Evaluation of fNIRS signal components elicited by cognitive and hypercapnic stimuli

Abstract

Functional near infrared spectroscopy (fNIRS) measurements are confounded by signal components originating from multiple physiological causes, whose activities may vary temporally and spatially (across tissue layers, and regions of the cortex). Furthermore, the stimuli can induce evoked effects, which may lead to over or underestimation of the actual effect of interest. Here, we conducted a temporal, spectral, and spatial analysis of fNIRS signals collected during cognitive and hypercapnic stimuli to characterize effects of functional versus systemic responses. We utilized wavelet analysis to discriminate physiological causes and employed long and short source-detector separation (SDS) channels to differentiate tissue layers. Multi-channel measures were analyzed further to distinguish hemispheric differences. The results highlight cardiac, respiratory, myogenic, and very low frequency (VLF) activities within fNIRS signals. Regardless of stimuli, activity within the VLF band had the largest contribution to the overall signal. The systemic activities dominated the measurements from the short SDS channels during cognitive stimulus, but not hypercapnic stimulus. Importantly, results indicate that characteristics of fNIRS signals vary with type of the stimuli administered as cognitive stimulus elicited variable responses between hemispheres in VLF band and task-evoked temporal effect in VLF, myogenic and respiratory bands, while hypercapnic stimulus induced a global response across both hemispheres.

Estimation of Respiratory Rate from Functional Near-Infrared Spectroscopy (fNIRS): A New Perspective on Respiratory Interference

“…On the other hand, respiratory fluctuations affect the cerebral blood volume and flow [ 38 ]. Therefore, it can be expected that alternation in RR is revealed in fNIRS, and in particular, in signals [ 26 ]. Our underlying assumption, which was inspired from PPG-based studies [ 25 ], was that fluctuations in the frequency of signal’s baseline wander can be related to RR.…”

“…Inspired by the studies that estimated RR from the photoplethysmography (PPG) [ 25 ], our hypothesis is that the baseline wander of fNIRS also might be related to alternations in the respiration. On the other hand, it has been shown that respiration has a stronger influence on the than the signal [ 26 ]. Hence, the basis of our method is to (i) extract the troughs of the signal, (ii) synthesize the baseline wander using the cubic spline interpolation of the extracted troughs, and (iii) find the dominant frequency of the baseline wander to estimate RR.…”

Individual differences in skill acquisition and transfer assessed by dual task training performance and brain activity

“…Similar, effects on brain activity changes using individual characteristics have been reported by other researchers [ 7 , 27 , 55 , 58 , 60 – 62 , 64 – 67 ]. The improvement was especially observed for HbR measures, which has been shown to be a more sensitive measure of cognitive activity than HbO for this particular task [ 45 ]. Furthermore, inclusion of these individual differences has provided further insight into interaction between performance and mental effort changes during multitasking training.…”

“…The model investigating behavioral measures used a random intercept design, while the model investigating mean fNIRS measures used non-correlated random intercept and slope design. The random slope term (0 + Short | ID) when evaluating mean fNIRS measures was used to separate task-induced extracerebral activity from that related to cerebral activity, which was shown to be significant in another study using the same dataset [ 45 ]. Lastly, to investigate performance and mental effort simultaneously, Eq.…”

Estimation of Respiratory Rate during Biking with a Single Sensor Functional Near-Infrared Spectroscopy (fNIRS) System

“…Although the HR estimation from fNIRS measurements has been addressed in several studies, no research, except for our previous study [ 16 ], has yet investigated the possibility of RR estimation from fNIRS. Respiration is one of the main physiological interferences in fNIRS, manifesting in its spectrum (∼0.2 to 0.4 Hz) [ 17 , 18 ]. The reason for such contamination is twofold; changes of the blood flow within the body during inhaling and exhaling, and the influence of respiratory oscillations on cerebral blood volume and flow [ 19 ].…”