Evidence map›Paper›PMID 32920988›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2021

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

Mohamad Habes, Raymond Pomponio, Haochang Shou, Jimit Doshi, Elizabeth Mamourian, Guray Erus, Ilya Nasrallah, Lenore J Launer, Tanweer Rashid, Murat Bilgel and 20 more

Open access · greenAbstract read
In one paragraph

Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers.

0numbers the graph read from it
0cells of the map it votes in
133citing papers in PubMed
14.5field-weighted citation impact, top 1% of its field
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

133 citing papers in PubMed, 166 citations in OpenAlex.

  1. Trial
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  8. Association of sleep duration with Alzheimer's disease and cognition.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
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  11. Charting Cervical Spinal Cord Morphometry Across the Lifespan.bioRxiv : the preprint server for biology · 2026
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  12. Article
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  14. Review
  15. Cardiovascular Health at Midlife and Alzheimer Disease Biomarkers.medRxiv : the preprint server for health sciences · 2026
    Article
  16. Article
  17. Review
  18. Article
  19. Midlife sleep characteristics and Alzheimer's disease biomarkers 20 years later.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  20. Article

73 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

30 authors at 14 institutions in 3 countries.

Mohamad HabesCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0001-9447-5805
Raymond PomponioCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Haochang ShouCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Jimit DoshiCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Elizabeth MamourianCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Guray ErusCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Ilya NasrallahCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Lenore J LaunerLaboratory of Epidemiology and Population Sciences, National Institute on Aging, Bethesda, Maryland, USA.
Tanweer RashidCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Murat BilgelLaboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, USA.
Yong FanCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Jon B ToledoDepartment of Pathology and Laboratory Medicine, Institute on Aging, Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA.
Kristine YaffeDepartments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA.
Aristeidis SotirasCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Dhivya SrinivasanCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Mark EspelandDepartment of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Colin MastersFlorey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia.
Paul MaruffFlorey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia.
Jurgen FrippCSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Australia.
Henry VölzkInstitute for Community Medicine, University of Greifswald, Greifswald, Germany.
Sterling C JohnsonWisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
John C MorrisDepartment of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA.
Marilyn S AlbertDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Michael I MillerDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
R Nick BryanDepartment of Diagnostic Medicine, University of Texas, Austin, Texas, USA.
Hans J GrabeDepartment of Psychiatry and Psychotherapy, University of Greifswald, Germany.
Susan M ResnickLaboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, USA.
David A WolkCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Christos DavatzikosCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
iSTAGING consortium, the Preclinical AD consortium, the ADNI, and the CARDIA studiesCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
University of Pennsylvania · USNational Institute on Aging · USJohns Hopkins University · USThe University of Melbourne · AUWashington University in St. Louis · USAustralian e-Health Research Centre · AUGerman Center for Neurodegenerative Diseases · DEHouston Methodist · USPenn Center for AIDS Research · USThe University of Texas at Austin · USThe University of Texas Health Science Center at San Antonio · USUniversitätsmedizin Greifswald · DEUniversity of California, San Francisco · USWake Forest University · US

Funding

Project 2 MeasurementU19AG033655 · NIA · JOHNS HOPKINS UNIVERSITY · PI MARILYN S. ALBERT · 2014 to 2026
$51.5M
Wisconsin Alzheimer's Disease Research CenterP30AG062715 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Barbara Brigitta Bendlin · 2019 to 2026
$34.5M
Research Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI MARILYN S. ALBERT · 2020 to 2026
$29.3M
Neuroimaging Predictors of Cognitive Decline, Impairment, and ResilienceZIAAG000191 · NIA · NATIONAL INSTITUTE ON AGING · PI FERRUCCI, LUIGI · 2009 to 2025
$26.4M
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7M
Wake Forest Alzheimer's Disease Core CenterP30AG049638 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ESPELAND, MARK ANDREW · 2016 to 2020
$12.2M
Preclinical AD ConsortiumRF1AG059869 · NIA · JOHNS HOPKINS UNIVERSITY · PI ALBERT, MARILYN S. · 2018 to 2018
$7.7M
Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortiumRF1AG054409 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2023
$6.3M
Personalized Functional Network Modeling to Characterize and Predict Psychopathology in YouthR01EB022573 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI Yong Fan, Theodore Satterthwaite · 2016 to 2026
$6.0M
Lifecourse CVD Risk and Midlife Cognitive Trajectories and Brain Aging: Implications for Alzheimer's and Dementia PreventionR01AG063887 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI YAFFE, KRISTINE · 2019 to 2022
$3.0M
Brain aging and cardiovascular risk factors and diseaseZIAAG007480 · NIA · NATIONAL INSTITUTE ON AGING · PI LAUNER, LENORE J · 2011 to 2025
$2.7M
Biomedical Image Computing and Informatics ClusterS10OD023495 · OD · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2017
$1.9M
NHLBI NIH HHS HHSN271201600059CNIA NIH HHS P30 AG049638NIA NIH HHS P30 AG062715NIA NIH HHS P30 AG066507NIA NIH HHS R01 AG063887NIA NIH HHS RF1 AG054409NIA NIH HHS RF1 AG059869NIA NIH HHS U01 AG068057NIA NIH HHS U19 AG033655NIBIB NIH HHS R01 EB022573NIDA NIH HHS HHSN271201600059CNIH HHS S10 OD023495
6 · The paper itself

Abstract

introductionRelationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).

methodsThree brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD.

resultsWMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. DISCUSSION: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

Indexed as

Machine LearningAdultAgedAged, 80 and overAgingAmyloid beta-PeptidesAtrophyBiomarkersBrainCerebral Small Vessel DiseasesCognitive DysfunctionDisease ProgressionFemaleHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingAmyloid beta-PeptidesBiomarkersAlzheimer's disease pathologybeta-amyloidbrain agingbrain signaturescognitive testingDementiaharmonized neuroimaging cohortsMachine LearningMRINeuroimagingPETpreclinical Alzheimer's diseasesmall vessel ischemic diseasetau

Identifiers

PMID32920988
PMCPMC7923395
OpenAlexW3084546953

What OpenQuestion holds

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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.