This application is in response to the RFA-MH-16-160, entitled ?Lifespan Human Connectome Project (HCP): Baby Connectome?. Investigators at The University of North Carolina at Chapel Hill (UNC) and The University of Minnesota (UMN) will join forces to accomplish the goals outlined by this RFA. The team at UNC has over 10 years of experience in recruiting and imaging typically developing and at-risk children, scanning over 1000 children from birth to five years1-40. Well established infrastructure at the Biomedical Research Imaging Center (BRIC) at UNC and Center for Magnetic Resonance Research (CMRR) at UMN are in place to recruit and retain pediatric subjects and facilitate the coordination of pediatric imaging studies. Our past and ongoing studies for imaging children (birth ? five years of age) without sedation have achieved an overall success rate of 81% and attrition rate of 29.3%. Our track record demonstrates that we possess the critical and essential components to successfully conduct longitudinal pediatric imaging studies focusing on early brain development, a critically-important aspect of this RFA. Our ability to recruit, retain, and image non-sedated, typically developing children is further strengthened by our image analysis team, which has developed novel image analysis tools specifically for early brain development. The expertise at UNC is complementary to and strengthened by the expertise of the team at UMN. The CMRR at UMN has been one of the leading groups in the HCP project and has developed novel MR imaging approaches to dramatically shorten data acquisition time. Furthermore, the team at UMN has extensive experience in behavioral and cognitive studies of early child development. Together, our combined team is well positioned to accomplish the goals of this RFA. To this end, a total of 500 typically developing children between birth and five years of age will be recruited across two data collection sites in a sequential cohort, accelerated longitudinal study design. The participants are divided into two main groups, longitudinal (n=285) and cross-sectional (n=215) groups, respectively. This hybrid longitudinal and cross-sectional design enables detailed characterization of early brain development from both brain structural/functional using MRI and behavioral aspects using behavioral assessments. All of the acquired images and behavioral assessments will undergo extensive quality assurance and control processes to ensure that high quality data is obtained and transferred to the Central Connectome Facility at Washington University. In addition, we will integrate novel image analysis tools, developed by our team onto the existing HCP pipelines.

Public Health Relevance

This application is in response to the RFA-MH-16-160, entitled ?Lifespan Human Connectome Project (HCP): Baby Connectome?. Investigators at The University of North Carolina at Chapel Hill (UNC) and The University of Minnesota (UMN) will join forces to accomplish the goals outlined by this RFA. The team at UNC has over 10 years of experience in recruiting and imaging typically developing and at-risk children, scanning over 1000 children from birth to five years. Well established infrastructure at the Biomedical Research Imaging Center (BRIC) at UNC and Center for Magnetic Resonance Research (CMRR) at UMN are in place to recruit and retain pediatric subjects and facilitate the coordination of pediatric imaging studies. A total of 500 typically developing children between birth and five years of age will be recruited across two data collection sites in a sequential cohort, accelerated longitudinal study design. The participants are divided into two main groups, longitudinal (n=285) and cross-sectional (n=215) groups, respectively. This hybrid longitudinal and cross-sectional design enables detailed characterization of early brain development from both brain structural/functional and behavioral aspects, balances between advantages offered by a longitudinal design and attribution rate, and accommodates the relatively short funding duration. Enrollment will include an equal proportion of males and females. The racial/ethnic diversity of the sample will reflect US Census data. Our team has also developed novel imaging analysis tools capable of providing quantitative measures of early brain development. We will integrate all of these novel pediatric imaging analysis tools onto HCP pipelines. All of the acquired image and behavioral data will be transferred to the Central Connectome Facility at Washington University.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project--Cooperative Agreements (U01)
Project #
3U01MH110274-03S1
Application #
9680173
Study Section
Special Emphasis Panel (ZMH1)
Program Officer
Kim, Douglas Sun-IL
Project Start
2016-09-01
Project End
2020-05-31
Budget Start
2018-08-15
Budget End
2019-05-31
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
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
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
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
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
Kim, Jaeil; Chen, Geng; Lin, Weili et al. (2017) Graph-Constrained Sparse Construction of Longitudinal Diffusion-Weighted Infant Atlases. Med Image Comput Comput Assist Interv 10433:49-56
Duan, Dingna; Xia, Shunren; Meng, Yu et al. (2017) Exploring Gyral Patterns of Infant Cortical Folding based on Multi-view Curvature Information. Med Image Comput Comput Assist Interv 10433:12-20
Hu, Shunbo; Wei, Lifang; Gao, Yaozong et al. (2017) Learning-based deformable image registration for infant MR images in the first year of life. Med Phys 44:158-170
Chen, Xiaobo; Zhang, Han; Zhang, Lichi et al. (2017) Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification. Hum Brain Mapp 38:5019-5034
Rekik, Islem; Li, Gang; Yap, Pew-Thian et al. (2017) Joint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI. Neuroimage 152:411-424

Showing the most recent 10 out of 21 publications