ArticleScience advances2022
An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- BrainAGE, brain health, and mental disorders: A systematic review.Neuroscience and biobehavioral reviews · 2024Pooled it
- Learning robust and task-invariant functional representation from fMRI through Siamese self-supervised learning.Medical image analysis · 2026Article
- Resting-State Cortical Network and Subcortical Hyperconnectivity in Youth With Generalized Anxiety Disorder in the ABCD Study.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Article
- MRI-derived estimation of biological aging in patients with affective disorders in a 9-year follow-up - a prospective marker of future recurrence.Molecular psychiatry · 2026Article
- Longitudinal brain-age predictions comprising long-duration spaceflight missions.NPJ microgravity · 2026Article
- The impact of balance exercise on brain age and brain morphometry: insights from MRI analysis.Aging clinical and experimental research · 2026Observational
- Ultra-fast MRI for brain-age prediction in a real-world cognitive disorders clinic.Frontiers in aging neuroscience · 2026Article
- Prediction of brain age using structural magnetic resonance imaging: a comparison of clinical utility of publicly available software packages.EBioMedicine · 2026Article
- Rate of brain aging associates with future executive function in Asian children and older adults.eLife · 2025Article
- Lifestyle, Early-life, and Genetic Health Risk Factors Underlying the Brain Age Gap: A Mega-Analysis Across 3,934 Individuals from the ENIGMA MDD Consortium.bioRxiv : the preprint server for biology · 2025Article
- Advanced Brain Age Prediction Using Multi-Head Self-Attention: A Comparative Analysis of Western and Middle Eastern MRI Datasets.Research square · 2025Article
- Investigating dynamic brain functional redundancy as a mechanism of cognitive reserve.Frontiers in aging neuroscience · 2025Article
- Automated quality control of T1-weighted brain MRI scans for clinical research datasets: methods comparison and design of a quality prediction classifier.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Network-level enrichment provides a framework for biological interpretation of machine learning results.Network neuroscience (Cambridge, Mass.) · 2024Article
- Geroscience-Centric Perspective for Geriatric Psychiatry: Integrating Aging Biology With Geriatric Mental Health Research.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2024Review
- Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test-retest reliability of publicly available software packages.Human brain mapping · 2023Article
- Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II.Frontiers in aging neuroscience · 2022Article
- Editorial: Predicting Chronological Age From Structural Neuroimaging: The Predictive Analytics Competition 2019.Frontiers in psychiatry · 2021Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
33 authors.
Funding
Abstract
The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk marker of cross-disorder brain changes, growing into a cornerstone of biological age research. However, machine learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared because of data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte Carlo dropout composite quantile regression (MCCQR) Neural Network trained on
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.