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. 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

Oscar Esteban  1 , Christopher J Markiewicz  2 , Ross W Blair  2 , Craig A Moodie  2 , A Ilkay Isik  3 , Asier Erramuzpe  4 , James D Kent  5 , Mathias Goncalves  6 , Elizabeth DuPre  7 , Madeleine Snyder  8 , Hiroyuki Oya  9 , Satrajit S Ghosh  6   10 , Jessey Wright  2 , Joke Durnez  2 , Russell A Poldrack #  2 , Krzysztof J Gorgolewski #  11
Affiliations

fMRIPrep: a robust preprocessing pipeline for functional MRI

Oscar Esteban et al. Nat Methods. 2019 Jan.

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.

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Figures

Figure 1.
Figure 1.. FMRIPrep is an fMRI preprocessing tool that adapts to the input dataset.
Leveraging the Brain Imaging Data Structure (BIDS21), the software self-adjusts automatically, configuring the optimal workflow for the given input dataset. Thus, no manual intervention is required to locate the required inputs (one T1-weighted image and one BOLD series), read acquisition parameters (such as the repetition time –TR– and the slice acquisition-times) or find additional acquisitions intended for specific preprocessing steps (like field maps and other alternatives for the estimation of the susceptibility distortion).
Figure 2.
Figure 2.. Integrating visual assessment into the software testing framework effectively increases the quality of results.
In an early assessment of quality using fMRIPrep version 1.0.0, the overall rating of two datasets was below the “poor” category and four below the “acceptable” level (left column of colored circles). After addressing some outstanding issues detected by the early assessment, the overall quality of processing is substantially improved (right column of circles), and no datasets are below the “poor” quality level. Only two datasets are rated below the “acceptable” level in the second assessment (using fMRIPrep version 1.0.7).
Figure 3.
Figure 3.. FMRIPrep affords the researcher finer control over the smoothness of their analysis.
A | Estimating the spatial smoothness of data before and after the initial smoothing step of the analysis workflow confirmed that results of preprocessing with feat are intrinsically smoother. B | Mapping the standard deviation of averaged BOLD time-series displayed greater variability around the brain outline (represented with a black contour) for data preprocessed with feat. This effect is generally associated with a lower performance of spatial normalization28. Reference contours correspond to the brain tissue segmentation of the MNI atlas.
Figure 4.
Figure 4.. The activation count maps from fMRIPrep are better aligned with the underlying anatomy.
The mosaics show thresholded activation count maps for the go vs. successful stop contrast in the “stopsignal” task after preprocessing using either fMRIPrep (top row) or FSL’s feat (bottom row), with identical single subject statistical modeling. Both tools obtained similar activation maps, with fMRIPrep results being slightly better aligned with the underlying anatomy.
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