Role of right dorsolateral prefrontal cortex–left primary motor cortex interaction in motor inhibition in Parkinson’s disease
Bibliographic record
Abstract
Background: Impaired motor inhibition in Parkinson's disease (PD) is associated with functional alterations in the frontal-basal ganglia (BG) neural circuits. The right dorsolateral prefrontal cortex (DLPFC), pre-supplementary motor area (pre-SMA), and primary motor cortex (M1) play key roles in regulating this inhibition. However, the changes in interhemispheric interactions during motor inhibition in PD have not been clearly defined. Methods: We used dual-site paired-pulse transcranial magnetic stimulation (ppTMS) to examine the interactions between the right DLPFC and pre-SMA and the left M1 in 30 patients with early-stage PD and 30 age-matched healthy controls (HC) during both resting and active conditions, specifically while performing a stop-signal task (SST). Results: Stop-signal reaction times (SSRT) were significantly longer in PD patients compared to HC. The right DLPFC-left M1 interaction, at both short- and long-latency intervals, showed enhanced inhibition in PD following the stop-signal. In PD patients, SSRT was correlated with the inhibition of the right DLPFC-left M1 interaction, with stronger inhibition associated with shorter SSRT. Conclusion: The deficit in reactive inhibition observed in PD is linked to an abnormal modulation of the right DLPFC-left M1 interaction during the stopping process.
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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".