Evaluation of fNIRS signal components elicited by cognitive and hypercapnic stimuli

Pratusha Reddy, Meltem Izzetoglu, Patricia A Shewokis, Michael Sangobowale, Ramon Diaz-Arrastia, Kurtulus Izzetoglu, Pratusha Reddy, Meltem Izzetoglu, Patricia A Shewokis, Michael Sangobowale, Ramon Diaz-Arrastia, Kurtulus Izzetoglu

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.

Trial registration: ClinicalTrials.gov NCT01762475 NCT01789164.

Conflict of interest statement

fNIRS Devices, LLC., manufactures the optical brain imaging instrument which was utilized in this study. M.I and K.I were involved in the technological development and thus offered a minor share in the startup firm, fNIRS Devices, LLC that licensed IP from Drexel University. The remaining authors declare no conflicts of interest.

© 2021. The Author(s).

Figures

Figure 1
Figure 1
Experimental protocols. (a) In cognitive task, each participant started with a brief instructional session regarding UAS controls and task objectives, followed by five 12 min task conditions of varying difficulties. The first three task conditions were of easier difficulty, while the last two were of harder difficulty. A 5 and 15-min breaks were given between instructional and first easy condition, and last easy and first hard condition. (b) In hypercapnic task, each participant began with a 30 s baseline period (Room Air – 0.04% CO2), followed by three hypercapnic (5% CO2) and normocapnic (Room Air) conditions of 60 s each.
Figure 2
Figure 2
Continuous wave functional Near Infrared Spectroscopy (CW-fNIRS) sensor used. (a) Sensor layout, where red squares represent light sources (LEDs), while grey squares represent the photodetectors. (b) Location of the 16 long and 2 short source-detector separation channels overlayed on the prefrontal cortex. Black boxes highlight the channels used to calculate measures for right and left middle frontal areas.
Figure 3
Figure 3
Continuous wavelet transforms of HbO signals from right-middle frontal area of long and short SDS measurements taken during cognitive task for one representative subject. (a) Changes in amplitude or absolute value of the wavelet coefficients as a function of frequency. (b) Changes in amplitude as a function of time and frequency. Dashed vertical lines in A and horizontal lines in B represent frequency intervals for very low frequency (VLF), myogenic (Myo), respiratory (Resp) and cardiac bands. Figure generated using custom code in Matlab (MathWorks, R2019b).
Figure 4
Figure 4
Differences in relative energy density (relε) between long- and short- source detector separation (SDS) measurements per biomarker. Colored lines represent mean of the data (channels, conditions and subjects), while associated shaded portions represent standard error of mean. Dashed black lines represent beginning and ending of very low frequency (VLF), myogenic (Myo), respiratory (Resp) and cardiac bands. The embedded plot represents log-transformed relε values of respiratory band (0.15 to 0.4 Hz). Figure generated using custom code in Matlab (MathWorks, R2019b).
Figure 5
Figure 5
Effect of condition on ε per SDS measurement, band and biomarker. Dots represent mixed model estimates, while lines represent confidence intervals. **p < 0.01, *p < 0.05. Significance bars reflect p values that were not adjusted using FDR correction. Only conditions pairs that were significant in both SDS measurements are shown here. Figure generated using custom code in R (R Core Team, 2019).
Figure 6
Figure 6
Differences in relative energy density (relε) between long- and short- source detector separation (SDS) measurements per biomarker during hypercapnic stimulus. Colored lines represent mean of the data (channels and subjects), while associated shaded portions represent standard error of mean. Dashed black lines represent beginning and ending of very low frequency (VLF), myogenic (Myo), respiratory (Resp) and cardiac bands. Figure generated using custom code in Matlab (MathWorks, R2019b).

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