Journal
NATURE METHODS
Volume 16, Issue 1, Pages 111-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41592-018-0235-4
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Funding
- Laura and John Arnold Foundation
- NIH [NBIB R01EB020740]
- NIMH [R24MH114705, R24MH117179]
- NINDS [U01NS103780]
- European Union [706561]
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R01EB020740] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF MENTAL HEALTH [R24MH114705, R24MH117179] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [U01NS103780] Funding Source: NIH RePORTER
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Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.
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