Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain.
Medical Psychology Unit, Department of Medicine, University of Barcelona, Barcelona, Catalonia, Spain.
Brain Connect. 2021 Jun;11(5):380-392. doi: 10.1089/brain.2020.0939. Epub 2021 Mar 31.
Probabilistic tractography, in combination with graph theory, has been used to reconstruct the structural whole-brain connectome. Threshold-free network-based statistics (TFNBS) is a useful technique to study structural connectivity in neurodegenerative disorders; however, there are no previous studies using TFNBS in Parkinson's disease (PD) with and without mild cognitive impairment (MCI). Sixty-two PD patients, 27 of whom classified as PD-MCI, and 51 healthy controls (HC) underwent diffusion-weighted 3T magnetic resonance imaging. Probabilistic tractography, using FMRIB Software Library (FSL), was used to compute the number of streamlines (NOS) between regions. NOS matrices were used to find group differences with TFNBS, and to calculate global and local measures of network integrity using graph theory. A binominal logistic regression was then used to assess the discrimination between PD with and without MCI using non-overlapping significant tracts. Tract-based spatial statistics were also performed with FSL to study changes in fractional anisotropy (FA) and mean diffusivity. PD-MCI showed 37 white matter connections with reduced connectivity strength compared with HC, mainly involving temporal/occipital regions. These were able to differentiate PD-MCI from PD without MCI with an area under the curve of 83-85%. PD without MCI showed disrupted connectivity in 18 connections involving frontal/temporal regions. No significant differences were found in graph measures. Only PD-MCI showed reduced FA compared with HC. TFNBS based on whole-brain probabilistic tractography can detect structural connectivity alterations in PD with and without MCI. Reduced structural connectivity in fronto-striatal and posterior cortico-cortical connections is associated with PD-MCI.
概率性轨迹追踪技术与图论相结合，已被用于重建全脑结构连接组。无阈值网络基础统计（TFNBS）是一种研究神经退行性疾病结构连接的有用技术；然而，在没有轻度认知障碍（MCI）的帕金森病（PD）患者中，尚无使用 TFNBS 的先前研究。62 名 PD 患者，其中 27 名被归类为 PD-MCI，以及 51 名健康对照者（HC）接受了 3T 磁共振弥散加权成像。使用 FMRIB 软件库（FSL）进行概率性轨迹追踪，以计算区域之间的流线数量（NOS）。NOS 矩阵用于使用 TFNBS 找到组间差异，并使用图论计算网络完整性的全局和局部度量。然后使用二项逻辑回归评估使用不重叠的显著束来区分有和无 MCI 的 PD。还使用 FSL 进行基于束的空间统计学研究，以研究分数各向异性（FA）和平均弥散度的变化。与 HC 相比，PD-MCI 显示 37 条白质连接的连接强度降低，主要涉及颞叶/枕叶区域。这些连接能够将 PD-MCI 与无 MCI 的 PD 区分开来，曲线下面积为 83-85%。无 MCI 的 PD 显示涉及额颞叶区域的 18 条连接的连接中断。在图论测量中未发现显著差异。只有 PD-MCI 与 HC 相比显示 FA 降低。基于全脑概率性轨迹追踪的 TFNBS 可以检测出有和无 MCI 的 PD 中的结构连接改变。额纹状体和后皮质皮质连接的结构连接减少与 PD-MCI 相关。
