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.

The brain is not a passive sensory-motor analyzer driven by environmental stimuli, but actively maintains ongoing representations that may be involved in the coding of expected sensory stimuli, prospective motor responses, and prior experience. Spontaneous cortical activity has been proposed to play an important part in maintaining these ongoing, internal representations, although its functional role is not well understood. One spontaneous signal being intensely investigated in the human brain is the interregional temporal correlation of the blood-oxygen level-dependent (BOLD) signal recorded at rest by functional MRI (functional connectivity-by-MRI, fcMRI, or BOLD connectivity). This signal is intrinsic and coherent within a number of distributed networks whose topography closely resembles that of functional networks recruited during tasks. While it is apparent that fcMRI networks reflect anatomical connectivity, it is less clear whether they have any dynamic functional importance. Here, we demonstrate that visual perceptual learning, an example of adult neural plasticity, modifies the resting covariance structure of spontaneous activity between networks engaged by the task. Specifically, after intense training on a shape-identification task constrained to one visual quadrant, resting BOLD functional connectivity and directed mutual interaction between trained visual cortex and frontalparietal areas involved in the control of spatial attention were significantly modified. Critically, these changes correlated with the degree of perceptual learning. We conclude that functional connectivity serves a dynamic role in brain function, supporting the consolidation of previous experience.fMRI ͉ functional connectivity ͉ perceptual learning ͉ resting state S pontaneous neural activity utilizes the majority of the brain's energy budget, but its function remains mysterious (1-8). At the level of single neurons, embedded in the local circuitry of a cortical area, spontaneous activity has been shown to emulate the pattern of activity evoked by the neuron's optimal stimulus, suggesting that at least at this level of description, spontaneous activity is likely to reflect the history of coactivation within local networks (9). At the level of distributed cortical systems, spontaneous activity measured by blood-oxygen level-dependent (BOLD) functional MRI (fMRI) exhibits covariance structures (or functional connectivity) at ultraslow frequencies (Ͻ0.1 Hz) that are stable across a wide range of behavioral states (anesthesia, task performance, resting wakefulness, and sleep) (10, 11). The topography of BOLD functional connectivity is compatible with both the underlying structural connectivity of the cortex and the functional anatomy of systems engaged by a broad range of tasks (12-16).Studies have suggested that BOLD functional connectivity is largely a physiological marker of anatomical connections or a correlate of intrinsic vascular dynamics without functional or behavioral significance (17). This hypothesis is consistent with t...

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 ...

The spontaneous activity of the brain shows different features at different scales. On one hand, neuroimaging studies show that longrange correlations are highly structured in spatiotemporal patterns, known as resting-state networks, on the other hand, neurophysiological reports show that short-range correlations between neighboring neurons are low, despite a large amount of shared presynaptic inputs. Different dynamical mechanisms of local decorrelation have been proposed, among which is feedback inhibition. Here, we investigated the effect of locally regulating the feedback inhibition on the global dynamics of a large-scale brain model, in which the long-range connections are given by diffusion imaging data of human subjects. We used simulations and analytical methods to show that locally constraining the feedback inhibition to compensate for the excess of long-range excitatory connectivity, to preserve the asynchronous state, crucially changes the characteristics of the emergent resting and evoked activity. First, it significantly improves the model's prediction of the empirical human functional connectivity. Second, relaxing this constraint leads to an unrealistic network evoked activity, with systematic coactivation of cortical areas which are components of the default-mode network, whereas regulation of feedback inhibition prevents this. Finally, information theoretic analysis shows that regulation of the local feedback inhibition increases both the entropy and the Fisher information of the network evoked responses. Hence, it enhances the information capacity and the discrimination accuracy of the global network. In conclusion, the local excitation-inhibition ratio impacts the structure of the spontaneous activity and the information transmission at the large-scale brain level.

Summary We used magneto-encephalography to study the temporal dynamics of band-limited power correlation at rest within and across six brain networks previously defined by prior fMRI studies. Epochs of transiently high within-network BLP correlation were identified and correlation of BLP time-series across networks was assessed in these epochs. These analyses demonstrate that functional networks are not equivalent with respect to cross-network interactions. The default-mode network and the posterior cingulate cortex, in particular, exhibit the highest degree of transient BLP correlation with other networks especially in the 14–25 Hz (beta band) frequency range. Our results indicate that the previously demonstrated neuroanatomical centrality of the PCC and DMN has a physiological counterpart in the temporal dynamics of network interaction at behaviorally relevant time scales. This interaction involved subsets of nodes from other networks during periods in which their internal correlation was low.

A dorsal frontoparietal network, including regions in intraparietal sulcus (IPS) and frontal eye field (FEF), has been hypothesized to control the allocation of spatial attention to environmental stimuli. One putative mechanism of control is the desynchronization of electroencephalography (EEG) alpha rhythms (ϳ8 -12 Hz) in visual cortex in anticipation of a visual target. We show that brief interference by repetitive transcranial magnetic stimulation (rTMS) with preparatory activity in right IPS or right FEF while subjects attend to a spatial location impairs identification of target visual stimuli ϳ2 s later. This behavioral effect is associated with the disruption of anticipatory (prestimulus) alpha desynchronization and its spatially selective topography in parieto-occipital cortex. Finally, the disruption of anticipatory alpha rhythms in occipital cortex after right IPS-or right FEF-rTMS correlates with deficits of visual identification. These results support the causal role of the dorsal frontoparietal network in the control of visuospatial attention, and suggest that this is partly exerted through the synchronization of occipital visual neurons.

The default mode network (DMN) is often considered a functionally homogeneous system that is broadly associated with internally directed cognition (e.g. episodic memory, theory of mind, self-evaluation). However, few studies have examined how this network interacts with other networks during putative “default” processes such as episodic memory retrieval. Using fMRI, we investigated the topography and response profile of human parietal regions inside and outside the DMN, independently defined using task-evoked deactivations and resting state functional connectivity, during episodic memory retrieval. Memory retrieval activated posterior nodes of the DMN, particularly the angular gyrus, but also more anterior and dorsal parietal regions that were anatomically separate from the DMN. The two sets of parietal regions showed different resting-state functional connectivity and response profiles. During memory retrieval, responses in DMN regions peaked sooner than non-DMN regions, which in turn showed responses that were sustained until a final memory judgment was reached. Moreover, a parahippocampal region that showed strong resting-state connectivity with parietal DMN regions also exhibited a pattern of task-evoked activity similar to that exhibited by DMN regions. These results suggest that DMN parietal regions directly supported memory retrieval, whereas non-DMN parietal regions were more involved in post-retrieval processes such as memory-based decision making. Finally, a robust functional dissociation within the DMN was observed. While angular gyrus and posterior cingulate/precuneus were significantly activated during memory retrieval, an anterior DMN node in medial prefrontal cortex was strongly deactivated. This latter finding demonstrates functional heterogeneity rather than homogeneity within the DMN during episodic memory retrieval.