Evidence mapPaperPMID 39349179Full record

ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2025

Brain Age Is Not a Significant Predictor of Relapse Risk in Late-Life Depression.

Helmet T Karim, Andrew Gerlach, Meryl A Butters, Robert Krafty, Brian D Boyd, Layla Banihashemi, Bennett A Landman, Olusola Ajilore, Warren D Taylor, Carmen Andreescu

Abstract readMulticenter Study
In one paragraph

Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Moderators of treatment response in late-life depression.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Review
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

10 authors.

Helmet T KarimDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania. Electronic address: hek26@pitt.edu.
Andrew GerlachDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania.
Meryl A ButtersDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania.
Robert KraftyDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia.
Brian D BoydCenter for Cognitive Medicine, Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, Tennessee.
Layla BanihashemiDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania.
Bennett A LandmanDepartments of Computer Science, Electrical Engineering, and Biomedical Engineering, Vanderbilt University, Nashville, Tennessee; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.
Olusola AjiloreDepartment of Psychiatry, University of Illinois, Chicago, Illinois.
Warren D TaylorCenter for Cognitive Medicine, Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, Tennessee; Geriatric Research, Education, and Clinical Center, Veterans Affairs Tennessee Valley Health System, Nashville, Tennessee.
Carmen AndreescuDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania. Electronic address: andrcx@upmc.edu.

Funding

Vanderbilt Institute for Clinical and Translational Research (VICTR)UL1TR002243 · VANDERBILT UNIVERSITY MEDICAL CENTER · 2025 to 2025
$10.7M
1/3-Recurrence Markers, Cognitive Burden and Neurobiological Homeostasis in Late-life Depression (Rembrandt)R01MH121620 · NIMH · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Patricia Serrano Andrews · 2022 to 2024
$2.8M
2/3: Recurrence markers, cognitive burden and neurobiological homeostasis in late-life depression (REMBRANDT)R01MH121619 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Carmen Andreescu · 2022 to 2024
$2.8M
3/3-Recurrence markers, cognitive burden and neurobiological homeostasis in late-life depressionR01MH121384 · NIMH · UNIVERSITY OF ILLINOIS AT CHICAGO · PI Olusola A. Ajilore · 2022 to 2024
$1.7M
CLINICAL RESEARCH TRAINING IN LATE-LIFE MOOD DISORDERST32MH019986 · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 1997 to 2025
$1.7M
The RAW Brain - The Effect of Rumination, Anxiety and Worry on Aging and Dementia RiskR01MH108509 · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2025 to 2025
$1.5M
Machine Learning Models for Identifying Neural Predictors of TMS Treatment Response in MDDK01MH122741 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 to 2025
$682k
NCATS NIH HHS UL1 TR000445NCATS NIH HHS UL1 TR002243NIMH NIH HHS K01 MH122741NIMH NIH HHS R01 MH108509NIMH NIH HHS R01 MH121384NIMH NIH HHS R01 MH121619NIMH NIH HHS R01 MH121620NIMH NIH HHS R21 MH138955NIMH NIH HHS T32 MH019986
6 · The paper itself

Abstract

backgroundLate-life depression (LLD) has been associated cross-sectionally with lower brain structural volumes and accelerated brain aging compared with healthy control participants (HCs). There are few longitudinal studies on the neurobiological predictors of recurrence in LLD. We tested a machine learning brain age model and its prospective association with LLD recurrence risk.

methodsWe recruited individuals with LLD (n = 102) and HCs (n = 43) into a multisite, 2-year longitudinal study. Individuals with LLD were enrolled within 4 months of remission. Remitted participants with LLD underwent baseline neuroimaging and longitudinal clinical follow-up. Over 2 years, 43 participants with LLD relapsed and 59 stayed in remission. We used a previously developed machine learning brain age algorithm to compute brain age at baseline, and we evaluated brain age group differences (HC vs. LLD and HC vs. remitted LLD vs. relapsed LLD). We conducted a Cox proportional hazards model to evaluate whether baseline brain age predicted time to relapse.

resultsWe found that brain age did not significantly differ between the HC and LLD groups or between the HC, remitted LLD, and relapsed LLD groups. Brain age did not significantly predict time to relapse.

conclusionsIn contrast to our hypothesis, we found that brain age did not differ between control participants without depression and individuals with remitted LLD, and brain age was not associated with subsequent recurrence. This is in contrast to existing literature which has identified baseline brain age differences in late life but consistent with work that has shown no differences between people who do and do not relapse on gross structural measures.

Indexed as

BrainRecurrenceAgedAgingFemaleHumansLongitudinal StudiesMachine LearningMagnetic Resonance ImagingMajor Depressive DisorderMaleMiddle AgedNeuroimagingBrain ageLate-life depressionRecurrenceRelapseRemission

Identifiers

PMID39349179
PMCPMC11710984

What Socratic 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 Socratic graph.