Parkinson's Disease (PD) is a progressive neurodegenerative disorder caused by a depletion of dopamine (DA) neurons in the striatum. Its major symptoms are movement- related, including tremors, bradykinesis, stooped posture, and masked facial affect. There are several monkey models for studying the pathophysiology of PD. The most widely used involves a MPTP, a substance that is toxic to dopaminergic neurons and very accurately mirrors the underlying cause, symptoms and progression of PD in humans. To assess the motor-related impairments in MPTP monkeys, researchers have adapted traditional clinical rating systems, but until recently, no standardized measurement system has been available to assess deficits in facial expressivity, despite this being a debilitating symptom of PD in humans. The FACS (Facial Action Coding System) is the gold standard for measuring facial movement in humans. FACS is anatomically-based as it identifies facial movement according to the underlying action of facial muscles. Researchers have used FACS to reveal deficits in both the type and intensity of facial expressions made by people with PD. Investigators have recently developed a monkey FACS (MaqFACS) and pilot data in MPTP monkeys suggest that this system may be a more sensitive measure of early onset PD than traditional rating systems. This project will directly assess the effectiveness of MaqFACS, compared to other rating systems (activity monitoring and a behavioral rating scale), in characterizing the progression of PD symptoms in MPTP-treated monkeys. Facial movement will be video recorded and then coded using MaqFACS during baseline sessions as well as two emotional provocations (exposure to a mirror and response to movies of other monkeys) to evoke facial expressivity. Inter-individual variability in facial movement will be quantified and changes in the type and amount of facial movement will be compared across the MPTP-treatment progression. The PD monkeys will then be given a DA agonist to reverse their symptoms and recovery of function will be assessed using the same testing and measurement procedures. These data will validate the effectiveness of MaqFACS in identifying the early onset of PD symptoms in the most common monkey model of PD and aid in the development of effective treatments.
Deficits in facial expressivity represent a core feature of numerous psychiatric and neurodegenerative disorders in humans, such as Parkinson's disease. Animal models for studying these disorders would benefit greatly from a standardized measurement system for quantifying these facial movement deficits. A recently developed system, the MaqFACS, proves to be such a system and this project aims to validate the usefulness of MaqFACS to quantify facial movement deficits in a monkey model of Parkinson's disease.