Neurodevelopmental MRI brain templates for children from 2 weeks to 4 years of age.

Spatial normalization and segmentation of pediatric brain magnetic resonance images (MRI) data with adult templates may impose biases and limitations in pediatric neuroimaging work. To remedy this issue, we created a single database made up of a series of pediatric, age-specific MRI average brain templates. These average, age-specific templates were constructed from brain scans of individual children obtained from two sources: (1) the NIH MRI Study of Normal Brain Development and (2) MRIs from University of South Carolina's McCausland Brain Imaging Center. Participants included young children enrolled at ages ranging from 8 days through 4.3 years of age. A total of 13 age group cohorts spanning the developmental progression from birth through 4.3 years of age were used to construct age-specific MRI brain templates (2 weeks, 3, 4.5, 6, 7.5, 9, 12, 15, 18 months, 2, 2.5, 3, 4 years). Widely used processing programs (FSL, SPM, and ANTS) extracted the brain and constructed average templates separately for 1.5T and 3T MRI volumes. The resulting age-specific, average templates showed clear changes in head and brain size across ages and between males and females, as well as changes in regional brain structural characteristics (e.g., myelin development). This average brain template database is available via our website (http://jerlab.psych.sc.edu/neurodevelopmentalmridatabase) for use by other researchers. Use of these age-specific, average pediatric brain templates by the research community will enhance our ability to gain a clearer understanding of the early postnatal development of the human brain in health and in disease.

[1]  Alan C. Evans,et al.  Total and regional brain volumes in a population-based normative sample from 4 to 18 years: the NIH MRI Study of Normal Brain Development. , 2012, Cerebral cortex.

[2]  J. Gilmore,et al.  Infant Brain Atlases from Neonates to 1- and 2-Year-Olds , 2011, PloS one.

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

[4]  J. Richards,et al.  Infant attention and visual preferences: converging evidence from behavior, event-related potentials, and cortical source localization. , 2010, Developmental psychology.

[5]  John E Richards,et al.  The development of attention to simple and complex visual stimuli in infants: Behavioral and psychophysiological measures. , 2010, Developmental review : DR.

[6]  Nicholas Lange,et al.  Associations Between IQ, Total and Regional Brain Volumes, and Demography in a Large Normative Sample of Healthy Children and Adolescents , 2010, Developmental neuropsychology.

[7]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

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

[9]  Simon K. Warfield,et al.  Automatic segmentation of newborn brain MRI , 2009, NeuroImage.

[10]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[11]  John E Richards,et al.  Cortical Source Localization of Infant Cognition , 2009, Developmental neuropsychology.

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

[13]  Carlo Pierpaoli,et al.  T2 relaxometry of normal pediatric brain development , 2009, Journal of magnetic resonance imaging : JMRI.

[14]  Scott Holland,et al.  Infant brain probability templates for MRI segmentation and normalization , 2008, NeuroImage.

[15]  D. Louis Collins,et al.  Human Brain Myelination from Birth to 4.5 Years , 2008, MICCAI.

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

[17]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[18]  Daniel Rueckert,et al.  Automatic segmentation and reconstruction of the cortex from neonatal MRI , 2007, NeuroImage.

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

[20]  Judith Rumsey,et al.  The NIH MRI study of normal brain development: Performance of a population based sample of healthy children aged 6 to 18 years on a neuropsychological battery , 2007, Journal of the International Neuropsychological Society.

[21]  J. Allsop,et al.  Quantification of Deep Gray Matter in Preterm Infants at Term-Equivalent Age Using Manual Volumetry of 3-Tesla Magnetic Resonance Images , 2007, Pediatrics.

[22]  Brain Development Cooperative Group,et al.  The NIH MRI study of normal brain development (Objective-2): Newborns, infants, toddlers, and preschoolers , 2007, NeuroImage.

[23]  Rebecca C. Knickmeyer,et al.  Regional Gray Matter Growth, Sexual Dimorphism, and Cerebral Asymmetry in the Neonatal Brain , 2007, The Journal of Neuroscience.

[24]  F. Rybicki,et al.  Regional Brain Development in Serial Magnetic Resonance Imaging of Low-Risk Preterm Infants , 2006, Pediatrics.

[25]  Alan C. Evans,et al.  The NIH MRI study of normal brain development , 2006, NeuroImage.

[26]  A. Evans,et al.  Large-scale morphometric analysis of neuroanatomy and neuropathology , 2005, Anatomy and Embryology.

[27]  John H. Gilmore,et al.  Automatic segmentation of MR images of the developing newborn brain , 2005, Medical Image Anal..

[28]  A. Barkovich Magnetic resonance techniques in the assessment of myelin and myelination , 2005, Journal of Inherited Metabolic Disease.

[29]  Richard N Aslin,et al.  Near-infrared spectroscopy for functional studies of brain activity in human infants: promise, prospects, and challenges. , 2005, Journal of biomedical optics.

[30]  Guido Gerig,et al.  Unbiased diffeomorphic atlas construction for computational anatomy , 2004, NeuroImage.

[31]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[32]  E. Darcy Burgund,et al.  Comparison of functional activation foci in children and adults using a common stereotactic space , 2003, NeuroImage.

[33]  Abraham Z. Snyder,et al.  The Feasibility of a Common Stereotactic Space for Children and Adults in fMRI Studies of Development , 2002, NeuroImage.

[34]  Marko Wilke,et al.  Assessment of spatial normalization of whole‐brain magnetic resonance images in children , 2002, Human brain mapping.

[35]  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.

[36]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[37]  William Davis Gaillard,et al.  Developmental Aspects of Pediatric fMRI: Considerations for Image Acquisition, Analysis, and Interpretation , 2001, NeuroImage.

[38]  John C. Mazziotta,et al.  A Probabilistic Atlas and Reference System for the Human Brain , 2001 .

[39]  O. Muzik,et al.  Statistical Parametric Mapping: Assessment of Application in Children , 2000, NeuroImage.

[40]  Jean Meunier,et al.  Average Brain Models: A Convergence Study , 2000, Comput. Vis. Image Underst..

[41]  J. Ashburner,et al.  Nonlinear spatial normalization using basis functions , 1999, Human brain mapping.

[42]  Terry M. Peters,et al.  3D statistical neuroanatomical models from 305 MRI volumes , 1993, 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference.

[43]  J. Talairach,et al.  Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging , 1988 .