Pooled analysis of feline neural network models reveals promising anti-epileptic strategies
An international team of researchers from Brazil, Scotland and Germany are expanding the research base on the brain’s complex suite of connections called neural networks using computer simulations and a technique called cluster analysis. Basically, they built a computer model of the corticocortical network of a cat brain separated into 65 zones and connected by fibers of different densities. The zones were separated into four groups, or cognitive regions: visual, auditory, somatosensory-motor and frontolimbic.
The researchers subjected the computer model to seizure-type conditions and tested three different ways of controlling or avoiding seizure-type electrical patterns in the computer model. Their results appear in the current edition of the journal chaos. In it, the team reports the results of their investigation into the specific crisis-related issue known as sync suppression using cluster analysis to evaluate three interventions.
The results show that of the three methods with therapeutic potential for correcting electrical disturbances in the brain, delayed feedback control is the most productive for synchronization suppression. The other two methods of achieving synchronization suppression that the team analyzed were periodic external driving and activation of selected neurons.
“We studied the destruction of synchronization in a realistic neural network model whose connection architecture is formed by a cluster of subnets, and we found that the most significant and interesting aspect is the verification that The efficiency of synchronization suppression by the delayed feedback control is higher and more efficient than for the other two methods: the external periodic driving and the activation of selected neurons. And most importantly, the delayed feedback control is a intervention that does not damage neurons, âsaid Antonio M. Batista, Ph.D., the study team leader of the Department of Mathematics and Statistics at Ponta Grossa State University, at Brazil.
Cluster analysis refers to an algorithm-based method commonly used to identify, classify, and compare studied objects into structures (clusters) that share a similar measure. It is widely used in many disciplines to analyze a range of characteristics, from pattern recognition and economic data to textual analysis and biological parameters, in this case the analysis of the electrical production of the brain.
It has long been documented that the mammalian brain has a cerebral cortex that exhibits complex networks and is involved in complex cognitive functions and perceptual tasks. There is also an abundant literature on cortical networks in the cerebral cortex of the cat. Clinical evidence indicates that timing problems in a small group of neurons play a key role in certain pathological conditions such as Parkinson’s disease, tremors, and epilepsy.
“For this reason, it is important to study ways to control or suppress burst rhythms synchronized with the neural phase to restore normal spike burst activity in a neural network,” Batista said. They hope that a new analytical approach using cluster analysis will have great practical value in identifying potential productive pathways for future treatments. âThe studies on synchronization suppression are important because of their link to mental illness,â Batista explained.
Suppression of phase synchronization in a cat brain-based network, Ewandson L. Lameu, Fernando S. Borges, Rafael R. Borges, Kelly C. Iarosz, Ibere ââL. Caldas, Antonio M. Batista, Ricardo L. Viana and Jurgen Kurths, chaos April 19, 2016. DOI: 10.1063 / 1.4945796
Quote: Cluster analysis of cat neural network models reveals promising anti-epileptic strategies (April 19, 2016, retrieved August 22, 2021 from https://phys.org/news/2016-04-cluster-analysis -cat-neural-network.html
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