Unbiased Age-Appropriate Structural Brain Atlases for Chinese Pediatrics

In magnetic resonance imaging (MRI) studies of children brain development, structural brain atlases usually serve as important references of pediatric population in which individual images are spatially normalized into a common or standard stereotactic space. However, the existing popular children brain atlases (e.g., National Institutes of Health pediatric atlases, NIH-PD atlases) are made mostly based on MR images from Western populations, and are thus insufficient to characterize the brains of Chinese children due to the neuroanatomical differences that are relevant to genetic and environmental factors. By collecting high-quality T1- and T2- weighted MR images from 328 typically developing Chinese children aged from 6 to 12 years old, we created a set of age-appropriate Chinese pediatric (CHN-PD) atlases using an unbiased template construction algorithm. The CHN-PD atlases included the head/brain templates, the symmetric brain template, the gender-specific brain templates and the corresponding tissue probability atlases. Moreover, the atlases contained multiple age-specific templates with a one-year interval. A direct comparison of the CHN-PD and the NIH-PD atlases revealed remarkable anatomical differences bilaterally in the lateral frontal and parietal regions and somatosensory cortex. While applying the CHN-PD atlases to two independent Chinese pediatric datasets (N = 114 and N = 71, respectively), machine-learning regression approaches revealed higher prediction accuracy on brain ages than the usage of NIH-PD atlases. These results suggest that the CHN-PD brain atlases are necessary and important for future typical and atypical developmental studies in Chinese pediatric population. Currently, the CHN-PD atlases have been released on the NITRC website (https://www.nitrc.org/projects/chn-pd).

[1]  Hans J. Johnson,et al.  Advanced Normalization Tools (ANTs) , 2020 .

[2]  Hao Huang,et al.  Baby brain atlases , 2019, NeuroImage.

[3]  G. Gong,et al.  The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features , 2018, NeuroImage.

[4]  John Ashburner,et al.  A comparison of various MRI feature types for characterizing whole brain anatomical differences using linear pattern recognition methods , 2018, NeuroImage.

[5]  D. Louis Collins,et al.  A comparison of publicly available linear MRI stereotaxic registration techniques , 2018, NeuroImage.

[6]  Yonggang Shi,et al.  Brain structure differences between Chinese and Caucasian cohorts: A comprehensive morphometry study , 2018, Human brain mapping.

[7]  D. Louis Collins,et al.  Comparison of different methods for average anatomical templates creation: do we really gain anything from a diffeomorphic framework? , 2018, bioRxiv.

[8]  Julien Cohen-Adad,et al.  PAM50: Unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space , 2018, NeuroImage.

[9]  J. Cole,et al.  Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers , 2017, Trends in Neurosciences.

[10]  Emi Takahashi,et al.  A pediatric structural MRI analysis of healthy brain development from newborns to young adults , 2017, Human brain mapping.

[11]  Efstathios D. Gennatas,et al.  Age-Related Effects and Sex Differences in Gray Matter Density, Volume, Mass, and Cortical Thickness from Childhood to Young Adulthood , 2017, The Journal of Neuroscience.

[12]  Yong He,et al.  Developmental Changes in Topological Asymmetry Between Hemispheric Brain White Matter Networks from Adolescence to Young Adulthood , 2016, Cerebral cortex.

[13]  A. Dale,et al.  Through Thick and Thin: a Need to Reconcile Contradictory Results on Trajectories in Human Cortical Development , 2016, Cerebral cortex.

[14]  Yong He,et al.  Toward Developmental Connectomics of the Human Brain , 2016, Front. Neuroanat..

[15]  John E. Richards,et al.  A database of age-appropriate average MRI templates , 2016, NeuroImage.

[16]  Kuncheng Li,et al.  Construction of brain atlases based on a multi-center MRI dataset of 2020 Chinese adults , 2015, Scientific Reports.

[17]  A. Galaburda,et al.  Asymmetry of White Matter Pathways in Developing Human Brains. , 2015, Cerebral cortex.

[18]  Qiyong Gong,et al.  The construction of MRI brain/head templates for Chinese children from 7 to 16 years of age , 2015, Developmental Cognitive Neuroscience.

[19]  Gui Xue,et al.  Long-term experience with Chinese language shapes the fusiform asymmetry of English reading , 2015, NeuroImage.

[20]  Qiyong Gong,et al.  Comparison of the brain development trajectory between Chinese and U.S. children and adolescents , 2015, Front. Syst. Neurosci..

[21]  Vince D. Calhoun,et al.  Lateralization of resting state networks and relationship to age and gender , 2015, NeuroImage.

[22]  E. Bullmore,et al.  Annual Research Review: Growth connectomics – the organization and reorganization of brain networks during normal and abnormal development , 2014, Journal of child psychology and psychiatry, and allied disciplines.

[23]  Wanze Xie,et al.  Brains for all the ages: structural neurodevelopment in infants and children from a life-span perspective. , 2015, Advances in child development and behavior.

[24]  D. L. Collins,et al.  Framework for integrated MRI average of the spinal cord white and gray matter: The MNI–Poly–AMU template , 2014, NeuroImage.

[25]  Lin Shi,et al.  Intensity and sulci landmark combined brain atlas construction for Chinese pediatric population , 2014, Human brain mapping.

[26]  Tanya M. Evans,et al.  Sex-specific gray matter volume differences in females with developmental dyslexia , 2014, Brain Structure and Function.

[27]  Alan C. Evans,et al.  Cortical thickness asymmetry from childhood to older adulthood , 2013, NeuroImage.

[28]  Abraham Z. Snyder,et al.  Human Connectome Project informatics: Quality control, database services, and data visualization , 2013, NeuroImage.

[29]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[30]  Janaina Mourão Miranda,et al.  PRoNTo: Pattern Recognition for Neuroimaging Toolbox , 2013, Neuroinformatics.

[31]  Swathi P. Iyer,et al.  Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data , 2012, Front. Syst. Neurosci..

[32]  Eileen Luders,et al.  Brain maturation: Predicting individual BrainAGE in children and adolescents using structural MRI , 2012, NeuroImage.

[33]  Anqi Qiu,et al.  Population Differences in Brain Morphology and Microstructure among Chinese, Malay, and Indian Neonates , 2012, PloS one.

[34]  M. Milham,et al.  The ADHD-200 Consortium: A Model to Advance the Translational Potential of Neuroimaging in Clinical Neuroscience , 2012, Front. Syst. Neurosci..

[35]  D. Louis Collins,et al.  Brain templates and atlases , 2012, NeuroImage.

[36]  Carmen E Sanchez,et al.  Age-Specific MRI Templates for Pediatric Neuroimaging , 2012, Developmental neuropsychology.

[37]  Martijn P. van den Heuvel,et al.  Sex steroids and connectivity in the human brain: A review of neuroimaging studies , 2011, Psychoneuroendocrinology.

[38]  C. Lebel,et al.  Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood , 2011, The Journal of Neuroscience.

[39]  Michael W. L. Chee,et al.  Brain Structure in Young and Old East Asians and Westerners: Comparisons of Structural Volume and Cortical Thickness , 2011, Journal of Cognitive Neuroscience.

[40]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

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

[42]  Jonathan D. Power,et al.  Prediction of Individual Brain Maturity Using fMRI , 2010, Science.

[43]  Arthur W. Toga,et al.  The construction of a Chinese MRI brain atlas: A morphometric comparison study between Chinese and Caucasian cohorts , 2010, NeuroImage.

[44]  Stefan Klöppel,et al.  Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters , 2010, NeuroImage.

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

[46]  James R. Booth,et al.  Cultural Constraints on Brain Development: Evidence from a Developmental Study of Visual Word Processing in Mandarin Chinese , 2009, Cerebral cortex.

[47]  Gilles Faÿ,et al.  Características inmunológicas claves en la fisiopatología de la sepsis. Infectio , 2009 .

[48]  Alan C. Evans,et al.  Development of cortical asymmetry in typically developing children and its disruption in attention-deficit/hyperactivity disorder. , 2009, Archives of general psychiatry.

[49]  C. Almli,et al.  Unbiased nonlinear average age-appropriate brain templates from birth to adulthood , 2009, NeuroImage.

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

[51]  M. Eckert,et al.  Asymmetry and Dyslexia , 2008, Developmental neuropsychology.

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

[53]  Zhendong Niu,et al.  A structural–functional basis for dyslexia in the cortex of Chinese readers , 2008, Proceedings of the National Academy of Sciences.

[54]  Michael J. Martinez,et al.  Bias between MNI and Talairach coordinates analyzed using the ICBM‐152 brain template , 2007, Human brain mapping.

[55]  Li Yao,et al.  Brain development in Chinese children and adolescents: a structural MRI study , 2007, Neuroreport.

[56]  Wen Gao,et al.  Image Matching by Normalized Cross-Correlation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[57]  Andrew Simmons,et al.  Influence of X Chromosome and Hormones on Human Brain Development: A Magnetic Resonance Imaging and Proton Magnetic Resonance Spectroscopy Study of Turner Syndrome , 2006, Biological Psychiatry.

[58]  Kewei Chen,et al.  Cerebral asymmetry in children when reading Chinese characters. , 2005, Brain research. Cognitive brain research.

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

[60]  L. Tan,et al.  Biological abnormality of impaired reading is constrained by culture , 2004, Nature.

[61]  Suzanne E. Welcome,et al.  Mapping cortical change across the human life span , 2003, Nature Neuroscience.

[62]  A. Toga,et al.  Mapping brain asymmetry , 2003, Nature Reviews Neuroscience.

[63]  David A. Ziegler,et al.  Abnormal asymmetry in language association cortex in autism , 2002, Annals of neurology.

[64]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

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

[66]  Karl J. Friston,et al.  Cerebral Asymmetry and the Effects of Sex and Handedness on Brain Structure: A Voxel-Based Morphometric Analysis of 465 Normal Adult Human Brains , 2001, NeuroImage.

[67]  Michael E. Tipping Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..

[68]  M. Keshavan,et al.  Sex differences in brain maturation during childhood and adolescence. , 2001, Cerebral cortex.

[69]  Nicholas Ayache,et al.  Three-dimensional multimodal brain warping using the Demons algorithm and adaptive intensity corrections , 2001, IEEE Transactions on Medical Imaging.

[70]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevan e Ve tor Ma hine , 2001 .

[71]  M. Unser,et al.  Interpolation revisited [medical images application] , 2000, IEEE Transactions on Medical Imaging.

[72]  David J. Hawkes,et al.  Voxel Similarity Measures for 3D Serial MR Brain Image Registration , 2000, IEEE Trans. Medical Imaging.

[73]  M. Unser,et al.  Interpolation Revisited , 2000, IEEE Trans. Medical Imaging.

[74]  L G Nyúl,et al.  On standardizing the MR image intensity scale , 1999, Magnetic resonance in medicine.

[75]  N. Ayache,et al.  Multimodal Brain Warping Using the Demons Algorithm and Adaptative Intensity Corrections , 1999 .

[76]  Alan C. Evans,et al.  Brain development during childhood and adolescence: a longitudinal MRI study , 1999, Nature Neuroscience.

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

[78]  Jean Meunier,et al.  Automatic Computation of Average Brain Models , 1998, MICCAI.

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

[80]  A. Beaton,et al.  The Relation of Planum Temporale Asymmetry and Morphology of the Corpus Callosum to Handedness, Gender, and Dyslexia: A Review of the Evidence , 1997, Brain and Language.

[81]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[82]  D. Louis Collins,et al.  Automatic 3‐D model‐based neuroanatomical segmentation , 1995 .

[83]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[84]  B Horwitz,et al.  X-chromosome effects on female brain: a magnetic resonance imaging study of Turner's syndrome , 1993, The Lancet.