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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