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.
Highlights
Techniques, which first entails thorough characterization and analysis of signal components arising because of the task being studied
Our results indicate that functional near infrared spectroscopy (fNIRS) signal components have unique temporal, spatial and frequency responses to different types of stimuli
The following discussion of these results begins by identifying physiological factors affecting fNIRS signals, followed by how much these factors contribute to various fNIRS markers, and how their contributions vary axially, laterally, and temporally with different stimuli
Summary
Techniques, which first entails thorough characterization and analysis of signal components arising because of the task being studied. The simplest and the most utilized class of methods to remove frequencies associated with cardiac, respiratory and myogenic activities, consist of applying a band pass (cut-off frequencies at 0.01 and 0.09 Hz) or low pass (cut-off frequency at 0.09 Hz) Finite Impulse Response, or Butterworth filters[25] Other filters such as Gaussian, Kalman, Wiener and hemodynamic response filters have been commonly used to remove aforementioned systemic noises in real time or off-line signal processing applications[26]. Another study reported that including end-tidal C O2 (EtCO2—surrogate for PaCO2 measures) in the interpretation of fNIRS signals improved ability to monitor pain[59] These findings indicate that to comprehensively evaluate cerebral hemodynamic changes due to a task/stimulus, it is important to not just remove the non-neuronal effects, but to analyze contribution of each of the signal components to the fNIRS measurements, separately and in relation to each other.
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