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bioRxiv · 10.1101/2020.09.11.257634

LIONirs: flexible Matlab toolbox for fNIRS data analysis.

Tremblay, J.·Martinez-Montes, E.·Husser, A.·Caron-Desrochers, L.·Pouliot, P.·Vannasing, P.·Gallagher, A.
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Abstract

BackgroundFunctional near-infrared spectroscopy (fNIRS) is a suitable tool for recording brain function in pediatric or challenging populations. As with other neuroimaging techniques, the scientific community is engaged in an evolving debate regarding the most adequate methods for performing fNIRS data analyses. New methodWe introduce LIONirs, a neuroinformatics toolbox for fNIRS data analysis, designed to follow two main goals: (1) flexibility, to explore several methods in parallel and verify results using 3D visualization; (2) simplicity, to apply a defined processing pipeline to a large dataset of subjects by using the MATLAB Batch System. ResultsWithin the graphical user interfaces (DisplayGUI), the user can reject noisy intervals and correct artifacts, while visualizing the topographical projection of the data onto the 3D head representation. Data decomposition methods are available for the identification of relevant signatures, such as brain responses or artifacts. Multimodal data recorded simultaneously to fNIRS, such as physiology, electroencephalography or audio-video, can be visualized using the DisplayGUI. The toolbox includes several functions that allow one to read, preprocess, and analyze fNIRS data, including task-based and functional connectivity measures. Comparison with existing methodsSeveral good neuroinformatics tools for fNIRS data analysis are currently available. None of them emphasize multimodal visualization of the data throughout the preprocessing steps and multidimensional decomposition, which are essential for understanding challenging data. Furthermore, LIONirs provides compatibility and complementarity with other existing tools by supporting common data format. ConclusionsLIONirs offers a flexible platform for basic and advanced fNIRS data analysis, shown through real experimental examples. HighlightsO_LIThe LIONirs toolbox is designed for fNIRS data inspection and visualization. C_LIO_LIMethods are integrated for isolation of relevant activity and correction of artifacts. C_LIO_LIMultimodal auxiliary, EEG or audio-video are visualized alongside the fNIRS data. C_LIO_LITask-based and functional connectivity measure analysis tools are available. C_LIO_LIThe code structure allows to automated and standardized analysis of large data set. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/257634v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@9cced8org.highwire.dtl.DTLVardef@cfdcc3org.highwire.dtl.DTLVardef@a694fcorg.highwire.dtl.DTLVardef@7f35f4_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Tremblay, J., Martinez-Montes, E., Husser, A., Caron-Desrochers, L., Pouliot, P., Vannasing, P., Gallagher, A.. 2020-09-11. LIONirs: flexible Matlab toolbox for fNIRS data analysis.. https://doi.org/10.1101/2020.09.11.257634

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