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.”