Journal article
fMRIPrep: a robust preprocessing pipeline for functional MRI
Nature methods, Vol.16(1), pp.111-116
01/2019
DOI: 10.1101/306951
PMCID: PMC6319393
PMID: 30532080
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.
Details
- Title: Subtitle
- fMRIPrep: a robust preprocessing pipeline for functional MRI
- Creators
- Oscar Esteban - Department of Psychology, Stanford University, Stanford, CA, USA. phd@oscaresteban.esChristopher J Markiewicz - Department of Psychology, Stanford University, Stanford, CA, USARoss W Blair - Department of Psychology, Stanford University, Stanford, CA, USACraig A Moodie - Department of Psychology, Stanford University, Stanford, CA, USAA Ilkay Isik - Max Planck Institute for Empirical Aesthetics, Hesse, GermanyAsier Erramuzpe - Computational Neuroimaging Lab, Biocruces Health Research Institute, Bilbao, SpainJames D Kent - University of Iowa, Psychological and Brain SciencesMathias Goncalves - McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA, USAElizabeth DuPre - Montreal Neurological Institute, McGill University, Montreal, QC, CanadaMadeleine Snyder - Department of Psychiatry, Stanford Medical School, Stanford University, Stanford, CA, USAHiroyuki Oya - University of Iowa, NeurosurgerySatrajit S Ghosh - Department of Otolaryngology, Harvard Medical School, Boston, MA, USAJessey Wright - Department of Psychology, Stanford University, Stanford, CA, USAJoke Durnez - Department of Psychology, Stanford University, Stanford, CA, USARussell A Poldrack - Department of Psychology, Stanford University, Stanford, CA, USAKrzysztof J Gorgolewski - Department of Psychology, Stanford University, Stanford, CA, USA. krzysztof.gorgolewski@gmail.com
- Resource Type
- Journal article
- Publication Details
- Nature methods, Vol.16(1), pp.111-116
- DOI
- 10.1101/306951
- PMID
- 30532080
- PMCID
- PMC6319393
- NLM abbreviation
- Nat Methods
- ISSN
- 1548-7091
- eISSN
- 1548-7105
- Publisher
- Nature Publishing Group; United States
- Grant note
- R01 EB020740 / NIBIB NIH HHS R24 MH114705 / NIMH NIH HHS R24 MH117179 / NIMH NIH HHS U01 NS103780 / NINDS NIH HHS
- Language
- English
- Date published
- 01/2019
- Academic Unit
- Psychological and Brain Sciences; Iowa Neuroscience Institute; Neurosurgery
- Record Identifier
- 9984051400202771
Metrics
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