ArticleHuman brain mapping2023
Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias.
Article in Human brain mapping, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Trial
- Increased Brain-Age Gap in Young Adults With Psychotic Experiences.Biological psychiatry global open science · 2026Article
- Longitudinal brain-age predictions comprising long-duration spaceflight missions.NPJ microgravity · 2026Article
- 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
- Mapping Multimodal Risk Factors to Mental Health Outcomes.Nature. Mental health · 2025Article
- Rate of brain aging associates with future executive function in Asian children and older adults.eLife · 2025Article
- Resting-state functional connectivity correlates of brain structural aging in schizophrenia.European archives of psychiatry and clinical neuroscience · 2025Article
- Temporal autocorrelation is predictive of age-An extensive MEG time-series analysis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Cross-Sectional Comparison of Structural MRI Markers of Impairment in a Diverse Cohort of Older Adults.Human brain mapping · 2025Article
- A review of artificial intelligence-based brain age estimation and its applications for related diseases.Briefings in functional genomics · 2025Review
- A multimodal ensemble stacking model improves brain age prediction and reveals associations with schizophrenia symptoms.Frontiers in psychiatry · 2025Article
- Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines.Brain informatics · 2024Article
- Exploring associations between psychotic experiences and structural brain age: a population-based study in late adolescence.medRxiv : the preprint server for health sciences · 2024Article
- Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review.Tomography (Ann Arbor, Mich.) · 2024Review
- Brain age has limited utility as a biomarker for capturing fluid cognition in older individuals.eLife · 2024Article
- Brain asymmetries from mid- to late life and hemispheric brain age.Nature communications · 2024Article
- GPT-based normative models of brain sMRI correlate with dimensional psychopathology.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- 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
- The alteration of cortical microstructure similarity in drug-resistant epilepsy correlated with mTOR pathway genes.EBioMedicine · 2023Article
- Considerations on brain age predictions from repeatedly sampled data across time.Brain and behavior · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Machine learning has been increasingly applied to neuroimaging data to predict age, deriving a personalized biomarker with potential clinical applications. The scientific and clinical value of these models depends on their applicability to independently acquired scans from diverse sources. Accordingly, we evaluated the generalizability of two brain age models that were trained across the lifespan by applying them to three distinct early-life samples with participants aged 8-22 years. These models were chosen based on the size and diversity of their training data, but they also differed greatly in their processing methods and predictive algorithms. Specifically, one brain age model was built by applying gradient tree boosting (GTB) to extracted features of cortical thickness, surface area, and brain volume. The other model applied a 2D convolutional neural network (DBN) to minimally preprocessed slices of T1-weighted scans. Additional model variants were created to understand how generalizability changed when each model was trained with data that became more similar to the test samples in terms of age and acquisition protocols. Our results illustrated numerous trade-offs. The GTB predictions were relatively more accurate overall and yielded more reliable predictions when applied to lower quality scans. In contrast, the DBN displayed the most utility in detecting associations between brain age gaps and cognitive functioning. Broadly speaking, the largest limitations affecting generalizability were acquisition protocol differences and biased brain age estimates. If such confounds could eventually be removed without post-hoc corrections, brain age predictions may have greater utility as personalized biomarkers of healthy aging.
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