Impact of Stimulation Frequency on Verbal Fluency Following Bilateral Subthalamic Nucleus Deep Brain Stimulation in Parkinson’s Disease
Bibliographic record
Abstract
OBJECTIVE: The effects of stimulation frequency on verbal fluency (VF) following subthalamic nucleus deep brain stimulation (STN-DBS) in Parkinson's disease (PD) are not well understood. The present study examines the impact stimulation frequency has on VF following bilateral STN-DBS in PD. METHODS: Prospective study of 38 consecutive patients with PD with low frequency STN-DBS (LFS) (n = 10) and high frequency STN-DBS (HFS) (n = 14), and a non-operated PD control group consisting of patients with fluctuating response to dopaminergic medication (n = 14) homogeneous in age, education, disease duration, and global cognitive function. Patients were evaluated on VF tasks (letter, semantic, action verbs, alternating). A one-way analysis of variance (ANOVA) was conducted to assess distinctions between groups. Pre- and post-surgical comparisons of fluencies were performed for operated groups. A mixed ANOVA was applied to the data to evaluate the interaction between treatment (HFS vs. LFS) and time (pre- vs. post-surgery). Strategy use (clustering and switching) was evaluated. RESULTS: Semantic and letter fluency performance revealed significant differences between HFS and LFS groups. Pre- and post-surgical comparisons revealed HFS negatively affected letter, semantic, and action fluencies, but LFS had no effect on VF. No interaction effect or main effect of treatment was found. Main effect of time was significant for semantic and action fluencies indicating a decrease in postoperative fluency performance. Patients with LFS produced larger average cluster sizes than patients with HFS. CONCLUSION: LFS may be less detrimental to VF, but these findings suggest that VF decline following STN-DBS is not caused by stimulation frequency alone.
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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".