Effects of Deep Brain Stimulation of the Subthalamic Nucleus Settings on Voice Quality, Intensity, and Prosody in Parkinson’s Disease: Preliminary Evidence for Speech Optimization
Bibliographic record
Abstract
OBJECTIVE: To systematically evaluate how different deep brain stimulation of the subthalamic nucleus (STN-DBS) amplitude, frequency, and pulse-width electrical parameter settings impact speech intensity, voice quality, and prosody of speech in Parkinson's disease (PD). METHODS: Ten individuals with PD receiving bilateral STN-DBS treatments were seen for three baseline and five treatment visits. The five treatment visits involved an examination of the standard clinical settings as well as manipulation of different combinations of frequency (low, mid, and high), pulse width (low, mid, and high), and voltage (low, mid, and high) of stimulation. Measures of speech intensity, jitter, shimmer, harmonics-noise ratio, semitone standard deviation, and listener ratings of voice quality and prosody were obtained for each STN-DBS manipulation. RESULTS: The combinations of lower frequency, lower pulse width, and higher voltage settings were associated with improved speech outcomes compared to the current standard clinical settings. In addition, decreased total electrical energy delivered to the STN appears to be associated with speech improvements. CONCLUSIONS: This study provides preliminary evidence that STN-DBS may be optimized for Parkinson-related problems with voice quality, speech intensity, and prosody of speech.
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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".