Ex Machina: Mapping the brain in the age of social networks

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If you use social media sites like Facebook or Twitter, you’re part of a massive social network. Think about your personal network. How many different social circles are represented? Do you communicate with people in some circles more than others? Does that change sometimes? For example, among your old friends, there may be a flurry of activity around an upcoming high school reunion, and then silence for months.

Companies like Facebook dig deep to find patterns in your habits. Using algorithms originally developed for airline schedules, they get a sense of who you’re connected to and how. Now, these algorithms are being repurposed yet again: instead of mapping social networks, they are mapping neural ones.

With over 100 billion cells and 100 trillion connections, the brain staggeringly complex. Danielle Bassett, a professor of bioengineering at the University of Pennsylvania, uses community detection algorithms to make sense of it all. In a recently published study, Bassett and graduate student Shi Gu scanned the brains of a whopping 780 people, aged 8-22. These scans relied on functional magnetic resonance imaging (fMRI), which tracks changes in blood flow that reflect neural activity. Sophisticated algorithms then flagged important similarities and differences between the scans of people in different age groups. By identifying the brain’s tight-knit microneighborhoods and information superhighways, she hopes to create road maps for guiding learning or diagnosing mental illness.

“Far from a spaghetti like mess, the connections between different parts of our brain are fairly organized, but by a rule that none of us have been able to define,” Bassett wrote in a recent Reddit AMA (Ask-Me-Anything) session. “We would have loved the answer to be simple: That brain regions connect to other brain regions that are close by (similar to what might happen in grade school when you become friends with kids in your own school more so than with kids in the school district next door). But interestingly, the brain shows long-distance connections as well.”

Some areas of the brain only communicate with nearby areas, forming tightly modular hotbeds of activity. Other areas act as hubs, connecting faraway areas to each other. Based on these characteristics, Bassett and Gu assigned brain areas to networks, each with a different job. Their algorithms boiled a massive amount of data down into just two dimensions: communication within brain networks, and communication between brain networks.

These patterns of communication change as children become adults. Bassett and Gu found that networks for sensory and motor function wire up early, becoming self-sufficient and independent from the rest of the brain in childhood. Throughout adolescence, the influence of networks involved in abstract thought becomes wider-reaching, linking up with many different brain areas. Bassett and Gu believe that as these networks expand their influence, adults achieve greater control over the flow of their own thoughts, focusing their attention and pausing for reflection much more easily than children can. For example, in the “default mode network,” a network involved in daydreaming and mind wandering, synchronized waves of activity grow stronger with age. These waves become so strong, they can affect activity clear across the brain. At the same time, networks for abstract thought processes like decision making and rule switching also start to influence increasingly distant areas, but with flexibly changing codes instead of uniform waves.

These patterns show key similarities between individuals that can act as a road map for development. Deviations from the map could be warning signs of mental illness. But it is important to note that some variation is also normal. “Each of us have different task-switching abilities,” Bassett wrote. “For some of us, these transitions are quick and for others, these transitions are slow. Part of my research program is focused on explaining what makes us different!”

Bassett and Gu also found a trade-off in the maturation of sensorimotor and cognitive networks. This could reflect developmental delays, where book-smart children have simply fallen behind on their sensorimotor development. However, it may represent more permanent individual differences that make everyone unique.

Members of Bassett’s laboratory are currently working to identify distinct learning styles associated with different configurations of brain networks. With this information, it might be possible to tailor more efficient learning environments. “What I would really love to do next is to understand how we can use our new knowledge to enhance learning,” Bassett told Reddit. “What interventions could enhance learning? What environments are most conducive to learning, and how [do] they change the brain to enable learning to occur?”

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