Abstract：
OBJECTIVE: Parkinson\'s disease (PD) involves pathological alterations that include cortical impairments at levels of region and network. However, its microstructural abnormalities remain to be further elucidated via an appropriate diffusion neuroimaging approach. This study aimed to comprehensively demonstrate the microstructural patterns of PD as mapped by diffusion kurtosis imaging (DKI).
METHODS: The microstructure of grey matter in both the PD group and the matched healthy control group was quantified by a DKI metric (mean kurtosis). The intergroup difference and classification performance of global microstructural complexity were analyzed in a voxelwise manner and via a machine learning approach, respectively. The patterns of information flows were explored in terms of structural connectivity, network covariance and modular connectivity.
RESULTS: Patients with PD exhibited global microstructural impairments that served as an efficient diagnostic indicator. Disrupted structural connections between the striatum and cortices as well as between the thalamus and cortices were widely distributed in the PD group. Aberrant covariance of the striatocortical circuitry and thalamocortical circuitry was observed in patients with PD, who also showed disrupted modular connectivity within the striatum and thalamus as well as across structures of the cortex, striatum and thalamus.
CONCLUSIONS: These findings verified the potential clinical application of DKI for the exploration of microstructural patterns in PD, contributing complementary imaging features that offer a deeper insight into the neurodegenerative process.
METHODS: The microstructure of grey matter in both the PD group and the matched healthy control group was quantified by a DKI metric (mean kurtosis). The intergroup difference and classification performance of global microstructural complexity were analyzed in a voxelwise manner and via a machine learning approach, respectively. The patterns of information flows were explored in terms of structural connectivity, network covariance and modular connectivity.
RESULTS: Patients with PD exhibited global microstructural impairments that served as an efficient diagnostic indicator. Disrupted structural connections between the striatum and cortices as well as between the thalamus and cortices were widely distributed in the PD group. Aberrant covariance of the striatocortical circuitry and thalamocortical circuitry was observed in patients with PD, who also showed disrupted modular connectivity within the striatum and thalamus as well as across structures of the cortex, striatum and thalamus.
CONCLUSIONS: These findings verified the potential clinical application of DKI for the exploration of microstructural patterns in PD, contributing complementary imaging features that offer a deeper insight into the neurodegenerative process.
摘要：
目的：帕金森病（PD）涉及病理改变，包括区域和网络水平的皮质损伤。然而,其微观结构异常仍有待通过适当的扩散神经成像方法进一步阐明.本研究旨在全面展示通过扩散峰度成像（DKI）绘制的PD的微观结构模式。
方法：通过DKI指标（平均峰度）对PD组和匹配的健康对照组的灰质微结构进行定量。通过机器学习方法，以体素方式分析了全局微结构复杂性的组间差异和分类性能，分别。从结构连通性方面探讨了信息流的模式，网络协方差和模块化连通性。
结果：PD患者表现出作为有效诊断指标的整体微结构损伤。纹状体和皮质之间以及丘脑和皮质之间的结构连接中断在PD组中广泛分布。在PD患者中观察到纹状体皮质回路和丘脑皮质回路的异常协方差，他还显示纹状体和丘脑以及皮质结构之间的模块化连通性中断，纹状体和丘脑.
结论：这些发现证实了DKI在探索PD的微结构模式方面的潜在临床应用。有助于补充成像功能，提供对神经退行性过程的更深入了解。
方法：通过DKI指标（平均峰度）对PD组和匹配的健康对照组的灰质微结构进行定量。通过机器学习方法，以体素方式分析了全局微结构复杂性的组间差异和分类性能，分别。从结构连通性方面探讨了信息流的模式，网络协方差和模块化连通性。
结果：PD患者表现出作为有效诊断指标的整体微结构损伤。纹状体和皮质之间以及丘脑和皮质之间的结构连接中断在PD组中广泛分布。在PD患者中观察到纹状体皮质回路和丘脑皮质回路的异常协方差，他还显示纹状体和丘脑以及皮质结构之间的模块化连通性中断，纹状体和丘脑.
结论：这些发现证实了DKI在探索PD的微结构模式方面的潜在临床应用。有助于补充成像功能，提供对神经退行性过程的更深入了解。
