Biology subjects

Bazhenov, M.

Publications and source records attributed to Bazhenov, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Novelty detection in early olfactory processing of the honey bee, Apis mellifera

Animals are constantly bombarded with stimuli, which presents a fundamental problem of sorting among pervasive uninformative stimuli and novel, possibly meaningful stimuli. We evaluated novelty detection behaviorally in honey bees as they position their antennae differentially in an air stream carrying familiar or novel odors. We then characterized neuronal responses to familiar and novel odors in the first synaptic integration center in the brain - the antennal lobes. We found that the neurons that exhibited stronger initial responses to the odor that was to be familiarized are the same units that later distinguish familiar and novel odors, independently of chemical identities. These units, including both projection neurons and local neurons, showed a decreased response to the familiar odor but an increased response to the novel odor. Our results suggest that the antennal lobe may assign a category of familiarity or novelty to an odor stimulus in addition to its chemical identity code. Therefore, the mechanisms for novelty detection may be present in early sensory processing, either as a result of local synaptic interaction or via feedback from higher brain centers.

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bioRxiv2020

Multielectrode Cortical Stimulation Selectively Induces Unidirectional Wave Propagation in Biophysical/Neural Model

Cortical stimulation is emerging as an experimental tool in basic research and a promising therapy for a range of neuropsychiatric conditions. As multielectrode arrays enter clinical practice, the possibility of using spatiotemporal patterns of electrical stimulation to induce desired physiological patterns has become theoretically possible, but in practice can only be implemented by trial-and-error because of a lack of predictive models. Experimental evidence increasingly establishes travelling waves as fundamental to cortical information-processing, but we lack understanding how to control wave properties despite rapidly improving technologies. This study uses a hybrid biophysical-anatomical and neural-computational model to predict and understand how a simple pattern of cortical surface stimulation could induce directional traveling waves via asymmetric activation of inhibitory interneurons. It reveals local circuit mechanisms to control spatiotemporal cortical dynamics and predicts interventions that can be developed to treat a broad range of cognitive disorders.

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bioRxiv2020

Differential covariance: A new method to estimate functional connectivity in fMRI

Measuring functional connectivity from fMRI recordings is important in understanding processing in cortical networks. However, because the brains connection pattern is complex, currently used methods are prone to producing false functional connections. We introduce differential covariance analysis, a new method that uses derivatives of the signal for estimating functional connectivity. We generated neural activities from Dynamical Causal Modeling and a neural network of Hodgkin-Huxley neurons and then converted them to hemodynamic signals using the forward Balloon model. The simulated fMRI signals together with the ground truth connectivity pattern were used to benchmark our method with other commonly used methods. Differential covariance achieved better results in complex network simulations. This new method opens an alternative way to estimate functional connectivity.

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bioRxiv2019

Interleaved training prevents catastrophic forgetting in spiking neural networks

Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new learning is interleaved with periods of sleep for memory consolidation. In this study, we used spiking network to study mechanisms behind catastrophic forgetting and the role of sleep in preventing it. The network could be trained to learn a complex foraging task but exhibited catastrophic forgetting when trained sequentially on multiple tasks. New task training moved the synaptic weight configuration away from the manifold representing old tasks leading to forgetting. Interleaving new task training with periods of off-line reactivation, mimicking biological sleep, mitigated catastrophic forgetting by pushing the synaptic weight configuration towards the intersection of the solution manifolds representing multiple tasks. The study reveals a possible strategy of synaptic weights dynamics the brain applies during sleep to prevent forgetting and optimize learning.

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bioRxiv2019

Can sleep protect memories from catastrophic forgetting?

Continual learning remains to be an unsolved problem in artificial neural networks. Biological systems have evolved mechanisms by which they can prevent catastrophic forgetting of old knowledge during new training and allow lifelong learning. Building upon data suggesting the importance of sleep in learning and memory, here we test a hypothesis that sleep protects memories from catastrophic forgetting. We found that training in a thalamocortical network model of a \"new\" memory that interferes with previously stored \"old\" memory may result in degradation and forgetting of the old memory trace. Simulating NREM sleep immediately after new learning leads to replay, which reverses the damage and ultimately enhances both old and new memory traces. Surprisingly, we found that sleep replay goes beyond recovering old memory traces that were damaged by new learning. When a new memory competes for the neuronal/synaptic resources previously allocated to the old memory, sleep replay changes the synaptic footprint of the old memory trace to allow for the overlapping populations of neurons to store multiple memories. Different neurons become preferentially supporting different memory traces to allow successful recall. We compared synaptic weight dynamics during sleep replay with that during interleaved training - a common approach to overcome catastrophic forgetting in artificial networks - and found that interleaved training promotes synaptic competition and weakening of reciprocal synapses, effectively reducing an ensemble of neurons contributing to memory recall. This leads to suboptimal recall performance compared to that after sleep. Together, our results suggest that sleep provides a powerful mechanism to achieve continual learning by combining consolidation of new memory traces with reconsolidation of old memory traces to minimize memory interference.

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bioRxiv2019

Interaction of Hippocampal Ripples and Cortical Slow Waves Leads to Coordinated Large-Scale Sleep Rhythm

The dialogue between cortex and hippocampus is known to be crucial for sleep dependent consolidation of long lasting memories. During slow wave sleep memory replay depends on slow oscillation (SO) and spindles in the (neo)cortex and sharp wave-ripple complexes (SWR) in the hippocampus, however, the mechanisms underlying interaction of these rhythms are poorly understood. Here, we examined the interaction between cortical SOs and hippocampal SWRs in a computational model of the hippocampo-cortico-thalamic network and compared the results with human intracranial recordings during sleep. We observed that ripple occurrence peaked following the onset of SO (Down-to-Up-state transition) and that cortical input to hippocampus was crucial to maintain this relationship. Ripples influenced the spatiotemporal structure of cortical SO and duration of the Up/Down-states. In particular, ripples were capable of synchronizing Up-to-Down state transition events across the cortical network. Slow waves had a tendency to initiate at cortical locations receiving hippocampal ripples, and these \"initiators\" were able to influence sequential reactivation within cortical Up states. We concluded that during slow wave sleep, hippocampus and neocortex maintain a complex interaction, where SOs bias the onset of ripples, while ripples influence the spatiotemporal pattern of SOs.

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bioRxiv2019

Optimal sparse olfactory representations persist in a plastic network

The neural representation of a stimulus is repeatedly transformed as it moves from the sensory periphery to deeper layers of the nervous system. Sparsening transformations are thought to increase the separation between similar representations, encode stimuli with great specificity, maximize storage capacity as associative memories, and provide an energy efficient instantiation of information in neural circuits. In the insect olfactory system, odors are initially represented in the periphery as a combinatorial code with simple temporal dynamics. Subsequently, in the antennal lobe this representation is transformed into a dense spatiotemporal activity pattern. Next, in the mushroom body Kenyon cells (KCs), the representation is dramatically sparsened. Then in mushroom body output neurons (MBONs), the representation takes on a new dense spatiotemporal format. Here, we develop a computational model to simulate this chain of olfactory processing from the receptor neurons to MBONs. We demonstrate that representations of similar odorants are maximally separated, measured by the distance between the corresponding MBON activity vectors, when KC responses are sparse and that the sparseness is maintained across variations in odor concentration by adjusting the feedback inhibition KCs receive. Different odor concentrations require different strength and timing of feedback inhibition for optimal processing. Further, in vivo, the KC-MBON synapse is highly plastic, and changes in synaptic strength after learning can change the balance of excitation and inhibition and may lead to a change in the distance between MBON activity vectors of two odorants for the same level of KC population sparseness. Thus, what is an optimal degree of sparseness before odor learning, could be rendered sub-optimal post learning. Here, we show, however, that synaptic weight changes caused by spike timing dependent plasticity increase the distance between the odor representations from the perspective of MBONs and do not lead to a concomitant change in the optimal sparseness. Author SummaryKenyon cells (KCs) of the mushroom body represent odors as a sparse code. When viewed from the perspective of follower neurons, mushroom body output neurons (MBONs), an optimal level of KC sparseness maximally separates the representations of odors. However, the KC-MBON synapse is highly plastic and may be potentiated or depressed by odor-driven experience that could, in turn, perturb the optimality formed by pre-synaptic circuits. Contrary to this expectation, we show that synaptic plasticity based on spike timing of pre- and postsynaptic neurons improves the ability of the system to distinguish between the representations of similar odors while preserving the optimality determined by pre-synaptic circuits.

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bioRxiv2018

Timing between cortical slow oscillations and heart rate bursts during sleep predicts perceptual speed, but not offline consolidation

Central and autonomic nervous system activity are coupled during sleep. Cortical slow oscillations (SOs, <1Hz) coincide with brief bursts in heart rate (HR), but the functional consequence of this coupling in cognition remains elusive. We measured SO-HR temporal coupling (i.e., the peak-to-peak interval between downstate of SO event and HR burst) during a daytime nap, and asked whether this SO-HR timing measure was associated with perceptual speed and learning on a texture discrimination task, by testing subjects before and after a nap. The coherence of SO-HR events during sleep strongly correlated with an individuals perceptual speed in the morning and evening test sessions, but not with their change in performance after the nap (i.e., consolidation). We confirmed this result in two additional experimental visits, and also discovered that this association was visit-specific, indicating a reliable state (not trait) marker. Thus, we introduce a novel physiological index that may be a useful marker of state-dependent processing speed of an individual.\n\nSignificance StatementStudies show that autonomic and central nervous system activity is coupled. For example, increases in heart rate follow cortical slow oscillations during sleep. However, the functional significance of this coupling for cognition is not understood. In three experimental visits, we show that the timing between these sleep events (the peak-to-peak delay between the slow oscillation and the heart rate burst) is highly correlated with waking perceptual processing speed. This reliable individual difference measure may be a useful marker of generalized processing speed.

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bioRxiv2018

Large time step discrete-time modeling of sharp wave activity in hippocampal area CA3.

Reduced models of neuronal spiking activity simulated with a fixed integration time step are frequently used in studies of spatio-temporal dynamics of neurobiological networks. The choice of fixed time step integration provides computational simplicity and efficiency, especially in cases dealing with large number of neurons and synapses operating at a different level of activity across the population at any given time. A network model tuned to generate a particular type of oscillations or wave patterns is sensitive to the intrinsic properties of neurons and synapses and, therefore, commonly susceptible to changes in the time step of integration. In this study, we analyzed a model of sharp-wave activity in the network of hippocampal area CA3, to examine how an increase of the integration time step affects network behavior and to propose adjustments of intrinsic properties of neurons and synapses that help minimize or remove the damage caused by the time step increase.\n\nHighlightsO_LISpiking models of neural network activity are sensitive to the integration step\nC_LIO_LILarger integration time steps are preferable in simulating large networks\nC_LIO_LICase study of CA3 sharp waves shows time step increase damages network dynamics\nC_LIO_LINeuronal and synaptic parameters adjustments rescue the dynamics at large time step1\nC_LI

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bioRxiv2018

Characterizing concentration-dependent neural dynamics of 4-aminopyridine-induced epileptiform activity

Epilepsy remains one of the most common neurological disorders. In patients, it is characterized by unprovoked, spontaneous, and recurring seizures or ictal events. Typically, inter-ictal events or large bouts of population level activity can be measured between seizures and are generally asymptomatic. Decades of research has focused on understanding the mechanisms leading to the development of seizure-like activity using various proconvulsive pharmacological agents, including 4-aimnopyridine (4AP). However, the lack of consistency in the concentrations used for studying 4AP-induced epileptiform activity in animal models may give rise to differences in the results and interpretation thereof. Indeed, the range of 4AP concentration in both in vivo and in vitro studies varies from 3M to 40mM. Here, we explored the effects of various 4AP concentrations on the development and characteristics of hippocampal epileptiform activity in acute mouse brain slices of either sex. Using multielectrode array recordings, we show that 4AP induces hippocampal epileptiform activity for broad range of concentrations. The frequency component and the spatio-temporal patterns of the epileptiform activity revealed a dose-dependent response. Finally, in the presence of 4AP, reduction of KCC2 co-transporter activity by KCC2 antagonist VU0240551 prevented the manifestation of the frequency component differences between different concentrations of 4AP. Overall, the study predicts that different concentrations of 4AP can result in the different mechanisms behind hippocampal epileptiform activity, of which some are dependent on the KCC2 co-transporter function.

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bioRxiv2017

Selective Recruitment of Cortical Neurons by Electrical Stimulation

Despite its critical importance in experimental and clinical neuroscience, at present there is no systematic method to predict which neural elements will be activated by a given stimulation regime. Here we develop a novel approach to model the effect of cortical stimulation on spiking probability of neurons in a volume of tissue, by applying an analytical estimate of stimulation-induced activation of different cell types across cortical layers. We utilize the morphology and properties of axonal arborization profiles obtained from publicly available anatomical reconstructions of the twelve main categories of neocortical neurons to derive the dependence of activation probability on cell type, layer and distance from the source. We then propagate this activity through the local network incorporating connectivity, synaptic and cellular properties. Our work predicts that (a) intracranial cortical stimulation induces selective activation across cell types and layers; (b) superficial anodal stimulation is more effective than cathodal at cell activation; (c) cortical surface stimulation focally activates layer I axons, and (d) an optimal stimulation intensity exists capable of eliciting cell activation lasting beyond the end of stimulation.

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bioRxiv2017

Learning-induced sequence reactivation during sharp-wave ripples: a computational study

During sleep, memories formed during the day are consolidated in a dialogue between cortex and hippocampus. The reactivation of specific neural activity patterns - replay - during sleep has been observed in both structures and is hypothesized to represent a neuronal substrate of consolidation. In the hippocampus, replay happens during sharp wave - ripples (SWR), short bouts of excitatory activity in area CA3 which induce high frequency oscillations in area CA1. In particular, recordings of hippocampal cells which spike at a specific location ( place cells) show that recently learned trajectories are reactivated during SWR in the following sleep SWR. Despite the importance of sleep replay, its underlying neural mechanisms are still poorly understood.\n\nWe developed a model of SWR activity, to study the effects of learning-induced synaptic changes on spontaneous sequence reactivation during SWR. The model implemented a paradigm including three epochs: Pre-sleep, learning and Post-sleep activity. We first tested the effects of learning on the hippocampal network activity through changes in a minimal number of synapses connecting selected pyramidal cells. We then introduced an explicit trajectory-learning task to the model, to obtain behavior-induced synaptic changes. The model revealed that the recently learned trajectory reactivates during sleep more often than other trajectories in the training field. The study predicts that the gain of reactivation rate during sleep following vs sleep preceding learning for a trained sequence of pyramidal cells depends on Pre-sleep activation of the same sequence, and on the amount of trajectory repetitions included in the training phase.

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bioRxiv2017

Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics

BackgroundAlthough they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain.\n\nMethodWe present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors.\n\nResultsThe simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted.\n\nComparison with Existing MethodsPrevious models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales simultaneously.\n\nConclusionsThis multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.

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bioRxiv2017

Computational model of brainstem circuit for state dependent control of hypoglossal motoneurons

In patients with obstructive sleep apnea (OSA) the pharyngeal muscles become relaxed during sleep, which leads to a partial or complete closure of upper airway. Empirical studies suggest that withdrawal of noradrenergic and serotonergic drives importantly contribute to depression of hypoglossal motoneurons during rapid eye-movement (REM) sleep and, therefore, may contribute to OSA pathophysiology; however, specific cellular and synaptic mechanisms remain unknown. It was recently suggested that, in order to explain experimental observations, the neuronal network for monoaminergic control of excitability of hypoglossal motoneurons has to include excitatory and inhibitory perihypoglossal interneurons that would mediate noradrenergic and serotonergic drives to the motoneurons. In this study, we applied a biophysical network model to validate the rationality of the proposed circuit and to investigate the dynamics of its neuronal populations during REM sleep-induced withdrawal of noradrenergic and serotonergic drives. The state-dependent activity of the model hypoglossal motoneurons during simulated REM sleep with or without a virtual application of noradrenergic and serotonergic drugs was in qualitative agreement with in vivo data. The study predicts the dynamics of the perihypoglossal interneurons during these conditions and corroborates the hypothesis that the excitatory interneurons may integrate both noradrenergic and serotonergic drives. The latter drive has to be mediated by the inhibitory interneurons. The study suggests that perihypoglossal interneurons may serve as novel potential targets for pharmacological treatment of OSA.

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bioRxiv2017

Thalamocortical and Intracortical Laminar Connectivity Determines Sleep Spindle Properties

Spindle oscillations are brief oscillatory activity during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the properties of the core vs. matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, projecting to the middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. Our model demonstrates that this property is sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Futhermore, consistent with human recordings, in the model, spindles occurred independently in the core system but matrix system spindles commonly co-occurred with core one. We found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of the intra and inter cortical connectivity can potentially be a mechanism for increasing in spindle density as observed during learning.\n\nAuthor summaryThe density of sleep spindles has been shown to correlate with memory consolidation. Further, sleep spindles occur more often in human MEG than EEG. We developed thalamocortical network model that is capable of spontaneous generation of spindles across cortical layers and that captures the essential statistical features of spindles observed in experiments. We predict that differences in thalamo-cortical connectivity, known from anatomical studies, lead to the differences in the spindle properties between EEG and MEG as observed in human recordings. Further, we predict that the intracortical connectivity between cortical layers, a property influenced by sleep preceding learning, increases spindle density. Results from our study highlight the role of cortical and thalamic projections on the occurrence and properties of spindles.

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bioRxiv2017

Coupling of autonomic and central events during sleep boosts declarative memory consolidation

While anatomical pathways between forebrain cognitive and brainstem autonomic nervous centers are well defined, autonomic-central interactions during sleep and their contribution to waking performance are not understood. Here, we analyzed simultaneous central activity via electroencephalography (EEG) and autonomic heart beat-to-beat intervals (RR intervals) from electrocardiography (ECG) during wake and daytime sleep. We identified bursts of ECG activity that lasted 4-5 seconds and predominated in non-rapid-eye-movement sleep (NREM). Using event-based analysis of NREM sleep, we found an increase in delta (0.5-4Hz) and sigma (12-15Hz) power and an elevated density of slow oscillations (0.5-1Hz) about 5 secs prior to peak of the heart rate burst, as well as a surge in vagal activity, assessed by high-frequency (HF) component of RR intervals. Using regression framework, we show that these Autonomic/Central Events (ACE) positively predicted post-nap improvement in a declarative memory task after controlling for the effects of spindles and slow oscillations from sleep periods without ACE. No such relation was found between memory performance and a control nap. Additionally, NREM ACE negatively correlated with REM sleep and learning in a non-declarative memory task. These results provide the first evidence that coordinated autonomic and central events play a significant role in declarative memory consolidation.

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bioRxiv2017

Reactive astrocytes - comprehending when neurons play 4’33”

Homeostatic regulation is a powerful tool utilized by virtually all biological systems, brain included. Broadly speaking, each homeostatic process embodies two components: the sensing component, whereby a deviation is detected and quantified, and the effecting component that executes the homeostatic adjustment with the goal of alleviating the deviation. In the central nervous system, homeostatic plasticity has been suggested to play an important role in shaping the dynamics of single neurons and neuronal networks. However, existing \"biophysical\" models of homeostatic plasticity are exceedingly simplistic. These models usually describe the sensor component in terms of simple averaging over neuronal activity and offer no explanation of the relevant biochemical pathways. Here, we attempt to fill this gap in our understanding of homeostatic plasticity by proposing a biophysical framework to explain detection of prolonged synaptic inactivity that may occur in some scenarios of brain injury. We propose that sensing of, and response to, synaptic inactivity involves detection of the extracellular glutamate level and occurs via the activation of metabotropic glutamate receptors (mGluRs), while the inactivity-induced synthesis of one of the homeostatic plasticity effectors, tumor necrosis factor alpha (TNF), serves as an effecting component of the system. This model can help to explain the experimental observations linking prolonged neuronal inactivity to TNF signaling. Importantly, the proposed signaling scheme is not limited to mGluRs and astrocytes, but rather is potentially applicable to any cells expressing receptors that activate the relevant G protein units. The proposed signaling scheme is likely to be useful for developing pharmacological interventions targeting homeostatic plasticity pathways.\n\nAuthor summaryHomeostatic regulation refers to the self-regulation ability of a system aimed at remaining in the same (or nearly the same) state. Homeostatic plasticity, a form of homeostatic regulation that arises in the context of neural dynamics, has been shown to shape neuronal and network dynamics by attempting to maintain physiological levels of neuronal activity in brain networks. Thus, it changes the excitation-inhibition balance in a network when sufficiently large and long-lasting deviations from physiological levels of activity are detected. However, the biophysical mechanisms of homeostatic plasticity and in particular those related to the ability to sense neuronal inactivity, remain elusive. We propose a feasible biophysical model of a homeostatic sensor, in which the sensing of inactivity depends on the presence and activation of G-protein-coupled receptors with different activation thresholds. In this model, the activation of the higher-threshold receptor suppresses the detection of inactivity, while the activation of the lower-threshold receptor promotes the detection of inactivity. The proposed model helps to explain a growing body of experimental data relating synaptic inactivity to production of homeostatic plasticity effectors such as tumor necrosis factor alpha. Although we consider a specific case study of metabotropic glutamate receptors on astrocytes, the model conclusions are likely to be applicable to any cells expressing receptors that activate the relevant G protein units.

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bioRxiv2017

Origin of Slow Spontaneous Resting-State Neuronal Fluctuations in Brain Networks

Resting or baseline state low frequency (0.01-0.2 Hz) brain activity has been observed in fMRI, EEG and LFP recordings. These fluctuations were found to be correlated across brain regions, and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (< 0.05 Hz) fluctuations could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra and extracellular K+ and Na+ and intracellular Cl- ions, Na+/K+ exchange pump, and KCC2 co-transporter. In the network model representing resting awake-like brain state, we observed slow fluctuations in the extracellular K+ concentration, Na+/K+ pump activation, firing rate of neurons and local field potentials. Holding K+ concentration constant prevented generation of these fluctuations. The amplitude and peak frequency of this activity were modulated by Na+/K+ pump, AMPA/GABA synaptic currents and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state networks observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are observed as the resting-state fluctuations in fMRI and EEG recordings.

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