Cholinergic mechanisms of high-frequency stimulation in entopeduncular nucleus
Bibliographic record
Abstract
Chronic, high-frequency (>100 Hz) electrical stimulation, known as deep brain stimulation (DBS), of the internal segment of the globus pallidus (GPi) is a highly effective therapy for Parkinson's disease (PD) and dystonia. Despite some understanding of how it works acutely in PD models, there remain questions about its mechanisms of action. Several hypotheses have been proposed, such as depolarization blockade, activation of inhibitory synapses, depletion of neurotransmitters, and/or disruption/alteration of network oscillations. In this study we investigated the cellular mechanisms of high-frequency stimulation (HFS) in entopeduncular nucleus (EP; rat equivalent of GPi) neurons using whole cell patch-clamp recordings. We found that HFS applied inside the EP nucleus induced a prolonged afterdepolarization that was dependent on stimulation frequency, pulse duration, and current amplitude. The high frequencies (>100 Hz) and pulse widths (>0.15 ms) used clinically for dystonia DBS could reliably induce these afterdepolarizations, which persisted under blockade of ionotropic glutamate (kynurenic acid, 2 mM), GABAA (picrotoxin, 50 μM), GABAB (CGP 55845, 1 μM), and acetylcholine nicotinic receptors (DHβE, 2 μM). However, this effect was blocked by atropine (2 μM; nonselective muscarinic antagonist) or tetrodotoxin (0.5 μM). Finally, the muscarinic-dependent afterdepolarizations were sensitive to Ca(2+)-sensitive nonspecific cationic (CAN) channel blockade. Hence, these data suggest that muscarinic receptor activation during HFS can lead to feedforward excitation through the opening of CAN channels. This study for the first time describes a cholinergic mechanism of HFS in EP neurons and provides new insight into the underlying mechanisms of DBS.
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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 | 0.000 |
| 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".