Pharmacological Modulation of Long-Term Potentiation-Like Activity in the Dorsolateral Prefrontal Cortex
Bibliographic record
Abstract
Background: Long-term potentiation (LTP) depends on glutamatergic neurotransmission and is modulated by cholinergic, dopaminergic, and GABAergic inputs. Paired Associative Stimulation (PAS) is a neurostimulation paradigm that, when combined with electroencephalography, assesses LTP-like activity (PAS-induced LTP) in the dorsolateral prefrontal cortex (DLPFC). Thus, we conducted a study to assess the role of cholinergic, dopaminergic, GABAergic, and glutamatergic neurotransmission on PAS-induced LTP in the DLPFC. We hypothesized that increasing the dopaminergic tone with L-DOPA and the cholinergic tone with rivastigmine will enhance PAS-induced LTP, while increasing the GABAergic tone with baclofen and inhibiting glutamatergic neurotransmission with dextromethorphan will reduce it compared to placebo. Methods: In this randomized controlled, double-blind cross-over within-subject study, 12 healthy participants received five sessions of PAS to the DLPFC in a random order, each preceded by the administration of placebo or one of the four active drugs. PAS-induced LTP was assessed after each drug administration and compared to PAS-induced LTP after placebo. Results: As predicted, L-DOPA and rivastigmine resulted in enhanced PAS-induced LTP in the DLPFC and dextromethorphan inhibited it compared to placebo. In contrast, baclofen did not significantly suppress PAS-induced LTP compared to placebo. Conclusions: This study provides a novel approach to study DLPFC neuroplasticity and its modulation in patients with brain disorders that are associated with abnormalities in these neurochemical systems. This study was based on a single dose administration of each drug. Given that these drugs are typically administered chronically, future studies should assess the effects of chronic administration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".