Anxiety‐provoked gait changes are selectively dopa‐responsive in Parkinson's disease
Bibliographic record
Abstract
In order to understand how dopamine modulates the effect of anxiety on gait, the goal of this study was to use virtual reality to provoke anxiety in Parkinson's disease (PD) (in both ON and OFF states) and quantify its effect on gait. Seventeen participants with PD and 20 healthy age-matched controls were instructed to walk in a virtual environment in two anxiety-provoking conditions: (i) across a plank that was located on the GROUND and (ii) across an ELEVATED plank. All participants with PD completed this experiment in both the ON and OFF states, and were then striated into groups based on baseline trait anxiety scores for further analyses. Anxiety (skin conductance and self-report) and spatiotemporal aspects of gait were measured. Overall, the ELEVATED condition resulted in greater skin conductance levels and self-reported anxiety levels. Additionally, all participants demonstrated slower gait with increased step-to-step variability when crossing the ELEVATED plank compared with the plank on the GROUND. The results showed that dopaminergic treatment selectively improved gait in only the highly anxious PD group, by significantly improving velocity, step length, step time and step-to-step variability specifically when walking across the ELEVATED plank (ON vs. OFF comparison). In conclusion, only highly trait anxious participants with PD benefitted from dopaminergic treatment, specifically when walking in the anxiety-provoking environment. Improvements to gait during anxious walking might be a result of dopaminergic medication acting in two ways: (i) improving the basal ganglia's capacity to process information and (ii) reducing the load from anxiety and subsequently making more resources available to effectively process other competing inputs.
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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".