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.brain rhythms ͉ multimodal imaging ͉ resting fluctuations A fundamental issue in neuroscience is understanding how large neuronal assemblies cooperate in the brain, and what mechanisms underlie this cooperation, that is the basis for all sensory, cognitive, and motor activities. Traditional physiological models of brain function emphasize the importance of spike rate as a medium for encoding and transferring signals in the brain, and often delineate electrophysiological spontaneous activity as internal noise (1, 2). More recent models propose that spontaneous activity may also play an important functional role by providing important endogenous or top-down constraints to sensory-, cognitive-, or motor-driven activity or temporal windows of opportunity for long-range communication (3)(4)(5).Functional neuroimaging studies have provided evidence for a baseline of neuronal ongoing activity, from which transient changes induced by specific perceptual and cognitive tasks, generally named activations, arise (6, 7). Interestingly, spontaneous activity, as measured with blood oxygen level-dependent (BOLD) functional MRI (fMRI) in the resting awake or anesthetized brain, is organized in multiple highly specific functional anatomical networks (resting state networks, RSNs) (8, 9). These RSNs fluctuate at frequencies between 0.01 and 0.1 Hz, and strongly overlap with sensory-motor, visual, auditory, attention, language, and default networks that are commonly modulated during active behavioral tasks (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20).A critical step toward understanding the functional role of spontaneous activity is to clarify the neurophysiological basis of these RSN...

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.

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 ...