Investigation of functional near-infrared spectroscopy signal quality and development of the hemodynamic phase correlation signal

Abstract

Significance: There is a longstanding recommendation within the field of fNIRS to use oxygenated ([Formula: see text]) and deoxygenated (HHb) hemoglobin when analyzing and interpreting results. Despite this, many fNIRS studies do focus on [Formula: see text] only. Previous work has shown that [Formula: see text] on its own is susceptible to systemic interference and results may mostly reflect that rather than functional activation. Studies using both [Formula: see text] and HHb to draw their conclusions do so with varying methods and can lead to discrepancies between studies. The combination of [Formula: see text] and HHb has been recommended as a method to utilize both signals in analysis. Aim: We present the development of the hemodynamic phase correlation (HPC) signal to combine [Formula: see text] and HHb as recommended to utilize both signals in the analysis. We use synthetic and experimental data to evaluate how the HPC and current signals used for fNIRS analysis compare. Approach: About 18 synthetic datasets were formed using resting-state fNIRS data acquired from 16 channels over the frontal lobe. To simulate fNIRS data for a block-design task, we superimposed a synthetic task-related hemodynamic response to the resting state data. This data was used to develop an HPC-general linear model (GLM) framework. Experiments were conducted to investigate the performance of each signal at different SNR and to investigate the effect of false positives on the data. Performance was based on each signal’s mean [Formula: see text]-value across channels. Experimental data recorded from 128 participants across 134 channels during a finger-tapping task were used to investigate the performance of multiple signals [[Formula: see text] , HHb, HbT, HbD, correlation-based signal improvement (CBSI), and HPC] on real data. Signal performance was evaluated on its ability to localize activation to a specific region of interest. Results: Results from varying the SNR show that the HPC signal has the highest performance for high SNRs. The CBSI performed the best for medium-low SNR. The next analysis evaluated how false positives affect the signals. The analyses evaluating the effect of false positives showed that the HPC and CBSI signals reflect the effect of false positives on [Formula: see text] and HHb. The analysis of real experimental data revealed that the HPC and HHb signals provide localization to the primary motor cortex with the highest accuracy. Conclusions: We developed a new hemodynamic signal (HPC) with the potential to overcome the current limitations of using [Formula: see text] and HHb separately. Our results suggest that the HPC signal provides comparable accuracy to HHb to localize functional activation while at the same time being more robust against false positives