Abstract
The employment of functional near-infrared spectroscopy (fNIRS) as a method of brain imaging has increased over the last few years due to its portability, low-cost and robustness to subject movement. Experiments with fNIRS are designed in the face of a limited number of sources and detectors (optodes) to be positioned on selected portion(s) of the scalp. The optodes locations represent an expectation of assessing cortical regions relevant to the experiment’s hypothesis. However, this translation process remains a challenge for fNIRS experimental design. In the present study, we propose an approach that automatically decides the location of fNIRS optodes from a set of predefined positions with the aim of maximizing the anatomical specificity to brain regions-of-interest. The implemented method is based on photon transport simulations on two head atlases. The results are compiled into the publicly available “fNIRS Optodes’ Location Decider” (fOLD). This toolbox is a first-order approach to bring the achieved advancements of parcellation methods and meta-analyses from functional magnetic resonance imaging to more precisely guide the selection of optode positions for fNIRS experiments.
Highlights
Functional near-infrared spectroscopy is a technique capable of measuring concentration changes of oxygenated and deoxygenated hemoglobin from the variations of absorbed near-infrared light during its transportation in tissues[1]
The results were compiled into a toolbox to facilitate the definition of optodes positions, the functional near-infrared spectroscopy (fNIRS) Optodes’ Location Decider
From the methods described in Methods section and the derived results for both 10–10 and 10–5 extended positions for all brain parcellation methods considered and based on the two head atlases of reference, we developed, in Matlab2017a App Designer[42], the toolbox “fNIRS Optodes’ Location Decider”
Summary
To create a single image file for the final tissue segmentation, we have defined each voxel to be part of a given tissue if its probability was higher than all other tissues and if it was greater than 0.2. The latter was important for boundary voxels e.g. between scalp and air. In addition to the Colin[27] head model, which is based on 27 averages of a single subject, we have considered a second head atlas based on the tissue probability maps provided in SPM12. Once the tissue segmentation procedure was performed, the scalp boundaries were defined for both brain atlases of interest, and we proceeded with the spatial localization of optodes to be used for the photon migration simulations
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