ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting‐state and task‐based fMRI data. Issue 9 (19th March 2022)

• Record Type: Journal Article
• Authors: Waller, Lea; Erk, Susanne; Pozzi, Elena; Toenders, Yara J.; Haswell, Courtney C.; Büttner, Marc; Thompson, Paul M.; Schmaal, Lianne; Morey, Rajendra A.; Walter, Henrik; Veer, Ilya M.
• Is Part Of: Human brain mapping. Volume 43:Issue 9(2022)
• Page Start: 2727
• Page End: 2742
• Publication Date: 2022-03-19
• Subjects: fMRI -- harmonization -- image analysis -- reproducibility -- meta‐analysis pipeline -- open source; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25829
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe ( H armonized A na l ysis of F unctional MRI pipe line), an open‐source, containerized, user‐friendly tool that facilitates reproducible analysis of task‐based and resting‐state fMRI data through uniform application of preprocessing, quality assessment, single‐subject feature extraction, and group‐level statistics. It provides state‐of‐the‐art preprocessing using fMRIPrep without the requirement for input data in Brain Imaging Data Structure (BIDS) format. HALFpipe extends the functionality of fMRIPrep with additional preprocessing steps, which include spatial smoothing, grand mean scaling, temporal filtering, and confound regression. HALFpipe generates an interactive quality assessment (QA) webpage to rate the quality of key preprocessing outputs and raw data in general. HALFpipe features myriad post‐processing functions at the individual subject level, including calculation of task‐based activation, seed‐based connectivity, network‐template (or dual) regression, atlas‐based functional connectivity matrices, regional homogeneity (ReHo), and fractional amplitude of low‐frequency fluctuations (fALFF), offering support to evaluate a combinatorial number of features or preprocessing settings in one run. Finally, flexible factorial models can be defined for mixed‐effects regression analysis at the group level, including multiple comparison correction. Here, we introduce the theoretical framework in which HALFpipe was developed, and present an overview of the main functions of the pipeline. HALFpipe offers the scientific community a major advance toward addressing the reproducibility crisis in neuroimaging, providing a workflow that encompasses preprocessing, post‐processing, and QA of fMRI data, while broadening core principles of data analysis for producing reproducible results. Instructions and code can be found at https://github.com/HALFpipe/HALFpipe . Abstract : HALFpipe is a user‐friendly software that facilitates reproducible analysis of fMRI data, including preprocessing, single‐subject, and group analysis. It provides state‐of‐the‐art preprocessing using fMRIPrep, but removes the necessity to convert data to the BIDS format. Common resting‐state and task‐based fMRI features can then be calculated on the fly using FSL and Nipype for statistics.