Stop‐related subthalamic beta activity indexes global motor suppression in Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: Rapid action stopping leads to global motor suppression. This is shown by studies using transcranial magnetic stimulation to measure corticospinal excitability of task-unrelated effectors (e.g., from the hand during speech stopping). We hypothesize that this global suppression relates to the STN of the basal ganglia. Several STN local field potential studies in PD patients have shown increased ß-band power during successful stopping. OBJECTIVES: Here, we aimed to test whether this STN ß-band activity indexes global motor suppression measured by transcranial magnetic stimulation. METHODS: We studied 9 medicated PD patients (age, 47-67 years; mean, 55.8; 3 female) who were implanted with STN-DBS electrodes. Participants performed a vocal stop-signal task (i.e., they had to occasionally stop a vocal response) while we simultaneously recorded local field potentials from right STN and delivered transcranial magnetic stimulation to primary motor cortex to measure corticospinal excitability from a task-unrelated hand muscle (first dorsal interosseous). RESULTS: Replicating previous results, STN ß-band power was increased (P < 0.005) and corticospinal excitability was reduced (P = 0.024; global motor suppression) during successful stopping. As hypothesized, global motor suppression was greater for successful stop trials with higher STN ß-power (median split: P = 0.043), which was further evident in a negative correlation between single-trial STN ß-power and corticospinal excitability (mean, r = -0.176; P = 0.011). CONCLUSION: These findings link stopping-related global motor suppression to STN ß-band activity through simultaneous recordings of STN and corticospinal excitability. The results support models of basal ganglia function that propose the STN has broad motor suppressive effects. © 2016 International Parkinson and Movement Disorder Society.
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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".