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NMDA Antagonist Ketamine Reduces Task Selectivity in Macaque Dorsolateral Prefrontal Neurons and Impairs Performance of Randomly Interleaved Prosaccades and Antisaccades

2012· article· en· W2041408167 on OpenAlexafffund
Kevin Skoblenick, Stefan Everling

Bibliographic record

VenueJournal of Neuroscience · 2012
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsAntisaccade taskNeuroscienceMacaqueNMDA receptorPsychologyKetamineSaccadeCognitionPrefrontal cortexDorsolateral prefrontal cortexAntagonistEye movementReceptorMedicine

Abstract

fetched live from OpenAlex

Ketamine, an NMDA receptor antagonist, has been shown to induce behavioral abnormalities in humans that mimic the positive, negative, and most importantly cognitive deficits observed in schizophrenia. Similar cognitive deficits have been observed in nonhuman primates after a subanesthetic dose of ketamine, including an impairment in their ability to perform the antisaccade task, which requires the suppression of a prosaccade toward a flashed stimulus and the generation of a saccade in the opposite direction. The neural basis underlying these cognitive impairments remains unknown. Here, we recorded single-neuron activity in the lateral prefrontal cortex of macaque monkeys before and after the administration of subanesthetic doses of ketamine during the performance of randomly interleaved prosaccade and antisaccade trials. Ketamine impeded the monkeys' ability to maintain and apply the correct task rule and increased reaction times of prosaccades and antisaccades. These behavioral changes were associated with an overall increase in activity of PFC neurons and a reduction in their task selectivity. Our results suggest that the mechanism underlying ketamine-induced cognitive abnormalities may be the nonspecific increase in PFC activity and the associated reduction of task selectivity.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.443

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 communication0.0000.001
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 head0.015
GPT teacher head0.274
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 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

Citations56
Published2012
Admission routes2
Has abstractyes

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