Evidence map›Paper›PMID 34190372›Full record

ArticleHuman brain mapping2021

Pitfalls in brain age analyses.

Ellyn R Butler, Andrew Chen, Rabie Ramadan, Trang T Le, Kosha Ruparel, Tyler M Moore, Theodore D Satterthwaite, Fengqing Zhang, Haochang Shou, Ruben C Gur and 2 more

Abstract read
In one paragraph

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 72 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
72citing papers in PubMed, 2 pooled it
–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

72 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  8. Review
  9. Article
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  12. Article
  13. Increased Brain-Age Gap in Young Adults With Psychotic Experiences.Biological psychiatry global open science · 2026
    Article
  14. Article
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12 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Ellyn R ButlerBrain Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0001-6316-6444
Andrew ChenPenn Statistics in Imaging and Visualization Endeavor, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-5027-6422
Rabie RamadanMathematics Department, Temple University, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-5107-9762
Trang T LeDepartment of Biostatistics, Epidemiology and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0003-3737-6565
Kosha RuparelBrain Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Tyler M MooreBrain Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-1384-0151
Theodore D SatterthwaitePenn Lifespan Informatics & Neuroimaging Center, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0001-7072-9399
Fengqing ZhangDepartment of Psychology, Drexel University, Philadelphia, Pennsylvania, USA.ORCID 0000-0003-2736-2303
Haochang ShouPenn Statistics in Imaging and Visualization Endeavor, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-3043-047X
Ruben C GurBrain Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-9657-1996
Thomas E NicholsBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.ORCID 0000-0002-4516-5103
Russell T ShinoharaPenn Statistics in Imaging and Visualization Endeavor, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0001-8627-8203

Funding

Statistical Methods for Multilevel Multivariate Functional StudiesR01NS060910 · NINDS · JOHNS HOPKINS UNIVERSITY · PI Ciprian M Crainiceanu · 2009 to 2026
$8.3M
Inter-modal Coupling Image AnalyticsR01MH112847 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI Theodore Satterthwaite, Russell Takeshi Shinohara · 2017 to 2026
$5.9M
Harmonization of Multi-Site Neuroimaging Data from Complex Study DesignsR01MH123550 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI SHINOHARA, RUSSELL TAKESHI · 2020 to 2025
$4.0M
Evolution of Psychosis in Youth: Multimodal Risk and Resilience MarkersR01MH119219 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI GUR, RAQUEL E, GUR, RUBEN C. · 2019 to 2023
$3.8M
Creating an adaptive screening tool for detecting neurocognitive deficits and psychopathology across the lifespanR01MH117014 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI GUR, RUBEN C. · 2019 to 2022
$2.9M
Advanced Statistical Analytics of MRI in MSR01NS112274 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI SHINOHARA, RUSSELL TAKESHI · 2020 to 2024
$2.8M
Biomedical Image Computing and Informatics ClusterS10OD023495 · OD · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2017
$1.9M
Multimodal brain maturation indices modulating psychopathology and neurocognitionR01MH107235 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI GUR, RUBEN C. · 2015 to 2017
$1.5M
NIH HHS S10 OD023495NIMH NIH HHS MH107235NIMH NIH HHS MH112847NIMH NIH HHS MH117014NIMH NIH HHS MH119219NIMH NIH HHS MH123550NIMH NIH HHS R01 MH107235NIMH NIH HHS R01 MH112847NIMH NIH HHS R01 MH117014NIMH NIH HHS R01 MH119219NIMH NIH HHS R01 MH123550NINDS NIH HHS NS060910NINDS NIH HHS NS112274NINDS NIH HHS R01 NS060910NINDS NIH HHS R01 NS112274
6 · The paper itself

Abstract

Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the "brain age gap." Researchers have identified that the brain age gap, as a linear transformation of an out-of-sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R

Indexed as

Models, TheoreticalAge FactorsBrainHumansNeuroimagingagebraindevelopmentdeviationpredictionresidual

Identifiers

PMID34190372
PMCPMC8357007

What OpenQuestion holds

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Registered trials

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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.