P.124 Subnuclear contact localization within the subthalamic nucleus in deep brain stimulation for Parkinson Disease
Bibliographic record
Abstract
Background: Therapeutic response from subthalamic nucleus (STN) deep brain stimulation (DBS) for Parkinson disease (PD) has been associated with proximity to an ideal target, commonly in the dorsal sensorimotor STN. Automated registration and atlas-based segmentation has allowed for contact localization within STN subnuclei. We sought to apply these methods to characterize the spatial distribution of our active contact placements. Methods: We conducted a retrospective analysis of 55 patients who underwent bilateral STN DBS for PD. Post-operative CT/MRI scans were non-linearly registered into a standard space, and DBS-electrodes were localized using Lead-DBS. 3-dimensional meshes from a segmented atlas (Ewert 2017) were utilized. Analysis was performed in MATLAB R2019b. Results: Mean active contacts were within sensorimotor STN bilaterally, located posteroinferiorly compared to reported ideal targets. Centroids fell within (left/right): sensorimotor (46%/40%), associative (22%/22%), limbic (0%/2%) and outside STN (32%/36%). Principal components analysis demonstrated most spatial variance is explained by the first component (left 65.8%, right 61.9%). Conclusions: We obtained contact locations in relation to STN subnuclei, allowing for an anatomically guided approach to our analysis. 66% of the active contacts were located within the STN, and most of the spatial variation occurred along a single dimension. Future directions include utilizing subnuclei localizations to investigate clinical outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".