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fMRIPrep: a robust preprocessing pipeline for functional MRI

Nature Methods, ISSN: 1548-7105, Vol: 16, Issue: 1, Page: 111-116
2019
  • 1,602
    Citations
  • 0
    Usage
  • 1,960
    Captures
  • 6
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    1,602
    • Citation Indexes
      1,600
    • Patent Family Citations
      2
      • Patent Families
        2
  • Captures
    1,960
  • Mentions
    6
    • Blog Mentions
      4
      • Blog
        4
    • News Mentions
      2
      • News
        2

Most Recent News

Functional neuroimaging as a catalyst for integrated neuroscience

Nature, Published online: 08 November 2023; doi:10.1038/s41586-023-06670-9 This Perspective reviews successful applications of functional magnetic resonance imaging (fMRI) and presents a case for fMRI as a central hub on which to integrate the dispersed subfields of systems, cognitive, computational and clinical neuroscience.

Article Description

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.

Bibliographic Details

Esteban, Oscar; Markiewicz, Christopher J; Blair, Ross W; Moodie, Craig A; Isik, A Ilkay; Erramuzpe, Asier; Kent, James D; Goncalves, Mathias; DuPre, Elizabeth; Snyder, Madeleine; Oya, Hiroyuki; Ghosh, Satrajit S; Wright, Jessey; Durnez, Joke; Poldrack, Russell A; Gorgolewski, Krzysztof J

Springer Science and Business Media LLC

Biochemistry, Genetics and Molecular Biology

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