An international team of investigators from Brazil, Scotland and Germany is expanding the research base on the brain’s complex suite of connections known as neural networks using computer simulations and a technique called cluster analysis. Basically, they built a computer model of a cat brain’s corticocortical network separated into 65 areas and connected by fibers of different densities. The areas were separated into four clusters, or cognitive regions: visual, auditory, somatosensory-motor, and frontolimbic.
The researchers subjected the computer model to seizure-like conditions and tested three different ways to control or avert seizure-like electrical patterns in the computer model. Their results appear in the current edition of the journal Chaos. In it, the team reports results of its investigation into the specific seizure-related problem known as synchronization suppression using cluster analysis to evaluate three interventions.
Results show that of the three methods with therapeutic potential for correcting electrical disorders in the brain, the delayed feedback control is the most productive for synchronization suppression. The other two methods for achieving synchronization suppression that the team analyzed were external time-periodic driving and activation of selected neurons.
“We investigated the destruction of synchronization in a realistic neural network model whose connecting architecture is formed by a cluster of sub-network, and we found the most significant and interesting aspect to be the verification that the efficiency of synchronization suppression by delayed feedback control is higher and more efficient than for the two other methods: external time-periodic driving and activation of selected neurons. And importantly, the delayed feedback control is an intervention that does not damage the neurons,” said Antonio M. Batista, Ph.D., the team leader of the study from the Department of Mathematics and Statistics at State University of Ponta Grossa, Brazil.
Cluster analysis refers to an algorithm-based method commonly used to identify, classify and compare objects being studied into structures — clusters — that share a similar measure. It is broadly used in many disciplines to analyze a range of characteristics, from pattern recognition to economic data to textual analysis and biological parameters, in this case analyzing the brain’s electrical output.
It has long been documented that the mammalian brain has a cerebral cortex that features complex networks, and is involved in cognitive functions and complex perceptual tasks. There is also substantial literature on cortical networks in the cat cerebral cortex. Clinical evidence points to problems with synchronization of a small group of neurons as playing a key role in some pathological conditions such as Parkinson’s disease, tremor, and epilepsy.
“For this reason, it is important to study ways to control or suppress neuronal phase synchronized bursting rhythms to restore normal spiking-bursting activity in a neuronal network,” Batista said. Their hope is that a novel analytic approach using cluster analysis will have high practical value in identifying productive potential paths for future treatments. “Studies about suppression of synchronization are important due to their link with mental illness,” Batista explained.
About this neuroscience research
Source: American Institute of Physics Image Source: The image is credited to Kelly C. Iarosz, figure based on experimental data obtained by Scannell et. al. (1995). Original Research: Full open access research for “Suppression of phase synchronisation in network based on cat’s brain” by Ewandson L. Lameu, Fernando S. Borges, Rafael R. Borges, Kelly C. Iarosz, Ibere L. Caldas, Antonio M. Batista, Ricardo L. Viana, and Jurgen Kurths in Chaos. Published online April 19 2016 doi:10.1063/1.4945796
Suppression of phase synchronisation in network based on cat’s brain
We have studied the effects of perturbations on the cat’s cerebral cortex. According to the literature, this cortex structure can be described by a clustered network. This way, we construct a clustered network with the same number of areas as in the cat matrix, where each area is described as a sub-network with a small-world property. We focus on the suppression of neuronal phase synchronisation considering different kinds of perturbations. Among the various controlling interventions, we choose three methods: delayed feedback control, external time-periodic driving, and activation of selected neurons. We simulate these interventions to provide a procedure to suppress undesired and pathological abnormal rhythms that can be associated with many forms of synchronisation. In our simulations, we have verified that the efficiency of synchronisation suppression by delayed feedback control is higher than external time-periodic driving and activation of selected neurons of the cat’s cerebral cortex with the same coupling strengths.
“Suppression of phase synchronisation in network based on cat’s brain” by Ewandson L. Lameu, Fernando S. Borges, Rafael R. Borges, Kelly C. Iarosz, Ibere L. Caldas, Antonio M. Batista, Ricardo L. Viana, and Jurgen Kurths in Chaos. Published online April 19 2016 doi:10.1063/1.4945796