Maintenance and termination of neocortical oscillations by dynamic modulation of intrinsic and synaptic excitability

Individual brain structure and modelling predict seizure propagation

See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions.

In vivo models of cortical acquired epilepsy

The neocortex is the site of origin of several forms of acquired epilepsy. Here we provide a brief review of experimental models that were recently developed to study neocortical epileptogenesis as well as some major results obtained with these methods. Most of neocortical seizures appear to be nocturnal and it is known that neuronal activities reveal high levels of synchrony during slow-wave sleep. Therefore, we start the review with a description of mechanisms of neuronal synchronization and major forms of synchronized normal and pathological activities. Then, we describe three experimental models of seizures and epileptogenesis: ketamine-xylazine anesthesia as feline seizure triggered factor, cortical undercut as cortical penetrating wound model and neocortical kindling. Besides specific technical details describing these models we also provide major features of pathological brain activities recorded during epileptogenesis and seizures. The most common feature of all models of neocortical epileptogenesis is the increased duration of network silent states that up-regulates neuronal excitability and eventually leads to epilepsy.

Mild KCC2 Hypofunction Causes Inconspicuous Chloride Dysregulation that Degrades Neural Coding

Disinhibition caused by Cl⁻ dysregulation is implicated in several neurological disorders. This form of disinhibition, which stems primarily from impaired Cl⁻ extrusion through the co-transporter KCC2, is typically identified by a depolarizing shift in GABA reversal potential (E_GABA). Here we show, using computer simulations, that intracellular [Cl⁻] exhibits exaggerated fluctuations during transient Cl⁻ loads and recovers more slowly to baseline when KCC2 level is even modestly reduced. Using information theory and signal detection theory, we show that increased Cl⁻ lability and settling time degrade neural coding. Importantly, these deleterious effects manifest after less KCC2 reduction than needed to produce the gross changes in E_GABA required for detection by most experiments, which assess KCC2 function under weak Cl⁻ load conditions. By demonstrating the existence and functional consequences of “occult” Cl⁻ dysregulation, these results suggest that modest KCC2 hypofunction plays a greater role in neurological disorders than previously believed.

Modeling of Age-Dependent Epileptogenesis by Differential Homeostatic Synaptic Scaling

Homeostatic synaptic plasticity (HSP) has been implicated in the development of hyperexcitability and epileptic seizures following traumatic brain injury (TBI). Our in vivo experimental studies in cats revealed that the severity of TBI-mediated epileptogenesis depends on the age of the animal. To characterize mechanisms of these differences, we studied the properties of the TBI-induced epileptogenesis in a biophysically realistic cortical network model with dynamic ion concentrations. After deafferentation, which was induced by dissection of the afferent inputs, there was a reduction of the network activity and upregulation of excitatory connections leading to spontaneous spike-and-wave type seizures. When axonal sprouting was implemented, the seizure threshold increased in the model of young but not the older animals, which had slower or unidirectional homeostatic processes. Our study suggests that age-related changes in the HSP mechanisms are sufficient to explain the difference in the likelihood of seizure onset in young versus older animals. Significance statement: Traumatic brain injury (TBI) is one of the leading causes of intractable epilepsy. Likelihood of developing epilepsy and seizures following severe brain trauma has been shown to increase with age. Specific mechanisms of TBI-related epileptogenesis and how these mechanisms are affected by age remain to be understood. We test a hypothesis that the failure of homeostatic synaptic regulation, a slow negative feedback mechanism that maintains neural activity within a physiological range through activity-dependent modulation of synaptic strength, in older animals may augment TBI-induced epileptogenesis. Our results provide new insight into understanding this debilitating disorder and may lead to novel avenues for the development of effective treatments of TBI-induced epilepsy.

Information theoretic measures of network coordination in high-frequency scalp EEG reveal dynamic patterns associated with seizure termination

How a seizure terminates is still under-studied and, despite its clinical importance, remains an obscure phase of seizure evolution. Recent studies of seizure-related scalp EEGs at frequencies >100 Hz suggest that neural activity, in the form of oscillations and/or neuronal network interactions, may play an important role in preictal/ictal seizure evolution (Andrade-Valenca et al., 2011; Stamoulis et al., 2012). However, the role of high-frequency activity in seizure termination, is unknown, if it exists at all. Using information theoretic measures of network coordination, this study investigated ictal and immediate postictal neurodynamic interactions encoded in scalp EEGs from a relatively small sample of 8 patients with focal epilepsy and multiple seizures originating in temporal and/or frontal brain regions, at frequencies ≤ 100 Hz and >100 Hz, respectively. Despite some heterogeneity in the dynamics of these interactions, consistent patterns were also estimated. Specifically, in several seizures, linear or non-linear increase in high-frequency neuronal coordination during ictal intervals, coincided with a corresponding decrease in coordination at frequencies <100 Hz, suggesting a potential interference role of high-frequency activity, to disrupt abnormal ictal synchrony at lower frequencies. These changes in network synchrony started at least 20-30s prior to seizure offset, depending on the seizure duration. Opposite patterns were estimated at frequencies ≤ 100 Hz in several seizures. These results raise the possibility that high-frequency interference may occur in the form of progressive network coordination during the ictal interval, which continues during the postictal interval. This may be one of several possible mechanisms that facilitate seizure termination. In fact, inhibition of pairwise interactions between EEGs by other signals in their spatial neighborhood, quantified by negative interaction information, was estimated at frequencies ≤ 100 Hz, at least in some seizures.

Human seizures self-terminate across spatial scales via a critical transition

Why seizures spontaneously terminate remains an unanswered fundamental question of epileptology. Here we present evidence that seizures self-terminate via a discontinuous critical transition or bifurcation. We show that human brain electrical activity at various spatial scales exhibits common dynamical signatures of an impending critical transition--slowing, increased correlation, and flickering--in the approach to seizure termination. In contrast, prolonged seizures (status epilepticus) repeatedly approach, but do not cross, the critical transition. To support these results, we implement a computational model that demonstrates that alternative stable attractors, representing the ictal and postictal states, emulate the observed dynamics. These results suggest that self-terminating seizures end through a common dynamical mechanism. This description constrains the specific biophysical mechanisms underlying seizure termination, suggests a dynamical understanding of status epilepticus, and demonstrates an accessible system for studying critical transitions in nature.

Ionic dynamics mediate spontaneous termination of seizures and postictal depression state

Epileptic seizures are characterized by periods of recurrent, highly synchronized activity that spontaneously terminates, followed by postictal state when neuronal activity is generally depressed. The mechanisms for spontaneous seizure termination and postictal depression remain poorly understood. Using a realistic computational model, we demonstrate that termination of seizure and postictal depression state may be mediated by dynamics of the intracellular and extracellular ion concentrations. Spontaneous termination was linked to progressive increase of intracellular sodium concentration mediated by activation of sodium channels during highly active epileptic state. In contrast, an increase of intracellular chloride concentration extended seizure duration making possible long-lasting epileptic activity characterized by multiple transitions between tonic and clonic states. After seizure termination, the extracellular potassium was reduced below baseline, resulting in postictal depression. Our study suggests that the coupled dynamics of sodium, potassium, and chloride ions play a critical role in the development and termination of seizures. Findings from this study could help identify novel therapeutics for seizure disorder.

Network bistability mediates spontaneous transitions between normal and pathological brain states

Little is known about how cortical networks support the emergence of remarkably different activity patterns. Physiological activity interspersed with epochs of pathological hyperactivity in the epileptic brain represents a clinically relevant yet poorly understood case of such rich dynamic repertoire. Using a realistic computational model, we demonstrate that physiological sparse and pathological tonic-clonic activity may coexist in the same cortical network for identical afferent input level. Transient perturbations in the afferent input were sufficient to switch the network between these two stable states. The effectiveness of the potassium regulatory apparatus determined the stability of the physiological state and the threshold for seizure initiation. Our findings contrast with the common notions of (1) pathological brain activity representing dynamic instabilities and (2) necessary adjustments of experimental conditions to elicit different network states. Rather, we propose that the rich dynamic repertoire of cortical networks may be based on multistabilities intrinsic to the network.

Cellular and network mechanisms of electrographic seizures

Epileptic seizures constitute a complex multiscale phenomenon that is characterized by synchronized hyperexcitation of neurons in neuronal networks. Recent progress in understanding pathological seizure dynamics provides crucial insights into underlying mechanisms and possible new avenues for the development of novel treatment modalities. Here we review some recent work that combines in vivo experiments and computational modeling to unravel the pathophysiology of seizures of cortical origin. We particularly focus on how activity-dependent changes in extracellular potassium concentration affects the intrinsic dynamics of neurons involved in cortical seizures characterized by spike/wave complexes and fast runs.