Diagnosing Parkinson's disease (PD) depends on the clinical history of the patient and the patient's response to specific treatments such as levodopa. Unfortunately, a definitive diagnosis of PD is still limited to post-mortem evaluation of brain tissues. Furthermore, diagnosis of idiopathic PD is even more challenging because symptoms of PD overlap with symptoms of other conditions such as essential tremor (ET) or Parkinsonian syndromes (PSs) such as progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal degeneration (CBD), or vascular Parkinsonism (VaP). Based on the principle that PD and PSs affect brain areas involved in eye movement control, this trial will utilize a platform that records complex eye movements and use a proprietary algorithm to characterize PSs. Preliminary data demonstrate that by monitoring oculomotor alterations, the process can assign PD-specific oculomotor patterns, which have the potential to serve as a diagnostic tool for PD. This study will evaluate capabilities of the process and its ability to differentiate PD from other PSs with statistical significance. The specific aims of this proposal are: To optimize the detection and analysis algorithms, and then to evaluate the process against neurological diagnoses of PD patients in a clinical study.
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Percent of patients accurately diagnosed with SaccadeDX
Timeframe: One test (approx. 1 hour)