P.017 Convergent and contrasting modulation of saccade and pupil responses by several neurodegenerative diseases during free viewing of video clips
Bibliographic record
Abstract
Background: Saccade and pupil responses are potential neurodegenerative disease biomarkers due to overlap between oculomotor circuitry and disease-affected areas. Instruction-based tasks have previously been examined as biomarker sources, but are arduous for patients with limited cognitive abilities; additionally, few studies have evaluated multiple neurodegenerative pathologies concurrently. Methods: The Ontario Neurodegenerative Disease Research Initiative recruited individuals with Alzheimer’s disease (AD), mild cognitive impairment (MCI), amyotrophic lateral sclerosis (ALS), frontotemporal dementia, progressive supranuclear palsy, or Parkinson’s disease (PD). Patients (n=274, age 40-86) and healthy controls (n=101, age 55-86) viewed 10 minutes of frequently changing video clips without instruction while their eyes were tracked. We evaluated differences in saccade and pupil parameters (e.g. saccade frequency and amplitude, pupil size, responses to clip changes) between groups. Results: Preliminary data indicates low-level behavioural alterations in multiple disease cohorts: increased centre bias, lower overall saccade rate and reduced saccade amplitude. After clip changes, patient groups generally demonstrated lower saccade rate but higher microsaccade rate following clip change to varying degrees. Additionally, pupil responses were blunted (AD, MCI, ALS) or exaggerated (PD). Conclusions: This task may generate behavioural biomarkers even in cognitively impaired populations. Future work should explore the possible effects of factors such as medication and disease stage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".