Observational studyNature communications2021
A deep learning framework identifies dimensional representations of Alzheimer's Disease from brain structure.
Observational study in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 1 of them a synthesis that pooled it.
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
61 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Discovering Heterogeneous Neurodegenerative Disease Patterns From MRI Data for Improved Prediction.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2027Article
- Cell-based polygenic risk scores predict clinical progression and prioritize network-based therapeutic targets in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Mapping Neuroanatomical Heterogeneity of Brain Aging Within a Clinically Defined Aging Reference Cohort.Bioengineering (Basel, Switzerland) · 2026Article
- Coupled cross-sectional and longitudinal non-negative matrix factorization reveals dominant brain aging trajectories in 48,949 individuals.Nature communications · 2026Article
- Multi-organ AI endophenotypes chart the heterogeneity of brain, eye and heart pan-disease.Nature. Mental health · 2026Article
- Investigating biotypes and neural characteristics among anxiety, depression, and comorbidity via a novel noisy label learning method.Psychoradiology · 2026Article
- Subtyping Autism Spectrum Disorder With a Population Graph-Based Dual Autoencoder: Revealing Two Distinct Biotypes.CNS neuroscience & therapeutics · 2025Article
- Distinct neuroimaging subtypes of ADHD among adolescents based on semi-supervised learning.Translational psychiatry · 2025Article
- Neuroimaging endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders.Nature biomedical engineering · 2025Article
- Dissecting real-world memory clinical cohort heterogeneity: analysis of neuroanatomical subtypes using HYDRA.Alzheimer's research & therapy · 2025Article
- Towards a multi-organ, multi-omics medical digital twin.Nature biomedical engineering · 2025Article
- Predicting Alzheimer's Disease Progression from Sparse Multimodal Data by NeuralODE Models.bioRxiv : the preprint server for biology · 2025Article
- A single-snapshot inverse solver for two-species graph model of tau pathology spreading in human Alzheimer's disease.Brain informatics · 2025Article
- A dynamic prediction model for predicting the time at which patients with MCI progress to AD based on time-dependent covariates.BMC medical informatics and decision making · 2025Article
- Generative Artificial Intelligence in Prostate Cancer Imaging.Balkan medical journal · 2025Review
- A genetically informed brain atlas for enhancing brain imaging genomics.Nature communications · 2025Article
- Pioneering new paths: the role of generative modelling in neurological disease research.Pflugers Archiv : European journal of physiology · 2025Review
- MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics.Briefings in bioinformatics · 2025Article
- Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
Abstract
Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI (two studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neurodegeneration. Applying this framework to longitudinal data revealed two distinct progression pathways. Measures of expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered complementary performance to amyloid/tau in predicting clinical progression. These deep-learning derived biomarkers offer potential for precision diagnostics and targeted clinical trial recruitment.
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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.