PT  - JOURNAL ARTICLE
AU  - Lea Waller
AU  - Susanne Erk
AU  - Elena Pozzi
AU  - Yara J. Toenders
AU  - Courtney C. Haswell
AU  - Marc Büttner
AU  - Paul M. Thompson
AU  - Lianne Schmaal
AU  - Rajendra A. Morey
AU  - Henrik Walter
AU  - Ilya M. Veer
TI  - ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data
AID  - 10.1101/2021.05.07.442790
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.05.07.442790
4099  - http://biorxiv.org/content/early/2021/05/09/2021.05.07.442790.short
4100  - http://biorxiv.org/content/early/2021/05/09/2021.05.07.442790.full
AB  - 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 StatementThe authors have declared no competing interest.