Evidence map›Paper›PMID 41483685›Full record

ArticleEBioMedicine2026

Prediction of brain age using structural magnetic resonance imaging: a comparison of clinical utility of publicly available software packages.

Ruben P Dörfel, Brice Ozenne, Melanie Ganz, Jonas E Svensson, Pontus Plavén-Sigray, Alzheimer's Disease Neuroimaging Initiative (ADNI)

Abstract readComparative Study
In one paragraph

Article in EBioMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Ruben P DörfelCentre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Sweden; Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Denmark; Department of Computer Science, University of Copenhagen, Denmark. Electronic address: ruben.dorfel@ki.se.
Brice OzenneNeurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Denmark; Section of Biostatistics, University of Copenhagen, Denmark.
Melanie GanzNeurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Denmark; Department of Computer Science, University of Copenhagen, Denmark.
Jonas E SvenssonCentre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Sweden.
Pontus Plavén-SigrayCentre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Sweden; Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Denmark.
Alzheimer's Disease Neuroimaging Initiative (ADNI)

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI MONICA G. RIVERA-MINDT · 2016 to 2026
$226.7M
NIA NIH HHS U19 AG024904
6 · The paper itself

Abstract

backgroundBrain age estimated from structural magnetic resonance images is commonly used as a biomarker of biological ageing and brain health. Ideally, as a clinically useful biomarker, brain age should indicate the current state of health and be predictive of future disease onset and detrimental changes in brain biology.

methodsIn this preregistered study, we evaluated and compared the clinical utility, i.e., diagnostic and prognostic performance, of six publicly available brain age prediction packages using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

findingsBaseline brain age differed significantly between groups consisting of individuals with normal cognitive function, mild cognitive impairment, and Alzheimer's disease for all packages, but with comparable performance to estimates of grey matter volume. Further, brain age estimates were not centred around zero for participants with normal cognition and showed considerable variation between packages. Finally, brain age was only weakly correlated with disease onset, memory decline, and grey matter atrophy within four years from baseline in individuals without neurodegenerative disease.

interpretationThe systematic discrepancy between chronological age and brain age among healthy subjects, combined with the weak associations between brain age and longitudinal changes in memory performance or grey matter volume, suggests that the current brain age estimates have limited clinical utility as a biomarker for biological ageing.

fundingThis work was supported by a Longevity Impetus Grant from the Norn Group, the Karolinska Institutet Loo och Hans Ostermans Stiftelse, Gun och Bertil Stohnes Stiftelse, Stiftelsen Gamla Tjänarinnor, Stiftelsen Söderström - Königska and Åhlén-stiftelsen (243016). PPS was supported by a grant from the Swedish Brain Foundation (PD2024-0444) and the Åke Wibergs Stiftelse (M24-0117).

Indexed as

AgingBrainMagnetic Resonance ImagingSoftwareAgedAged, 80 and overAlzheimer DiseaseCognitive DysfunctionFemaleGray MatterHumansImage Processing, Computer-AssistedMaleMiddle AgedNeuroimagingBiomarkerBrain ageClinical utilityMachine learningPredicted age deviationStructural MRI

Identifiers

PMID41483685
PMCPMC12805294

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.