Evidence map›Paper›PMID 37166071›Full record

ArticleAlcohol, clinical & experimental research2023

Neural oscillations in the ventral striatum reveal differences between the encoding of palatable food and ethanol consumption.

Lucas L Dwiel, Angela M Henricks, Elise Bragg, Jiang Gui, Wilder T Doucette

Open access · bronzeAbstract read
In one paragraph

Article in Alcohol, clinical & experimental research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 56% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

5 authors at 2 institutions in 1 country.

Lucas L DwielDepartment of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.ORCID https://orcid.org/0000-0002-9440-7842
Angela M HenricksDepartment of Psychology, Washington State University, Pullman, Washington, USA.
Elise BraggDepartment of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.
Jiang GuiDepartment of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.
Wilder T DoucetteDepartment of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.
Dartmouth College · USWashington State University · US

Funding

Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Fred W Kolling IV · 1985 to 2026
$91.3M
Treatment Development & Evaluation CoreP30DA029926 · NIDA · DARTMOUTH COLLEGE · PI Lisa A. Marsch · 2011 to 2026
$21.5M
Training in the Science of Co-Occurring DisordersT32DA037202 · NIDA · DARTMOUTH COLLEGE · PI Lisa A. Marsch · 2014 to 2026
$4.7M
SYNERGY: The Dartmouth Center for clinical and Translational ScienceKL2TR001088 · NCATS · DARTMOUTH COLLEGE · PI GREEN, ALAN I · 2013 to 2017
$3.3M
Computationally guided modulation of cortical-striatal activity: Toward brain stimulation-based treatments for impulsive and risky decision makingK08MH117347 · NIMH · DARTMOUTH-HITCHCOCK CLINIC · PI DOUCETTE, WILDER T · 2019 to 2022
$692k
Targeting corticostriatal synchrony with deep brain stimulation to reduce alcohol drinkingF31AA027441 · NIAAA · DARTMOUTH COLLEGE · PI DWIEL, LUCAS · 2018 to 2019
$90k
NCATS NIH HHS KL2 TR001088NCATS NIH HHS KL2TR001088NCI NIH HHS P30 CA023108NIAAA NIH HHS F31 AA027441NIAAA NIH HHS F31AA027441NIDA NIH HHS DA037202NIDA NIH HHS P30 DA029926NIDA NIH HHS T32 DA037202NIMH NIH HHS 1K08MH117347NIMH NIH HHS K08 MH117347
6 · The paper itself

Abstract

backgroundAcross multiple levels of investigation, there appear to be convergent neuronal processes underlying substance use and other motivated behaviors (i.e., the pursuit and consumption of rewarding substances). The consumption of alcohol and sweet, high-fat food engages many of the same brain regions, especially, the ventral striatum. In the current study, we hypothesized that ventral striatal local field potentials (LFPs) recorded during self-administration sessions could be used to detect when the consumption of 10% ethanol or sweet-fat food (SF) was occurring compared to all other behaviors, including naturalistic controls (i.e., water or house-chow).

methodsWe used an intermittent limited access approach to condition Sprague-Dawley rats to consume either ethanol or SF while we recorded LFPs. We used machine learning and simple logistic regressions to determine whether LFP features could classify when consumption of each substance was occurring, and whether a general model could predict consumption of both substances. We report performance as the average area under the receiver operator characteristic curve (AUROC).

resultsConsumption of a single substance was differentiable from all other behaviors, as evidenced by the AUROC (ethanol = 0.84 and SF = 0.83, p < 0.01). Models built from the combined dataset (general) did modestly overall (general → general = 0.68, p < 0.05), and did not detect the consumption of the two substances similarly (general → SF = 0.5 and general → ethanol = 0.63, p > 0.05).

conclusionsModels successfully classified ethanol and SF consumption versus all other behavior/naturalistic controls. However, the findings highlight differences in how the ventral striatum represents the consumption of ethanol and SF and show that, although there is potential for finding biomarkers related to substance use, it may be difficult to build a model that performs well detecting multiple substances.

Indexed as

biomarkerlocal field potentialsmachine learningsubstance useventral striatum

Identifiers

PMID37166071
PMCPMC10601443
OpenAlexW4376131469

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.