Abstract
White matter microstructures have been studied most commonly using diffusion tensor imaging (DTI) that models diffusivity in each voxel of diffusion MRI images as a tensor. Classic DTI parameters (e.g., mean diffusivity or MD, fractional anisotropy or FA) derived from the eigenvalues of tensors have been widely used to describe white matter properties. More recently, novel metrics like neurite orientation dispersion and density imaging (NODDI) have broadened the spectrum over which we can both characterize healthy connectivity and investigate pathology. When looking at specific brain regions, previous works combining DTI and NODDI have focused on regions of interest (ROI) analysis where regional masks were generated by mapping known atlas to standard spaces and applied to skeletonized FA maps from tract-based spatial statistics (TBSS). Recent advancement in probabilistic tractography, e.g., the FSL XTRACT toolbox, provides an alternative method of ROI analysis by estimating tract regions in an individual native diffusion space, but the exact advantages and disadvantages compared to using a standard space have not been well documented. In the present study, we perform ROI analysis on DTI and NODDI parameters from diffusion MRI (dMRI) of 39 healthy adults collected from two time points, using both standard-space method (“TBSS ROI analysis”) and native-space method (“XTRACT ROI analysis”). We compare the test-retest reliability of these two methods by evaluating the coefficient of variation (\(C_{V}\)) at each time point, the Pearson’s correlation (R) between the two time points, and the intra-class correlation coefficient (ICC) between the two time points. With these statistics, we aim to determine the precision of the TBSS ROI analysis and the XTRACT ROI analysis quantitatively in the practice of analyzing a particular dataset. The prospective results will provide a new and general reference for choosing analysis methods in future dMRI studies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alexander, A.L., Lee, J.E., Lazar, M., Field, A.S.: Diffusion tensor imaging of the brain. Neurotherapeutics 4(3), 316–329 (2007)
Andersson, J.L., Graham, M.S., Drobnjak, I., Zhang, H., Campbell, J.: Susceptibility-induced distortion that varies due to motion: correction in diffusion MR without acquiring additional data. Neuroimage 171, 277–295 (2018)
Andersson, J.L., Graham, M.S., Drobnjak, I., Zhang, H., Filippini, N., Bastiani, M.: Towards a comprehensive framework for movement and distortion correction of diffusion MR images: within volume movement. Neuroimage 152, 450–466 (2017)
Andersson, J.L., Graham, M.S., Zsoldos, E., Sotiropoulos, S.N.: Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. Neuroimage 141, 556–572 (2016)
Andersson, J.L., Skare, S., Ashburner, J.: How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20(2), 870–888 (2003)
Andersson, J.L., Sotiropoulos, S.N.: An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125, 1063–1078 (2016)
Andica, C., et al.: Scan-rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics. Neuroradiology 62(4), 483–494 (2020)
Bouyagoub, S., Dowell, N.G., Gabel, M., Cercignani, M.: Comparing multiband and singleband EPI in NODDI at 3 T: what are the implications for reproducibility and study sample sizes? Magn. Reson. Mater. Phys. Biol. Med. 34, 1–13 (2020)
Daducci, A., Canales-Rodríguez, E.J., Zhang, H., Dyrby, T.B., Alexander, D.C., Thiran, J.P.: Accelerated microstructure imaging via convex optimization (AMICO) from diffusion MRI data. NeuroImage 105, 32–44 (2015)
Farquharson, S., et al.: White matter fiber tractography: why we need to move beyond DTI. J. Neurosurg. 118(6), 1367–1377 (2013)
Gardner, R.C., Yaffe, K.: Epidemiology of mild traumatic brain injury and neurodegenerative disease. Mol. Cell. Neurosci. 66, 75–80 (2015)
Jones, D.K., Leemans, A.: Diffusion tensor imaging. In: Modo, M.J., Bulte, J.W.M. (eds.) Magnetic Resonance Neuroimaging. Methods in Molecular Biology (Methods and Protocols), 711, 127–144. Springer. Cham (2011). https://doi.org/10.1007/978-1-61737-992-5_6
Le Bihan, D., et al.: Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging: Official J. Int. Soc. Magn. Reson. Med. 13(4), 534–546 (2001)
Lash, R.S., Bell, J.F., Reed, S.C.: Epidemiology. In: Todd, K.H., Thomas, C.R., Alagappan, K. (eds.) Oncologic Emergency Medicine, pp. 3–12. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67123-5_1
Lerma-Usabiaga, G., Mukherjee, P., Perry, M.L., Wandell, B.A.: Data-science ready, multisite, human diffusion MRI white-matter-tract statistics. Sci. Data 7(1), 1–9 (2020)
Lerma-Usabiaga, G., Mukherjee, P., Ren, Z., Perry, M.L., Wandell, B.A.: Replication and generalization in applied neuroimaging. Neuroimage 202, 116048 (2019)
Lucignani, M., Breschi, L., Espagnet, M.C.R., Longo, D., Talamanca, L.F., Placidi, E., Napolitano, A.: Reliability on multiband diffusion NODDI models: a test retest study on children and adults. NeuroImage 238, 118234 (2021)
Oishi, K., et al.: Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer’s disease participants. Neuroimage 46(2), 486–499 (2009)
Palacios, E.M., et al.: The evolution of white matter microstructural changes after mild traumatic brain injury: a longitudinal DTI and NODDI study. Sci. Adv. 6(32), eaaz6892 (2020)
Smith, S.M., et al.: Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31(4), 1487–1505 (2006)
Theaud, G., Houde, J.C., Boré, A., Rheault, F., Morency, F., Descoteaux, M.: Tractoflow: a robust, efficient and reproducible diffusion MRI pipeline leveraging nextflow & singularity. NeuroImage 218, 116889 (2020)
Warrington, S., et al.: Xtract-standardised protocols for automated tractography in the human and macaque brain. NeuroImage 217, 116923 (2020)
Yue, J.K., et al.: Transforming research and clinical knowledge in traumatic brain injury pilot: multicenter implementation of the common data elements for traumatic brain injury. J. Neurotrauma 30(22), 1831–1844 (2013)
Yuh, E.L., et al.: Diffusion tensor imaging for outcome prediction in mild traumatic brain injury: a track-TBI study. J. Neurotrauma 31(17), 1457–1477 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Cai, L.T., Baida, M., Wren-Jarvis, J., Bourla, I., Mukherjee, P. (2021). Diffusion MRI Automated Region of Interest Analysis in Standard Atlas Space versus the Individual’s Native Space. In: Cetin-Karayumak, S., et al. Computational Diffusion MRI. CDMRI 2021. Lecture Notes in Computer Science(), vol 13006. Springer, Cham. https://doi.org/10.1007/978-3-030-87615-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-87615-9_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87614-2
Online ISBN: 978-3-030-87615-9
eBook Packages: Computer ScienceComputer Science (R0)