Changes to Saccade Behaviors in Parkinson’s Disease Following Dancing and Observation of Dancing
Bibliographic record
Abstract
BACKGROUND: The traditional view of Parkinson's disease (PD) as a motor disorder only treated by dopaminergic medications is now shifting to include non-pharmacologic interventions. We have noticed that patients with PD obtain an immediate, short-lasting benefit to mobility by the end of a dance class, suggesting some mechanism by which dancing reduces bradykinetic symptoms. We have also found that patients with PD are unimpaired at initiating highly automatic eye movements to visual stimuli (pro-saccades) but are impaired at generating willful eye movements away from visual stimuli (anti-saccades). We hypothesized that the mechanisms by which a dance class improves movement initiation may generalize to the brain networks impacted in PD (frontal lobe and basal ganglia, BG), and thus could be assessed objectively by measuring eye movements, which rely on the same neural circuitry. METHODS: Participants with PD performed pro- and anti-saccades before, and after, a dance class. "Before" and "after" saccade performance measurements were compared. These measurements were then contrasted with a control condition (observing a dance class in a video), and with older and younger adult populations, who rested for an hour between measurements. RESULTS: We found an improvement in anti-saccade performance following the observation of dance (but not following dancing), but we found a detriment in pro-saccade performance following dancing. CONCLUSION: We suggest that observation of dance induced plasticity changes in frontal-BG networks that are important for executive control. Dancing, in contrast, increased voluntary movement signals that benefited mobility, but interfered with the automaticity of efficient pro-saccade execution.
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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".