Summary: Study reveals the mechanisms behind distinct patterns of electrical activity in neuron groups that accompany the onset of seizures.
Source: University of Exeter
Scientists have made a significant breakthrough in the quest to understand the intricate processes that occur in the brain during seizures that are the key symptom of epilepsy.
A team of scientists from the University of Exeter has studied the mechanisms behind distinctive patterns of electrical activity of neuron groups in the brain that accompany the onset of seizures.
In healthy brains, networks of neurons move through states of similar behavior – known as synchronization – and dissimilar behavior, called desynchronization. These processes are also associated with both memory and attention.
However, in a brain with a neurological disorder, such as epilepsy, this synchronization can grow to an almost dangerous extent, when a collection of brain cells begins to emit excess electricity.
In a series of new studies, published recently in PLoS Computational Biology and the SIAM Journal on Applied Dynamical Systems, the research team used sophisticated mathematical modelling approach to explore the interplay between groups of neurons, that leads to transitions in synchronization changes.
Jennifer Creaser, co-author of the study and from the University of Exeter said: “Synchronization is thought to be important for information processing. But too much synchronization–such as what occurs in epileptic seizures or Parkinson’s disease–is associated with disease states and can impair brain function.”
The study, which took place at the Engineering and Physical Science Research Council’s Centre for Predictive Modelling in Healthcare at the University of Exeter and University of Birmingham, used an extended version of an existing mathematical model that represents the brain as a network connecting multiple nodes of neuron groups.
The model network consists of bi-stable nodes, meaning that each node can switch between two stable states – resting and seizure. These nodes remain in their current state until they receive a stimulus that gives them the appropriate ‘kick’ to escape to the other state.
This stimulus comes from both other connected nodes and in the form of “noise” — outside sources of neural activity, such as endocrine responses that are associated with an emotional state or physiological changes due to disease.
Adding a small amount of noise to the system caused each node to transition to the active state — but the system’s geometry was such that returning to the resting state took much longer than leaving.
Previously, the research team found that this leads to a cascade of escapes to the active state–much like a falling line of dominos–that spreads activity across the network.
The new research builds on this ‘domino effect’ to identify the circumstances that bring about these changes in synchrony and investigate how the type of coupling in a network affects its behaviour.
It found that, when the model incorporated more general amplitude and phase coupling, the nodes’ synchrony could change between consecutive escapes during the domino effect.
Professor Peter Ashwin, co-author of the study said: “Although this is a theoretical study of an idealized model, it is inspired by challenges posed by understanding transitions between healthy and pathological activity in the brain.”
Professor Krasimira Tsaneva-Atanasova, also co-author of the study added: “The mathematical modeling of seizure initiation and propagation can not only help to uncover seizures’ complex underlying mechanisms, but also provide a means for enabling in silico experiments to predict the outcome of manipulating neural systems.”
Sequential Escapes and Synchrony Breaking for Networks of Bistable Oscillatory Nodes
Progression through different synchronized and desynchronized regimes in brain networks has been reported to reflect physiological and behavioral states, such as working memory and attention. Moreover, intracranial recordings of epileptic seizures show a progression towards synchronization as brain regions are recruited and the seizures evolve. In this paper, we build on our previous work on noise- induced transitions on networks to explore the interplay between transitions and synchronization. We consider a bistable dynamical system that is initially at a stable equilibrium (quiescent) that coexists with an oscillatory state (active). The addition of noise will typically lead to escape from the quiescent to the active state. If a number of such systems are coupled, these escapes can spread sequentially in the manner of a “domino effect.” We illustrate our findings numerically in an example system with three coupled nodes. We first show that a symmetrically coupled network with amplitude-dependent coupling exhibits new phenomena of accelerating and decelerating domino effects modulated by the strength and sign of the coupling. This is quantified by numerically computing escape times for the system with weak coupling. We then apply phase-amplitude-dependent coupling and explore the interplay between synchronized and desynchronized dynamics in the system. We consider escape phases between nodes where the cascade of noise-induced escapes is associated with various types of partial synchrony along the sequence. We show examples for the three-node system in which there is multistability between in-phase and antiphase solutions where solutions switch between the two as the sequence of escapes progresses.
Domino-like transient dynamics at seizure onset in epilepsy
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.