Characterization of the relative contributions from systemic physiological noise to whole-brain resting-state functional near-infrared spectroscopy data using single-channel independent component analysis

Abstract

Characterization of the relative contributions from systemic physiological noise to whole-brain resting-state functional near-infrared spectroscopy data using single-channel independent component analysis," Neurophoton. Abstract. Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique used to measure changes in oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) in the brain. In this study, we present a decomposition approach based on single-channel independent component analysis (scICA) to investigate the contribution of physiological noise to fNIRS signals during rest. Single-channel ICA is an underdetermined decomposition method, which separates a single time series into components containing nonredundant spectral information. Using scICA, fNIRS signals from a total of 17 subjects were decomposed into the constituent physiological components. The percentage contribution of the classes of physiology to the fNIRS signals including low-frequency (LF) fluctuations, respiration, and cardiac oscillations was estimated using spectral domain classification methods. Our results show that LF oscillations accounted for 40% to 55% of total power of both the oxy-Hb and deoxy-Hb signals. Respiration and its harmonics accounted for 10% to 30% of the power, and cardiac pulsations and cardio-respiratory components accounted for 10% to 30%. We describe this scICA method for decomposing fNIRS signals, which unlike other approaches to spatial covariance reduction is applicable to both single-or multiple-channel fNIRS signals and discuss how this approach allows functionally distinct sources of noise with disjoint spectral support to be separated from obscuring systemic physiology.

Characterization and correction of the false-discovery rates in resting state connectivity using functional near-infrared spectroscopy

“…Using short-separation measurements to remove physiology-based artifacts assumes that the same components are found in both the skin and brain. In a recently published paper, 71 we found up to a 7-s delay between different measurements sites; this delay might be found between the skin and brain regions. Therefore, a simple regressor may not be efficient enough to remove the physiological noise.…”

Short-channel regression in functional near-infrared spectroscopy is more effective when considering heterogeneous scalp hemodynamics

“…We deliberately used real resting-state measurements instead of purely simulated systemic signals, because we expected that simulated noise could only insufficiently reflect heterogeneous behavior in the scalp and brain layers. A possible limitation of this approach is that resting-state measurements may contain spontaneous neural activity with amplitudes comparable to functional brain activity, 7 , 54 , 55 hampering the efficacy of the regression. Therefore, it is important to investigate the performance of the different GLM approaches through simulations and actual motor-execution runs.…”

“… 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 – 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 ( ) 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.…”

An Augmented-Reality fNIRS-Based Brain-Computer Interface: A Proof-of-Concept Study

“…It is important to note that fNIRS measurements are contaminated by systemic interference of especially (but not limited to) extracerebral regions, which is mainly caused by cardiac pulsations, respiration, and blood-pressure variations (Boas et al, 2004;Tachtsidis and Scholkmann, 2016). Several approaches have been reported in the literature to reduce these noises: conventional band-pass filtering (Hocke et al, 2018;Pinti et al, 2019); modeling physiological noises as a sum of sinusoidal functions with known frequencies where their amplitudes are estimated by using the extended Kalman filter and regressed out using a general linear model (Prince et al, 2003); global signalcovariance removal by either principal/independent component analysis (Zhang et al, 2005;Aarabi and Huppert, 2016) or global average procedures (Batula et al, 2017); adaptive filters that use recursive least-squares estimation methods (Nguyen et al, 2018) or short-distance channel (SDC) regression (Saager and Berger, 2005;Saager et al, 2011;Goodwin et al, 2014). In fNIRS measurements these SDCs are channels that have reduced inter-optode separations such that the interrogated volume is confined primarily to extracerebral regions (Goodwin et al, 2014).…”