Brain sample (male, 10.9-year-old) was cut in half and scanned on a 14T/8.9cm vertical bore MRI spectrometer (Bruker Biospin) with a 20-mm linear millipede coil (m2m Imaging). The 14T microimager has stronger magnetic field and gradient strength, but the bore size can only fit half marmoset brain. Thus, we acquired multi-shell dMRI data at 64 µm for one half of the brain (the right hemisphere). The multi-shell diffusion MRI was collected using a 3D diffusion-weighted multi-shot spin-echo EPI sequence: TR = 160 ms, TE = 23 ms, number of averages = 3, number of segments = 51, FOV = 25.6 × 17.4 × 13.3 mm, matrix size = 400x272x208, resolution = 64 um isotropic, a total of 192 DWI images with three b values ( and 126 b = 4800, 64 b =2400, 1 b = 0), and the total acquisition time was about 11.3 days. Multi-shell gradient sampling schemes of the dMRI were generated by the IMOC method of the DMRITool (Cheng et al., 2017).
Download Raw DWI data*We masked out most black background and converted the data to 16bit integer format (.nii.gz) to reduce the size of the data. *A few slice of the original b0 images had artifacts due to insuffient gradient spoiler. We collected two extra b0 (with stronger spoilers) in a different session to replace the orginial affected slices. Thus, the final b0 was a combination-and-averaging of 3 b0.
Software: Mrtrix3.0, FSL 5.0.1
Note: We used b0, b2400, and b4800 for the preprocessing. b30 was excluded in this preprocessing pipeline
1. Denoising DWI (by Mrtrix3)
$ dwidenoise raw.nii.gz dwi_denoised.nii.gz -noise noise.nii.gz -extent 7,7,7 -force
2. Eddy current correction and DTI-fitting (by FSL)
$ eddy_correct dwi_denoised.nii.gz data.nii.gz 0 spline
$ fdt_rotate_bvecs raw.bvecs bvecs data.ecclog
$ dtifit -k data.nii.gz -o DTIFIT -m mask.nii.gz -r bvecs -b bvals
*We masked out most black background and converted the data to 16bit integer format (.nii.gz) to reduce the size of the data.
Mrtrix3-based pipeline for dMRI tracktography in Liu C, et al, 2020, including multi-tissue constrained spherical deconvolution, were similar as the pipeline for the 80um data.
For the track map shown in Figure 4 of Liu C, et al, 2020, we used the following command:
$ tckgen -select 10000000 -angle 60 -minlength 3 -maxlength 75 -seed_dynamic fod_masked.mif -seed_cutoff 0.1 -mask mask.nii.gz fod_masked.mif tracks_10_million.tck -force -nthread $NumofThreads
$ tckmap tracks_10_million.tck tckmap_fod.mif -vox 0.05 -dec -force -nthreads $NumofThreads