Evidence map›Paper›PMID 33005147›Full record

ArticleFrontiers in aging neuroscience2020

Age-Related Regional Network Covariance of Magnetic Resonance Imaging Gray Matter in the Rat.

Gene E Alexander, Lan Lin, Eriko S Yoshimaru, Pradyumna K Bharadwaj, Kaitlin L Bergfield, Lan T Hoang, Monica K Chawla, Kewei Chen, James R Moeller, Carol A Barnes and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in aging neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

19 citing papers in PubMed, 29 citations in OpenAlex.

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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

11 authors at 5 institutions in 1 country.

Gene E AlexanderDepartment of Psychology, University of Arizona, Tucson, AZ, United States.
Lan LinDepartment of Psychology, University of Arizona, Tucson, AZ, United States.
Eriko S YoshimaruDepartment of Biomedical Engineering, University of Arizona, Tucson, AZ, United States.
Pradyumna K BharadwajDepartment of Psychology, University of Arizona, Tucson, AZ, United States.
Kaitlin L BergfieldEvelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States.
Lan T HoangEvelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States.
Monica K ChawlaEvelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States.
Kewei ChenArizona Alzheimer's Consortium, Phoenix, AZ, United States.
James R MoellerDepartment of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, Columbia University, New York, NY, United States.
Carol A BarnesDepartment of Psychology, University of Arizona, Tucson, AZ, United States.
Theodore P TrouardEvelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, United States.
Alzheimer's Association · USUniversity of Arizona · USArizona Alzheimer’s ConsortiumColumbia University · USGood Samaritan Medical Center · US

Funding

Research Education ComponentP30AG019610 · NIA · SUN HEALTH RESEARCH INSTITUTE · PI GONZALEZ HERNANDEZ, GRACIELA · 2001 to 2020
$32.5M
NEUROBEHAVIORAL RELATIONS IN SENESCENT HIPPOCAMPUSR01AG003376 · NIA · UNIVERSITY OF COLORADO AT BOULDER · PI BARNES, CAROL A. · 1985 to 2025
$16.4M
Neural System Dynamics & Gene Expression Supporting Successful Cognitive AgingR01AG049465 · NIA · UNIVERSITY OF ARIZONA · PI BARNES, CAROL A. · 2014 to 2018
$3.6M
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
NIA NIH HHS P30 AG019610NIA NIH HHS R01 AG003376NIA NIH HHS R01 AG025526NIA NIH HHS R01 AG049464NIA NIH HHS R01 AG049465
6 · The paper itself

Abstract

Healthy human aging has been associated with brain atrophy in prefrontal and selective temporal regions, but reductions in other brain areas have been observed. We previously found regional covariance patterns of gray matter with magnetic resonance imaging (MRI) in healthy humans and rhesus macaques, using multivariate network Scaled Subprofile Model (SSM) analysis and voxel-based morphometry (VBM), supporting aging effects including in prefrontal and temporal cortices. This approach has yet to be applied to neuroimaging in rodent models of aging. We investigated 7.0T MRI gray matter covariance in 10 young and 10 aged adult male Fischer 344 rats to identify, using SSM VBM, the age-related regional network gray matter covariance pattern in the rodent. SSM VBM identified a regional pattern that distinguished young from aged rats, characterized by reductions in prefrontal, temporal association/perirhinal, and cerebellar areas with relative increases in somatosensory, thalamic, midbrain, and hippocampal regions. Greater expression of the age-related MRI gray matter pattern was associated with poorer spatial learning in the age groups combined. Aging in the rat is characterized by a regional network pattern of gray matter reductions corresponding to aging effects previously observed in humans and non-human primates. SSM MRI network analyses can advance translational aging neuroscience research, extending from human to small animal models, with potential for evaluating mechanisms and interventions for cognitive aging.

Indexed as

agingbehaviorperirhinal cortexprefrontal cortexscaled subprofile modelstructural covariance

Identifiers

PMID33005147
PMCPMC7479213
OpenAlexW3080814340

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.