Early childhood development of white matter fiber density and morphology Abstract   Early childhood is an important period for cognitive and brain development, though white matter changes specific to this period remain understudied. Here we utilize a novel analytic approach to quantify and track developmental changes in white matter micro and macrostructure, calculated from individually oriented fiberbundle populations, termed xe2x80x9cfixelsxe2x80x9d. Fixelbased analysis and mixedeffects models were used to assess tractwise changes in fiber density and bundle morphology in 73 girls scanned at baseline (ages 4.09xe2x80x937.02, meanxe2x80xafxe2x80xaf5.47, SDxe2x80xafxe2x80xaf0.81), 6month (Nxe2x80xafxe2x80xaf7), and oneyear followup (Nxe2x80xafxe2x80xaf42). For comparison, we also assessed changes in commonly utilized diffusion tensor metrics: fractional anisotropy (FA), and mean, radial and axial diffusivity (MD, RD, AD). Maturational increases in fixelmetrics were seen in most major white matter tracts, with the most rapid increases in the corticospinal tract and slowest or nonsignificant increases in the genu of the corpus callosum and uncinate fasciculi. As expected, we observed developmental increases in FA and decreases in MD, RD and AD, though percentage changes were smaller relative to fixelmetrics. The majority of tracts showed more substantial morphological than microstructural changes. These findings highlight early childhood as a period of dynamic white matter maturation, characterized by large increases in macroscopic fiber bundle size, mild changes in axonal density, and parallel, albeit less substantial, changes in diffusion tensor metrics.

Reducing variability in alongtract analysis with diffusion profile realignment Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction of the brain9s white matter structures through tractography. Analyzing dMRI measures along the trajectory of white matter bundles can provide a more specific investigation than considering a region of interest or tractaveraged measurements. However, performing group analyses with this alongtract strategy requires correspondence between points of tract pathways across subjects. This is usually achieved by creating a new common space where the representative streamlines from every subject are resampled to the same number of points. If the underlying anatomy of some subjects was altered due to, e.g. disease or developmental changes, such information might be lost by resampling to a fixed number of points. In this work, we propose to address the issue of possible misalignment, which might be present even after resampling, by realigning the representative streamline of each subject in this 1D space with a new method, coined diffusion profile realignment (DPR). Experiments on synthetic datasets show that DPR reduces the coefficient of variation for the mean diffusivity, fractional anisotropy and apparent fiber density when compared to the unaligned case. Using 100 in vivo datasets from the HCP, we simulated changes in mean diffusivity, fractional anisotropy and apparent fiber density. Pairwise Student9s ttests between these altered subjects and the original subjects indicate that regional changes are identified after realignment with the DPR algorithm, while preserving differences previously detected in the unaligned case. This new correction strategy contributes to revealing effects of interest which might be hidden by misalignment and has the potential to improve the specificity in longitudinal population studies beyond the traditional region of interest based analysis and alongtract analysis workflows.