Effects of Systemic Physiology on Mapping Resting-State Networks Using Functional Near-Infrared Spectroscopy

Abstract

Resting-state functional connectivity (rsFC) has gained popularity mainly due to its simplicity and potential for providing insights into various brain disorders. In this vein, functional near-infrared spectroscopy (fNIRS) is an attractive choice due to its portability, flexibility, and low cost, allowing for bedside imaging of brain function. While promising, fNIRS suffers from non-neural signal contaminations (i.e., systemic physiological noise), which can increase correlation across fNIRS channels, leading to spurious rsFC networks. In the present work, we hypothesized that additional measurements with short channels, heart rate, mean arterial pressure, and end-tidal CO2 could provide a better understanding of the effects of systemic physiology on fNIRS-based resting-state networks. To test our hypothesis, we acquired 12 min of resting-state data from 10 healthy participants. Unlike previous studies, we investigated the efficacy of different pre-processing approaches in extracting resting-state networks. Our results are in agreement with previous studies and reinforce the fact that systemic physiology can overestimate rsFC. We expanded on previous work by showing that removal of systemic physiology decreases intra- and inter-subject variability, increasing the ability to detect neural changes in rsFC across groups and over longitudinal studies. Our results show that by removing systemic physiology, fNIRS can reproduce resting-state networks often reported with functional magnetic resonance imaging (fMRI). Finally, the present work details the effects of systemic physiology and outlines how to remove (or at least ameliorate) their contributions to fNIRS signals acquired at rest.

Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain

“…Concerning the question how the systemic physiological signals should be processed and analyzed within the SPA-fNIRS approach, there is currently no standard procedure available, and the research how to analyze fNIRS data along systemic physiological data just started. Different methods have been employed so far, including the calculation of block-averages of stimulus-evoked changes in the signals, 95 , 96 block-averaging with subsequent correlation analysis to investigate the neurosystemic functional connectivity, 87 the use of a general linear model (GLM) that treats the systemic physiological signals as additional regressors, 89 , 91 , 92 , 97 – 99 wavelet coherence analysis, 97 the use coupling functions derived from the phases of the signals via the continuous wavelet transform, 24 oblique subspace projections signal decomposition, 100 or the recent approach using a GLM and regularized temporally embedded canonical correlation analysis (tCCA). 27 , 101 The use of tCCA allows one to create optimal nuisance regressors by considering non-instantaneous and non-constant coupling between the recorded signals and by intelligently combining any available auxiliary signals (e.g., systemic physiology and short-channel fNIRS recordings).…”

“…A comparison between short-channel regression, and SPA-fNIRS-based regression has been recently published by Abdalmalak et al. 99 investigating the impact of both methods on the detection and quantification of functional resting-state brain networks. The authors concluded that both approaches are useful and necessary to detect the functional networks and that short-channels and systemic physiological signals “provided complementary information that could not be obtained by either regressor separately.” Figure 9 shows the impact of different regression methods of fNIRS resting-state functional connectivity measurements.…”

“… Comparison between different techniques to remove confounding physiological factors in resting-state fNIRS measurements. Shown are the findings of Abdalmalak et al 99 They investigated the impact of applying different physiological interference reduction methods on detecting resting-state functional brain networks. Depicted are the functional brain networks determined using raw data, with short-channel regression, with short-channel and systemic physiology (HR, MAP, and ) regression, and with PCA-based regression.…”

“…The study shows the necessity and usefulness of the SPA-fNIRS to determine functional resting-state networks. Subfigures (a) and (b) modified from Abdalmalak et al 99 …”

Using preregistration as a tool for transparent fNIRS study design

“…So far, the most accurate way of removing the extracerebral part of such artifacts is by using the hardware-based solution: shortdistance channels (SDCs). SDCs are generated by placing the source and detector at a distance of < 1 cm, and ideally at ~0.8 cm for adults 72 , to capture the hemodynamic activity from extracerebral tissue only 33,100,[114][115][116][117][118] . SDC signals can be used for correcting the regular distance channels, for instance, by applying a regression-based approach.…”