Subthalamic nucleus conditioning reduces premotor-motor interaction in Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation of the subthalamic nucleus is effective to alleviate motor symptoms in advanced Parkinson's disease. Using a novel conditioning paradigm, it has been shown that deep brain stimulation pulses from electrodes in the subthalamic nucleus modulate corticospinal excitability as determined with transcranial magnetic stimulation applied to the motor cortex. The mechanism of action is unclear. OBJECTIVE: To investigate the effects of subthalamic nucleus and dorsal premotor cortex conditioning on corticospinal excitability as a function of interstimulus intervals between target areas and deep brain stimulation frequencies. METHODS: In 19 patients with Parkinson's disease with subthalamic nucleus deep brain stimulation, the premotor-motor interaction was investigated in four different deep brain stimulation conditions (off, clinically used settings, 3 Hz, 20 Hz). Transcranial magnetic pulses were applied to the premotor and motor cortex and paired at certain intervals with deep brain stimulation pulses. The volume of tissue activated by deep brain stimulation was correlated with neurophysiological findings. RESULTS: There was distinct motor cortex inhibition by premotor cortex conditioning at an interstimulus interval of 1 ms before the motor cortex stimulation. Subthalamic nucleus conditioning with deep brain stimulation frequencies of 3 and 20 Hz at an interstimulus interval of 10 ms between subthalamic nucleus and primary motor cortex reduced premotor-motor inhibition. The volume of tissue activated by deep brain stimulation correlated positively with this effect. Corticospinal excitability was not affected by subthalamic nucleus conditioning as used here. CONCLUSIONS: Premotor-motor inhibition is modulated by subthalamic nucleus conditioning, presumably through the monosynaptic hyperdirect pathway.
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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) | 0.001 | 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".