Sweet spots of standard and directional leads in patients with refractory essential tremor: white matter pathways associated with maximal tremor improvement
Bibliographic record
Abstract
OBJECTIVE: In patients with essential tremor (ET) treated with standard deep brain stimulation (sDBS) whose ET had progressed and who no longer received optimal benefit from sDBS, directional deep brain stimulation (dDBS) may provide better tremor control. Current steering may provide better coverage of subcortical structures related to tremor control in patients with ET and significant progression without optimal response to sDBS. METHODS: This study included 6 patients with ET initially treated with sDBS whose tremor later progressed and who then underwent reimplantation with dDBS to optimize their tremor control. To investigate the differences in the local effects of sDBS and dDBS, the authors generated the volume of tissue activation (VTA) to calculate the sweet spots associated with the best possible tremor control with no side effects. Then, to investigate the anatomical structures associated with maximal tremor control, the white matter pathways of the posterior subthalamic areas (PSAs) were generated and their involvement with the sDBS and dDBS sweet spots was calculated. RESULTS: Tremor improvement was significantly better with dDBS (68.4%) than with sDBS (48.7%) (p = 0.017). The sDBS sweet spot was located within the ventral intermediate nucleus, whereas the sweet spot of the dDBS was mainly located within the PSA. The sweet spots of both sDBS and dDBS involved a similar portion of the cerebellothalamic pathway. However, the dDBS had greater involvement of the pallidofugal pathways than the sDBS. CONCLUSIONS: In patients with ET treated with sDBS who later had ET progression, dDBS provided better tremor control, which was related to directionality and a more ventral position. The involvement of both the cerebellothalamic and pallidofugal pathways obtained with dDBS is associated with additional improvement over the sDBS.
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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".