Neuromodulation of Prefrontal Cortex in Non-Human Primates by Dopaminergic Receptors during Rule-Guided Flexible Behavior and Cognitive Control
Bibliographic record
Abstract
The prefrontal cortex (PFC) is indispensable for several higher-order cognitive and executive capacities of primates, including representation of salient stimuli in working memory (WM), maintenance of cognitive task set, inhibition of inappropriate responses and rule-guided flexible behavior. PFC networks are subject to robust neuromodulation from ascending catecholaminergic systems. Disruption of these systems in PFC has been implicated in cognitive deficits associated with several neuropsychiatric disorders. Over the past four decades, a considerable body of work has examined the influence of dopamine on macaque PFC activity representing spatial WM. There has also been burgeoning interest in neuromodulation of PFC circuits involved in other cognitive functions of PFC, including representation of rules to guide flexible behavior. Here, we review recent neuropharmacological investigations conducted in our laboratory and others of the role of PFC dopamine receptors in regulating rule-guided behavior in non-human primates. Employing iontophoresis, we examined the effects of local manipulation of dopaminergic subtypes on neuronal activity during performance of rule-guided pro- and antisaccades, an experimental paradigm sensitive to PFC integrity, wherein deficits in performance are reliably observed in many neuropsychiatric disorders. We found dissociable effects of dopamine receptors on neuronal activity for rule representation and oculomotor responses and discuss these findings in the context of prior studies that have examined the role of dopamine in spatial delayed response tasks, attention, target selection, abstract rules, visuomotor learning and reward. The findings we describe here highlight the common features, as well as heterogeneity and context dependence of dopaminergic neuromodulation in regulating the efficacy of cognitive functions of PFC in health and disease.
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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 | 0.001 |
| 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".