Evidence map›Paper›PMID 40436879›Full record

Articlenpj aging2025

Subcortical brain volumetric differences related to white matter lesion volume and cognition in healthy aging.

Hyun Song, Pradyumna K Bharadwaj, Matthew D Grilli, David A Raichlen, Christian G Habeck, Matthew J Huentelman, Georg A Hishaw, Theodore P Trouard, Gene E Alexander

Abstract read
In one paragraph

Article in npj aging, 2025. 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

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

9 authors.

Hyun SongDepartment of Psychology, University of Arizona, Tucson, AZ, USA.
Pradyumna K BharadwajDepartment of Psychology, University of Arizona, Tucson, AZ, USA.
Matthew D GrilliDepartment of Psychology, University of Arizona, Tucson, AZ, USA.
David A RaichlenHuman and Evolutionary Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA.
Christian G HabeckCognitive Neuroscience Division, Department of Neurology and Taub Institute, Columbia University, New York, NY, USA.
Matthew J HuentelmanEvelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA.
Georg A HishawDepartment of Neurology, University of Arizona, Tucson, AZ, USA.
Theodore P TrouardEvelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA.
Gene E AlexanderDepartment of Psychology, University of Arizona, Tucson, AZ, USA. gene.alexander@arizona.edu.

Funding

Research Education ComponentP30AG019610 · NIA · SUN HEALTH RESEARCH INSTITUTE · PI REIMAN, ERIC MICHAEL · 2001 to 2020
$32.5M
Research Education ComponentP30AG072980 · NIA · BANNER HEALTH · PI ALIREZA ATRI · 2021 to 2026
$24.9M
Revitalizing Cognition in Older Adults at Risk for Alzheimer's Disease with Near-Infrared Photobiomodulation: Diversity SupplementR01AG064587 · NIA · UNIVERSITY OF FLORIDA · PI ALEXANDER, GENE E, BOWERS, DAWN · 2019 to 2023
$4.0M
Inactivity, sedentary behavior, and the risk for Alzheimer’s disease in middle aged to older adultsR01AG072445 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI GENE E ALEXANDER, Yann Charles Klimentidis · 2022 to 2026
$3.3M
Epigenetic, Neuroimaging & Behavioral Effects of Hypertension in the Aging BrainR01AG049464 · NIA · UNIVERSITY OF ARIZONA · PI ALEXANDER, GENE E, BARNES, CAROL A. · 2014 to 2018
$2.4M
Neuroanatomical Substrates of Aging & Cognitive DeclineR01AG025526 · NIA · UNIVERSITY OF ARIZONA · PI ALEXANDER, GENE E · 2006 to 2010
$2.2M
Physical activity predictors of cognitive and brain health in the risk for Alzheimer's diseaseR56AG067200 · NIA · UNIVERSITY OF ARIZONA · PI ALEXANDER, GENE E, RAICHLEN, DAVID · 2020 to 2020
$767k
NIA NIH HHS P30 AG019610NIA NIH HHS P30 AG072980NIA NIH HHS R01 AG025526NIA NIH HHS R01 AG049464NIA NIH HHS R01 AG064587NIA NIH HHS R01 AG072445NIA NIH HHS R56 AG067200NIH HHS AG025526, AG019610, AG072980, AG049464, AG064587, AG067200, AG072445
6 · The paper itself

Abstract

White matter hyperintensity (WMH) lesions associated with small vessel cerebrovascular disease (CVD) are common structural neuroimaging findings in older adults. Greater global brain WMH burden related to aging has been implicated in dementia but has also been linked to brain atrophy and cognitive dysfunction in old age. We sought to investigate the regionally distributed association of global WMH lesion load with subcortical gray matter (SGM) volumes using a multivariate network analysis method in 178 community-dwelling, healthy older adults (mean age = 69.77 ± 10.22 years). We additionally applied mediation models with WMH-related subcortical volumetric differences as a mediator to evaluate a potential global WMH-related vascular risk pathway leading to cognitive aging. Global WMH burden was associated with a regionally distributed pattern of SGM atrophy involving bilateral putamen and left nucleus accumbens, with relative volume increases in bilateral caudate nucleus. Mediation analyses revealed that increasing age predicted greater WMH-SGM pattern expression, which then predicted slowed processing speed that was, in turn, associated with decrements in other age-sensitive cognitive domains of memory, executive functioning, and fine motor function. These results suggest that the multivariate WMH-SGM pattern and its association with processing speed may provide an important early indicator of age-related decrements in higher-order cognitive processes, reflecting a potential link between CVD and broader cognitive dysfunction across multiple domains in healthy aging.

Identifiers

PMID40436879
PMCPMC12120003

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.