The reproducibility crisis in neuroimaging has led to an increased demand for standardized data processing workflows. Within the ENIGMA consortium, we developed HALFpipe (Harmonized Analysis of Functional MRI pipeline), 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.
|Number of pages||16|
|Journal||Human Brain Mapping|
|Publication status||E-pub ahead of print - 19 Mar 2022|
Bibliographical noteFunding Information:
This work has been supported by a Grant from the German Research Foundation to S.E. and H.W. (DFG ER 724/4–1, WA 1539/11–1). P.M.T. received research grant funding from Biogen, Inc., for research unrelated to this manuscript. L.S. received funding from the National Institutes of Health (NIH R01 MH117601 and NHMRC Career Development Fellowship 1140764). R.A.M received funding from National Institutes of Health (NIH R01 MH111671). I.M.V. and H.W. received funding from the European Union's Horizon 2020 research and innovation programme under under Grant Agreement number 777084 (DynaMORE project).
German Research Foundation, Grant/Award Numbers: DFG ER 724/4‐1, WA 1539/11‐1; Biogen, Inc.; National Institutes of Health, Grant/Award Numbers: NIH R01, MH117601; NHMRC Career Development Fellowship, Grant/Award Number: 1140764; National Institutes of Health, Grant/Award Numbers: NIH R01, MH111671; European Union's Horizon 2020 research andinnovation programme, Grant/Award Number: 777084 Funding information
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