Low/High Multi‐Frequency Stimulation of the Subthalamic Nucleus Improves Verbal Fluency Maintaining Motor Control in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: High frequency deep brain stimulation of the subthalamic nucleus (STN-DBS) is a well-established therapy for Parkinson's disease (PD) motor symptoms, however, its effect on non-motor symptoms is controversial. Low frequency DBS can improve cognition, but its effects on motor functions are detrimental. OBJECTIVE: Our goal was to evaluate the effect on verbal fluency (VF) of dual frequency STN-DBS combining high and low frequency (130 + 10 Hz) as compared to 130 Hz or 10 Hz alone and to OFF stimulation. The effect on motor symptoms, working memory, and subjective feelings was also assessed. METHODS: We used a randomized order of experimental conditions with a double-blind design to assess the effects of 130 Hz, 10 Hz, and 130 + 10 Hz stimulation as compared to OFF stimulation in 18 PD patients with STN-DBS. In each condition, participants completed: phonemic and action VF, N-back task, and visual analogue scales for fatigue and stress level. Motor functions and gait velocity were also assessed. Friedman analysis of variance were conducted to determine whether change scores from baseline OFF stimulation, in our primary (VF) and secondary outcomes measures (motor functions, N-back task, subjective feelings) were different in the three stimulation conditions. RESULTS: (2) = 11.1, P = 0.004), it being worse at 10 Hz than 130 Hz (P = 0.002) and 130 + 10 Hz (P = 0.01). CONCLUSIONS: Dual frequency STN-DBS improves phonemic VF while maintaining a beneficial effect on motor signs of PD. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.000 |
| 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".