Changes in Vowel Articulation with Subthalamic Nucleus Deep Brain Stimulation in Dysarthric Speakers with Parkinson’s Disease
Bibliographic record
Abstract
Purpose. To investigate changes in vowel articulation with the electrical deep brain stimulation (DBS) of the subthalamic nucleus (STN) in dysarthric speakers with Parkinson's disease (PD). Methods. Eight Quebec-French speakers diagnosed with idiopathic PD who had undergone STN DBS were evaluated ON-stimulation and OFF-stimulation (1 hour after DBS was turned off). Vowel articulation was compared ON-simulation versus OFF-stimulation using acoustic vowel space and formant centralization ratio, calculated with the first (F1) and second formant (F2) of the vowels /i/, /u/, and /a/. The impact of the preceding consonant context on articulation, which represents a measure of coarticulation, was also analyzed as a function of the stimulation state. Results. Maximum vowel articulation increased during ON-stimulation. Analyses also indicate that vowel articulation was modulated by the consonant context but this relationship did not change with STN DBS. Conclusions. Results suggest that STN DBS may improve articulation in dysarthric speakers with PD, in terms of range of movement. Optimization of the electrical parameters for each patient is important and may lead to improvement in speech fine motor control. However, the impact on overall speech intelligibility may still be small. Clinical considerations are discussed and new research avenues are suggested.
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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".