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

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 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.

Keywords: fMRI; harmonization; image analysis; meta-analysis pipeline; open source; reproducibility.

FIGURE 1

HALFpipe workflow. HALFpipe is configured in a user interface where the user is asked a series of questions about their data and the processing steps to perform. Data are then converted to BIDS format (Gorgolewski et al., 2016) to allow standardized processing (white). After minimal preprocessing of the structural (blue) and functional (green and orange) data with fMRIPrep (Esteban, Blair, et al., 2019), additional preprocessing steps can be selected (red). Using the preprocessed data, statistical maps can be calculated during feature extraction (turquoise). Finally, group statistics can be performed (yellow). Note that not all preprocessing steps are available for each feature, as is outlined in Table 3. The diagram omits this information to increase visual clarity

FIGURE 2

Quality assessment user interface. The top panel shows the charts view, containing one chart for processing status, one for quality ratings and one for image quality metrics. In the top left corner, the navigation menu is open, which shows the option to export ratings for use in group statistics. The bottom panel contains a screenshot of the explorer view that allows the user to navigate across subjects and image types. The explorer view shows the currently selected report image on the right, along with its rating, related images, and the source files that were used to construct it. By clicking on the image, or selecting the report detail view in the navigation menu, the image can be zoomed and panned using the mouse