At 32, a year beyond a postdoctoral fellowship, Danielle Bassett could only express unreserved astonishment when she learned that she was one of 21 winners of a 2014 MacArthur Fellowship. Bassett was the youngest this year for one of the so-called “genius” prizes totaling $625,000.

For 12 months, Bassett has held the position of the Skirkanich Assistant Professor of Innovation at the University of Pennsylvania’s department of bioengineering. During that time, she received another surprise phone call to inform her that she had received a $50,000 grant from the Sloan Foundation.

What may have attracted the judges is Bassett’s fascination with crossing disciplines by using complex systems theory and other mathematical tools to unravel the obtuse workings of the trillions of connections that constitute the networks that criss-cross the human brain.

Coming from a family with conservative values in Pennsylvania, she began her academic career at the Reading Hospital School of Nursing. But she—and her teachers there as well --realized soon enough that she had erred in studying nursing. Bassett sorely missed using her prodigious skills in physics and math, which had already become apparent during high school.

She left nursing school and switched to Pennsylvania State University, where she received an undergraduate degree in physics before moving on to a doctorate in theoretical physics at the University of Cambridge. Bassett has already set her sights on helping to found a new sub-discipline called dynamic network neuroscience to chart the constantly shifting on-and-off activity of connected brain cells.

An edited interview with Bassett follows.

Scientific American: The entry for “graph theory” in Wikipedia doesn’t mention either the brain or neuroscience, even though it does talk about the theory’s importance for computer networks and linguistics. Can you explain what graph theory is very briefly and why it may be one of the techniques needed to help neuroscience as a field move forward?

Danielle Bassett: The study of graphs is the study of nodes connected by edges. Another name for a graph is a network. Mathematically, graph theory is the field of understanding the structure of these graphs or networks. It’s purely a branch of math. It has nothing to do with neuroscience.

But there’s been a growing appreciation over the last few decades that the brain can be characterized as a network. It is composed of individual parts that can be represented as nodes. And their interactions with each other can be represented as edges. The question in our group is how can we understand the configuration of those edges to better understand cognitive functioning. So graph theory is a field that’s perfectly appropriate for the kind of system that the brain is.

SA: One of the most important findings in neuroscience in recent years has been that there is a lot going on in the brain during moments when it doesn’t seem to be doing anything—it’s even called the resting-state by scientists. But that work has expanded and it now seems that people are beginning to talk about the role of networks in explaining what’s going on in the brain when it’s doing things like listening or remembering something. Can you talk about this a bit? Are we getting closer to a theory about how different parts of the brain work together—both when we’re doing something like playing chess or literally at rest during sleep?

DB: There’s definitely been a growing appreciation that the resting state can be looked at as a functional network. In the last year or two, three years max, we have really been able to start asking additional questions by probing how the million different tasks that your brain does are characterized by different kinds of networks. What my group is focusing on is looking at the brain as a set of dynamic networks. It is not really characterized by a single network or a single graph. It’s characterized by networks that are constantly reconfiguring depending on what your brain is doing. These are really exciting questions at the vanguard of the field, questions that are asking how does the brain reconfigure, what are the constraints on that reconfiguration, how do we manipulate or optimize these activities and how can we predict changes that are important for our behavior.

SA: You did work that got some attention that showed that the ability to switch neural activity among these networks equates to how well a person learns. Can you explain what that study showed and what you mean when you talk about what you call network flexibility?

DB: Network flexibility is a measure that tells you how often a brain region changes its allegiance to another partner in the network. The way the brain works is that different parts of the brain tend to coordinate with each other at different points in time. And you can imagine it’s a little bit like a dance. If you go into a group dance, you’ll dance with one partner for a while, and then you’ll switch and dance with another.

And the number of switches that brain regions have with other partners represent the flexibility of the network. We were able to show that people who have very flexible networks—and switch partners between other regions often—learn better than people whose networks are less flexible. Flexibility is something that’s driven by multiple factors. Some of it may be related, on a psychological level, to someone who changes learning strategies. Someone who tries different strategies often is better able to learn than someone who just sticks with the same strategy.

I think our work is definitely highlighting the fact that networks are reconfiguring much more than previously imagined. Previous work had really focused on capturing single networks that were thought to be representing single tasks.

SA: Aren’t there implications for some of this work in medical settings? Does your research help explain why some of these networks go awry in schizophrenia and other disorders?

In schizophrenia, we’re actually finding that networks reconfigure more quickly than they potentially should. And we think that could have implications for the symptoms of disorganized thought that are found in the disease. We also think that our general results are important for rehabilitation following stroke. They give us a biological target to enhance rehabilitation.

SA: These methods can also be used to model interesting social settings such as how well people evacuate during an emergency and also things that have nothing to do with a social milieu like the properties of granular materials? Can these other areas lend insight into the brain as well?

DB: They definitely can. The physical systems are very interesting because they help us think about how networks are embedded into space. The brain network is another embedded system so we’re very interested in understanding where the physical constraints inside a three-dimensional space inform the process of reconfiguring neural connections. Just as particles in a granular material exert force on nearby particles, parts of the brain tend to connect to other parts that are physically close by.

SA: What are some of the big questions you’d like to answer as you continue along with your career?

DB: I’d like to understand what network reconfigurations are present in the brain as we use it in our everyday life and how we can intervene to optimize these reconfigurations in neurological disorders. What I call the emerging field of dynamic network neuroscience is a field that I would like to help build in coming years.

Image Source: University of Pennsylvania