Report of a patient undergoing chronic responsive deep brain stimulation for Tourette syndrome: proof of concept
Bibliographic record
Abstract
Deep brain stimulation (DBS) has emerged as a promising intervention for the treatment of select movement and neuropsychiatric disorders. Current DBS therapies deliver electrical stimulation continuously and are not designed to adapt to a patient's symptoms. Continuous DBS can lead to rapid battery depletion, which necessitates frequent surgery for battery replacement. Next-generation neurostimulation devices can monitor neural signals from implanted DBS leads, where stimulation can be delivered responsively, moving the field of neuromodulation away from continuous paradigms. To this end, the authors designed and chronically implemented a responsive stimulation paradigm in a patient with medically refractory Tourette syndrome. The patient underwent implantation of a responsive neurostimulator, which is capable of responsive DBS, with bilateral leads in the centromedian-parafascicular (Cm-Pf) region of the thalamus. A spectral feature in the 5- to 15-Hz band was identified as the control signal. Clinical data collected prior to and after 12 months of responsive therapy revealed improvements from baseline scores in both Modified Rush Tic Rating Scale and Yale Global Tic Severity Scale scores (64% and 48% improvement, respectively). The effectiveness of responsive stimulation (p = 0.16) was statistically identical to that of scheduled duty cycle stimulation (p = 0.33; 2-sided Wilcoxon unpaired rank-sum t-test). Overall, responsive stimulation resulted in a 63.3% improvement in the neurostimulator's projected mean battery life. Herein, to their knowledge, the authors present the first proof of concept for responsive stimulation in a patient with Tourette syndrome.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".