Schizophrenia is a debilitating mental disorder with early neurodevelopmental origins. Genetic high-risk infants born to schizophrenic mothers are ideal candidates for improving our understanding of developmental origins and abnormal trajectories in schizophrenia. The University of North Carolina at Chapel Hill has collected a unique cohort of longitudinal MRI dataset of typically developing infants and also infants at high-risk for schizophrenia in their first two years of life, which allows us to track dynamic developmental trajectories of the cortex in both typical and high-risk infants during this critical stage. Cortical surface-based analysis of neuroimaging data is playing an increasingly critical role in adult schizophrenia studies, and has revealed widespread structural and functional abnormalities. However, existing cortical surface-based analysis tools developed for adult brains are ill-suited for infant studies, due to their dramatic differences in image contrast, cortical sie, shape, and folding degree. Moreover, independent processing of image for each time-point in the longitudinal infant studies leads to temporally inconsistent and inaccurate measurements. To become an independent investigator on pediatric neuroimaging research, the candidate proposes in this K01 application to receive training in developmental neurobiology and neurodevelopmental disorders, advanced biostatistics, and infant MR imaging techniques. These training activities will greatly augment the candidate's background in infant neuroimaging mapping and establish a solid foundation for his long-term goal of being a leading researcher on early brain development study. In the research plan, the candidate will create a unique suite of infant-specific, 4D cortical surface based neuroimaging analysis tools that enable accurate characterization of early brain development in both typically developing infants and infants at high-risk for schizophrenia. Specifically, a method for consistent parcellation of 4D infant corticl surface will be developed (Aim 1). Then, a method for brain-size-adaptive measurement of 4D infant cortical regional and local gyrification development will be created (Aim 2). After that, th first 4D infant cortical surface atlases will be constructed, based on the dynamic developmental trajectories of the cortex (Aim 3). These methods and atlases will be then used to characterize the dynamic cortex developmental trajectories in both typical infants and high-risk infants (Aim 4). Results from this research will help to identify early biomarkers of risk for schizophrenia and to design targeted preemptive intervention strategies. All created tools and atlases will be integrated and released freely to the public, such as NITRC (www.nitrc.org).
This project aims to create a unique suite of infant-specific, 4D cortical surface based neuroimaging analysis tools for accurate characterization of early dynamic brain development in both typically developing infants and infants at high-risk for schizophrenia. In particular, we will develop novel methods for longitudinally-consistent 4D parcellation and measurement of regional/local gyrification of infant cortical surfaces. Then, we will construct the first (longitudinal) 4D infant cortical surface atlases, based on the dynamic developmental trajectories of the cortex. Finally, we will quantitatively characterize and compare the dynamic cortex development between typical infants and high-risk infants.
| 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 |
| Li, Guannan; Liu, Mingxia; Sun, Quansen et al. (2018) Early Diagnosis of Autism Disease by Multi-channel CNNs. Mach Learn Med Imaging 11046:303-309 |
| Meng, Yu; Li, Gang; Wang, Li et al. (2018) Discovering cortical sulcal folding patterns in neonates using large-scale dataset. Hum Brain Mapp : |
| Rekik, Islem; Li, Gang; Lin, Weili et al. (2018) ESTIMATION OF SHAPE AND GROWTH BRAIN NETWORK ATLASES FOR CONNECTOMIC BRAIN MAPPING IN DEVELOPING INFANTS. Proc IEEE Int Symp Biomed Imaging 2018:985-989 |
| Wu, Zhengwang; Li, Gang; Wang, Li et al. (2018) CONSTRUCTION OF SPATIOTEMPORAL NEONATAL CORTICAL SURFACE ATLASES USING A LARGE-SCALE DATASET. Proc IEEE Int Symp Biomed Imaging 2018:1056-1059 |
| Wang, Fan; Lian, Chunfeng; Xia, Jing et al. (2018) CONSTRUCTION OF SPATIOTEMPORAL INFANT CORTICAL SURFACE ATLAS OF RHESUS MACAQUE. Proc IEEE Int Symp Biomed Imaging 2018:704-707 |
| He, Ping; Xu, Xiaohua; Zhang, Han et al. (2018) Spatiotemporal Analysis of Developing Brain Networks. Front Neuroinform 12:48 |
| Xia, Jing; Wang, Fan; Meng, Yu et al. (2018) A computational method for longitudinal mapping of orientation-specific expansion of cortical surface in infants. Med Image Anal 49:46-59 |
| 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 |
Showing the most recent 10 out of 48 publications
