Characterization of Mayer wave oscillations in functional near-infrared spectroscopy using a physiologically informed model of the neural power spectra

Significance: Mayer waves are spontaneous oscillations in arterial blood pressure that can mask cortical hemodynamic responses associated with neural activity of interest. Aim: To characterize the properties of oscillations in the fNIRS signal generated by Mayer waves in a large sample of fNIRS recordings. Further, we aim to determine the impact of short-channel correction for the attenuation of these unwanted signal components. Approach: Mayer wave oscillation parameters were extracted from 310 fNIRS measurements using the Fitting Oscillations & One-Over-F (FOOOF) method to compute normative values. The effect of short-channel correction on Mayer wave oscillation power was quantified on 222 measurements. The practical benefit of the short-channel correction approach for reducing Mayer waves and improving response detection was also evaluated on a subgroup of 17 fNIRS measurements collected during a passive auditory speech detection experiment. Results: Mayer wave oscillations had a mean frequency of 0.108 Hz, bandwidth of 0.075 Hz, and power of 3.5 μM2/Hz. The distribution of oscillation signal power was positively skewed, with some measurements containing large Mayer waves. Short-channel correction significantly reduced the amplitude of these undesired signals; greater attenuation was observed for measurements containing larger Mayer wave oscillations. Conclusions: A robust method for quantifying Mayer wave oscillations in the fNIRS signal spectrum was presented and used to provide normative parameterization. Short-channel correction is recommended as an approach for attenuating Mayer waves, particularly in participants with large oscillations.

Phase Velocity of Facial Blood Volume Oscillation at a Frequency of 0.1 Hz

Facial blood flow, which typically exhibits distinctive oscillation at a frequency of around 0.1 Hz, has been extensively studied. Although this oscillation may include important information about blood flow regulation, its origin remains unknown. The spatial phase distribution of the oscillation is thus desirable. Therefore, we visualized facial blood volume oscillation at a frequency of around 0.1 Hz using a digital camera imaging method with an improved approximation equation, which enabled precise analysis over a large area. We observed a slow spatial movement of the 0.1-Hz oscillation. The oscillation phase was not synchronized, but instead moved slowly. The phase velocity varies with person, measurement location, and time. An average phase velocity of 3.8 mm/s was obtained for several subjects. The results are consistent with previous studies; however, the conventional explanation that the blood flow at a certain point oscillates independently of adjacent areas should be corrected. If the primary origin of the movement is myogenic activity, the movement may ascend along a blood vessel toward the upstream. Otherwise, the oscillation and its propagation can be considered to be related to Mayer waves. By determining the mechanism, some questions regarding Mayer waves can be answered. The direction of the wave (upstream or downstream) provides important information.

Characteristic of Mayer Waves in Electrophysiological, Hemodynamic and Vascular Signals

We evaluated the properties of oscillations in the Mayer waves (MW) frequency range ([Formula: see text][Formula: see text]Hz) detected in blood pressure, heart rate variability, cerebral blood oxygenation changes and evolution of electroencephalographic (EEG) rhythms to elucidate the mechanisms of MW generation. We examined the persistence of MW in different signals and stability of their oscillations on the level of individual MW waveforms, which was achieved by applying matching pursuit (MP). MP yields adaptive time-frequency approximation of signal’s structures in terms of frequency, amplitude, time occurrence, and time-span. The number of waveforms contributing to 95% of the energy of the signals was vastly different for the time series, but the average number of waveforms conforming to the MW criteria was almost the same ([Formula: see text] per 120[Formula: see text]s epoch). In all the investigated signals, MW had the same distributions of frequency and the number of cycles. We show that the MW energy ratios in different signals varied strongly, [Formula: see text]. The highest percentage of MW energy was observed in blood pressure signals, heart rate variability, and reduced hemoglobin, in contrast to brain signals and oxygenated hemoglobin. The percentage of MW energy was related to the strength of causal influence exerted by them on the other signals. Our results indicate existence of a common mechanism of MW generation and support the hypothesis of MW generation in the baroreflex loop; however, they do not exclude the action of a central pacemaker.