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Record W4414825062 · doi:10.1016/j.neuron.2025.09.009

Hunger modulates exploration through suppression of dopamine signaling in the tail of the striatum

2025· article· en· W4414825062 on OpenAlexaff
Tarun Kamath, Bart Lodder, Eliana Bilsel, Isobel Green, Rochelin Dalangin, Michelle Raghubardayal, Wengang Wang, Paolo Capelli, Jessie Legister, Lauren Hulshof, Janet Berrios Wallace, Lin Tian, Naoshige Uchida, Mitsuko Watabe‐Uchida, Bernardo L. Sabatini

Bibliographic record

VenueNeuron · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsUniversité Laval
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthNational Institute of General Medical SciencesHoward Hughes Medical InstituteNational Institute on Drug AbuseNational Institutes of Health
KeywordsDopamineDopaminergicStriatumArcuate nucleusHypothalamusLateral hypothalamusOptogeneticsZebrafishSignal transduction

Abstract

fetched live from OpenAlex

Caloric depletion induces behavioral changes that help an animal find food and restore its homeostatic balance. Hunger increases exploration and risk-taking behavior, allowing an animal to forage for food despite risks; however, it is unknown which neural systems coordinate such behavioral adaptations. Here, we characterize how hunger restructures an animal's spontaneous behavior as well as its directed exploration of a novel object. We show that hunger-induced changes in exploration are accompanied by and result from the modulation of dopamine signaling in the tail of the striatum (TOS). Dopamine signaling in the TOS is in turn modulated by hunger through the activity of agouti-related peptide (AgRP) neurons, putative "hunger neurons" in the arcuate nucleus of the hypothalamus that are polysynaptically connected to the TOS through the lateral hypothalamus. Thus, we delineate how hypothalamic systems modulate dopaminergic circuitry to mediate changes in exploratory behavior in the hungry state.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.112

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.303
Teacher spread0.282 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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