Connettività dinamica funzionale nella malattia di Parkinson

Parkinson’s Disease is a neurodegenerative disorder characterized by different symptoms such as: bradykinesia, resting tremor and rigidity, the main motor symptoms of the disease, and the cognitive impairments affecting different cognitive domains. Two distinct forms of PD exist: the normal cognition (NC) and the mild cognitive impaired condition (MCI). Here, we investigated redundant functional patterns across Parkinson disease (PD) subjects according to different subclasses of the cognitive impairment. Dynamic Functional Connectivity (dFC) analysis was conducted in a cohort of 114 participants (CogPhenoPark, recruited from two centers: Lille, France (N=55) and Maastricht, The Netherlands (N=59), between March 2013 and August 2014), in order to investigate potential similarities of brain region connections in PD and in 24 healthy control participants. Resting state-functional MRI acquisition was performed, and the raw data were preprocessed with fmriprep toolbox. ICA analysis was used to extract 8 resting state networks. Then, the sliding window method was applied within a defined time interval (60 sec), spanning the entire acquisition period. In this way, connectivity matrices were obtained performing a Pearson coefficient correlation between all the possible ICA pairs and then k-means algorithm was performed to obtain two states for each group; this step was followed by statistical analysis, performed on NBS, to test if there were significant differences between the PD subclasses and the healthy control group. Afterwards, performing a t-test, significant differences emerged comparing the group of healthy controls with the Parkinson disease one, while no differences were found within the PD subtypes.