| Literature DB >> 32514178 |
Oscar Esteban1, Rastko Ciric2, Karolina Finc3, Ross W Blair2, Christopher J Markiewicz2, Craig A Moodie2, James D Kent4, Mathias Goncalves5, Elizabeth DuPre6, Daniel E P Gomez7, Zhifang Ye8, Taylor Salo9, Romain Valabregue10, Inge K Amlien11, Franziskus Liem12, Nir Jacoby13, Hrvoje Stojić14, Matthew Cieslak15, Sebastian Urchs6, Yaroslav O Halchenko16, Satrajit S Ghosh5,17, Alejandro De La Vega18, Tal Yarkoni18, Jessey Wright2, William H Thompson2,19, Russell A Poldrack, Krzysztof J Gorgolewski2.
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 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.Entities:
Mesh:
Year: 2020 PMID: 32514178 PMCID: PMC7404612 DOI: 10.1038/s41596-020-0327-3
Source DB: PubMed Journal: Nat Protoc ISSN: 1750-2799 Impact factor: 13.491