Four NIH Institutes (NICHD, NIMH, NINDS, and NIDA) jointly sponsored a multicenter study to advance our understanding about how the brain develops in typical, healthy children and adolescents. This study enrolled approximately 500 children, ranging from infancy to young adulthood, who were seen at different time points over a six-year period at several clinical centers around the U.S.. Magnetic Resonance Images (MRI) of the brain were acquired and the imaging findings and standardized neuropsycological tests were administered. Our role in this clinical research project has been to serve as a Diffusion Tensor Data Processing Center (DPC). This entails processing and analyzing all diffusion tensor MRI (DT-MRI or DTI) data that were acquired by the various clinical Centers during the course of the study. Previously, we developed and implemented a DTI data processing pipeline. Specifically we developed procedures for sorting, displaying, and co-registering diffusion weighted images (DWI) from which the DTI 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 loss in data quality. We also proposed a new strategy for robust estimation of the diffusion tensor and quantities derived from it. We addressed the task of registering DTI data to other structural MRI data contained in the database using rigid body and linear (affine) transformations. More recently we addressed the task of correcting residual image distortion originating from the Echo-Planar DWI (DW-EPI) acquisition used for DTI. 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 only 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 recent expansion has improved the quality of data collected through longer or additional scanning sessions for older subjects, and through the recruitment of new subjects and scanning sessions for the younger cohort. Our group designed the expanded DTI acquisition protocol and coordinated its implementation at each clinical site where DTI data was collected. We have developed our DTI processing software (TORTOISE) and have disseminated it to the public through the website . The software is actively maintained and has an increasing number of registered users. All diffusion data from the Normal Pediatric Brain MRI project have been successfully processed using the TORTOISE pipeline and have been deposited in a database that will be accessible by the greater scientific community when the NIH project officers of the Institutes sponsoring this study decide to release the data. In the interim, investigators who have been part of the project have access to the data and are providing feedback and suggest potential improvements.

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Walker, Lindsay; Chang, Lin-Ching; Nayak, Amritha et al. (2016) The diffusion tensor imaging (DTI) component of the NIH MRI study of normal brain development (PedsDTI). Neuroimage 124:1125-30
Sadeghi, Neda; Nayak, Amritha; Walker, Lindsay et al. (2015) Analysis of the contribution of experimental bias, experimental noise, and inter-subject biological variability on the assessment of developmental trajectories in diffusion MRI studies of the brain. Neuroimage 109:480-92
Walker, Lindsay; Curry, Michael; Nayak, Amritha et al. (2013) A framework for the analysis of phantom data in multicenter diffusion tensor imaging studies. Hum Brain Mapp 34:2439-54
Irfanoglu, M Okan; Walker, Lindsay; Sarlls, Joelle et al. (2012) Effects of image distortions originating from susceptibility variations and concomitant fields on diffusion MRI tractography results. Neuroimage 61:275-88
Walker, Lindsay; Gozzi, Marta; Lenroot, Rhoshel et al. (2012) Diffusion tensor imaging in young children with autism: biological effects and potential confounds. Biol Psychiatry 72:1043-51
Chang, Lin-Ching; Walker, Lindsay; Pierpaoli, Carlo (2012) Informed RESTORE: A method for robust estimation of diffusion tensor from low redundancy datasets in the presence of physiological noise artifacts. Magn Reson Med 68:1654-63
Walker, Lindsay; Chang, Lin-Ching; Koay, Cheng Guan et al. (2011) Effects of physiological noise in population analysis of diffusion tensor MRI data. Neuroimage 54:1168-77
Fonov, Vladimir; Evans, Alan C; Botteron, Kelly et al. (2011) Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54:313-27
Pierpaoli, Carlo (2010) Quantitative brain MRI. Top Magn Reson Imaging 21:63
Sarlls, Joelle E; Pierpaoli, Carlo (2009) In vivo diffusion tensor imaging of the human optic chiasm at sub-millimeter resolution. Neuroimage 47:1244-51

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