Computational modeling of epilepsy

Multiscale neuro-inspired models for interpretation of EEG signals in patients with epilepsy

OBJECTIVE

The aim is to gain insight into the pathophysiological mechanisms underlying interictal epileptiform discharges observed in electroencephalographic (EEG) and stereo-EEG (SEEG, depth electrodes) recordings performed during pre-surgical evaluation of patients with drug-resistant epilepsy.

METHODS

We developed novel neuro-inspired computational models of the human cerebral cortex at three different levels of description: i) microscale (detailed neuron models), ii) mesoscale (neuronal mass models) and iii) macroscale (whole brain models). Although conceptually different, micro- and mesoscale models share some similar features, such as the typology of neurons (pyramidal cells and three types of interneurons), their spatial arrangement in cortical layers, and their synaptic connectivity (excitatory and inhibitory). The whole brain model consists of a large-scale network of interconnected neuronal masses, with connectivity based on the human connectome.

RESULTS

For these three levels of description, the fine-tuning of free parameters and the quantitative comparison with real data allowed us to reproduce interictal epileptiform discharges with a high degree of fidelity and to formulate hypotheses about the cell- and network-related mechanisms underlying the generation of fast ripples and SEEG-recorded epileptic spikes and spike-waves.

CONCLUSIONS

The proposed models provide valuable insights into the pathophysiological mechanisms underlying the generation of epileptic events. The knowledge gained from these models effectively complements the clinical analysis of SEEG data collected during the evaluation of patients with epilepsy.

SIGNIFICANCE

These models are likely to play a key role in the mechanistic interpretation of epileptiform activity.

Brain injuries can set up an epileptogenic neuronal network

Development of epilepsy or epileptogenesis promotes recurrent seizures. As of today, there are no effective prophylactic therapies to prevent the onset of epilepsy. Contributing to this deficiency of preventive therapy is the lack of clarity in fundamental neurobiological mechanisms underlying epileptogenesis and lack of reliable biomarkers to identify patients at risk for developing epilepsy. This limits the development of prophylactic therapies in epilepsy. Here, neural network dysfunctions reflected by oscillopathies and microepileptiform activities, including neuronal hyperexcitability and hypersynchrony, drawn from both clinical and experimental epilepsy models, have been reviewed. This review suggests that epileptogenesis reflects a progressive and dynamic dysfunction of specific neuronal networks which recruit further interconnected groups of neurons, with this resultant pathological network mediating seizure occurrence, recurrence, and progression. In the future, combining spatial and temporal resolution of neuronal non-invasive recordings from patients at risk of developing epilepsy, together with analytics and computational tools, may contribute to determining whether the brain is undergoing epileptogenesis in asymptomatic patients following brain injury.

Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders

The investigation of synaptic functions remains one of the most fascinating challenges in the field of neuroscience and a large number of experimental methods have been tuned to dissect the mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the insights of neurological disorders originating from alterations in neurotransmission often requires the development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological approaches. In the last decades, additional tools to explore neurological diseases have been provided to the scientific community. A wide range of computational models in fact have been developed to explore the alterations of the mechanisms involved in neurotransmission following the emergence of neurological pathologies. Here, we review some of the advancements in the development of computational methods employed to investigate neuronal circuits with a particular focus on the application to the most diffuse neurological disorders.

The effect of external voltage stimulation on absence seizures

BACKGROUND:

Absence epilepsy (AE) is a systemic disease of the brain, which mainly occurs during childhood and adolescence. The control mechanism is still unclear, and few theoretical studies have been conducted to investigate this.

OBJECTIVE:

In this paper, we employed external direct voltage stimulation in the subthalamic nucleus to explore mechanisms that inhibit absence seizures.

METHODS:

All simulation results are obtained by the four-order Runge-Kutta method in the MATLAB environment. The inhibition mechanism can be inferred from the results.

RESULTS:

We found that the seizures may be inhibited by tuning the strength of the voltage to suitable ranges. This regulation may be achieved through the competition between the inhibitory projections from the basal ganglia to the thalamus.

CONCLUSION:

Because the mechanism underlying the treatment of epilepsy with a uniform direct current electric field is unclear, we hope that these results can inspire further experimental studies.

Regulation and control roles of the basal ganglia in the development of absence epileptiform activities

Absence epileptiform activities are traditionally considered to be primarily induced by abnormal interactions between the cortical and thalamic neurons, which form the thalamocortical circuit in the brain. The basal ganglia, as an organizational unit in the brain, has close input and output relationships with the thalamocortical circuit. Although several studies report that the basal ganglia may participate in controlling and regulating absence epileptiform activities, to date, there have been no studies regarding whether the basal ganglia directly cause absence epileptiform activities. In this paper, we built a basal ganglia-corticothalamic network model to determine the role of basal ganglia in this disease. We determined that absence epileptiform activities might be directly induced by abnormal coupling strengths on certain pivotal pathways in the basal ganglia. These epileptiform activities can be well controlled by the coupling strengths of three major pathways that project from the thalamocortical network to the basal ganglia. The results implied that the substantia nigra pars compacta (SNc) can be considered to be the effective treatment target area for inhibiting epileptiform activities, which supports the observations of previous studies. Particularly, as a major contribution of this paper, we determined that the final presentation position of the epileptic slow spike waves is not limited to the cerebral cortex; these waves may additionally appear in the thalamus, striatal medium spiny neurons, striatal fast spiking interneuron, the SNc, subthalamic nucleus, substantia nigra pars reticulata and globus pallidus pars externa. In addition, consistent with several previous studies, the delay in the network was observed to be a critical factor for inducing transitions between different types of absence epileptiform activities. Our new model not only explains the onset and control mechanism but also provides a unified framework to study similar problems in neuron systems.

The generation mechanism of spike-and-slow wave discharges appearing on thalamic relay nuclei

In this paper, we use a model modified from classic corticothalamic network(CT) to explore the mechanism of absence seizures appearing on specific relay nuclei (SRN) of the thalamus. It is found that typical seizure states appear on SRN through tuning several critical connection strengths in the model. In view of previous experimental and theoretical works which were mainly on epilepsy seizure phenomena appearing on excitatory pyramidal neurons (EPN) of the cortex, this is a novel model to consider the seizure observed on thalamus. In particular, the onset mechanism is different from previous theoretical studies. Inspired by some previous clinical and experimental studies, we employ the external stimuli voltage on EPN and SRN in the network, and observe that the seizure can be well inhibited by tuning the stimulus intensity appropriately. We further explore the effect of the signal transmission delays on seizures, and found that the polyspike phenomenon appears only when the delay is sufficiently large. The experimental data also confirmed our model. Since there is a complex network in the brain and all organizations are interacting closely with each other, the results obtained in this paper provide not only biological insights into the regulatory mechanisms but also a reference for the prevention and treatment of epilepsy in future.

Control of absence epilepsy seizures in specific relay nuclei of thalamus

In this paper, we used a classic basal ganglia-corticothalamic model(BGCT) to study the onset and control mechanism of absence epilepsy in specific relay nuclei (SRN) of thalamus. It was found that the seizure state may appear in SRN by turning the coupling strength -v_sr and signal transmission delay τ on the route "Thalamic reticular nuclei (TRN) of thalamus ⟶ SRN". With increasing of -v_sr, the seizure state appeared two times, and its onset mechanism has not been discussed in previous studies. The seizure activity can be well controlled by adjusting the activation level of the substantia nigra pars reticulata (SNr) in basal ganglia, which is a main output tissue to the corticothalamic system through two direct inhibitory pathways "SNr ⟶ SRN" and "SNr ⟶ TRN" in our model. We found that the interesting bidirectional regulation phenomenon appeared as considering the single pathway "SNr ⟶ SRN" and "SNr ⟶ TRN", or when they coexisted in one network, the mechanism of which is also different from some previous theoretical studies. At last, we pointed out that the mechanism obtained above can also explain the onset and control of the poly-spikes slow wave appeared in SRN by turning τ to large enough. Therefore, the results in the paper will further deepen our understanding of the generation and control mechanism of epilepsy disease.

Controlling mechanism of absence seizures by deep brain stimulus applied on subthalamic nucleus

Based on a classical model of the basal ganglia thalamocortical network, in this paper, we employed a type of the deep brain stimulus voltage on the subthalamic nucleus to study the control mechanism of absence epilepsy seizures. We found that the seizure can be well controlled by turning the period and the duration of current stimulation into suitable ranges. It is the very interesting bidirectional periodic adjustment phenomenon. These parameters are easily regulated in clinical practice, therefore, the results obtained in this paper may further help us to understand the treatment mechanism of the epilepsy seizure.

Overexpression of the immediate-early genes Egr1, Egr2, and Egr3 in two strains of rodents susceptible to audiogenic seizures

Genetic animal models of epilepsy are an important tool for further understanding the basic cellular mechanisms underlying epileptogenesis and for developing novel antiepileptic drugs. We conducted a comparative study of gene expression in the inferior colliculus, a nucleus that triggers audiogenic seizures, using two animal models, the Wistar audiogenic rat (WAR) and the genetic audiogenic seizure hamster (GASH:Sal). For this purpose, both models were exposed to high intensity auditory stimulation, and 60min later, the inferior colliculi were collected. As controls, intact Wistar rats and Syrian hamsters were subjected to stimulation and tissue preparation protocols identical to those performed on the experimental animals. Ribonucleic acid was isolated, and microarray analysis comparing the stimulated Wistar and WAR rats showed that the genomic profile of these animals displayed significant (fold change, |FC|≥2.0 and p<0.05) upregulation of 38 genes and downregulation of 47 genes. Comparison of gene expression profiles between stimulated control hamsters and stimulated GASH:Sal revealed the upregulation of 10 genes and the downregulation of 5 genes. Among the common genes that were altered in both models, we identified the zinc finger immediate-early growth response gene Egr3. The Egr3 protein is a transcription factor that is induced by distinct stress-elicited factors. Based on immunohistochemistry, this protein was expressed in the cochlear nucleus complex, the inferior colliculus, and the hippocampus of both animal models as well as in lymphoma tumors of the GASH:Sal. Our results support that the overexpression of the Egr3 gene in both models might contribute to neuronal viability and development of lymphoma in response to stress associated with audiogenic seizures. This article is part of a Special Issue entitled "Genetic and Reflex Epilepsies, Audiogenic Seizures and Strains: From Experimental Models to the Clinic".

The Wistar Audiogenic Rat (WAR) strain and its contributions to epileptology and related comorbidities: History and perspectives

In the context of modeling epilepsy and neuropsychiatric comorbidities, we review the Wistar Audiogenic Rat (WAR), first introduced to the neuroscience international community more than 25years ago. The WAR strain is a genetically selected reflex model susceptible to audiogenic seizures (AS), acutely mimicking brainstem-dependent tonic-clonic seizures and chronically (by audiogenic kindling), temporal lobe epilepsy (TLE). Seminal neuroethological, electrophysiological, cellular, and molecular protocols support the WAR strain as a suitable and reliable animal model to study the complexity and emergent functions typical of epileptogenic networks. Furthermore, since epilepsy comorbidities have emerged as a hot topic in epilepsy research, we discuss the use of WARs in fields such as neuropsychiatry, memory and learning, neuroplasticity, neuroendocrinology, and cardio-respiratory autonomic regulation. Last, but not least, we propose that this strain be used in "omics" studies, as well as with the most advanced molecular and computational modeling techniques. Collectively, pioneering and recent findings reinforce the complexity associated with WAR alterations, consequent to the combination of their genetically-dependent background and seizure profile. To add to previous studies, we are currently developing more powerful behavioral, EEG, and molecular methods, combined with computational neuroscience/network modeling tools, to further increase the WAR strain's contributions to contemporary neuroscience in addition to increasing knowledge in a wide array of neuropsychiatric and other comorbidities, given shared neural networks. During the many years that the WAR strain has been studied, a constantly expanding network of multidisciplinary collaborators has generated a growing research and knowledge network. Our current and major wish is to make the WARs available internationally to share our knowledge and to facilitate the planning and execution of multi-institutional projects, eagerly needed to contribute to paradigm shifts in epileptology. This article is part of a Special Issue entitled "Genetic and Reflex Epilepsies, Audiogenic Seizures and Strains: From Experimental Models to the Clinic".

New Insights on Temporal Lobe Epilepsy Based on Plasticity-Related Network Changes and High-Order Statistics

Epilepsy is a disorder of the brain characterized by the predisposition to generate recurrent unprovoked seizures, which involves reshaping of neuronal circuitries based on intense neuronal activity. In this review, we first detailed the regulation of plasticity-associated genes, such as ARC, GAP-43, PSD-95, synapsin, and synaptophysin. Indeed, reshaping of neuronal connectivity after the primary, acute epileptogenesis event increases the excitability of the temporal lobe. Herein, we also discussed the heterogeneity of neuronal populations regarding the number of synaptic connections, which in the theoretical field is commonly referred as degree. Employing integrate-and-fire neuronal model, we determined that in addition to increased synaptic strength, degree correlations might play essential and unsuspected roles in the control of network activity. Indeed, assortativity, which can be described as a condition where high-degree correlations are observed, increases the excitability of neural networks. In this review, we summarized recent topics in the field, and data were discussed according to newly developed or unusual tools, as provided by mathematical graph analysis and high-order statistics. With this, we were able to present new foundations for the pathological activity observed in temporal lobe epilepsy.

Astrocyte Hypertrophy Contributes to Aberrant Neurogenesis after Traumatic Brain Injury

Traumatic brain injury (TBI) is a widespread epidemic with severe cognitive, affective, and behavioral consequences. TBIs typically result in a relatively rapid inflammatory and neuroinflammatory response. A major component of the neuroinflammatory response is astrocytes, a type of glial cell in the brain. Astrocytes are important in maintaining the integrity of neuronal functioning, and it is possible that astrocyte hypertrophy after TBIs might contribute to pathogenesis. The hippocampus is a unique brain region, because neurogenesis persists in adults. Accumulating evidence supports the functional importance of these newborn neurons and their associated astrocytes. Alterations to either of these cell types can influence neuronal functioning. To determine if hypertrophied astrocytes might negatively influence immature neurons in the dentate gyrus, astrocyte and newborn neurons were analyzed at 30 days following a TBI in mice. The results demonstrate a loss of radial glial-like processes extending through the granule cell layer after TBI, as well as ectopic growth and migration of immature dentate neurons. The results further show newborn neurons in close association with hypertrophied astrocytes, suggesting a role for the astrocytes in aberrant neurogenesis. Future studies are needed to determine the functional significance of these alterations to the astrocyte/immature neurons after TBI.

Potassium Channels in Epilepsy

This review attempts to give a concise and up-to-date overview on the role of potassium channels in epilepsies. Their role can be defined from a genetic perspective, focusing on variants and de novo mutations identified in genetic studies or animal models with targeted, specific mutations in genes coding for a member of the large potassium channel family. In these genetic studies, a demonstrated functional link to hyperexcitability often remains elusive. However, their role can also be defined from a functional perspective, based on dynamic, aggravating, or adaptive transcriptional and posttranslational alterations. In these cases, it often remains elusive whether the alteration is causal or merely incidental. With ∼80 potassium channel types, of which ∼10% are known to be associated with epilepsies (in humans) or a seizure phenotype (in animals), if genetically mutated, a comprehensive review is a challenging endeavor. This goal may seem all the more ambitious once the data on posttranslational alterations, found both in human tissue from epilepsy patients and in chronic or acute animal models, are included. We therefore summarize the literature, and expand only on key findings, particularly regarding functional alterations found in patient brain tissue and chronic animal models.

Microcircuits in Epilepsy: Heterogeneity and Hub Cells in Network Synchronization

Epilepsy is a complex disorder involving neurological alterations that lead to the pathological development of spontaneous, recurrent seizures. For decades, seizures were thought to be largely repetitive, and had been examined at the macrocircuit level using electrophysiological recordings. However, research mapping the dynamics of large neuronal populations has revealed that seizures are not simply recurrent bursts of hypersynchrony. Instead, it is becoming clear that seizures involve a complex interplay of different neurons and circuits. Herein, we will review studies examining microcircuit changes that may underlie network hyperexcitability, discussing observations from network theory, computational modeling, and optogenetics. We will delve into the idea of hub cells as pathological centers for seizure activity, and will explore optogenetics as a novel avenue to target and treat pathological circuits. Finally, we will conclude with a discussion on future directions in the field.

Computational modeling of neurostimulation in brain diseases

Neurostimulation as a therapeutic tool has been developed and used for a range of different diseases such as Parkinson's disease, epilepsy, and migraine. However, it is not known why the efficacy of the stimulation varies dramatically across patients or why some patients suffer from severe side effects. This is largely due to the lack of mechanistic understanding of neurostimulation. Hence, theoretical computational approaches to address this issue are in demand. This chapter provides a review of mechanistic computational modeling of brain stimulation. In particular, we will focus on brain diseases, where mechanistic models (e.g., neural population models or detailed neuronal models) have been used to bridge the gap between cellular-level processes of affected neural circuits and the symptomatic expression of disease dynamics. We show how such models have been, and can be, used to investigate the effects of neurostimulation in the diseased brain. We argue that these models are crucial for the mechanistic understanding of the effect of stimulation, allowing for a rational design of stimulation protocols. Based on mechanistic models, we argue that the development of closed-loop stimulation is essential in order to avoid inference with healthy ongoing brain activity. Furthermore, patient-specific data, such as neuroanatomic information and connectivity profiles obtainable from neuroimaging, can be readily incorporated to address the clinical issue of variability in efficacy between subjects. We conclude that mechanistic computational models can and should play a key role in the rational design of effective, fully integrated, patient-specific therapeutic brain stimulation.

Future of seizure prediction and intervention: closing the loop

The ultimate goal of epilepsy therapies is to provide seizure control for all patients while eliminating side effects. Improved specificity of intervention through on-demand approaches may overcome many of the limitations of current intervention strategies. This article reviews the progress in seizure prediction and detection, potential new therapies to provide improved specificity, and devices to achieve these ends. Specifically, we discuss (1) potential signal modalities and algorithms for seizure detection and prediction, (2) closed-loop intervention approaches, and (3) hardware for implementing these algorithms and interventions. Seizure prediction and therapies maximize efficacy, whereas minimizing side effects through improved specificity may represent the future of epilepsy treatments.

Graph theory findings in the pathophysiology of temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Accumulating evidence has shown that TLE is a disorder of abnormal epileptogenic networks, rather than focal sources. Graph theory allows for a network-based representation of TLE brain networks, and has potential to illuminate characteristics of brain topology conducive to TLE pathophysiology, including seizure initiation and spread. We review basic concepts which we believe will prove helpful in interpreting results rapidly emerging from graph theory research in TLE. In addition, we summarize the current state of graph theory findings in TLE as they pertain its pathophysiology. Several common findings have emerged from the many modalities which have been used to study TLE using graph theory, including structural MRI, diffusion tensor imaging, surface EEG, intracranial EEG, magnetoencephalography, functional MRI, cell cultures, simulated models, and mouse models, involving increased regularity of the interictal network configuration, altered local segregation and global integration of the TLE network, and network reorganization of temporal lobe and limbic structures. As different modalities provide different views of the same phenomenon, future studies integrating data from multiple modalities are needed to clarify findings and contribute to the formation of a coherent theory on the pathophysiology of TLE.

Large-scale modeling of epileptic seizures: scaling properties of two parallel neuronal network simulation algorithms

Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.

The epilepsies: complex challenges needing complex solutions

It is widely accepted that epilepsies are complex syndromes due to their multi-factorial origins and manifestations. Different mathematical and computational descriptions use appropriate methods to address nonlinear relationships, chaotic behaviors and emergent properties. These theoretical approaches can be divided into two major categories: descriptive, such as flowcharts, graphs and other statistical analyses, and explicative, which include both realistic and abstract models. Although these modeling tools have brought great advances, a common framework to guide their design, implementation and evaluation, with the goal of future integration, is still needed. In the current review, we discuss two examples of complexity analysis that can be performed with epilepsy data: behavioral sequences of temporal lobe seizures and alterations in an experimental cellular model. We also highlight the importance of the creation of model repositories for the epileptology field and encourage the development of mathematical descriptions of complex systems, together with more accurate simulation techniques.

Medial temporal lobe epilepsy is associated with neuronal fibre loss and paradoxical increase in structural connectivity of limbic structures

BACKGROUND

It has been hypothesised that seizure induced neuronal loss and axonal damage in medial temporal lobe epilepsy (MTLE) may lead to the development of aberrant connections between limbic structures and eventually result in the reorganisation of the limbic network. In this study, limbic structural connectivity in patients with MTLE was investigated, using diffusion tensor MRI, probabilistic tractography and graph theory based network analysis.

METHODS

12 patients with unilateral MTLE and hippocampal sclerosis (five left and seven right MTLE) and 26 healthy controls were studied. The connectivity of 10 bilateral limbic regions of interest was mapped with probabilistic tractography, and the probabilistic fibre density between each pair of regions was used as the measure of their weighted structural connectivity. Binary connectivity matrices were then obtained from the weighted connectivity matrix using a range of fixed density thresholds. Graph theory based properties of nodes (degree, local efficiency, clustering coefficient and betweenness centrality) and the network (global efficiency and average clustering coefficient) were calculated from the weight and binary connectivity matrices of each subject and compared between patients and controls.

RESULTS

MTLE was associated with a regional reduction in fibre density compared with controls. Paradoxically, patients exhibited (1) increased limbic network clustering and (2) increased nodal efficiency, degree and clustering coefficient in the ipsilateral insula, superior temporal region and thalamus. There was also a significant reduction in clustering coefficient and efficiency of the ipsilateral hippocampus, accompanied by increased nodal degree.

CONCLUSIONS

These results suggest that MTLE is associated with reorganisation of the limbic system. These results corroborate the concept of MTLE as a network disease, and may contribute to the understanding of network excitability dynamics in epilepsy and MTLE.

Computational modeling of epilepsy for an experimental neurologist

Computational modeling can be a powerful tool for an experimentalist, providing a rigorous mathematical model of the system you are studying. This can be valuable in testing your hypotheses and developing experimental protocols prior to experimenting. This paper reviews models of seizures and epilepsy at different scales, including cellular, network, cortical region, and brain scales by looking at how they have been used in conjunction with experimental data. At each scale, models with different levels of abstraction, the extraction of physiological detail, are presented. Varying levels of detail are necessary in different situations. Physiologically realistic models are valuable surrogates for experimental systems because, unlike in an experiment, every parameter can be changed and every variable can be observed. Abstract models are useful in determining essential parameters of a system, allowing the experimentalist to extract principles that explain the relationship between mechanisms and the behavior of the system. Modeling is becoming easier with the emergence of platforms dedicated to neuronal modeling and databases of models that can be downloaded. Modeling will never be a replacement for animal and clinical experiments, but it should be a starting point in designing experiments and understanding their results.