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Variability in diffusion kurtosis imaging: Impact on study design, statistical power and interpretation.

Szczepankiewicz, Filip LU ; Lätt, Jimmy LU ; Wirestam, Ronnie LU ; Leemans, Alexander; Sundgren, Pia LU ; van Westen, Danielle LU ; Ståhlberg, Freddy LU and Nilsson, Markus LU (2013) In NeuroImage 76(1). p.145-154
Abstract
Diffusion kurtosis imaging (DKI) is an emerging technique with the potential to quantify properties of tissue microstructure that may not be observable using diffusion tensor imaging (DTI). In order to help design DKI studies and improve interpretation of DKI results, we employed statistical power analysis to characterize three aspects of variability in four DKI parameters; the mean diffusivity, fractional anisotropy, mean kurtosis, and radial kurtosis. First, we quantified the variability in terms of the group size required to obtain a statistical power of 0.9. Second, we investigated the relative contribution of imaging and post-processing noise to the total variance, in order to estimate the benefits of longer scan times versus the... (More)
Diffusion kurtosis imaging (DKI) is an emerging technique with the potential to quantify properties of tissue microstructure that may not be observable using diffusion tensor imaging (DTI). In order to help design DKI studies and improve interpretation of DKI results, we employed statistical power analysis to characterize three aspects of variability in four DKI parameters; the mean diffusivity, fractional anisotropy, mean kurtosis, and radial kurtosis. First, we quantified the variability in terms of the group size required to obtain a statistical power of 0.9. Second, we investigated the relative contribution of imaging and post-processing noise to the total variance, in order to estimate the benefits of longer scan times versus the inclusion of more subjects. Third, we evaluated the potential benefit of including additional covariates such as the size of the structure when testing for differences in group means. The analysis was performed in three major white matter structures of the brain: the superior cingulum, the corticospinal tract, and the mid-sagittal corpus callosum, extracted using diffusion tensor tractography and DKI data acquired in a healthy cohort. The results showed heterogeneous variability across and within the white matter structures. Thus, the statistical power varies depending on parameter and location, which is important to consider if a pathogenesis pattern is inferred from DKI data. In the data presented, inter-subject differences contributed more than imaging noise to the total variability, making it more efficient to include more subjects rather than extending the scan-time per subject. Finally, strong correlations between DKI parameters and the structure size were found for the cingulum and corpus callosum. Structure size should thus be considered when quantifying DKI parameters, either to control for its potentially confounding effect, or as a means of reducing unexplained variance. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Diffusion kurtosis imaging, Diffusion tensor imaging, DKI, DTI, Statistical power, Study design, Group size, Tractography, Statistics, Effect size, Random effects model
in
NeuroImage
volume
76
issue
1
pages
145 - 154
publisher
Elsevier
external identifiers
  • wos:000319090300015
  • pmid:23507377
  • scopus:84876345487
ISSN
1095-9572
DOI
10.1016/j.neuroimage.2013.02.078
language
English
LU publication?
yes
id
c5abb21f-2bef-46aa-9a22-dfcbdf53f2d5 (old id 3628009)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/23507377?dopt=Abstract
date added to LUP
2013-04-05 10:48:43
date last changed
2019-07-02 01:15:22
@article{c5abb21f-2bef-46aa-9a22-dfcbdf53f2d5,
  abstract     = {Diffusion kurtosis imaging (DKI) is an emerging technique with the potential to quantify properties of tissue microstructure that may not be observable using diffusion tensor imaging (DTI). In order to help design DKI studies and improve interpretation of DKI results, we employed statistical power analysis to characterize three aspects of variability in four DKI parameters; the mean diffusivity, fractional anisotropy, mean kurtosis, and radial kurtosis. First, we quantified the variability in terms of the group size required to obtain a statistical power of 0.9. Second, we investigated the relative contribution of imaging and post-processing noise to the total variance, in order to estimate the benefits of longer scan times versus the inclusion of more subjects. Third, we evaluated the potential benefit of including additional covariates such as the size of the structure when testing for differences in group means. The analysis was performed in three major white matter structures of the brain: the superior cingulum, the corticospinal tract, and the mid-sagittal corpus callosum, extracted using diffusion tensor tractography and DKI data acquired in a healthy cohort. The results showed heterogeneous variability across and within the white matter structures. Thus, the statistical power varies depending on parameter and location, which is important to consider if a pathogenesis pattern is inferred from DKI data. In the data presented, inter-subject differences contributed more than imaging noise to the total variability, making it more efficient to include more subjects rather than extending the scan-time per subject. Finally, strong correlations between DKI parameters and the structure size were found for the cingulum and corpus callosum. Structure size should thus be considered when quantifying DKI parameters, either to control for its potentially confounding effect, or as a means of reducing unexplained variance.},
  author       = {Szczepankiewicz, Filip and Lätt, Jimmy and Wirestam, Ronnie and Leemans, Alexander and Sundgren, Pia and van Westen, Danielle and Ståhlberg, Freddy and Nilsson, Markus},
  issn         = {1095-9572},
  keyword      = {Diffusion kurtosis imaging,Diffusion tensor imaging,DKI,DTI,Statistical power,Study design,Group size,Tractography,Statistics,Effect size,Random effects model},
  language     = {eng},
  number       = {1},
  pages        = {145--154},
  publisher    = {Elsevier},
  series       = {NeuroImage},
  title        = {Variability in diffusion kurtosis imaging: Impact on study design, statistical power and interpretation.},
  url          = {http://dx.doi.org/10.1016/j.neuroimage.2013.02.078},
  volume       = {76},
  year         = {2013},
}