Evidence map›Paper›PMID 41575378›Full record

ArticleAlcohol, clinical & experimental research2026

Behavioral profile predicts ethanol preference in adolescent mice, but not in adults: A machine learning approach.

Liana C L Portugal, Bruno da Silva Gonçalves, Emily de Assis Fagundes, Maria Fernandes Freire de Sá, Cláudio Carneiro Filgueiras, Ana Carolina Dutra-Tavares, Alex C Manhães, Yael Abreu-Villaça, Anderson Ribeiro-Carvalho

Abstract read
In one paragraph

Article in Alcohol, clinical & experimental research, 2026. 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
–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

1 citing paper in PubMed.

  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

9 authors.

Liana C L PortugalLaboratório de Neurofisiologia, Departamento de Ciências Fisiológicas, Instituto de Biologia Roberto Alcântara Gomes, Centro Biomédico, Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, Brazil.
Bruno da Silva GonçalvesLaboratório de Neurofisiologia, Departamento de Ciências Fisiológicas, Instituto de Biologia Roberto Alcântara Gomes, Centro Biomédico, Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, Brazil.
Emily de Assis FagundesLaboratório de Neurofisiologia, Departamento de Ciências Fisiológicas, Instituto de Biologia Roberto Alcântara Gomes, Centro Biomédico, Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, Brazil.
Maria Fernandes Freire de SáLaboratório de Neurofisiologia, Departamento de Ciências Fisiológicas, Instituto de Biologia Roberto Alcântara Gomes, Centro Biomédico, Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, Brazil.
Cláudio Carneiro FilgueirasLaboratório de Neurofisiologia, Departamento de Ciências Fisiológicas, Instituto de Biologia Roberto Alcântara Gomes, Centro Biomédico, Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, Brazil.
Ana Carolina Dutra-TavaresDepartamento de Ciências Biomédicas e Saúde, Instituto de Biologia Roberto Alcantara Gomes, Universidade do Estado do Rio de Janeiro (UERJ), Cabo Frio, Brazil.
Alex C ManhãesLaboratório de Neurofisiologia, Departamento de Ciências Fisiológicas, Instituto de Biologia Roberto Alcântara Gomes, Centro Biomédico, Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, Brazil.
Yael Abreu-VillaçaLaboratório de Neurofisiologia, Departamento de Ciências Fisiológicas, Instituto de Biologia Roberto Alcântara Gomes, Centro Biomédico, Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, Brazil.
Anderson Ribeiro-CarvalhoDepartamento de Ciências, Faculdade de Formação de Professores da Universidade do Estado do Rio de Janeiro, São Gonçalo, Brazil.ORCID https://orcid.org/0000-0003-4324-1413

Funding

Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro E-26/202.759/2019
6 · The paper itself

Abstract

backgroundClinical studies suggest that adolescents display a complex behavioral profile characterized by traits that increase their susceptibility to alcohol experimentation and impaired control over use. In the present study, we applied a machine learning model to predict the impact of diverse behavioral phenotypes on ethanol preference during adolescence and adulthood in mice.

methodsC57BL/6 and Swiss mice were assigned to one of two age groups: adolescents (starting at PN40) or adults (starting at PN120). Over the next 3 days, novelty-seeking, anxiety-like behavior, sociability, coping behavior, and natural reward response were evaluated using the following behavioral tests: hole-board, elevated plus maze, three-chamber sociability, forced swimming, and sucrose preference, respectively. During the subsequent 5 days, alcohol preference behavior was assessed using the two-bottle choice paradigm (10% ethanol). We trained machine learning regression models to predict alcohol preference in each age group.

resultsThe analysis model significantly predicted ethanol preference based on behavioral phenotypic profiles in mice during adolescence, but not in adulthood. Notably, the behavioral traits that contributed most to the prediction were sucrose preference and sociability time. Sucrose preference had a positive predictive value. Distinctively, sociability time had a negative predictive value, indicating an inverse relationship with ethanol preference.

conclusionsThese findings suggest that behavioral phenotypes during adolescence, particularly natural reward sensitivity and sociability, are key predictors of ethanol preference. The negative association between sociability and alcohol intake highlights the potential protective role of social interaction. The absence of predictive value in adulthood underscores adolescence as a critical developmental window during which behavioral traits may influence vulnerability to alcohol use.

Indexed as

Alcohol DrinkingBehavior, AnimalEthanolMachine LearningAge FactorsAnimalsChoice BehaviorMaleMiceMice, Inbred C57BLPredictive Learning ModelsRewardEthanoladolescenceethanolindividual differencesmachine learning

Identifiers

PMID41575378
PMCPMC12829523

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.