Constructing and investigating Morphometric Similarity Networks from structural and diffusion weighted imaging data in a large, longitudinal cohort of patients with Parkinson’s Disease Psychosis.
Using multi-level meta-analytic tools to investigate if a specific profile of impaired cognition and visual function is linked to vulnerability to visual hallucinations in Parkinson’s Disease. The overall aim is to better understand the complex relationship between psychosis and cognitive decline in Parkinson’s patients.
Using graph theoretical approaches and resting fMRI data to better characterise the neural fingerprints of visual hallucinations in Parkinson’s Disease. This includes (i) evaluating group differences in FC in terms of both Von Economo cytoarchitectonic principles and well-established functional connectivity networks, (ii) NBS analyses, and (iii) machine learning approaches to identify patterns of covariance between rsfMRI networks and cognitive and clinical biomarkers of interest (cognitive tasks, MCI tests, cerebrospinal fluid biomarkers such as β-Amyloid, T-Tau and α-Synuclein)
Developing targets for understanding current treatments and developing novel treatments The aim of this pharmachological intervention is to implement and enhance a neuroimaging protocol to test for whole brain impairment in PD patients with and without psychosis to (i) enhance understanding of the neural basis of PD psychosis, (ii) estimate the magnitude of impairment both in predefined brain regions and across brain networks and (iii) test for drug effects (5-HT2a inverse agonism) in these networks.