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

Abstract

AimWe 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.ApproachWe synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis.ResultsThis 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.ConclusionsThe 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.SignificanceImage 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.

Frontotemporal activation differs between perception of simulated cochlear implant speech and speech in background noise: An image-based fNIRS study

“…Finally, data were converted to hemoglobin concentration values using a differential path-length factor of 6 for both wavelengths. Volumetric timeseries data were constructed from these cleaned channel data following the procedure outlined by Forbes et al (2021) .…”

“…After reconstruction, general linear modeling is used to estimate the amplitude of HbO and HbR for each condition and for each subject across the measured voxels.We used an HRF derived from diffuse optical tomography (DOT) data for both HbO and HbR responses because it has shown to be a better fit than HRFs derived from fMRI ( Forbes et al, 2021 ; Hassanpour et al, 2014 ). The GLM comprised of eight regressors, including (1) speech in quiet (SQ), (2) speech in noise with high-intelligibility (HN), (3) vocoded speech with high-intelligibility (HV), (4) correct speech in noise with low-intelligibility (LN c ), (5) correct vocoded speech with low-intelligibility (LV c ), (6) incorrect speech in noise with low-intelligibility (LN i ), (7) incorrect vocoded speech with low-intelligibility (LV i ), and (8) time stamps associated with the vocal responses after each trial.…”

“…Therefore, it is important to check for consistency after the image reconstruction process. Following the procedures described in Forbes et al (2021) (see section 6.2), we correlated the channel-based time series data with the image-reconstructed time series for all subjects in this study. The mean amplitude of HbO and HbR were extracted from a 2 cm size sphere of voxels around the voxel with maximum sensitivity for each channel.…”

“…First, we designed an fNIRS probe to cover left frontal and temporal regions and conducted volumetric analyses which can provide better alignment of data across participants and localize activation to cortical regions. This analysis method has been validated through comparison with concurrently-measured fMRI data ( Eggebrecht et al, 2012 ; Wijeakumar et al, 2017 ) and has been applied in studies using only fNIRS ( Forbes et al, 2021 ; Wijeakumar et al, 2019 ). This method will allow us to assess the degree to which these compensatory mechanisms activate across temporal and frontal cortices, and further, determine how they might differ during recognition of artificial versus natural forms of distorted speech.…”

Disentangling Age and Schooling Effects on Inhibitory Control Development: An fNIRS Investigation

“…A potential way to improve upon this would be to conduct more targeted analyses. For instance, novel image reconstruction uses a head model to generate functional images of the fNIRS data, transforming surface level channel-based data into a volumetric representation within the brain (Forbes et al, 2021). This would allow for greater comparability with fMRI investigations.…”