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, and a lack of an adequate means for sharing data analytic tools.? Four NIH Institutes (NICHD, NIMH, NINDS, and NIDA) have jointly sponsored a multi-center study to advance our understanding about how the brain develops in typical, healthy children and adolescents. This study is enrolling approximately 500 children, ranging from infancy to young adulthood, who are being seen at different time points over a six-year period at several clinical centers around the US. Magnetic Resonance Images (MRI) of the brain are being performed and the imaging findings are being related to the results of standardized neuropsycological tests. ? This project serves 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: Our role is to serve as a Diffusion Tensor Data Processing Center (DPC). This entails processing and analyzing all diffusion tensor MRI (DT-MRI) data that is being acquired by the various Centers during the course of the study. ? Previously, we have been developing and implementing the DT-MRI data processing """"""""pipeline"""""""". Specifically we developed procedures for sorting, displaying, and co-registering diffusion weighted images from which the DT-MRI data is computed. The image registration procedure accomplishes the tasks of removing the effects of subject motion and eddy current distortion as well as aligning the images to a given template with only one interpolation step, ensuring minimal data quality loss. We also proposed a new strategy for robust estimation of the diffusion tensor and quantities derived from it. We addressed the task of registering DT-MRI data to other structural MRI data contained in the database using rigid body and linear transformations and we are currently experimenting with several non-linear deformation strategies for Echo-Planar Image (EPI) distortion correction.? In 2005 the project was awarded Neuroscience Blueprint funds to expand the DTI portion of the study. The original DTI data collection was limited to a 10-minute protocol performed on those subjects able to tolerate additional time in the scanner following the collection of the core structural MRI data. This expansion will increase the quality and quantity of data collected through a longer or an additional scanning session for older subjects, and through the addition of new subjects and scanning sessions for the younger cohort. Our group has designed the expanded DTI protocol and coordinated its implementation at each acquisition site. The sites are now succesfully acquiring DTI data with that protocol.

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U.S. National Inst/Child Hlth/Human Dev
United States
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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
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
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
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
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
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