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cnievergelt@ucsd.edu

Lea Waller 1, , Susanne Erk 1, Elena Pozzi 2,3, Yara J. Toenders 2,3, Courtney C. Haswell 4, Marc Büttner 1, Paul M. Thompson 5, Lianne Schmaal 2,3, Rajendra A. Morey 4†, Henrik Walter 1†, Ilya M. Veer 1,6,7†

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 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

Read the full paper here.

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