsupervised deep representation learning of fractional anisotropy maps.Research square · 2025Article
- An effective encoding of human medical conditions in disease space provides a versatile framework for deciphering disease associations.Quantitative biology (Beijing, China) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.
Indexed as
Identifiers
What Socratic 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 Socratic graph.