Changes of Brain Structural Network Connection in Parkinson’s Disease Patients with Mild Cognitive Dysfunction: a Study Based on Diffusion Tensor Imaging
Journal of Neurology(2019)
Department of Neurology
Abstract
Introduction Previous studies have found that white matter (WM) alterations might be correlated in Parkinson’s disease (PD) patients with cognitive impairment. This study aimed to investigate WM structural network connectome alterations in PD patients with mild cognitive impairment (PD-MCI) and assess the relationship between cognitive impairment and structural topological network changes in PD patients. Methods All 31 healthy controls (HCs) and 71 PD patients (43 PD-NC and 28 PD-MCI) matched for age, sex and education underwent 3.0 T MRI and diffusion tensor imaging (DTI) scan. Graph theoretical analyses and network-based statistical (NBS) analyses were performed to identify the structural WM networks and subnetwork changes in PD-MCI. Results PD-MCI patients showed significantly decreased global efficiency ( E glob ) and increased shortest path length ( L p ) compared with the HC group. Several nodal efficiencies showed significant differences in multiple brain regions among the three groups. The nodal efficiency of the orbitofrontal part was closely related to the overall cognitive ability and multiple sub-cognitive domains. Moreover, NBS analyses identified eight one-connect subnetworks, three two-connect subnetworks and two multi-connect subnetworks with reduced connectivity that characterizes the WM structural organization in PD-MCI patients. The two multi-connect subnetworks were located on the bilateral lobe, and both were centered on the orbitofrontal part. Conclusions This study provided new evidence that PD with cognitive dysfunction is associated with WM structural alterations. The nodal efficiency and sub-network analyses focusing on the orbitofrontal part might provide new ideas to explore the physiological mechanism of PD-MCI.
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Key words
Brain structural network connections,Network,Parkinson’s disease,Mild cognitive dysfunction,Diffusion tensor imaging,Network-based statistics
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