Neural Engineering Laboratory, Department of Biomedical Engineering, University of Neyshabur, Neyshabur, Iran.
Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
Brain Behav. 2021 May;11(5):e02101. doi: 10.1002/brb3.2101. Epub 2021 Mar 30.
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.
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.
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.
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.
静息态功能磁共振成像（Rs-fMRI）可用于研究帕金森病（PD）患者与健康对照（HC）相比静息态脑网络（RSNs）的改变。本研究旨在确定个体 RSN 之间的差异，并揭示 RSN 在 HC 和 PD 之间的最重要鉴别特征。
本研究使用了 23 名 PD 患者和 18 名 HC 的 Rs-fMRI 数据。进行组独立成分分析（ICA），并通过与模板 RSN 空间重叠的组件提取 23 个组件。将提取的组件用于以下三种方法比较 PD 患者和 HC 的 RSN：（1）基于组 RSN 和双回归方法的基于个体的得分（即 RSN 得分）；（2）使用非参数置换检验对 PD 患者和 HC 组的 RSN 进行体素级比较；（3）对 PD 患者和 HC 组的 RSN 进行层次聚类分析。
RSN 得分的结果显示，与 HC 相比，PD 患者的 7 个 IC 中的连通性明显降低，而在后外侧和内侧枕叶皮质上的降低尤为明显。PD 患者和 HC 组 RSN 的层次聚类结果显示，默认模式网络的聚类在 PD 患者中分为三个其他聚类。
与 HC 相比，我们发现 PD 患者 RSN 的改变具有多种特征。我们的结果表明，RSN 的不同特征为 PD 的生物学机制提供了深入了解。
