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Abstract #2473

dMRIPrep: a robust preprocessing pipeline for diffusion MRI

Michael J Joseph1, Derek Pisner2, Adam Richie-Halford3, Garikoitz Lerma-Usabiaga4, Salim Mansour1, James D Kent5, Anisha Keshavan3, Matthew Cieslak6, Erin W Dickie1, Sebastian Tourbier7, Aristotle N Voineskos1, Theodore D Satterthwaite6, Russell A Poldrack8, Jelle Veraart9, Ariel Rokem10, and Oscar Esteban7
1The Centre for Addiction and Mental Health, Toronto, ON, Canada, 2Department of Psychology, University of Texas at Austin, Austin, TX, United States, 3eScience Institute, The University of Washington, Seattle, WA, United States, 4Basque Center on Cognition, Brain and Language, Donostia - San Sebastian, Spain, 5Neuroscience Program, University of Iowa, Iowa City, IA, United States, 6Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, 7Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 8Department of Psychology, Stanford University, Stanford, CA, United States, 9NYU Grossman School of Medicine, New York City, NY, United States, 10Department of Psychology, The University of Washington, Seattle, WA, United States

We present dMRIPrep, a preprocessing pipeline for diffusion MRI (dMRI) inspired by the approach and wide uptake of fMRIPrep. dMRIPrep reliably and consistently performs on diverse data acquired by different studies. dMRIPrep equips researchers with a reliable and transparent tool developed with the best available engineering practices and neuroimaging standards, maintained as part of NiPreps, a community software framework that ensures long-lasting support and public-interest steering.

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