Longitudinal reliability of tract-based spatial statistics in diffusion tensor imaging
Article first published online: 3 APR 2014
Copyright © 2014 Wiley Periodicals, Inc.
Human Brain Mapping
Volume 35, Issue 9, pages 4544–4555, September 2014
How to Cite
Madhyastha, T., Mérillat, S., Hirsiger, S., Bezzola, L., Liem, F., Grabowski, T. and Jäncke, L. (2014), Longitudinal reliability of tract-based spatial statistics in diffusion tensor imaging. Hum. Brain Mapp., 35: 4544–4555. doi: 10.1002/hbm.22493
- Issue published online: 18 JUL 2014
- Article first published online: 3 APR 2014
- Manuscript Accepted: 6 FEB 2014
- Manuscript Received: 16 JAN 2014
- Velux Stiftung . Grant Number: project No. 369
- Swiss National Science Foundation.
- diffusion tensor imaging;
- tract-based spatial statistics;
Relatively little is known about reliability of longitudinal diffusion-tensor imaging (DTI) measurements despite growing interest in using DTI to track change in white matter structure. The purpose of this study is to quantify within- and between session scan-rescan reliability of DTI-derived measures that are commonly used to describe the characteristics of neural white matter in the context of neural plasticity research. DTI data were acquired from 16 cognitively healthy older adults (mean age 68.4). We used the Tract-Based Spatial Statistics (TBSS) approach implemented in FSL, evaluating how different DTI preprocessing choices affect reliability indices. Test-Retest reliability, quantified as ICC averaged across the voxels of the TBSS skeleton, ranged from 0.524 to 0.798 depending on the specific DTI-derived measure and the applied preprocessing steps. The two main preprocessing steps that we found to improve TBSS reliability were (a) the use of a common individual template and (b) smoothing DTI data using a 1-voxel median filter. Overall our data indicate that small choices in the preprocessing pipeline have a significant effect on test-retest reliability, therefore influencing the power to detect change within a longitudinal study. Furthermore, differences in the data processing pipeline limit the comparability of results across studies. Hum Brain Mapp 35:4544–4555, 2014. © 2014 Wiley Periodicals, Inc.