Eye movement function captured via an electronic tablet informs on cognition and disease severity in Parkinson’s disease

Eye movement function captured via an electronic tablet informs on cognition and disease severity in Parkinson’s disease

Studying the oculomotor system provides a unique window to assess brain health and function in various clinical populations. Although the use of detailed oculomotor parameters in clinical research has been limited due to the scalability of the required equipment, the development of novel tablet-based technologies has created opportunities for fast, easy, cost-effective, and reliable eye tracking. Oculomotor measures captured via a mobile tablet-based technology have previously been shown to reliably discriminate between Parkinson’s Disease (PD) patients and healthy controls. Here we further investigate the use of oculomotor measures from tablet-based eye-tracking to inform on various cognitive abilities and disease severity in PD patients. When combined using partial least square regression, the extracted oculomotor parameters can explain up to 71% of the variance in cognitive test scores (e.g. Trail Making Test). Moreover, using a receiver operating characteristics (ROC) analysis we show that eye-tracking parameters can be used in a support vector classifier to discriminate between individuals with mild PD from those with moderate PD (based on UPDRS cut-off scores) with an accuracy of 90%. Taken together, our findings highlight the potential usefulness of mobile tablet-based technology to rapidly scale eye-tracking use and usefulness in both research and clinical settings by informing on disease stage and cognitive outcomes.


Neurodegenerative disorders have long been known to produce a broad variety of oculomotor alterations as a result of deteriorating brain health. Many of these have been previously described in Parkinson's disease (PD) and include, but are not limited to, increased pro-saccade latency1, presence of multistep pro-saccades2,3, increased saccadic intrusions during fixation4, and increased antisaccade error rate1,5. Although primarily referred to as a motor neurodegenerative disorder, PD is a multisystem disorder that leads to several non-motor issues, including cognitive dysfunction, dementia, and depression, that contribute greatly to the overall disease burden6.

Cognitive dysfunction is one of the more frequent–up to six times more common in individuals with PD than in the healthy population7 –and debilitating non-motor symptoms of PD, as it significantly affects the patient’s quality of life8. Although it was traditionally believed that cognitive dysfunction does not emerge until the later stages of PD, recent evidence suggests that mild-to-moderate cognitive impairments are often present during the early disease stages, occurring in up to 35% of individuals with early-stage PD9. In fact, the onset of cognitive decline appears to be highly unpredictable in PD individuals, which can occur a few years or decades after diagnosis as much as it can appear at the time of, or even prior to, PD diagnosis10.

The accurate diagnosis of cognitive impairment in individuals with PD is important for clinical management, and research, including trial selection. Although screening of cognitive function in patients with PD is not performed regularly, it has been argued that it should be part of routine clinical care11. The Montreal Cognitive Assessment (MoCA) is the most frequently used cognitive screening instrument in PD research and clinical practice, and the optimal cut-off point of 23/24 has a sensitivity of 0.41 and a specificity of 0.82, with 68% correct diagnoses of PD-MCI12. The main drawbacks of such a cognitive screening approach is the limited information gleaned about the detailed cognitive profile and the reduced reliability compared with a comprehensive neuropsychological assessment. However, performing a full neuropsychological assessment is generally too time-consuming to become part of the clinical practice standard of care. Inferring cognitive ability from analysis of oculomotor parameters shows great potential and promise in bridging this gap.

Indeed, a growing body of evidence suggests that eye-tracking data can serve as a viable marker of cognition and cognitive impairments13,14. Specifically, several oculomotor metrics measured in individuals with PD have been shown to correlate with measures of general cognition such as the Mini-Mental Status Exam (MMSE)15,16 or the MoCA17,18. More recently, in a study of individuals with multiple sclerosis, we showed that several oculomotor parameters, when jointly considered, could account for a large proportion of the variance in cognitive test scores19.

Despite the promise of oculomotor analysis as a potential marker of cognition and disease severity, this has not previously been practical or scalable given the costs and operational limitations of the required equipment, such as infrared eye-tracking cameras. These limitations acted as important barriers to adoption of eye tracking in clinical practice. To address this critical technological gap, a novel gaze-tracking tool was recently developed and requires only the embedded camera of an iPad Pro (Eye-Tracking Neurological Assessment (ETNA™); Innodem Neurosciences). This approach allows for the precise quantification of several eye movement parameters with a precision comparable to those of research-grade infrared eye tracking devices, such as the latency, velocity, accuracy of saccades, and the presence of saccadic intrusions during fixation. Using this novel technology, we recently replicated sets of well-known oculomotor findings in both individuals with MS19 and PD20, with the latter study having primarily focused on distinguishing individuals with PD from healthy controls on the basis of recorded eye movement parameters. The main objective of the present paper was to determine to what extent the oculomotor parameters extracted by this mobile eye-tracking tool could serve as viable markers of both disease stage (or severity) using standard PD clinical staging tools, and of cognition in individuals with PD. To address the question of cognition, we evaluated four of the cognitive domains outlined in the Movement Disorder Society Task Force Guidelines21 – using one cognitive test per cognitive domain: MoCA (global cognitive), Trail Making Test (attention and working memory), Controlled Oral Word Association Test (COWAT) of verbal fluency (executive function), Hopkins Verbal Learning Test (HVLT; memory). Oculomotor parameters were measured during 5 visual tests that are typically used to reveal eye movement anomalies in various patient populations such as PD1,4,5,17: fixation task, pro-saccade task, anti-saccade task, smooth pursuit task, and optokinetic nystagmus task.

In a first preliminary step, we investigated correlations between each cognitive/motor outcome measure of interest and all individual eye movement parameters. We hypothesized based on the known literature that several of these correlations would be of moderate strength (0.3 < r < 0.5), particularly for pro- and anti-saccade parameters. In a subsequent step, we used partial least squares (PLS) regression approaches to determine the extent of clinical score variance that could be explained using the eye movement features and hypothesized that although significant proportions of the variance of the cognitive test scores could be explained, that these proportions wouldn’t be as high as those observed for clinical motor scale scores, as we have previously shown in a sample of patients with MS19. Finally, we developed a support vector classifier to discriminate between individuals with mild PD from those with moderate PD (based on UPDRS cut-off scores). Given the strong relationship known to exist between several oculomotor parameters and the UPDRS scores and our own previously published data20, we hypothesized that we should be able to distinguish between both PD patient subgroups with a high level of accuracy. The overall aim of the study is to generate evidence that oculomotor parameters collected with a novel tablet-based technology can assist in clinical assessment and management of PD patients by informing on disease severity and cognitive abilities.

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