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Record W2586285480 · doi:10.1016/j.celrep.2017.01.030

Projection-Target-Defined Effects of Orexin and Dynorphin on VTA Dopamine Neurons

2017· article· en· W2586285480 on OpenAlexafffund
Corey Baimel, Benjamin K. Lau, Min Qiao, Stephanie L. Borgland

Bibliographic record

VenueCell Reports · 2017
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsUniversity of CalgaryUniversity of British Columbia
FundersFondation Brain Canada
KeywordsDynorphinOrexinDopamineVentral tegmental areaNeuroscienceProjection (relational algebra)ChemistryBiologyNeuropeptideComputer scienceDopaminergicBiochemistryOpioidOpioid peptide

Abstract

fetched live from OpenAlex

Circuit-specific signaling of ventral tegmental area (VTA) dopamine neurons drives different aspects of motivated behavior, but the neuromodulatory control of these circuits is unclear. We tested the actions of co-expressed lateral hypothalamic peptides, orexin A (oxA) and dynorphin (dyn), on projection-target-defined dopamine neurons in mice. We determined that VTA dopamine neurons that project to the nucleus accumbens lateral shell (lAcbSh), medial shell (mAcbSh), and basolateral amygdala (BLA) are largely non-overlapping cell populations with different electrophysiological properties. Moreover, the neuromodulatory effects of oxA and dyn on these three projections differed. OxA selectively increased firing in lAcbSh- and mAcbSh-projecting dopamine neurons. Dyn decreased firing in the majority of mAcbSh- and BLA-projecting dopamine neurons but reduced firing only in a small fraction of those that project to the lAcbSh. In conclusion, the oxA-dyn input to the VTA may drive reward-seeking behavior by tuning dopaminergic output in a projection-target-dependent manner.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.287
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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

Citations144
Published2017
Admission routes2
Has abstractyes

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