Longitudinal regression analysis of spatial–temporal growth patterns of geometrical diffusion measures in early postnatal brain development with diffusion tensor imaging

Although diffusion tensor imaging (DTI) has provided substantial insights into early brain development, most DTI studies based on fractional anisotropy (FA) and mean diffusivity (MD) may not capitalize on the information derived from the three principal diffusivities (e.g. eigenvalues). In this study, we explored the spatial and temporal evolution of white matter structures during early brain development using two geometrical diffusion measures, namely, linear (Cl) and planar (Cp) diffusion anisotropies, from 71 longitudinal datasets acquired from 29 healthy, full-term pediatric subjects. The growth trajectories were estimated with generalized estimating equations (GEE) using linear fitting with logarithm of age (days). The presence of the white matter structures in Cl and Cp was observed in neonates, suggesting that both the cylindrical and fanning or crossing structures in various white matter regions may already have been formed at birth. Moreover, we found that both Cl and Cp evolved in a temporally nonlinear and spatially inhomogeneous manner. The growth velocities of Cl in central white matter were significantly higher when compared to peripheral, or more laterally located, white matter: central growth velocity Cl=0.0465±0.0273/log(days), versus peripheral growth velocity Cl=0.0198±0.0127/log(days), p<10⁻⁶. In contrast, the growth velocities of Cp in central white matter were significantly lower than that in peripheral white matter: central growth velocity Cp=0.0014±0.0058/log(days), versus peripheral growth velocity Cp=0.0289±0.0101/log(days), p<10⁻⁶. Depending on the underlying white matter site which is analyzed, our findings suggest that ongoing physiologic and microstructural changes in the developing brain may exert different effects on the temporal evolution of these two geometrical diffusion measures. Thus, future studies utilizing DTI with correlative histological analysis in the study of early brain development are warranted.

[1]  Guido Gerig,et al.  Comparisons of regional white matter diffusion in healthy neonates and adults performed with a 3.0-T head-only MR imaging unit. , 2003, Radiology.

[2]  A. Snyder,et al.  Normal brain in human newborns: apparent diffusion coefficient and diffusion anisotropy measured by using diffusion tensor MR imaging. , 1998, Radiology.

[3]  Alexander Leemans,et al.  Microstructural maturation of the human brain from childhood to adulthood , 2008, NeuroImage.

[4]  P. Hüppi,et al.  Diffusion tensor imaging of normal and injured developing human brain ‐ a technical review , 2002, NMR in biomedicine.

[5]  Dinggang Shen,et al.  Image registration by local histogram matching , 2007, Pattern Recognit..

[6]  J. K. Smith,et al.  Early Postnatal Development of Corpus Callosum and Corticospinal White Matter Assessed with Quantitative Tractography , 2007, American Journal of Neuroradiology.

[7]  P. Diggle,et al.  Analysis of Longitudinal Data. , 1997 .

[8]  Thomas E. Nichols,et al.  Controlling the familywise error rate in functional neuroimaging: a comparative review , 2003, Statistical methods in medical research.

[9]  W. Kaiser,et al.  Diffusion tensor imaging: the normal evolution of ADC, RA, FA, and eigenvalues studied in multiple anatomical regions of the brain , 2009, Neuroradiology.

[10]  Hangyi Jiang,et al.  Pediatric diffusion tensor imaging: Normal database and observation of the white matter maturation in early childhood , 2006, NeuroImage.

[11]  J K Smith,et al.  Temporal and Spatial Development of Axonal Maturation and Myelination of White Matter in the Developing Brain , 2008, American Journal of Neuroradiology.

[12]  Hao Huang,et al.  Evidence of slow maturation of the superior longitudinal fasciculus in early childhood by diffusion tensor imaging , 2007, NeuroImage.

[13]  A. Snyder,et al.  Radial organization of developing preterm human cerebral cortex revealed by non-invasive water diffusion anisotropy MRI. , 2002, Cerebral cortex.

[14]  P. Grenier,et al.  MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. , 1986, Radiology.

[15]  Guido Gerig,et al.  Diffusion tensor imaging: Application to the study of the developing brain. , 2007, Journal of the American Academy of Child and Adolescent Psychiatry.

[16]  V. Wedeen,et al.  Fiber crossing in human brain depicted with diffusion tensor MR imaging. , 2000, Radiology.

[17]  Lei Guo,et al.  Brain tissue segmentation based on DTI data , 2007, NeuroImage.

[18]  Leonard E White,et al.  Diffusion tensor imaging assessment of brain white matter maturation during the first postnatal year. , 2007, AJR. American journal of roentgenology.

[19]  H. Akaike A new look at the statistical model identification , 1974 .

[20]  C. Beaulieu,et al.  The basis of anisotropic water diffusion in the nervous system – a technical review , 2002, NMR in biomedicine.

[21]  J. Shimony,et al.  Normal brain maturation during childhood: developmental trends characterized with diffusion-tensor MR imaging. , 2001, Radiology.

[22]  Thomas E. Nichols,et al.  Acquisition and voxelwise analysis of multi-subject diffusion data with Tract-Based Spatial Statistics , 2007, Nature Protocols.

[23]  S. Zeger,et al.  Longitudinal data analysis using generalized linear models , 1986 .

[24]  A. Snyder,et al.  Diffusion-tensor MR imaging of gray and white matter development during normal human brain maturation. , 2002, AJNR. American journal of neuroradiology.

[25]  J. Dubois,et al.  Diffusion tensor imaging of brain development. , 2006, Seminars in fetal & neonatal medicine.

[26]  Lucie Hertz-Pannier,et al.  Assessment of the early organization and maturation of infants' cerebral white matter fiber bundles: A feasibility study using quantitative diffusion tensor imaging and tractography , 2006, NeuroImage.

[27]  Hongtu Zhu,et al.  Statistical Modelling of Brain Morphological Measures Within Family Pedigrees. , 2008, Statistica Sinica.

[28]  若菜 勢津 Fiber tract-based atlas of human white matter anatomy , 2006 .

[29]  Roland G. Henry,et al.  Quantitative diffusion tensor MRI fiber tractography of sensorimotor white matter development in premature infants , 2005, NeuroImage.

[30]  R. McKinstry,et al.  Diffusion tensor imaging and tractography of human brain development. , 2006, Neuroimaging clinics of North America.

[31]  Shu-Wei Sun,et al.  Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia , 2003, NeuroImage.

[32]  Susumu Mori,et al.  Image contrast using the secondary and tertiary eigenvectors in diffusion tensor imaging , 2006, Magnetic resonance in medicine.

[33]  J. Kucharczyk,et al.  Identification of “Premyelination” by Diffusion‐Weighted MRI , 1995, Journal of computer assisted tomography.

[34]  Peter J. Brophy,et al.  Mechanisms of axon ensheathment and myelin growth , 2005, Nature Reviews Neuroscience.

[35]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. , 1996, Journal of magnetic resonance. Series B.

[36]  Michael I. Miller,et al.  Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detection , 2010, NeuroImage.

[37]  Dinggang Shen,et al.  HAMMER: hierarchical attribute matching mechanism for elastic registration , 2002, IEEE Transactions on Medical Imaging.

[38]  Hao Huang,et al.  White and gray matter development in human fetal, newborn and pediatric brains , 2006, NeuroImage.

[39]  K. A. Il’yasov,et al.  Fast quantitative diffusion-tensor imaging of cerebral white matter from the neonatal period to adolescence , 2004, Neuroradiology.

[40]  Khader M Hasan,et al.  Diffusion tensor imaging of the developing human cerebrum , 2005, Journal of neuroscience research.