Navigation in Virtual Reality Floor Mazes: Added Cognitive Demand and Its Effects on Gait and Balance in Parkinson’s Disease
Bibliographic record
Abstract
Along with motor dysfunction, people with Parkinson's Disease (PD) often develop cognitive dysfunction, linked to the gait abnormality - freezing of gait (FOG). Spatial navigation in Virtual Reality Floor Mazes (VR-FM) provides a unique framework for studying the effects of cognitive load on walking, with the ability to manipulate the complexity of the cognitive load. In addition, mazes include turns which simulate indoor home environments that people with PD frequently traverse in their daily life. This study is aimed to examine the effects of increasing cognitive load, applied with VR-FM, on motor performance in PD subjects with and without FOG. This is particularly important in understanding Parkinson's Disease, as cognitive decline is a strong contributor to morbidity and mortality as the disease progresses and may be a contributing factor to FOG. Fourteen subjects with PD, including eight who exhibited FOG, completed VR-FM under three conditions: 1) control mazes where the path to the goal is displayed; 2) easy mazes with two or less decision points; and 3) hard mazes, with more than two decision points. In comparison to non-freezers, freezers took fewer spin steps, shorter and slower strides, and reduced medial-lateral sway of the center of mass. These deficits became worse with maze difficulty, accompanied by further degradation in balance measured by margin of stability. Increased cognitive load imposed by the VR-FM led to gait deterioration and a prioritization for balance in both freezers and non-freezers. This supports the use of VR-FM as a tool to investigate motor-cognitive interplay in PD. Freezers exhibit more pronounced deterioration in gait and balance in VR-FM. Hence, VR-FM can serve as a potential tool to characterize and identify freezers.
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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) | 0.000 | 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".