Localization of Deep Brain Stimulation Contacts Using Corticospinal/Corticobulbar Tracts Stimulation
Bibliographic record
Abstract
BACKGROUND: Successful deep brain stimulation (DBS) in Parkinson's disease (PD) requires optimal electrode placement. One technique of intraoperative electrode testing is determination of stimulation thresholds inducing corticospinal/corticobulbar tracts (CSBT) motor contractions. OBJECTIVE: This study aims to analyze correlations between DBS electrode distance to CSBT and contraction thresholds, with either visual or electromyography (EMG) detection, to establish an intraoperative tool devoted to ensure safe distance of the electrode to the CSBT. METHODS: Twelve PD patients with subthalamic nucleus DBS participated. Thresholds of muscular contractions were assessed clinically and with EMG, for three different sets of stimulation parameters, all monopolar: 130 Hz high-frequency stimulation (HFS); 2 Hz low-frequency stimulation with either 60 or 210 µs (LFS-60, LFS-210). The anatomical distance of electrode contacts to CSBT was measured from fused CT-MRI. RESULTS: = 0.50). When using LFS, the correlation found remained lower than for HFS but increased when EMG was used. Indeed, the detection of contraction thresholds with EMG versus visual inspection did allow more frequent detection of face contractions, contributing to improve that correlation. CONCLUSION: The correlation between electrode distance to the CSBT and contraction thresholds was found better when estimated with currents rather than voltage, eliminating the variance due to electrode impedance. Using LFS did not improve the precision of that evaluation, but EMG did. This technique provides a prediction band to ensure minimum distance of the electrode contacts to the CSBT, integrating the variance that can be encountered between prediction of models and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".