In two studies published in Physical Review Letters and PNAS, British mathematicians have attempted to explain how the structure of the brain relates to its function.
Lobes of the human cerebrum
In the first study, researchers from the Queen Mary University of London’s School of Mathematical Sciences describe how different areas in the brain can have an association despite a lack of direct interaction.
The team combined two different human brain networks – one that maps all the physical connections among brain areas known as the backbone network, and another that reports the activity of different regions as blood flow changes, known as the functional network. They showed that the presence of symmetrical neurons within the backbone network might be responsible for the synchronized activity of physically distant brain regions.
“We don’t fully understand how the human brain works. So far the focus has been more on the analysis of the function of single, localized regions. However, there isn’t a complete model that brings the whole functionality of the brain together. Hopefully, our research will help neuroscientists to develop a more accurate map of the brain and investigate its functioning beyond single areas,” said lead author Dr Vincenzo Nicosia.
This study adds to the findings published in PNAS (Proceedings of the National Academy of Sciences) in which the team analyzed the development of the brain of a small worm called Caenorhabditis elegans.
In the PNAS study, the team examined the number of links formed in the brain during the worm’s lifespan, and observed an unexpected abrupt change in the pattern of growth, corresponding with the time of egg hatching.
“The research is important as it’s the first time that a sharp transition in the growth of a neural network has ever been observed,” Dr Nicosia explained.
“Although we don’t know which biological factors are responsible for the change in the growth pattern, we were able to reproduce the pattern using a simple economical model of synaptic formation. This result can pave the way to a deeper understanding of how neural networks grow in more complex organisms.”
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Bibliographic information: Vincenzo Nicosia et al. 2013. Remote Synchronization Reveals Network Symmetries and Functional Modules. Phys. Rev. Lett. 110, 174102; doi: 10.1103/PhysRevLett.110.174102
Vincenzo Nicosia et al. Phase transition in the economically modeled growth of a cellular nervous system. PNAS, published online before print April 22, 2013; doi: 10.1073/pnas.1300753110
