Developmental topography of cortical thickness during infancy

Significance During the first 2 postnatal years, the human brain undergoes dynamic growth and shows rapid expansions in behavioral and cognitive abilities. Charting the developmental patterns of cortical thickness in healthy infants is important for understanding many neurodevelopmental disorders, which unfortunately remains unexplored. Therefore, we investigate the infantile developmental regionalization of cortical thickness, which describes regions naturally formed during the dynamic development of cortical thickness and differs markedly from conventional anatomical parcellations. Meanwhile, this study delineates the inverted U-shaped developmental trajectory and peak age of cortical thickness, which clarifies the previous ambiguity on the peaking time of cortical thickness during early brain development. During the first 2 postnatal years, cortical thickness of the human brain develops dynamically and spatially heterogeneously and likely peaks between 1 and 2 y of age. The striking development renders this period critical for later cognitive outcomes and vulnerable to early neurodevelopmental disorders. However, due to the difficulties in longitudinal infant brain MRI acquisition and processing, our knowledge still remains limited on the dynamic changes, peak age, and spatial heterogeneities of cortical thickness during infancy. To fill this knowledge gap, in this study, we discover the developmental regionalization of cortical thickness, i.e., developmentally distinct regions, each of which is composed of a set of codeveloping cortical vertices, for better understanding of the spatiotemporal heterogeneities of cortical thickness development. We leverage an infant-dedicated computational pipeline, an advanced multivariate analysis method (i.e., nonnegative matrix factorization), and a densely sampled longitudinal dataset with 210 serial MRI scans from 43 healthy infants, with each infant being scheduled to have 7 longitudinal scans at around 1, 3, 6, 9, 12, 18, and 24 mo of age. Our results suggest that, during the first 2 y, the whole-brain average cortical thickness increases rapidly and reaches a plateau at about 14 mo of age and then decreases at a slow pace thereafter. More importantly, each discovered region is structurally and functionally meaningful and exhibits a distinctive developmental pattern, with several regions peaking at varied ages while others keep increasing in the first 2 postnatal years. Our findings provide valuable references and insights for early brain development.

[1]  Clifford H. Spiegelman,et al.  Testing the Goodness of Fit of a Linear Model via Nonparametric Regression Techniques , 1990 .

[2]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[3]  Yaozong Gao,et al.  Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation , 2014, NeuroImage.

[4]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[5]  Carolyn R. Bertozzi,et al.  Methods and Applications , 2009 .

[6]  J. Gilmore,et al.  Structural and Maturational Covariance in Early Childhood Brain Development , 2016, Cerebral cortex.

[7]  R. C. Macridis A review , 1963 .

[8]  Alan C. Evans,et al.  Early brain development in infants at high risk for autism spectrum disorder , 2017, Nature.

[9]  Erkki Oja,et al.  Linear and Nonlinear Projective Nonnegative Matrix Factorization , 2010, IEEE Transactions on Neural Networks.

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

[11]  J. Gilmore,et al.  Mapping Longitudinal Development of Local Cortical Gyrification in Infants from Birth to 2 Years of Age , 2014, The Journal of Neuroscience.

[12]  Jianming Ye On Measuring and Correcting the Effects of Data Mining and Model Selection , 1998 .

[13]  Dinggang Shen,et al.  Automatic segmentation of neonatal images using convex optimization and coupled level sets , 2011, NeuroImage.

[14]  M. Sherman,et al.  A comparison between bootstrap methods and generalized estimating equations for correlated outcomes in generalized linear models , 1997 .

[15]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[16]  Dinggang Shen,et al.  journal homepage: www.elsevier.com/locate/ynimg , 2022 .

[17]  A. Mikami,et al.  Developmental trajectory of the corpus callosum from infancy to the juvenile stage: Comparative MRI between chimpanzees and humans , 2017, PloS one.

[18]  M. Fox,et al.  Individual Variability in Functional Connectivity Architecture of the Human Brain , 2013, Neuron.

[19]  Armin Raznahan,et al.  How Does Your Cortex Grow? , 2011, The Journal of Neuroscience.

[20]  Alan C. Evans,et al.  Intellectual ability and cortical development in children and adolescents , 2006, Nature.

[21]  Christos Davatzikos,et al.  Differential cortical microstructural maturation in the preterm human brain with diffusion kurtosis and tensor imaging , 2019, Proceedings of the National Academy of Sciences.

[22]  Yaozong Gao,et al.  LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images , 2014, NeuroImage.

[23]  M. Miller,et al.  Anatomical Characterization of Human Fetal Brain Development with Diffusion Tensor Magnetic Resonance Imaging , 2009, The Journal of Neuroscience.

[24]  X. Lin,et al.  Inference in generalized additive mixed modelsby using smoothing splines , 1999 .

[25]  J. Gilmore,et al.  Dynamic Development of Regional Cortical Thickness and Surface Area in Early Childhood. , 2015, Cerebral cortex.

[26]  J. Gilmore,et al.  Spatial Patterns, Longitudinal Development, and Hemispheric Asymmetries of Cortical Thickness in Infants from Birth to 2 Years of Age , 2015, The Journal of Neuroscience.

[27]  Dinggang Shen,et al.  Environmental Influences on Infant Cortical Thickness and Surface Area , 2019, Cerebral cortex.

[28]  Dinggang Shen,et al.  The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development , 2019, NeuroImage.

[29]  John H. Gilmore,et al.  Imaging structural and functional brain development in early childhood , 2018, Nature Reviews Neuroscience.

[30]  Bruce Fischl,et al.  Genetic topography of brain morphology , 2013, Proceedings of the National Academy of Sciences.

[31]  Dinggang Shen,et al.  4D Multi-Modality Tissue Segmentation of Serial Infant Images , 2012, PloS one.

[32]  Siqi Wu,et al.  Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks , 2016, Proceedings of the National Academy of Sciences.

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

[34]  Gang Li,et al.  Learning‐based subject‐specific estimation of dynamic maps of cortical morphology at missing time points in longitudinal infant studies , 2016, Human brain mapping.

[35]  D. B. Leitch,et al.  Neuron densities vary across and within cortical areas in primates , 2010, Proceedings of the National Academy of Sciences.

[36]  Dinggang Shen,et al.  Consistent reconstruction of cortical surfaces from longitudinal brain MR images , 2012, NeuroImage.

[37]  Dinggang Shen,et al.  Construction of 4D high-definition cortical surface atlases of infants: Methods and applications , 2015, Medical Image Anal..

[38]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[39]  Hongtu Zhu,et al.  Intersubject Variability of and Genetic Effects on the Brain's Functional Connectivity during Infancy , 2014, The Journal of Neuroscience.

[40]  Jay N. Giedd,et al.  Motion Artifact in Magnetic Resonance Imaging: Implications for Automated Analysis , 2002, NeuroImage.

[41]  Alan C. Evans,et al.  Trajectories of cortical thickness maturation in normal brain development — The importance of quality control procedures , 2016, NeuroImage.

[42]  Dinggang Shen,et al.  Measuring the dynamic longitudinal cortex development in infants by reconstruction of temporally consistent cortical surfaces , 2014, NeuroImage.

[43]  J. Gilmore,et al.  Mapping region-specific longitudinal cortical surface expansion from birth to 2 years of age. , 2013, Cerebral cortex.

[44]  R. Gur,et al.  Patterns of coordinated cortical remodeling during adolescence and their associations with functional specialization and evolutionary expansion , 2017, Proceedings of the National Academy of Sciences.

[45]  Alan C. Evans,et al.  Testosterone-related cortical maturation across childhood and adolescence. , 2012, Cerebral cortex.

[46]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

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

[48]  John W. Harwell,et al.  Similar patterns of cortical expansion during human development and evolution , 2010, Proceedings of the National Academy of Sciences.

[49]  Suzanne E. Welcome,et al.  Longitudinal Mapping of Cortical Thickness and Brain Growth in Normal Children , 2022 .

[50]  Dafnis Batalle,et al.  Annual Research Review: Not just a small adult brain: understanding later neurodevelopment through imaging the neonatal brain , 2017, Journal of child psychology and psychiatry, and allied disciplines.

[51]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[52]  Thomas A. Severini,et al.  Diagnostics for Assessing Regression Models , 1991 .

[53]  Elizabeth A. Molloy,et al.  Motion artifact in magnetic resonance imaging: Implications for automated analysis of clinical samples , 2000, NeuroImage.

[54]  Alan C. Evans,et al.  Changes in thickness and surface area of the human cortex and their relationship with intelligence. , 2015, Cerebral cortex.

[55]  Dinggang Shen,et al.  Computational neuroanatomy of baby brains: A review , 2019, NeuroImage.

[56]  J. Gilmore,et al.  Discovering cortical sulcal folding patterns in neonates using large‐scale dataset , 2018, Human brain mapping.