Analysis of temporal saccade prediction in Parkinson’s Disease using video-based eye tracking
Bibliographic record
Abstract
In Parkinson’s Disease, key brain regions involved in generating saccades and producing adaptive anticipatory behaviour are impacted, however the intersection of these deficits is not well characterized. Effective Parkinson’s Disease biomarkers are lacking, and video-based eye tracking provides a low-cost, non-invasive means to quantify eye-movement behaviour and address this knowledge gap. In a preliminary study, we analyzed predictive saccade behaviour in eight Parkinson’s patients (ON and OFF medication) and twenty controls aged 51-80 years. Participants performed a visual metronome task, moving their eyes in synchrony with a visual target jumping at a fixed rate on a computer screen. This was contrasted with a random task where the timing of target jumps was not predictable. Saccades made in anticipation of target appearance were classified as predictive, while those made significantly after were classified as reactive. There were no significant differences in saccadic metrics (i.e., reaction time, peak velocity, and amplitude) between groups. Parkinson’s Disease’s impact on saccade reaction time and predictive saccade generation was subtle, however these patients generated multi-stepping, hypometric saccades with reduced velocity compared to controls. The effects of dopaminergic medication on saccade metrics were inconsistent, with some improvement of saccade amplitude. Weak to moderate correlations were obtained between saccade metrics and disease severity and duration. This pilot study contributes to the understanding of saccade performance in evaluating the neural underpinnings of motor impairments in Parkinson’s Disease. Further investigation with more participant recruitment will be necessary to identify which saccade features are sensitive and specific to Parkinson’s Disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 teacher head, 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".