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Record W4405338850 · doi:10.1016/j.ejphar.2024.177185

Cognitive and kinematic markers of ketamine effects in behaving non-human primates

2024· article· en· W4405338850 on OpenAlexaff
Pierre Pouget, Pierre Daye, Martin Paré

Bibliographic record

VenueEuropean Journal of Pharmacology · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsSaccadeNeuroscienceNMDA receptorKinematicsCognitionNeurotransmissionBrainstemPsychologyKetamineBiologyMedicineReceptorEye movementPhysicsInternal medicine

Abstract

fetched live from OpenAlex

Ketamine is widely used to probe cognitive functions relying on the properties of methyl-D-aspartate receptor (NMDAR) synaptic transmission. Numerous works have proved that cognitive performance and adjustments in the decision or perceptual domains are affected after ketamine injection in general circulation of primates. Here, we take advantage of that in the brain stem; horizontal saccade deceleration is controlled by glycine-NMDAR-gated current, while gamma-aminobutyric acid (GABA) current controls vertical deceleration to demonstrate that despite general circulation level manipulation of NMDAR synaptic transmission, the kinematic of the saccade appeared to be in the motor brainstem generator circuit differentially maintained. The results show that the deacceleration of the saccade elicited toward a horizontal target was substantially decreased, while the deacceleration of a vertical saccade remained largely unaffected. These results provide functional distinct markers for estimating cognitive and kinematic NMDAR-gated specificity acting in the pre-frontal cortex while maintaining specificity among the GABA circuit of drugs in general circulation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

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 studies0.0000.001
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 head0.035
GPT teacher head0.386
Teacher spread0.352 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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