Temporal dynamics of spontaneous MEG activity in brain networks

Abstract

Functional MRI (fMRI) studies have shown that low-frequency (<0.1 Hz) spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal during restful wakefulness are coherent within distributed large-scale cortical and subcortical networks (resting state networks, RSNs). The neuronal mechanisms underlying RSNs remain poorly understood. Here, we describe magnetoencephalographic correspondents of two well-characterized RSNs: the dorsal attention and the default mode networks. Seed-based correlation mapping was performed using time-dependent MEG power reconstructed at each voxel within the brain. The topography of RSNs computed on the basis of extended (5 min) epochs was similar to that observed with fMRI but confined to the same hemisphere as the seed region. Analyses taking into account the nonstationarity of MEG activity showed transient formation of more complete RSNs, including nodes in the contralateral hemisphere. Spectral analysis indicated that RSNs manifest in MEG as synchronous modulation of bandlimited power primarily within the theta, alpha, and beta bands-that is, in frequencies slower than those associated with the local electrophysiological correlates of event-related BOLD responses.resting state networks | default mode network | dorsal attention network | functional MRI T he existence of resting state networks (RSNs) is now a wellestablished fMRI phenomenon (1). The basic finding is that in awake, quietly resting humans, spontaneous, slow (<0.1 Hz) fluctuations of the blood oxygen level dependent (BOLD) signal are temporally coherent within widely distributed functional networks closely resembling those evoked by sensory, motor, and cognitive paradigms (2). Interindividual differences in RSN properties may correlate with cognitive abilities both in health (2) and disease (3). Thus, correlated spontaneous neural activity in distributed brain networks represents a fundamental aspect of brain physiology and psychology. Though there is significant evidence linking stimulusevoked BOLD responses, activations and deactivations both (4), and changes in local field potential (LFP) power, especially in the gamma (40-160 Hz) band, data bearing on the electrophysiological correlates of RSNs are scarce. Recent electrocorticography (ECoG) recordings in human subjects have shown a relationship between the topography of a sensory-motor RSN and slow cortical potentials (5). Slow (∼0.1 Hz) fluctuations of the band-limited gamma power have been also reported as an electrophysiological correlate of BOLD signal fluctuations between brain areas within (5, 6) and across hemispheres in both humans (7) and monkeys (8). Though invasive recordings of electrophysiological activity in animals (8) or humans undergoing surgical management of epilepsy (5, 7) provide high spatial and temporal resolution and specificity, they are not ideal for the study of large-scale RSN in healthy volunteers. Not only are these methods invasive, but recordings through grids or electrodes grids typically cover only a small fraction ...

Mesoscale infraslow spontaneous membrane potential fluctuations recapitulate high-frequency activity cortical motifs

“…Furthermore, we were not limited to only sensorimotor ROIs in our approach. The interconnected nature of the activity structure of infraslow and slow activity is consistent with a cross-frequency phase-amplitude coupling relationship that has previously been indicated for infraslow fluctuations in EEG and MEG recordings in human subjects [13][14][15]60 .…”

Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity

“…Note that, in agreement with other results (de Pasquale et al, 2010, Baker et al, 2012 there is significant temporal variation in resting state correlation. As with the simulated data, the dynamic statistical threshold and mean canonical correlation calculated for the null distribution shows significant temporal structure.…”

“…Pasquale and colleagues have published multiple papers (de Pasquale et al, 2010, de Pasquale et al, 2012 showing that accounting for temporal non-stationarity aids in the detection of several resting state networks, suggesting that networks transiently engage with other networks during periods of high internal correlation, with the default mode network acting as a hub of cross network interaction. Also using MEG, Baker et al (Baker et al, 2012) show evidence of a bi-state nature to band limited power correlation, with periods of zero functional connectivity interspersed with periods of high transient functional connectivity.…”

“…(Scholvinck et al, 2013)). Throughout the remainder of this manuscript, we use the term functional connectivity to mean temporal correlation between the 6 amplitude envelopes of band limited neural oscillations (de Pasquale et al, 2010, Liu et al, 2010, Brookes et al, 2011a, Brookes et al, 2011b, Hipp et al, 2012, Luckhoo et al, 2012, Hall et al, 2013. …”

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

“…Various methods are used to reveal these networks, leading to different interpretations regarding their spatial and temporal organization. Functional magnetic resonance imaging studies of brain networks aim to demonstrate correlations among BOLD fluctuations in different brain regions (Biswal et al, 1995), while those involving electro-or magnetoencephalography (EEG/MEG) typically evaluate correlations among fluctuations in the amplitude of oscillatory activity in different brain regions (de Pasquale et al, 2010;Fries, 2015). Researchers have proposed that the resting-state networks (RSNs) measured using fMRI (rsfMRI) reflect a sort of "constant inner state of exploration" that optimizes the system for a given impending input, thus influencing perception and cognitive processing (Deco et al, 2011).…”