title: Variability in diffusion kurtosis imaging: impact on study design, statistical power and interpretation.
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.
authors.affiliation: Department of Medical Radiation Physics, Lund University, Lund, Sweden. filip.szczepankiewicz@med.lu.se
authors.forename: Filip
authors.initials: F
authors.lastname: Szczepankiewicz
authors.forename: Jimmy
authors.initials: J
authors.lastname: Lätt
authors.forename: Ronnie
authors.initials: R
authors.lastname: Wirestam
authors.forename: Alexander
authors.initials: A
authors.lastname: Leemans
authors.forename: Pia
authors.initials: P
authors.lastname: Sundgren
authors.forename: Danielle
authors.initials: D
authors.lastname: van Westen
authors.forename: Freddy
authors.initials: F
authors.lastname: Ståhlberg
authors.forename: Markus
authors.initials: M
authors.lastname: Nilsson
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male