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Application of diffusional kurtosis imaging for insights into structurally aberrant topology in Parkinson's disease

峰度 医学 磁共振弥散成像 聚类系数 断开 疾病 默认模式网络 拓扑(电路) 神经科学 病理 聚类分析 计算机科学 人工智能 数学 磁共振成像 生物 放射科 组合数学 统计 功能磁共振成像 法学 政治学
作者
Siying Zhu,Lijuan Wang,Xiang Lv,Yao Xu,Weiqiang Dou,Hongying Zhang,Jing Ye
出处
期刊:Acta Radiologica [SAGE Publishing]
卷期号:65 (2): 233-240 被引量:1
标识
DOI:10.1177/02841851231216039
摘要

Background Parkinson's disease (PD) has been regarded as a disconnection syndrome with functional and structural disturbances. However, as the anatomic determinants, the structural disconnections in PD have yet to be fully elucidated. Purpose To non-invasively construct structural networks based on microstructural complexity and to further investigate their potential topological abnormalities in PD given the technical superiority of diffusion kurtosis imaging (DKI) to the quantification of microstructure. Material and Methods The microstructural data of gray matter in both the PD group and the healthy control (HC) group were acquired using DKI. The structural networks were constructed at the group level by a covariation approach, followed by the calculation of topological properties based on graph theory and statistical comparisons between groups. Results A total of 51 patients with PD and 50 HCs were enrolled. Individuals were matched between groups with respect to demographic characteristics ( P >0.05). The constructed structural networks in both the PD and HC groups featured small-world properties. In comparison with the HC group, the PD group exhibited significantly altered global properties, with higher normalized characteristic path lengths, clustering coefficients, local efficiency values, and characteristic path lengths and lower global efficiency values ( P <0.05). In terms of nodal centralities, extensive nodal disruptions were observed in patients with PD ( P <0.05); these disruptions were mainly distributed in the sensorimotor network, default mode network, frontal-parietal network, visual network, and subcortical network. Conclusion These findings contribute to the technical application of DKI and the elucidation of disconnection syndrome in PD.
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