NMDA Antagonist Ketamine Reduces Task Selectivity in Macaque Dorsolateral Prefrontal Neurons and Impairs Performance of Randomly Interleaved Prosaccades and Antisaccades
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".