Networking in the brain – Bringing the outside in
When we look at the networks around us, we view connections between many people, or connections between many groups. As we have learned in the calss so far, these connections can be strong or weak ties, and with those two come the triadic and strong triadic closures that describe the entire structure of the network. As technology becomes more advanced, we are able to look into our brains and deconstruct our internal networks. Mirroring the external networks between people, the networks of out brain follow some of the same rules.
Taking a fMRI of a brain, a researcher is able to view the response of different brain areas to an input stimulus. With new fMRI machines, researchers have started to measure the correlation between different brain regions as they respond to stimuli. This correlation can be constructed into a network; each node is a neuron or brain region and each edge has a weight measuring the correlation. These networks have terms to describe high correlation and low correlation, which are analogous to triadic closure and no-triadic closure respectively. The brain networks additionally have areas with large amount of connections and bridges that pass information between large connected areas, just as the bridges that connect friend groups in out larger networks. Looking specifically at motion control, there are much more edges that connect insie of modules (76% of edges) than the edges that connect between modules (24% of edges). Partially this is due to our definition of modules (those areas that are highly connected) although it is also shown that each of these areas has a somewhat separate function, analogous to different friend groups. The brain networks even follow the same efficiency rules as large scale networks. If two areas need to talk more often, an edge will connect the two, just as a road network will connect two points if the efficiency determines the need.
Throughout this class we will continue to learn about the networks that surround us; the theory behind them, the most efficient paths, how to construct and classify them. We will even learn the complex (and maybe simple) equations to traverse a graph, find endpoints, connect paths. Throughout all of this just remember that you have a network – more complex than anything we can imagine, let a lone compute – within our heads, using the same rules that we are writing down in our notes.
More information can be found here: http://www.brainmapping.org/NITP/images/Summer2013Slides/Graph%20Theory_NITP2013_SteffieTomson_sm.pdf