Brain network analysis between Parkinson's Disease and Health Control based on edge functional connectivity

Abstract

Parkinson's Disease (PD) is the second largest neurodegenerative disease. Brain functional connectivity (FC) studies for PD were useful. In this study, we employed a novel brain network construction method, edge functional connectivity (eFC), to explore FC differences between healthy control (HC) subjects and PD patients. The data used in this study included 34 HCs and 47 PDs from Huashan Hospital, Fudan University, China. Resting state functional magnetic resonance imaging (rsfMRI) and clinical information were selected. Firstly, we constructed eFC brain network and calculated network matrix for the HC and PD groups. Then, we compared brain network matrix between eFC and the traditional nodal functional connectivity (nFC) method. Receiver operating characteristic curve (ROC) analysis was applied to validate the efficiency of the eFC brain network. The results showed that both nFC and eFC brain networks could identify significantly different characteristics between the HC and PD groups. Important hubs were mainly concentrated in visual network, sensorimotor network, subcortex and cerebellum. In addition, new hubs in basal ganglia and cerebellum regions were found in eFC. Furthermore, eFC achieved better classification results (AUC=0.985) than nFC (AUC=0.861) in discriminating PD from CN subjects.