مقاله انگلیسی رایگان در مورد ارتباط عملی در بیماران مبتلا به بیماری پارکینسون با اختلال شناختی – الزویر 2018

The objective was to assess dynamic functional connectivity (FC) and local/global connectivity in Parkinson’s disease (PD) patients with mild cognitive impairment (PD-MCI) and with normal cognition (PD-NC). The sample included 35 PD patients and 26 healthy controls (HC). Cognitive assessment followed an extensive neuropsychological battery. For resting-state functional MRI (rs-fMRI) analysis, independent component analysis (ICA) was performed and components were located in 7 networks: Subcortical (SC), Auditory (AUD), Somatomotor (SM), visual (VI), cognitive-control (CC), default-mode (DMN), and cerebellar (CB). Dynamic FC analysis was performed using the GIFT toolbox. FC differences between groups in each FC state were analysed with the network-based statistic (NBS) approach. Finally, a graph-theoretical analysis for local/global parameters was performed. The whole sample showed 2 dynamic FC states during the rs-fMRI. PD-MCI patients showed decreased mean dwell time in the hypo-connectivity state (p = 0.030) and showed increased number of state transitions (p = 0.007) compared with the HC. In addition, in the hypo-connectivity state, PD-MCI patients showed reduced inter-network FC between the SM-CC, SM-VI, SM-AUD, CC-VI and SC-DMN compared with the HC (p < 0.05- FDR). These FC alterations in PD-MCI were accompanied by graph-topological alterations in nodes located in the SM network (p < 0.001). In contrast, no differences were found between the PD-NC and HC. Findings suggest the presence of dynamic functional brain deteriorations in PD-MCI that are not present in PD-NC, showing the PD-MCI group dynamic FC dysfunctions, reduced FC mostly between SM-CC networks and graph-topological deteriorations in the SM network. A dynamic FC approach could be helpful to understand cognitive deterioration in PD.

Introduction

Cognitive deficits in Parkinson’s disease (PD) patients are common from early to moderate stages (Aarsland et al., 2009; Elgh et al., 2009) and up to 80% of the patients may develop dementia in the course of the disease (Hely et al., 2008). Moreover, the presence of cognitive impairment is related to a reduction in quality of life and functional disability in PD (Leroi et al., 2012; Rosenthal et al., 2010). Magnetic resonance imaging (MRI) studies have shown that cognitive deficits in PD patients are correlated with structural (Duncan et al., 2016) and functional brain dysfunctions (Christopher and Strafella, 2013; Gao and Wu, 2016). More specifically, resting-state functional MRI (rs-fMRI) is a non-invasive method that shows reliability and high reproducibility to easily explore the functional activity of the different brain networks (Biswal et al., 2010; Van Den Heuvel et al., 2010). To date, most rs-fMRI studies in PD have investigated functional connectivity (FC) patterns as a static phenomenon. While these studies have shown a general impairment in PD patients compared with healthy controls (HC), [for a review see: (Gao and Wu, 2016)], others have focused on assessing differences between PD patients with mild cognitive impairment (PD-MCI) and with normal cognition (PD-NC). PD-MCI patients seemed to present FC alterations within (Amboni et al., 2015; Baggio et al., 2015; Gorges et al., 2015) and between networks (Baggio et al., 2015) compared with HC, and also compared with PD NC (Baggio et al., 2015; Gorges et al., 2015). Less pronounced FC differences between PD-NC and HC have also been reported (Amboni et al., 2015; Baggio et al., 2015; Gorges et al., 2015). However, more recently, rs-fMRI studies have shown that FC may actually vary during the acquisition time (i.e. dynamic FC) (Allen et al., 2014; Calhoun et al., 2014; Hutchison et al., 2013). A widely applied method for temporal dynamic FC analysis is the sliding time window method (Allen et al., 2014; Damaraju et al., 2014; Du et al., 2016; Hutchison et al., 2013). This method divides acquired rs-fMRI into windows and calculates the variation of FC across those windows. The results represent the dynamic characteristic of FC. Given that static FC has helped to understand the cerebral correlates of cognitive impairment in PD, a dynamic FC approach may add relevant information as it represents more accurately the dynamic nature of the brain (Calhoun et al., 2014; Hutchison et al., 2013).Therefore, a dynamic approach to study FC may help clarify the neurobiological substrates of presence of MCI in PD. To date, only one dynamic FC study has been recently published in PD, and showed dynamic FC alterations in PD patients compared to HC (Kim et al., 2017).