Citing Prepdwi

All structural and diffusion data were processed and analyzed using an open-source and containerized application, prepdwi, which uses the BIDS [1] and BIDS Apps [2] standards to perform standardized pre-processing, fitting, image registration, and tractography.

T1w images were skull-stripped using bet (FSL, using the -f 0.4 -B options, [3]), corrected for non-uniformities N4BiasFieldCorrection (ANTS, [4]), and normalized by the mean intensity within the brain mask. Pre-processed T1w images were registered with standard templates (MNI ICBM152 non-linear 6th generation symmetric template, referred to as MNI152_1mm in FSL, and the MNI152NLin2009cAsym template) using an initial affine registration using block-matching [5], followed by deformable b-spline registration [6], both implemented in NiftyReg v1.3.9. Overlay visualizations depicting the skull-stripping, affine registration, and deformable registration were generated for each subject to check for failures. Failures in affine registration could occur, but were corrected by forcing initialization with an existing transformation matrix. Discrete and probabilistic segmentation images in the template spaces were automatically propagated to each subject’s T1w space, using nearest neighbour interpolation for discrete segmentations, and linear for probabilistic segmentations.

Diffusion-weighted MRI data were pre-processed with denoising using a local PCA method with (dwidenoise from mrtrix3, [7]), and correction of ringing artifacts with the unring tool [8]. Eddy current distortions were corrected using eddy (FSL, [9]), with the --repol option enabled for outlier replacement [10]. If multiple phase-encoding polarities were used in the acquisition, top-up [11]was used to correct for susceptibility distortions, with the resulting parameters fed into eddy. If data were not sufficient to run top-up, a registration-based susceptibility distortion correction was performed following eddy, using B-spline deformable registration [6] between the average b0 image and a T1w volume with inverted intensities. Finally, within-subject rigid registration of the corrected DWI volume and the T1w volume was performed using block-matching[5], to bring the DWI images in the same space as the T1w, where atlas labels were also propagated. If gradient non-linearities were provided through spherical harmonic coefficients (e.g. for the AC84 gradient system on the 7T), these were used with the gradient_unwarp tool [12] to generate a non-linear transformation, which was composed with the T1w linear transformation to resample the DWI images into the corrected T1w space in a single step. Modulation with the determinant of the Jacobian of the unwarping was used to correct for intensity differences in the magnitude images due to gradient non-linearities. Pre-processed DWI images in the T1w space were then used to estimate diffusion tensor metrics using dtifit (FSL, [13]), and ball and stick modelling for probablistic tractography using bedpostx [14]. If multi-shell diffusion data was detected, diffusion kurtosis metrics were computed using the DKE toolbox [15].

References:

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  2. Gorgolewski KJ, Alfaro-Almagro F, Auer T, Bellec P, Capotă M, Chakravarty MM, et al. BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Comput Biol. 2017;13: e1005209. doi:10.1371/journal.pcbi.1005209
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#versions: neuroglia-dwi:latest mrtrix 3.0_RC3 camino 2019-02-01 1c4ef77615d103d43adcff6c79b72d0bbdac0897 unring 2017-02-17 dke v1.0 niftyreg 1.3.9 fsl v6.0 (fslinstaller 3.0.12)