MétaCan
Menu
Back to cohort
Record W3045742345 · doi:10.1038/s41467-020-17543-4

Dissociable mesolimbic dopamine circuits control responding triggered by alcohol-predictive discrete cues and contexts

2020· article· en· W3045742345 on OpenAlexafffund
Milan D. Valyear, Iulia Glovaci, Audrey Zaari, Soraya Lahlou, Ivan Trujillo‐Pisanty, C. Andrew Chapman, Nadia Chaudhri

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsConcordia University
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchConcordia UniversityNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsDopamineNeuroscienceControl (management)PsychologyCognitive psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Context can influence reactions to environmental cues and this elemental process has implications for substance use disorder. Using an animal model, we show that an alcohol-associated context elevates entry into a fluid port triggered by a conditioned stimulus (CS) that predicted alcohol (CS-triggered alcohol-seeking). This effect persists across multiple sessions and, after it diminishes in extinction, the alcohol context retains the capacity to augment reinstatement. Systemically administered eticlopride and chemogenetic inhibition of ventral tegmental area (VTA) dopamine neurons reduce CS-triggered alcohol-seeking. Chemogenetically silencing VTA dopamine terminals in the nucleus accumbens (NAc) core reduces CS-triggered alcohol-seeking, irrespective of context, whereas silencing VTA dopamine terminals in the NAc shell selectively reduces the elevation of CS-triggered alcohol-seeking in an alcohol context. This dissociation reveals new roles for divergent mesolimbic dopamine circuits in the control of responding to a discrete cue for alcohol and in the amplification of this behaviour in an alcohol context.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.297
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations39
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueNature CommunicationsSame topicNeural dynamics and brain functionFrench-language works237,207