Neuropsychiatric symptoms (NPS) (depression, anxiety, hallucinations, etc.) are among the most common non-motor symptoms in Parkinson's disease. They affect the majority of patients during the course of the disease, and are even more common in people with mild cognitive impairment or dementia.

Screening for emerging NPS can be an effective method of identifying a population at high risk of dementia, particularly in people with Parkinson's disease. Mild behavioral impairment (MBI) is a neurobehavioral syndrome associated with the emergence, later in life, of a long-lasting NPS, characterized by a change in behavior or personality. Early identification of the NPS that make up MBI can contribute to earlier detection of dementia, before the onset of cognitive impairment.

The Mild Behavioral Impairment-C (MBI-C) test battery was developed to assess early non-cognitive markers of dementia. It is an easy-to-apply MBI case ascertainment tool designed to recognize emerging SNPs in an elderly, functionally independent, community-dwelling population. Our preliminary results indicate a significant correlation between MBI-C scores and cognitive deficits in PD. However, the relationship between MBI-C scores, cognitive deficits and PD progression has not yet been investigated. The aim of this project is to shed light on this relationship.

To date, the database is complete, and XX participants with Parkinson's disease have had their neuropsychiatric and neuropsychological symptoms assessed and have anatomical and functional brain imaging data, as well as their DNA extracted for genetic analysis.

Several projects are currently in progress by members of the laboratory and will enable us to better identify mild behavioral impairments as simple early clinical biomarkers of dementia developed in Parkinson's disease, as well as to better understand the neural mechanisms linking cognitive decline and neuropsychiatric symptoms in Parkinson's disease. In order to generalize our results, other databases are also being explored and analyzed using artificial intelligence methods.

This project is funded by a CIHR grant obtained in 2019.