Evidence map›Paper›PMID 35429100›Full record

ArticleHuman brain mapping2022

Associations of white matter hyperintensities with networks of gray matter blood flow and volume in midlife adults: A coronary artery risk development in young adults magnetic resonance imaging substudy.

William S H Kim, Nicholas J Luciw, Sarah Atwi, Zahra Shirzadi, Sudipto Dolui, John A Detre, Ilya M Nasrallah, Walter Swardfager, Robert Nick Bryan, Lenore J Launer and 1 more

Open access · goldAbstract read
In one paragraph

Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.1field-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

5 citing papers in PubMed, 12 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. 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

11 authors at 4 institutions in 2 countries.

William S H KimDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0003-1257-1348
Nicholas J LuciwDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0003-4043-990X
Sarah AtwiDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0003-2312-2281
Zahra ShirzadiDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0001-6854-9356
Sudipto DoluiCenter for Functional Neuroimaging, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-9035-3795
John A DetreCenter for Functional Neuroimaging, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-8115-6343
Ilya M NasrallahDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0003-2346-7562
Walter SwardfagerHurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada.ORCID 0000-0002-0030-8908
Robert Nick BryanDepartment of Diagnostic Medicine, University of Texas, Austin, Texas, USA.ORCID 0000-0002-2327-4579
Lenore J LaunerLaboratory of Epidemiology and Population Science, National Institute on Aging, Bethesda, Maryland, USA.ORCID 0000-0002-3238-7612
Bradley J MacIntoshDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0001-7300-2355
Sunnybrook Health Science Centre · CAUniversity of Pennsylvania · USNational Institute on Aging · USThe University of Texas at Austin · US

Funding

Brain aging and cardiovascular risk factors and diseaseZIAAG007480 · NIA · NATIONAL INSTITUTE ON AGING · PI LAUNER, LENORE J · 2011 to 2025
$2.7M
Cerebral Blood Flow trajectory in the Alzheimer's Disease continuumR03AG063213 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DOLUI, SUDIPTO · 2019 to 2020
$323k
CIHR 165981NHLBI NIH HHS HHSN268201800003INHLBI NIH HHS HHSN268201800004INHLBI NIH HHS HHSN268201800005INHLBI NIH HHS HHSN268201800006INHLBI NIH HHS HHSN268201800007INIA NIH HHS R03 AG063213
6 · The paper itself

Abstract

White matter hyperintensities (WMHs) are emblematic of cerebral small vessel disease, yet effects on the brain have not been well characterized at midlife. Here, we investigated whether WMH volume is associated with brain network alterations in midlife adults. Two hundred and fifty-four participants from the Coronary Artery Risk Development in Young Adults study were selected and stratified by WMH burden into Lo-WMH (mean age = 50 ± 3.5 years) and Hi-WMH (mean age = 51 ± 3.7 years) groups of equal size. We constructed group-level covariance networks based on cerebral blood flow (CBF) and gray matter volume (GMV) maps across 74 gray matter regions. Through consensus clustering, we found that both CBF and GMV covariance networks partitioned into modules that were largely consistent between groups. Next, CBF and GMV covariance network topologies were compared between Lo- and Hi-WMH groups at global (clustering coefficient, characteristic path length, global efficiency) and regional (degree, betweenness centrality, local efficiency) levels. At the global level, there were no between-group differences in either CBF or GMV covariance networks. In contrast, we found between-group differences in the regional degree, betweenness centrality, and local efficiency of several brain regions in both CBF and GMV covariance networks. Overall, CBF and GMV covariance analyses provide evidence that WMH-related network alterations are present at midlife.

Indexed as

LeukoaraiosisWhite MatterCoronary VesselsGray MatterHumansMagnetic Resonance ImagingMiddle AgedYoung AdultCARDIAcerebral blood flowcovariancegraph theorygray matter volumesmall vessel diseasewhite matter hyperintensities

Identifiers

PMID35429100
PMCPMC9294299
OpenAlexW4223900775

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.