Mohammadi Siawoosh, Keller Simon S, Glauche Volkmar, Kugel Harald, Jansen Andreas, Hutton Chloe, Flöel Agnes, Deppe Michael
Department of Neurology, University of Münster, Münster, Germany.
PLoS One. 2012;7(6):e36851. doi: 10.1371/journal.pone.0036851. Epub 2012 Jun 5.
The sensitivity of diffusion tensor imaging (DTI) for detecting microstructural white matter alterations has motivated the application of voxel-based statistics (VBS) to fractional anisotropy (FA) images (FA-VBS). However, detected group differences may depend on the spatial registration method used. The objective of this study was to investigate the influence of spatial registration on detecting cerebral asymmetries in FA-VBS analyses with reference to data obtained using Tract-Based Spatial Statistics (TBSS). In the first part of this study we performed FA-VBS analyses using three single-contrast and one multi-contrast registration: (i) whole-brain registration based on T2 contrast, (ii) whole-brain registration based on FA contrast, (iii) individual-hemisphere registration based on FA contrast, and (iv) a combination of (i) and (iii). We then compared the FA-VBS results with those obtained from TBSS. We found that the FA-VBS results depended strongly on the employed registration approach, with the best correspondence between FA-VBS and TBSS results when approach (iv), the "multi-contrast individual-hemisphere" method was employed. In the second part of the study, we investigated the spatial distribution of residual misregistration for each registration approach and the effect on FA-VBS results. For the FA-VBS analyses using the three single-contrast registration methods, we identified FA asymmetries that were (a) located in regions prone to misregistrations, (b) not detected by TBSS, and (c) specific to the applied registration approach. These asymmetries were considered candidates for apparent FA asymmetries due to systematic misregistrations associated with the FA-VBS approach. Finally, we demonstrated that the "multi-contrast individual-hemisphere" approach showed the least residual spatial misregistrations and thus might be most appropriate for cerebral FA-VBS analyses.
扩散张量成像（DTI）检测微观结构白质改变的敏感性促使基于体素的统计方法（VBS）应用于分数各向异性（FA）图像（FA-VBS）。然而，检测到的组间差异可能取决于所使用的空间配准方法。本研究的目的是参照基于纤维束的空间统计（TBSS）获得的数据，研究空间配准对FA-VBS分析中检测大脑不对称性的影响。在本研究的第一部分，我们使用三种单对比度和一种多对比度配准方法进行FA-VBS分析：（i）基于T2对比度的全脑配准，（ii）基于FA对比度的全脑配准，（iii）基于FA对比度的个体半球配准，以及（iv）（i）和（iii）的组合。然后，我们将FA-VBS结果与从TBSS获得的结果进行比较。我们发现FA-VBS结果在很大程度上取决于所采用的配准方法，当采用方法（iv），即“多对比度个体半球”方法时，FA-VBS和TBSS结果之间的对应性最佳。在研究的第二部分，我们研究了每种配准方法的残余配准误差的空间分布及其对FA-VBS结果的影响。对于使用三种单对比度配准方法的FA-VBS分析，我们确定了FA不对称性，这些不对称性（a）位于易于出现配准误差的区域，（b）未被TBSS检测到，并且（c）特定于所应用的配准方法。这些不对称性被认为是由于与FA-VBS方法相关的系统性配准误差导致的明显FA不对称性的候选因素。最后，我们证明“多对比度个体半球”方法显示出最少的残余空间配准误差，因此可能最适合大脑FA-VBS分析。
