Evidence map›Paper›PMID 35272096›Full record

ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2022

High-Risk Drinkers Engage Distinct Stress-Predictive Brain Networks.

Elizabeth V Goldfarb, Dustin Scheinost, Nia Fogelman, Dongju Seo, Rajita Sinha

Open access · greenAbstract read
In one paragraph

Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 7% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Network state dynamics underpin craving in a transdiagnostic population.medRxiv : the preprint server for health sciences · 2023
    Article
  11. Article
  12. Candidate biomarkers in psychiatric disorders: state of the field.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2023
    Article
  13. Constructs derived from the addiction cycle predict alcohol use disorder treatment outcomes and recovery 3 years following treatment.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2023
    Article
  14. 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

5 authors at 1 institution in 1 country.

Elizabeth V GoldfarbDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; Yale Stress Center, Yale University School of Medicine, New Haven, Connecticut; Department of Psychology, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut. Electronic address: elizabeth.goldfarb@yale.edu.
Dustin ScheinostDepartment of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut; Child Study Center, Yale University School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut.
Nia FogelmanDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; Yale Stress Center, Yale University School of Medicine, New Haven, Connecticut.
Dongju SeoDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; Yale Stress Center, Yale University School of Medicine, New Haven, Connecticut.
Rajita SinhaDepartment of Psychiatry, Yale University School of Medicine, New Haven, Connecticut; Yale Stress Center, Yale University School of Medicine, New Haven, Connecticut; Child Study Center, Yale University School of Medicine, New Haven, Connecticut; Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut. Electronic address: rajita.sinha@yale.edu.
Yale University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Institutional Career Development CoreKL2TR001862 · NCATS · YALE UNIVERSITY · PI CANTLEY, LLOYD G, EDELMAN, E. JENNIFER · 2016 to 2025
$12.2M
Chronic Alcohol and Brain Stress Circuit ResponseR01AA013892 · NIAAA · YALE UNIVERSITY · PI Rajita Sinha · 2003 to 2026
$10.1M
Impaired dynamic neurobiological responses in alcoholism and early trauma to predict relapse after treatmentR01AA026844 · NIAAA · YALE UNIVERSITY · PI SEO, DONGJU · 2019 to 2023
$3.2M
Neuroimaging multiple memory processes, glucocorticoids and alcoholism riskK01AA027832 · NIAAA · YALE UNIVERSITY · PI GOLDFARB, ELIZABETH · 2020 to 2024
$879k
NCATS NIH HHS KL2 TR001862NCATS NIH HHS UL1 TR001863NIAAA NIH HHS K01 AA027832NIAAA NIH HHS R01 AA013892NIAAA NIH HHS R01 AA026844
6 · The paper itself

Abstract

backgroundExcessive alcohol intake is a major public health problem and can be triggered by stress. Heavy drinking in patients with alcohol use disorder also alters neural, physiological, and emotional stress responses. However, it is unclear whether adaptations in stress-predictive brain networks can be an early marker of risky drinking behavior.

methodsRisky social drinkers (regular bingers; n = 53) and light drinker control subjects (n = 51) aged 18 to 53 years completed a functional magnetic resonance imaging-based sustained stress protocol with repeated measures of subjective stress state, during which whole-brain functional connectivity was computed. This was followed by prospective daily ecological momentary assessment for 30 days. We used brain computational predictive modeling with cross-validation to identify unique brain connectivity predictors of stress in risky drinkers and determine the prospective utility of stress-brain networks for subsequent loss of control over drinking.

resultsRisky drinkers had anatomically and functionally distinct stress-predictive brain networks (showing stronger predictions from visual and motor networks) compared with light drinkers (default mode and frontoparietal networks). Stress-predictive brain networks defined for risky drinkers selectively predicted future real-world stress levels for risky drinkers and successfully predicted prospective future real-world loss of control over drinking across all participants.

conclusionsThese results indicate adaptations in computationally derived stress-related brain circuitry among high-risk drinkers, suggesting potential targets for early preventive intervention and revealing the malleability of the neural processes that govern stress responses.

Indexed as

Alcohol DrinkingAlcoholismBrainHumansMagnetic Resonance ImagingRisk-TakingBinge drinkingEcological momentary assessmentfMRILoss of controlPredictive modelingStressVentromedial prefrontal cortexVisuomotor networks

Identifiers

PMID35272096
PMCPMC9378362
OpenAlexW4221097286

What OpenQuestion holds

Textmetadata
LicenceTDM
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.