ArticleImaging neuroscience (Cambridge, Mass.)2025
Characterising ongoing brain aging and baseline effects from cross-sectional data.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Interpretable Local-to-Global Estimation of Brain Aging Speed From Morphological Changes Using Longitudinal Structural MRI Data.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2027Article
- Sex-specific biological aging clocks across organs and omics.Nature medicine · 2026Article
- Psychosis as a multisystem disorder of aberrant aging.npj aging · 2026Review
- Prediction of brain age using structural magnetic resonance imaging: a comparison of clinical utility of publicly available software packages.EBioMedicine · 2026Article
- Plasma proteomics links brain and immune system aging with healthspan and longevity.Nature medicine · 2025Article
- Cross-Sectional Brain Age Assessments Are Limited in Predicting Future Brain Change.Human brain mapping · 2025Article
- Stable individual differences dominate adult brain volume variation until later life.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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
"Brain age delta" is the difference between age estimated from brain imaging data and actual age. Positive delta in adults is normally interpreted as implying that an individual is aging (or has aged) faster than the population norm, an indicator of unhealthy aging. Unfortunately, from cross-sectional (single timepoint) imaging data, it is impossible to know whether a single individual's positive delta reflects a state of faster ongoing aging, or an unvarying trait (in other words, a "historical baseline effect" in the context of the population being studied). However, for a cross-sectional dataset comprising many individuals, one could attempt to disambiguate varying aging rates from fixed baseline effects. We present a method for doing this, and show that for the common approach of estimating a single delta per subject, baseline effects are likely to dominate. If instead one estimates multiple biologically distinct modes of brain aging, we find that some modes do reflect aging rates varying strongly across subjects. We demonstrate this, and verify our modelling, using longitudinal (two timepoint) data from 4,400 participants in UK Biobank. In addition, whereas previous work found incompatibility between cross-sectional and longitudinal brain aging, we show that careful data processing does show consistency between cross-sectional and longitudinal results.
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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.