Multi-contrast human neonatal brain atlas: Application to normal neonate development analysis

MRI is a sensitive method for detecting subtle anatomic abnormalities in the neonatal brain. To optimize the usefulness for neonatal and pediatric care, systematic research, based on quantitative image analysis and functional correlation, is required. Normalization-based image analysis is one of the most effective methods for image quantification and statistical comparison. However, the application of this methodology to neonatal brain MRI scans is rare. Some of the difficulties are the rapid changes in T1 and T2 contrasts and the lack of contrast between brain structures, which prohibits accurate cross-subject image registration. Diffusion tensor imaging (DTI), which provides rich and quantitative anatomical contrast in neonate brains, is an ideal technology for normalization-based neonatal brain analysis. In this paper, we report the development of neonatal brain atlases with detailed anatomic information derived from DTI and co-registered anatomical MRI. Combined with a diffeomorphic transformation, we were able to normalize neonatal brain images to the atlas space and three-dimensionally parcellate images into 122 regions. The accuracy of the normalization was comparable to the reliability of human raters. This method was then applied to babies of 37-53 post-conceptional weeks to characterize developmental changes of the white matter, which indicated a posterior-to-anterior and a central-to-peripheral direction of maturation. We expect that future applications of this atlas will include investigations of the effect of prenatal events and the effects of preterm birth or low birth weights, as well as clinical applications, such as determining imaging biomarkers for various neurological disorders.

[1]  R. Grebe,et al.  Neonatal probabilistic models for brain, CSF and skull using T1-MRI data: Preliminary results , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Michael I. Miller,et al.  Validation of semiautomated methods for quantifying cingulate cortical metrics in schizophrenia , 2004, Psychiatry Research: Neuroimaging.

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

[4]  John H. Gilmore,et al.  Group analysis of DTI fiber tract statistics with application to neurodevelopment , 2009, NeuroImage.

[5]  Dinggang Shen,et al.  Neonatal brain image segmentation in longitudinal MRI studies , 2010, NeuroImage.

[6]  Fabio Mosca,et al.  Neonatal neuroimaging: going beyond the pictures. , 2009, Early human development.

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

[8]  P. Boesiger,et al.  SENSE: Sensitivity encoding for fast MRI , 1999, Magnetic resonance in medicine.

[9]  D. Louis Collins,et al.  Unbiased average age-appropriate atlases for pediatric studies , 2011, NeuroImage.

[10]  D. Tayloe,et al.  Pediatrics , 1927, The Indian Medical Gazette.

[11]  A. Fanaroff,et al.  Improved Survival Rates With Increased Neurodevelopmental Disability for Extremely Low Birth Weight Infants in the 1990s , 2005, Pediatrics.

[12]  M Wilke,et al.  Normative pediatric brain data for spatial normalization and segmentation differs from standard adult data , 2003, Magnetic resonance in medicine.

[13]  Sinisa Pajevic,et al.  Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: Application to white matter fiber tract mapping in the human brain , 1999, Magnetic resonance in medicine.

[14]  A. Fanaroff,et al.  Outcomes of children of extremely low birthweight and gestational age in the 1990's. , 1999, Early human development.

[15]  L. D. de Vries,et al.  Asymmetrical myelination of the posterior limb of the internal capsule in infants with periventricular haemorrhagic infarction: an early predictor of hemiplegia. , 1999, Neuropediatrics.

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

[17]  M. Horsfield,et al.  Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging , 1999, Magnetic resonance in medicine.

[18]  M. Schluchter,et al.  Neurodevelopment and predictors of outcomes of children with birth weights of less than 1000 g: 1992-1995. , 2000, Archives of pediatrics & adolescent medicine.

[19]  B. Vohr,et al.  Neurodevelopmental and functional outcomes of extremely low birth weight infants in the National Institute of Child Health and Human Development Neonatal Research Network, 1993-1994. , 2000, Pediatrics.

[20]  Y. Okuno,et al.  Mr Imaging of Spastic Diplegia , 1996, Acta radiologica.

[21]  P. Boesiger,et al.  SENSE‐DTI at 3 T , 2004, Magnetic resonance in medicine.

[22]  et al.,et al.  The Effect of Template Choice on Morphometric Analysis of Pediatric Brain Data ☆ , 2022 .

[23]  Anatole S. Dekaban.  Regional Development of the Brain in Early Life: A Symposium , 1967, Pediatrics.

[24]  Alain Trouvé,et al.  Geodesic Shooting for Computational Anatomy , 2006, Journal of Mathematical Imaging and Vision.

[25]  Alain Trouvé,et al.  Bayesian template estimation in computational anatomy , 2008, NeuroImage.

[26]  Arthur W. Toga,et al.  A Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development The International Consortium for Brain Mapping (ICBM) , 1995, NeuroImage.

[27]  Arthur W. Toga,et al.  Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer's disease participants , 2009, NeuroImage.

[28]  Hong Wang,et al.  Abnormal Cerebral Structure Is Present at Term in Premature Infants , 2005, Pediatrics.

[29]  N Marlow,et al.  Neurocognitive outcome after very preterm birth , 2004, Archives of Disease in Childhood - Fetal and Neonatal Edition.

[30]  Sudhish Chandra,et al.  Parenchymal Brain Injury in the Preterm Infant: Comparison of Cranial Ultrasound, MRI and Neurodevelopmental Outcome , 2002 .

[31]  E. Duchesnay,et al.  Asynchrony of the early maturation of white matter bundles in healthy infants: Quantitative landmarks revealed noninvasively by diffusion tensor imaging , 2008, Human brain mapping.

[32]  D. Shen,et al.  Spatial normalization of diffusion tensor fields , 2003, Magnetic resonance in medicine.

[33]  Hangyi Jiang,et al.  DtiStudio: Resource program for diffusion tensor computation and fiber bundle tracking , 2006, Comput. Methods Programs Biomed..

[34]  Peter Lorenzen,et al.  Multi-modal image set registration and atlas formation , 2006, Medical Image Anal..

[35]  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.

[36]  Alain Trouvé,et al.  Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms , 2005, International Journal of Computer Vision.

[37]  Simon K Warfield,et al.  Reduced occipital regional volumes at term predict impaired visual function in early childhood in very low birth weight infants. , 2006, Investigative ophthalmology & visual science.

[38]  Mary A. Rutherford,et al.  Diffusion tensor imaging with tract-based spatial statistics reveals local white matter abnormalities in preterm infants , 2007, NeuroImage.

[39]  Scott T. Grafton,et al.  Automated image registration: I. General methods and intrasubject, intramodality validation. , 1998, Journal of computer assisted tomography.

[40]  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.

[41]  S. Maier,et al.  Microstructural Development of Human Newborn Cerebral White Matter Assessed in Vivo by Diffusion Tensor Magnetic Resonance Imaging , 1998, Pediatric Research.

[42]  Edgar Erdfelder,et al.  G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences , 2007, Behavior research methods.

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

[44]  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.

[45]  F. Duffy,et al.  Early experience alters brain function and structure. , 2004, Pediatrics.

[46]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

[47]  T. L. Davis,et al.  Morphometry of in vivo human white matter association pathways with diffusion‐weighted magnetic resonance imaging , 1997, Annals of neurology.

[48]  J. Perlman,et al.  Neurobehavioral deficits in premature graduates of intensive care--potential medical and neonatal environmental risk factors. , 2001, Pediatrics.

[49]  Koen Van Leemput,et al.  Encoding Probabilistic Brain Atlases Using Bayesian Inference , 2009, IEEE Transactions on Medical Imaging.

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

[51]  Arthur W. Toga,et al.  Human brain white matter atlas: Identification and assignment of common anatomical structures in superficial white matter , 2008, NeuroImage.

[52]  T. Inder,et al.  Neonatal MRI to predict neurodevelopmental outcomes in preterm infants. , 2006, The New England journal of medicine.

[53]  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.

[54]  Stephen L. Keeling,et al.  Diffusion Tensor Imaging using SENSE-single-shot EPI , 2001 .

[55]  S. Mori,et al.  Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research , 2006, Neuron.

[56]  Steven P. Miller,et al.  Tractography‐based quantitation of diffusion tensor imaging parameters in white matter tracts of preterm newborns , 2005, Journal of magnetic resonance imaging : JMRI.

[57]  Alan C. Evans,et al.  Maturation of white matter in the human brain: a review of magnetic resonance studies , 2001, Brain Research Bulletin.

[58]  Dinggang Shen,et al.  Construction of multi-region-multi-reference atlases for neonatal brain MRI segmentation , 2010, NeuroImage.

[59]  Scott Holland,et al.  Template-O-Matic: A toolbox for creating customized pediatric templates , 2008, NeuroImage.

[60]  P. Basser,et al.  Toward a quantitative assessment of diffusion anisotropy , 1996, Magnetic resonance in medicine.

[61]  P. Basser,et al.  Water Diffusion Changes in Wallerian Degeneration and Their Dependence on White Matter Architecture , 2000 .

[62]  D. Louis Collins,et al.  A new improved version of the realistic digital brain phantom , 2006, NeuroImage.

[63]  Alan C. Evans,et al.  Enhancement of MR Images Using Registration for Signal Averaging , 1998, Journal of Computer Assisted Tomography.

[64]  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.

[65]  Michael I. Miller,et al.  Multi-contrast large deformation diffeomorphic metric mapping for diffusion tensor imaging , 2009, NeuroImage.

[66]  D. Phelps,et al.  Longitudinal, 15-year follow-up of children born at less than 29 weeks' gestation after introduction of surfactant therapy into a region: neurologic, cognitive, and educational outcomes. , 2002, Pediatrics.

[67]  Arthur W. Toga,et al.  Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template , 2008, NeuroImage.

[68]  J Mazziotta,et al.  A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[69]  Mert R. Sabuncu,et al.  Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy , 2007, MICCAI.

[70]  Daniel Rueckert,et al.  Consistent groupwise non-rigid registration for atlas construction , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[71]  Mijna Hadders-Algra,et al.  A systematic review of the effects of early intervention on motor development. , 2005, Developmental medicine and child neurology.

[72]  M. Torrens.  Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .

[73]  S. Wakana,et al.  MRI Atlas of Human White Matter , 2005 .

[74]  Hamid Abrishami Moghaddam,et al.  A neonatal atlas template for spatial normalization of whole-brain magnetic resonance images of newborns: Preliminary results , 2007, NeuroImage.

[75]  M. Miller,et al.  Correction of B0 susceptibility induced distortion in diffusion-weighted images using large-deformation diffeomorphic metric mapping. , 2008, Magnetic resonance imaging.

[76]  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.