FMRIPrep: extending the scanner to produce ready-for-analysis fMRI data

Presented During:

Poster No:


Submission Type:

Abstract Submission 


Mathias Goncalves1, Christopher Markiewicz1, Karolina Finc2, Russell Poldrack1, Oscar Esteban1


1Stanford University, Stanford, CA, 2Nicolaus Copernicus University in Toruń, Toruń, Kuyavian-Pomeranian

First Author:

Mathias Goncalves  
Stanford University
Stanford, CA


Christopher Markiewicz  
Stanford University
Stanford, CA
Karolina Finc  
Nicolaus Copernicus University in Toruń
Toruń, Kuyavian-Pomeranian
Russell Poldrack  
Stanford University
Stanford, CA
Oscar Esteban  
Stanford University
Stanford, CA


Analyses of blood-oxygen-level-dependent (BOLD) data -like other functional magnetic resonance imaging (fMRI) modalities- cannot operate directly on the images reconstructed by the scanner. Researchers have typically addressed this problem by inserting a data "preprocessing" step before analysis. fMRIPrep (Esteban et al., 2018) fulfills such a task with an easy-to-use interface that minimizes user intervention by self-adapting to the input data. The ever-increasing number of fMRIPrep users demonstrates the adequacy of the approach to simplify the neuroimaging workflow while maximizing the transparency and reproducibility of results.


The fMRIPrep pipeline (Fig 1) uses a combination of tools from well-known software packages, including FSL (Jenkinson et al., 2012), ANTs (Avants et al., 2008), FreeSurfer (Fischl, 2012) and AFNI (Cox and Hyde, 1997), with the goal of providing the best software implementation for each preprocessing step. The tool leverages BIDS (Gorgolewski et al., 2016) to automatically read and understand the input datasets and all the corresponding metadata (i.e. acquisition parameters and settings). Since its inception, fMRIPrep has shifted from a monolithic approach to an aggregation of microservices. Such modularization enables reusing these sub-pipelines in other preprocessing tools and improves overall software testing.
Supporting Image: OHBM2020-fmriprep-FIG1-Page-1.png


We have focused on improving fMRIPrep in a number of ways:

1) Better support for mixed volume-surface based analysis: with the full implementation of the Grayordinates data structure (Glasser et al., 2013). The Grayordinates structure is a spatially normalized sampling scheme that combines BOLD signals on a regular grid for the subcortical brain structures and along two hemispherical surfaces. This new output expands the compatibility of fMRIPrep with individual and group-level grayordinate-based analyses.

2) Major improvements to user documentation: with an emphasis on accessibility, readability, and educational value. The bulk of this work focused on confounds table output, containing a set of time-series with a potential non-neuronal origin that can further be used to denoise fMRI data. By thoroughly describing these confounding variables, and providing insight on potential usage and pitfalls, we hope to improve the average quality of results any user can produce using our tool.

3) More granular and powerful interface to specify output spaces: Due to the sheer number of potential output spaces, we augmented the interface capability of concurrent spaces. For example, users can specify:

--output-spaces T1w:res-native:res-bold

to output BOLD data aligned to the same subject structural image in two resolutions, the T1 template and the BOLD run. In addition, we addressed potential file bloat by reducing outputs to only explicitly requested spatial normalizations.

4) Modularization: The anatomical and susceptibility distortion correction processing (Fig 2) have been stripped out from the preprocessing workflow as standalone modules (sMRIPrep and SDCFlows, respectively).

In this software demonstration, we will walk users along the process of running fMRIPrep on a dataset. Additionally, we will give a brief overview of the improved documentation, highlighting the outputs and confounds section. We will show how derivatives are organized following the BIDS Derivatives Draft. Finally, we will conduct the inspection of the visual reports generated.
Supporting Image: OHBM2020-fmriprep-FIG2.png


fMRIPrep is a robust and easy-to-use pipeline for the preprocessing of diverse functional MRI data. The tool can be streamed to the MRI scanner to afford researchers data ready for analysis. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results. With its expansive collection of neuroimaging tools, simplified user experience, and informative reporting, fMRIPrep hopes to help researchers to better understand each step of the process while producing high-quality outputs.

Modeling and Analysis Methods:

Motion Correction and Preprocessing 2

Neuroinformatics and Data Sharing:

Brain Atlases
Workflows 1


Data analysis

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.


Please indicate below if your study was a "resting state" or "task-activation” study.


Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.


Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Not applicable

Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI
Structural MRI
Diffusion MRI
Other, Please specify  -   Software design

Provide references using author date format

Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C., 2008. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41.

Cox, R.W., Hyde, J.S., 1997. Software tools for analysis and visualization of fMRI data. NMR Biomed. 10, 171–178.<171::AID-NBM453>3.0.CO;2-L

Esteban, O., Markiewicz, C., Blair, R.W., Moodie, C., Isik, A.I., Aliaga, A.E., Kent, J., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S., Wright, J., Durnez, J., Poldrack, R., Gorgolewski, K.J., 2018. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111-116,

Fischl, B., 2012. FreeSurfer. NeuroImage 62, 774–781.

Glasser, M., Sotiropoulos, S., Wilson, J., Coalson, T., Fischl, B., Andersson, J., Xu, J., Jbabdi, S., Webster, M., Polimeni, J., Van Essen, D., Jenkinson, M., 2013. The minimal preprocessing pipelines for the human connectome project. NeuroImage 80, 105-124.

Gorgolewski, K.J., Auer, T., Calhoun, V.D., Craddock, R.C., Das, S., Duff, E.P., Flandin, G., Ghosh, S.S., Glatard, T., Halchenko, Y.O., Handwerker, D.A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B.N., Nichols, T.E., Pellman, J., Poline, J.-B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J.A., Varoquaux, G., Poldrack, R.A., 2016. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci. Data 3, 160044.
Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M., 2012. FSL. NeuroImage 62, 782–790.