Effects of Ketamine on Frontoparietal Interactions in a Rule-Based Antisaccade Task in Macaque Monkeys
Bibliographic record
Abstract
Cognitive control is engaged by working memory processes and high-demand situations like antisaccade, where one must suppress a prepotent response. While it is known to be supported by the frontoparietal control network, how intra- and interareal dynamics contribute to cognitive control processes remains unclear. N -Methyl- d -aspartate glutamate receptors (NMDARs) play a key role in prefrontal dynamics that support cognitive control. NMDAR antagonists, such as ketamine, are known to alter task-related prefrontal activities and impair cognitive performance. However, the role of NMDAR in cognitive control-related frontoparietal dynamics remains underexplored. Here, we simultaneously recorded local field potentials and single-unit activities from the lateral prefrontal (lPFC) and posterior parietal cortices (PPC) in two male macaque monkeys during a rule-based antisaccade task, with both rule-visible (RV) and rule-memorized (RM) conditions. In addition to altering the E/I balance in both areas, ketamine had a negative impact on rule coding in true oscillatory activities. It also reduced frontoparietal coherence in a frequency- and rule-dependent manner. Granger prediction analysis revealed that ketamine induced an overall reduction in bidirectional connectivity. Among antisaccade trials, a greater reduction in lPFC–PPC connectivity during the delay period preceded a greater delay in saccadic onset under the RM condition and a greater deficit in performance under the RV condition. Lastly, ketamine compromised rule coding in lPFC neurons in both RV and RM conditions and in PPC neurons only in the RV condition. Our findings demonstrate the utility of acute NMDAR antagonists in understanding the mechanisms through which frontoparietal dynamics support cognitive control processes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".