Abstract
.Significance: The reliability of functional near-infrared spectroscopy (fNIRS) measurements is reduced by systemic physiology. Short-channel regression algorithms aim at removing systemic “noise” by subtracting the signal measured at a short source–detector separation (mainly scalp hemodynamics) from the one of a long separation (brain and scalp hemodynamics). In literature, incongruent approaches on the selection of the optimal regressor signal are reported based on different assumptions on scalp hemodynamics properties.Aim: We investigated the spatial and temporal distribution of scalp hemodynamics over the sensorimotor cortex and evaluated its influence on the effectiveness of short-channel regressions.Approach: We performed hand-grasping and resting-state experiments with five subjects, measuring with 16 optodes over sensorimotor areas, including eight 8-mm channels. We performed detailed correlation analyses of scalp hemodynamics and evaluated 180 hand-grasping and 270 simulated (overlaid on resting-state measurements) trials. Five short-channel regressor combinations were implemented with general linear models. Three were chosen according to literature, and two were proposed based on additional physiological assumptions [considering multiple short channels and their Mayer wave (MW) oscillations].Results: We found heterogeneous hemodynamics in the scalp, coming on top of a global close-to-homogeneous behavior (correlation 0.69 to 0.92). The results further demonstrate that short-channel regression always improves brain activity estimates but that better results are obtained when heterogeneity is assumed. In particular, we highlight that short-channel regression is more effective when combining multiple scalp regressors and when MWs are additionally included.Conclusion: We shed light on the selection of optimal regressor signals for improving the removal of systemic physiological artifacts in fNIRS. We conclude that short-channel regression is most effective when assuming heterogeneous hemodynamics, in particular when combining spatial- and frequency-specific information. A better understanding of scalp hemodynamics and more effective short-channel regression will promote more accurate assessments of functional brain activity in clinical and research settings.
Highlights
The susceptibility to non-neuronal signals is specific to the measurement principle of Functional near-infrared spectroscopy (fNIRS), all technologies that infer brain activity via hemodynamic changes, i.e., fNIRS, functional magnetic resonance imaging, and positron emission tomography, are affected
The spatiotemporal distribution of scalp hemodynamics over sensorimotor areas is presented with respect to the incongruent assumptions of heterogeneity and homogeneity (Sec. 3.1)
The performance of the five general linear model (GLM) regression approaches is reported for simulated hemodynamic responses (HRs), which were overlaid on actual resting-state measurements (Sec. 3.2)
Summary
Functional near-infrared spectroscopy (fNIRS) enables the noninvasive measurement of human brain activity by monitoring concentration changes of oxygenated hemoglobin (O2Hb) and deoxygenated hemoglobin (HHb) in the blood.[1,2,3,4] fNIRS has evolved from a tool for basic research to a widely used technique to investigate brain activity in nonconstrained environments.[5,6] Despite its versatile use, there remain several challenges, in particular, the sensitivity of continuous-wave fNIRS to hemodynamic changes of non-neuronal origin.[2,7,8,9,10] These are often referred to as physiological “noise” or “interference” and include systemic activities, such as cardiac pulsation (1 to 2 Hz), respiration (0.2 to 0.4 Hz), low-frequency oscillations (∼0.1 Hz) and very low-frequency oscillations (0.01 to 0.05 Hz),[11] and an increase in blood flow through sympathetic nervous activity.[12] These artifacts generate signal changes that may mimic or mask true task-evoked hemodynamic responses (HRs) and may lead to false positives or false negatives.[8,10,13] This challenge has been acknowledged and its significance recognized in the recent years by the fNIRS community.[8] the susceptibility to non-neuronal signals is specific to the measurement principle of fNIRS, all technologies that infer brain activity via hemodynamic changes, i.e., fNIRS, functional magnetic resonance imaging, and positron emission tomography, are affected
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