2.4.2. MRI Analysis
CONTRACTION
Following data acquisition, the mcDESPOT and T1-weighted MP-RAGE images were visually examined using SCLD for motion artifacts and, if necessary, excluded from analyses.From the mcDESPOT data, voxel-wise T1, T2, and myelin water fraction (MWF) measures were estimated using an established processing pipeline that includes linear registration to account for subtle motion between the 26 individual acquisitions, removal of non-brain signals (skull stripping), correction for main and transmit magnetic field (B0 and B1) inhomogeneities, and fitting of single- and multi-component tissue relaxation models using stochastic region contraction. The quantitative T1, T2, and MWF images were then non-linearly aligned to a custom pediatric template in MNI space following a previously described multi-step, multi-scale registration approach using Advanced Normalization Tools (ANTS v2.1 toolbox). Briefly, the high-flip-angle T1-weighted image from the mcDESPOT acquisition was non-linearly aligned to the most appropriate existing age-matched template (constructed for 3, 6, 12, 18, and 24 months) and the transformation applied to the quantitative images. The age-aligned images were then aligned to a single overall template using pre-computed transformations between the age and the study template. Once aligned, mean regional T1, T2, and MWF values were calculated for each child and time point in six a priori (cerebellum; frontal and parietal lobes; and corpus callosum (CC) body, genu, and splenium) and two post hoc (occipital and temporal lobe) white matter (WM) regions of interest (ROIs). In addition, mean values were also calculated for 174 anatomically localized regions throughout the brain, with functional relevance identified through prior analysis.Anatomical volumetric data were derived from the high-resolution T1-weighted MP-RAGE data. Whole-brain WM, gray matter (GM), cerebellum, and CC volumes were calculated using an atlas-matching approach in which tissue masks in MNI space were aligned to each participant’s image using the inverse of the image transformations calculated in the mcDESPOT processing, thresholded at 0.95, and the volume calculated.Diffusion tensor and neurite orientation and dispersion density imaging (DTI and NODDI-DTI) processing were performed using the FSL Diffusion Toolbox and the NODDI MATLAB Toolbox, respectively. DTI analysis included eddy current correction, inter-image motion estimation and outlier detection, re-orientation of the b-matrix, and voxel-wise calculation of tensor parameters, including mean, axial, and radial diffusivities (MD, AD, and RD) and fraction anisotropy (FA). For the NODDI analysis, the multi-parameter model was fit to the eddy-current-corrected diffusion-weighted images, providing measures of the orientation dispersion index (ODI) as well as the volume fraction of the intra-cellular or restricted diffusion compartment (VIC) and the volume fraction of an isotropic diffusion compartment (VISO). All diffusion-related parameter images were then aligned to our study template in MNI space using the same multi-step registration approach with the b0 reference image for each child and time point. Once aligned, the mcDESPOT and NODDI parameter images were combined to calculate the myelin g-ratio, a measure of the thickness of the myelin sheath relative to the axon diameter. Mean regional values for all parameters were then calculated for the cerebellum and frontal, parietal, occipital, and temporal lobe WM; the CC body, genu, and splenium WM; and the 174 functionally relevant areas.Functional connectivity processing was performed for infants 12 months of age and older using the CONN Toolbox (v21). Analysis was restricted to older infants to ensure appropriate contrast within their T1-weighted anatomical images needed for cortical alignment and segmentation.