The importance of modeling epileptic seizure dynamics as spatio-temporal patterns.

Published onlineJournal ArticleThis is the final version of the article. Available from Frontiers Media via the DOI in this record.The occurrence of seizures is the common feature across the spectrum of epileptic disorders. We describe how the use of mechanistic neural population models leads to novel insight into the dynamic mechanisms underlying two important types of epileptic seizures. We specifically stress the need for a spatio-temporal description of the rhythms to deal with the complexity of the pathophenotype. Adapted to functional and structural patient data, the macroscopic models may allow a patient-specific description of seizures and prediction of treatment outcome.We thank British research councils EPSRC and BBSRC and the University of Manchester for financial support. We thank Kaspar Schindler, Ulrich Stephani, Hiltrud Muhle, Rainer Boor, Michael Siniatchkin, Fernando Lopes da Silva, and Gilles van Luijtelaar for discussions. EEG data are from the University Hospital Inselspital, Bern, Switzerland

Understanding Epileptiform After-Discharges as Rhythmic Oscillatory Transients

Electro-cortical activity in patients with epilepsy may show abnormal rhythmic transients in response to stimulation. Even when using the same stimulation parameters in the same patient, wide variability in the duration of transient response has been reported. These transients have long been considered important for the mapping of the excitability levels in the epileptic brain but their dynamic mechanism is still not well understood. To understand the occurrence of abnormal transients dynamically, we use a thalamo-cortical neural population model of epileptic spike-wave activity and study the interaction between slow and fast subsystems. In a reduced version of the thalamo-cortical model, slow wave oscillations arise from a fold of cycles (FoC) bifurcation. This marks the onset of a region of bistability between a high amplitude oscillatory rhythm and the background state. In vicinity of the bistability in parameter space, the model has excitable dynamics, showing prolonged rhythmic transients in response to suprathreshold pulse stimulation. We analyse the state space geometry of the bistable and excitable states, and find that the rhythmic transient arises when the impending FoC bifurcation deforms the state space and creates an area of locally reduced attraction to the fixed point. This area essentially allows trajectories to dwell there before escaping to the stable steady state, thus creating rhythmic transients. In the full thalamo-cortical model, we find a similar FoC bifurcation structure. Based on the analysis, we propose an explanation of why stimulation induced epileptiform activity may vary between trials, and predict how the variability could be related to ongoing oscillatory background activity.Comment: http://journal.frontiersin.org/article/10.3389/fncom.2017.00025/ful

Dynamic causal modelling of seizure activity in a rat model

This paper presents a physiological account of seizure activity and its evolution over time using a rat model of induced epilepsy. We analyse spectral activity recorded in the hippocampi of three rats who received kainic acid injections in the right hippocampus. We use dynamic causal modelling of seizure activity and Bayesian model reduction to identify the key synaptic and connectivity parameters that underlie seizure onset. Using recent advances in hierarchical modelling (parametric empirical Bayes), we characterise seizure onset in terms of slow fluctuations in synaptic excitability of specific neuronal populations. Our results suggest differences in the pathophysiology – of seizure activity in the lesioned versus the non-lesioned hippocampus – with pronounced changes in excitation-inhibition balance and temporal summation on the lesioned side. In particular, our analyses suggest that marked reductions in the synaptic time constant of the deep pyramidal cells and the self-inhibition of inhibitory interneurons (in the lesioned hippocampus) are sufficient to explain changes in spectral activity. Although these synaptic changes are consistent over rats, the resulting electrophysiological phenotype can be quite diverse

Dynamic Causal Modelling of Dynamic Dysfunction in NMDA-Receptor Antibody Encephalitis

Using electroencephalography (EEG) dynamic brain function can be measured and its abnormalities identified and described. However, inferring pathological mechanisms from EEG recordings is an ill-posed, inverse problem. Here we illustrate the use of neural mass model based dynamic causal modelling to address this inverse problem. Using Bayesian model inversion and model comparison, DCM allows evaluation of different hypotheses regarding pathomechanisms leading to dynamic brain dysfunction in NMDA receptor encephalitis

Self-organised transients in a neural mass model of epileptogenic tissue dynamics

Stimulation of human epileptic tissue can induce rhythmic, self-terminating responses on the EEG or ECoG. These responses play a potentially important role in localising tissue involved in the generation of seizure activity, yet the underlying mechanisms are unknown. However, in vitro evidence suggests that self-terminating oscillations in nervous tissue are underpinned by non-trivial spatio-temporal dynamics in an excitable medium. In this study, we investigate this hypothesis in spatial extensions to a neural mass model for epileptiform dynamics. We demonstrate that spatial extensions to this model in one and two dimensions display propagating travelling waves but also more complex transient dynamics in response to local perturbations. The neural mass formulation with local excitatory and inhibitory circuits, allows the direct incorporation of spatially distributed, functional heterogeneities into the model. We show that such heterogeneities can lead to prolonged reverberating responses to a single pulse perturbation, depending upon the location at which the stimulus is delivered. This leads to the hypothesis that prolonged rhythmic responses to local stimulation in epileptogenic tissue result from repeated self-excitation of regions of tissue with diminished inhibitory capabilities. Combined with previous models of the dynamics of focal seizures this macroscopic framework is a first step towards an explicit spatial formulation of the concept of the epileptogenic zone. Ultimately, an improved understanding of the pathophysiologic mechanisms of the epileptogenic zone will help to improve diagnostic and therapeutic measures for treating epilepsy

How models of canonical microcircuits implement cognitive functions

Major cognitive functions such as language, memory, and decision-making are thought to rely on distributed networks of a large number of fundamental neural elements, called canonical microcircuits. A mechanistic understanding of the interaction of these canonical microcircuits promises a better comprehension of cognitive functions as well as their potential disorders and corresponding treatment techniques. This thesis establishes a generative modeling framework that rests on canonical microcircuits and employs it to investigate composite mechanisms of cognitive functions. A generic, biologically plausible neural mass model was derived to parsimoniously represent conceivable architectures of canonical microcircuits. Time domain simulations and bifurcation and stability analyses were used to evaluate the model’s capability for basic information processing operations in response to transient stimulations, namely signal flow gating and working memory. Analysis shows that these basic operations rest upon the bistable activity of a neural population and the selectivity for the stimulus’ intensity and temporal consistency and transiency. In the model’s state space, this selectivity is marked by the distance of the system’s working point to a saddle-node bifurcation and the existence of a Hopf separatrix. The local network balance, in regard of synaptic gains, is shown to modify the model’s state space and thus its operational repertoire. Among the investigated architectures, only a three-population model that separates input-receiving and output-emitting excitatory populations exhibits the necessary state space characteristics. It is thus specified as minimal canonical microcircuit. In this three-population model, facilitative feedback information modifies the retention of sensory feedforward information. Consequently, meta-circuits of two hierarchically interacting minimal canonical microcircuits feature a temporal processing history that enables state-dependent processing operations. The relevance of these composite operations is demonstrated for the neural operations of priming and structure-building. Structure-building, that is the sequential and selective activation of neural circuits, is identified as an essential mechanism in a neural network for syntax parsing. This insight into cognitive processing proves the modeling framework’s potential in neurocognitive research. This thesis substantiates the connectionist notion that higher processing operations emerge from the combination of minimal processing elements and advances the understanding how cognitive functions are implemented in the neocortical matter of the brain.Kognitive Fähigkeiten wie Sprache, Gedächtnis und Entscheidungsfindung resultieren vermutlich aus der Interaktion vieler fundamentaler neuronaler Elemente, sogenannter kanonischer Schaltkreise. Eine vertiefte Einsicht in das Zusammenwirken dieser kanonischen Schaltkreise verspricht ein besseres Verständnis kognitiver Fähigkeiten, möglicher Funktionsstörungen und Therapieansätze. Die vorliegende Dissertation untersucht ein generatives Modell kanonischer Schaltkreise und erforscht mit dessen Hilfe die Zusammensetzung kognitiver Fähigkeiten aus konstitutiven Mechanismen neuronaler Interaktion. Es wurde ein biologisch-plausibles neuronales Massenmodell erstellt, das mögliche Architekturen kanonischer Schaltkreise generisch berücksichtigt. Anhand von Simulationen sowie Bifurkations- und Stabilitätsanalysen wurde untersucht, inwiefern das Modell grundlegende Operationen der Informationsverarbeitung, nämlich Selektion und temporäre Speicherung einer transienten Stimulation, unterstützt. Die Untersuchung zeigt, dass eine bistabile Aktivität einer neuronalen Population und die Beurteilung der Salienz des Signals den grundlegenden Operationen zugrunde liegen. Die Beurteilung der Salienz beruht dabei hinsichtlich der Signalstärke auf dem Abstand des Arbeitspunktes zu einer Sattel-Knoten-Bifurkation und hinsichtlich der Signalkonsistenz und-–vergänglichkeit auf einer Hopf-Separatrix im Zustandsraum des Systems. Die Netzwerkbalance modifiziert diesen Zustandsraum und damit die Funktionsfähigkeit des Modells. Nur ein Drei-Populationenmodell mit getrennten erregenden Populationen für Signalempfang und -emission weist die notwendigen Bedingungen im Zustandsraum auf und genügt der Definition eines minimalen kanonischen Schaltkreises. In diesem Drei-Populationenmodell erleichtert ein Feedbacksignal die Speicherfähigkeit für sensorische Feedforwardsignale. Dementsprechend weisen hierarchisch interagierende minimale kanonische Schaltkreise ein zeitliches Verarbeitungsgedächtnis auf, das zustandsabhängige Verarbeitungsoperationen erlaubt. Die Bedeutung dieser konstitutiven Operationen wird für die neuronalen Operationen Priming und Strukturbildung verdeutlicht. Letztere wurde als wichtiger Mechanismus in einem Netzwerk zur Syntaxanalyse identifiziert und belegt das Potential des Modellansatzes für die neurokognitive Forschung. Diese Dissertation konkretisiert die konnektionistische Ansicht höhere Verarbeitungsoperationen als Ergebnis der Kombination minimaler Verarbeitungselemente zu verstehen und befördert das Verständnis für die Frage wie kognitive Fähigkeiten im Nervengewebe des Gehirns implementiert sind

Quantitative Methods For Guiding Epilepsy Surgery From Intracranial Eeg

Despite advances in intracranial EEG (iEEG) technique, technology and neuroimaging, patients today are no more likely to achieve seizure freedom after epilepsy surgery than they were 20 years ago. These poor outcomes are in part due to the difficulty and subjectivity associated with interpreting iEEG recordings, and have led to widespread interest in developing quantitative methods to localize the epileptogenic zone. Approaches to computational iEEG analysis vary widely, spanning studies of both seizures and interictal periods, and encompassing a range of techniques including electrographic signal analysis and graph theory. However, many current methods often fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Indeed, none have completed prospective clinical trials. In this dissertation, I develop and validate tools for guiding epilepsy surgery through the quantitative analysis of intracranial EEG. Specifically, I leverage methods from graph theory for mapping network synchronizability to predict surgical outcome from ictal recordings, and also investigate the effects of sampling bias on network models. Finally, I construct a normative intracranial EEG atlas as a framework for objectively identifying patterns of abnormal neural activity and connectivity. Overall, the methods and results of this dissertation support the implementation of quantitative iEEG analysis in epilepsy surgical evaluation

Spatio-temporal modelling and analysis of epileptiform EEG

In this thesis we investigate the mechanisms underlying the generation of abnormal EEG rhythms in epilepsy, which is a crucial step towards better treatment of this disorder in the future. To this end, macroscopic scale mathematical models of the interactions between neuronal populations are examined. In particular, the role of interactions between neural masses that are spatially distributed in cortical networks are explored. In addition, two other important aspects of the modelling process are addressed, namely the conversion of macroscopic model variables into EEG output and the comparison of multivariate, spatio-temporal data. For the latter, we adopt a vectorisation of the correlation matrix of windowed data and subsequent comparison of data by vector distance measures. Our modelling studies indicate that excitatory connectivity between neural masses facilitates self-organised dynamics. In particular, we report for the first time the production of complex rhythmic transients and the generation of intermittent periods of 'abnormal' rhythmic activity in two different models of epileptogenic tissue. These models therefore provide novel accounts of the spontaneous, intermittent transition between normal and pathological rhythms in primarily generalised epilepsies and the evocation of complex, self-terminating, spatio-temporal dynamics by brief stimulation in focal epilepsies. Two key properties of these models are excitability at the macroscopic level and the presence of spatial heterogeneities. The identification of neural mass excitability as an important processes in spatially extended brain networks is a step towards uncovering the multi-scale nature of the pathological mechanisms of epilepsy. A direct consequence of this work is therefore that novel experimental investigations are proposed, which in itself is a validation of our modelling approach. In addition, new considerations regarding the nature of dynamical systems as applied to problems of transitions between rhythmic states are proposed and will prompt future investigations of complex transients in spatio-temporal excitable systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Simulation and Analysis of Stimulus Evoked and Seizure-like Activity in an Acute Rat Neocortical Brain Slice Preparation

Ph. D. ThesisThis thesis aims to provide tools for the simulation and analysis of acute brain slice experiments that have recorded spontaneous seizure-like activity and activity evoked using electric eld stimulation. The Virtual Electrode Recording Tool for Extracellular Potentials (VERTEX) (Tomsett et al., 2015) is a simulation framework that can act as a sca old for anatomical and physiological knowledge and can be used to test how interventions a ect the dynamics observed in the extracellular potentials. We extend VERTEX so that one can model a greater range of experimental setups, in particular those that involve electric eld stimulation. We also devise a software pipeline for the identi cation and analysis of epileptiform neuronal activity, which we apply to in vitro recordings from acute neocortical slices from a chronic model of epilepsy in rat. In Chapter 1 we look at the intersection of electric eld stimulation, synaptic plasticity and epilepsy. In Chapter 2 we look at the implementation of electric eld stimulation that we have added to the VERTEX simulator. We show that our simulation compares well with simulations using detailed neuron models, and with previously published in vitro data. We also describe our implementations of short term plasticity and spike-timing dependent plasticity. In Chapter 3 we describe some example simulations of focal electric eld stimulation in neocortex, investigating a single pulse of stimulation, a paired pulse of stimulation and the role of short term plasticity in the response. We also use theta burst stimulation to provoke a potentiation of the response when we apply spike-timing dependent plasticity to the synapses of the network. We then look to the experimental context of our framework. Chapter 4 describes an analysis tool for identifying and evaluating epileptiform activity recorded in vitro and outlines a method devised to measure the abruptness of seizure build up. Chapter 5 uses this analysis pipeline to analyse seizure-like events recorded in vitro from slices of motor cortex prepared from rats with chronic seizures induced by injection of tetanus toxin. In this chapter we also describe two VERTEX simulations; one that uses short term plasticity as the vehicle for the breakdown in inhibition and the build up of activity during a seizure-like event, and another of stimulus evoked activity in a seizure-prone neocortical slice. We compare the latter with stimulus evoked potential in an example in vitro multi-electrode array recording from the chronic epilepsy model. In Chapter 6 we discuss the future uses of VERTEX in modelling stimulus evoked activity and epileptiform activity

Building And Validating Next-Generation Neurodevices Using Novel Materials, Fabrication, And Analytic Strategies

Technologies that enable scientists to record and modulate neural activity across spatial scales are advancing the way that neurological disorders are diagnosed and treated, and fueling breakthroughs in our fundamental understanding of brain function. Despite the rapid pace of technology development, significant challenges remain in realizing safe, stable, and functional interfaces between manmade electronics and soft biological tissues. Additionally, technologies that employ multimodal methods to interrogate brain function across temporal and spatial scales, from single cells to large networks, offer insights beyond what is possible with electrical monitoring alone. However, the tools and methodologies to enable these studies are still in their infancy. Recently, carbon nanomaterials have shown great promise to improve performance and multimodal capabilities of bioelectronic interfaces through their unique optical and electronic properties, flexibility, biocompatibility, and nanoscale topology. Unfortunately, their translation beyond the lab has lagged due to a lack of scalable assembly methods for incorporating such nanomaterials into functional devices. In this thesis, I leverage carbon nanomaterials to address several key limitations in the field of bioelectronic interfaces and establish scalable fabrication methods to enable their translation beyond the lab. First, I demonstrate the value of transparent, flexible electronics by analyzing simultaneous optical and electrical recordings of brain activity at the microscale using custom-fabricated graphene electronics. Second, I leverage a recently discovered 2D nanomaterial, Ti3C2 MXene, to improve the capabilities and performance of neural microelectronic devices. Third, I fabricate and validate human-scale Ti3C2 MXene epidermal electrode arrays in clinical applications. Leveraging the unique solution-processability of Ti3C2 MXene, I establish novel fabrication methods for both high-resolution microelectrode arrays and macroscale epidermal electrode arrays that are scalable and sufficiently cost-effective to allow translation of MXene bioelectronics beyond the lab and into clinical use. Thetechnologies and methodologies developed in this thesis advance bioelectronic technology for both research and clinical applications, with the goal of improving patient quality of life and illuminating complex brain dynamics across spatial scales