Evidence map›Paper›PMID 39686721›Full record

ArticleAddiction biology2024

Reduced brain network segregation in alcohol use disorder: Associations with neurocognition.

Xinying Wang, Peter Manza, Xinyi Li, Astrid Ramos-Rolón, Nathan Hager, Gene-Jack Wang, Nora D Volkow, Yuzheng Hu, Zhenhao Shi, Corinde E Wiers

Abstract read
In one paragraph

Article in Addiction biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Accelerated brain aging in alcohol users: Insights from machine learning.Alcohol, clinical & experimental research · 2025
    Article
  7. Article
  8. 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

10 authors.

Xinying WangCenter for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-0960-2660
Peter ManzaLaboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland, USA.
Xinyi LiCenter for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Astrid Ramos-RolónCenter for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Nathan HagerCenter for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0003-2083-7337
Gene-Jack WangLaboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland, USA.
Nora D VolkowLaboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, Maryland, USA.
Yuzheng HuDepartment of Psychology and Behavioral Sciences, Zhejiang University, Zijingang Campus, Hangzhou, Zhejiang Province, China.ORCID 0000-0003-3427-0650
Zhenhao ShiCenter for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Corinde E WiersCenter for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-2934-8794

Funding

T32 Translational Addiction Research Fellowship ProgramT32DA028874 · NIDA · UNIVERSITY OF PENNSYLVANIA · PI Julie A Blendy, Anna Rose Childress · 2010 to 2026
$5.0M
Multivariate Modeling of the Neural Mechanisms of Treatment Response in Opioid AddictionK01DA051709 · NIDA · UNIVERSITY OF PENNSYLVANIA · PI SHI, ZHENHAO · 2021 to 2025
$916k
Ketone ester intervention in alcohol use disorderR00AA026892 · NIAAA · UNIVERSITY OF PENNSYLVANIA · PI WIERS, CORINDE E · 2020 to 2022
$747k
Brain and Behavior Research Foundation 28778Brain and Behavior Research Foundation 30780NIAAA NIH HHS R00 AA026892NIAAA NIH HHS Y1AA-3009NIDA NIH HHS K01 DA051709NIDA NIH HHS T32 DA028874NIH HHS AA026892NIH HHS DA028874NIH HHS DA051709
6 · The paper itself

Abstract

The human brain consists of functionally segregated networks, characterized by strong connections among regions belonging to the same network and weak connections between those of different networks. Alcohol use disorder (AUD) is associated with premature brain aging and neurocognitive impairments. Given the link between decreased brain network segregation and age-related cognitive decline, we hypothesized lower brain segregation in patients with AUD than healthy controls (HCs). Thirty AUD patients (9 females, 21 males) and 61 HCs (35 females, 26 males) underwent resting-state functional MRI (rs-fMRI), whose data were processed to assess segregation within the brain sensorimotor and association networks. We found that, compared to HCs, AUD patients had significantly lower segregation in both brain networks as well as poorer performance on a spatial working memory task. In the HC group, brain network segregation correlated negatively with age and positively with spatial working memory. Our findings suggest reduced brain network segregation in individuals with AUD that may contribute to cognitive impairment and is consistent with premature brain aging in this population.

Indexed as

AlcoholismBrainCognitive DysfunctionMagnetic Resonance ImagingMemory, Short-TermAdultCase-Control StudiesCognitionFemaleHumansMaleMiddle AgedNerve NetNeural PathwaysNeuropsychological Testsalcohol use disordercognitionsegregation

Identifiers

PMID39686721
PMCPMC11649955

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.