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.
Highlights
Parkinson’s disease (PD) is a common progressive neurodegenerative disorder, characterized by tremor, rigidity, bradykinesia, and postural instability/gait disorders (Lees et al, 2009)
Dynamic Functional Network Connectivity Strength Changes Between PDTD and PDNTD Patients. Both PDTD and PDNTD patients displayed higher connectivity between dorsal somatomotor network (dSMN) and FPN in state II and state III compared with the controls
We found that PDNTD patients exhibited decreased FNC between basal ganglia (BG) and FPN compared with both PDTD patients and controls
Summary
Parkinson’s disease (PD) is a common progressive neurodegenerative disorder, characterized by tremor, rigidity, bradykinesia, and postural instability/gait disorders (Lees et al, 2009). Patients with PD can be grouped into tremor-dominant (TD) and non-tremor-dominant (NTD) phenotypes according to the presence of resting tremor or not (Helmich et al, 2012; Marras and Lang, 2013). Relative to the TD phenotypes, the NTD phenotypes suffer more from postural and gait problems and have greater risk of dementia, worse level of cognitive decline (Williams-Gray et al, 2007, 2009), higher sensitivity for depression (Dissanayaka et al, 2010), and increased executive control deficits (Wylie et al, 2012). 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
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
