Parkinson’s Disease affects the contextual control, but not the expression, of a rapid visuomotor response that initiates visually-guided reaching: Evidence for multiple, interacting motor pathways and implications for motor symptoms in Parkinson’s Disease
Bibliographic record
Abstract
Abstract Despite significant deficits in voluntary motor control, patients with Parkinson’s disease (PD) can generate reflexive or stimulus-driven movements. How are such spared capabilities realized? Here, we recorded upper limb muscle activity in patients with PD and age-matched healthy controls (HCs) as they reached either toward or away from a visual stimulus. The task promoted express visuomotor responses (EVRs), which are brief bursts of muscle recruitment time-locked (<100 ms) to stimulus presentation that are thought to originate from the midbrain superior colliculus. Across two experiments, we observed a remarkable sparing of the latency and magnitude of EVRs in patients with PD, but a decreased ability for patients with PD to contextually modulate the EVR depending on trial instruction. EVR Magnitudes were strikingly strongly correlated with PD Reaction times and Error rates, despite compromised levels of electromyographic (EMG) recruitment in subsequent phases of muscle activity, which predicted lower Peak velocities. Our results are consistent with a differential influence of PD on parallel-but-interacting subcortical and cortical pathways that converge onto brainstem and spinal circuits during reaching. This differential influence is discriminable even within a single trial in the selective sparing of stimulus-aligned but not movement-aligned muscle recruitment, and has implications for our understanding of the motor and cognitive deficits seen in PD.
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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".