Mild cognitive impairment (MCI) and dementia are common in Parkinson’s disease. However, presently, it is difficult to recognize which aspects of cognitive changes are related to the motor symptoms of PD and its treatment, and which are due to disease progression. This project will study well-characterized PD patients with and without cognitive impairment to define the mildest evidence supportive of impairment and use functional MRI (fMRI) and EEG imaging techniques to describe the characteristic networks that underlie cognition in PD.
First, researchers will examine archived data to define the MCI phase of PD and the transition from PD to PD-MCI to PD-dementia. This will be accomplished using a novel statistical approach called Multiphase Random Change Point modeling that allows analyses of data at the individual level. Second, researchers will examine 80 individuals (20 each of healthy controls and people with PD, PD-MCI or PD-dementia) over two years with neuropsychological testing, fMRI and EEG to map the network activity of the brain and determine which changes are due to age and which are due to PD. They have developed novel methods for combining information from fMRI and EEG on the same reference map.
Relevance to Diagnosis/Treatment of Parkinson’s Disease:
By defining PD-MCI, this project will improve the ability to detect cognitive change at the earliest possible stage. And by focusing on high-dimensional network analysis, researchers obtain cutting-edge information on the global brain network changes.
The investigators predict that cognitive decline from PD to PD-dementia passes through a definable phase of PD-MCI, with the greatest decline in the visuospatial, attention and perceptual speed domains. These data can be used to predict future PD-dementia for clinical trials. They also anticipate that multimodal analyses of fMRI and EEG will be superior to either approach alone in defining PD-MCI and characterizing network dynamics.