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

Synopsis

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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