Evidence map›Paper›PMID 36607335›Full record

Observational studyeLife2023

Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study.

Peter R Millar, Brian A Gordon, Patrick H Luckett, Tammie L S Benzinger, Carlos Cruchaga, Anne M Fagan, Jason J Hassenstab, Richard J Perrin, Suzanne E Schindler, Ricardo F Allegri and 8 more

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 2 pooled it
11.8field-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

43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.

  1. Pooled it
  2. BrainAGE, brain health, and mental disorders: A systematic review.Neuroscience and biobehavioral reviews · 2024
    Pooled it
  3. Trial
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  9. Article
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  12. When Age Is More Than a Number: Acceleration of Brain Aging in Neurodegenerative Diseases.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025
    Article
  13. Review
  14. Article
  15. Review
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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

18 authors at 6 institutions in 4 countries.

Peter R MillarDepartment of Neurology, Washington University in St. Louis, St Louis, United States.ORCID 0000-0003-4588-739X
Brian A GordonDepartment of Radiology, Washington University in St. Louis, St Louis, United States.
Patrick H LuckettDepartment of Neurosurgery, Washington University in St. Louis, St Louis, United States.
Tammie L S BenzingerDepartment of Radiology, Washington University in St. Louis, St Louis, United States.
Carlos CruchagaDepartment of Psychiatry, Washington University in St. Louis, St Louis, United States.
Anne M FaganDepartment of Neurology, Washington University in St. Louis, St Louis, United States.
Jason J HassenstabDepartment of Neurology, Washington University in St. Louis, St Louis, United States.
Richard J PerrinDepartment of Neurology, Washington University in St. Louis, St Louis, United States.
Suzanne E SchindlerDepartment of Neurology, Washington University in St. Louis, St Louis, United States.
Ricardo F AllegriDepartment of Cognitive Neurology, Institute for Neurological Research (FLENI), Buenos Aires, Argentina.
Gregory S DayDepartment of Neurology, Mayo Clinic Florida, Jacksonville, United States.
Martin R FarlowDepartment of Neurology, Indiana University School of Medicine, Indianapolis, United States.
Hiroshi MoriDepartment of Clinical Neuroscience, Osaka Metropolitan University Medical School, Nagaoka Sutoku University, Osaka, Japan.
Georg NüblingDepartment of Neurology, Ludwig-Maximilians University, Munich, Germany.
Dominantly Inherited Alzheimer Network
Randall J BatemanDepartment of Neurology, Washington University in St. Louis, St Louis, United States.
John C MorrisDepartment of Neurology, Washington University in St. Louis, St Louis, United States.
Beau M AncesDepartment of Neurology, Washington University in St. Louis, St Louis, United States.ORCID 0000-0003-3862-7397
Washington University in St. Louis · USFundación para la Lucha contra las Enfermedades Neurológicas de la Infancia · ARGerman Center for Neurodegenerative Diseases · DEIndiana University School of MedicineJacksonville College · USOsaka Metropolitan University · JP

Funding

WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8M
Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5M
Imaging CoreU19AG032438 · NIA · WASHINGTON UNIVERSITY · PI BATEMAN, RANDALL J · 2010 to 2025
$53.9M
The natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 2005 to 2025
$49.5M
Research Education ComponentP30AG066444 · NIA · WASHINGTON UNIVERSITY · PI DAVID M. HOLTZMAN · 2020 to 2026
$28.7M
Cross Sectional and Longitudinal Racial Disparity in Molecular Biomarkers of Alzheimer DiseaseR01AG067505 · NIA · WASHINGTON UNIVERSITY · PI XIONG, CHENGJIE · 2020 to 2024
$14.7M
PET Imaging of Neuroinflammation in Genetic Alzheimer's DiseaseR01AG052550 · NIA · WASHINGTON UNIVERSITY · PI Beau M Ances, Tammie Lee Smith Benzinger · 2018 to 2026
$4.2M
FALLS: A MARKER OF PRECLINICAL ALZHEIMERS DISEASER01AG057680 · NIA · WASHINGTON UNIVERSITY · PI ANCES, BEAU M, STARK, SUSAN LYNN · 2018 to 2022
$3.4M
HIGH PERFORMANCE BIOMEDICAL IMAGING COMPUTER RESOURCESS10RR022984 · NCRR · WASHINGTON UNIVERSITY · PI PRIOR, FRED WILLIAM · 2009 to 2009
$1.9M
POSTMORTEM VALIDATION OF BIOMARKERS FOR IMAGING PARKINSON DISEASER01NS092865 · NINDS · WASHINGTON UNIVERSITY · PI XU, JINBIN · 2016 to 2020
$1.7M
Neuroimaging Markers of Emerging Dysfunction In Preclinical Alzheimer DiseaseK01AG053474 · NIA · WASHINGTON UNIVERSITY · PI GORDON, BRIAN ANDREW · 2017 to 2021
$623k
Alzheimer's Association SG-20-690363-DIANNCRR NIH HHS S10 RR022984NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P30 AG066444NIA NIH HHS R01 AG052550NIA NIH HHS R01 AG057680NIA NIH HHS R01 AG067505NIA NIH HHS U19 AG032438NIH HHS 5-R01-AG052550NIH HHS 5-R01-AG057680NIH HHS P01-AG026276NIH HHS P01-AG03991NIH HHS P30-AG066444NIH HHS U19-AG032438
6 · The paper itself

Abstract

Background: Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. Methods: We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. Results: All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. Conclusions: Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. Funding: This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN).

Indexed as

Alzheimer DiseaseCognitive DysfunctionAdolescentAdultAgedAged, 80 and overAmyloid beta-PeptidesBiomarkersBrainCognitionHumansMagnetic Resonance ImagingMiddle AgedYoung AdultAmyloid beta-PeptidesBiomarkersAlzheimer diseasebrain aginghumanmachine learningmedicineneuroscienceresting-state functional connectivitystructural MRI

Identifiers

PMID36607335
PMCPMC9988262
OpenAlexW4313645584

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.