Eye movement study in essential tremor patients and its clinical correlates

Rekik, Arwa; Mrabet, S.; Nasri, Amina; Abida, Youssef; Gharbi, Alya; Gargouri, Amina; Kacem, Imen; Gouider, Riadh

doi:10.1007/s00702-023-02614-9

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…MoCA test scores have been shown to correlate with pro-saccade parameters of latency 17 , 39 and accuracy 17 , and anti-saccade latency 1 . In a sample of essential tremor patients, the presence of pro-saccade, anti-saccade and smooth pursuit impairments (relative to healthy controls) strongly correlated with low MMSE scores and impaired verbal fluency 40 . Similarly, van Stockum et al 41 identified a link between verbal fluency ability and anti-saccade latency in individuals with PD.…”

Section: Discussionmentioning

confidence: 92%

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

Koch,

Voss,

Cisneros-Franco

et al. 2024

Sci Rep

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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.

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