Methodological considerations on tract-based spatial statistics (TBSS)
Methodological considerations on tract-based spatial statistics (TBSS)
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DOI：
10.1016/j.neuroimage.2014.06.021
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发表时间：
2014-10-15
期刊：
影响因子：
5.7
通讯作者：
Maier-Hein, Klaus H.
中科院分区：
文献类型：
--
作者：
Bach, Michael;Laun, Frederik B.;Maier-Hein, Klaus H.
自2006年引入以来，基于区域的空间统计(TBSS)得到了极大的普及，现在可以被认为是基于体素的扩散张量成像(DTI)数据分析(VBA)的标准方法。为了提高多受试者DTI研究的敏感性、客观性和可解释性，TBSS包括一个骨架化步骤，该步骤减轻了残余图像的不对准，并消除了数据平滑的需要。尽管TBSS代表了一个优雅和用户友好的框架，解决了传统VBA方法中存在的许多问题，但它也有自己的局限性，其中一些局限性已经在最近的文献中进行了详细描述。在这项工作中，我们提出了关于TBSS的一般方法学考虑，并报告了以前没有描述的陷阱。特别是，我们已经确定了在典型条件下可能不满足的关于TBSS的具体假设。此外，我们还证明了这种违规行为的存在会严重影响TBSS结果的可靠性。随着TBSS的使用越来越多，让TBSS用户了解这些问题至关重要，这样才能在知情的情况下就是否以及如何进行TBSS分析作出决定。最后，除了通过提供我们的新见解来提高人们的认识外，我们还提供了建设性的建议，这些建议可以显著提高TBSS的有效性和影响力。(C)2014 Elsevier Inc.保留所有权利。
Having gained a tremendous amount of popularity since its introduction in 2006, tract-based spatial statistics (TBSS) can now be considered as the standard approach for voxel-based analysis (VBA) of diffusion tensor imaging (DTI) data. Aiming to improve the sensitivity, objectivity, and interpretability of multi-subject DTI studies, TBSS includes a skeletonization step that alleviates residual image misalignment and obviates the need for data smoothing. Although TBSS represents an elegant and user-friendly framework that tackles numerous concerns existing in conventional VBA methods, it has limitations of its own, some of which have already been detailed in recent literature. In this work, we present general methodological considerations on TBSS and report on pitfalls that have not been described previously. In particular, we have identified specific assumptions of TBSS that may not be satisfied under typical conditions. Moreover, we demonstrate that the existence of such violations can severely affect the reliability of TBSS results. With TBSS being used increasingly, it is of paramount importance to acquaint TBSS users with these concerns, such that a well-informed decision can be made as to whether and how to pursue a TBSS analysis. Finally, in addition to raising awareness by providing our new insights, we provide constructive suggestions that could improve the validity and increase the impact of TBSS drastically. (C) 2014 Elsevier Inc. All rights reserved.