Format

Send to

Choose Destination
Neuroimage. 2014 Oct 15;100:358-69. doi: 10.1016/j.neuroimage.2014.06.021. Epub 2014 Jun 16.

Methodological considerations on tract-based spatial statistics (TBSS).

Author information

1
Section Quantitative Imaging-based Disease Characterization, Department of Radiology, German Cancer Research Center (DKFZ), Germany; Department of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Germany.
2
Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
3
Department of Neurology, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands.
4
Section Quantitative Imaging-based Disease Characterization, Department of Radiology, German Cancer Research Center (DKFZ), Germany.
5
Section Quantitative Imaging-based Disease Characterization, Department of Radiology, German Cancer Research Center (DKFZ), Germany; Medical Image Computing Group, Div. Medical and Biological Informatics, German Cancer Research Center (DKFZ), Germany. Electronic address: k.maier-hein@dkfz-heidelberg.de.

Abstract

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.

KEYWORDS:

DTI; Evaluation; FA; Pitfalls; Quantitative; TBSS

[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center