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. 2022 Dec 27;36(2):172–191. doi: 10.1007/s10548-022-00935-8

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 5 — Example of general output figures generated by fMRIflows after executing the functional preprocessing pipeline. The dataset used to generate these figures was recorded with a TR of 600 ms and had a total of 600 volumes per run. Preprocessing included a low-pass filter at 0.2 Hz. Distribution plots on the right side of the figures in part A and B represent value frequency in y-direction. A Depiction of the nuisance confounds FD, DVARS and TV. Detected outlier volumes are highlighted with vertical black bars. B Estimation of translation head motion after application of low-pass filtering at 0.2 Hz in color, and before temporal filtering in light gray. C Depiction of brain masks used to compute DVARS (red), and temporal (green) and anatomical (blue) CompCor confounds, overlaid on the mean functional image (grey) (Color figure online)