Applying event-related deep brain stimulation to investigate the causal role of the subthalamic nucleus in stopping motor responses
Bibliographic record
Abstract
There is growing evidence implicating the subthalamic nucleus (STN) as a critical neural structure for the voluntary control of movement, especially in situations which require the stopping of movements. Evidence to date lacks the fine temporal scale to investigate the causal role of the STN in stopping. The present study takes a novel approach to manipulate STN functioning, using a short-train of disruptive deep brain stimulation time-locked to stopping events to observe its causal effects on stopping performance. Parkinson's disease patients with externalized bilateral STN electrodes performed a stop-signal task requiring a left or right arrow key press in response to a visual go-signal, but inhibit this response if a stop-signal was subsequently presented. Patients first performed a training block without stimulation to determine their stopping ability at a given stop-signal delay (SSD). Stimulation was then randomly delivered in the testing block during go-trials, and stop-trials with various SSD's (early, middle, late). We applied 250 ms of bilateral 130 Hz stimulation to the STN starting 50 ms after stop-signal onset (or theoretical onset during go-trials). Preliminary results reveal that stimulation has no effect on go-trial reaction time, but appears to have an effect on stopping performance depending on the SSD. Stimulation reduces the probability of stopping at the early SSD, but has no effect at the middle and late SSD. Though patient recruitment is ongoing, these initial findings provide novel human evidence for the causal role of the STN in stopping which will further inform current theories of behaviour control.
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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".