Analysis of task-based functional MRI data preprocessed with fMRIPrep

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.

The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses

“…Results included in this manuscript come from preprocessing performed using Fmriprep version 1.4.1 (RRID:SCR_016216) 42 , 43 , a Nipype based tool (RRID:SCR_002502) 44 , 45 . Each T1w (T1-weighted) volume was corrected for INU (intensity non-uniformity) using N4BiasFieldCorrection v2.1.0 46 and skull-stripped using antsBrainExtraction.sh v2.1.0 (using the OASIS template).…”

The Dual Mechanisms of Cognitive Control (DMCC) Project

“…As such, we found that surface-based alignment, combined with the cortical parcellation approach, exhibited significant benefits in statistical sensitivity to task effects relative to more standard volume-based alignment approaches; consequently, the data and analyses reported here utilize this approach. Additionally, we are now in the process of shifting our preprocessing pipeline to fMRIprep (Esteban et al, 2018(Esteban et al, , 2020, as we have that it yields further benefits in terms of standardization, QA/QC, and statistical sensitivity, relative to the HCP pipelines (Etzel et al, 2019). Most recently, we have explored a second stage pre-processing approach using a whole-brain neural model derived from resting-state fMRI scans to filter out intrinsic activity dynamics and provide even greater sensitivity and temporal precision regarding event-related activity dynamics (Wang et al, 2020).…”

Whole-brain dynamics of human sensorimotor adaptation

“…Preprocessing was performed using fMRIPrep 1.4.0 (Esteban, Ciric, et al, 2019; Esteban et al, 2018) RRID:SCR_016216), which is based on Nipype 1.2.0 (Esteban, Markiewicz, et al, 2019); RRID:SCR_002502).…”