The first years of life are the most dynamic and perhaps the most critical phase of postnatal brain development. Abnormalities in early childhood brain development have been implicated in neurodevelopmental disorders, including autism and schizophrenia, though very little is known about this crucial time period. In the previous grant cycle, we studied brain development in a unique cohort of normal children longitudinally followed and scanned at birth, 1 and 2 years of age, including 386 neonates, 297 one year olds and 251 two year olds. In addition, we have developed the innovative image analysis tools necessary to study brain development in very young children. Our studies found rapid gray matter development and white matter maturation in the first 2 years of life, with marked regional differences in the cortex and in white matter tracts, consistent with temporal patterns of sensory/motor and higher integrative function development. We also found significant relationships between white matter maturation and working memory. Preliminary data indicates that regional gray matter volume and white matter tract diffusivity in neonates is predictive of subsequent gray matter volume and white matter structure at ages 1 and 2 years. These findings indicate that neonatal brain structure, reflective of prenatal brain development and the rapid growth trajectories of the first two years of life, likely play an important role in longer trm outcome. In this competitive renewal, we will extend follow-up of our cohort to 6 years of age and focus on structure/function relationships and the predictive value of early brain structure for later childhood brain structure and cognitive function. MRIs, including structural and diffusion tensor imaging, will be done at ages 1, 2, 4, and 6 years. Cognitive development, including general cognitive function and working memory will also be assessed. Developmental trajectories of cortical gray matter (including cortical thickness and surface area) and white matter (including tract-based spatial statistics and quantitative tractography) will be studied. We predict that neonatal brain structure and developmental trajectories in the first two years of life are critical for, and predictive of, subsequent structural and cognitive development. Relevance New knowledge gained in this study will provide a dramatically improved framework for understanding abnormalities of early childhood brain development in neurodevelopmental disorders such as autism and schizophrenia and will provide the fundamental information critical for developing preventative strategies for these disorders.
Abnormalities in early childhood brain development have been implicated in neurodevelopmental disorders, including autism and schizophrenia, though very little is known about this critical period of human brain development. We will study early childhood brain development from birth to age 6 years in a large group of children using MRI. New knowledge gained in this study will provide a dramatically improved framework for understanding abnormalities of early childhood brain development in neurodevelopmental disorders provide the basic information critical to developing preventative strategies for these disorders.
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|Li, Gang; Nie, Jingxin; Wang, Li et al. (2014) Measuring the dynamic longitudinal cortex development in infants by reconstruction of temporally consistent cortical surfaces. Neuroimage 90:266-79|
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|Knickmeyer, Rebecca C; Meltzer-Brody, Samantha; Woolson, Sandra et al. (2014) Rate of Chiari I malformation in children of mothers with depression with and without prenatal SSRI exposure. Neuropsychopharmacology 39:2611-21|
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|Meng, Yu; Li, Gang; Lin, Weili et al. (2014) Spatial distribution and longitudinal development of deep cortical sulcal landmarks in infants. Neuroimage 100:206-18|
|Chen, Yasheng; Zhu, Hongtu; An, Hongyu et al. (2014) More insights into early brain development through statistical analyses of eigen-structural elements of diffusion tensor imaging using multivariate adaptive regression splines. Brain Struct Funct 219:551-69|
|Yuan, Ying; Gilmore, John H; Geng, Xiujuan et al. (2014) FMEM: functional mixed effects modeling for the analysis of longitudinal white matter Tract data. Neuroimage 84:753-64|
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