A Noninvasive Imaging Approach to Understanding Speech Changes Following Deep Brain Stimulation in Parkinson’s Disease
Bibliographic record
Abstract
PURPOSE: To explore the use of noninvasive functional imaging and "virtual" lesion techniques to study the neural mechanisms underlying motor speech disorders in Parkinson's disease. Here, we report the use of positron emission tomography (PET) and transcranial magnetic stimulation (TMS) to explain exacerbated speech impairment following subthalamic nucleus deep brain stimulation (STN-DBS) in a patient with Parkinson's disease. METHOD: Perceptual and acoustic speech measures, as well as cerebral blood flow during speech as measured by PET, were obtained with STN-DBS on and off. TMS was applied to a region in the speech motor network found to be abnormally active during DBS. Speech disruption by TMS was compared both perceptually and acoustically with speech produced with DBS on. RESULTS: Speech production was perceptually inferior and acoustically less contrastive during left STN stimulation compared to no stimulation. Increased neural activity in left dorsal premotor cortex (PMd) was observed during DBS on. "Virtual" lesioning of this region resulted in speech characterized by decreased speech segment duration, increased pause duration, and decreased intelligibility. CONCLUSIONS: This case report provides evidence that impaired speech production accompanying STN-DBS may result from unintended activation of PMd. Clinical application of functional imaging and TMS may lead to optimizing the delivery of STN-DBS to improve outcomes for speech production as well as general motor abilities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".