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ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data

View ORCID ProfileLea Waller, View ORCID ProfileSusanne Erk, View ORCID ProfileElena Pozzi, View ORCID ProfileYara J. Toenders, Courtney C. Haswell, View ORCID ProfileMarc Büttner, View ORCID ProfilePaul M. Thompson, View ORCID ProfileLianne Schmaal, View ORCID ProfileRajendra A. Morey, View ORCID ProfileHenrik Walter, View ORCID ProfileIlya M. Veer
doi: https://doi.org/10.1101/2021.05.07.442790
Lea Waller
1Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy CCM, Berlin, Germany
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  • For correspondence: lea@fmri.science
Susanne Erk
1Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy CCM, Berlin, Germany
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Elena Pozzi
2Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
3Orygen, Parkville, Australia
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Yara J. Toenders
2Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
3Orygen, Parkville, Australia
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Courtney C. Haswell
4Duke University School of Medicine, Durham, NC, USA
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Marc Büttner
1Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy CCM, Berlin, Germany
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Paul M. Thompson
5Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Lianne Schmaal
2Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
3Orygen, Parkville, Australia
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Rajendra A. Morey
4Duke University School of Medicine, Durham, NC, USA
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Henrik Walter
1Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy CCM, Berlin, Germany
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Ilya M. Veer
1Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy CCM, Berlin, Germany
6Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands
7Radboud University Medical Center, Nijmegen, the Netherlands
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Abstract

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 assess 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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/HALFpipe/HALFpipe

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 09, 2021.
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ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data
Lea Waller, Susanne Erk, Elena Pozzi, Yara J. Toenders, Courtney C. Haswell, Marc Büttner, Paul M. Thompson, Lianne Schmaal, Rajendra A. Morey, Henrik Walter, Ilya M. Veer
bioRxiv 2021.05.07.442790; doi: https://doi.org/10.1101/2021.05.07.442790
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ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data
Lea Waller, Susanne Erk, Elena Pozzi, Yara J. Toenders, Courtney C. Haswell, Marc Büttner, Paul M. Thompson, Lianne Schmaal, Rajendra A. Morey, Henrik Walter, Ilya M. Veer
bioRxiv 2021.05.07.442790; doi: https://doi.org/10.1101/2021.05.07.442790

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