We present cross-metric and cross-tract assessment of test-retest repeatability for microstructure measures on an ultra strong gradient MRI scanner (CONNECTOM 3T) in the human brain. We show that several MRI metrics of tissue microstructure are reliable and present relative sample sizes required to provide sufficient statistical power across different white matter pathways and microstructure metrics.
MRI brain scans were collected on an ultra strong gradient (300mT/m) 3T MRI scanner (MAGNETOM Skyra CONNECTOM) in six healthy adults (3 female, age range 24-30). Each MRI session lasted approximately 2 hours, and was repeated 5 times within a two-week period. Care was taken to avoid potential diurnal effects by performing scans for each participant at approximately the same time of day. The MRI protocol comprised the following sequences: multi-shell diffusion CHARMED3, multicomponent relaxometry McDESPOT1 and quantitative magnetisation transfer (QMT2, Table 1).
To assess test-retest repeatability, a white matter projection tract (cortico-spinal), association tract (arcuate fasciculus) and the fornix were virtually dissected with probabilistic tractography (MRTrix iFOD214, Fig. 1). Track density maps of the resultant tracts were computed (TDI15) and thresholded to exclude voxels through which streamlines passed less than 20 percent. Metrics were extracted for each vertex along each tract for statistical comparison. The intra-class correlation coefficient (two-way mixed, absolute agreement) and coefficient of variation were computed for assessment of test-retest repeatability (Table 2). Required sample sizes were estimated for a Group (2) x Time (2) between-within groups ANOVA (Fig. 3) across all metrics and tracts at small, medium and large effect sizes to reach statistical power of .84 and significance 𝜶 = .05. Pearson correlation coefficients were used to account for the correlation among repeated measures for sample size estimation (Table 2).
1. Deoni, S. C., Rutt, B. K., Arun, T., Pierpaoli, C., & Jones, D. K. (2008). Gleaning Multi-Component T1 and T2 Information from Steady-State Imaging Data. Mag Res Med, 60, 2008, 1372-1387
2. Wood (2018). QUIT: Quantitative Imaging Tools. Journal of Open Source Software, 3(26), 656.
3. Assaf, Y., Freidlin, R. Z., Rohde, G. K., & Basser, P. J. (2004). New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter. Mag Res Med, 52(5), 965–978. https://doi.org/10.1002/mrm.20274
4. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Biometrics, 26(3), 588. https://doi.org/10.2307/2529115
5. Veraart, J., Novikov, D. S., Christiaens, D., Ades-aron, B., Sijbers, J., & Fieremans, E. (2016). Denoising of diffusion MRI using random matrix theory. NeuroImage, 142, 394–406. https://doi.org/10.1016/J.NEUROIMAGE.2016.08.016
6. Vos, S. B., Tax, C. M. W., Luijten, P. R., Ourselin, S., Leemans, A., & Froeling, M. (2017). The importance of correcting for signal drift in diffusion MRI. Mag Res Med, 77(1), 285–299. https://doi.org/10.1002/mrm.26124
7. Andersson, J. L. R., Skare, S., & Ashburner, J. (2003). How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage, 20(2), 870–888. https://doi.org/10.1016/S1053-8119(03)00336-7
8. Andersson, J. L. R., & Sotiropoulos, S. N. (2016). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage, 125, 1063–1078. https://doi.org/10.1016/J.NEUROIMAGE.2015.10.019
9. Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., … WU-Minn HCP Consortium, for the W.-M. H. (2013). The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80, 105–24.
10. Kellner, E., Dhital, B., Kiselev, V. G., & Reisert, M. (2016). Gibbs-ringing artifact removal based on local subvoxel-shifts. Mag Res Med, 76(5), 1574–1581. https://doi.org/10.1002/mrm.26054
11. Hoy, A. R., Koay, C. G., Kecskemeti, S. R., & Alexander, A. L. (2014). Optimization of a free water elimination two-compartment model for diffusion tensor imaging. NeuroImage, 103, 323–333. https://doi.org/10.1016/J.NEUROIMAGE.2014.09.053
12. Zhang, Y. and Brady, M. and Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imag, 20(1):45-57, 2001.
13. Jeurissen, Ben., et al. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage 103 (2014): 411-426.
14. Tournier, J.D., Calamante, F. & Connelly, A. Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Proc Int Soc Mag Res Med, 2010, 1670, 15.
15. Calamante, F., Tournier, J. D., Jackson, G. D., & Connelly, A. (2010). Track-density imaging (TDI): Super-resolution white matter imaging using whole-brain track-density mapping. NeuroImage, 53(4), 1233–1243. https://doi.org/10.1016/j.neuroimage.2010.07.024 https://doi.org/10.1016/j.neuroimage.2013.04.127
16. Fisher, R. A. (1915). Frequency distribution of the values of the correlation coefficient in samples of an indefinitely large population. Biometrika. Biometrika Trust. 10 (4): 507521. doi:10.2307/2331838. JSTOR 2331838.
Figure 2. Intra-class correlation coefficients (two-way mixed, absolute agreement) for test-retest repeatability of microstructure metrics measured 5 times in 6 participants.