propagation of seizure-like activity in a model of neocortex

Distinctive Properties and Powerful Neuromodulation of Nav1.6 Sodium Channels Regulates Neuronal Excitability

Voltage-gated sodium channels (Navs) are critical determinants of cellular excitability. These ion channels exist as large heteromultimeric structures and their activity is tightly controlled. In neurons, the isoform Nav1.6 is highly enriched at the axon initial segment and nodes, making it critical for the initiation and propagation of neuronal impulses. Changes in Nav1.6 expression and function profoundly impact the input-output properties of neurons in normal and pathological conditions. While mutations in Nav1.6 may cause channel dysfunction, aberrant changes may also be the result of complex modes of regulation, including various protein-protein interactions and post-translational modifications, which can alter membrane excitability and neuronal firing properties. Despite decades of research, the complexities of Nav1.6 modulation in health and disease are still being determined. While some modulatory mechanisms have similar effects on other Nav isoforms, others are isoform-specific. Additionally, considerable progress has been made toward understanding how individual protein interactions and/or modifications affect Nav1.6 function. However, there is still more to be learned about how these different modes of modulation interact. Here, we examine the role of Nav1.6 in neuronal function and provide a thorough review of this channel's complex regulatory mechanisms and how they may contribute to neuromodulation.

Propagating wave activity in a tangential cortical slice

Propagating neural waves in the cerebral cortex influence the integration of incoming sensory information with ongoing cortical activity. However, the neural circuit elements that support these cortical waves remain to be fully defined. Here, a novel tangential slice preparation was developed that exhibited propagating wave activity across the dorsal cortical sheet, as assessed using autofluorescence imaging following focal electrical stimulation. Analysis of functional connectivity in the slice preparation with laser-scanning photostimulation via glutamate uncaging revealed a lack of short-latency, presumed monosynaptic, long-range connections (>300 μm) in the slice preparation. These results establish a novel slice preparation for assessing cortical dynamics and support the proposition that interactions among local cortical elements are sufficient to enable widespread propagating wave activity.

A Proposed Mechanism for Spontaneous Transitions between Interictal and Ictal Activity

Epileptic networks are characterized by two outputs: brief interictal spikes and rarer, more prolonged seizures. Although either output state is readily modeled in silico and induced experimentally, the transition mechanisms are unknown, in part because no models exhibit both output states spontaneously. In silico small-world neural networks were built using single-compartment neurons whose physiological parameters were derived from dual whole-cell recordings of pyramidal cells in organotypic hippocampal slice cultures that were generating spontaneous seizure-like activity. In silico, neurons were connected by abundant local synapses and rare long-distance synapses. Activity-dependent synaptic depression and gradual recovery delimited synchronous activity. Full synaptic recovery engendered interictal population spikes that spread via long-distance synapses. When synaptic recovery was incomplete, postsynaptic neurons required coincident activation of multiple presynaptic terminals to reach firing threshold. Only local connections were sufficiently dense to spread activity under these conditions. This coalesced network activity into traveling waves whose velocity varied with synaptic recovery. Seizures were comprised of sustained traveling waves that were similar to those recorded during experimental and human neocortical seizures. Sustained traveling waves occurred only when wave velocity, network dimensions, and the rate of synaptic recovery enabled wave reentry into previously depressed areas at precisely ictogenic levels of synaptic recovery. Wide-field, cellular-resolution GCamP7b calcium imaging demonstrated similar initial patterns of activation in the hippocampus, although the anatomical distribution of traveling waves of synaptic activation was altered by the pattern of synaptic connectivity in the organotypic hippocampal cultures.SIGNIFICANCE STATEMENT When computerized distributed neural network models are required to generate both features of epileptic networks (i.e., spontaneous interictal population spikes and seizures), the network structure is substantially constrained. These constraints provide important new hypotheses regarding the nature of epileptic networks and mechanisms of seizure onset.

Cross-scale effects of neural interactions during human neocortical seizure activity

Small-scale neuronal networks may impose widespread effects on large network dynamics. To unravel this relationship, we analyzed eight multiscale recordings of spontaneous seizures from four patients with epilepsy. During seizures, multiunit spike activity organizes into a submillimeter-sized wavefront, and this activity correlates significantly with low-frequency rhythms from electrocorticographic recordings across a 10-cm-sized neocortical network. Notably, this correlation effect is specific to the ictal wavefront and is absent interictally or from action potential activity outside the wavefront territory. To examine the multiscale interactions, we created a model using a multiscale, nonlinear system and found evidence for a dual role for feedforward inhibition in seizures: while inhibition at the wavefront fails, allowing seizure propagation, feedforward inhibition of the surrounding centimeter-scale networks is activated via long-range excitatory connections. Bifurcation analysis revealed that distinct dynamical pathways for seizure termination depend on the surrounding inhibition strength. Using our model, we found that the mesoscopic, local wavefront acts as the forcing term of the ictal process, while the macroscopic, centimeter-sized network modulates the oscillatory seizure activity.

High-Throughput Analysis of in-vitro LFP Electrophysiological Signals: A validated workflow/software package

Synchronized brain activity in the form of alternating epochs of massive persistent network activity and periods of generalized neural silence, has been extensively studied as a fundamental form of circuit dynamics, important for many cognitive functions including short-term memory, memory consolidation, or attentional modulation. A key element in such studies is the accurate determination of the timing and duration of those network events. The local field potential (LFP) is a particularly attractive method for recording network activity, because it allows for long and stable recordings from multiple sites, allowing researchers to estimate the functional connectivity of local networks. Here, we present a computational method for the automatic detection and quantification of in-vitro LFP events, aiming to overcome the limitations of current approaches (e.g. slow analysis speed, arbitrary threshold-based detection and lack of reproducibility across and within experiments). The developed method is based on the implementation of established signal processing and machine learning approaches, is fully automated and depends solely on the data. In addition, it is fast, highly efficient and reproducible. The performance of the software is compared against semi-manual analysis and validated by verification of prior biological knowledge.

Spontaneous Up states in vitro: a single-metric index of the functional maturation and regional differentiation of the cerebral cortex

Understanding the development and differentiation of the neocortex remains a central focus of neuroscience. While previous studies have examined isolated aspects of cellular and synaptic organization, an integrated functional index of the cortical microcircuit is still lacking. Here we aimed to provide such an index, in the form of spontaneously recurring periods of persistent network activity -or Up states- recorded in mouse cortical slices. These coordinated network dynamics emerge through the orchestrated regulation of multiple cellular and synaptic elements and represent the default activity of the cortical microcircuit. To explore whether spontaneous Up states can capture developmental changes in intracortical networks we obtained local field potential recordings throughout the mouse lifespan. Two independent and complementary methodologies revealed that Up state activity is systematically modified by age, with the largest changes occurring during early development and adolescence. To explore possible regional heterogeneities we also compared the development of Up states in two distinct cortical areas and show that primary somatosensory cortex develops at a faster pace than primary motor cortex. Our findings suggest that in vitro Up states can serve as a functional index of cortical development and differentiation and can provide a baseline for comparing experimental and/or genetic mouse models.

Coalescence of deep and superficial epileptic foci into larger discharge units in adult rat neocortex

Epilepsy is a disease of neuronal hyper-synchrony that can involve both neocortical and hippocampal brain regions. While much is known about the network properties of the hippocampus little is known of how epileptic neocortical hyper-synchrony develops. We aimed at characterizing the properties of epileptic discharges of a neocortical epileptic focus. We established a multi-electrode-array method to record the spatial patterns of epileptiform potentials in acute adult rat brain slices evoked by 4-Aminopyridine in the absence of magnesium. Locations of discharges mapped to two anatomical regions over the somatosensory cortex and over the lateral convexity separated by a gap at a location matching the dysgranular zone. Focal epileptiform discharges were recorded in superficial and deep neocortical layers but over superficial layers, they exhibited larger surface areas. They were often independent even when closely spaced to one another but they became progressively coupled resulting in larger zones of coherent discharge. The gradual coupling of multiple, independent, closely spaced, spatially restricted, focal discharges between deep and superficial neocortical layers represents a possible mechanism of the development of an epileptogenic zone.

Modern concepts of seizure modeling

Seizures are complex phenomena spanning multiple spatial and temporal scales, from ion dynamics to communication between brain regions, from milliseconds (spikes) to days (interseizure intervals). Because of the existence of such multiple scales, the experimental evaluation of the mechanisms underlying the initiation, propagation, and termination of epileptic seizures is a difficult problem. Theoretical models and numerical simulations provide new tools to investigate seizure mechanisms at multiple scales. In this chapter, we review different theoretical approaches and their contributions to our understanding of seizure mechanisms.

Modeling Neural Activity

This paper provides an overview of different types of models for studying activity of nerve cells and their networks with a special emphasis on neural oscillations. One part describes the neuronal models based on the Hodgkin and Huxley formalism first described in the 1950s. It is discussed how further simplifications of this formalism enable mathematical analysis of the process of neural excitability. The focus of the paper’s second component is on network activity. Understanding network function is one of the important frontiers remaining in neuroscience. At present, experimental techniques can only provide global recordings or samples of the activity of the huge networks that form the nervous system. Models in neuroscience can therefore play a critical role by providing a framework for integration of necessarily incomplete datasets, thereby providing insight into the mechanisms of neural function. Network models can either explicitly contain individual network nodes that model the neurons, or they can be based on representations of compound population activity. The latter approach was pioneered by Wilson and Cowan in the 1970s. Finally I provide an overview and discuss how network models are employed in the study of neuronal network pathology such as epilepsy.

Rhythmic intrinsic bursting neurons in human neocortex obtained from pediatric patients with epilepsy

Neocortical oscillations result from synchronized activity of a synaptically coupled network and can be strongly influenced by the intrinsic firing properties of individual neurons. As such, the intrinsic electroresponsive properties of individual neurons may have important implications for overall network function. Rhythmic intrinsic bursting (rIB) neurons are of particular interest, as they are poised to initiate and/or strongly influence network oscillations. Although neocortical rIB neurons have been recognized in multiple species, the current study is the first to identify and characterize rIB neurons in the human neocortex. Using whole-cell current-clamp recordings, rIB neurons (n = 12) are identified in human neocortical tissue resected from pediatric patients with intractable epilepsy. In contrast to human regular spiking neurons (n = 12), human rIB neurons exhibit rhythmic bursts of action potentials at frequencies of 0.1-4 Hz. These bursts persist after blockade of fast excitatory neurotransmission and voltage-gated calcium channels. However, bursting is eliminated by subsequent application of the persistent sodium current (I(NaP)) blocker, riluzole. In the presence of riluzole (either 10 or 20 μm), human rIB neurons no longer burst, but fire tonically like regular spiking neurons. These data demonstrate that I(NaP) plays a critical role in intrinsic oscillatory activity observed in rIB neurons in the human neocortex. It is hypothesized that aberrant changes in I(NaP) expression and/or function may ultimately contribute to neurological diseases that are linked to abnormal network activity, such as epilepsy.

An integrative view of mechanisms underlying generalized spike-and-wave epileptic seizures and its implication on optimal therapeutic treatments

Many types of epileptic seizures are characterized by generalized spike-and-wave discharges. In the past, notable effort has been devoted to understanding seizure dynamics and various hypotheses have been proposed to explain the underlying mechanisms. In this paper, by taking an integrative view of the underlying mechanisms, we demonstrate that epileptic seizures can be generated by many different combinations of synaptic strengths and intrinsic membrane properties. This integrative view has important medical implications: the specific state of a patient characterized by a set of biophysical characteristics ultimately determines the optimal therapeutic treatment. Through the same view, we further demonstrate the potentiation effect of rational polypharmacy in the treatment of epilepsy and provide a new angle to resolve the debate on polypharmacy. Our results underscore the need for personalized medicine and demonstrate that computer modeling and simulation may play an important role in assisting the clinicians in selecting the optimal treatment on an individual basis.

N-methyl-D-aspartate-induced oscillatory properties in neocortical pyramidal neurons from patients with epilepsy

N-Methyl-D-aspartate (NMDA) receptors have been implicated in epileptogenesis, but how these receptors contribute to epilepsy remains unknown. In particular, their role is likely to be complicated because of their voltage-dependent behavior. Here, the authors investigate how activation of NMDA receptors can affect the intrinsic production of oscillation and the resonance properties of neocortical pyramidal neurons from children with intractable epilepsy. Intracellular whole-cell patch clamp recordings in cortical slices from these patients revealed that pyramidal neurons do not produce spontaneous oscillation under control conditions. However, they did exhibit resonance around 1.5 Hz. On NMDA receptor activation, with bath-applied NMDA (10 μM), the majority of neurons produced voltage-dependent intrinsic oscillation associated with a change in the stability of the neuronal system as reflected by the whole-cell I-V curve. Furthermore, the degree of resonance was amplified while the frequency of resonance was shifted to lower frequencies (∼1 Hz) in NMDA. These results suggest that NMDA receptors may both promote the production of low-frequency oscillation and sharpen the response of the cell to lower frequencies. Both these behaviors may be amplified in tissue from patients with epilepsy, resulting in an increased propensity to generate seizures.

The electrocortical effects of enflurane: experiment and theory

BACKGROUND

High concentrations of enflurane will induce a characteristic electroencephalogram pattern consisting of periods of suppression alternating with large short paroxysmal epileptiform discharges (PEDs). In this study, we compared a theoretical computer model of this activity with real local field potential (LFP) data obtained from anesthetized rats.

METHODS

After implantation of a high-density 8 x 8 electrode array in the visual cortex, the patterns of LFP and multiunit spike activity were recorded in rats during 0.5, 1.0, 1.5, and 2.0 minimum alveolar anesthetic concentration (MAC) enflurane anesthesia. These recordings were compared with computer simulations from a mean field model of neocortical dynamics. The neuronal effect of increasing enflurane concentration was simulated by prolonging the decay time constant of the inhibitory postsynaptic potential (IPSP). The amplitude of the excitatory postsynaptic potential (EPSP) was modulated, inverse to the neocortical firing rate.

RESULTS

In the anesthetized rats, increasing enflurane concentrations consistently caused the appearance of suppression pattern (>1.5 MAC) in the LFP recordings. The mean rate of multiunit spike activity decreased from 2.54/s (0.5 MAC) to 0.19/s (2.0 MAC). At high MAC, the majority of the multiunit action potential events became synchronous with the PED. In the theoretical model, prolongation of the IPSP decay time and activity-dependent EPSP modulation resulted in output that was similar in morphology to that obtained from the experimental data. The propensity for rhythmic seizure-like activity in the model could be determined by analysis of the eigenvalues of the equations.

CONCLUSION

It is possible to use a mean field theory of neocortical dynamics to replicate the PED pattern observed in LFPs in rats under enflurane anesthesia. This pattern requires a combination of a moderately increased total area under the IPSP, prolonged IPSP decay time, and also activity-dependent modulation of EPSP amplitude.

The extracellular matrix protein SC1/hevin localizes to excitatory synapses following status epilepticus in the rat lithium-pilocarpine seizure model

The epileptic brain is characterized by increased susceptibility to neuronal hyperexcitability. The rat lithium-pilocarpine model, which mimics many features of temporal lobe epilepsy, has been used to study processes leading to the development of recurrent seizures. After a prolonged seizure episode, termed status epilepticus (SE), neural changes occur during a period known as epileptogenesis and include neuronal cell death, reactive gliosis, axonal sprouting, and synaptogenesis. Extracellular matrix adhesion molecules are important regulators of synaptogenesis and axonal sprouting resulting from SE. SC1, also known as hevin, is an antiadhesive extracellular matrix molecule that localizes to synapses in the mammalian brain. In this study, the distribution of SC1 protein in neurons following SE was examined using the lithium-pilocarpine model. SC1 protein levels in neuronal cell bodies showed a transient decrease at 1 day post-SE, which coincided with an increase of SC1 in the synapse-rich neuropil that was identified with the synaptic marker synaptophysin. Immunoelectron microscopy confirmed the decrease of SC1 signal in neurons at 1 day post-SE and showed that SC1 remained localized to postsynaptic elements throughout the seizure time course. Increased colocalization of SC1 was detected with the excitatory synaptic markers vesicular glutamate transporter 1 (VGLUT1), AMPA receptor subunit GluR1, and N-methyl-D-aspartate receptor subunit NR1, but not with the inhibitory synaptic markers vesicular gamma-aminobutyric acid (GABA) transporter (VGAT) and GABA(A) receptor subunit beta2 (GABA(A) beta2), which could reflect enhanced association of SC1 with excitatory synapses. These findings suggest that SC1 may be involved in synaptic modifications underlying epileptogenesis.

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.

Computational models of epileptic activity: a bridge between observation and pathophysiological interpretation

Epilepsy is a neurological disorder characterized by the recurrence of seizures. It affects 50 million people worldwide. Although a considerable number of new antiepileptic drugs with reduced side effects and toxicity have been introduced since the 1950s, 30% of patients remain pharmacoresistant. Although epilepsy research is making progress, advances in understanding drug resistance have been hampered by the complexity of the underlying neuronal systems responsible for epileptic activity. In such systems where short- or long-term plasticity plays a role, pathophysiological alterations may take place at subcellular (i.e., membrane ion channels and neurotransmitter receptors), cellular (neurons), tissular (networks of neurons) and regional (networks of networks of neurons) scales. In such a context, the demand for integrative approaches is high and neurocomputational models become recognized tools for tackling the complexity of epileptic phenomena. The purpose of this report is to provide an overview on computational modeling as a way of structuring and interpreting multimodal data recorded from the epileptic brain. Some examples are briefly described, which illustrate how computational models closely related with either experimental or clinical data can markedly advance our understanding of essential issues in epilepsy such as the transition from background to seizure activity. A commentary is also made on the potential use of such models in the study of therapeutic strategies such as rational drug design or electrical stimulations.