Key role of coupling, delay, and noise in resting brain fluctuations (original) (raw)
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Electrophysiological signatures of resting state networks in the human brain
Proceedings of the National Academy of Sciences, 2007
Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus-or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.
The Dynamical Balance of the Brain at Rest
The Neuroscientist, 2011
The authors review evidence that spontaneous, that is, not stimulus or task driven, activity in the brain at the level of large-scale neural systems is not noise, but orderly and organized in a series of functional networks that maintain, at all times, a high level of coherence. These networks of spontaneous activity correlation or resting state networks (RSN) are closely related to the underlying anatomical connectivity, but their topography is also gated by the history of prior task activation. Network coherence does not depend on covert cognitive activity, but its strength and integrity relates to behavioral performance. Some RSN are functionally organized as dynamically competing systems both at rest and during tasks. Computational studies show that one of such dynamics, the anticorrelation between networks, depends on noise-driven transitions between different multistable cluster synchronization states. These multistable states emerge because of transmission delays between regions that are modeled as coupled oscillators systems. Large-scale systems dynamics are useful for keeping different functional subnetworks in a state of heightened competition, which can be stabilized and fired by even small modulations of either sensory or internal signals.
Brain activity at rest: a multiscale hierarchical functional organization
Journal of Neurophysiology, 2011
Spontaneous brain activity was mapped with functional MRI (fMRI) in a sample of 180 subjects while in a conscious resting-state condition. With the use of independent component analysis (ICA) of each individual fMRI signal and classification of the ICA-defined components across subjects, a set of 23 resting-state networks (RNs) was identified. Functional connectivity between each pair of RNs was assessed using temporal correlation analyses in the 0.01-to 0.1-Hz frequency band, and the corresponding set of correlation coefficients was used to obtain a hierarchical clustering of the 23 RNs. At the highest hierarchical level, we found two anticorrelated systems in charge of intrinsic and extrinsic processing, respectively. At a lower level, the intrinsic system appears to be partitioned in three modules that subserve generation of spontaneous thoughts (M1a; default mode), inner maintenance and manipulation of information (M1b), and cognitive control and switching activity (M1c), respectively. The extrinsic system was found to be made of two distinct modules: one including primary somatosensory and auditory areas and the dorsal attentional network (M2a) and the other encompassing the visual areas (M2b). Functional connectivity analyses revealed that M1b played a central role in the functioning of the intrinsic system, whereas M1c seems to mediate exchange of information between the intrinsic and extrinsic systems. conscious resting state; fMRI; functional connectivity; independent component analysis; networks AN INCREASING NUMBER OF NEUROIMAGING studies have investigated the functional organization of the brain at rest (see reviews of Bressler and Menon 2010; Fox and Raichle 2007
Temporally-independent functional modes of spontaneous brain activity
Proceedings of the National Academy of Sciences, 2012
Resting-state functional magnetic resonance imaging has become a powerful tool for the study of functional networks in the brain. Even "at rest," the brain's different functional networks spontaneously fluctuate in their activity level; each network's spatial extent can therefore be mapped by finding temporal correlations between its different subregions. Current correlation-based approaches measure the average functional connectivity between regions, but this average is less meaningful for regions that are part of multiple networks; one ideally wants a network model that explicitly allows overlap, for example, allowing a region's activity pattern to reflect one network's activity some of the time, and another network's activity at other times. However, even those approaches that do allow overlap have often maximized mutual spatial independence, which may be suboptimal if distinct networks have significant overlap. In this work, we identify functionally distinct networks by virtue of their temporal independence, taking advantage of the additional temporal richness available via improvements in functional magnetic resonance imaging sampling rate. We identify multiple "temporal functional modes," including several that subdivide the default-mode network (and the regions anticorrelated with it) into several functionally distinct, spatially overlapping, networks, each with its own pattern of correlations and anticorrelations. These functionally distinct modes of spontaneous brain activity are, in general, quite different from resting-state networks previously reported, and may have greater biological interpretability. F unctional connectivity in the brain can be observed using resting-state functional magnetic resonance imaging (FMRI), because the spontaneous temporal fluctuations from functionally related regions show temporal correlation with each other (1). However, brain function is mediated by many functionally distinct networks. These networks may overlap each other, either because a given region contains distinct functional units that cannot be separated with the limited spatial resolution of the data, or because a region truly is a part of more than one distinct functional network. If two regions participate in multiple functional networks, their apparent temporal correlation will reflect the combined contribution from all networks, obscuring the true underlying functional organization. The correlation will not necessarily be meaningful, being some unknown combination of correlations caused by various distinct processes. In this paper, we describe an approach that allows for overlap between functional networks, but differentiates the networks from each other on the basis of their temporal, rather than spatial, independence. The rationale is that in a resting FMRI dataset of sufficient duration, functionally distinct networks should be largely temporally distinct, even if spatially overlapping.
Exploring the network dynamics underlying brain activity during rest
Progress in Neurobiology, 2014
Since the mid 1990s, the intriguing dynamics of the brain at rest has been attracting a growing body of research in neuroscience. Neuroimaging studies have revealed distinct functional networks that slowly activate and deactivate, pointing to the existence of an underlying network dynamics emerging spontaneously during rest, with specific spatial, temporal and spectral characteristics. Several theoretical scenarios have been proposed and tested with the use of large-scale computational models of coupled brain areas. However, a mechanistic explanation that encompasses all the phenomena observed in the brain during rest is still to come. In this review, we provide an overview of the key findings of resting-state activity covering a range of neuroimaging modalities including fMRI, EEG and MEG. We describe how to best define and analyze anatomical and functional brain networks and how unbalancing these networks may lead to problems with mental health. Finally, we review existing large-scale models of resting-state dynamics in health and disease. An important common feature of resting-state models is that the emergence of resting-state functional networks is obtained when the model parameters are such that the system operates at the edge of a bifurcation. At this critical working point, the global network dynamics reveals correlation patterns that are spatially shaped by the underlying anatomical structure, leading to an optimal fit with the empirical BOLD functional connectivity. However, new insights coming from recent studies, including faster oscillatory dynamics and non-stationary functional connectivity, must be taken into account in future models to fully understand the network mechanisms leading to the resting-state activity.
The resting brain: unconstrained yet reliable
Cerebral …, 2009
The human brain is a complex dynamic system capable of generating a multitude of oscillatory waves in support of brain function. Using fMRI, we examined the amplitude of spontaneous low-frequency oscillations (LFO) observed in the human resting brain and the test-retest reliability of relevant amplitude measures. We confirmed prior reports that gray matter exhibits higher LFO amplitude than white matter. Within gray matter, the largest amplitudes appeared along mid-brain structures associated with the "default-mode" network. Additionally, we found that high-amplitude LFO activity in specific brain regions was reliable across time. Furthermore, parcellation-based results revealed significant and highly reliable ranking orders of LFO amplitudes among anatomical parcellation units. Detailed examination of individual low frequency bands showed distinct spatial profiles. Intriguingly, LFO amplitudes in the slow-4 (0.027-0.073 Hz) band, as defined by Buzsáki et al., were most robust in the basal ganglia, as has been found in spontaneous electrophysiological recordings in the awake rat. These results suggest that amplitude measures of LFO can contribute to further between-group characterization of existing and future "resting-state" fMRI datasets.
Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors
Journal of Neuroscience, 2012
The ongoing activity of the brain at rest, i.e., under no stimulation and in absence of any task, is astonishingly highly structured into spatiotemporal patterns. These spatiotemporal patterns, called resting state networks, display low-frequency characteristics (Ͻ0.1 Hz) observed typically in the BOLD-fMRI signal of human subjects. We aim here to understand the origins of resting state activity through modeling via a global spiking attractor network of the brain. This approach offers a realistic mechanistic model at the level of each single brain area based on spiking neurons and realistic AMPA, NMDA, and GABA synapses. Integrating the biologically realistic diffusion tensor imaging/diffusion spectrum imaging-based neuroanatomical connectivity into the brain model, the resultant emerging resting state functional connectivity of the brain network fits quantitatively best the experimentally observed functional connectivity in humans when the brain network operates at the edge of instability. Under these conditions, the slow fluctuating (Ͻ0.1 Hz) resting state networks emerge as structured noise fluctuations around a stable low firing activity equilibrium state in the presence of latent "ghost" multistable attractors. The multistable attractor landscape defines a functionally meaningful dynamic repertoire of the brain network that is inherently present in the neuroanatomical connectivity.
Brain activity fluctuations propagate as waves traversing the cortical hierarchy
2020
The brain exhibits highly organized patterns of spontaneous activity as measured by resting-state fMRI fluctuations that are being widely used to assess the brain’s functional connectivity. Some evidence suggests that spatiotemporally coherent waves are a core feature of spontaneous activity that shapes functional connectivity, though this has been difficult to establish using fMRI given the temporal constraints of the hemodynamic signal. Here we investigated the structure of spontaneous waves in human fMRI and monkey electrocorticography. In both species, we found clear, repeatable, and directionally constrained activity waves coursed along a spatial axis approximately representing cortical hierarchical organization. These cortical propagations were closely associated with activity changes in distinct subcortical structures, particularly those related to arousal regulation, and modulated across different states of vigilance. The findings demonstrate a neural origin of spatiotempora...
NeuroImage, 2012
During conscious rest, the mind switches into a state of wandering. Although this rich inner experience occupies a large portion of the time spent awake, how it relates to brain activity has not been well explored. Here, we report the results of a behavioral and functional magnetic resonance imaging (fMRI) study of the continuous resting state in 307 healthy participants. The analysis focused on the relationship between the nature of inner experience and the temporal correlations computed between the low-frequency blood oxygen level-dependent (BOLD) fluctuations (0.01-0.1 Hz) of five large-scale modules. The subjects' self-reported time spontaneously spent on visual mental imagery and/or inner language was used as the behavioral variable. Decreased temporal correlations between modules were revealed when subjects reported more time spent thinking in mental images and inner language. These changes segregated the three modules supporting inner-oriented activities from those associated with sensory-related and externally guided activities. Among the brain areas associated with inner-oriented processing, the module including the lateral parietal and frontal regions (commonly described as being engaged in the manipulation and maintenance of internal information) was implicated in the majority of these effects. The preponderance of segregation appears to be the signature of the spontaneous sequence of thoughts during rest that are not constrained by logic, causality, or even a rigorous temporal organization. In other words, though goal-directed tasks have been demonstrated to rely on specific regional integration, mind wandering can be characterized by widespread modular segregation. Overall, the present study provides evidence that modulation of spontaneous low-frequency fluctuations in the brain is at least partially explained by spontaneous conscious cognition while at rest.