A processing pipeline for image reconstructed fNIRS analysis using both MRI templates and individual anatomy

Samuel Forbes, Sobanawartiny Wijeakumar, Adam Eggebrecht, Vincent Magnotta, John Spencer

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Abstract

Significance: Image reconstruction of fNIRS data is a useful technique for transforming channel-based fNIRS into a volumetric representation and managing spatial variance based on optode location. We present a novel integrated pipeline for image reconstruction of fNIRS data using either MRI templates or individual anatomy.
Aim: We demonstrate a pipeline with accompanying code to allow users to clean and prepare optode location information, prepare and standardize individual anatomical images, create the light model, run the 3D image reconstruction, and analyze data in group space.
Approach: We synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis.
Results: This pipeline has been tested using both templates and individual anatomy, and on data from different fNIRS data collection systems. We show high temporal correlations between channel-based and image-based fNIRS data. In addition, we demonstrate the reliability of this pipeline with a sample dataset that included 74 children as part of a longitudinal study taking place in Scotland. We demonstrate good correspondence between data in channel space and image reconstructed data.
Conclusions: The pipeline presented here makes a unique contribution by integrating multiple tools to assemble a complete pipeline for image reconstruction in fNIRS. We highlight further issues that may be of interest to future software developers in the field.
Original languageEnglish
JournalNeurophotonics
Publication statusAccepted/In press - 18 May 2021

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