## **Using HALFpipe for automated data analysis and quality control** ## **Lea Waller**, Charité Universitatsmedizin Berlin **Relevant work:** The [ENIGMA HALFpipe paper][1] in Human Brain Mapping **Softwares/programs requirements:** [Singularity][2] or [Docker][3] to be able to download HALFpipe, and [MRIcron][4] or [Fsleyes][5] to view the results **AOMICS dataset:** [ID1000][6], [PIOP1][7], [PIOP2][8] **Modalities:** T1w, DWI, fMRI **Abstract:** Increasing reproducibility in neuroimaging often means combining datasets for larger sample sizes and/or direct replications of findings. HALF pipe (Harmonized Analysis of Functional MRI pipeline) is a tool that can help in these scenarios, because it can automatically construct processing pipelines to derive task-based activation and functional connectivity from virtually any dataset (see also https://doi.org/gddf). In this course, we will demonstrate how to set up HALF pipe for your data using an example. We will also discuss the lessons we learned when processing diverse datasets within the ENIGMA consortium and how these might inform your analysis plan. [1]: https://doi.org/10.1002/hbm.25829 [2]: https://sylabs.io/ [3]: https://www.docker.com/ [4]: https://www.nitrc.org/projects/mricron [5]: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes [6]: https://openneuro.org/datasets/ds003097 [7]: https://openneuro.org/datasets/ds002785/versions/2.0.0 [8]: https://openneuro.org/datasets/ds002790/versions/2.0.0