Chimera States and Seizures in a Mouse Neuronal Model

Chimera states—the coexistence of synchrony and asynchrony in a nonlocally-coupled network of identical oscillators—are often sought as a model for epileptic seizures. This work investigates that connection, seeking chimera states in a network of modified Hindmarsh-Rose neurons connected in the graph of the mesoscale mouse connectome. After an overview of chimera states for neurologists, and an overview of neurology for mathematicians, previous connections between chimera states and seizures are reviewed in the current scientific literature. The model was found to be of sufficient quality to produce superficially epileptiform activity. The limitations of the model were investigated, depending on the strength of connections between subcortices within a cortex and between cortices. A wide swath of parameter space revealed persistent chimera states

Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy

Recent studies have shown that seizures can spread and terminate across brain areas via a rich diversity of spatiotemporal patterns. In particular, while the location of the seizure onset area is usually in-variant across seizures in a same patient, the source of traveling (2-3 Hz) spike-and-wave discharges (SWDs) during seizures can either move with the slower propagating ictal wavefront or remain stationary at the seizure onset area. In addition, although most focal seizures terminate quasi-synchronously across brain areas, some evolve into distinct ictal clusters and terminate asynchronously. To provide a unifying perspective on the observed diversity of spatiotemporal dynamics for seizure spread and termination, we introduce here the Epileptor neural field model. Two mechanisms play an essential role. First, while the slow ictal wavefront propagates as a front in excitable neural media, the faster SWDs propagation results from coupled-oscillator dynamics. Second, multiple time scales interact during seizure spread, allowing for low-voltage fast-activity (>10 Hz) to hamper seizure spread and for SWD propagation to affect the way a seizure terminates. These dynamics, together with variations in short and long-range connectivity strength, play a central role on seizure spread, maintenance and termination. We demonstrate how Epileptor field models incorporating the above mechanisms predict the previously reported diversity in seizure spread patterns. Furthermore, we confirm the predictions for synchronous or asynchronous (clustered) seizure termination in human seizures recorded via stereotactic EEG. Our new insights into seizure spatiotemporal dynamics may also contribute to the development of new closed-loop neuromodulation therapies for focal epilepsy.Comment: 10 pages + 9 pages Supporting Information (SI), 7 figures, 1 SI table, 7 SI figure

Computational Modeling of Seizure Dynamics Using Coupled Neuronal Networks: Factors Shaping Epileptiform Activity

International audienceEpileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion that slow variations of global excitability, due to exogenous fluctuations from extracellular environment, and gap junction communication push the system into paroxysmal regimes. We discuss potential mechanisms underlying such machinery and the relevance of our approach, supporting previous detailed modeling studies and reflecting on the limitations of our methodology

Optimizing electrical brain stimulation for seizure disorders

University of Minnesota Ph.D. dissertation. March 2017. Major: Neuroscience. Advisor: Theoden Netoff. 1 computer file (PDF); x, 145 pages.Approximately 1% of the world population is afflicted with Epilepsy. For many patients, antiepileptic drugs do not fully control seizures. Electrical brain stimulation therapies have been effective in reducing seizure rates in some patients. While current neuromodulation devices provide a benefit to patients, efficacy can be improved by optimizing brain stimulation so that the therapy is tuned on a patient by patient basis. One optimization approach is to target deep brain regions that strongly modulate seizure prone regions. I will present data on the effects of stimulation of two different anatomical regions for seizure control, and establish my experimental platform for testing closed-loop algorithms. There are two general methods to implementing closed-loop algorithms to modulate neural activity: 1) Model-free algorithms that require a learning period to establish an optimal mapping between neural states and best therapeutic parameters, and 2) Model-based algorithms that use forward predictions of the neural system to determine the appropriate stimulation therapy to be administered. In this thesis, I will propose and test two closed-loop control schemes to control the brain activity to prevent epileptogenic activity while reducing stimulation energy. I will also present techniques to remove stimulation artifacts so that neural biomarkers can be measured while simultaneously applying stimulation. The methods I will present could potentially be implemented in next generation electrical brain stimulation hardware for seizure disorders and other neurological diseases

Adiabatic dynamic causal modelling

This technical note introduces adiabatic dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions in brain activity (e.g., paroxysmal seizure activity). The scheme is efficient and yet retains a biophysical interpretation, in virtue of being based on established neural mass models that are equipped with a slow dynamic on the parameters (such as synaptic rate constants or effective connectivity). In brief, we use an adiabatic approximation to summarise fast fluctuations in hidden neuronal states (and their expression in sensors) in terms of their second order statistics; namely, their complex cross spectra. This allows one to specify and compare models of slowly changing parameters (using Bayesian model reduction) that generate a sequence of empirical cross spectra of electrophysiological recordings. Crucially, we use the slow fluctuations in the spectral power of neuronal activity as empirical priors on changes in synaptic parameters. This introduces a circular causality, in which synaptic parameters underwrite fast neuronal activity that, in turn, induces activity-dependent plasticity in synaptic parameters. In this foundational paper, we describe the underlying model, establish its face validity using simulations and provide an illustrative application to a chemoconvulsant animal model of seizure activity

Whole-Brain Models to Explore Altered States of Consciousness from the Bottom Up.

The scope of human consciousness includes states departing from what most of us experience as ordinary wakefulness. These altered states of consciousness constitute a prime opportunity to study how global changes in brain activity relate to different varieties of subjective experience. We consider the problem of explaining how global signatures of altered consciousness arise from the interplay between large-scale connectivity and local dynamical rules that can be traced to known properties of neural tissue. For this purpose, we advocate a research program aimed at bridging the gap between bottom-up generative models of whole-brain activity and the top-down signatures proposed by theories of consciousness. Throughout this paper, we define altered states of consciousness, discuss relevant signatures of consciousness observed in brain activity, and introduce whole-brain models to explore the biophysics of altered consciousness from the bottom-up. We discuss the potential of our proposal in view of the current state of the art, give specific examples of how this research agenda might play out, and emphasize how a systematic investigation of altered states of consciousness via bottom-up modeling may help us better understand the biophysical, informational, and dynamical underpinnings of consciousness

Domino-like transient dynamics at seizure onset in epilepsy

This is the final version. Available on open access from Public Library of Science via the DOI in this recordData Availability: We have made publicly available the 15 epochs of human EEG data containing generalized paroxysms classified as focal onset, and all 15 epochs containing seizures from one individual used in the manuscript, 1252 EEG epochs containing seizures classified as generalized onset and the 6 mouse mEC recordings. All data and the code used for the data analysis and model simulations they can be accessed via DOI 10.17605/OSF.IO/G2EXK.The International League Against Epilepsy (ILAE) groups seizures into “focal”, “generalized” and “unknown” based on whether the seizure onset is confined to a brain region in one hemisphere, arises in several brain region simultaneously, or is not known, respectively. This separation fails to account for the rich diversity of clinically and experimentally observed spatiotemporal patterns of seizure onset and even less so for the properties of the brain networks generating them. We consider three different patterns of domino-like seizure onset in Idiopathic Generalized Epilepsy (IGE) and present a novel approach to classification of seizures. To understand how these patterns are generated on networks requires understanding of the relationship between intrinsic node dynamics and coupling between nodes in the presence of noise, which currently is unknown. We investigate this interplay here in the framework of domino-like recruitment across a network. In particular, we use a phenomenological model of seizure onset with heterogeneous coupling and node properties, and show that in combination they generate a range of domino-like onset patterns observed in the IGE seizures. We further explore the individual contribution of heterogeneous node dynamics and coupling by interpreting in-vitro experimental data in which the speed of onset can be chemically modulated. This work contributes to a better understanding of possible drivers for the spatiotemporal patterns observed at seizure onset and may ultimately contribute to a more personalized approach to classification of seizure types in clinical practice.Engineering and Physical Sciences Research Council (EPSRC)Medical Research Council (MRC