Deep brain stimulation of the substantia nigra pars reticulata improves forelimb akinesia in the hemiparkinsonian rat
Bibliographic record
Abstract
Deep brain stimulation (DBS) employing high-frequency stimulation (HFS) is commonly used in the globus pallidus interna (GPi) and the subthalamic nucleus (STN) for treating motor symptoms of patients with Parkinson's disease (PD). Although DBS improves motor function in most PD patients, disease progression and stimulation-induced nonmotor complications limit DBS in these areas. In this study, we assessed whether stimulation of the substantia nigra pars reticulata (SNr) improved motor function. Hemiparkinsonian rats predominantly touched with their unimpaired forepaw >90% of the time in the stepping and limb-use asymmetry tests. After SNr-HFS (150 Hz), rats touched equally with both forepaws, similar to naive and sham-lesioned rats. In vivo, SNr-HFS decreased beta oscillations (12-30 Hz) in the SNr of freely moving hemiparkinsonian rats and decreased SNr neuronal spiking activity from 28 ± 1.9 Hz before stimulation to 0.8 ± 1.9 Hz during DBS in anesthetized animals; also, neuronal spiking activity increased from 7 ± 1.6 to 18 ± 1.6 Hz in the ventromedial portion of the thalamus (VM), the primary SNr efferent. In addition, HFS of the SNr in brain slices from normal and reserpine-treated rat pups resulted in a depolarization block of SNr neuronal activity. We demonstrate improvement of forelimb akinesia with SNr-HFS and suggest that this motor effect may have resulted from the attenuation of SNr neuronal activity, decreased SNr beta oscillations, and increased activity of VM thalamic neurons, suggesting that the SNr may be a plausible DBS target for treating motor symptoms 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) | 0.001 | 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".