Format

Send to

Choose Destination
Nat Methods. 2019 Jan;16(1):111-116. doi: 10.1038/s41592-018-0235-4. Epub 2018 Dec 10.

fMRIPrep: a robust preprocessing pipeline for functional MRI.

Author information

1
Department of Psychology, Stanford University, Stanford, CA, USA. phd@oscaresteban.es.
2
Department of Psychology, Stanford University, Stanford, CA, USA.
3
Max Planck Institute for Empirical Aesthetics, Hesse, Germany.
4
Computational Neuroimaging Lab, Biocruces Health Research Institute, Bilbao, Spain.
5
Neuroscience Program, University of Iowa, Iowa City, IA, USA.
6
McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA.
7
Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
8
Department of Psychiatry, Stanford Medical School, Stanford University, Stanford, CA, USA.
9
Department of Neurosurgery, University of Iowa Health Care, Iowa City, IA, USA.
10
Department of Otolaryngology, Harvard Medical School, Boston, MA, USA.
11
Department of Psychology, Stanford University, Stanford, CA, USA. krzysztof.gorgolewski@gmail.com.

Abstract

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.

PMID:
30532080
PMCID:
PMC6319393
DOI:
10.1038/s41592-018-0235-4
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center