Deep Brain Stimulation of the Subthalamic Nucleus Parameter Optimization for Vowel Acoustics and Speech Intelligibility in Parkinson's Disease
Bibliographic record
Abstract
Purpose: The settings of 3 electrical stimulation parameters were adjusted in 12 speakers with Parkinson's disease (PD) with deep brain stimulation of the subthalamic nucleus (STN-DBS) to examine their effects on vowel acoustics and speech intelligibility. Method: Participants were tested under permutations of low, mid, and high STN-DBS frequency, voltage, and pulse width settings. At each session, participants recited a sentence. Acoustic characteristics of vowel production were extracted, and naive listeners provided estimates of speech intelligibility. Results: Overall, lower-frequency STN-DBS stimulation (60 Hz) was found to lead to improvements in intelligibility and acoustic vowel expansion. An interaction between speaker sex and STN-DBS stimulation was found for vowel measures. The combination of low frequency, mid to high voltage, and low to mid pulse width led to optimal speech outcomes; however, these settings did not demonstrate significant speech outcome differences compared with the standard clinical STN-DBS settings, likely due to substantial individual variability. Conclusions: Although lower-frequency STN-DBS stimulation was found to yield consistent improvements in speech outcomes, it was not found to necessarily lead to the best speech outcomes for all participants. Nevertheless, frequency may serve as a starting point to explore settings that will optimize an individual's speech outcomes following STN-DBS surgery. Supplemental Material: https://doi.org/10.23641/asha.5899228.
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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) | 0.002 | 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".