FMRIPrep: a robust preprocessing pipeline for functional MRI

By Oscar Esteban, Christopher J. Markiewicz, Ross W. Blair, Craig A. S. Moodie, A. I. Isik, A. Erramuzpe, James D. Kent, M. Goncalves, E. Dupre, Madeleine C. Snyder and 6 more. Nature Methods, 2018

3,579 citations recorded by the source corpus.

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. fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/6db95cb9720d8093c71064550c95fdb12356a118

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

The OpenNeuro resource for sharing of neuroscience data (2021, positive): “A significant number of publications reused multiple datasets; 31 of the 165 papers reused at least two datasets, with a maximum of 40 datasets reused ( Esteban et al., 2019 ).”
A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence (2021, background): “Although we think our pre-processing is general and likely suitable for most downstream uses of the data, the raw data are also available for those who want to explore other pre-processing approaches, such as fmriprep 76 .”
Data-Efficient Mutual Information Neural Estimator (2019, methodology): “All fMRI data were formatted according to the Brain Imaging Data Structure (BIDS) standard [22] and preprocessed using fMRIPrep [10]10.”
Variability in the analysis of a single neuroimaging dataset by many teams (2019, neutral): “18 (9), Brazil (3), Canada (16), China (2), Finland (1), France (4), Germany (21), Italy (13), the Netherlands (8), Spain (4), Sweden (4), Switzerland (3), Taiwan (3), UK (7) and the USA (76).”
Cortical functional hierarchy disruption following subcortical stroke (2026, unknown): “Structural and resting-state fMRI data were preprocessed using fMRIPrep [44].”
Hierarchical Reconfiguration of Neurocognitive Task Set Representations Mediates Cognitive Flexibility (2026, unknown): “All fMRI data were preprocessed using fMRIPrep version 20.1.1 (Esteban et al., 2019) to reduce noise and transform data from subject native space to the ICBM 152 Nonlinear Asymmetrical template version 2009c for group analysis (Fonov et al., 2011).”
Frontoparietal Hubs Leverage Probabilistic Representations and Integrated Uncertainty to Guide Cognitive Flexibility (2025, unknown): “Our integration framework predicts that regions that integrate internal and external information—such as the MFG and IPS—should exhibit hub-like properties.”
Revealing the co-existence of written and spoken language coding neural populations in the visual word form area (2025, unknown): “Pre-processing was conducted by using fMRIPrep 20.2.0 ( Esteban et al., 2019).”