Precision targeting in the globus pallidus interna: insights from the multicenter, prospective, blinded VA/NINDS CSP 468 study
Bibliographic record
Abstract
OBJECTIVE: Deep brain stimulation (DBS) targeting the globus pallidus interna (GPi) has been shown to significantly improve motor symptoms for the treatment of medication-refractory Parkinson's disease. Yet, heterogeneity in clinical outcomes persists, possibly due to suboptimal target identification within the GPi. By leveraging robust sampling of the GPi and 6-month postsurgical outcomes, this study aims to determine optimal symptom-specific GPi DBS targets. METHODS: In this study, the authors analyzed the anatomical lead location and 6-month postsurgical, double-blinded outcome measures of 86 patients who underwent bilateral GPi DBS. These patients were selected from the multicenter Veterans Affairs (VA)/National Institutes of Neurological Disorders and Stroke (NINDS) Cooperative Studies Program (CSP) 468 study to identify the optimal target zones ("sweet spots") for the control of overall motor (United Parkinson's Disease Rating Scale [UPDRS]-III), axial, tremor, rigidity, and bradykinesia symptoms. Lead coordinates were normalized to Montreal Neurological Institute space and the optimal target zones were identified and validated using a leave-one-patient-out approach. RESULTS: The authors' findings revealed statistically significant optimal target zones for UPDRS-III (R = 0.37, p < 0.001), axial (R = 0.22, p = 0.042), rigidity (R = 0.20, p = 0.021), and bradykinesia (R = 0.23, p = 0.004) symptoms. These zones were localized within the primary motor and premotor subdivisions of the GPi. Interestingly, these zones extended beyond the GPi lateral border into the GPi-globus pallidus externa (GPe) lamina and into the GPe, but they did not reach the GPi ventral border, challenging traditional surgical approaches based on pallidotomies. CONCLUSIONS: Drawing upon a robust dataset, this research effectively delineates specific optimal target zones for not only overall motor improvement but also symptom subscores. These insights hold the potential to enhance the precision of targeting in subsequent bilateral GPi DBS surgical procedures.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".