Predicting epilepsy from neural network models

Date:
December 8, 2020
Source:
Springer
Summary:
A new study shows how 'tipping points' in the brain, responsible for
diseases including epilepsy, can be better predicted by accounting
for branches in networks of neurons.



FULL STORY ========================================================================== Within the staggeringly complex networks of neurons which make up our
brains, electric currents display intricate dynamics in the electric
currents they convey. To better understand how these networks behave, researchers in the past have developed models which aim to mimic their dynamics. In some rare circumstances, their results have indicated that 'tipping points' can occur, where the systems abruptly transition from
one state to another: events now commonly thought to be associated with episodes of epilepsy. In a new study published in EPJ B, researchers led
by Fahimeh Nazarimehr at the University of Technology, Tehran, Iran,
show how these dangerous events can be better predicted by accounting
for branches in networks of neurons.


==========================================================================
The team's findings could give researchers a better understanding of
suddenly occurring episodes including epilepsy and asthma attacks, and
may enable them to develop better early warning systems for patients
who suffer from them. To do this, the study considered how the dynamics
of neuron activity are influenced by branches in the networks they
form. Previous models have shown that these dynamics will often slow
down at these points -- yet so far, they have been unable to predict
how the process unfolds in larger, more complex networks of neurons.

Nazarimehr's team improved on these techniques using updated models, where
the degree to which adjacent neurons influence each other's dynamics can
be manually adjusted. In addition, they considered how the dynamics of
complex neuron networks compare with those of isolated cells. Together,
these techniques enabled the researchers to better predict where
branching occurs; and subsequently, how the network's dynamics are
affected. Their results represent an advance in our understanding of
the brain's intricate structure, and how the dynamics of the electric
currents it contains can be directly related to instances of epilepsy.


========================================================================== Story Source: Materials provided by Springer. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Zahra Faghani, Sajad Jafari, Chao-Yang Chen, Fahimeh Nazarimehr.

Investigating bifurcation points of neural networks: application
to the epileptic seizure. The European Physical Journal B, 2020;
93 (12) DOI: 10.1140/epjb/e2020-10477-6 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/12/201208111559.htm

--- up 15 weeks, 1 day, 7 hours, 50 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1337:3/111)