At age 16, Danielle Bassett spent most of her day at the piano, trying to train her fingers and ignoring a throbbing pain in her forearms. She hoped to pursue a career in music and had been assigning herself relentless practice sessions. But the more she rehearsed Johannes Brahms's feverish Rhapsody in B Minor on her family's Steinway, the clearer it became that something was wrong. Finally, a surgeon confirmed it: Stress fractures would force her to give up the instrument for a year.
"What was left in my life was rather bleak," Bassett says. Her home-schooled upbringing in rural central Pennsylvania had instilled a love of math, science, and the arts. But by 17, discouraged by her parents from attending college and disheartened at her loss of skill while away from the keys, she expected that responsibilities as a housewife and mother would soon eclipse any hopes of a career. "I wasn't happy with that plan," she says.
Instead, Bassett catapulted herself into a life of research in a largely uncharted scientific field now known as network neuroscience. A Ph.D. physicist and a MacArthur fellow by age 32, she has pioneered the use of concepts from physics and math to describe the dynamic connections in the human brain. "She's now the doyenne of network science," says theoretical neuroscientist Karl Friston of University College London. "She came from a formal physics background but was … confronted with some of the deepest questions in neuroscience."
Now 37, Bassett runs a lab at the University of Pennsylvania (UPenn) that tackles a whiplash-inducing variety of questions. A sampling from one morning's worth of meetings: Do our brains navigate words in written text the way they navigate physical space? Does the structure of college students' brains interact with the structure of their social networks to influence their ability to abstain from alcohol? Does the network of connections in the mouse brain predict how a disease-causing protein will spread?
Other projects focus on a theme that has captivated her since her childhood passion for books and the piano: learning and mastery. Bassett wants to find ways to optimize learning by using networks to represent both the brain and the material it learns.
"If you came to most thinking scientists, who try to be conservative and skeptical and cautious, and you spelled out to them what Dani's research program was going to be, they'd question anybody's sanity who was going to bite off that big of a chunk of science," says Steven Schiff, a neurosurgeon at Pennsylvania State University in State College and an admirer of Bassett's work.
But Bassett routinely disregards disciplinary boundaries and follows her curiosity with abandon. "What I think is beautiful about network science," she says, "is that you can use it to derive very simple intuitions about really complex systems that … just look like a big hairball."
That bid to simplify one of nature's gnarliest hairballs—our 86-billion-neuron organ of thought—into a set of mathematical equations has been hard for some neuroscientists to get behind. Network science is "a new way of looking at the brain," says Martha Shenton, a neuroscientist at Harvard Medical School in Boston. "This is an advance in science—I do believe that—but it remains to be seen how much information it's going to give us." And whether Bassett's toolbox of equations can make reliable predictions that inform treatments, such as targeted stimulation for brain disorders, is still unknown.
But neuroscience is hungry for theory, says cognitive neuroscientist Michael Gazzaniga of the University of California (UC), Santa Barbara. "There's an uneasiness that I think is widespread that we're not quite capturing the framework … to understand how neurons generate behavior, mind, and all this," he says.
Bassett is part of a generation of physicists and mathematicians who are betting on new theories to capture the brain's higher-order organization. "They [have] the math to back them up … and that just brings tremendous power to the biological scene," Gazzaniga says. "The great advances in science come from trespassing," he adds, paraphrasing pioneering psychologist Wolfgang Köhler. "And Dani is a trespasser."
An uncommon education
On a recent Tuesday afternoon, Bassett—a slight figure with short hair that persistently sneaks in front of her right eye—stands before her class with a large, gilded-edged volume of Claudius Ptolemy. The course teaches undergraduate and graduate students to represent the brain as a network—a set of "nodes" joined by pairwise connections, or "edges." Depending on the study, researchers might define nodes as individual neurons or larger brain regions. And they might draw edges between nodes that are physically connected by neural fibers or that tend to be active at the same time. The approach formalizes a basic premise of neuroscience: that our thoughts, sensations, and experiences emerge as the brain's connected components interact.
But first, Ptolemy. Bassett, in a characteristically composed and formal tone, reads aloud from the second century Greek astronomer's famous treatise, The Almagest: "It is proper to try and fit as far as possible the simpler hypothesis to the movements of the heavens; and if this does not succeed, then any hypothesis possible." He was addressing apparent contradictions in his geocentric explanation of planetary motion. His theory, we now know, was destined to fall apart. But his message was a good one, Bassett tells the class: Strive for the simplest hypothesis.
Bassett's penchant for quoting the ancients reflects her unusual education. Her mother, Holly Perry, who home-schooled her 11 children, says her goal was "to teach them how to teach themselves anything they wanted to learn." Bassett was a natural autodidact. "When she decided that something interested her, she kind of couldn't stop until she knew everything there was about it," Perry says.
Bassett's twin brother, Perry Zurn, a philosopher at American University in Washington, D.C., describes their home schooling as research. They would choose a topic and build a constellation of projects around it, with little regard for where those projects fell among traditional school subjects.
Perry's insistence that her children prioritize primary texts stuck with Bassett. Reading antiquated, alien-sounding prose jolts the mind into "a much bigger space," she says. The twins now describe their education as "really wonderful" and "really fantastic." But their parents' conservative Christianity shaped what they could aspire to. "Because we both grew up being understood as female … we were actively discouraged from going to college," says Zurn, who is transgender.
After Bassett's hiatus from the piano, her father allowed her to attend nursing school. "He had finally given me a little bit of room, and I figured I should take it," she says. (Her father, John Perry, contends that he never discouraged his children from college or careers, though he says he "felt that being a good wife and mother was a high calling.")
An isolated childhood made the move to traditional school jarring for Bassett. "It took a long time to feel like I could laugh at the right times when somebody told a joke," she says. And nursing school was a bad fit. Confrontations with sickness and dying left her drained.
After a year and a half, she definitively broke with her family's expectations. She dropped out of nursing school and applied to Penn State to study physics. "I just wanted to do something that is clean and formal," she says, "and also, just with books."
Thinking in graphs
An hour into Bassett's Tuesday class, the students whip out their laptops and become subjects in one of her latest studies about learning. Their screens display a cloud of about 50 concepts she has selected from the course, such as prediction, network, behavior, and neurological disease. They draw lines to connect related words and phrases, stretching the lines to put distance between dissimilar concepts. Bassett will compare the structure of the maps at different points in the course, gauge the influence of class readings and lectures, and look for correlations between network structure and test scores.
The work seems miles away from Bassett's physics degree. But underlying that study—and nearly every other project in her lab—is a branch of math called graph theory. The approach, with roots in the 18th century, describes the structure of networks of discrete, interacting parts, be they friends linked on social media or grains in a sand pile.
Researchers first calculate the relationships between all nodes in a network: in the simplest case, either a zero (not connected) or a one (connected). Then, they ask questions about the features of the network: Is it a sparse web or a dense jungle of connections? Do certain nodes have an unusually large number of connections? Do nodes tend to organize themselves into tight-knit modules that mostly talk among themselves?
In the 1990s, a few researchers started to create such graphs to describe the layout of animal nervous systems. A graph for the nematode Caenorhabditis elegans could include all the connections among the 302 neurons that determine how the tiny worm wiggles through life. The brains of mammals were far too large and complex to map neuron by neuron, so researchers analyzed the connections between dozens of broad areas in the monkey and cat cortex according to the flow of tracer molecules along neurons.
"We worked in complete obscurity," neuroscientist Olaf Sporns says of the field that would become network neuroscience. Sporns, now at Indiana University in Bloomington, was among the first to use graph theory to analyze connections in the human brain. Few data sets were available, he says. But he and his collaborators hoped the approach could help explain how the brain's structure gives rise to thought and awareness.
By the mid-2000s, applications of graph theory were getting more ambitious. Neuropsychiatrist Edward Bullmore's group at the University of Cambridge in the United Kingdom used it to analyze human brain activity recorded with functional magnetic resonance imaging (fMRI), a technique that can indicate which regions are active in unison.
"It was a very exciting period, when [we] began to … explore these previously unmeasured properties of human brain networks," Bullmore says. "It was around that time when Dani started in the lab." Bullmore was one of Bassett's four advisers in a Ph.D. program sponsored by Cambridge and the U.S. National Institutes of Health. She took off running with graph theory, Bullmore recalls, stretching its uses to new types of brain data.
In one study, Bassett analyzed MRI data from people with and without schizophrenia. The condition seems to arise from broadly disorganized brain activity, not a defect in any one region. Bassett and colleagues showed that graph theory offered a new way to describe that disorganization. Brains with schizophrenia showed more random patterns of connectivity than healthy ones, and their hubs—the most highly connected regions—were less likely to be in the frontal cortex, the area that exerts executive control over the brain. That finding aligned with some of the symptoms of schizophrenia: deficits in executive functions such as planning, decision-making, and regulating behavior. But it didn't explain them.
And some neuroscientists were unimpressed by early results from network science. Graphs of brain networks were "obviously a radical simplification of the nervous system," Bullmore says. "The main criticism has always been, ‘Isn't this too simple to be meaningful, given the complexity of the system we're trying to understand?’"
Bassett saw a different limitation to graph theory. "It's great for characterizing the structure of something," she says, "but not necessarily what the thing does." A graph is static, but an active brain flows between connectivity patterns. So, as Bassett moved to her postdoc at UC Santa Barbara, she added another type of analysis to her study of networks: dynamical systems theory, a way of modeling how network structure changes. "Dani has excelled at bringing time into the game," Sporns says.
In a key experiment, Bassett studied people as they learned to tap their fingers quickly in a specific order by reading sequences of notes on a staff. The sequences weren't exactly Brahms rhapsodies; each was just 12 notes long. But participants took time to master them. During three training sessions, they lay in an fMRI scanner and practiced their finger work.
Bassett's group captured changes over time in the sets of brain areas that preferentially conversed with each other while participants learned. The researchers created a mathematical measure of overall "flexibility"—how likely regions were to change their "module allegiance" and sync up with a different set of partners. A brain's flexibility during a practice session, the researchers found, predicted how much faster the person would be able to play the note sequences in the next session.
The research, published in 2011, hinted that measurable, predictable features of the brain's configuration can prime it for learning. That "started to get a lot of people's attention," Bassett says, including representatives of the MacArthur Fellows Program, who pointed to the work in selecting Bassett for the 2014 award. Bassett, who was just getting her lab at UPenn off the ground, found herself in the academic spotlight. Her parents, who had separated when she was 18, cheered her on.
Bassett is now a hub in a lively network—a role that doesn't always suit her. On an endless circuit of invited talks, she seeks solitude in her hotel room. She shies away from group interactions, preferring one-on-one communication with trainees and collaborators.
But some of those pairwise connections have had far-reaching effects. In 2013, on a bench overlooking the Pacific Ocean in Santa Barbara, she and mechanical engineer Fabio Pasqualetti, then a fellow postdoc, realized they shared an ambition. They wondered whether network science could go beyond describing the brain to offering ways to change it. Pasqualetti studies control theory, a branch of engineering that uses sensors and feedback to guide the behavior of a system, whether that's an electrical grid or a fighter jet. Was it possible, he and Bassett wondered, to apply principles of control theory to brain networks?
In their initial study, published in 2015, Bassett and Pasqualetti modeled brain structure with data from an MRI-based technique that traces the diffusion of water through the brain to identify regions connected by bundles of neuronal fibers. By feeding that information into an equation from control theory, they identified areas of the brain that, when active, might help it shift into various other states. "It was a big jump, honestly, to make the assumption that this thing could work," says Pasqualetti, now at UC Riverside.
"It's a very important contribution," computational neuroscientist Marco Zorzi of the University of Padua in Italy says of the paper. Scientists are already experimenting with zapping the brain to improve various conditions, including severe depression and disability after stroke. But the approach, which often relies on magnetic stimulation of the scalp, involves trial and error. Control theory could help researchers decide where in the brain to stimulate, and at which intensities, to reliably steer it into a healthier state.
Still, Zorzi says, "It's not ready yet." To develop stimulation protocols based on control theory, "we just need much more theoretical work," he says. That work should include studying how many points of stimulation are necessary to induce a desired brain state, he adds.
Bassett and her team are now refining their control theory approach and using it to predict the spreading patterns of activity in epileptic seizures. The results, they hope, will show how doctors could place seizure-stifling electrical implants more precisely or slice out less brain tissue during surgery.
Before any clinical trials, Bassett and colleagues will also have to defend the work against a familiar charge: that it oversimplifies the brain. Signals don't pass predictably along every connection between neurons. Some get amplified; others run into gating mechanisms that inhibit them, and equations from control theory don't fully capture those details. "That makes the control problem enormously difficult," says Schiff, a former epilepsy surgeon who studies control theory. "That's an enormous frontier that we're just starting to crack into."
In response, Bassett channels Ptolemy. "Physicists … start with relatively simple models, and then they expand those models as it becomes necessary," she says. "If there's more than a few parameters, it's very difficult to understand why something happens."
Degrees of freedom
On the drive home from class, Bassett's 4-year-old son, Simeon, recounts his day care exploits from the back seat of the car and dictates the playlist.
When Bassett entered college, she swore she would never be a wife or mother. On campus, she found that the homemaker role her family had insisted on was, at times, discouraged. But she met Lee Bassett, a fellow physics student whom she married in 2006. Both now teach at UPenn, and the first of their two children was born in 2011.
That evening, after bedtime reading (The Berenstain Bears for Simeon and the children's fantasy novel Mossflower for Silas), Bassett pops open a can of cherry-flavored sour beer and brings out one of her own favorites: British philosopher Joseph Glanvill's 17th century volume The Vanity of Dogmatizing. In it, Glanvill marvels at humanity's ignorance of the natural world and condemns blind faith in both science and religion. Bassett has peppered its margins with notes.
Down the hall in the living room sits a Steinway grand piano, testimony to her continuing love of music. It's the only purchase Bassett has made so far with her $625,000 MacArthur award; for now, her lab is not hurting for funding. But the unspent money means freedom. If an idea sparks her imagination and funders won't get behind her, she plans to chase it anyway.
*Correction, 11 April, 11:55 a.m.: At the request of Bassett’s mother, we have changed her name to Holly Perry.