Graph theoretical approach to functional connectivity in prefrontal cortex via fNIRS

Abstract

Functional near-infrared spectroscopy (fNIRS) has been proposed as an affordable, fast, and robust alternative to many neuroimaging modalities yet it still has long way to go to be adapted in the clinic. One request from the clinicians has been the delivery of a simple and straightforward metric (a so-called biomarker) from the vast amount of data a multichannel fNIRS system provides. We propose a simple-straightforward signal processing algorithm derived from fNIRS-HbO(2) data collected during a modified version of the color-word matching Stroop task that consists of three different conditions. The algorithm starts with a wavelet-transform-based preprocessing, then uses partial correlation analysis to compute the functional connectivity matrices at each condition and then computes the global efficiency values. To this end, a continuous wave 16 channels fNIRS device (ARGES Cerebro, Hemosoft Inc., Turkey) was used to measure the changes in HbO(2) concentrations from 12 healthy volunteers. We have considered 10\% of strongest connections in each network. A strong Stroop interference effect was found between the incongruent against neutral condition (p = 0.01) while a similar significance was observed for the global efficiency values decreased from neutral to congruent to incongruent conditions {[}F(2

33thorn = 3.46, p = 0.043]. The findings bring us closer to delivering a biomarker derived from fNIRS data that can be reliably and easily adopted by the clinicians. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).