About this episode
Our brain’s network structure consists of many interconnected regions, each containing billions of neurons. Many neurons within one region fire electrical signals at the same time, in synchrony, and even neurons across different regions may synchronise. These are known as synchronous clusters. The collective firing of neurons in synchronous clusters is believed to create brainwaves. Brainwave measurements of patients with epilepsy have shown that during seizures, there can be episodes of excessive synchrony. The mechanisms behind these episodes are not well understood. More
A major question for researchers is why these spontaneous episodes of excessive brain synchrony start and why they end. It is also unclear how changes in the brain’s network structure can lead to epilepsy.
In a novel collaboration between physicists and neurologists, Professor Eckehard Schöll, student Moritz Gerster and their colleagues use computer simulations to better understand the interplay of network structure and synchrony in epilepsy.
To begin, the researchers constructed a realistic brain network using magnetic resonance imaging. They then simulated the dynamics of the neurons using a mathematical model of the human brain. By comparing simulated seizures to real seizures, the team found that their simulations could accurately reproduce the excessive synchronisation patterns observed in patients with epilepsy.
Professor Schöll and his colleagues then rewired the brain’s network structure in their model to explore the role of the so-called clustering coefficient, which describes how densely the different areas of the brain are connected.
They found that networks with intermediate clustering best mimicked the real connectivity of the human brain, whereas there was no consistent spontaneous synchronisation in the other simulated brain networks. Seizures either did not happen or the level of synchronisation remained too high throughout the simulation. This suggests that a balance between the regularity of high clustering and the randomness of low clustering must be involved in the initiation and termination of the synchronisation process during a seizure.
To further explore this observation, the team randomly rewired all the brain connections in their model, resulting in a network with a low clustering coefficient.
When testing their random model, seizures still occurred, but they tended to be more frequent and shorter. Synchronisation was much higher in their random model, even during the healthy state between seizures. This indicates that random network structures actually increase overall brain synchronisation. Next, the researchers tested the other extreme: a simulated brain network with a high clustering coefficient. This time, the brain synchronised fully, leading to a vastly prolonged seizure.
From these simulations, it became clear that both highly clustered, regular networks and those with low clustering and high randomness show increased synchronisation. The team concluded that a healthy brain network needs a delicate balance between clustering and randomness.
Professor Schöll and his colleagues speculate that patients with epilepsy may have brain networks where clustering is either too strong or too weak. A healthy human brain lies somewhere between these two extremes. The team hopes that these insights could help to further improve treatment methods which aim to change the network structures of epileptic brains.
Original Article Reference
Summary of the paper ‘FitzHugh–Nagumo oscillators on complex networks mimic epileptic-seizure-related synchronization phenomena’, by Moritz Gerster, Rico Berner, Jakub Sawicki, Anna Zakharova, Antonín Škoch, Jaroslav Hlinka, Klaus Lehnertz and Eckehard Schöll, in Chaos 30, 123130 (2020), doi.org/10.1063/5.0021420
For further information, you can connect with Professor Eckehard Schöll at email@example.com
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