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.

Demonstrating quality control procedures for fMRI in DPABI

“…For example, ICA-AROMA is an outstanding algorithm to control head-motion related artifacts based on independent component analysis (ICA). As this algorithm is extremely time-consuming, it is an optional method in DPABISurf but is not conducted in default, while some other pipelines would include ICA-based nuisance regressions using a modified ICA-AROMA algorithm ( Waller et al, 2022 ). Moreover, a detailed list of exclusion criteria and excluded subject IDs in the studies based on public datasets would save time for other researchers and improve the reproducibility of the findings.…”

“…DPABISurf used docker technology to wrap the whole computing environment for fMRIPrep ( Esteban et al, 2019 ), FreeSurfer ( Fischl, 2012 ), ANTs ( Tustison et al, 2014 ), FSL ( Jenkinson et al, 2012 ), AFNI ( Cox, 1996 ), SPM ( Ashburner, 2012 ), GNU Parallel ( Tange, 2011 ), PALM ( Winkler et al, 2014 ), MATLAB (The MathWorks Inc., Natick, MA, USA), Docker 1 and DPABI ( Yan et al, 2016 ), etc. The pipelines mentioned above have their own preprocessing and QC procedures and an elaborate comparison among these pipelines could be found in the ENIGMA HALFpipe references ( Waller et al, 2022 ). The resting-state functional images and T1-weighted images were preprocessed by the following steps.…”

“…The fMRIPrep would produce a series of intuitive dynamic graphs and charts to demonstrate the effectiveness of Bold-T1 image co-registration, brain surface reconstruction, spatial normalization, and the severity of head motion after fMRI preprocessing. These graphs and reports are frequently invoked by QC procedures in the other pipelines such as DPABISurf ( Yan et al, 2021 ) and ENIGMA HALFpipe ( Waller et al, 2022 ). For example, HALFpipe provides an interactive webpage for users to evaluate an integrated quality report derived from fMRIPrep and other tools for each participant.…”

“…As mentioned above, QC was essential for large-scale, multi-center imaging projects. Therefore, the recent large-scale projects like UKBiobank ( Alfaro-Almagro et al, 2018 ), ABCD ( Hagler et al, 2019 ), and ENIGMA ( Waller et al, 2022 ) also created their own (combination of) QC methods. In addition to these specialized QC tools, imaging formatter such as DCM2NIIX ( Li et al, 2016 ), BIDS-validator and DPABI_InputPreparer could also be used to check for the absence of imaging meta-data in QC.…”

Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses

“…In addition, data quality control (QC) procedures that often cannot be fully automated but require a qualitative assessment by a reader, can be standardized ( 23 ). Further, to reduce influences of different (combinations of) software versions, containerized, user-friendly software packages are often distributed ( 24 ).…”