The Association of Dexmedetomidine with Firing Properties in Pallidal Neurons
Bibliographic record
Abstract
BACKGROUND: Microelectrode recordings (MERs) are used during deep brain stimulation surgery (DBS) to optimize patient outcomes and provide a unique method of collecting data regarding neurological conditions. However, MERs can be affected by anesthetics such as dexmedetomidine. Little is known about the effects of dexmedetomidine (DEX) on the globus pallidus interna (GPi), a common target for DBS. The primary aim of this study is to investigate the hypothesis that DEX is associated with alterations in GPi MERs. METHODS: We conducted a retrospective analysis comparing MERs from patients with Parkinson's disease (PD) and dystonia who underwent insertion of DBS of the GPi under DEX sedation with those who went through the same procedure without DEX (No DEX). RESULTS: Firing rates for GPi neurons in the DEX group were lower (57.44 ± 2.04; mean ± SEM, n = 163 cells) than the No DEX group (69.53 ± 2.06, n = 112 cells, P < 0.0001). Overall, DEX was associated with a greater proportion of GPi cells classified as firing in bursty pattern compared to our No DEX group. (29.41%, n = 153 vs 14.81%, n = 108, P = 0.008). This effect was present for both PD and dystonia patients who underwent the procedure. High doses of DEX were associated with lower firing rates than low doses. CONCLUSIONS: Our results suggest that DEX is associated with a decrease in GPi firing rates and are associated with an increase in burstiness. Furthermore, these effects are similar between dystonia and PD patients. Lastly, the effects of DEX may differ between high doses and low doses.
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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 | 0.002 |
| 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".