Computational models of thalamocortical augmenting responses

Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SOs, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the mechanisms by which global and local SOs arise from micro-scale neuronal dynamics and network connectivity remain poorly understood. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and SWS, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. An increase in the overall synaptic strength led to synchronized global SO, while a decrease in synaptic connectivity produced only local slow-waves that would not propagate beyond local areas. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

Emergent effects of synaptic connectivity on the dynamics of global and local slow waves in a large-scale thalamocortical network model of the human brain

Slow-wave sleep (SWS), characterized by slow oscillations (SO, <1Hz) of alternating active and silent states in the thalamocortical network, is a primary brain state during Non-Rapid Eye Movement (NREM) sleep. In the last two decades, the traditional view of SWS as a global and uniform whole-brain state has been challenged by a growing body of evidence indicating that SO can be local and can coexist with wake-like activity. However, the understanding of how global and local SO emerges from micro-scale neuron dynamics and network connectivity remains unclear. We developed a multi-scale, biophysically realistic human whole-brain thalamocortical network model capable of transitioning between the awake state and slow-wave sleep, and we investigated the role of connectivity in the spatio-temporal dynamics of sleep SO. We found that the overall strength and a relative balance between long and short-range synaptic connections determined the network state. Importantly, for a range of synaptic strengths, the model demonstrated complex mixed SO states, where periods of synchronized global slow-wave activity were intermittent with the periods of asynchronous local slow-waves. Increase of the overall synaptic strength led to synchronized global SO, while decrease of synaptic connectivity produced only local slow-waves that would not propagate beyond local area. These results were compared to human data to validate probable models of biophysically realistic SO. The model producing mixed states provided the best match to the spatial coherence profile and the functional connectivity estimated from human subjects. These findings shed light on how the spatio-temporal properties of SO emerge from local and global cortical connectivity and provide a framework for further exploring the mechanisms and functions of SWS in health and disease.

Thalamic feedback shapes brain responses evoked by cortical stimulation in mice and humans

Cortical stimulation with single pulses is a common technique in clinical practice and research. However, we still do not understand the extent to which it engages subcortical circuits which contribute to the associated evoked potentials (EPs). Here we find that cortical stimulation generates remarkably similar EPs in humans and mice, with a late component similarly modulated by the subject's behavioral state. We optogenetically dissect the underlying circuit in mice, demonstrating that the late component of these EPs is caused by a thalamic hyperpolarization and rebound. The magnitude of this late component correlates with the bursting frequency and synchronicity of thalamic neurons, modulated by the subject's behavioral state. A simulation of the thalamo-cortical circuit highlights that both intrinsic thalamic currents as well as cortical and thalamic GABAergic neurons contribute to this response profile. We conclude that the cortical stimulation engages cortico-thalamo-cortical circuits highly preserved across different species and stimulation modalities.

Toward a cerebello-thalamo-cortical computational model of spinocerebellar ataxia

Computational neural network modelling is an emerging approach for optimization of drug treatment of neurological disorders and fine-tuning of rehabilitation strategies. In the current study, we constructed a cerebello-thalamo-cortical computational neural network model to simulate a mouse model of cerebellar ataxia (pcd^5J mice) by manipulating cerebellar bursts through reduction of GABAergic inhibitory input. Cerebellar output neurons were projected to the thalamus and bidirectionally connected with the cortical network. Our results showed that reduction of inhibitory input in the cerebellum orchestrated the cortical local field potential (LFP) dynamics to generate specific motor outputs of oscillations of the theta, alpha, and beta bands in the computational model as well as in mouse motor cortical neurons. The therapeutic potential of deep brain stimulation (DBS) was tested in the computational model by increasing the sensory input to restore cortical output. Ataxia mice showed normalization of the motor cortex LFP after cerebellum DBS. We provide a novel approach to computational modelling to investigate the effect of DBS by mimicking cerebellar ataxia involving degeneration of Purkinje cells. Simulated neural activity coincides with findings from neural recordings of ataxia mice. Our computational model could thus represent cerebellar pathologies and provide insight into how to improve disease symptoms by restoring neuronal electrophysiological properties using DBS.

Effects of Small-World Rewiring Probability and Noisy Synaptic Conductivity on Slow Waves: Cortical Network

Physiological rhythms play a critical role in the functional development of living beings. Many biological functions are executed with an interaction of rhythms produced by internal characteristics of scores of cells. While synchronized oscillations may be associated with normal brain functions, anomalies in these oscillations may cause or relate the emergence of some neurological or neuropsychological pathologies. This study was designed to investigate the effects of topological structure and synaptic conductivity noise on the spatial synchronization and temporal rhythmicity of the waves generated by cells in the network. Because of holding the ability of clustering and randomizing with change of parameters, small-world (SW) network topology was chosen. The oscillatory activity of network was tried out by manipulating an insulated SW, cortical network model whose morphology is very close to real world. According to the obtained results, it was observed that at the optimal probabilistic rates of conductivity noise and rewiring of SW, powerful synchronized oscillatory small waves are generated in relation to the internal dynamics of cells, which are in line with the network's input. These two parameters were observed to be quite effective on the excitation-inhibition balance of the network. Accordingly, it may be suggested that the topological dynamics of SW and noisy synaptic conductivity may be associated with the normal and abnormal development of neurobiological structure.

Cellular and neurochemical basis of sleep stages in the thalamocortical network

The link between the combined action of neuromodulators in the brain and global brain states remains a mystery. In this study, using biophysically realistic models of the thalamocortical network, we identified the critical intrinsic and synaptic mechanisms, associated with the putative action of acetylcholine (ACh), GABA and monoamines, which lead to transitions between primary brain vigilance states (waking, non-rapid eye movement sleep [NREM] and REM sleep) within an ultradian cycle. Using ECoG recordings from humans and LFP recordings from cats and mice, we found that during NREM sleep the power of spindle and delta oscillations is negatively correlated in humans and positively correlated in animal recordings. We explained this discrepancy by the differences in the relative level of ACh. Overall, our study revealed the critical intrinsic and synaptic mechanisms through which different neuromodulators acting in combination result in characteristic brain EEG rhythms and transitions between sleep stages.

A Humanized Clinically Calibrated Quantitative Systems Pharmacology Model for Hypokinetic Motor Symptoms in Parkinson's Disease

The current treatment of Parkinson’s disease with dopamine-centric approaches such as L-DOPA and dopamine agonists, although very successful, is in need of alternative treatment strategies, both in terms of disease modification and symptom management. Various non-dopaminergic treatment approaches did not result in a clear clinical benefit, despite showing a clear effect in preclinical animal models. In addition, polypharmacy is common, sometimes leading to unintended effects on non-motor cognitive and psychiatric symptoms. To explore novel targets for symptomatic treatment and possible synergistic pharmacodynamic effects between different drugs, we developed a computer-based Quantitative Systems Pharmacology (QSP) platform of the closed cortico-striatal-thalamic-cortical basal ganglia loop of the dorsal motor circuit. This mechanism-based simulation platform is based on the known neuro-anatomy and neurophysiology of the basal ganglia and explicitly incorporates domain expertise in a formalized way. The calculated beta/gamma power ratio of the local field potential in the subthalamic nucleus correlates well (R² = 0.71) with clinically observed extra-pyramidal symptoms triggered by antipsychotics during schizophrenia treatment (43 drug-dose combinations). When incorporating Parkinsonian (PD) pathology and reported compensatory changes, the computer model suggests a major increase in b/g ratio (corresponding to bradykinesia and rigidity) from a dopamine depletion of 70% onward. The correlation between the outcome of the QSP model and the reported changes in UPDRS III Motor Part for 22 placebo-normalized drug-dose combinations is R² = 0.84. The model also correctly recapitulates the lack of clinical benefit for perampanel, MK-0567 and flupirtine and offers a hypothesis for the translational disconnect. Finally, using human PET imaging studies with placebo response, the computer model predicts well the placebo response for chronic treatment, but not for acute treatment in PD.

Network models of frequency modulated sweep detection

Frequency modulated (FM) sweeps are common in species-specific vocalizations, including human speech. Auditory neurons selective for the direction and rate of frequency change in FM sweeps are present across species, but the synaptic mechanisms underlying such selectivity are only beginning to be understood. Even less is known about mechanisms of experience-dependent changes in FM sweep selectivity. We present three network models of synaptic mechanisms of FM sweep direction and rate selectivity that explains experimental data: (1) The 'facilitation' model contains frequency selective cells operating as coincidence detectors, summing up multiple excitatory inputs with different time delays. (2) The 'duration tuned' model depends on interactions between delayed excitation and early inhibition. The strength of delayed excitation determines the preferred duration. Inhibitory rebound can reinforce the delayed excitation. (3) The 'inhibitory sideband' model uses frequency selective inputs to a network of excitatory and inhibitory cells. The strength and asymmetry of these connections results in neurons responsive to sweeps in a single direction of sufficient sweep rate. Variations of these properties, can explain the diversity of rate-dependent direction selectivity seen across species. We show that the inhibitory sideband model can be trained using spike timing dependent plasticity (STDP) to develop direction selectivity from a non-selective network. These models provide a means to compare the proposed synaptic and spectrotemporal mechanisms of FM sweep processing and can be utilized to explore cellular mechanisms underlying experience- or training-dependent changes in spectrotemporal processing across animal models. Given the analogy between FM sweeps and visual motion, these models can serve a broader function in studying stimulus movement across sensory epithelia.

System identification of the nonlinear dynamics in the thalamocortical circuit in response to patterned thalamic microstimulation in vivo

OBJECTIVE

Nonlinear system identification approaches were used to develop a dynamical model of the network level response to patterns of microstimulation in vivo.

APPROACH

The thalamocortical circuit of the rodent vibrissa pathway was the model system, with voltage sensitive dye imaging capturing the cortical response to patterns of stimulation delivered from a single electrode in the ventral posteromedial thalamus. The results of simple paired stimulus experiments formed the basis for the development of a phenomenological model explicitly containing nonlinear elements observed experimentally. The phenomenological model was fit using datasets obtained with impulse train inputs, Poisson-distributed in time and uniformly varying in amplitude.

MAIN RESULTS

The phenomenological model explained 58% of the variance in the cortical response to out of sample patterns of thalamic microstimulation. Furthermore, while fit on trial-averaged data, the phenomenological model reproduced single trial response properties when simulated with noise added into the system during stimulus presentation. The simulations indicate that the single trial response properties were dependent on the relative sensitivity of the static nonlinearities in the two stages of the model, and ultimately suggest that electrical stimulation activates local circuitry through linear recruitment, but that this activity propagates in a highly nonlinear fashion to downstream targets.

SIGNIFICANCE

The development of nonlinear dynamical models of neural circuitry will guide information delivery for sensory prosthesis applications, and more generally reveal properties of population coding within neural circuits.

Median nerve stimulation modulates extracellular signals in the primary motor area of a macaque monkey

Aiming to better define the functional influence of somatosensory stimuli on the primary motor cortex (M1) of primates, we investigated changes in extracellular neural activity induced by repetitive median nerve stimulation (MNS). We described neural adaptation and signal integration in both the multiunit activity (MUA) and the local field potential (LFP). To identify integration of initial M1 activity in the MNS response, we tested the correlation between peak amplitude responses and band energy preceding the peaks. Most of the sites studied in the M1 resulted responsive to MNS. MUA response peak amplitude decreased significantly in time in all sites during repetitive MNS, LFP response peak amplitude instead resulted more variable. Similarly, correlation analysis with the initial activity revealed a significant influence when tested using MUA peak amplitude modulation and a less significant correlation when tested using LFP peak amplitude. Our findings improve current knowledge on mechanisms underlying early M1 changes consequent to afferent somatosensory stimuli.

Forebrain HCN1 channels contribute to hypnotic actions of ketamine

BACKGROUND

Ketamine is a commonly used anesthetic, but the mechanistic basis for its clinically relevant actions remains to be determined. The authors previously showed that HCN1 channels are inhibited by ketamine and demonstrated that global HCN1 knockout mice are twofold less sensitive to hypnotic actions of ketamine. Although that work identified HCN1 channels as a viable molecular target for ketamine, it did not determine the relevant neural substrate.

METHODS

To localize the brain region responsible for HCN1-mediated hypnotic actions of ketamine, the authors used a conditional knockout strategy to delete HCN1 channels selectively in excitatory cells of the mouse forebrain. A combination of molecular, immunohistochemical, and cellular electrophysiologic approaches was used to verify conditional HCN1 deletion; a loss-of-righting reflex assay served to ascertain effects of forebrain HCN1 channel ablation on hypnotic actions of ketamine.

RESULTS

In conditional knockout mice, HCN1 channels were selectively deleted in cortex and hippocampus, with expression retained in cerebellum. In cortical pyramidal neurons from forebrain-selective HCN1 knockout mice, effects of ketamine on HCN1-dependent membrane properties were absent; notably, ketamine was unable to evoke membrane hyperpolarization or enhance synaptic inputs. Finally, the EC50 for ketamine-induced loss-of-righting reflex was shifted to significantly higher concentrations (by approximately 31%).

CONCLUSIONS

These data indicate that forebrain principal cells represent a relevant neural substrate for HCN1-mediated hypnotic actions of ketamine. The authors suggest that ketamine inhibition of HCN1 shifts cortical neuron electroresponsive properties to contribute to ketamine-induced hypnosis.

Control of layer 5 pyramidal cell spiking by oscillatory inhibition in the distal apical dendrites: a computational modeling study

The distal apical dendrites of layer 5 pyramidal neurons receive cortico-cortical and thalamocortical top-down and feedback inputs, as well as local recurrent inputs. A prominent source of recurrent inhibition in the neocortical circuit is somatostatin-positive Martinotti cells, which preferentially target distal apical dendrites of pyramidal cells. These electrically coupled cells can fire synchronously at various frequencies, including over a relatively slow range (5∼30 Hz), thereby imposing oscillatory inhibition on the pyramidal apical tuft dendrites. We examined how such distal oscillatory inhibition influences the firing of a biophysically detailed layer 5 pyramidal neuron model, which reproduced the spatiotemporal properties of sodium, calcium, and N-methyl-D-aspartate receptor spikes found experimentally. We found that oscillatory synchronization strongly influences the impact of distal inhibition on the pyramidal cell firing. Whereas asynchronous inhibition largely cancels out the facilitatory effects of distal excitatory inputs, inhibition oscillating synchronously at around 10∼20 Hz allows distal excitation to drive axosomatic firing, as if distal inhibition were absent. Underlying this is a switch from relatively infrequent burst firing to single spike firing at every period of the inhibitory oscillation. This phenomenon depends on hyperpolarization-activated cation current-dependent membrane potential resonance in the dendrite, but also, in a novel manner, on a cooperative amplification of this resonance by N-methyl-D-aspartate-receptor-driven dendritic action potentials. Our results point to a surprising dependence of the effect of recurrent inhibition by Martinotti cells on their oscillatory synchronization, which may control not only the local circuit activity, but also how it is transmitted to and decoded by downstream circuits.

Optimal space-time precoding of artificial sensory feedback through mutichannel microstimulation in bi-directional brain-machine interfaces

Brain-machine interfaces (BMIs) aim to restore lost sensorimotor and cognitive function in subjects with severe neurological deficits. In particular, lost somatosensory function may be restored by artificially evoking patterns of neural activity through microstimulation to induce perception of tactile and proprioceptive feedback to the brain about the state of the limb. Despite an early proof of concept that subjects could learn to discriminate a limited vocabulary of intracortical microstimulation (ICMS) patterns that instruct the subject about the state of the limb, the dynamics of a moving limb are unlikely to be perceived by an arbitrarily-selected, discrete set of static microstimulation patterns, raising questions about the generalization and the scalability of this approach. In this work, we propose a microstimulation protocol intended to activate optimally the ascending somatosensory pathway. The optimization is achieved through a space-time precoder that maximizes the mutual information between the sensory feedback indicating the limb state and the cortical neural response evoked by thalamic microstimulation. Using a simplified multi-input multi-output model of the thalamocortical pathway, we show that this optimal precoder can deliver information more efficiently in the presence of noise compared to suboptimal precoders that do not account for the afferent pathway structure and/or cortical states. These results are expected to enhance the way microstimulation is used to induce somatosensory perception during sensorimotor control of artificial devices or paralyzed limbs.

Dynamics of action potential initiation in the GABAergic thalamic reticular nucleus in vivo

Understanding the neural mechanisms of action potential generation is critical to establish the way neural circuits generate and coordinate activity. Accordingly, we investigated the dynamics of action potential initiation in the GABAergic thalamic reticular nucleus (TRN) using in vivo intracellular recordings in cats in order to preserve anatomically-intact axo-dendritic distributions and naturally-occurring spatiotemporal patterns of synaptic activity in this structure that regulates the thalamic relay to neocortex. We found a wide operational range of voltage thresholds for action potentials, mostly due to intrinsic voltage-gated conductances and not synaptic activity driven by network oscillations. Varying levels of synchronous synaptic inputs produced fast rates of membrane potential depolarization preceding the action potential onset that were associated with lower thresholds and increased excitability, consistent with TRN neurons performing as coincidence detectors. On the other hand the presence of action potentials preceding any given spike was associated with more depolarized thresholds. The phase-plane trajectory of the action potential showed somato-dendritic propagation, but no obvious axon initial segment component, prominent in other neuronal classes and allegedly responsible for the high onset speed. Overall, our results suggest that TRN neurons could flexibly integrate synaptic inputs to discharge action potentials over wide voltage ranges, and perform as coincidence detectors and temporal integrators, supported by a dynamic action potential threshold.

Non-homogeneous extracellular resistivity affects the current-source density profiles of up-down state oscillations

Rhythmic local field potential (LFP) oscillations observed during deep sleep are the result of synchronized electrical activities of large neuronal ensembles, which consist of alternating periods of activity and silence, termed 'up' and 'down' states, respectively. Current-source density (CSD) analysis indicates that the up states of these slow oscillations are associated with current sources in superficial cortical layers and sinks in deep layers, while the down states display the opposite pattern of source-sink distribution. We show here that a network model of up and down states displays this CSD profile only if a frequency-filtering extracellular medium is assumed. When frequency filtering was modelled as inhomogeneous conductivity, this simple model had considerably more power in slow frequencies, resulting in significant differences in LFP and CSD profiles compared with the constant-resistivity model. These results suggest that the frequency-filtering properties of extracellular media may have important consequences for the interpretation of the results of CSD analysis.

Neural feedback for instantaneous spatiotemporal modulation of afferent pathways in bi-directional brain-machine interfaces

In bi-directional brain-machine interfaces (BMIs), precisely controlling the delivery of microstimulation, both in space and in time, is critical to continuously modulate the neural activity patterns that carry information about the state of the brain-actuated device to sensory areas in the brain. In this paper, we investigate the use of neural feedback to control the spatiotemporal firing patterns of neural ensembles in a model of the thalamocortical pathway. Control of pyramidal (PY) cells in the primary somatosensory cortex (S1) is achieved based on microstimulation of thalamic relay cells through multiple-input multiple-output (MIMO) feedback controllers. This closed loop feedback control mechanism is achieved by simultaneously varying the stimulation parameters across multiple stimulation electrodes in the thalamic circuit based on continuous monitoring of the difference between reference patterns and the evoked responses of the cortical PY cells. We demonstrate that it is feasible to achieve a desired level of performance by controlling the firing activity pattern of a few "key" neural elements in the network. Our results suggest that neural feedback could be an effective method to facilitate the delivery of information to the cortex to substitute lost sensory inputs in cortically controlled BMIs.

Neuronal plasticity and thalamocortical sleep and waking oscillations

Throughout life, thalamocortical (TC) network alternates between activated states (wake or rapid eye movement sleep) and slow oscillatory state dominating slow-wave sleep. The patterns of neuronal firing are different during these distinct states. I propose that due to relatively regular firing, the activated states preset some steady state synaptic plasticity and that the silent periods of slow-wave sleep contribute to a release from this steady state synaptic plasticity. In this respect, I discuss how states of vigilance affect short-, mid-, and long-term synaptic plasticity, intrinsic neuronal plasticity, as well as homeostatic plasticity. Finally, I suggest that slow oscillation is intrinsic property of cortical network and brain homeostatic mechanisms are tuned to use all forms of plasticity to bring cortical network to the state of slow oscillation. However, prolonged and profound shift from this homeostatic balance could lead to development of paroxysmal hyperexcitability and seizures as in the case of brain trauma.

Intracortical Augmenting Responses in Networks of Reduced Compartmental Models of Tufted Layer 5 Cells

Augmenting responses (ARs) are characteristic recruitment phenomena that can be generated in target neural populations by repetitive intracortical or thalamic stimulation and that may facilitate activity transmission from thalamic nuclei to the cortex or between cortical areas. Experimental evidence suggests a role for cortical layer 5 in initiating at least one form of augmentation. We present a three-compartment model of tufted layer 5 (TL5) cells that faithfully reproduces a wide range of dynamics in these neurons that previously has been achieved only partially and in much more complex models. Using this model, the simplest network exhibiting AR was a single pair of TL5 and inhibitory (IN5) neurons. Intracellularly, AR initiation was controlled by low-threshold Ca2+ current ( IT), which promoted TL5 rebound firing, whereas AR strength was dictated by inward-rectifying current ( Ih), which regulated TL5 multiple-spike firing and also prevented excessive firing under high-amplitude stimuli. Synaptically, AR was significantly more salient under concurrent stimulus delivery to superficial and deep dendritic zones of TL5 cells than under conventional single-zone stimuli. Moreover, slow GABA-B–mediated inhibition in TL5 cells controlled AR strength and frequency range. Finally, a network model of two cortical populations interacting across functional hierarchy showed that intracortical AR occurred prominently upon exciting superficial cortical layers either directly or via intrinsic connections, with AR frequency dictated by connection strength and background activity. Overall, the investigation supports a central role for a TL5–IN5 skeleton network in low-frequency cortical dynamics in vivo, particularly across functional hierarchies, and presents neuronal models that facilitate accurate large-scale simulations.

Subunit-specific effects of isoflurane on neuronal Ih in HCN1 knockout mice

The ionic mechanisms that contribute to general anesthetic actions have not been elucidated, although increasing evidence has pointed to roles for subthreshold ion channels, such as the HCN channels underlying the neuronal hyperpolarization-activated cationic current (Ih). Here, we used conventional HCN1 knockout mice to test directly the contributions of specific HCN subunits to effects of isoflurane, an inhalational anesthetic, on membrane and integrative properties of motor and cortical pyramidal neurons in vitro. Compared with wild-type mice, residual Ih from knockout animals was smaller in amplitude and presented with HCN2-like properties. Inhibition of Ih by isoflurane previously attributed to HCN1 subunit-containing channels (i.e., a hyperpolarizing shift in half-activation voltage [V1/2]) was absent in neurons from HCN1 knockout animals; the remaining inhibition of current amplitude could be attributed to effects on residual HCN2 channels. We also found that isoflurane increased temporal summation of excitatory postsynaptic potentials (EPSPs) in cortical neurons from wild-type mice; this effect was predicted by simulation of anesthetic-induced dendritic Ih inhibition, which also revealed more prominent summation accompanying shifts in V1/2 (an HCN1-like effect) than decreased current amplitude (an HCN2-like effect). Accordingly, anesthetic-induced EPSP summation was not observed in cortical cells from HCN1 knockout mice. In wild-type mice, the enhanced synaptic summation observed with low concentrations of isoflurane contributed to a net increase in cortical neuron excitability. In summary, HCN channel subunits account for distinct anesthetic effects on neuronal membrane properties and synaptic integration; inhibition of HCN1 in cortical neurons may contribute to the synaptically mediated slow-wave cortical synchronization that accompanies anesthetic-induced hypnosis.

Computer modelling of epilepsy

Epilepsy is a complex set of disorders that can involve many areas of the cortex, as well as underlying deep-brain systems. The myriad manifestations of seizures, which can be as varied as déjà vu and olfactory hallucination, can therefore give researchers insights into regional functions and relations. Epilepsy is also complex genetically and pathophysiologically: it involves microscopic (on the scale of ion channels and synaptic proteins), macroscopic (on the scale of brain trauma and rewiring) and intermediate changes in a complex interplay of causality. It has long been recognized that computer modelling will be required to disentangle causality, to better understand seizure spread and to understand and eventually predict treatment efficacy. Over the past few years, substantial progress has been made in modelling epilepsy at levels ranging from the molecular to the socioeconomic. We review these efforts and connect them to the medical goals of understanding and treating the disorder.

Focal generation of paroxysmal fast runs during electrographic seizures

PURPOSE

A cortically generated Lennox-Gastaut type seizure is associated with spike-wave/poly-spike-wave discharges at 1.0-2.5 Hz and fast runs at 7-16 Hz. Here we studied the patterns of synchronization during runs of paroxysmal fast spikes.

METHODS

Electrographic activities were recorded using multisite intracellular and field potential recordings in vivo from cats anesthetized with ketamine-xylazine. In different experiments, the recording electrodes were located either at short distances (<1 mm) or at longer distances (up to 12 mm). The main experimental findings were tested in computational models.

RESULTS

In the majority of cases, the onset and the offset of fast runs occurred almost simultaneously in different recording sites. The amplitude and duration of fast runs could vary by orders of magnitude. Within the fast runs, the patterns of synchronization recorded in different electrodes were as following: (1) synchronous, in phase, (2) synchronous, with phase shift, (3) patchy, repeated in phase/phase shift transitions, and (4) nonsynchronous, slightly different frequencies in different recording sites or absence of oscillatory activity in one of the recording sites; the synchronous patterns (in phase or with phase shifts) were most common. All these patterns could be recorded in the same pair of electrodes during different seizures, and they were reproduced in a computational network model. Intrinsically bursting (IB) neurons fired more spikes per cycle than any other neurons suggesting their leading role in the fast run generation.

CONCLUSIONS

Once started, the fast runs are generated locally with variable correlations between neighboring cortical foci.

Oscillations and synchrony in large-scale cortical network models

Intrinsic neuronal and circuit properties control the responses of large ensembles of neurons by creating spatiotemporal patterns of activity that are used for sensory processing, memory formation, and other cognitive tasks. The modeling of such systems requires computationally efficient single-neuron models capable of displaying realistic response properties. We developed a set of reduced models based on difference equations (map-based models) to simulate the intrinsic dynamics of biological neurons. These phenomenological models were designed to capture the main response properties of specific types of neurons while ensuring realistic model behavior across a sufficient dynamic range of inputs. This approach allows for fast simulations and efficient parameter space analysis of networks containing hundreds of thousands of neurons of different types using a conventional workstation. Drawing on results obtained using large-scale networks of map-based neurons, we discuss spatiotemporal cortical network dynamics as a function of parameters that affect synaptic interactions and intrinsic states of the neurons.

Spikes, synchrony, and attentive learning by laminar thalamocortical circuits

This article develops the Synchronous Matching Adaptive Resonance Theory (SMART) neural model to explain how the brain may coordinate multiple levels of thalamocortical and corticocortical processing to rapidly learn, and stably remember, important information about a changing world. The model clarifies how bottom-up and top-down processes work together to realize this goal, notably how processes of learning, expectation, attention, resonance, and synchrony are coordinated. The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single-cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current source densities and local field potentials; and single-cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. In particular, the model predicts how laminar circuits of multiple cortical areas interact with primary and higher-order specific thalamic nuclei and nonspecific thalamic nuclei to carry out attentive visual learning and information processing. The model simulates how synchronization of neuronal spiking occurs within and across brain regions, and triggers STDP. Matches between bottom-up adaptively filtered input patterns and learned top-down expectations cause gamma oscillations that support attention, resonance, learning, and consciousness. Mismatches inhibit learning while causing beta oscillations during reset and hypothesis testing operations that are initiated in the deeper cortical layers. The generality of learned recognition codes is controlled by a vigilance process mediated by acetylcholine.

Consciousness CLEARS the mind

A full understanding of consciousness requires that we identify the brain processes from which conscious experiences emerge. What are these processes, and what is their utility in supporting successful adaptive behaviors? Adaptive Resonance Theory (ART) predicted a functional link between processes of Consciousness, Learning, Expectation, Attention, Resonance and Synchrony (CLEARS), including the prediction that "all conscious states are resonant states". This connection clarifies how brain dynamics enable a behaving individual to autonomously adapt in real time to a rapidly changing world. The present article reviews theoretical considerations that predicted these functional links, how they work, and some of the rapidly growing body of behavioral and brain data that have provided support for these predictions. The article also summarizes ART models that predict functional roles for identified cells in laminar thalamocortical circuits, including the six layered neocortical circuits and their interactions with specific primary and higher-order specific thalamic nuclei and nonspecific nuclei. These predictions include explanations of how slow perceptual learning can occur without conscious awareness, and why oscillation frequencies in the lower layers of neocortex are sometimes slower beta oscillations, rather than the higher-frequency gamma oscillations that occur more frequently in superficial cortical layers. ART traces these properties to the existence of intracortical feedback loops, and to reset mechanisms whereby thalamocortical mismatches use circuits such as the one from specific thalamic nuclei to nonspecific thalamic nuclei and then to layer 4 of neocortical areas via layers 1-to-5-to-6-to-4.

Modeling the contribution of lamina 5 neuronal and network dynamics to low frequency EEG phenomena

The Electroencephalogram (EEG) is an important clinical and research tool in neurophysiology. With the advent of recording techniques, new evidence is emerging on the neuronal populations and wiring in the neocortex. A main challenge is to relate the EEG generation mechanisms to the underlying circuitry of the neocortex. In this paper, we look at the principal intrinsic properties of neocortical cells in layer 5 and their network behavior in simplified simulation models to explain the emergence of several important EEG phenomena such as the alpha rhythms, slow-wave sleep oscillations, and a form of cortical seizure. The models also predict the ability of layer 5 cells to produce a resonance-like neuronal recruitment known as the augmenting response. While previous models point to deeper brain structures, such as the thalamus, as the origin of many EEG rhythms (spindles), the current model suggests that the cortical circuitry itself has intrinsic oscillatory dynamics which could account for a wide variety of EEG phenomena.

State-dependent modulation of time-varying gustatory responses

Sensory processing is modulated by attention, which is a function of network states. Here we show that changes in such states do more than a simple gating of stimuli: they actually re-arrange cortical coding space to emphasize emotional valences. We delivered taste stimuli to rats before and after a spontaneous state change ("disengagement") that is associated with a reduction in attention and a concurrent emergence of cortical mu rhythms. The percentage of cortical neurons that responded to tastes, and the average response across neurons, remained stable with disengagement, but the particulars of the responses changed drastically. The distinctiveness of sucrose and quinine-which represent the high and low ends of the palatability spectrum-increased, the distinctiveness of the two aversive tastes (quinine and citric acid) decreased, and the distinctiveness of sucrose and NaCl, which were almost identically palatable to start with, did not change. Overall, then, the changes appeared to be palatability-specific. Two additional findings were consistent with this conclusion: rats' palatability-related behavioral responses to the tastes changed in similar ways with disengagement and disengagement-related neural changes specifically appeared late in the response, when palatability-specific information emerges in cortical responses. These data suggest that neural state changes can change the content of neural codes.

Photic-induced sensitization: acquisition of an augmenting spike-wave response in the adult rat through repeated strobe exposure

It is well established that patterns of sensory input can affect neuroplastic changes during early development. The scope and consequences of experience-dependent plasticity in the adult are less well understood. We studied the possibility that repeated exposure to trains of stroboscopic stimuli could induce a sensitized and potentially aberrant response in ordinary individuals. Chronic electrocorticographic recording electrodes enabled measurement of responses in awake, freely moving animals. Normal adult rats, primarily Sprague-Dawley, were exposed to 20-40 strobe trains per day after a strobe-free adaptation period. The common response to strobe trains changed in 34/36 rats with development of a high-amplitude spike-wave response that emerged fully by the third day of photic exposure. Onset of this sensitized response was marked by short-term augmentation of response to successive strobe flashes. The waveform generalized across the brain, reflected characteristics of the visual stimulus, as well as an inherent 6- to 8-Hz pacing, and was suppressed with ethosuximide administration. Spike-wave episodes were self-limiting but could persist beyond the strobe period. Sensitization lasted 2-4 wk after last strobe exposure. The results indicate visual stimulation, by itself, can induce in adult rats an enduring sensitization of visual response with epileptiform characteristics. The results raise the question of the effects of such neuroplastic change on sensation and epileptiform events.

Grouping of brain rhythms in corticothalamic systems

Different brain rhythms, with both low-frequency and fast-frequency, are grouped within complex wave-sequences. Instead of dissecting various frequency bands of the major oscillations that characterize the brain electrical activity during states of vigilance, it is conceptually more rewarding to analyze their coalescence, which is due to neuronal interactions in corticothalamic systems. This concept of unified brain rhythms does not only include low-frequency sleep oscillations but also fast (beta and gamma) activities that are not exclusively confined to brain-activated states, since they also occur during slow-wave sleep. The major factor behind this coalescence is the cortically generated slow oscillation that, through corticocortical and corticothalamic drives, is effective in grouping other brain rhythms. The experimental evidence for unified oscillations derived from simultaneous intracellular recordings of cortical and thalamic neurons in vivo, while recent studies in humans using global methods provided congruent results of grouping different types of slow and fast oscillatory activities. Far from being epiphenomena, spontaneous brain rhythms have an important role in synaptic plasticity. The role of slow-wave sleep oscillation in consolidating memory traces acquired during wakefulness is being explored in both experimental animals and human subjects. Highly synchronized sleep oscillations may develop into seizures that are generated intracortically and lead to inhibition of thalamocortical neurons, via activation of thalamic reticular neurons, which may explain the obliteration of signals from the external world and unconsciousness during some paroxysmal states.

Independent variable time-step integration of individual neurons for network simulations

Realistic neural networks involve the coexistence of stiff, coupled, continuous differential equations arising from the integrations of individual neurons, with the discrete events with delays used for modeling synaptic connections. We present here an integration method, the local variable time-step method (lvardt), that uses separate variable-step integrators for individual neurons in the network. Cells that are undergoing excitation tend to have small time steps, and cells that are at rest with little synaptic input tend to have large time steps. A synaptic input to a cell causes reinitialization of only that cell's integrator without affecting the integration of other cells. We illustrated the use of lvardt on three models: a worst-case synchronizing mutual-inhibition model, a best-case synfire chain model, and a more realistic thalamocortical network model.

Synaptic Enhancement Induced Through Callosal Pathways in Cat Association Cortex

The corpus callosum plays a major role in synchronizing neocortical activities in the two hemispheres. We investigated the changes in callosally elicited excitatory postsynaptic potentials (EPSPs) of neurons from cortical association areas 5 and 7 of cats under barbiturate or ketamine-xylazine anesthesia. Single pulses to callosal pathway evoked control EPSPs; pulse-trains were subsequently applied at different frequencies to homotopic sites in the contralateral cortex, as conditioning stimulation; thereafter, the single pulses were applied again to test changes in synaptic responsiveness by comparing the amplitudes of control and conditioned EPSPs. In 41 of 42 neurons recorded under barbiturate anesthesia, all frequencies of conditioning callosal stimuli induced short-term (5–30 min) enhancement of test EPSPs elicited by single stimuli. Neurons tested with successive conditioning pulse-trains at different frequencies displayed stronger enhancement with high-frequency (40–100 Hz) than with low-frequency (10–20 Hz) rhythmic pulse-trains; >100 Hz, the potentiation saturated. In a neuronal sample, microdialysis of an N-methyl-d-aspartate (NMDA) receptor blocker in barbiturate-treated cats suppressed this potentiation, and potentiation of callosally evoked EPSPs was not detected in neurons recorded under ketamine-xylazine anesthesia, thus indicating that EPSPs' potentiation implicates, at least partially, NMDA receptors. These data suggest that callosal activities occurring within low-frequency and fast-frequency oscillations play a role in cortical synaptic plasticity.

Estimation of neurophysiological parameters from the waking EEG using a biophysical model of brain dynamics

This paper presents the results from using electroencephalographic (EEG) data to estimate the values of key neurophysiological parameters using a detailed biophysical model of brain activity. The model incorporates spatial and temporal aspects of cortical function including axonal transmission delays, synapto-dendritic rates, range-dependent connectivities, excitatory and inhibitory neural populations, and intrathalamic, intracortical, corticocortical and corticothalamic pathways. Parameter estimates were obtained by fitting the model's theoretical spectrum to EEG spectra from each of 100 healthy human subjects. Statistical analysis was used to infer significant parameter variations occurring between eyes-closed and eyes-open states, and a correlation matrix was used to investigate links between the parameter variations and traditional measures of quantitative EEG (qEEG). Accurate fits to all experimental spectra were observed, and both inter-subject and between-state variability were accounted for by the variance in the fitted biophysical parameters, which were in turn consistent with known independent experimental and theoretical estimates. These values thus provide physiological information regarding the state. transitions (eyes-closed vs. eyes-open) and phenomena including cortical idling and alpha desynchronization. The parameters are also consistent with traditional qEEG, but are more informative, since they provide links to underlying physiological processes. To our knowledge, this is the first study where a detailed biophysical model of the brain is used to estimate neurophysiological parameters underlying the transitions in a broad range (0.25-50 Hz) of EEG spectra obtained from a large set of human data.

Neuronal mechanisms mediating the variability of somatosensory evoked potentials during sleep oscillations in cats

The slow oscillation (SO) generated within the corticothalamic system is composed of active and silent states. The studies of response variability during active versus silent network states within thalamocortical system of human and animals provided inconsistent results. To investigate this inconsistency, we used electrophysiological recordings from the main structures of the somatosensory system in anaesthetized cats. Stimulation of the median nerve (MN) elicited cortical responses during all phases of SO. Cortical responses to stimulation of the medial lemniscus (ML) were virtually absent during silent periods. At the ventral-posterior lateral (VPL) level, ML stimuli elicited either EPSPs in isolation or EPSPs crowned by spikes, as a function of membrane potential. Response to MN stimuli elicited compound synaptic responses and spiked at any physiological level of membrane potential. The responses of dorsal column nuclei neurones to MN stimuli were of similar latency, but the latencies of antidromic responses to ML stimuli were variable. Thus, the variable conductance velocity of ascending prethalamic axons was the most likely cause of the barrages of synaptic events in VPL neurones mediating their firing at different level of the membrane potential. We conclude that the preserved ability of the somatosensory system to transmit the peripheral stimuli to the cerebral cortex during all the phases of sleep slow oscillation is based on the functional properties of the medial lemniscus and on the intrinsic properties of the thalamocortical cells. However the reduced firing ability of the cortical neurones during the silent state may contribute to impair sensory processing during sleep.

The thalamus as the generator and modulator of EEG alpha rhythm: a combined PET/EEG study with lorazepam challenge in humans

BACKGROUND

Purpose of this study was to investigate the functional relationship between electroencephalographic (EEG) alpha power and cerebral glucose metabolism before and after pharmacological alpha suppression by lorazepam.

METHODS

Ten healthy male volunteers were examined undergoing two F18-fluorodeoxyglucose (18-FDG) positron emission tomography (PET) scans with simultaneous EEG recording: 1x placebo, 1x lorazepam. EEG power spectra were computed by means of Fourier analysis. The PET data were analyzed using SPM99, and the correlations between metabolism and alpha power were calculated for both conditions.

RESULTS

The comparison lorazepam versus placebo revealed reduced glucose metabolism of the bilateral thalamus and adjacent subthalamic areas, the occipital cortex and temporo-insular areas (P < 0.001). EEG alpha power was reduced in all derivations (P < 0.001). Under placebo, there was a positive correlation between alpha power and metabolism of the bilateral thalamus and the occipital and adjacent parietal cortex (P < 0.001). Under lorazepam, the thalamic and parietal correlations were maintained, whereas the occipital correlation was no longer detectable (P < 0.001). The correlation analysis of the difference lorazepam-placebo showed the alpha power exclusively correlated with the thalamic activity (P < 0.0001).

CONCLUSIONS

These results support the hypothesis of a close functional relationship between thalamic activity and alpha rhythm in humans mediated by corticothalamic loops which are independent of sensory afferences. The study paradigm could be a promising approach for the investigation of cortico-thalamo-cortical feedback loops in neuropsychiatric diseases.

Synaptic Interactions Between Thalamic and Cortical Inputs Onto Cortical Neurons In Vivo

To study the interactions between thalamic and cortical inputs onto neocortical neurons, we used paired-pulse stimulation (PPS) of thalamic and cortical inputs as well as PPS of two cortical or two thalamic inputs that converged, at different time intervals, onto intracellularly recorded cortical and thalamocortical neurons in anesthetized cats. PPS of homosynaptic cortico-cortical pathways produced facilitation, depression, or no significant effects in cortical pathways, whereas cortical responses to thalamocortical inputs were mostly facilitated at both short and long intervals. By contrast, heterosynaptic interactions between either cortical and thalamic, or thalamic and cortical, inputs generally produced decreases in the peak amplitudes and depolarization area of evoked excitatory postsynaptic potentials (EPSPs), with maximal effect at ∼10 ms and lasting from 60 to 100 ms. All neurons tested with thalamic followed by cortical stimuli showed a decrease in the apparent input resistance ( Rin), the time course of which paralleled that of decreased responses, suggesting that shunting is the factor accounting for EPSP's decrease. Only half of neurons tested with cortical followed by thalamic stimuli displayed changes in Rin. Spike shunting in the thalamus may account for those cases in which decreased synaptic responsiveness of cortical neurons was not associated with decreased Rin because thalamocortical neurons showed decreased firing probability during cortical stimulation. These results suggest a short-lasting but strong shunting between thalamocortical and cortical inputs onto cortical neurons.

Activity dynamics in nonlocal interacting neural fields

We study the activity of a synaptically coupled neuronal network consisting of an excitatory and an inhibitory layer with isotropic connections and nonlinear interactions. Using the mathematical model of Wilson and Cowan in two spatial dimensions, we first discuss a spatial hysteresis phenomenon. Then we analyze special traveling wave solutions with stationary shape. We establish existence conditions, derive analytic expressions of the particular solutions and their velocity, and finally present numerical simulations.

Neuronal plasticity in thalamocortical networks during sleep and waking oscillations

Spontaneous brain oscillations during states of vigilance are associated with neuronal plasticity due to rhythmic spike bursts and spike trains fired by thalamic and neocortical neurons during low-frequency rhythms that characterize slow-wave sleep and fast rhythms occurring during waking and REM sleep. Intracellular recordings from thalamic and related cortical neurons in vivo demonstrate that, during natural slow-wave sleep oscillations or their experimental models, both thalamic and cortical neurons progressively enhance their responsiveness. This potentiation lasts for several minutes after the end of oscillatory periods. Cortical neurons display self-sustained activity, similar to responses evoked during previous epochs of stimulation, despite the fact that thalamic neurons remain under a powerful hyperpolarizing pressure. These data suggest that, far from being a quiescent state during which the cortex and subcortical structures are globally inhibited, slow-wave sleep may consolidate memory traces acquired during wakefulness in corticothalamic networks. Similar phenomena occur as a consequence of fast oscillations during brain-activated states.

Frequency-selective augmenting responses by short-term synaptic depression in cat neocortex

Thalamic stimulation at frequencies between 5 and 15 Hz elicits incremental or 'augmenting' cortical responses. Augmenting responses can also be evoked in cortical slices and isolated cortical slabs in vivo. Here we show that a realistic network model of cortical pyramidal cells and interneurones including short-term plasticity of inhibitory and excitatory synapses replicates the main features of augmenting responses as obtained in isolated slabs in vivo. Repetitive stimulation of synaptic inputs at frequencies around 10 Hz produced postsynaptic potentials that grew in size and carried an increasing number of action potentials resulting from the depression of inhibitory synaptic currents. Frequency selectivity was obtained through the relatively weak depression of inhibitory synapses at low frequencies, and strong depression of excitatory synapses together with activation of a calcium-activated potassium current at high frequencies. This network resonance is a consequence of short-term synaptic plasticity in a network of neurones without intrinsic resonances. These results suggest that short-term plasticity of cortical synapses could shape the dynamics of synchronized oscillations in the brain.

Cortical hyperpolarization-activated depolarizing current takes part in the generation of focal paroxysmal activities

During paroxysmal neocortical oscillations, sudden depolarization leading to the next cycle occurs when the majority of cortical neurons are hyperpolarized. Both the Ca(2+)-dependent K(+) currents (I(K(Ca))) and disfacilitation play critical roles in the generation of hyperpolarizing potentials. In vivo experiments and computational models are used here to investigate whether the hyperpolarization-activated depolarizing current (I(h)) in cortical neurons also contributes to the generation of paroxysmal onsets. Hyperpolarizing current pulses revealed a depolarizing sag in approximately 20% of cortical neurons. Intracellular recordings from glial cells indirectly indicated an increase in extracellular potassium concentration ([K(+)](o)) during paroxysmal activities, leading to a positive shift in the reversal potential of K(+)-mediated currents, including I(h). In the paroxysmal neocortex, approximately 20% of neurons show repolarizing potentials originating from hyperpolarizations associated with depth-electroencephalogram positive waves of spike-wave complexes. The onset of these repolarizing potentials corresponds to maximal [K(+)](o) as estimated from dual simultaneous impalements from neurons and glial cells. Computational models showed how, after the increased [K(+)](o), the interplay between I(h), I(K(Ca)), and a persistent Na(+) current, I(Na(P)), could organize paroxysmal oscillations at a frequency of 2-3 Hz.

Natural waking and sleep states: a view from inside neocortical neurons

In this first intracellular study of neocortical activities during waking and sleep states, we hypothesized that synaptic activities during natural states of vigilance have a decisive impact on the observed electrophysiological properties of neurons that were previously studied under anesthesia or in brain slices. We investigated the incidence of different firing patterns in neocortical neurons of awake cats, the relation between membrane potential fluctuations and firing rates, and the input resistance during all states of vigilance. In awake animals, the neurons displaying fast-spiking firing patterns were more numerous, whereas the incidence of neurons with intrinsically bursting patterns was much lower than in our previous experiments conducted on the intact-cortex or isolated cortical slabs of anesthetized cats. Although cortical neurons displayed prolonged hyperpolarizing phases during slow-wave sleep, the firing rates during the depolarizing phases of the slow sleep oscillation was as high during these epochs as during waking and rapid-eye-movement sleep. Maximum firing rates, exceeding those of regular-spiking neurons, were reached by conventional fast-spiking neurons during both waking and sleep states, and by fast-rhythmic-bursting neurons during waking. The input resistance was more stable and it increased during quiet wakefulness, compared with sleep states. As waking is associated with high synaptic activity, we explain this result by a higher release of activating neuromodulators, which produce an increase in the input resistance of cortical neurons. In view of the high firing rates in the functionally disconnected state of slow-wave sleep, we suggest that neocortical neurons are engaged in processing internally generated signals.

Multiplicity in the high-frequency signals during the short-latency somatosensory evoked cortical activity in humans

OBJECTIVE

Recent studies using electroencephalography or magnetoencephalography have shown that peripheral nerve stimulations produce short-latency high-frequency signals in the human somatosensory cortex. The present study tested whether they consist of more than one distinct type of signal.

METHODS

Somatic evoked magnetic fields (SEFs) elicited by electrical stimulation of the median nerve were measured in 12 healthy volunteers. They were analyzed using a time-frequency analysis method based on Gabor filters and another based on autoregressive moving average, and also with bispectrum and bicoherence techniques and a new dispersion curve method.

RESULTS

Signals in two separate high-frequency bands (200 and 600 Hz) were distinguished from the main signal in the low frequency (LF) range during the time period of N20m and P25m. The novel 200 Hz-band signal was seen reliably in those channels where the LF band signal was weak, so that the former was not masked by the latter. The 600 Hz signal consisted of two distinct components or parts (p1 and p2) in 10 out of 12 subjects, one peaking during ascending slope and the second during the descending slope of the N20m. The latency of the p1 was shorter than the latencies of the 200 Hz and LF signals according to the dispersion curve analysis. The inter-peak interval of p1 became shorter for later peaks in all 12 subjects. Bicoherence analysis revealed a significant phase coupling between the 200 and 600 Hz bands.

CONCLUSIONS

There are three distinct types of signal during the time period of the short-latency cortical components of the SEF -- LF which gives rise to the commonly seen waveform of the SEF, the newly found 200 Hz signal and the 600 Hz signal which consists of two components. The possible origins of the high frequency signals are discussed in light of the new set of evidence found in the present study.

Impact of network activities on neuronal properties in corticothalamic systems

Data from in vivo and in vitro experiments are discussed to emphasize that synaptic activities in neocortex and thalamus have a decisive impact on intrinsic neuronal properties in intact-brain preparations under anesthesia and even more so during natural states of vigilance. Thus the firing patterns of cortical neuronal types are not inflexible but may change with the level of membrane potential and during periods rich in synaptic activity. The incidences of some cortical cell classes (defined by their responses to depolarizing current pulses) are different in isolated cortical slabs in vivo or in slices maintained in vitro compared with the intact cortex of naturally awake animals. Network activities, which include the actions of generalized modulatory systems, have a profound influence on the membrane potential, apparent input resistance, and backpropagation of action potentials. The analysis of various oscillatory types leads to the conclusion that in the intact brain, there are no "pure" rhythms, generated in simple circuits, but complex wave sequences (consisting of different, low- and fast-frequency oscillations) that result from synaptic interactions in corticocortical and corticothalamic neuronal loops under the control of activating systems arising in the brain stem core or forebrain structures. As an illustration, it is shown that the neocortex governs the synchronization of network or intrinsically generated oscillations in the thalamus. The rhythmic recurrence of spike bursts and spike trains fired by thalamic and cortical neurons during states of decreased vigilance may lead to plasticity processes in neocortical neurons. If these phenomena, which may contribute to the consolidation of memory traces, are not constrained by inhibitory processes, they induce seizures in which the neocortex initiates the paroxysms and controls their thalamic reflection. The results indicate that intact-brain preparations are necessary to investigate global brain functions such as behavioral states of vigilance and paroxysmal activities.

Corticothalamic resonance, states of vigilance and mentation

During various states of vigilance, brain oscillations are grouped together through reciprocal connections between the neocortex and thalamus. The coherent activity in corticothalamic networks, under the control of brainstem and forebrain modulatory systems, requires investigations in intact-brain animals. During behavioral states associated with brain disconnection from the external world, the large-scale synchronization of low-frequency oscillations is accompanied by the inhibition of synaptic transmission through thalamocortical neurons. Despite the coherent oscillatory activity, on the functional side there is dissociation between the thalamus and neocortex during slow-wave sleep. While dorsal thalamic neurons undergo inhibitory processes due to the prolonged spike-bursts of thalamic reticular neurons, the cortex displays, periodically, a rich spontaneous activity and preserves the capacity to process internally generated signals that dominate the state of sleep. In vivo experiments using simultaneous intracellular recordings from thalamic and cortical neurons show that short-term plasticity processes occur after prolonged and rhythmic spike-bursts fired by thalamic and cortical neurons during slow-wave sleep oscillations. This may serve to support resonant phenomena and reorganize corticothalamic circuitry, determine which synaptic modifications, formed during the waking state, are to be consolidated and generate a peculiar kind of dreaming mentation. In contrast to the long-range coherent oscillations that occur at low frequencies during slow-wave sleep, the sustained fast oscillations that characterize alert states are synchronized over restricted territories and are associated with discrete and differentiated patterns of conscious events.

Why do we sleep?

Slow-wave sleep consists in slowly recurring waves that are associated with a large-scale spatio-temporal synchrony across neocortex. These slow-wave complexes alternate with brief episodes of fast oscillations, similar to the sustained fast oscillations that occur during the wake state. We propose that alternating fast and slow waves consolidate information acquired previously during wakefulness. Slow-wave sleep would thus begin with spindle oscillations that open molecular gates to plasticity, then proceed by iteratively 'recalling' and 'storing' information primed in neural assemblies. This scenario provides a biophysical mechanism consistent with the growing evidence that sleep serves to consolidate memories.

Reciprocal inhibitory connections regulate the spatiotemporal properties of intrathalamic oscillations

Mice with an inactivated GABA(A) receptor beta(3) subunit gene have features of Angelman syndrome, including absence-like seizures. This suggests the occurrence of abnormal hypersynchrony in the thalamocortical system. Within the thalamus, the efficacy of inhibitory synapses between thalamic reticular (RE) neurons is selectively compromised, and thalamic oscillations in vitro are prolonged and lack spatial phase gradients (). Here we used computational models to examine how intra-RE inhibition regulates intrathalamic oscillations. A major effect is an abbreviation of network responses, which is caused by long-lasting intra-RE inhibition that shunts recurrent excitatory input. In addition, differential activation of RE cells desynchronizes network activity. Near the slice center, where many cells are initially activated, there is a resultant high level of intra-RE inhibition. This leads to RE cell burst truncation in the central region and a gradient in the timing of thalamocortical cell activity similar to that observed in vitro. Although RE cell burst durations were shortened by this mechanism, there was very little effect on the times at which RE cells began to burst. The above results depended on widespread stimuli that activated RE cells in regions larger than the diameter of intra-RE connections. By contrast, more focal stimuli could elicit oscillations that lasted several cycles and remained confined to a small region. These results suggest that intra-RE inhibition restricts intrathalamic activity to particular spatiotemporal patterns to allow focal recurrent activity that may be relevant for normal thalamocortical function while preventing widespread synchronization as occurs in seizures.

Coherent oscillations and short-term plasticity in corticothalamic networks

The neocortex and thalamus are a unified oscillatory machine. Different types of brain rhythms, which characterize various behavioral states, are combined within complex wave-sequences. During the stage of sleep that is associated with low-frequency and high-amplitude brain rhythms, the excitatory component of a cortically generated slow oscillation is effective in triggering thalamically generated rhythms and in increasing their spatiotemporal coherence over widespread territories. Thus, the study of coherent oscillations, as they appear naturally during states of vigilance in animals and humans, requires intact-brain preparations in which the neocortex and thalamus engage in a permanent dialog. Sleep oscillations are associated with rhythmic spike-bursts or spike-trains in thalamic and cortical neurons, which lead to persistent excitability changes consisting of increased depolarizing responses and decreased inhibitory responses. These short-term plasticity processes could be used to consolidate memory traces acquired during wakefulness, but can also lead to paroxysmal (hypersynchronous) episodes, similar to those observed in some epileptic seizures.

Motion integration in a thalamic visual nucleus

Thalamic nuclei have long been regarded as passive relay stations for sensory information en route to higher level processing in the cerebral cortex. Recently, physiological and theoretical studies have reassessed the role of the thalamus and it has been proposed that thalamic nuclei may actively participate with cortical areas in processing specific information. In support of this idea, we now show that a subset of neurons in an extrageniculate visual nucleus, the lateral-posterior pulvinar complex, can signal the true direction of motion of a plaid pattern, indicating that thalamic cells can integrate different motion signals into a coherent moving percept. This is the first time that these computations have been found to occur outside the higher-order cortical areas. Our findings implicate extrageniculate cortico-thalamo-cortical loops in the dynamic processing of image motion, and, more generally, as basic computational modules involved in analysing specific features of complex visual scenes.