Abstract
Parkinson’s disease (PD) is a neurodegenerative disease characterized by dysfunction in distributed functional brain networks. Previous studies have reported abnormal changes in static functional connectivity using resting-state functional magnetic resonance imaging (fMRI). However, the dynamic characteristics of brain networks in PD is still poorly understood. This study aimed to quantify the characteristics of dynamic functional connectivity in PD patients at nodal, intra- and inter-subnetwork levels. Resting-state fMRI data of a total of 42 PD patients and 40 normal controls (NCs) were investigated from the perspective of the temporal variability on the connectivity profiles across sliding windows. The results revealed that PD patients had greater nodal variability in precentral and postcentral area (in sensorimotor network, SMN), middle occipital gyrus (in visual network), putamen (in subcortical network) and cerebellum, compared with NCs. Furthermore, at the subnetwork level, PD patients had greater intra-network variability for the subcortical network, salience network and visual network, and distributed changes of inter-network variability across several subnetwork pairs. Specifically, the temporal variability within and between subcortical network and other cortical subnetworks involving SMN, visual, ventral and dorsal attention networks as well as cerebellum was positively associated with the severity of clinical symptoms in PD patients. Additionally, the increased inter-network variability of cerebellum-auditory pair was also correlated with clinical severity of symptoms in PD patients. These observations indicate that temporal variability can detect the distributed abnormalities of dynamic functional network of PD patients at nodal, intra- and inter-subnetwork scales, and may provide new insights into understanding PD.
Highlights
Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects millions of people around the world
Among the 13 subnetworks, we found that subnetworks including subcortical network, SAN and visual network tended to display greater intra-network variability in PD than normal controls (NCs) (p < 0.05, 10000 permutations, Figure 2A)
We found that the temporal variability including nodal, intra- and inter- network variability estimated from windows of different lengths were highly correlated, indicating that these metrics is not sensitive to the choice of window length
Summary
Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects millions of people around the world. Several previous studies highlighted that PD could be considered as a disease related to the disruptions in several networks using diffusion tensor imaging (DTI) (Melzer et al, 2013; Lopes et al, 2017), resting-state functional magnetic resonance imaging (fMRI) (Luo et al, 2014), task fMRI (Shine et al, 2013b) and other imaging techniques (Brooks and Pavese, 2011). Previous studies on large-scale network of PD patients by graph theoretic analysis revealed disruptions in the topological properties of brain networks and these network measures have been demonstrated to serve as potential biomarkers of PD for clinical diagnosis (Amboni et al, 2015). Altered modular organization of functional brain networks in PD patients has been reported (Ma et al, 2016; Peraza et al, 2017), implying an abnormal functional integration of PD
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