Traditional Parkinson’s disease (PD) detection and monitoring systems lack objective biomarkers and are often reliant on late-stage symptoms. By contrast, a masked face is often seen in early-stage PD and even years before diagnosis. In this study, we aim to validate the reliability of a tool that objectively captures and identifies the masked face and other early-stage facial expression impairments and micromovements.
We hypothesize that in PD, facial expressions may communicate disease state and provide an external manifestation of early, pre-nigral PD pathology. If this is true, spontaneous and posed facial expression impairments in PD may be objectively identified and used to detect early stages of PD and may differentiate between commonly misidentified disorders.
In this study, we will objectively measure and digitize the masked face associated with early stages of PD. Study participants will undergo a process to elicit and collect certain spontaneous and posed facial expressions. We will then determine if facial expression impairments are able to detect PD and differentiate between atypical parkinsonism disorders. We also hope to determine whether facial expression impairments can predict the severity and progression of PD.
Impact on Diagnosis/Treatment of Parkinson’s Disease:
If our hypothesis is correct, we will be able to use the developed system to objectively detect early stages of PD and measure disease progression. The developed system may also provide a noninvasive tool to aid in differential diagnosis.
Next Steps for Development:
If the results of this study are promising, we will conduct a larger longitudinal study to confirm them. We will also determine if the identified facial indicators are associated with underlying brain networks that are found to be abnormal in PD, which may ultimately be useful to test and develop disease-modifying therapeutics.