Many obstacles have impeded neuroimaging research on childhood brain disorders. These include difficulties in obtaining data from children given their limited ability to comply with procedures, the lack of an adequate normative data base with which to characterize normal brain development and against which to identify and measure aberrant brain development, the lack of adequate image analysis tools for characterizing developmental brain changes in the brain, and a lack of adequate means for sharing data analytic tools. Three NIH Institutes (NICHD, NIMH, and NINDS) have jointly sponsored a multi-center study to learn more about how the brain develops in typical, healthy children and adolescents. This study will enroll approximately 500 children, ranging from infancy to young adulthood, who will be seen at different time points over a six-year period at several clinical centers around the US. MRIs of the brain will be performed and the imaging findings will be related to the results of standardized neuropsycological tests. This study would serve several purposes: (1) to facilitate the study of normal human brain development; (2) to provide a necessary, representative, and reliable source of normal control data for studies of children with brain disorders and diseases; (3) to provide growth curves for normal brain anatomy; and (4) to aid in the development of pediatric neuroimaging tools. More information regarding this project can be found at the """"""""MRI Study of Normal Brain Development"""""""" website: www.brain-child.org/. Our role is to serve as a Data Processing Center (DPC), in particular, to process and analyze all DT-MRI data that will be acquired by the various Centers during the course of the study. A detailed description of our group's specific tasks will soon be available on the web. To date, Gustavo Rohde has been developing methods to warp and register multi-dimensional DT-MRI data sets obtained from children. Lin-Chin Chang has been developing several web-based applications that allow diffusion tensor data to be imported from the various Centers and examined for quality control purposes. Ferenc Horkay is developing new polymeric phantoms that can be used to calibrate each DT-MRI measurement system in each Center to ensure that their DT-MRI acquisitions are quantitative and of high quality.

Project Start
Project End
Budget Start
Budget End
Support Year
1
Fiscal Year
2003
Total Cost
Indirect Cost
Name
U.S. National Inst/Child Hlth/Human Dev
Department
Type
DUNS #
City
State
Country
United States
Zip Code
Koay, Cheng Guan; Ozarslan, Evren; Pierpaoli, Carlo (2009) Probabilistic Identification and Estimation of Noise (PIESNO): a self-consistent approach and its applications in MRI. J Magn Reson 199:94-103
Koay, Cheng Guan; Chang, Lin-Ching; Pierpaoli, Carlo et al. (2007) Error propagation framework for diffusion tensor imaging via diffusion tensor representations. IEEE Trans Med Imaging 26:1017-34
Chang, Lin-Ching; Koay, Cheng Guan; Pierpaoli, Carlo et al. (2007) Variance of estimated DTI-derived parameters via first-order perturbation methods. Magn Reson Med 57:141-9
Evans, Alan C; Brain Development Cooperative Group (2006) The NIH MRI study of normal brain development. Neuroimage 30:184-202
Jones, Derek K; Catani, Marco; Pierpaoli, Carlo et al. (2006) Age effects on diffusion tensor magnetic resonance imaging tractography measures of frontal cortex connections in schizophrenia. Hum Brain Mapp 27:230-8
Koay, Cheng Guan; Chang, Lin-Ching; Carew, John D et al. (2006) A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging. J Magn Reson 182:115-25
Chang, Lin-Ching; Jones, Derek K; Pierpaoli, Carlo (2005) RESTORE: robust estimation of tensors by outlier rejection. Magn Reson Med 53:1088-95
Rohde, Gustavo K; Barnett, Alan S; Basser, Peter J et al. (2005) Estimating intensity variance due to noise in registered images: applications to diffusion tensor MRI. Neuroimage 26:673-84
Rohde, G K; Barnett, A S; Basser, P J et al. (2004) Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI. Magn Reson Med 51:103-14
Taber, Katherine H; Pierpaoli, Carlo; Rose, Stephen E et al. (2002) The future for diffusion tensor imaging in neuropsychiatry. J Neuropsychiatry Clin Neurosci 14:1-5