Scientists have made a significant breakthrough in their quest to understand the complex processes that occur in the brain during the seizures that are the main symptom of epilepsy.

A team of scientists from the University of Exeter investigated the mechanism behind the distinctive electrical activity patterns of groups of neurons in the brain that accompany the onset of seizures.

In healthy brains, networks of neurons move through states of similar behavior – known as synchronism – and different behavior, known as desynchronisation. These processes are also involved in both memory and concentration.

However, in the brain with neurological disorders, such as epilepsy, this synchronization can progress to near-dangerous levels, when a subset of brain cells begin to generate excess electricity.

In a series of new studies, recently published in PLoS Computational Biology and SIAM Journal on Applied Dynamical Systems, the team used a complex mathematical modeling approach to explore the interplay between group of neurons, leading to a switch in synchronization change.

“Synchronization is thought to be important for information processing,” said Jennifer Creaser, co-author of the study and from the University of Exeter. But over-synchronizing — such as what happens in epilepsy or Parkinson’s disease — is associated with the condition and can impair brain function. “

The study, conducted at the Center for Predictive Modeling in Healthcare of the Engineering and Physical Sciences Research Council at the University of Exeter and the University of Birmingham, used an expanded version of a mathematical model. Existing studies represent the brain as a interconnected network of many nodes of groups of neurons.

The model network consists of dual stable nodes, which means that each node can switch between two steady states – resting and dynamic. These nodes remain in their current state until they receive a stimulus that gives them the appropriate ‘kick’ to move into another state.

This stimulus comes both from other connections and in the form of “noise” – external sources of neural activity, such as endocrine responses related to emotional states or physiological changes. due to illness.

Adding a small amount of noise to the system causes each node to transition to the active state – but the geometry of the system makes it take longer to return to rest than to move away.

Previously, the team found that this resulted in a series of jumps to the active state — like a falling stream of dominos — that propagated activity across the network.

New research builds on this ‘domino effect’ to identify the circumstances that lead to these changes in synchronicity, and to study how the type of connection in the network affects its behavior.

It has been found that, when the model incorporates more general amplitudes and phase coupling, the synchronization of nodes can change between successive hops in the domino effect.

“Although this is a theoretical study of an idealized model, it is inspired by the challenges posed by understanding transitions,” said Professor Peter Ashwin, co-author of the study. between healthy activity and disease in the brain.”

Professor Krasimira Tsaneva-Atanasova, co-author of the study, added: “Mathematical modeling of seizure initiation and propagation can not only help uncover the complex underlying mechanisms underlying seizures. but also provides a means of allowing in silico experiments to predict control outcomes of neural systems.”

Information sources:

Materials provided by University of Exeter. Note: Content may have been modified in presentation and length.

References:

1. Sequential Escapes and Synchrony Breaking for Networks of Bistable Oscillatory Nodes.

Jennifer Creaser, Peter Ashwin, Krasimira Tsaneva-Atanasova. SIAM Journal on Applied Dynamical Systems, 2020; 19 (4): 2829

DOI: https://epubs.siam.org/doi/10.1137/20M1345773

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Source: ScienceDaily

Link: https://www.sciencedaily.com/releases/2021/02/210205121253.htm

Author: Roxie Duong

Editing: Duong Ngoc