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Abstract #4083

NiBabies: A robust preprocessing workflow tailored for neonate and infant MRI

Mathias Goncalves1, Christopher J. Markiewicz1, Martin Styner2, Lucille A. Moore3, Kathy Snider4, Eric A. Earl3, Christopher D. Smyser5, Lilla Zöllei6, Russel A. Poldrack1, Oscar Esteban7, Eric Feczko4, and Damien A. Fair4
1Stanford University, Stanford, CA, United States, 2University of North Carolina School of Medicine, Chapel Hill, NC, United States, 3Oregon Health & Science University, Portland, OR, United States, 4University of Minnesota, Minneapolis, MN, United States, 5Washington University in St. Louis, St. Louis, MO, United States, 6Harvard Medical School, Boston, MA, United States, 7University of Lausanne, Lausanne, Switzerland


Advances in both data acquisition and processing methods have given magnetic resonance imaging researchers (MRI) a plethora of options on how best to clean and standardize data before statistical analysis. Recently, there has been a surge in standardized data processing workflows, but special populations, such infants, require modified techniques not normally found in general pipelines. Here we introduce NiBabies, a robust and open-source structural and functional MRI preprocessing pipeline designed for infant populations.

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