Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neuroimage.2014.06.021 | NeuroImage |
Keywords | Field | DocType |
TBSS,Pitfalls,DTI,Quantitative,FA,Evaluation | Spatial analysis,Data science,Interpretability,Data mining,Computer science,Cognitive psychology,Smoothing | Journal |
Volume | ISSN | Citations |
100 | 1053-8119 | 24 |
PageRank | References | Authors |
0.87 | 17 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Bach | 1 | 24 | 0.87 |
| Frederik B. Laun | 2 | 59 | 4.90 |
| Alexander Leemans | 3 | 237 | 20.37 |
| Chantal M W Tax | 4 | 108 | 6.73 |
| Geert Jan Biessels | 5 | 74 | 8.20 |
| Bram Stieltjes | 6 | 193 | 55.84 |
| Klaus H. Maier-Hein | 7 | 361 | 42.06 |