Metin Mehmet Özer, Gökçay Didem
Department of Health Informatics, Middle East Technical University, Ankara, Turkey.
Front Neurosci. 2021 Mar 22;15:625473. doi: 10.3389/fnins.2021.625473. eCollection 2021.
Group analysis in diffusion tensor imaging is challenging. Comparisons of tensor morphology across groups have typically been performed on scalar measures of diffusivity, such as fractional anisotropy (FA), disregarding the complex three-dimensional morphologies of diffusion tensors. Scalar measures consider only the magnitude of the diffusion but not directions. In the present study, we have introduced a new approach based on directional statistics to use directional information of diffusion tensors in statistical group analysis based on Bingham distribution. We have investigated different directional statistical models to find the best fit. During the experiments, we confirmed that carrying out directional statistical analysis along the tract is much more effective than voxel- or skeleton-guided directional statistics. Hence, we propose a new method called tract profiling and directional statistics (TPDS) applicable to fiber bundles. As a case study, the method has been applied to identify connectivity differences of patients with major depressive disorder. The results obtained with the directional statistic-based analysis are consistent with those of NBS, but additionally, we found significant changes in the right hemisphere striatum, ACC, and prefrontal, parietal, temporal, and occipital connections as well as left hemispheric differences in the limbic areas such as the thalamus, amygdala, and hippocampus. The results are also evaluated with respect to fiber lengths. Comparison with the output of the network-based statistical toolbox indicated that the benefit of the proposed method becomes much more distinctive as the tract length increases. The likelihood of finding clusters of voxels that differ in long tracts is higher in TPDS, while that relationship is not clearly established in NBS.
扩散张量成像中的组分析具有挑战性。跨组张量形态的比较通常是在扩散率的标量测量上进行的，例如分数各向异性（FA），而忽略了扩散张量复杂的三维形态。标量测量仅考虑扩散的大小，而不考虑方向。在本研究中，我们引入了一种基于方向统计的新方法，以在基于宾汉分布的统计组分析中使用扩散张量的方向信息。我们研究了不同的方向统计模型以找到最佳拟合。在实验过程中，我们证实沿着纤维束进行方向统计分析比体素或骨架引导的方向统计更有效。因此，我们提出了一种适用于纤维束的称为纤维束剖析和方向统计（TPDS）的新方法。作为一个案例研究，该方法已被应用于识别重度抑郁症患者的连接差异。基于方向统计分析获得的结果与基于网络的统计分析（NBS）的结果一致，但此外，我们还发现右半球纹状体、前扣带回以及前额叶、顶叶、颞叶和枕叶连接存在显著变化，以及左半球边缘区域如丘脑、杏仁核和海马体存在差异。还根据纤维长度对结果进行了评估。与基于网络的统计工具箱的输出进行比较表明，随着纤维束长度的增加，所提出方法的优势变得更加明显。在TPDS中，在长纤维束中发现不同体素簇的可能性更高，而在NBS中这种关系并未明确建立。
