Cerebral functional connectivity and Mayer waves in mice: Phenomena and separability

Abstract

Resting-state functional connectivity is a growing neuroimaging approach that analyses the spatiotemporal structure of spontaneous brain activity, often using low-frequency (<0.08 Hz) hemodynamics. In addition to these fluctuations, there are two other low-frequency hemodynamic oscillations in a nearby spectral region (0.1-0.4 Hz) that have been reported in the brain: vasomotion and Mayer waves. Despite how close in frequency these phenomena exist, there is little research on how vasomotion and Mayer waves are related to or affect resting-state functional connectivity. In this study, we analyze spontaneous hemodynamic fluctuations over the mouse cortex using optical intrinsic signal imaging. We found spontaneous occurrence of oscillatory hemodynamics $0.2 Hz consistent with the properties of Mayer waves reported in the literature. Across a group of mice (n ¼ 19), there was a large variability in the magnitude of Mayer waves. However, regardless of the magnitude of Mayer waves, functional connectivity patterns could be recovered from hemodynamic signals when filtered to the lower frequency band, 0.01-0.08 Hz. Our results demonstrate that both Mayer waves and resting-state functional connectivity patterns can co-exist simultaneously, and that they can be separated by applying bandpass filters.

Statistical parametric mapping of stimuli evoked changes in total blood flow velocity in the mouse cortex obtained with extended-focus optical coherence microscopy

“…These oscillations, present here in vessels with high baseline velocity (i.e. arteries or veins), have already been reported [14] and could originate from either vasomotion or Mayer Waves [15]. Finally, in a last set of experiments, we imaged the velocity response by stimulation two different limbs with our method to assess the viability of using different stimulus protocols on the same animal.…”

Visual experience sculpts whole-cortex spontaneous infraslow activity patterns through an Arc-dependent mechanism

“…This global signal was regressed from every pixel's time trace to remove global sources of variance; global signal regression was applied independently on each contiguous imaging session. Finally, data from some imaging sessions exhibited strongly oscillatory activity in the 0.04-to 0.08-Hz range, which likely reflects vascular (not neural) physiology (78). Because the spectral content of the BOLD signal is known to be ∼1/f (79), we excluded runs in which 50% of the power of the filtered, regressed data were found above 0.04 Hz.…”

Detailed view on slow sinusoidal, hemodynamic oscillations on the human brain cortex by Fourier transforming oxy/deoxy hyperspectral images

“…Instead of applying a relatively wide bandpass filter, where all oscillations are aggregated (Bumstead et al, 2016; Gratton et al, 1998; Zuo et al, 2010), or only looking at only one frequency of 0.1 Hz (Rayshubskiy et al, 2014), a more detailed view on a SSHO can be acquired using a very small bandwidth ( $\Delta f$ around 0.002 Hz) as with a Fourier transform. In this way, it becomes clear that different but also overlapping SSHO‐regions can be observed with slightly different SSHO frequencies.…”

“…These SSHOs are hypothesized to have two possible origins: global oscillations (Mayer waves) that are influenced by the sympatic nerve system and related to blood pressure fluctuations (Bumstead, Bauer, Wright, & Culver, 2016; Julien, 2006; Yücel et al, 2016) or vasomotion which has a spontaneous, local character (Aalkjaer, Boedtkjer, & Matchkov, 2011; Aalkjaer & Nilsson, 2005; Franceschini, Fantini, Toronov, Filiaci, & Gratton, 2000; Gratton et al, 1998). Normally, these SSHOs are seen as confounding signals and are removed before further analysis.…”