Studies of stimulus parameters for seizure disruption using neural network simulations

A Model for the Propagation of Seizure Activity in Normal Brain Tissue

Epilepsies are characterized by paroxysmal electrophysiological events and seizures, which can propagate across the brain. One of the main unsolved questions in epilepsy is how epileptic activity can invade normal tissue and thus propagate across the brain. To investigate this question, we consider three computational models at the neural network scale to study the underlying dynamics of seizure propagation, understand which specific features play a role, and relate them to clinical or experimental observations. We consider both the internal connectivity structure between neurons and the input properties in our characterization. We show that a paroxysmal input is sometimes controlled by the network while in other instances, it can lead the network activity to itself produce paroxysmal activity, and thus will further propagate to efferent networks. We further show how the details of the network architecture are essential to determine this switch to a seizure-like regime. We investigated the nature of the instability involved and in particular found a central role for the inhibitory connectivity. We propose a probabilistic approach to the propagative/non-propagative scenarios, which may serve as a guide to control the seizure by using appropriate stimuli.

Lateral Inhibition Organizes Beta Attentional Modulation in the Primary Visual Cortex

We have previously shown that during top-down attentional modulation (stimulus expectation) correlations of the beta signals across the primary visual cortex were uniform, while during bottom-up attentional processing (visual stimulation) their values were heterogeneous. These different patterns of attentional beta modulation may be caused by feed-forward lateral inhibitory interactions in the visual cortex, activated solely during stimulus processing. To test this hypothesis, we developed a large-scale computational model of the cortical network. We first identified the parameter range needed to support beta rhythm generation, and next, simulated the different activity states corresponding to experimental paradigms. The model matched our experimental data in terms of spatial organization of beta correlations during different attentional states and provided a computational confirmation of the hypothesis that the paradigm-specific beta activation spatial maps depend on the lateral inhibitory mechanism. The model also generated testable predictions that cross-correlation values depend on the distance between the activated columns and on their spatial position with respect to the location of the sensory inputs from the thalamus.

Modeling Neural Adaptation in Auditory Cortex

Neural responses recorded from auditory cortex exhibit adaptation, a stimulus-specific decrease that occurs when the same sound is presented repeatedly. Stimulus-specific adaptation is thought to facilitate perception in noisy environments. Although adaptation is assumed to arise independently from cortex, this has been difficult to validate directly in vivo. In this study, we used a neural network model of auditory cortex with multicompartmental cell modeling to investigate cortical adaptation. We found that repetitive, non-adapted inputs to layer IV neurons in the model elicited frequency-specific decreases in simulated single neuron, population-level and local field potential (LFP) activity, consistent with stimulus-specific cortical adaptation. Simulated recordings of LFPs, generated solely by excitatory post-synaptic inputs and recorded from layers II/III in the model, showed similar waveform morphologies and stimulus probability effects as auditory evoked responses recorded from human cortex. We tested two proposed mechanisms of cortical adaptation, neural fatigue and neural sharpening, by varying the strength and type of inter- and intra-layer synaptic connections (excitatory, inhibitory). Model simulations showed that synaptic depression modeled in excitatory (AMPA) synapses was sufficient to elicit a reduction in neural firing rate, consistent with neural fatigue. However, introduction of lateral inhibition from local layer II/III interneurons resulted in a reduction in the number of responding neurons, but not their firing rates, consistent with neural sharpening. These modeling results demonstrate that adaptation can arise from multiple neural mechanisms in auditory cortex.

The Relationship Between Ictal Multi-Unit Activity and the Electrocorticogram

During neocortical seizures in patients with epilepsy, microelectrode array recordings from the ictal core show a strong correlation between the fast, cellular spiking activities and the low-frequency component of the potential field, reflected in the electrocorticogram (ECoG). Here, we model the relationship between the cellular spike activity and this low-frequency component as the input and output signals of a linear time invariant system. Our approach is based on the observation that this relationship can be characterized by a so-called sinc function, the unit impulse response of an ideal (brick-wall) filter. Accordingly, using a brick-wall filter, we are able to convert ictal cellular spike inputs into an output that significantly correlates with the observed seizure activity in the ECoG (r = 0.40 - 0.56,p < 0.01) , while ECoG recordings of subsequent seizures within patients also show significant, but lower, correlations (r = 0.10 - 0.30,p < 0.01) . Furthermore, we can produce seizure-like output signals using synthetic spike trains with ictal properties. We propose a possible physiological mechanism to explain the observed properties associated with an ideal filter, and discuss the potential use of our approach for the evaluation of anticonvulsant strategies.

Optogenetic Approaches for Controlling Seizure Activity

Optogenetics, a technique that utilizes light-sensitive ion channels or pumps to activate or inhibit neurons, has allowed scientists unprecedented precision and control for manipulating neuronal activity. With the clinical need to develop more precise and effective therapies for patients with drug-resistant epilepsy, these tools have recently been explored as a novel treatment for halting seizure activity in various animal models. In this review, we provide a detailed and current summary of these optogenetic approaches and provide a perspective on their future clinical application as a potential neuromodulatory therapy.

Interaction between synaptic inhibition and glial-potassium dynamics leads to diverse seizure transition modes in biophysical models of human focal seizures

How focal seizures initiate and evolve in human neocortex remains a fundamental problem in neuroscience. Here, we use biophysical neuronal network models of neocortical patches to study how the interaction between inhibition and extracellular potassium ([K (+)] o ) dynamics may contribute to different types of focal seizures. Three main types of propagated focal seizures observed in recent intracortical microelectrode recordings in humans were modelled: seizures characterized by sustained (∼30-60 Hz) gamma local field potential (LFP) oscillations; seizures where the onset in the propagated site consisted of LFP spikes that later evolved into rhythmic (∼2-3 Hz) spike-wave complexes (SWCs); and seizures where a brief stage of low-amplitude fast-oscillation (∼10-20 Hz) LFPs preceded the SWC activity. Our findings are fourfold: (1) The interaction between elevated [K (+)] o (due to abnormal potassium buffering by glial cells) and the strength of synaptic inhibition plays a predominant role in shaping these three types of seizures. (2) Strengthening of inhibition leads to the onset of sustained narrowband gamma seizures. (3) Transition into SWC seizures is obtained either by the weakening of inhibitory synapses, or by a transient strengthening followed by an inhibitory breakdown (e.g. GABA depletion). This reduction or breakdown of inhibition among fast-spiking (FS) inhibitory interneurons increases their spiking activity and leads them eventually into depolarization block. Ictal spike-wave discharges in the model are then sustained solely by pyramidal neurons. (4) FS cell dynamics are also critical for seizures where the evolution into SWC activity is preceded by low-amplitude fast oscillations. Different levels of elevated [K (+)] o were important for transitions into and maintenance of sustained gamma oscillations and SWC discharges. Overall, our modelling study predicts that the interaction between inhibitory interneurons and [K (+)] o glial buffering under abnormal conditions may explain different types of ictal transitions and dynamics during propagated seizures in human focal epilepsy.

Anatomically Detailed and Large-Scale Simulations Studying Synapse Loss and Synchrony Using NeuroBox

The morphology of neurons and networks plays an important role in processing electrical and biochemical signals. Based on neuronal reconstructions, which are becoming abundantly available through databases such as NeuroMorpho.org, numerical simulations of Hodgkin-Huxley-type equations, coupled to biochemical models, can be performed in order to systematically investigate the influence of cellular morphology and the connectivity pattern in networks on the underlying function. Development in the area of synthetic neural network generation and morphology reconstruction from microscopy data has brought forth the software tool NeuGen. Coupling this morphology data (either from databases, synthetic, or reconstruction) to the simulation platform UG 4 (which harbors a neuroscientific portfolio) and VRL-Studio, has brought forth the extendible toolbox NeuroBox. NeuroBox allows users to perform numerical simulations on hybrid-dimensional morphology representations. The code basis is designed in a modular way, such that e.g., new channel or synapse types can be added to the library. Workflows can be specified through scripts or through the VRL-Studio graphical workflow representation. Third-party tools, such as ImageJ, can be added to NeuroBox workflows. In this paper, NeuroBox is used to study the electrical and biochemical effects of synapse loss vs. synchrony in neurons, to investigate large morphology data sets within detailed biophysical simulations, and used to demonstrate the capability of utilizing high-performance computing infrastructure for large scale network simulations. Using new synapse distribution methods and Finite Volume based numerical solvers for compartment-type models, our results demonstrate how an increase in synaptic synchronization can compensate synapse loss at the electrical and calcium level, and how detailed neuronal morphology can be integrated in large-scale network simulations.

Computational Modeling of Subdural Cortical Stimulation: A Quantitative Spatiotemporal Analysis of Action Potential Initiation in a High-Density Multicompartment Model

OBJECTIVE

Computational modeling studies were performed to identify presynaptic elements of cortical neurons that are activated by subdural electrical stimulation.

MATERIALS AND METHODS

The computer model consists of layers of multicompartmental neurons arranged in 3D space in an anatomically realistic fashion inside a 4.8 × 4.8 × 3.4 mm volume of gray matter modeled as a homogenous and isotropic medium. The model was subjected to an electric field generated by a circular disk electrode.

RESULTS

The initiation of presynaptic action potentials (PAPs) in neurons takes place predominantly in the axon initial segment (AIS) or ectopically in axonal branch terminals. PAPs that were initiated in only one axonal terminal were typically followed by a second PAP (spike duplet) resulting from the activation of the AIS by the antidromically propagating initial PAP. There were significant time delays (up to 0.5 ms) in the propagation of these ectopically initiated PAPs along the axons to nonactivated axonal branches and, associated with these delays, latencies in the occurrence of spike duplets in different axonal terminals. The effect of the dendritic arbor 3D structure on the AIS activation threshold was contingent on whether the net axonal and somato-dendritic current flows made an antagonistic or synergetic contribution.

CONCLUSIONS

This study examines the effects of subdural electrical stimulation on a high-density network consisting of several populations of multicompartment cell types. The effect of dendritic arbor structure on the axonal activation threshold is prominent in the case of multipolar neurons with large-diameter symmetric dendrites (basal/apical) that are oriented parallel to the electric field lines. The timing of presynaptic terminal activation after stimulation is not determined solely by the axonal delay (orthodromic propagation) but depends on the details of the applied stimulation field and axonal branching structure, which may be important factors in characterizing the effects of electrical stimulation in neuromodulation systems.

A study of the dynamics of seizure propagation across micro domains in the vicinity of the seizure onset zone

OBJECTIVE

The use of micro-electrode arrays to measure electrical activity from the surface of the brain is increasingly being investigated as a means to improve seizure onset zone (SOZ) localization. In this work, we used a multivariate autoregressive model to determine the evolution of seizure dynamics in the [Formula: see text] Hz high frequency band across micro-domains sampled by such micro-electrode arrays. We showed that a directed transfer function (DTF) can be used to estimate the flow of seizure activity in a set of simulated micro-electrode data with known propagation pattern.

APPROACH

We used seven complex partial seizures recorded from four patients undergoing intracranial monitoring for surgical evaluation to reconstruct the seizure propagation pattern over sliding windows using a DTF measure.

MAIN RESULTS

We showed that a DTF can be used to estimate the flow of seizure activity in a set of simulated micro-electrode data with a known propagation pattern. In general, depending on the location of the micro-electrode grid with respect to the clinical SOZ and the time from seizure onset, ictal propagation changed in directional characteristics over a 2-10 s time scale, with gross directionality limited to spatial dimensions of approximately [Formula: see text]. It was also seen that the strongest seizure patterns in the high frequency band and their sources over such micro-domains are more stable over time and across seizures bordering the clinically determined SOZ than inside.

SIGNIFICANCE

This type of propagation analysis might in future provide an additional tool to epileptologists for characterizing epileptogenic tissue. This will potentially help narrowing down resection zones without compromising essential brain functions as well as provide important information about targeting anti-epileptic stimulation devices.

A computational study of stimulus driven epileptic seizure abatement

Active brain stimulation to abate epileptic seizures has shown mixed success. In spike-wave (SW) seizures, where the seizure and background state were proposed to coexist, single-pulse stimulations have been suggested to be able to terminate the seizure prematurely. However, several factors can impact success in such a bistable setting. The factors contributing to this have not been fully investigated on a theoretical and mechanistic basis. Our aim is to elucidate mechanisms that influence the success of single-pulse stimulation in noise-induced SW seizures. In this work, we study a neural population model of SW seizures that allows the reconstruction of the basin of attraction of the background activity as a four dimensional geometric object. For the deterministic (noise-free) case, we show how the success of response to stimuli depends on the amplitude and phase of the SW cycle, in addition to the direction of the stimulus in state space. In the case of spontaneous noise-induced seizures, the basin becomes probabilistic introducing some degree of uncertainty to the stimulation outcome while maintaining qualitative features of the noise-free case. Additionally, due to the different time scales involved in SW generation, there is substantial variation between SW cycles, implying that there may not be a fixed set of optimal stimulation parameters for SW seizures. In contrast, the model suggests an adaptive approach to find optimal stimulation parameters patient-specifically, based on real-time estimation of the position in state space. We discuss how the modelling work can be exploited to rationally design a successful stimulation protocol for the abatement of SW seizures using real-time SW detection.

Afferent inputs to cortical fast-spiking interneurons organize pyramidal cell network oscillations at high-gamma frequencies (60-200 Hz)

High-gamma activity, ranging in frequency between ∼60 Hz and 200 Hz, has been observed in local field potential, electrocorticography, EEG and magnetoencephalography signals during cortical activation, in a variety of functional brain systems. The origin of these signals is yet unknown. Using computational modeling, we show that a cortical network model receiving thalamic input generates high-gamma responses comparable to those observed in local field potential recorded in monkey somatosensory cortex during vibrotactile stimulation. These high-gamma oscillations appear to be mediated mostly by an excited population of inhibitory fast-spiking interneurons firing at high-gamma frequencies and pacing excitatory regular-spiking pyramidal cells, which fire at lower rates but in phase with the population rhythm. The physiological correlates of high-gamma activity, in this model of local cortical circuits, appear to be similar to those proposed for hippocampal ripples generated by subsets of interneurons that regulate the discharge of principal cells.

Transcranial current brain stimulation (tCS): models and technologies

In this paper, we provide a broad overview of models and technologies pertaining to transcranial current brain stimulation (tCS), a family of related noninvasive techniques including direct current (tDCS), alternating current (tACS), and random noise current stimulation (tRNS). These techniques are based on the delivery of weak currents through the scalp (with electrode current intensity to area ratios of about 0.3-5 A/m2) at low frequencies (typically < 1 kHz) resulting in weak electric fields in the brain (with amplitudes of about 0.2-2 V/m). Here we review the biophysics and simulation of noninvasive, current-controlled generation of electric fields in the human brain and the models for the interaction of these electric fields with neurons, including a survey of in vitro and in vivo related studies. Finally, we outline directions for future fundamental and technological research.

Determination of seizure propagation across microdomains using spectral measures of causality

The use of microelectrode arrays to measure electrical activity from the surface of the brain is increasingly being investigated as a means to improve seizure focus localization. In this work, we determine seizure propagation across microdomains sampled by such microelectrode arrays and compare the results using two widely used frequency domain measures of causality, namely the partial directed coherence and the directed direct transfer function. We show that these two measures produce very similar propagation patterns for simulated microelectrode activity over a relatively smaller number of channels. However as the number of channels increases, partial directed coherence produces better estimates of the actual propagation pattern. Additionally, we apply these two measures to determine seizure propagation over microelectrode arrays measured from a patient undergoing intracranial monitoring for seizure focus localization and find very similar patterns which also agree with a threshold based reconstruction during seizure onset.

Predicting single-neuron activity in locally connected networks

The characterization of coordinated activity in neuronal populations has received renewed interest in the light of advancing experimental techniques that allow recordings from multiple units simultaneously. Across both in vitro and in vivo preparations, nearby neurons show coordinated responses when spontaneously active and when subject to external stimuli. Recent work (Truccolo, Hochberg, & Donoghue, 2010 ) has connected these coordinated responses to behavior, showing that small ensembles of neurons in arm-related areas of sensorimotor cortex can reliably predict single-neuron spikes in behaving monkeys and humans. We investigate this phenomenon using an analogous point process model, showing that in the case of a computational model of cortex responding to random background inputs, one is similarly able to predict the future state of a single neuron by considering its own spiking history, together with the spiking histories of randomly sampled ensembles of nearby neurons. This model exhibits realistic cortical architecture and displays bursting episodes in the two distinct connectivity schemes studied. We conjecture that the baseline predictability we find in these instances is characteristic of locally connected networks more broadly considered.

Distributed control in a mean-field cortical network model: implications for seizure suppression

Brain electrical stimulation (BES) has long been suggested as a means of controlling pathological brain activity. In epilepsy, control of a spatially localized source, the seizure focus, may normalize neuronal dynamics. Consequently, most BES research has been directed at controlling small, local, neuronal populations. At a higher level, pathological seizure activity can be viewed as a network event that may begin without a clear spatial focus or in multiple sites and spread rapidly through a distributed cortical network. In this paper, we begin to address the implications of local control in a network scenario. To do so, we explore the efficacy of local BES when deployed over a larger-scale neuronal network, for instance, using a grid of stimulating electrodes on the cortex. By introducing a mean-field model of neuronal interactions we are able to identify limitations in network controllability based on physiological constraints that suggest the need for more nuanced network control strategies.

Effects of transcranial Direct Current Stimulation (tDCS) on cortical activity: a computational modeling study

Although it is well-admitted that transcranial Direct Current Stimulation (tDCS) allows for interacting with brain endogenous rhythms, the exact mechanisms by which externally-applied fields modulate the activity of neurons remain elusive. In this study a novel computational model (a neural mass model including subpopulations of pyramidal cells and inhibitory interneurons mediating synaptic currents with either slow or fast kinetics) of the cerebral cortex was elaborated to investigate the local effects of tDCS on neuronal populations based on an in-vivo experimental study. Model parameters were adjusted to reproduce evoked potentials (EPs) recorded from the somatosensory cortex of the rabbit in response to air-puffs applied on the whiskers. EPs were simulated under control condition (no tDCS) as well as under anodal and cathodal tDCS fields. Results first revealed that a feed-forward inhibition mechanism must be included in the model for accurate simulation of actual EPs (peaks and latencies). Interestingly, results revealed that externally-applied fields are also likely to affect interneurons. Indeed, when interneurons get polarized then the characteristics of simulated EPs become closer to those of real EPs. In particular, under anodal tDCS condition, more realistic EPs could be obtained when pyramidal cells were depolarized and, simultaneously, slow (resp. fast) interneurons became de- (resp. hyper-) polarized. Geometrical characteristics of interneurons might provide some explanations for this effect.

Epileptic seizures from abnormal networks: why some seizures defy predictability

Seizure prediction has proven to be difficult in clinically realistic environments. Is it possible that fluctuations in cortical firing could influence the onset of seizures in an ictal zone? To test this, we have now used neural network simulations in a computational model of cortex having a total of 65,536 neurons with intercellular wiring patterned after histological data. A spatially distributed Poisson driven background input representing the activity of neighboring cortex affected 1% of the neurons. Gamma distributions were fit to the interbursting phase intervals, a non-parametric test for randomness was applied, and a dynamical systems analysis was performed to search for period-1 orbits in the intervals. The non-parametric analysis suggests that intervals are being drawn at random from their underlying joint distribution and the dynamical systems analysis is consistent with a nondeterministic dynamical interpretation of the generation of bursting phases. These results imply that in a region of cortex with abnormal connectivity analogous to a seizure focus, it is possible to initiate seizure activity with fluctuations of input from the surrounding cortical regions. These findings suggest one possibility for ictal generation from abnormal focal epileptic networks. This mechanism additionally could help explain the difficulty in predicting partial seizures in some patients.

Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo

Arrays of electrodes for recording and stimulating the brain are used throughout clinical medicine and basic neuroscience research, yet are unable to sample large areas of the brain while maintaining high spatial resolution because of the need to individually wire each passive sensor at the electrode-tissue interface. To overcome this constraint, we developed new devices that integrate ultrathin and flexible silicon nanomembrane transistors into the electrode array, enabling new dense arrays of thousands of amplified and multiplexed sensors that are connected using fewer wires. We used this system to record spatial properties of cat brain activity in vivo, including sleep spindles, single-trial visual evoked responses and electrographic seizures. We found that seizures may manifest as recurrent spiral waves that propagate in the neocortex. The developments reported here herald a new generation of diagnostic and therapeutic brain-machine interface devices.

Neuroscience: what we cannot model, we do not understand

To understand computations in neuronal circuits, a model of a small patch of cortex has been developed that can describe the firing regime in the visual system remarkably well.

Estimation of electrode location in a rat motor cortex by laminar analysis of electrophysiology and intracortical electrical stimulation

While the development of microelectrode arrays has enabled access to disparate regions of a cortex for neurorehabilitation, neuroprosthetic and basic neuroscience research, accurate interpretation of the signals and manipulation of the cortical neurons depend upon the anatomical placement of the electrode arrays in a layered cortex. Toward this end, this report compares two in vivo methods for identifying the placement of electrodes in a linear array spaced 100 µm apart based on in situ laminar analysis of (1) ketamine-xylazine-induced field potential oscillations in a rat motor cortex and (2) an intracortical electrical stimulation-induced movement threshold. The first method is based on finding the polarity reversal in laminar oscillations which is reported to appear at the transition between layers IV and V in laminar 'high voltage spindles' of the rat cortical column. Analysis of histological images in our dataset indicates that polarity reversal is detected 150.1 ± 104.2 µm below the start of layer V. The second method compares the intracortical microstimulation currents that elicit a physical movement for anodic versus cathodic stimulation. It is based on the hypothesis that neural elements perpendicular to the electrode surface are preferentially excited by anodic stimulation while cathodic stimulation excites those with a direction component parallel to its surface. With this method, we expect to see a change in the stimulation currents that elicits a movement at the beginning of layer V when comparing anodic versus cathodic stimulation as the upper cortical layers contain neuronal structures that are primarily parallel to the cortical surface and lower layers contain structures that are primarily perpendicular. Using this method, there was a 78.7 ± 68 µm offset in the estimate of the depth of the start of layer V. The polarity reversal method estimates the beginning of layer V within ±90 µm with 95% confidence and the intracortical stimulation method estimates it within ±69.3 µm. We propose that these methods can be used to estimate the in situ location of laminar electrodes implanted in the rat motor cortex.

Polarity of cortical electrical stimulation differentially affects neuronal activity of deep and superficial layers of rat motor cortex

BACKGROUND

Cortical electrical stimulation (CES) techniques are practical tools in neurorehabilitation that are currently being used to test models of functional recovery after neurologic injury. However, the mechanisms by which CES has therapeutic effects, are not fully understood.

OBJECTIVE

In this study, we investigated the effects of CES on unit activity of different neuronal elements in layers of rat primary motor cortex after the offset of stimulation. We evaluated the effects of monopolar CES pulse polarity (anodic-first versus cathodic-first) using various stimulation frequencies and amplitudes on unit activity after stimulation.

METHODS

A penetrating single shank silicon microelectrode array enabled us to span the entirety of six layer motor cortex allowing simultaneous electrophysiologic recordings from different depths after monopolar CES. Neural spiking activity before the onset and after the offset of CES was modeled using point processes fit to capture neural spiking dynamics as a function of extrinsic stimuli based on generalized linear model methods.

RESULTS

We found that neurons in lower layers have a higher probability of being excited after anodic CES. Conversely, neurons located in upper cortical layers have a higher probability of being excited after cathodic stimulation. The opposing effects observed following anodic versus cathodic stimulation in upper and lower layers were frequency- and amplitude-dependent.

CONCLUSIONS

The data demonstrates that the poststimulus changes in neural activity after manipulation of CES parameters changes according to the location (depth) of the recorded units in rat primary motor cortex. The most effective pulse polarity for eliciting action potentials after stimulation in lower layers was not as effective in upper layers. Likewise, lower amplitudes and frequencies of CES were more effective than higher amplitudes and frequencies for eliciting action potentials. These results have important implications in the context of maximizing efficacy of CES for neurorehabilitation and neuroprosthetic applications.

Observed network dynamics from altering the balance between excitatory and inhibitory neurons in cultured networks

Complexity in the temporal organization of neural systems may be a reflection of the diversity of their neural constituents. These constituents, excitatory and inhibitory neurons, comprise a well-defined ratio in vivo and form the substrate for rhythmic oscillatory activity. To begin to elucidate the dynamical implications that underlie this balance, we construct neural circuits not ordinarily found in nature and study the resulting temporal patterns. We culture several networks of neurons composed of varying fractions of excitatory and inhibitory cells and use a multielectrode array to study their temporal dynamics as this balance is modulated. We use the electrode burst as the temporal imprimatur to signify the presence of network activity. Burst durations, interburst intervals, and the number of spikes participating within a burst are used to illustrate the vivid differences in the temporal organization between the various cultured networks. When the network consists largely of excitatory neurons, no network temporal structure is apparent. However, the addition of inhibitory neurons evokes a temporal order. Calculation of the temporal autocorrelation shows that when the number of inhibitory neurons is a major fraction of the network, a striking network pattern materializes when none was previously present.

Toward rational design of electrical stimulation strategies for epilepsy control

Electrical stimulation is emerging as a viable alternative for patients with epilepsy whose seizures are not alleviated by drugs or surgery. Its attractions are temporal and spatial specificity of action, flexibility of waveform parameters and timing, and the perception that its effects are reversible unlike resective surgery. However, despite significant advances in our understanding of mechanisms of neural electrical stimulation, clinical electrotherapy for seizures relies heavily on empirical tuning of parameters and protocols. We highlight concurrent treatment goals with potentially conflicting design constraints that must be resolved when formulating rational strategies for epilepsy electrotherapy, namely, seizure reduction versus cognitive impairment, stimulation efficacy versus tissue safety, and mechanistic insight versus clinical pragmatism. First, treatment markers, objectives, and metrics relevant to electrical stimulation for epilepsy are discussed from a clinical perspective. Then the experimental perspective is presented, with the biophysical mechanisms and modalities of open-loop electrical stimulation, and the potential benefits of closed-loop control for epilepsy.

Phase-dependent stimulation effects on bursting activity in a neural network cortical simulation

PURPOSE

A neural network simulation with realistic cortical architecture has been used to study synchronized bursting as a seizure representation. This model has the property that bursting epochs arise and cease spontaneously, and bursting epochs can be induced by external stimulation. We have used this simulation to study the time-frequency properties of the evolving bursting activity, as well as effects due to network stimulation.

METHODS

The model represents a cortical region of 1.6 mm x 1.6mm, and includes seven neuron classes organized by cortical layer, inhibitory or excitatory properties, and electrophysiological characteristics. There are a total of 65,536 modeled single compartment neurons that operate according to a version of Hodgkin-Huxley dynamics. The intercellular wiring is based on histological studies and our previous modeling efforts.

RESULTS

The bursting phase is characterized by a flat frequency spectrum. Stimulation pulses are applied to this modeled network, with an electric field provided by a 1mm radius circular electrode represented mathematically in the simulation. A phase dependence to the post-stimulation quiescence is demonstrated, with local relative maxima in efficacy occurring before or during the network depolarization phase in the underlying activity. Brief periods of network insensitivity to stimulation are also demonstrated. The phase dependence was irregular and did not reach statistical significance when averaged over the full 2.5s of simulated bursting investigated. This result provides comparison with previous in vivo studies which have also demonstrated increased efficacy of stimulation when pulses are applied at the peak of the local field potential during cortical after discharges. The network bursting is synchronous when comparing the different neuron classes represented up to an uncertainty of 10 ms. Studies performed with an excitatory chandelier cell component demonstrated increased synchronous bursting in the model, as predicted from experimental work.

CONCLUSIONS

This large-scale multi-neuron neural network simulation reproduces many aspects of evolving cortical bursting behavior as well as the timing-dependent effects of electrical stimulation on that bursting.