Event‐related deep brain stimulation of the subthalamic nucleus affects conflict processing
Bibliographic record
Abstract
OBJECTIVE: Many lines of evidence suggest that response conflict recruits brain regions in the cortical-basal ganglia system. Within the basal ganglia, deep brain recordings from the subthalamic nucleus (STN) have shown that conflict triggers a transient increase in low-frequency oscillations (LFOs; 2-8Hz). Here, we deployed a new method of delivering short trains of event-related deep brain stimulation (DBS) to the STN to test the causal role of the STN and its associated circuits in conflict-related processing. METHODS: In a double-blind design, we stimulated the STN in patients with Parkinson disease by locking brief trains of DBS to specific periods of the trial within a Stroop task. RESULTS: Stimulation had a specific effect on conflict compared to nonconflict trials by relatively speeding responses on conflict trials (ie, reducing the Stroop effect, defined as the difference in reaction time between conflict and nonconflict trials) when it was delivered in the preresponse period in the preparation phase. Stimulation also increased errors when it was delivered early in the response window. This latter result corresponded to the timing of the conflict-induced increase in LFOs observed in the absence of stimulation but was not directly related to the reduction in the Stroop effect. INTERPRETATION: These results support the theory that the time of LFO increase recorded from the STN corresponds to a conflict-processing function. They also provide one of the first demonstrations of event-related DBS of the STN in humans during a cognitive control paradigm. Ann Neurol 2018;84:515-526.
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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".