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Record W7132965317

Biomimetic Optogenetic Stimulation of Ventral Tegmental Area GABAergic Projections to the Tegmental Pedunculopontine Nucleus is Sufficient to Elicit Artificial Naïve Opiate Reward

2023· dissertation· W7132965317 on OpenAlexaff
Sabine Rasa Lovejoy

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVentral tegmental areaOptogeneticsPedunculopontine Tegmental NucleusGABAergicDopamineNucleus accumbensVentral pallidumStimulationChannelrhodopsinConditioned place preference
DOInot available

Abstract

fetched live from OpenAlex

Morphine injections into the Ventral Tegmental Area (VTA) produce reward behaviour. Lesions of the Tegmental Pedunculopontine Nucleus (TPP) block these rewarding effects in formerly drug-naïve animals. We hypothesize TPP-projecting VTA GABA neuron circuits produce morphine reward in previously opiate-naïve animals. We employed optogenetics to activate TPP-projecting VTA GABAergic neurons in real-time place preference (RTPP) paradigms. GAD65-cre mice expressing a high fidelity channelrhodopsin in the axon terminals or cell bodies of TPP-projecting VTA GABA neurons received biomimetic laser stimulation that replicated the action potentials of a VTA GABA neuron firing trace recorded from a mouse acutely injected with morphine. Stimulation of the cell bodies or axons of this TPP-projecting VTA GABA population (and not other projection populations) led to reward responses, even during dopamine antagonism. This work indicates dopamine-independent direct projections from VTA GABA neurons to the TPP may underlie the rewarding effects of morphine in previously naïve mice.

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 score0.004

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 integrity0.0000.000
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 head0.067
GPT teacher head0.378
Teacher spread0.311 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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