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. 2020 Jul-Dec:12:100598.
doi: 10.1016/j.softx.2020.100598. Epub 2020 Oct 21.

Graphical interface for automated management of motion artifact within fMRI acquisitions: INFOBAR

Manish Anand  1 , Jed A Diekfuss  1   2   3 , Alexis B Slutsky-Ganesh  4 , Scott Bonnette  1 , Dustin R Grooms  5   6   7 , Gregory D Myer  1   8   9   10   2   3
Affiliations

Graphical interface for automated management of motion artifact within fMRI acquisitions: INFOBAR

Manish Anand et al. SoftwareX. 2020 Jul-Dec.

Abstract

Independent Component Analysis-based Automatic Removal of Motion Artifacts (ICA-AROMA; Pruim et al., 2015) is a robust approach to remove brain activity related to head motion within functional magnetic resonance imaging (fMRI) datasets. However, ICA-AROMA requires command line implementation and customized code to batch process large datasets. We developed a cross-platform, open-source graphical user Interface for Batch processing fMRI datasets using ICA-AROMA (INFOBAR). INFOBAR allows a user to search directories, identify appropriate datasets, and batch execute ICA-AROMA. INFOBAR also has additional data processing options and visualization features to support all researchers interested in mitigating head motion artifact in post-processing using ICA-AROMA.

Keywords: Batch process; GUI; ICA-AROMA; Python.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1.
Fig. 1.
The main window of the GUI showing the control panel on the top (green), results table in the middle (red) and status bar at the bottom (blue).
Fig. 2.
Fig. 2.
Settings menu allows the user to set path to ICA-AROMA, and configure various options provided by the program.
Fig. 3.
Fig. 3.
(Top) Left clicking a dataset shows the translation, rotation and displacement graphs generated through FSL, (Bottom) Right/middle-clicking or pressing ‘w’ on the dataset shows independent components associated with motion, as well as post-processed primary zstat and model fit curves, if data has been post-processed.
Fig. 4.
Fig. 4.
Process flow chart representing the example case.
See this image and copyright information in PMC

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