基于道的空间统计 (TBSS) 的方法论考虑,NeuroImage

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基于道的空间统计 (TBSS) 的方法论考虑
NeuroImage ( IF 4.7 ) Pub Date : 2014-10-01 , DOI: 10.1016/j.neuroimage.2014.06.021
Michael Bach ₁ , Frederik B Laun ₁ , Alexander Leemans ₂ , Chantal M W Tax ₂ , Geert J Biessels ₃ , Bram Stieltjes ₄ , Klaus H Maier-Hein ₅

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自 2006 年推出以来，基于道的空间统计 (TBSS) 获得了极大的普及，现在可以将其视为基于体素分析 (VBA) 的扩散张量成像 (DTI) 数据的标准方法。为了提高多主题 DTI 研究的灵敏度、客观性和可解释性，TBSS 包括一个骨架化步骤，可减轻残留图像错位并避免数据平滑的需要。尽管 TBSS 代表了一个优雅且用户友好的框架，它解决了传统 VBA 方法中存在的许多问题，但它也有其自身的局限性，其中一些已经在最近的文献中进行了详细说明。在这项工作中，我们提出了关于 TBSS 的一般方法论考虑，并报告了以前没有描述过的陷阱。特别是，我们已经确定了在典型条件下可能无法满足的 TBSS 的特定假设。此外，我们证明此类违规的存在会严重影响 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，这样才能就是否以及如何进行 TBSS 分析做出明智的决定。最后，除了通过提供我们的新见解来提高认识之外，我们还提供了建设性的建议，可以提高 TBSS 的有效性和影响。让 TBSS 用户了解这些问题至关重要，这样才能就是否以及如何进行 TBSS 分析做出明智的决定。最后，除了通过提供我们的新见解来提高认识之外，我们还提供了建设性的建议，可以大大提高 TBSS 的有效性和影响。让 TBSS 用户了解这些问题至关重要，这样才能就是否以及如何进行 TBSS 分析做出明智的决定。最后，除了通过提供我们的新见解来提高认识之外，我们还提供了建设性的建议，可以提高 TBSS 的有效性和影响。

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Methodological considerations on tract-based spatial statistics (TBSS)

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.

更新日期：2014-10-01

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