Evidence map›Paper›PMID 40945409›Full record

ArticleDrug and alcohol dependence2025

Brain network segregation is associated with drug use severity in individuals with opioid use disorder.

Nathan M Hager, Xinying Wang, Astrid P Ramos-Rolón, Anna Rose Childress, Daniel D Langleben, Corinde E Wiers, Zhenhao Shi

Abstract read
In one paragraph

Article in Drug and alcohol dependence, 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.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Nathan M HagerDepartment of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA. Electronic address: hagern@pennmedicine.upenn.edu.
Xinying WangDepartment of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Astrid P Ramos-RolónDepartment of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
Anna Rose ChildressDepartment of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
Daniel D LanglebenDepartment of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
Corinde E WiersDepartment of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
Zhenhao ShiDepartment of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.

Funding

Radiochemistry CoreP30DA046345 · NIDA · UNIVERSITY OF PENNSYLVANIA · PI KRANZLER, HENRY RICHARD, MACH, ROBERT H · 2019 to 2023
$9.4M
T32 Translational Addiction Research Fellowship ProgramT32DA028874 · NIDA · UNIVERSITY OF PENNSYLVANIA · PI Julie A Blendy, Anna Rose Childress · 2010 to 2026
$5.0M
Cross-disciplinary training in translational neuroimaging of ADRDT32AG076411 · NIA · UNIVERSITY OF PENNSYLVANIA · PI JOHN A DETRE, MURRAY GROSSMAN · 2022 to 2026
$1.5M
Multivariate Modeling of the Neural Mechanisms of Treatment Response in Opioid AddictionK01DA051709 · NIDA · UNIVERSITY OF PENNSYLVANIA · PI SHI, ZHENHAO · 2021 to 2025
$916k
Modulation of alcohol sensitivity and alcohol tolerance by exogenous ketones in humansR21AA031088 · NIAAA · UNIVERSITY OF PENNSYLVANIA · PI NANGA, RAVI PRAKASH REDDY, WIERS, CORINDE E · 2024 to 2025
$421k
Ketone supplementation as an intervention to alleviate alcohol withdrawal and improve brain energetics in Alcohol Use DisorderR21AA031337 · NIAAA · UNIVERSITY OF PENNSYLVANIA · PI NANGA, RAVI PRAKASH REDDY, WIERS, CORINDE E · 2024 to 2025
$376k
NIAAA NIH HHS R21 AA031088NIAAA NIH HHS R21 AA031337NIA NIH HHS T32 AG076411NIDA NIH HHS K01 DA051709NIDA NIH HHS P30 DA046345NIDA NIH HHS T32 DA028874
6 · The paper itself

Abstract

backgroundOpioid use disorder (OUD) is associated with altered brain network connectivity, particularly in the fronto-parietal, default mode, and salience networks. Brain efficiency is maximized when networks are distinct ('segregated') yet maintain partial connectivity with other networks ('integrated'). 'Brain network segregation' quantifies this balance by comparing the functional connectivity of nodes within and between networks. Previous research found lower brain network segregation in people with cognitive impairment, alcohol use disorder, and older age. We hypothesized that recent drug use severity in people with OUD would relate to reduced brain network segregation.

methodForty treatment-seeking adults with OUD (72.5 % male) completed resting-state functional magnetic resonance imaging. We grouped 264 brain regions into 10 networks, categorized as "association" (higher-order cognition) or "sensorimotor" (sensory and motor) networks. Regression analysis tested the relation between drug use severity and brain network segregation of association and sensorimotor categories and specific networks. Partial correlations explored the effects of cognition (IQ and working memory), mood, and affect.

resultsDrug use severity predicted lower brain network segregation of the association networks, particularly the fronto-parietal and salience networks, but not the default mode network. The relation between drug use severity and lower segregation of the sensorimotor networks depended on age. In exploratory analyses, positive affect related to greater salience network segregation.

conclusionsAn altered balance of connectivity within and between brain networks may correspond with drug use severity, particularly in cognitive and salience-detection networks. Lower brain network segregation may indicate accelerated brain aging and be a target for OUD treatment.

Indexed as

Brain network segregationGraph theoryOpioidsResting-state fMRI

Identifiers

PMID40945409
PMCPMC12947781

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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 OpenQuestion graph.