A study from the Human Connectome Project

Investigating the performance of Diffusional Kurtosis Imaging for group-wise analyses: A study from the Human Connectome Project Hamed Y. Mesri1, Szabolcs David1, Max A. Viergever1, and Alexander A. Leemans1 1

Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, Netherlands

Synopsis Diffusional Kurtosis Imaging (DKI) is an extension to Diffusion Tensor Imaging (DTI), which allows the quantification of non-Gaussian water diffusion and the quantification of parameters related to microstructural changes. In this work, we used high quality datasets from the HCP and non-parametric statistical inference to evaluate the performance of DKI measures for group-wise studies. To this end, we used the gender information to group the subjects and study the differences. Our results demonstrated that DKI metrics could reveal the differences more accurately compared to DTI metrics.

Background and Purpose Diffusional Kurtosis Imaging (DKI) is used for quantification of the non-Gaussian water diffusion and extraction of additional information about the underlying tissue microstructure [1]–[4]. DKI can be used to identify microstructural changes in the tissue due to pathology or group-wise differences. In this work, we utilize high-quality datasets from the Human Connectome Project (HCP) [5] to investigate the performance of DKI for identifying group-wise differences in the regional values of DKI metrics. To this end, we use the gender difference, which has already been reported in the literature to highlight group-wise differences [6]– [10].

Methods Subjects: The subjects for this study were taken from the HCP 500 release dataset, out of which 410 subjects (244 females and 166 males) aged between 22 and 36 years, had their full DKI data available. Diffusion data: We used the pre-processed [11] multi-shell diffusion-weighted MRI data (b=1000, 2000 and 3000 s/mm2) collected with 90 unique gradient directions and 6 non-diffusion weighted acquisitions per shell (288 volumes in total). Estimation of DKI parameters was implemented using the REKINDLE [12] approach in ExploreDTI [13] with constraints on kurtosis tensor [14]. Correction for diffusion gradient nonlinearities was performed [15]–[17] and the gradient field tensor for each subject was used to correct the magnitude and direction of the diffusion-sensitizing gradients at each brain voxel [11], [15], [16]. DTI and DKI metrics were then calculated for each understudied subject. Parcellation of brain regions using the FreeSurfer [18] toolbox with “wmparc” atlas had already been performed by the HCP team. 179 brain regions common among all the subjects were identified in total out of which 165 regions were ultimately considered. Regions consisting of CSF (ventricles) and the right and left vessels were excluded due to huge flow and noise-related artefacts. The parcellation masks were then used to calculate the mean and standard deviation of all the DTI and DKI of each subject. The mean and standard deviation per regions were inspected to ensure that all the regions consist of normal diffusion values.

Statistical Comparisons: Comparison of gender differences were carried out on the mean values per regions using non-parametric two-sample permutation based t-tests [19] in Permutation Analysis of Linear Models (PALM) version alpha104 with 10,000 permutations [20]. To eliminate the nuisance effect of volume on the statistical analyses [21], volume was considered as a covariate of no-interest. Tests were applied to all the 8 DTI and DKI scalars. Calculation speed was accelerated using the tail approximation [22]. P-values were corrected for multiple comparisons with family-wise error-rate adjustment, by considering multiple contrasts and metrics [20]. Corrected p-values and effect sizes (Cohen’s d) were provided for every region per metric. The significance of a test was determined at corrected p-value (pCORR)