Almost two decades ago, four NIH Institutes (NICHD, NIMH, NINDS, and NIDA) began jointly sponsoring 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 neuropsychological tests were administered. A research consortium formed from this study, The Brain Development Cooperative Group (, still continues to collaborate and publish research findings together. We served as a Diffusion Tensor Data Processing Center (DPC) of this consortium. This entailed processing and analyzing all diffusion tensor MRI (DT-MRI or DTI) data acquired by the various clinical centers participating in this study. Previously, we developed and implemented a DTI data processing pipeline. Under the aegis of Carlo Pierpaoli (formerly a Staff Scientist in SQITS and now a Tenure-track Principal Investigator in NIBIB), 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 strategies 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. Several new methods have been published that use diffeomorphic registration schemes, and which use the information provided in the diffusion tensor itself to help warp and register DWI data. The recently proposed DRBUDDI uses blip-up/blip-down DWI data to correct for residual image distortion originating from the Echo-Planar DWI (DW-EPI) acquisition used for DTI. Dr. Pierpaoli's group developed a comprehensive DTI processing software package (TORTOISE) while in SQITS and is continuing to disseminate it to the public through the website The software is actively being curated has an increasing number of registered users. Several versions of TORTOISE have been released, the most recent being TORTOISEv2. All diffusion data from the Normal Pediatric Brain MRI project were successfully processed using the TORTOISE pipeline and were deposited in a database that is accessible by the larger scientific community. We have also produced age-specific brain atlases from these data and we investigated developmental trajectories for several brain regions. The imaging data can now be analyzed in conjunction with cognitive and other behavioral data available in the database.

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