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Research Paper|Volume 12, Issue 16|pp 16341—16356

Abnormal static and dynamic functional connectivity of resting-state fMRI in multiple system atrophy

Weimin Zheng1, Yunxiang Ge2, Shan Ren3, Weizheng Ran4, Xinning Zhang4, Wenyang Tian4, Zhigang Chen3, Weibei Dou2, Zhiqun Wang1
  • 1Department of Radiology, Aerospace Center Hospital, Beijing 100049, China
  • 2Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
  • 3Department of Neurology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, China
  • 4Beijing University of Chinese Medicine, Beijing 100029, China
* Equal contribution
Received: February 13, 2020Accepted: June 29, 2020Published: August 27, 2020

Copyright © 2020 Zheng et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

In order to explore the topological alterations in functional brain networks between multiple system atrophy (MSA) patients and healthy controls (HC), a new joint analysis method of static and dynamic functional connectivity (FC) is proposed in this paper. Twenty-four MSA patients and twenty HCs were enrolled in this study. We constructed static and dynamic brain networks from resting-state fMRI data and calculated four graph theory attributes. Statistical comparisons and correlation analysis were carried out for static and dynamic FC separately before combining both cases. We found decreased local efficiency (LE) and weighted degree (WD) in cerebellum from both static and dynamic graph attributes. For static FC alone, we identified increased betweenness centrality (BC) at left dorsolateral prefrontal cortex, left Cerebellum_Crus9 and decreased WD at Vermis_6. For dynamic FC alone, decreased BC, clustering coefficients and LE at several cortical regions and cerebellum were identified. All the features had significant correlation with total UMSARS scores. Receiver operating characteristic analysis showed that dynamic features had the highest area under the curve value. Our work not only added new evidence for the underlying neurobiology and disrupted dynamic disconnection syndrome of MSA, but also proved the possibility of disease diagnosis and progression tracking using rs-fMRI.