The primary function of the Administrative Core is to ensure that the specific aims andscientific objectives of the Center and its projects are carried out efficiently and successfully.Responsibility for this function falls primarily to the components and personnel of the AdministrativeCore (i.e., the Director, Associate Director, Steering Committee). Several mechanisms have beenestablished that promote the integration of Center research, achieve cross-fertilization andsynergies between labs and projects, and ensure that information from one project informs thedirections and interpretation of results in other projects. The goal is to ensure that the Center ismore than the sum of its parts and provides added value beyond that which the individual projectswould achieve if they were carried out independently (although it is highly unlikely that without aCenter they would even be done). These mechanisms include monthly seminars, a monthly journalclub, bi-annual scientific reviews by a National Scientific Advisory Committee, monthly SteeringCommittee meetings, a website and intranet for Center information and communications, a Centerpublication policy that encourages the development of translational reports and joint theoreticalarticles, integrated training programs, and a plan for information dissemination and outreach.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Specialized Center (P50)
Project #
2P50MH064065-06
Application #
7333014
Study Section
Special Emphasis Panel (ZMH1-ERB-S (03))
Project Start
2007-08-01
Project End
2012-07-31
Budget Start
2007-08-01
Budget End
2008-07-31
Support Year
6
Fiscal Year
2007
Total Cost
$82,214
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Type
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Tu, Liyun; Styner, Martin; Vicory, Jared et al. (2018) Skeletal Shape Correspondence Through Entropy. IEEE Trans Med Imaging 37:1-11
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Wei, Lifang; Cao, Xiaohuan; Wang, Zhensong et al. (2017) Learning-based deformable registration for infant MRI by integrating random forest with auto-context model. Med Phys 44:6289-6303
Gao, Wei; Lin, Weili; Grewen, Karen et al. (2017) Functional Connectivity of the Infant Human Brain: Plastic and Modifiable. Neuroscientist 23:169-184

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