Optimal Stimulation Sites and Connectomes for <scp>GPi</scp> and <scp>STN</scp>‐<scp>DBS</scp> in Cervical Dystonia
Bibliographic record
Abstract
AIMS: To map optimal stimulation targets (sweet spots) and neural networks for globus pallidus internus (GPi)- and subthalamic nucleus (STN)-deep brain stimulation (DBS) in cervical dystonia (CD), and compare their structural/functional connectivity profiles and predictive validity for clinical outcomes. METHODS: Retrospective analysis of 76 stimulation settings from 38 CD patients across four centers. Volume of tissue activated was reconstructed; connectivity-based sweet spots were identified. Structural/functional connectivity models were developed using normative connectomes and validated externally. Clinical outcomes were assessed using validated scales. RESULTS: Optimal targets localized to the posterior ventral medial GPi and dorsolateral STN. The ideal probabilistic stimulation maps of STN-DBS exhibited predictive clinical improvement. Both targets showed beneficial connections to the motor cortex, with GPi-DBS negatively connected to the occipital lobe and STN-DBS positively connected to the premotor cortex and cerebellum. Functional connectivity patterns further highlighted shared and distinct regions linked to CD symptoms. Moreover, the structural and functional connectivity models predicted postoperative improvement through internal and external validation. CONCLUSION: GPi- and STN-DBS engage distinct but overlapping networks in CD. Connectivity-based models robustly predict clinical improvement, offering tools for personalized targeting and programming. These findings clarify network mechanisms of DBS in dystonia and advance precision neuromodulation strategies.
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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 | 0.002 |
| 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".