Computational protocol: Evolution of structural neuroimaging biomarkers in a series of adult patients with Niemann-Pick type C under treatment

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Protocol publication

[…] MRI acquisitions were performed on 1.5 and 3 T MR units (General Electric, I, USA) using a standard protocol applied for the exploration and follow-up of neurometabolic patients at our institution. A standard 3D T1-weighted image (TR = 9.5 ms, TE = 3 ms, matrix = 256 × 256, field of view (FOV) = 256 × 256 (n = 77), 250 × 250 or 512 × 512 (n = 34), 240 × 240 or 220 × 220) was acquired for localization of brain regions and volumetric analysis. DTI was performed in some patients (b value = 1000 s/mm2, 12 directions (6 directions in 2 patients), matrix = 256 × 256, FOV either 240 × 240, 280 × 280 or 380 × 380, TR = 12000 ms, TE = 80 ms) to evaluate the integrity of white matter microstructure. Volumetry and DTI analysis were run using the FMRIB Software Library (FSL) (https://www.fmrib.ox.ac.uk/fsl).The voxel-based-morphometry (VBM) tool of FSL was used on the 3D T1 images for brain extraction and segmentation. The segmented images were then non-linearly registered to a study-specific grey matter template, smoothed and a statistic map was created using a threshold-free cluster enhancement (TFCE) approach. Region of interest (ROI) analysis was also performed, and segmentation of cortical and subcortical structures was automated with FreeSurfer 5.3 (https://surfer.nmr.mgh.harvard.edu). Brain volumes were normalized to the total intracranial volume of each subject to eliminate the bias from differences in skull sizes.For DTI analyses, quality control of DTI images was performed by visual inspection for spikes and other artifacts. Eddy correction was performed to correct for the effects of head movement and eddy current induced geometric distortions. DTI provides information on microstructure integrity using metrics such as fractional anisotropy (FA) and radial diffusivity (RD). FA measures the global diffusion where lower values correspond to reduced diffusion and thus reduced fiber bundles. RD measures diffusion across the fibers and increased values usually indicate myelin injury. The diffusion tensor model was fitted to generate FA maps. Thus, we extracted FA and other diffusion metrics such as RD and axial diffusivity from different ROI (based on the JHU-atlas). We also performed statistical analysis using the voxel-wise manner with the tract based spatial statistics (TBSS) tool of FSL. First, the FA-maps were non-linearly transformed to the MNI space, and an average FA image corresponding to the mean FA was created. A threshold skeletonized mean FA image was created onto which all subjects’ FA was projected before cross-subject voxel-wise statistics were performed using FSL’s randomise. Multiple comparison correction was performed with a threshold of p < 0.05. Radial diffusivity data were studied in the same manner. […]

Pipeline specifications

Software tools FSL, FreeSurfer
Application Neuroimaging analysis
Organisms Homo sapiens