Neuroimaging studies of the dynamic and critical cortex development during early postnatal stages would greatly increase our very limited knowledge on normal early brain development, and also provide important insights into neurodevelopmental origins and abnormal trajectories of neuropsychiatric disorders. In neuroimaging studies, cortical surface-based brain atlases play fundamental and increasingly important roles for normalization, analysis, visualization, and comparison of the highly-folded cortex across different studies. Existing cortical surface atlases developed for adults and neonates are problematic when used for studying the dynamic developing cortex in infants, due to dramatic differences of cortical size, shape, and folding degree. Meanwhile, parcellations in these cortical surfaces atlases based on the sulcal-gyral landmarks are also problematic when used for localization of functional regions, due to poor matching of sulcal-gyral patterns with the microstructural borders. To address all these limitations, we aim to construct longitudinally-consistent, age-specific population representative shapes of multi-scale cortical folding at 1, 3, 6, 9, 12, 18 and 24 months of age (Aim 1). To ensure the longitudinal consistency of 4D atlases, we will capitalize on within- subject longitudinal constraints to establish consistent inter-subjec cortical correspondences. To capture the multi-scale nature of cortical folding, we will characterize cortical folding by using spherical wavelet decomposition of curvature information. To ensure the clarity and representativeness of cortical folding in atlases, we will develop a sparse representation method to adaptively integrate individuals' cortical folding. Next, we aim to parcellate 4D infant cortical surface atlases into distinct regions based on the dynamic developmental trajectories of cortical thickness, surface area, and cortical local gyrification (Ai 2). The dynamic cortical developmental trajectories indicate the underlying changes of microstructures, which essentially determine the molecular organization and functional principles of the cortex, and thus can better define the developmentally, microstructurally, and functionally distinct regions than the conventional sulcalgyral landmarks. Finally, we will package our 4D infant cortical surface atlases and release them freely to the community.

Public Health Relevance

This project aims to create the first longitudinal four-dimensional (4D) infant cortical surface atlases to enable the accurate neuroimaging mapping of the dynamic infant brain development. In particular, we will construct longitudinally-consistent, age-specific population representative shapes of multi-scale cortical folding at 1, 3, 6, 9, 12, 18 and 24 months of age. Then, we will parcellate the 4D infant cortical surface atlases into distinct regions based on the dynamic developmental trajectories of infant cortical attributes.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21MH108914-02
Application #
9249106
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Zhan, Ming
Project Start
2016-04-01
Project End
2018-03-31
Budget Start
2017-04-01
Budget End
2018-03-31
Support Year
2
Fiscal Year
2017
Total Cost
$205,200
Indirect Cost
$70,200
Name
University of North Carolina Chapel Hill
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
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Xia, Jing; Zhang, Caiming; Wang, Fan et al. (2018) FETAL CORTICAL PARCELLATION BASED ON GROWTH PATTERNS. Proc IEEE Int Symp Biomed Imaging 2018:696-699
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Wang, Li; Li, Gang; Adeli, Ehsan et al. (2018) Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism. Hum Brain Mapp 39:2609-2623
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Xia, Jing; Zhang, Caiming; Wang, Fan et al. (2018) A COMPUTATIONAL METHOD FOR LONGITUDINAL MAPPING OF ORIENTATION-SPECIFIC EXPANSION OF CORTICAL SURFACE AREA IN INFANTS. Proc IEEE Int Symp Biomed Imaging 2018:683-686
Zhang, Changqing; Adeli, Ehsan; Wu, Zhengwang et al. (2018) INFANT BRAIN DEVELOPMENT PREDICTION WITH LATENT PARTIAL MULTI-VIEW REPRESENTATION LEARNING. Proc IEEE Int Symp Biomed Imaging 2018:1048-1051
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Meng, Yu; Li, Gang; Wang, Li et al. (2018) Discovering cortical sulcal folding patterns in neonates using large-scale dataset. Hum Brain Mapp :

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