ArticleBrain and behavior2023
Considerations on brain age predictions from repeatedly sampled data across time.
Article in Brain and behavior, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Cross-Sectional Brain Age Assessments Are Limited in Predicting Future Brain Change.Human brain mapping · 2025Article
- Brain age in multiple sclerosis: a study with deep learning and traditional machine learning.Brain communications · 2025Article
- Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis.Frontiers in aging neuroscience · 2025Article
- FreeSurfer version-shuffling can enhance brain age predictions.Neuroimage. Reports · 2024Article
- Brain asymmetries from mid- to late life and hemispheric brain age.Nature communications · 2024Article
- The intra-individual reliability ofHuman brain mapping · 2024Article
- Considerations on brain age predictions from repeatedly sampled data across time.Brain and behavior · 2023Article
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
Abstract
introductionBrain age, the estimation of a person's age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for the same individual at various time points and validate our findings with age-matched healthy controls.
methodsWe used densely sampled T1-weighted MRI data from four individuals (from two densely sampled datasets) to observe how brain age corresponds to age and is influenced by acquisition and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pretrained deep learning model.
resultsWe found small within-subject correlations between age and brain age. We also found evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data and inconclusive effects of scan quality.
conclusionThe absence of maturation effects for the age range in the presented sample, brain age model bias (including training age distribution and field strength), and model error are potential reasons for small relationships between age and brain age in densely sampled longitudinal data. Clinical applications of brain age models should consider of the possibility of apparent biases caused by variation in the data acquisition process.
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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.