Characterizing resting‐state networks in Parkinson’s disease: A multi‐aspect functional connectivity study

Abstract

Purpose Resting‐state functional magnetic resonance imaging (Rs‐fMRI) can be used to investigate the alteration of resting‐state brain networks (RSNs) in patients with Parkinson's disease (PD) when compared with healthy controls (HCs). The aim of this study was to identify the differences between individual RSNs and reveal the most important discriminatory characteristic of RSNs between the HCs and PDs. Methods This study used Rs‐fMRI data of 23 patients with PD and 18 HCs. Group independent component analysis (ICA) was performed, and 23 components were extracted by spatially overlapping the components with a template RSN. The extracted components were used in the following three methods to compare RSNs of PD patients and HCs: (1) a subject‐specific score based on group RSNs and a dual‐regression approach (namely RSN scores); (2) voxel‐wise comparison of the RSNs in the PD patient and HC groups using a nonparametric permutation test; and (3) a hierarchical clustering analysis of RSNs in the PD patient and HC groups. Results The results of RSN scores showed a significant decrease in connectivity in seven ICs in patients with PD compared with HCs, and this decrease was particularly striking on the lateral and medial posterior occipital cortices. The results of hierarchical clustering of the RSNs revealed that the cluster of the default mode network breaks down into the three other clusters in PD patients. Conclusion We found various characteristics of the alteration of the RSNs in PD patients compared with HCs. Our results suggest that different characteristics of RSNs provide insights into the biological mechanism of PD.

Alteration of the resting-state brain networks: a multi-aspect study of functional and effective connectivity in Parkinson's disease

“…[28] also evaluated the time courses and found disruptions in SMN's connectivity, which is consistent with our results. Ghasemi et al [29] also used RSNs' timedomain dependence to create a network of connections. A hierarchical network is formed as a result of the strength of the connections between the components.…”

“…First, non-brain tissues were eliminated from structural images using FSL's Brain Extraction Tool (BET) [32]. The functional data's rst ve volumes were removed to eliminate initial equilibrium effects [29]. Afterward, the remaining 205 volumes of EPI data were pre-processed with the several steps via FSL's FEAT tool (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT/) [33] including head motion correction, slice timing correction, spatial smoothing using a Gaussian lter 6 mm full width at half maximum (FWHM), high-pass temporal ltering with a cutoff frequency of 0.01 Hz, as suggested for resting-state fMRI data studies [29,34].…”

“…The functional data's rst ve volumes were removed to eliminate initial equilibrium effects [29]. Afterward, the remaining 205 volumes of EPI data were pre-processed with the several steps via FSL's FEAT tool (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT/) [33] including head motion correction, slice timing correction, spatial smoothing using a Gaussian lter 6 mm full width at half maximum (FWHM), high-pass temporal ltering with a cutoff frequency of 0.01 Hz, as suggested for resting-state fMRI data studies [29,34]. The functional image is then registered to the T1-weighted structural image using 6 degrees-of freedom, and then the high resolution structural image is normalized to standard space (MNI152, T1-weighted, 3×3×3 𝑚𝑚3) with 12 degrees-of freedom.…”

“…The ndings reveal that PD with mild cognitive impairment suffers from dynamic functional brain degeneration, FC disruption, lower FC of SM-CC networks, as well as graph structure deterioration in the SM network in the PD mild cognitive impairment group. Ghasemi et al [29] also gathered the correlation matrices of RSN time courses, and discovered that clustering of such time courses in the DMN leads to three clusters in PD patients.…”