Altered Brain Functional Network in Subtypes of Parkinson's Disease: A Dynamic Perspective

Junlan Zhu; Qiaoling Zeng; Qiao Shi; Jiao Li; Shuwen Dong; Chao Lai; Guanxun Cheng

doi:10.3389/fnagi.2021.710735

Altered Brain Functional Network in Subtypes of Parkinson's Disease: A Dynamic Perspective

Front Aging Neurosci. 2021 Sep 7:13:710735. doi: 10.3389/fnagi.2021.710735. eCollection 2021.

Authors

Junlan Zhu^{1

2}, Qiaoling Zeng¹, Qiao Shi¹, Jiao Li¹, Shuwen Dong¹, Chao Lai¹, Guanxun Cheng¹

Affiliations

¹ Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, China.
² Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.

Abstract

Background: Parkinson's disease (PD) is a highly heterogeneous disease, especially in the clinical characteristics and prognosis. The PD is divided into two subgroups: tremor-dominant phenotype and non-tremor-dominant phenotype. Previous studies reported abnormal changes between the two PD phenotypes by using the static functional connectivity analysis. However, the dynamic properties of brain networks between the two PD phenotypes are not yet clear. Therefore, we aimed to uncover the dynamic functional network connectivity (dFNC) between the two PD phenotypes at the subnetwork level, focusing on the temporal properties of dFNC and the variability of network efficiency. Methods: We investigated the resting-state functional MRI (fMRI) data from 29 tremor-dominant PD patients (PDTD), 25 non-tremor-dominant PD patients (PDNTD), and 20 healthy controls (HCs). Sliding window approach, k-means clustering, independent component analysis (ICA), and graph theory analysis were applied to analyze the dFNC. Furthermore, the relationship between alterations in the dynamic properties and clinical features was assessed. Results: The dFNC analyses identified four reoccurring states, one of them showing sparse connections (state I). PDTD patients stayed longer time in state I and showed increased FNC between BG and vSMN in state IV. Both PD phenotypes exhibited higher FNC between dSMN and FPN in state II and state III compared with the controls. PDNTD patients showed decreased FNC between BG and FPN but increased FNC in the bilateral FPN compared with both PDTD patients and controls. In addition, PDNTD patients exhibited greater variability in global network efficiency. Tremor scores were positively correlated with dwell time in state I along with increased FNC between BG and vSMN in state IV. Conclusions: This study explores the dFNC between the PDTD and PDNTD patients, which offers new evidence on the abnormal time-varying brain functional connectivity and their network destruction of the two PD phenotypes, and may help better understand the neural substrates underlying different types of PD.

Keywords: Parkinson's disease; dynamic; functional connectivity; functional magnet resonance imaging; graph theory.