Subthalamic suppression defines therapeutic threshold of deep brain stimulation in Parkinson’s disease
Bibliographic record
Abstract
INTRODUCTION: Subthalamic deep brain stimulation (DBS) is beneficial when delivered at a high frequency. However, the effects of current amplitude and pulse width on subthalamic neuronal activity during high-frequency stimulation have not been investigated. METHODS: In 20 patients with Parkinson's disease each undergoing subthalamic DBS, we recorded single-unit subthalamic activity using one microelectrode, while a separate microelectrode was used to deliver 5-10 s trains of stimulation at 100 Hz using varying current amplitudes and pulse widths (44 neurons investigated). RESULTS: Analysis of variance tests confirmed significant (p<0.001) main effects of both current amplitude and pulse width on subthalamic neuronal firing during stimulation and on poststimulus inhibitory silent periods. Prolonged silent periods were often followed by postinhibitory rebound burst excitations. Additionally, a significant (p<0.0001) correlation was found between neuronal firing and total electrical energy delivered (TEED). With TEED values≤31.2 µJ/s (associated with DBS parameters of ≤2.0 mA, 130 Hz stimulation frequency and 60 µs pulse width, assuming 1 kΩ impedance), neuronal firing was sustained at a rate of 32.4%±3.3% (mean±SE), while with values>31.2 µJ/s, neurons fired at only 4.3%±1.2%. CONCLUSIONS: Neuronal suppression is likely an important mechanism of action of therapeutically beneficial subthalamic DBS, which may underlie clinically relevant behavioural changes.
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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 | 0.001 |
| 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".