3555
Delineation of white matter microstructural maturation of infant brain with DKI1Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States, 2Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, United States, 3Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
Synopsis
Diffusion kurtosis imaging (DKI) based on multi-shell diffusion MRI may offer extra information on WM microstructural complexity changes during infant development compared to conventional DTI. After acquiring DKI and DTI from 18 normal infants aged 0-1 year, we measured the mean kurtosis (MK) and fractional anisotropy (FA) of all major WM tracts by combining WM skeletonization process and WM tract labels. Significant age-related increases in both MK and FA were found in all tracts. Distinctive developmental trajectories of WM tracts with MK measurements compared to those with FA measurements were revealed, indicating heterogeneous increases of microstructural complexity among WM tracts.
Purpose
Infant brain development is characterized with probably most dynamic structural and functional changes across the lifespan. White matter (WM) microstructural maturation in infants aged 0-1 year is associated with cellular processes such as fiber myelination and membranes proliferation around fibers[1,2]. Diffusion tensor imaging (DTI) has been widely used to quantify WM microstructural maturation. Diffusion kurtosis imaging (DKI), characterizing non-Gaussian diffusion of water, is sensitive to microstructural complexity[3] that cannot be quantified by DTI. So far no study has delineated the microstructural complexity of all major WM tracts in infants[4]. Here we acquired DKI and DTI from normal infants and comprehensively quantified the microstructural complexity maturation of all major WM tracts during 0-1 year.Methods
Subjects and data acquisition: 18 infants aged from 1.1 to 13.8 months were included in this study and scanned on a 3T Siemens Prisma scanner. T2-weighted images (T2w) were acquired with a voxel size of 0.8x0.8x0.8 mm3. High-resolution multi-shell diffusion MR images (dMRI) were acquired using Human Connectome Project (HCP)-styled, multi-band EPI sequences with both phase encoding directions of anterior-posterior (AP) and posterior-anterior (PA). The dMRI parameters were: TR/TE = 3222/89.2 ms, FOV = 210x210 mm2, in plane resolution = 1.5x1.5 mm2, slice thickness = 1.5 mm without slice gap, slice number = 92, b-values of 1500 and 3000 s/mm2 with 46 non-identical independent diffusion gradient directions in each shell. Data analysis: dMRI data underwent eddy-current and EPI distortion correction in FSL (http://www.fmrib.ox.ac.uk/fsl). Corrected AP/PA images with the same gradient direction were averaged after registration to the first b0 image. Diffusion tensor fitting was conducted in DTIstudio (http://www.MRIstudio.org), and mean kurtosis (MK) maps were fitted in DKE (http://academicdepartments.musc.edu/cbi/dki). A digital WM atlas JHU ICBM-DTI-81 (http://cmrm.med.jhmi.edu/) was applied to parcellate WM tracts and tract groups. Nonlinear registration, skeletonization and projection steps from TBSS from FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TBSS) were used to map the atlas labels to the WM skeleton in averaged FA maps in the atlas space. Each voxel from skeletonized MK or FA maps was categorized in one of 27 tracts (left/right combined, Figure 1) and one of the five tract groups: brainstem, projection, commissural, association, and limbic tract group (Figure 4 legend). Details of these procedures can be found in the literature[5]. Linear regression was conducted between the averaged skeletonized MK (or FA) from a specific tract/tract group and age, respectively.Results
High-resolution MK maps from four representative infants aged 0-1 year are shown in Figure 2, with T2w as anatomical reference. MK values of WM tracts, such as splenium of corpus callosum (SCC) and internal capsule, increased dramatically from 1.3 to 11.5 months. Distinctive developmental trajectories among the tracts and tract groups with MK measurements are delineated in Figure 3 and 4a, respectively, and those with FA measurements are shown in Figure 4b. MK values in the brainstem tracts were much higher than those in other tracts in the entire age range of 0-1 year (Figure 4a). FA values in the commissural tracts, in contrast, were much higher than those in other tracts (Figure 4b). Table 1 shows top ten WM tracts with the highest increase rates of MK and FA values, respectively. Interestingly, the increase rate of MK values in SCC was much higher than those in other tracts, whereas the increase rate of FA values in SCC was not as prominent as its MK rate.Discussion and Conclusions
The asynchrony of WM microstructural maturation across the five WM tract groups in infant brains was delineated with DKI. Distinctive developmental trajectories of WM tracts with MK measurements compared to those with FA measurements were revealed. Higher MK values in the brainstem tracts were found across 0-1 year, suggesting higher level of diffusion barriers and earlier microstructural maturation in brainstem tracts than other tracts. However, different from the microstructural distribution pattern delineated with MK measurements among the WM tracts, FA values in commissural tracts were much higher than other tracts. MK values increased fastest in SCC, consistent with the caudo-rostral sequence of myelination of CC[6,7]. Such developmental pattern is much more prominent in developmental trajectories with MK measurements than those with FA measurements. The microstructural complexity changes indicated by MK measurements may be associated with the first stage of myelination with proliferation of glial cell bodies and prolongations, which is relatively isotropic[2,8] and can hardly be delineated with FA. DKI measurements of the non-Gaussianity in water diffusion in WM offer unique information of microstructural complexity maturation and could serve as potential biomarkers for atypical brain development.Acknowledgements
This study is funded by NIH MH092535, MH092535-S1 and HD086984.References
[1] Ouyang, M., Dubois, J., Yu, Q., Mukherjee, P., & Huang, H. (2018). Delineation of early brain development from fetuses to infants with diffusion MRI and beyond. NeuroImage (In press).
[2] Dubois, J., Dehaene‐Lambertz, G., Perrin, M., Mangin, J. F., Cointepas, Y., Duchesnay, E., ... & Hertz‐Pannier, L. (2008). Asynchrony of the early maturation of white matter bundles in healthy infants: quantitative landmarks revealed noninvasively by diffusion tensor imaging. Human brain mapping, 29(1), 14-27.
[3] Jensen, J. H., Helpern, J. A., Ramani, A., Lu, H., & Kaczynski, K. (2005). Diffusional kurtosis imaging: the quantification of non‐gaussian water diffusion by means of magnetic resonance imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 53(6), 1432-1440.
[4] Paydar, A., Fieremans, E., Nwankwo, J. I., Lazar, M., Sheth, H. D., Adisetiyo, V., ... & Milla, S. S. (2014). Diffusional kurtosis imaging of the developing brain. American Journal of Neuroradiology, 35(4), 808-814.
[5] Ouyang, M., Cheng, H., Mishra, V., Gong, G., Mosconi, M. W., Sweeney, J., ... & Huang, H. (2016). Atypical age‐dependent effects of autism on white matter microstructure in children of 2–7 years. Human brain mapping, 37(2), 819-832.
[6] Brody, B. A., Kinney, H. C., Kloman, A. S., & Gilles, F. H. (1987). Sequence of central nervous system myelination in human infancy. I. An autopsy study of myelination. Journal of Neuropathology & Experimental Neurology, 46(3), 283-301.
[7] Kinney, H. C., Brody, B. A., Kloman, A. S., & Gilles, F. H. (1988). Sequence of central nervous system myelination in human infancy: II. Patterns of myelination in autopsied infants. Journal of Neuropathology & Experimental Neurology, 47(3), 217-234.
[8] Baumann, N., & Pham-Dinh, D. (2001). Biology of oligodendrocyte and myelin in the mammalian central nervous system. Physiological reviews, 81(2), 871-927.
Figures
