How does our healthy brain grow? The simplicity of this question masks the highly complex and multifaceted nature of human neurodevelopment. Across fetal development, infancy and early childhood, our brain undergoes remarkable change in response to diverse genetic and environmental pressures. Processes including myelination and synaptogenesis are at their peak across the first 2-3yrs of life, contributing to the emergence of nearly all cognitive and behavioral skills, and laying the foundation for future learning and academic success. While the importance of this early life period to life-long mental health is widely recognized, important questions remain regarding the influences that shape brain growth and cognitive development: 1. How is brain growth altered by specific pre- and post-natal environmental or genetic factors; 2. How are patterns of brain growth associated with, and predictive of, emerging cognitive and behavioral abilities; and 3. How are these brain-behavior relationships influenced by modifiable factors experienced throughout childhood? This proposal seeks to address these fundamental questions using a unique longitudinal neuroimaging dataset that spans fetal, infant and childhood development (22wks to 10yrs of age) and contains more than 2500 measures from ~650 children with diverse birth outcomes, environmental exposures and genotypes. Alongside multimodal MRI, extensive neurocognitive, sociodemographic, physical health, family and medical history, anthropometric, nutrition, sleep, and biospecimen (DNA, oral and fecal microbiome, and shed deciduous teeth) data have been collected on each child and updated at biannual or annual visits. Using this extensive dataset, we aim to address our central hypothesis: that intrauterine events, early life environmental exposures and genetic factors influence cognitive/behavioral outcomes by altering patterns of brain growth. We will examine this hypothesis in three incremental steps. First, we will demonstrate that intrauterine events, early life exposures and specific genetic polymorphisms give rise to altered trajectories of brain development. Next, we will show that differing patterns of neurodevelopment are associated with varying cognitive and behavioral profiles. Finally, we will take a holistic approach and examine how modifiable factors, specifically child nutrition and obesity, sleep health, and our microbiome may mediate these brain-cognition/behavior relationships within the context of related pre- and post-natal environmental and genetic influences. This marks a distinct departure from prior studies, which have typically examined these factors in relative isolation and using cross-sectional study designs. Over the course of this proposal, an additional 500 children will be recruited and 3000 longitudinal measures acquired (bringing the totals to ~1100 children and ~6100 measures). This represents the largest pediatric neuroimaging database that spans birth to 10yrs, and the only that includes fetal and infant measures on the same children. This study and dataset, therefore, represent an unprecedented opportunity to examine the diverse influences, and their complex interactions, that shape brain growth across a fundamental yet understudied period of development.
Improved understanding and characterization of the intrauterine, environmental and genetic influences that shape early infant and childhood brain and cognitive development is a critical step towards uncovering the origins of psychiatric, behavioral, and intellectual disorders. This study aims to holistically examine the complex cascade of early life environmental, genetic and modifiable factors that influence brain development using a uniquely large and rich longitudinal neuroimaging dataset than spans fetal development, infancy and early childhood. Successful results, therefore, address pressing public health concerns and will have lasting impacts on educational polices and clinical standards of care.
|Lebel, Catherine; Deoni, Sean (2018) The development of brain white matter microstructure. Neuroimage 182:207-218|
|Gabard-Durnam, L J; O'Muircheartaigh, J; Dirks, H et al. (2018) Human amygdala functional network development: A cross-sectional study from 3 months to 5 years of age. Dev Cogn Neurosci 34:63-74|
|Ji, Hao; Müller, Hans-Georg; Papadopoulos, Nikos T et al. (2017) Quantifying functionals of age distributions in the wild by solving an operator equation. J Math Biol 75:973-984|
|Remer, Justin; Croteau-Chonka, Elise; Dean 3rd, Douglas C et al. (2017) Quantifying cortical development in typically developing toddlers and young children, 1-6 years of age. Neuroimage 153:246-261|
|Petersen, Alexander; Zhao, Jianyang; Carmichael, Owen et al. (2016) Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks. Brain Connect 6:540-7|
|Carey, James R; Liedo, Pablo; Xu, Cong et al. (2016) Diet Shapes Mortality Response to Trauma in Old Tephritid Fruit Flies. PLoS One 11:e0158468|