Abstract

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time-consuming, error-prone, and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure (BIDS) to standardize both the input datasets -MRI data as stored by the scanner- and the outputs -data ready for modeling and analysis-, fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

Footnotes

* https://openneuro.org/datasets/ds000003/versions/00001

Details

Title
Analysis of task-based functional MRI data preprocessed with fMRIPrep
Author
Esteban, Oscar; Ciric, Rastko; Finc, Karolina; Blair, Ross W; Markiewicz, Christopher J; Moodie, Craig A; Kent, James D; Goncalves, Mathias; Dupre, Elizabeth; Gomez, Daniel Ep; Ye, Zhifang; Salo, Taylor; Valabregue, Romain; Amlien, Inge K; Liem, Franziskus; Jacoby, Nir; Stojic, Hrvoje; Cieslak, Matthew; Urchs, Sebastian; Halchenko, Yaroslav O; Ghosh, Satrajit S; De La Vega, Alejandro; Yarkoni, Tal; Jessey Ak Wright; Thompson, William H; Poldrack, Russell A; Gorgolewski, Krzysztof J
University/institution
Cold Spring Harbor Laboratory Press
Section
New Results
Publication year
2020
Publication date
Jan 3, 2020
Publisher
Cold Spring Harbor Laboratory Press
ISSN
2692-8205
Source type
Working Paper
Language of publication
English
ProQuest document ID
2253571687
Copyright
© 2020. This article is published under http://creativecommons.org/licenses/by/4.0/ (“the License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.