Saccadic Behaviour in an Eye-Tracking Task is Differentially Altered by Neurodegenerative Diseases (2184)
Bibliographic record
Abstract
Objective: Characterize saccadic behaviour across several neurodegenerative diseases to determine patterns of behavioural alterations that may be used as disease-specific biomarkers. Background: The overlap of oculomotor circuitry and brain regions affected by neurodegenerative disease suggests assessment of eye movements can differentiate and monitor such diseases. Typifying saccadic behavioural “fingerprints” found in neurodegenerative diseases, in combination with clinical measures, may enhance screening, diagnosis, and tracking of disease progression. Design/Methods: The Ontario Neurodegenerative Disease Research Initiative has collected data from individuals with one of six neurodegenerative diseases: Alzheimer’s disease (AD), mild cognitive impairment (MCI), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and vascular cognitive impairment (VCI). Patients (n=520, age 40–87) and a cohort of healthy age-matched controls (n=133, age 50–93) completed a randomly interleaved pro- and anti-saccade task while their eye movements were tracked with high-speed video. The colour of a central fixation point conveyed the instruction for a prosaccade (look at peripheral target) or antisaccade (look away from peripheral target). We assessed saccade parameters including task errors, reaction times, and their association with clinical parameters (e.g. MoCA score). Results: Patterns of abnormality differed across disease groups. Each group displayed abnormalities on a unique subset of task-related parameters – e.g., antisaccade reaction time significantly increased in PD and VCI relative to controls; antisaccade direction errors (erroneously looking at the peripheral target) at very short latencies significantly increased in FTD and PD; and fixation breaks (looking away from the fixation point) significantly increased in AD and VCI. A subset of performance parameters (fixation breaks, antisaccade direction errors) were progressively worsened between controls, MCI, and AD. Conclusions: Neurodegenerative diseases display unique oculomotor “fingerprints” that provide insight into disease-specific brain dysfunction. These fingerprints signify unique behavioural biomarkers for neurodegeneration that, in combination with clinical measures, can powerfully inform novel diagnostic tools and treatments. Disclosure: Dr. Riek has nothing to disclose. Dr. Coe has nothing to disclose. Dr. Brien has nothing to disclose. Dr. Black has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Hoffman La Roche. Dr. Black has received research support from Genentech, Roche, Biogen, GE Healthcare, and Avid/Eli Lilly.. Dr. Borrie has received research support from Biogen, Merck, Eisai, Eli Lilly, Abbvie, Roche, Genentech, Novartis. Dr. Dowlatshahi has nothing to disclose. Dr. Finger has received personal compensation in an editorial capacity for NeuroImage:Clinical.Dr. Freedman has nothing to disclose. Dr. Kwan has nothing to disclose. Dr. Lang has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Consultant: AbbVie, Acorda, AFFiRis, Biogen, Janssen, Lilly, Lundbeck, Merck, Paladin, Roche, Seelos, Syneos, Sun Pharma, Theravance. Dr. Lang has received royalty, license fees, or contractual rights payments from Elsevier, Saunders, Wiley-Blackwell, Johns Hopkins Press, Cambridge University Press.Dr. Marras has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Grey Matter Technologies LLC, Acorda Therapeutics, EMD Serono. Dr. Marras has received research support from Acorda Therapeutics.Dr. Masellis has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Arkuda Therapeutics, Ionis Pharmaceuticals, and Alector Pharmaceuticals. Dr. Masellis has received research support from Roche, Novartis, Alector Pharmaceuticals.Dr. Shoesmith has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Mitsubishi Tanabe Pharma Canada. Dr. Swartz has nothing to disclose. Dr. Tan has nothing to disclose. Dr. Tartaglia has nothing to disclose. Dr. Zinman has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Mitsubishi Tanabe Pharma Canada.Dr. Investigators has nothing to disclose. Dr. Munoz has nothing to disclose.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".