Dose-Dependent Dissociation of Pro-cognitive Effects of Donepezil on Attention and Cognitive Flexibility in Rhesus Monkeys
Bibliographic record
Abstract
Background: Donepezil exerts pro-cognitive effects by nonselectively enhancing acetylcholine (ACh) across multiple brain systems. Two brain systems that mediate pro-cognitive effects of attentional control and cognitive flexibility are the prefrontal cortex and the anterior striatum, which have different pharmacokinetic sensitivities to ACh modulation. We speculated that these area-specific ACh profiles lead to distinct optimal dose ranges for donepezil to enhance the cognitive domains of attention and flexible learning. Methods: To test for dose-specific effects of donepezil on different cognitive domains, we devised a multitask paradigm for nonhuman primates that assessed attention and cognitive flexibility. The nonhuman primates received either vehicle or variable doses of donepezil before task performance. We measured intracerebral donepezil and its strength in preventing the breakdown of ACh within the prefrontal cortex and anterior striatum using solid phase microextraction neurochemistry. Results: The highest administered donepezil dose improved attention and made the subjects more robust against distractor interference, but it did not improve flexible learning. In contrast, only a lower dose range of donepezil improved flexible learning and reduced perseveration, but without distractor-dependent attentional improvement. Neurochemical measurements confirmed a dose-dependent increase of extracellular donepezil and decreases in choline within the prefrontal cortex and the striatum. Conclusions: The donepezil dose for maximally improving attention differed from the dose range that enhanced cognitive flexibility despite the availability of the drug in two major brain systems supporting these functions. These results suggest that in our cohort of adult monkeys, donepezil traded improvements in attention for improvements in cognitive flexibility at a given dose range.
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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".