Eye Tracking as a Digital Biomarker in Neurodegenerative Diseases
Oculomotor abnormalities are a common finding across neurodegenerative diseases, arising from degeneration of the dorsolateral prefrontal cortex, basal ganglia, superior colliculus, and cerebellum — all regions critical for eye-movement control. These abnormalities produce subtle changes in eye-movement metrics that may escape clinical examination but can be captured quantitatively by eye-tracking technology.
Key Eye-Movement Metrics
- Saccades — Rapid eye movements between fixation points; metrics include latency, velocity, accuracy
- Anti-saccades — Volitional saccades away from a visual target; tests inhibitory control (frontal lobe function)
- Fixation — Ability to maintain steady gaze; instability signals cerebellar/brainstem dysfunction
- Smooth pursuit — Tracking a moving target; impairment reflects cortical and cerebellar pathology
Clinical Utility
Eye-movement metrics serve three primary roles:
- Prognostic — Predict disease progression across conditions
- Diagnostic — Differentiate pathologic subtypes (e.g., PSP vs. Parkinson’s, AD vs. FTD)
- Monitoring — Early detection of worsening motor and cognitive function
Conditions Covered
| Condition | Key Oculomotor Findings |
|---|---|
| Multiple sclerosis | Internuclear ophthalmoplegia, saccadic dysmetria, fixation instability |
| Parkinson’s disease | Hypometric saccades, increased anti-saccade error rates, impaired smooth pursuit |
| Alzheimer’s disease / dementias | Prolonged saccade latencies, increased anti-saccade errors, impaired smooth pursuit |
| Amyotrophic lateral sclerosis (ALS) | Saccadic abnormalities, particularly in bulbar-onset forms |
| Progressive supranuclear palsy (PSP) | Vertical gaze palsy, severely impaired saccades |
| Huntington’s disease | Choreic saccadic intrusions, impaired initiation |
Technology and Accessibility
New eye-tracking technologies incorporated into widely-accessible digital devices (smartphones, tablets) now permit detailed assessments with minimal equipment requirements. This addresses two major care gaps:
- Cost — Traditional clinical eye-tracking equipment is expensive
- Specialist shortage — Assessments can be deployed by family physicians, nurses, and pharmacists, not only subspecialist neurologists
Concrete Implementations
VECA — VR Eye-tracking Cognitive Assessment
A portable dementia screening tool integrating VR-based eye tracking with machine learning, developed by Xu et al. (2024). Deployed with 201 participants from Shenzhen Baoan Chronic Hospital to predict MoCA scores and classify cognitive impairment across educational backgrounds:
- Technology: VR headset captures eye-tracking data
- Model: Support vector regression predicts MoCA score
- Performance: r = 0.9 correlation with MoCA; 88.5% sensitivity, 83% specificity
- Advantage: Non-invasive, efficient, suitable for large-scale deployment
- Target condition: Alzheimer’s disease and MCI screening 1
VECA addresses limitations of traditional AD biomarkers (costly, invasive) and neuropsychological tests like MoCA (unsuitable for mass screening), directly embodying the portable digital biomarker paradigm described in this review.
Relationship to Other Oculomotor Conditions
While eye-tracking in vergence-control disorders (e.g., acute-acquired-concomitant-esotropia) focuses on binocular alignment and vergence, the neurodegenerative biomarker application targets a different set of oculomotor parameters (saccades, anti-saccades, smooth pursuit, fixation stability) as indicators of central nervous system degeneration. Both fields benefit from the same enabling technology — portable eye-tracking in consumer devices.
References
- Giacomini et al. (2026). Eye tracking as a digital biomarker in neurodegenerative diseases. Journal of Neurology, 273, 133. 2
- Xu et al. (2024). A portable and efficient dementia screening tool using eye tracking machine learning and virtual reality. npj Digital Medicine, 7, 219. 1