Dataset Information

On the nature of seizure dynamics.

ABSTRACT: Seizures can occur spontaneously and in a recurrent manner, which defines epilepsy; or they can be induced in a normal brain under a variety of conditions in most neuronal networks and species from flies to humans. Such universality raises the possibility that invariant properties exist that characterize seizures under different physiological and pathological conditions. Here, we analysed seizure dynamics mathematically and established a taxonomy of seizures based on first principles. For the predominant seizure class we developed a generic model called Epileptor. As an experimental model system, we used ictal-like discharges induced in vitro in mouse hippocampi. We show that only five state variables linked by integral-differential equations are sufficient to describe the onset, time course and offset of ictal-like discharges as well as their recurrence. Two state variables are responsible for generating rapid discharges (fast time scale), two for spike and wave events (intermediate time scale) and one for the control of time course, including the alternation between 'normal' and ictal periods (slow time scale). We propose that normal and ictal activities coexist: a separatrix acts as a barrier (or seizure threshold) between these states. Seizure onset is reached upon the collision of normal brain trajectories with the separatrix. We show theoretically and experimentally how a system can be pushed toward seizure under a wide variety of conditions. Within our experimental model, the onset and offset of ictal-like discharges are well-defined mathematical events: a saddle-node and homoclinic bifurcation, respectively. These bifurcations necessitate a baseline shift at onset and a logarithmic scaling of interspike intervals at offset. These predictions were not only confirmed in our in vitro experiments, but also for focal seizures recorded in different syndromes, brain regions and species (humans and zebrafish). Finally, we identified several possible biophysical parameters contributing to the five state variables in our model system. We show that these parameters apply to specific experimental conditions and propose that there exists a wide array of possible biophysical mechanisms for seizure genesis, while preserving central invariant properties. Epileptor and the seizure taxonomy will guide future modeling and translational research by identifying universal rules governing the initiation and termination of seizures and predicting the conditions necessary for those transitions.

SUBMITTER: Jirsa VK

PROVIDER: S-EPMC4107736 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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On the nature of seizure dynamics.

Jirsa Viktor K VK Stacey William C WC Quilichini Pascale P PP Ivanov Anton I AI Bernard Christophe C

Brain : a journal of neurology 20140611 Pt 8

Seizures can occur spontaneously and in a recurrent manner, which defines epilepsy; or they can be induced in a normal brain under a variety of conditions in most neuronal networks and species from flies to humans. Such universality raises the possibility that invariant properties exist that characterize seizures under different physiological and pathological conditions. Here, we analysed seizure dynamics mathematically and established a taxonomy of seizures based on first principles. For the pr ...[more]

PMID: 24919973

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Project description:Abstract Dynamic functional brain connectivity facilitates adaptive cognition and behaviour. Abnormal alterations within such connectivity could result in disrupted functions observed across various neurological conditions. As one of the most common neurological disorders, epilepsy is defined by the seemingly random occurrence of spontaneous seizures. A central but unresolved question concerns the mechanisms by which extraordinarily diverse propagation dynamics of seizures emerge. Here, we applied a graph-theoretical approach to assess dynamic reconfigurations in the functional brain connectivity before, during and after seizures that display heterogeneous propagation patterns despite sharing similar cortical onsets. We computed time-varying functional brain connectivity networks from human intracranial recordings of 67 seizures (across 14 patients) that had a focal origin—49 of these focal seizures remained focal and 18 underwent a bilateral spread (focal to bilateral tonic-clonic seizures). We utilized functional connectivity networks estimated from interictal periods across patients as control. Our results characterize network features that quantify the underlying functional dynamics associated with the observed heterogeneity of seizure propagation across these two types of focal seizures. Decoding these network features demonstrate that bilateral propagation of seizure activity is an outcome of the imbalance of global integration and segregation in the brain prior to seizure onset. We show that there exist intrinsic network signatures preceding seizure onset that are associated with the extent to which an impending seizure will propagate throughout the brain (i.e. staying within one hemisphere versus spreading transcallosally). Additionally, these features characterize an increase in segregation and a decrease in excitability within the brain network (i.e. high modularity and low spectral radius). Importantly, seizure-type-specific differences in these features emerge several minutes prior to seizure onset, suggesting the potential utility of such measures in intervention strategies. Finally, our results reveal network characteristics after the onset that are unique to the propagation mechanisms of two most common focal seizure subtypes, indicative of distinct reconfiguration processes that may assist termination of each seizure type. Together, our findings provide insights into the relationship between the temporal evolution of seizure activity and the underlying functional connectivity dynamics. These results offer exciting avenues where graph-theoretical measures could potentially guide personalized clinical interventions for epilepsy and other neurological disorders in which extensive heterogeneity is observed across subtypes as well as across and within individual patients. Using a graph-theoretical technique, Rungratsameetaweemana et al. demonstrate that propagation mechanisms of seizure activity can be decoded from the network properties of functional brain connectivity as inferred from electrocorticography recordings. Graphical Abstract Graphical abstract

Project description:Epileptic seizure is a paroxysmal and self-limited phenomenon characterized by abnormal hypersynchrony of a large population of neurons. However, our current understanding of seizure dynamics is still limited. Here we propose a novel in vivo model of seizure-like afterdischarges using optogenetics, and report on investigation of directional network dynamics during seizure along the septo-temporal (ST) axis of hippocampus. Repetitive pulse photostimulation was applied to the rodent hippocampus, in which channelrhodopsin-2 (ChR2) was expressed, under simultaneous recording of local field potentials (LFPs). Seizure-like afterdischarges were successfully induced after the stimulation in both W-TChR2V4 transgenic (ChR2V-TG) rats and in wild type rats transfected with adeno-associated virus (AAV) vectors carrying ChR2. Pulse frequency at 10 and 20 Hz, and a 0.05 duty ratio were optimal for afterdischarge induction. Immunohistochemical c-Fos staining after a single induced afterdischarge confirmed neuronal activation of the entire hippocampus. LFPs were recorded during seizure-like afterdischarges with a multi-contact array electrode inserted along the ST axis of hippocampus. Granger causality analysis of the LFPs showed a bidirectional but asymmetric increase in signal flow along the ST direction. State space presentation of the causality and coherence revealed three discrete states of the seizure-like afterdischarge phenomenon: 1) resting state; 2) afterdischarge initiation with moderate coherence and dominant septal-to-temporal causality; and 3) afterdischarge termination with increased coherence and dominant temporal-to-septal causality. A novel in vivo model of seizure-like afterdischarge was developed using optogenetics, which was advantageous in its reproducibility and artifact-free electrophysiological observations. Our results provide additional evidence for the potential role of hippocampal septo-temporal interactions in seizure dynamics in vivo. Bidirectional networks work hierarchically along the ST hippocampus in the genesis and termination of epileptic seizures.

Project description:It is not fully understood how seizures terminate and why some seizures are followed by a period of complete brain activity suppression, postictal generalized EEG suppression. This is clinically relevant as there is a potential association between postictal generalized EEG suppression, cardiorespiratory arrest and sudden death following a seizure. We combined human encephalographic seizure data with data of a computational model of seizures to elucidate the neuronal network dynamics underlying seizure termination and the postictal generalized EEG suppression state. A multi-unit computational neural mass model of epileptic seizure termination and postictal recovery was developed. The model provided three predictions that were validated in EEG recordings of 48 convulsive seizures from 48 subjects with refractory focal epilepsy (20 females, age range 15-61 years). The duration of ictal and postictal generalized EEG suppression periods in human EEG followed a gamma probability distribution indicative of a deterministic process (shape parameter 2.6 and 1.5, respectively) as predicted by the model. In the model and in humans, the time between two clonic bursts increased exponentially from the start of the clonic phase of the seizure. The terminal interclonic interval, calculated using the projected terminal value of the log-linear fit of the clonic frequency decrease was correlated with the presence and duration of postictal suppression. The projected terminal interclonic interval explained 41% of the variation in postictal generalized EEG suppression duration (P < 0.02). Conversely, postictal generalized EEG suppression duration explained 34% of the variation in the last interclonic interval duration. Our findings suggest that postictal generalized EEG suppression is a separate brain state and that seizure termination is a plastic and autonomous process, reflected in increased duration of interclonic intervals that determine the duration of postictal generalized EEG suppression.

Dataset Information

On the nature of seizure dynamics.

Publications

On the nature of seizure dynamics.

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