fMRIflows: a consortium of fully automatic univariate and multivariate fMRI processing pipelines

Abstract

AbstractHow functional MRI (fMRI) data are analyzed depends on the researcher and the toolbox used. It is not uncommon that the processing pipeline is rewritten for each new dataset. Consequently, code transparency, quality control and objective analysis pipelines are important for improving reproducibility in neuroimaging studies. Toolboxes, such as Nipype and fMRIPrep, have documented the need for and interest in automated analysis pipelines. Here, we introduce fMRIflows: a consortium of fully automatic neuroimaging pipelines for fMRI analysis, which performs standard preprocessing, as well as 1st- and 2nd-level univariate and multivariate analysis. In addition to the standardized processing pipelines, fMRIflows also provides flexible temporal and spatial filtering to account for datasets with increasingly high temporal resolution and to help appropriately prepare data for multivariate analysis and improve signal decoding accuracy. This paper first describes fMRIflows’ structure and functionality, then explains its infrastructure and access, and lastly validates the toolbox by comparing it to other neuroimaging processing pipelines such as fMRIPrep, FSL and SPM. This validation was performed on three datasets with varying temporal resolution to ensure flexibility and robustness, as well as to showcase the improved capability of fMRIflows. The outcome of the validation analysis shows that fMRIflows preprocessing pipeline performs comparably to the ones obtained from other toolboxes. Importantly, fMRIflows’ flexible temporal filtering approach leads to improved signal-to-noise-ratio after preprocessing and increased statistical sensitivity, particularly in datasets with high temporal resolution. fMRIflows is a fully automatic fMRI processing pipeline which uniquely offers univariate and multivariate single-subject and group analyses as well as preprocessing. It offers flexible spatial and temporal filtering and thereby leads to more pronounced results for datasets with temporal resolutions at or below 1000ms.