Ed Bullimore, a professor at the University of Cambridge, recently gave a talk to an audience of eager Britons about the amazing complexity of the brain. He shared how it is known that the human brain contains billions of neural cells connected to each other by trillions of synapses. However, as Professor Bullimore is studying the brain from a network point-of-view, his description of the brain shifted as he explained how he reduced his vision of the brain to mathematical equations rather than a large number of biological parts.
Bullimore explained that by viewing the brain in this manner, he discovered that the organ has much in common with computer chips and stock markets in the way they process information. Brain networks represent a balance of efficiency of information transfer and connection cost. Moreover, different patterns of network connections in the brain correspond with different types of thinking, and age and neurological disorders can rapidly affect different network “configurations”.
To illustrate some of these concepts, Bullimore performed a live experiment during his talk using Twitter. Members of the audience were asked to tweet during the lecture about the concepts that were being discussed using a special hashtag. At the conclusion of his talk, he displayed an image that showed the interconnectivity of the hashtagged tweets (image above) and explained how the “twitterbrain” network is analogous to the human brain network.
“We found that the #twitterbrain network was somewhat like the brain network in being small-world and modular with highly connected hub nodes," explains Bullimore, "however the brain network was more clustered and less efficient than the twitter network. So at first sight there were some points in common and some points of difference between these two information processing networks.”
Take a look at the video clip below that shows a simple model of the human brain network:
Caption and explanation:
Each node of the network represents a different brain region and is colour-coded according to the larger area is located in. Pairs of nodes are linked if the activity of the two regions is found to synchronize a lot of the time during an fMRI brain scan, and the size of nodes represents how many other regions a given node is linked to.
The resulting network is used to analyze information flow in brains of healthy people as well as patients with disorders such as schizophrenia. To better understand these networks, we can decompose them into communities of nodes which are more densely connected with each other than with the rest of the network. This gives rise to a different picture, where the nodes are layed out in space according to the communities they participate in, rather than their location in real anatomical space.
The above video shows the transition from a network showing the connections between different brain regions in their anatomical locations, and a new layout emphasizing the network’s structure, with nodes relocated and re-coloured based on their membership in network communities.
Article from the University of Cambridge: Neuro-tweets: #hashtagging the brain…