Dose-dependent dissociation of pro-cognitive effects of donepezil on attention and cognitive flexibility in rhesus monkeys
Bibliographic record
Abstract
ABSTRACT BACKGROUND Donepezil exerts pro-cognitive effects by non-selectively enhancing acetylcholine (ACh) across multiple brain systems. The 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 multi-task paradigm for nonhuman primates (NHPs) that assessed attention and cognitive flexibility. NHPs received either vehicle or variable doses of donepezil prior to task performance. We measured donepezil intracerebral and how strong it prevented the breakdown of ACh within prefrontal cortex and anterior striatum using solid-phase-microextraction neurochemistry. RESULTS The highest administered donepezil dose improved attention and made 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 functions differed from the dose range that enhanced cognitive flexibility despite the availability of the drug in the major brain systems supporting these cognitive functions. Thus, the non-selective acetylcholine esterase inhibitor donepezil inherently trades improvement in the attention domain for improvement in the cognitive flexibility domain 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".