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Record W3037147769 · doi:10.1016/j.celrep.2020.107812

Dopamine Inputs from the Ventral Tegmental Area into the Medial Prefrontal Cortex Modulate Neuropathic Pain-Associated Behaviors in Mice

2020· article· en· W3037147769 on OpenAlexafffund
Shuo Huang, Zizhen Zhang, Eder Gambeta, Shi Chen Xu, Catherine S. Thomas, Nathan Godfrey, Lina Chen, Saïd M’Dahoma, Stephanie L. Borgland, Gerald W. Zamponi

Bibliographic record

VenueCell Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersCanadian Institutes of Health ResearchCanada Research ChairsInternational Development Research Centre
KeywordsVentral tegmental areaPrefrontal cortexNeuroscienceOptogeneticsPeriaqueductal grayDopaminePhotostimulationNeuropathic painConditioned place preferencePsychologyNeuroplasticityMidbrainDopaminergicCognitionCentral nervous system

Abstract

fetched live from OpenAlex

The medial prefrontal cortex (mPFC) is a brain region involved in the affective components of pain and undergoes plasticity during the development of chronic pain. Dopamine (DA) is a key neuromodulator in the mesocortical circuit and modulates working memory and aversion. Although DA inputs into the mPFC are known to modulate plasticity, whether and how these inputs affect pain remains incompletely understood. By using optogenetics, we find that phasic activation of DA inputs from the ventral tegmental area (VTA) into the mPFC reduce mechanical hypersensitivity during neuropathic pain states. Mice with neuropathic pain exhibit a preference for contexts paired with photostimulation of DA terminals in the mPFC. Fiber photometry-based calcium imaging reveals that DA increases the activity of mPFC neurons projecting to the ventrolateral periaqueductal gray (vlPAG). Together, our findings indicate an important role of mPFC DA signaling in pain modulation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.013
GPT teacher head0.224
Teacher spread0.211 · 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 designObservational
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

Citations98
Published2020
Admission routes2
Has abstractyes

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