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

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.

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Altered dynamic functional network connectivity in levodopa-induced dyskinesia of Parkinson's disease

10.21203/rs.3.rs-50663/v1 ◽

2020 ◽

Author(s):

Qianqian Si ◽

Yongsheng Yuan ◽

Caiting Gan ◽

Min Wang ◽

Lina Wang ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Large Scale ◽

Network Connectivity ◽

Functional Network ◽

Sensorimotor Network ◽

Functional Network Connectivity ◽

Strongly Connected ◽

Window Approach ◽

Dynamic Functional Network Connectivity

Abstract Background Traditional measures of static functional connectivity may not completely reflect the dynamic neural activity of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD). This study was aimed to investigate the dynamic changes of large-scale functional network connectivity in the temporal domain in PD patients with and without LID. Methods Using dynamic functional network connectivity (dFNC) analysis, we evaluated 41 PD patients with LID (LID group) and 34 PD patients without LID (No-LID group), on and off their levodopa medications. Group spatial independent component analysis, sliding-window approach and k-means clusters were employed. Results In OFF phase, we found no differences between PD subgroups in temporal properties. In ON phase, compared than No-LID group, LID group occurred more frequently and dwelled longer in strongly connected State 1, characterized by strong connections between visual network (VIS) and other networks. When switching from OFF to ON phase, LID group occurred more frequently and dwelled longer in State 2 and occurred less frequently and dwelled shorter in State 3 (both states were strongly connected), while No-LID group occurred more frequently and dwelled longer in State 5 (weakly connected). Additionally, correlation analysis further demonstrated that the severity of dyskinesia was only associated with frequency of occurrence and dwell time in State 2, dominated by inferior frontal cortex in cognitive executive network (CEN), strongly connecting with sensorimotor network (SMN) and VIS. Conclusions Using dFNC analysis, we found that compared to those without LID, PD patients with LID may be involved in the superexcitation of VIS, as well as interconnections between CEN and SMN, VIS, having impact on inhibition of motor circuits. The dFNC analysis might provide new insights into the neural mechanisms of LID in PD.

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