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.
journal_name
Nat Methodsjournal_title
Nature methodsauthors
Esteban O,Markiewicz CJ,Blair RW,Moodie CA,Isik AI,Erramuzpe A,Kent JD,Goncalves M,DuPre E,Snyder M,Oya H,Ghosh SS,Wright J,Durnez J,Poldrack RA,Gorgolewski KJdoi
10.1038/s41592-018-0235-4subject
Has Abstractpub_date
2019-01-01 00:00:00pages
111-116issue
1eissn
1548-7091issn
1548-7105pii
10.1038/s41592-018-0235-4journal_volume
16pub_type
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