Virtual brains modeling epilepsy and schizophrenia display less complexity among functional connections, and other differences compared to healthy brain models, researchers at Case Western Reserve University School of Medicine report.
The researchers worked backward from brain rhythms the oscillating patterns of electrical activity in the brain recorded on electroencephalograms - from both healthy and ill individuals.
These oscillations relate to the state of awareness. But, instead of seeking answers to how the rhythms emerge, the investigators built models that, when they reproduced the different neural activity patterns seen in real brains, revealed underlying structural differences among the healthy and ill.
Their work is published in the online journal PLoS Computational Biology.
"Our hypothesis is that healthy brains share features with the virtual healthy brains and unhealthy brains share features with virtual unhealthy brains," said Roberto Fernndez Galn, a professor of neurosciences at Case Western Reserve School of Medicine. Galn has a background in physics, electrophysiology and computational neuroscience.
Galn worked with G. Karl Steinke, a former graduate student in Biomedical Engineering at Case School of Engineering, and now a researcher at Boston Scientific Neroumodulation.
After breaking down the oscillating patterns of brain activity collected from real EEGs and MEGs into a usable form, the researchers applied inverse calculations and reverse engineering to develop brain models they refer to as virtual brains.
The most striking difference they found is in the hierarchical networks of brain connections among the models of healthy and unhealthy brains, Galn said. "The more complex the network, the more normal the EEG pattern."
"A healthy brain network is similar to the airport network," he explained. "There are a small number of hubs with many connections to othe
|Contact: Kevin Mayhood|
Case Western Reserve University