Quasi-periodic patterns (QPP): Large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity

Abstract

Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders.

Ultra-fast magnetic resonance encephalography of physiological brain activity – Glymphatic pulsation mechanisms?

“…7,8,16,42 However, currently the widest agreement for the source of LF BOLD fluctuations in functionally connected brain regions (or networks) is electrophysiological activity coupled with neurovascular activity. 19,23,[43][44][45] The previously detected quasi-periodic pulsations seem to be reminiscent of VLF/LF vasomotor waves that in theory could influence glymphatic convection of CSF. As the arterial wall pressure clearly affects the convection of CSF into the brain, 46 slow waves in vasomotor tone, i.e.…”

“…[17][18][19] The QPP algorithm uses a data-driven and correlation-based approach to find high SNR (quasi-) periodic, repeating spatiotemporal patterns (like cardiac impulse, respiratory rhythms and LF/VLF fluctuations) from the band-passed MREG brain data, c.f. Figure 2.…”

“…19,23,44,48,49 The DC-EEG potential has a strong contribution from non-neuronal potential over the blood-brain barrier (BBB). 19,23,44,48,[49][50][51] It is unlikely that the permeability of the BBB oscillates in a healthy brain at even low frequencies, as its function is to provide a stable barrier between the brain cells and blood. We propose that fluctuation in the glymphatic fluid fluxes in the perivascular space surrounding the BBB could contribute to the slow DC-EEG potential oscillations.…”

“…7,8,[16][17][18][19] Much of the interactions and mechanisms of these pulsations affecting the cerebral BOLD signal still remain unclear. One reason for the uncertainty of BOLD signal pulsations is the fact that whole-brain fMRI studies sample data slowly, i.e.…”

“…Novel image analysis algorithms can further effectively separate periodic pulsations of the brain in order to characterize their spatiotemporal patterns over a wide range of frequencies. 17,19 In this study, a collaborative effort was undertaken to comprehensively monitor the spatiotemporal effects of physiological pulsations in brain tissue with novel multimodal ultra-fast MREG technology. This technology removes aliasing and minimizes motion-induced spinhistory effects and offers increased statistical power for analysis.…”