Acute low frequency dorsal subthalamic nucleus stimulation improves verbal fluency in Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disorder that results in movement-related dysfunction and has variable cognitive impairment. Deep brain stimulation (DBS) of the dorsal subthalamic nucleus (STN) has been shown to be effective in improving motor symptoms; however, cognitive impairment is often unchanged, and in some cases, worsened particularly on tasks of verbal fluency. Traditional DBS strategies use high frequency gamma stimulation for motor symptoms (∼130 Hz), but there is evidence that low frequency theta oscillations (5-12 Hz) are important in cognition. METHODS: We tested the effects of stimulation frequency and location on verbal fluency among patients who underwent STN DBS implantation with externalized leads. During baseline cognitive testing, STN field potentials were recorded and the individual patients' peak theta frequency power was identified during each cognitive task. Patients repeated cognitive testing at five different stimulation settings: no stimulation, dorsal contact gamma (130 Hz), ventral contact gamma, dorsal theta (peak baseline theta) and ventral theta (peak baseline theta) frequency stimulation. RESULTS: Acute left dorsal peak theta frequency STN stimulation improves overall verbal fluency compared to no stimulation and to either dorsal or ventral gamma stimulation. Stratifying by type of verbal fluency probes, verbal fluency in episodic categories was improved with dorsal theta stimulation compared to all other conditions, while there were no differences between stimulation conditions in non-episodic probe conditions. CONCLUSION: Here, we provide evidence that dorsal STN theta stimulation may improve verbal fluency, suggesting a potential possibility of integrating theta stimulation into current DBS paradigms to improve cognitive outcomes.
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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".