Abstract
Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD.Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4–8 Hz), alpha (8–13 Hz), beta1 (13–20 Hz), and beta2 (20–30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels.Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p < 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p < 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p < 0.05).Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.
Highlights
Over the last few decades, the demonstration of alteration in the structural network or functional network via neuroimaging data has gained increasing attention in neuroscience and cognitive neuroscience research [1,2,3,4]
There were no significant differences in age and gender between the patients with Parkinson’s disease (PD) and healthy controls (HC)
The results of the frequency-based analysis show that compared with HC, there was a significant increase of PSD in the theta (p = 0.003) and decrease of PSD in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD in the DBSOFF and deep brain stimulation (DBS)-ON states
Summary
Over the last few decades, the demonstration of alteration in the structural network or functional network via neuroimaging data has gained increasing attention in neuroscience and cognitive neuroscience research [1,2,3,4]. Modern network science including dynamic systems theory, graph theory, statistics, and connectivity analysis has been applied to investigate topological properties of the brain under various states and conditions. A powerful mathematical approach, illustrates a complex network architecture based on the modern theory of networks, which can offer new insights into the structure and function of the brain network, including their architecture, evolution, development, and clinical disorders. The nodes and edges defined from neuroimaging data can be used to represent the brain network to study topological properties (organization) and functional connectivity by network-based statistics. Several neuroimaging approaches have been used to demonstrate functional changes of the brain in many conditions such as epilepsy [5], Parkinson’s disease (PD) [6], and Alzheimer’s disease [7] and have achieved many significant insights
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