College of Information Engineering, Zhejiang University of Technology, Hangzhou, China.
Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Behav Brain Res. 2020 Sep 15;394:112805. doi: 10.1016/j.bbr.2020.112805. Epub 2020 Jul 13.
The deficits of white matter (WM) microstructure are involved during Parkinson's disease (PD) progression. Most current methods identify key WM tracts relying on cortical regions of interest (ROIs). However, such ROI methods can be challenged due to low diffusion anisotropy near the gray matter (GM), which could result in a low sensitivity of tract identification. This work proposes an automatic WM parcellation method to improve the accuracy of WM tract identification and locate abnormal tracts by using sensitive features. The proposed method consists of 1) whole brain WM parcellation using an established fiber clustering method, without using any ROIs, 2) features of fasciculus were calculated to quantify diffusion measures at each equal cross-section along the whole cluster. Then, we use the proposed features to investigate the WM difference in PD compared with healthy controls (HC). We also use these features to investigate the relationship of clinical symptoms and specific fiber tracts. The novelty of the proposed method is that it automatically identifies the abnormal WM fibers in cluster degree. Experiment results indicated that the proposed method had advantage in detecting the local WM abnormality by performing between-group statistical analysis in 30 patients with PD and 28 HC. We found 13 hemisphere clusters and 8 commissural clusters had significant group difference (p < 0.05, corrected by FDR method) in local regions, which belonged to multiple fiber tracts including cingulum bundle (CB), inferior occipito-frontal fasciculus (IoFF), corpus callosum (CC), external capsule (EC), uncinate fasciculus (UF), superior longitudinal fasciculus (SLF) and thalamo front (TF). We also found clusters that had relevance with clinical indices of cognitive function (2 clusters), athletic function (6 clusters), and depressive state (2 clusters) in these significant clusters. From the experiment results, it confirmed the ability of the proposed method to identify potential WM microstructure abnormality.
在帕金森病（PD）的进展过程中，存在白质（WM）微观结构的缺陷。目前大多数方法都是基于皮质感兴趣区域（ROI）来识别关键的 WM 束。然而，由于灰质（GM）附近的扩散各向异性较低，这种 ROI 方法可能会受到挑战，从而导致束识别的灵敏度降低。本研究提出了一种自动 WM 分割方法，通过使用敏感特征来提高 WM 束识别的准确性并定位异常束。该方法包括：1）使用已建立的纤维聚类方法对全脑 WM 进行分割，无需使用任何 ROI；2）计算束的特征，以量化沿整个聚类的每个等分截面的扩散测量值。然后，我们使用提出的特征来研究 PD 与健康对照组（HC）之间的 WM 差异。我们还使用这些特征来研究临床症状与特定纤维束之间的关系。该方法的新颖之处在于它能够自动识别聚类程度的异常 WM 纤维。实验结果表明，与基于 ROI 的方法相比，该方法在 30 名 PD 患者和 28 名 HC 之间进行组间统计分析时具有检测局部 WM 异常的优势。我们在局部区域发现 13 个半球聚类和 8 个连合聚类存在显著的组间差异（p < 0.05，经 FDR 方法校正），这些聚类属于多个纤维束，包括扣带束（CB）、下额枕额束（IoFF）、胼胝体（CC）、外囊（EC）、钩束（UF）、上纵束（SLF）和丘脑前束（TF）。我们还在这些显著聚类中发现了与认知功能（2 个聚类）、运动功能（6 个聚类）和抑郁状态（2 个聚类）相关的聚类。从实验结果中可以确认该方法识别潜在 WM 微观结构异常的能力。
