RT Journal Article
SR Electronic
T1 Analysis of task-based functional MRI data preprocessed with fMRIPrep
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 694364
DO 10.1101/694364
A1 Oscar Esteban
A1 Rastko Ciric
A1 Karolina Finc
A1 Ross Blair
A1 Christopher J. Markiewicz
A1 Craig A. Moodie
A1 James D. Kent
A1 Mathias Goncalves
A1 Elizabeth DuPre
A1 Daniel E. P. Gomez
A1 Zhifang Ye
A1 Taylor Salo
A1 Romain Valabregue
A1 Inge K. Amlien
A1 Franziskus Liem
A1 Nir Jacoby
A1 Hrvoje Stojić
A1 Matthew Cieslak
A1 Sebastian Urchs
A1 Yaroslav O. Halchenko
A1 Satrajit S. Ghosh
A1 Alejandro De La Vega
A1 Tal Yarkoni
A1 Jessey Wright
A1 William H. Thompson
A1 Russell A. Poldrack
A1 Krzysztof J. Gorgolewski
YR 2020
UL http://biorxiv.org/content/early/2020/01/05/694364.abstract
AB 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.