ArticleHuman brain mapping2021
Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 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
53 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium.Molecular psychiatry · 2023Pooled it
- Resting-state brain age is associated with cognitive and sensorimotor abnormalities in schizophrenia spectrum disorders: a validation & longitudinal study.Translational psychiatry · 2026Article
- Investigating the Contribution of Molecular-Enriched Functional Connectivity to Brain-Age Analysis.Human brain mapping · 2026Article
- A comparative evaluation of brain MRI modalities, modality-specific measures, brain regions, and cognitive tests for brain age prediction.GeroScience · 2026Article
- Source-space EEG alpha activity reveals brain age gaps due to neurodegeneration and disparity.Communications biology · 2026Article
- Article
- Resting-State Brain Age Predicts Cognitive and Sensorimotor Deficits in Schizophrenia Spectrum Disorders: A Validation & Longitudinal Study.Research square · 2026Article
- Neonatal brain age estimation from T2-weighted magnetic resonance images using shallow regression analysis.Scientific reports · 2026Article
- Article
- Investigating the Contribution of Molecular-Enriched Functional Connectivity to Brain-Age Analysis.bioRxiv : the preprint server for biology · 2025Article
- Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective.ArXiv · 2025Article
- Neuroanatomical features reveal accelerated brain age in alcohol use disorder.bioRxiv : the preprint server for biology · 2025Article
- Integration of Genetic Information to Improve Brain Age Gap Estimation Models in the UK Biobank.Human brain mapping · 2025Article
- When Age Is More Than a Number: Acceleration of Brain Aging in Neurodegenerative Diseases.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Development and Validation of a Brain Aging Biomarker in Middle-Aged and Older Adults: Deep Learning Approach.JMIR aging · 2025Article
- Do Transformers and CNNs Learn Different Concepts of Brain Age?Human brain mapping · 2025Article
- Neurofind: using deep learning to make individualised inferences in brain-based disorders.Translational psychiatry · 2025Article
- A review of artificial intelligence-based brain age estimation and its applications for related diseases.Briefings in functional genomics · 2025Review
- Brain age in multiple sclerosis: a study with deep learning and traditional machine learning.Brain communications · 2025Article
- A multimodal ensemble stacking model improves brain age prediction and reveals associations with schizophrenia symptoms.Frontiers in psychiatry · 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
10 authors.
Funding
Abstract
Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such "brain age prediction" vary widely in terms of their methods and type of data, so at present the most accurate and generalisable methodological approach is unclear. Therefore, we used the UK Biobank data set (N = 10,824, age range 47-73) to compare the performance of the machine learning models support vector regression, relevance vector regression and Gaussian process regression on whole-brain region-based or voxel-based structural magnetic resonance imaging data with or without dimensionality reduction through principal component analysis. Performance was assessed in the validation set through cross-validation as well as an independent test set. The models achieved mean absolute errors between 3.7 and 4.7 years, with those trained on voxel-level data with principal component analysis performing best. Overall, we observed little difference in performance between models trained on the same data type, indicating that the type of input data had greater impact on performance than model choice. All code is provided online in the hope that this will aid future research.
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.