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A prosthesis driven by the nervous system helps people with amputation walk naturally

Anne Trafton |

State-of-the-art prosthetic limbs can help people with amputations achieve a natural walking gait, but they don’t give the user full neural control over the limb. Instead, they rely on robotic sensors and controllers that move the limb using predefined gait algorithms.

Using a new type of surgical intervention and neuroprosthetic interface, MIT researchers, in collaboration with colleagues from Brigham and Women’s Hospital, have shown that a natural walking gait is achievable using a prosthetic leg fully driven by the body’s own nervous system. The surgical amputation procedure reconnects muscles in the residual limb, which allows patients to receive “proprioceptive” feedback about where their prosthetic limb is in space.

In a study of seven patients who had this surgery, the MIT team found that they were able to walk faster, avoid obstacles, and climb stairs much more naturally than people with a traditional amputation.

“This is the first prosthetic study in history that shows a leg prosthesis under full neural modulation, where a biomimetic gait emerges. No one has been able to show this level of brain control that produces a natural gait, where the human’s nervous system is controlling the movement, not a robotic control algorithm,” says Hugh Herr, a professor of media arts and sciences, co-director of the K. Lisa Yang Center for Bionics at MIT, an associate member of MIT’s McGovern Institute for Brain Research, and the senior author of the new study.

Patients also experienced less pain and less muscle atrophy following this surgery, which is known as the agonist-antagonist myoneural interface (AMI). So far, about 60 patients around the world have received this type of surgery, which can also be done for people with arm amputations.

Hyungeun Song, a postdoc in MIT’s Media Lab, is the lead author of the paper, which appears today in Nature Medicine.

Sensory feedback

Most limb movement is controlled by pairs of muscles that take turns stretching and contracting. During a traditional below-the-knee amputation, the interactions of these paired muscles are disrupted. This makes it very difficult for the nervous system to sense the position of a muscle and how fast it’s contracting — sensory information that is critical for the brain to decide how to move the limb.

People with this kind of amputation may have trouble controlling their prosthetic limb because they can’t accurately sense where the limb is in space. Instead, they rely on robotic controllers built into the prosthetic limb. These limbs also include sensors that can detect and adjust to slopes and obstacles.

To try to help people achieve a natural gait under full nervous system control, Herr and his colleagues began developing the AMI surgery several years ago. Instead of severing natural agonist-antagonist muscle interactions, they connect the two ends of the muscles so that they still dynamically communicate with each other within the residual limb. This surgery can be done during a primary amputation, or the muscles can be reconnected after the initial amputation as part of a revision procedure.

“With the AMI amputation procedure, to the greatest extent possible, we attempt to connect native agonists to native antagonists in a physiological way so that after amputation, a person can move their full phantom limb with physiologic levels of proprioception and range of movement,” Herr says.

In a 2021 study, Herr’s lab found that patients who had this surgery were able to more precisely control the muscles of their amputated limb, and that those muscles produced electrical signals similar to those from their intact limb.

After those encouraging results, the researchers set out to explore whether those electrical signals could generate commands for a prosthetic limb and at the same time give the user feedback about the limb’s position in space. The person wearing the prosthetic limb could then use that proprioceptive feedback to volitionally adjust their gait as needed.

In the new Nature Medicine study, the MIT team found this sensory feedback did indeed translate into a smooth, near-natural ability to walk and navigate obstacles.

“Because of the AMI neuroprosthetic interface, we were able to boost that neural signaling, preserving as much as we could. This was able to restore a person’s neural capability to continuously and directly control the full gait, across different walking speeds, stairs, slopes, even going over obstacles,” Song says.

A natural gait

For this study, the researchers compared seven people who had the AMI surgery with seven who had traditional below-the-knee amputations. All of the subjects used the same type of bionic limb: a prosthesis with a powered ankle as well as electrodes that can sense electromyography (EMG) signals from the tibialis anterior the gastrocnemius muscles. These signals are fed into a robotic controller that helps the prosthesis calculate how much to bend the ankle, how much torque to apply, or how much power to deliver.

The researchers tested the subjects in several different situations: level-ground walking across a 10-meter pathway, walking up a slope, walking down a ramp, walking up and down stairs, and walking on a level surface while avoiding obstacles.

In all of these tasks, the people with the AMI neuroprosthetic interface were able to walk faster — at about the same rate as people without amputations — and navigate around obstacles more easily. They also showed more natural movements, such as pointing the toes of the prosthesis upward while going up stairs or stepping over an obstacle, and they were better able to coordinate the movements of their prosthetic limb and their intact limb. They were also able to push off the ground with the same amount of force as someone without an amputation.

“With the AMI cohort, we saw natural biomimetic behaviors emerge,” Herr says. “The cohort that didn’t have the AMI, they were able to walk, but the prosthetic movements weren’t natural, and their movements were generally slower.”

These natural behaviors emerged even though the amount of sensory feedback provided by the AMI was less than 20 percent of what would normally be received in people without an amputation.

“One of the main findings here is that a small increase in neural feedback from your amputated limb can restore significant bionic neural controllability, to a point where you allow people to directly neurally control the speed of walking, adapt to different terrain, and avoid obstacles,” Song says.

“This work represents yet another step in us demonstrating what is possible in terms of restoring function in patients who suffer from severe limb injury. It is through collaborative efforts such as this that we are able to make transformational progress in patient care,” says Matthew Carty, a surgeon at Brigham and Women’s Hospital and associate professor at Harvard Medical School, who is also an author of the paper.

Enabling neural control by the person using the limb is a step toward Herr’s lab’s goal of “rebuilding human bodies,” rather than having people rely on ever more sophisticated robotic controllers and sensors — tools that are powerful but do not feel like part of the user’s body.

“The problem with that long-term approach is that the user would never feel embodied with their prosthesis. They would never view the prosthesis as part of their body, part of self,” Herr says. “The approach we’re taking is trying to comprehensively connect the brain of the human to the electromechanics.”

The research was funded by the MIT K. Lisa Yang Center for Bionics and the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

A new strategy to cope with emotional stress

by Rubina Veerakone |

Some people, especially those in public service, perform admirable feats—healthcare workers fighting to keep patients alive or a first responder arriving at the scene of a car crash. But the emotional weight can become a mental burden. Research has shown that emergency personnel are at elevated risk for mental health challenges like post-traumatic stress disorder. How can people undergo such stressful experiences and also maintain their well-being?

A new study from the McGovern Institute reveals that a cognitive strategy focused on social good may be effective in helping people cope with distressing events. The research team found that the approach was comparable to another well-established emotion regulation strategy, unlocking a new tool for dealing with highly adverse situations.

“How you think can improve how you feel.”
– John Gabrieli

“This research suggests that the social good approach might be particularly useful in improving well-being for those constantly exposed to emotionally taxing events,” says John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology and a professor of brain and cognitive sciences at MIT, who is a senior author of the paper.

The study, published today in PLOS ONE, is the first to examine the efficacy of this cognitive strategy. Nancy Tsai, a postdoctoral research scientist in Gabrieli’s lab at the McGovern Institute, is the lead author of the paper.

Emotion regulation tools

Emotion regulation is the ability to mentally reframe how we experience emotions—a skill critical to maintaining good mental health. Doing so can make one feel better when dealing with adverse events, and emotion regulation has been shown to boost emotional, social, cognitive, and physiological outcomes across the lifespan.

Female scientist poses with her arms crossed.
MIT postdoctoral researcher Nancy Tsai. Photo: Steph Stevens

One emotion regulation strategy is “distancing,” where a person copes with a negative event by imagining it as happening far away, a long time ago, or from a third-person perspective. Distancing has been well-documented as a useful cognitive tool, but it may be less effective in certain situations, especially ones that are socially charged—like a firefighter rescuing a family from a burning home. Rather than distancing themselves, a person may instead be forced to engage directly with the situation.

“In these cases, the ‘social good’ approach may be a powerful alternative,” says Tsai. “When a person uses the social good method, they view a negative situation as an opportunity to help others or prevent further harm.” For example, a firefighter experiencing emotional distress might focus on the fact that their work enables them to save lives. The idea had yet to be backed by scientific investigation, so Tsai and her team, alongside Gabrieli, saw an opportunity to rigorously probe this strategy.

A novel study

The MIT researchers recruited a cohort of adults and had them complete a questionnaire to gather information including demographics, personality traits, and current well-being, as well as how they regulated their emotions and dealt with stress. The cohort was randomly split into two groups: a distancing group and a social good group. In the online study, each group was shown a series of images that were either neutral (such as fruit) or contained highly aversive content (such as bodily injury). Participants were fully informed of the types of images they might see and could opt out of the study at any time.

Each group was asked to use their assigned cognitive strategy to respond to half of the negative images. For example, while looking at a distressing image, a person in the distancing group could have imagined that it was a screenshot from a movie. Conversely, a subject in the social good group might have responded to the image by envisioning that they were a first responder saving people from harm. For the other half of the negative images, participants were asked to only look at them and pay close attention to their emotions. The researchers asked the participants how they felt after each image was shown.

Social good as a potent strategy

The MIT team found that distancing and social good approaches helped diminish negative emotions. Participants reported feeling better when they used these strategies after viewing adverse content compared to when they did not and stated that both strategies were easy to implement.

The results also revealed that, overall, distancing yielded a stronger effect. Importantly, however, Tsai and Gabrieli believe that this study offers compelling evidence for social good as a powerful method better suited to situations when people cannot distance themselves, like rescuing someone from a car crash, “Which is more probable for people in the real world,” notes Tsai. Moreover, the team discovered that people who most successfully used the social good approach were more likely to view stress as enhancing rather than debilitating. Tsai says this link may point to psychological mechanisms that underlie both emotion regulation and how people respond to stress.

“The social good approach may be a potent strategy to combat the immense emotional demands of certain professions.”
– John Gabrieli

Additionally, the results showed that older adults used the cognitive strategies more effectively than younger adults. The team suspects that this is probably because, as prior research has shown, older adults are more adept at regulating their emotions likely due to having greater life experiences. The authors note that successful emotion regulation also requires cognitive flexibility, or having a malleable mindset to adapt well to different situations.

“This is not to say that people, such as physicians, should reframe their emotions to the point where they fully detach themselves from negative situations,” says Gabrieli. “But our study shows that the social good approach may be a potent strategy to combat the immense emotional demands of certain professions.”

The MIT team says that future studies are needed to further validate this work, and that such research is promising in that it can uncover new cognitive tools to equip individuals to take care of themselves as they bravely assume the challenge of taking care of others.

What is language for?

by Jennifer Michalowski |

Language is a defining feature of humanity, and for centuries, philosophers and scientists have contemplated its true purpose. We use language to share information and exchange ideas—but is it more than that? Do we use language not just to communicate, but to think?

McGovern Investivator Ev Fedorenko in the Martinos Imaging Center at MIT. Photo: Caitlin Cunningham

In the June 19, 2024, issue of the journal Nature, McGovern Institute neuroscientist Evelina Fedorenko and colleagues argue that we do not. Language, they say, is primarily a tool for communication.

Fedorenko acknowledges that there is an intuitive link between language and thought. Many people experience an inner voice that seems to narrate their own thoughts. And it’s not unreasonable to expect that well-spoken, articulate individuals are also clear thinkers. But as compelling as these associations can be, they are not evidence that we actually use language to think.

 “I think there are a few strands of intuition and confusions that have led people to believe very strongly that language is the medium of thought,” she says.

“But when they are pulled apart thread by thread, they don’t really hold up to empirical scrutiny.”

Separating language and thought

For centuries, language’s potential role in facilitating thinking was nearly impossible to evaluate scientifically. But neuroscientists and cognitive scientists now have tools that enable a more rigorous consideration of the idea. Evidence from both fields, which Fedorenko, MIT cognitive scientist and linguist Edward Gibson, and University of California Berkeley cognitive scientist Steven Piantadosi review in their Nature Perspective, supports the idea that language is a tool for communication, not for thought.

“What we’ve learned by using methods that actually tell us about the engagement of the linguistic processing mechanisms is that those mechanisms are not really engaged when we think,” Fedorenko says. Also, she adds, “you can take those mechanisms away, and it seems that thinking can go on just fine.”

Over the past 20 years, Fedorenko and other neuroscientists have advanced our understanding of what happens in the brain as it generates and understands language. Now, using functional MRI to find parts of the brain that are specifically engaged when someone reads or listens to sentences or passages, they can reliably identify an individual’s language-processing network. Then they can monitor those brain regions while the person performs other tasks, from solving a sudoku puzzle to reasoning about other people’s beliefs.

“Your language system is basically silent when you do all sorts of thinking.” – Ev Fedorenko

“Pretty much everything we’ve tested so far, we don’t see any evidence of the engagement of the language mechanisms,” Fedorenko says. “Your language system is basically silent when you do all sorts of thinking.”

That’s consistent with observations from people who have lost the ability to process language due to an injury or stroke. Severely affected patients can be completely unable to process words, yet this does not interfere with their ability to solve math problems, play chess, or plan for future events. “They can do all the things that they could do before their injury. They just can’t take those mental representations and convert them into a format which would allow them to talk about them with others,” Fedorenko says. “If language gives us the core representations that we use for reasoning, then…destroying the language system should lead to problems in thinking as well, and it really doesn’t.”

Conversely, intellectual impairments do not always associate with language impairment; people with intellectual disability disorders or neuropsychiatric disorders that limit their ability to think and reason do not necessarily have problems with basic linguistic functions. Just as language does not appear to be necessary for thought, Fedorenko and colleagues conclude that it is also not sufficient to produce clear thinking.

Language optimization

In addition to arguing that language is unlikely to be used for thinking, the scientists considered its suitability as a communication tool, drawing on findings from linguistic analyses. Analyses across dozens of diverse languages, both spoken and signed, have found recurring features that make them easy to produce and understand. “It turns out that pretty much any property you look at, you can find evidence that languages are optimized in a way that makes information transfer as efficient as possible,” Fedorenko says.

That’s not a new idea, but it has held up as linguists analyze larger corpora across more diverse sets of languages, which has become possible in recent years as the field has assembled corpora that are annotated for various linguistic features. Such studies find that across languages, sounds and words tend to be pieced together in ways that minimize effort for the language producer without muddling the message. For example, commonly used words tend to be short, while words whose meanings depend on one another tend to cluster close together in sentences. Likewise, linguists have noted features that help languages convey meaning despite potential “signal distortions,” whether due to attention lapses or ambient noise.

“All of these features seem to suggest that the forms of languages are optimized to make communication easier,” Fedorenko says, pointing out that such features would be irrelevant if language was primarily a tool for internal thought.

“Given that languages have all these properties, it’s likely that we use language for communication,” she says. She and her coauthors conclude that as a powerful tool for transmitting knowledge, language reflects the sophistication of human cognition—but does not give rise to it.

Symposium highlights scale of mental health crisis and novel methods of diagnosis and treatment

by Lauren Rebecca Thacker | MIT Open Learning |

Digital technologies, such as smartphones and machine learning, have revolutionized education. At the McGovern Institute for Brain Research’s 2024 Spring Symposium, “Transformational Strategies in Mental Health,” experts from across the sciences — including psychiatry, psychology, neuroscience, computer science, and others — agreed that these technologies could also play a significant role in advancing the diagnosis and treatment of mental health disorders and neurological conditions.

Co-hosted by the McGovern Institute, MIT Open Learning, McClean Hospital, the Poitras Center for Psychiatric Disorders Research at MIT, and the Wellcome Trust, the symposium raised the alarm about the rise in mental health challenges and showcased the potential for novel diagnostic and treatment methods.

“We have to do something together as a community of scientists and partners of all kinds to make a difference.” – John Gabrieli

John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology at MIT, kicked off the symposium with a call for an effort on par with the Manhattan Project, which in the 1940s saw leading scientists collaborate to do what seemed impossible. While the challenge of mental health is quite different, Gabrieli stressed, the complexity and urgency of the issue are similar. In his later talk, “How can science serve psychiatry to enhance mental health?,” he noted a 35 percent rise in teen suicide deaths between 1999 and 2000 and, between 2007 and 2015, a 100 percent increase in emergency room visits for youths ages 5 to 18 who experienced a suicide attempt or suicidal ideation.

“We have no moral ambiguity, but all of us speaking today are having this meeting in part because we feel this urgency,” said Gabrieli, who is also a professor of brain and cognitive sciences, the director of the Integrated Learning Initiative (MITili) at MIT Open Learning, and a member of the McGovern Institute. “We have to do something together as a community of scientists and partners of all kinds to make a difference.”

An urgent problem

In 2021, U.S. Surgeon General Vivek Murthy issued an advisory on the increase in mental health challenges in youth; in 2023, he issued another, warning of the effects of social media on youth mental health. At the symposium, Susan Whitfield-Gabrieli, a research affiliate at the McGovern Institute and a professor of psychology and director of the Biomedical Imaging Center at Northeastern University, cited these recent advisories, saying they underscore the need to “innovate new methods of intervention.”

Other symposium speakers also highlighted evidence of growing mental health challenges for youth and adolescents. Christian Webb, associate professor of psychology at Harvard Medical School, stated that by the end of adolescence, 15-20 percent of teens will have experienced at least one episode of clinical depression, with girls facing the highest risk. Most teens who experience depression receive no treatment, he added.

Adults who experience mental health challenges need new interventions, too. John Krystal, the Robert L. McNeil Jr. Professor of Translational Research and chair of the Department of Psychiatry at Yale University School of Medicine, pointed to the limited efficacy of antidepressants, which typically take about two months to have an effect on the patient. Patients with treatment-resistant depression face a 75 percent likelihood of relapse within a year of starting antidepressants. Treatments for other mental health disorders, including bipolar and psychotic disorders, have serious side effects that can deter patients from adherence, said Virginie-Anne Chouinard, director of research at McLean OnTrackTM, a program for first episode psychosis at McLean Hospital.

New treatments, new technologies

Emerging technologies, including smartphone technology and artificial intelligence, are key to the interventions that symposium speakers shared.

In a talk on AI and the brain, Dina Katabi, the Thuan and Nicole Pham Professor of Electrical Engineering and Computer Science at MIT, discussed novel ways to detect Parkinson’s and Alzheimer’s, among other diseases. Early-stage research involved developing devices that can analyze how movement within a space impacts the surrounding electromagnetic field, as well as how wireless signals can detect breathing and sleep stages.

“I realize this may sound like la-la land,” Katabi said. “But it’s not! This device is used today by real patients, enabled by a revolution in neural networks and AI.”

Parkinson’s disease often cannot be diagnosed until significant impairment has already occurred. In a set of studies, Katabi’s team collected data on nocturnal breathing and trained a custom neural network to detect occurrences of Parkinson’s. They found the network was over 90 percent accurate in its detection. Next, the team used AI to analyze two sets of breathing data collected from patients at a six-year interval. Could their custom neural network identify patients who did not have a Parkinson’s diagnosis on the first visit, but subsequently received one? The answer was largely yes: Machine learning identified 75 percent of patients who would go on to receive a diagnosis.

Detecting high-risk patients at an early stage could make a substantial difference for intervention and treatment. Similarly, research by Jordan Smoller, professor of psychiatry at Harvard Medical School and director of the Center for Precision Psychiatry at Massachusetts General Hospital, demonstrated that AI-aided suicide risk prediction model could detect 45 percent of suicide attempts or deaths with 90 percent specificity, about two to three years in advance.

Other presentations, including a series of lightning talks, shared new and emerging treatments, such as the use of ketamine to treat depression; the use of smartphones, including daily text surveys and mindfulness apps, in treating depression in adolescents; metabolic interventions for psychotic disorders; the use of machine learning to detect impairment from THC intoxication; and family-focused treatment, rather than individual therapy, for youth depression.

Advancing understanding

The frequency and severity of adverse mental health events for children, adolescents, and adults demonstrate the necessity of funding for mental health research — and the open sharing of these findings.

Niall Boyce, head of mental health field building at the Wellcome Trust — a global charitable foundation dedicated to using science to solve urgent health challenges — outlined the foundation’s funding philosophy of supporting research that is “collaborative, coherent, and focused” and centers on “What is most important to those most affected?” Wellcome research managers Anum Farid and Tayla McCloud stressed the importance of projects that involve people with lived experience of mental health challenges and “blue sky thinking” that takes risks and can advance understanding in innovative ways. Wellcome requires that all published research resulting from its funding be open and accessible in order to maximize their benefits.

Whether through therapeutic models, pharmaceutical treatments, or machine learning, symposium speakers agreed that transformative approaches to mental health call for collaboration and innovation.

“Understanding mental health requires us to understand the unbelievable diversity of humans,” Gabrieli said. “We have to use all the tools we have now to develop new treatments that will work for people for whom our conventional treatments don’t.”

Just thinking about a location activates mental maps in the brain

Anne Trafton |

As you travel your usual route to work or the grocery store, your brain engages cognitive maps stored in your hippocampus and entorhinal cortex. These maps store information about paths you have taken and locations you have been to before, so you can navigate whenever you go there.

New research from MIT has found that such mental maps also are created and activated when you merely think about sequences of experiences, in the absence of any physical movement or sensory input. In an animal study, the researchers found that the entorhinal cortex harbors a cognitive map of what animals experience while they use a joystick to browse through a sequence of images. These cognitive maps are then activated when thinking about these sequences, even when the images are not visible.

This is the first study to show the cellular basis of mental simulation and imagination in a nonspatial domain through activation of a cognitive map in the entorhinal cortex.

“These cognitive maps are being recruited to perform mental navigation, without any sensory input or motor output. We are able to see a signature of this map presenting itself as the animal is going through these experiences mentally,” says Mehrdad Jazayeri, an associate professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

McGovern Institute Research Scientist Sujaya Neupane is the lead author of the paper, which appears today in Nature. Ila Fiete, a professor of brain and cognitive sciences at MIT, a member of MIT’s McGovern Institute for Brain Research, and director of the K. Lisa Yang Integrative Computational Neuroscience Center, is also an author of the paper.

Mental maps

A great deal of work in animal models and humans has shown that representations of physical locations are stored in the hippocampus, a small seahorse-shaped structure, and the nearby entorhinal cortex. These representations are activated whenever an animal moves through a space that it has been in before, just before it traverses the space, or when it is asleep.

“Most prior studies have focused on how these areas reflect the structures and the details of the environment as an animal moves physically through space,” Jazayeri says. “When an animal moves in a room, its sensory experiences are nicely encoded by the activity of neurons in the hippocampus and entorhinal cortex.”

In the new study, Jazayeri and his colleagues wanted to explore whether these cognitive maps are also built and then used during purely mental run-throughs or imagining of movement through nonspatial domains.

To explore that possibility, the researchers trained animals to use a joystick to trace a path through a sequence of images (“landmarks”) spaced at regular temporal intervals. During the training, the animals were shown only a subset of pairs of images but not all the pairs. Once the animals had learned to navigate through the training pairs, the researchers tested if animals could handle the new pairs they had never seen before.

One possibility is that animals do not learn a cognitive map of the sequence, and instead solve the task using a memorization strategy. If so, they would be expected to struggle with the new pairs. Instead, if the animals were to rely on a cognitive map, they should be able to generalize their knowledge to the new pairs.

“The results were unequivocal,” Jazayeri says. “Animals were able to mentally navigate between the new pairs of images from the very first time they were tested. This finding provided strong behavioral evidence for the presence of a cognitive map. But how does the brain establish such a map?”

To address this question, the researchers recorded from single neurons in the entorhinal cortex as the animals performed this task. Neural responses had a striking feature: As the animals used the joystick to navigate between two landmarks, neurons featured distinctive bumps of activity associated with the mental representation of the intervening landmarks.

“The brain goes through these bumps of activity at the expected time when the intervening images would have passed by the animal’s eyes, which they never did,” Jazayeri says. “And the timing between these bumps, critically, was exactly the timing that the animal would have expected to reach each of those, which in this case was 0.65 seconds.”

The researchers also showed that the speed of the mental simulation was related to the animals’ performance on the task: When they were a little late or early in completing the task, their brain activity showed a corresponding change in timing. The researchers also found evidence that the mental representations in the entorhinal cortex don’t encode specific visual features of the images, but rather the ordinal arrangement of the landmarks.

A model of learning

To further explore how these cognitive maps may work, the researchers built a computational model to mimic the brain activity that they found and demonstrate how it could be generated. They used a type of model known as a continuous attractor model, which was originally developed to model how the entorhinal cortex tracks an animal’s position as it moves, based on sensory input.

The researchers customized the model by adding a component that was able to learn the activity patterns generated by sensory input. This model was then able to learn to use those patterns to reconstruct those experiences later, when there was no sensory input.

“The key element that we needed to add is that this system has the capacity to learn bidirectionally by communicating with sensory inputs. Through the associational learning that the model goes through, it will actually recreate those sensory experiences,” Jazayeri says.

The researchers now plan to investigate what happens in the brain if the landmarks are not evenly spaced, or if they’re arranged in a ring. They also hope to record brain activity in the hippocampus and entorhinal cortex as the animals first learn to perform the navigation task.

“Seeing the memory of the structure become crystallized in the mind, and how that leads to the neural activity that emerges, is a really valuable way of asking how learning happens,” Jazayeri says.

The research was funded by the Natural Sciences and Engineering Research Council of Canada, the Québec Research Funds, the National Institutes of Health, and the Paul and Lilah Newton Brain Science Award.

Nancy Kanwisher, Robert Langer, and Sara Seager named Kavli Prize Laureates

Anne Trafton |

MIT faculty members Nancy Kanwisher, Robert Langer, and Sara Seager are among eight researchers worldwide to receive this year’s Kavli Prizes.

A partnership among the Norwegian Academy of Science and Letters, the Norwegian Ministry of Education and Research, and the Kavli Foundation, the Kavli Prizes are awarded every two years to “honor scientists for breakthroughs in astrophysics, nanoscience and neuroscience that transform our understanding of the big, the small and the complex.” The laureates in each field will share $1 million.

Understanding recognition of faces

Nancy Kanwisher, the Walter A Rosenblith Professor of Brain and Cognitive Sciences and McGovern Institute for Brain Research investigator, has been awarded the 2024 Kavli Prize in Neuroscience with Doris Tsao, professor in the Department of Molecular and Cell Biology at the University of California at Berkeley, and Winrich Freiwald, the Denise A. and Eugene W. Chinery Professor at the Rockefeller University.

Kanwisher, Tsao, and Freiwald discovered a specialized system within the brain to recognize faces. Their discoveries have provided basic principles of neural organization and made the starting point for further research on how the processing of visual information is integrated with other cognitive functions.

Kanwisher was the first to prove that a specific area in the human neocortex is dedicated to recognizing faces, now called the fusiform face area. Using functional magnetic resonance imaging, she found individual differences in the location of this area and devised an analysis technique to effectively localize specialized functional regions in the brain. This technique is now widely used and applied to domains beyond the face recognition system.

Integrating nanomaterials for biomedical advances

Robert Langer, the David H. Koch Institute Professor, has been awarded the 2024 Kavli Prize in Nanoscience with Paul Alivisatos, president of the University of Chicago and John D. MacArthur Distinguished Service Professor in the Department of Chemistry, and Chad Mirkin, professor of chemistry at Northwestern University.

Langer, Alivisatos, and Mirkin each revolutionized the field of nanomedicine by demonstrating how engineering at the nano scale can advance biomedical research and application. Their discoveries contributed foundationally to the development of therapeutics, vaccines, bioimaging, and diagnostics.

Langer was the first to develop nanoengineered materials that enabled the controlled release, or regular flow, of drug molecules. This capability has had an immense impact for the treatment of a range of diseases, such as aggressive brain cancer, prostate cancer, and schizophrenia. His work also showed that tiny particles, containing protein antigens, can be used in vaccination, and was instrumental in the development of the delivery of messenger RNA vaccines.

Searching for life beyond Earth

Sara Seager, the Class of 1941 Professor of Planetary Sciences in the Department of Earth, Atmospheric and Planetary Sciences and a professor in the departments of Physics and of Aeronautics and Astronautics, has been awarded the 2024 Kavli Prize in Astrophysics along with David Charbonneau, the Fred Kavli Professor of Astrophysics at Harvard University.

Seager and Charbonneau are recognized for discoveries of exoplanets and the characterization of their atmospheres. They pioneered methods for the detection of atomic species in planetary atmospheres and the measurement of their thermal infrared emission, setting the stage for finding the molecular fingerprints of atmospheres around both giant and rocky planets. Their contributions have been key to the enormous progress seen in the last 20 years in the exploration of myriad exoplanets.

Kanwisher, Langer, and Seager bring the number of all-time MIT faculty recipients of the Kavli Prize to eight. Prior winners include Rainer Weiss in astrophysics (2016), Alan Guth in astrophysics (2014), Mildred Dresselhaus in nanoscience (2012), Ann Graybiel in neuroscience (2012), and Jane Luu in astrophysics (2012).

Nancy Kanwisher Shares 2024 Kavli Prize in Neuroscience

by Julie Pryor |

The Norwegian Academy of Science and Letters today announced the 2024 Kavli Prize Laureates in the fields of astrophysics, nanoscience, and neuroscience. The 2024 Kavli Prize in Neuroscience honors Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience at MIT and an investigator at the McGovern Institute, along with UC Berkeley neurobiologist Doris Tsao, and Rockefeller University neuroscientist Winrich Freiwald for their discovery of a highly localized and specialized system for representation of faces in human and non-human primate neocortex. The neuroscience laureates will share $1 million USD.

“Kanwisher, Freiwald, and Tsao together discovered a localized and specialized neocortical system for face recognition,” says Kristine Walhovd, Chair of the Kavli Neuroscience Committee. “Their outstanding research will ultimately further our understanding of recognition not only of faces, but objects and scenes.”

Overcoming failure

As a graduate student at MIT in the early days of functional brain imaging, Kanwisher was fascinated by the potential of the emerging technology to answer a suite of questions about the human mind. But a lack of brain imaging resources and a series of failed experiments led Kanwisher consider leaving the field for good. She credits her advisor, MIT Professor of Psychology Molly Potter, for supporting her through this challenging time and for teaching her how to make powerful inferences about the inner workings of the mind from behavioral data alone.

After receiving her PhD from MIT, Kanwisher spent a year studying nuclear strategy with a MacArthur Foundation Fellowship in Peace and International Security, but eventually returned to science by accepting a faculty position at Harvard University where she could use the latest brain imaging technology to pursue the scientific questions that had always fascinated her.

Zeroing in on faces

Recognizing faces is important for social interaction in many animals. Previous work in human psychology and animal research had suggested the existence of a functionally specialized system for face recognition, but this system had not clearly been identified with brain imaging technology. It is here that Kanwisher saw her opportunity.

Using a new method at the time, called functional magnetic resonance imaging or fMRI, Kanwisher’s team scanned people while they looked at faces and while they looked at objects, and searched for brain regions that responded more to one than the other. They found a small patch of neocortex, now called the fusiform face area (FFA), that is dedicated specifically to the task of face recognition. She found individual differences in the location of this area and devised an analysis technique to effectively localize specialized functional regions in the brain. This technique is now widely used and applied to domains beyond the face recognition system. Notably, Kanwisher’s first FFA paper was co-authored with Josh McDermott, who was an undergrad at Harvard University at the time, and is now an associate investigator at the McGovern Institute and holds a faculty position alongside Kanwisher in MIT’s Department of Brain and Cognitive Sciences.

A group of five scientists standing and smiling in front of a whiteboard.
The Kanwisher lab at Harvard University circa 1996. From left to right: Nancy Kanwisher, Josh McDermott (then an undergrad), Marvin Chun (postdoc), Ewa Wojciulik (postdoc), and Jody Culham (grad student). Photo: Nancy Kanwisher

From humans to monkeys

Inspired by Kanwisher´s findings, Winrich Freiwald and Doris Tsao together used fMRI to localize similar face patches in macaque monkeys. They mapped out six distinct brain regions, known as the face patch system, including these regions’ functional specialization and how they are connected. By recording the activity of individual brain cells, they revealed how cells in some face patches specialize in faces with particular views.

Tsao proceeded to identify how the face patches work together to identify a face, through a specific code that enables single cells to identify faces by assembling information of facial features. For example, some cells respond to the presence of hair, others to the distance between the eyes. Freiwald uncovered that a separate brain region, called the temporal pole, accelerates our recognition of familiar faces, and that some cells are selectively responsive to familiar faces.

“It was a special thrill for me when Doris and Winrich found face patches in monkeys using fMRI,” says Kanwisher, whose lab at MIT’s McGovern Institute has gone on to uncover many other regions of the human brain that engage in specific aspects of perception and cognition. “They are scientific heroes to me, and it is a thrill to receive the Kavli Prize in neuroscience jointly with them.”

“Nancy and her students have identified neocortical subregions that differentially engage in the perception of faces, places, music and even what others think,” says McGovern Institute Director Robert Desimone. “We are delighted that her groundbreaking work into the functional organization of the human brain is being honored this year with the Kavli Prize.”

Together, the laureates, with their work on neocortical specialization for face recognition, have provided basic principles of neural organization which will further our understanding of how we perceive the world around us.

About the Kavli Prize

The Kavli Prize is a partnership among The Norwegian Academy of Science and Letters, The Norwegian Ministry of Education and Research, and The Kavli Foundation (USA). The Kavli Prize honors scientists for breakthroughs in astrophysics, nanoscience and neuroscience that transform our understanding of the big, the small and the complex. Three one-million-dollar prizes are awarded every other year in each of the three fields. The Norwegian Academy of Science and Letters selects the laureates based on recommendations from three independent prize committees whose members are nominated by The Chinese Academy of Sciences, The French Academy of Sciences, The Max Planck Society of Germany, The U.S. National Academy of Sciences, and The Royal Society, UK.

MIT scientists learn how to control muscles with light

Anne Trafton |

For people with paralysis or amputation, neuroprosthetic systems that artificially stimulate muscle contraction with electrical current can help them regain limb function. However, despite many years of research, this type of prosthesis is not widely used because it leads to rapid muscle fatigue and poor control.

McGovern Institute Associate Investigator Hugh Herr. Photo: Jimmy Day / MIT Media Lab

MIT researchers have developed a new approach that they hope could someday offer better muscle control with less fatigue. Instead of using electricity to stimulate muscles, they used light. In a study in mice, the researchers showed that this optogenetic technique offers more precise muscle control, along with a dramatic decrease in fatigue.

“It turns out that by using light, through optogenetics, one can control muscle more naturally. In terms of clinical application, this type of interface could have very broad utility,” says Hugh Herr, a professor of media arts and sciences, co-director of the K. Lisa Yang Center for Bionics at MIT, and an associate member of MIT’s McGovern Institute for Brain Research.

Optogenetics is a method based on genetically engineering cells to express light-sensitive proteins, which allows researchers to control activity of those cells by exposing them to light. This approach is currently not feasible in humans, but Herr, MIT graduate student Guillermo Herrera-Arcos, and their colleagues at the K. Lisa Yang Center for Bionics are now working on ways to deliver light-sensitive proteins safely and effectively into human tissue.

Herr is the senior author of the study, which appears today in Science Robotics. Herrera-Arcos is the lead author of the paper.

Optogenetic control

For decades, researchers have been exploring the use of functional electrical stimulation (FES) to control muscles in the body. This method involves implanting electrodes that stimulate nerve fibers, causing a muscle to contract. However, this stimulation tends to activate the entire muscle at once, which is not the way that the human body naturally controls muscle contraction.

“Humans have this incredible control fidelity that is achieved by a natural recruitment of the muscle, where small motor units, then moderate-sized, then large motor units are recruited, in that order, as signal strength is increased,” Herr says. “With FES, when you artificially blast the muscle with electricity, the largest units are recruited first. So, as you increase signal, you get no force at the beginning, and then suddenly you get too much force.”

This large force not only makes it harder to achieve fine muscle control, it also wears out the muscle quickly, within five or 10 minutes.

The MIT team wanted to see if they could replace that entire interface with something different. Instead of electrodes, they decided to try controlling muscle contraction using optical molecular machines via optogenetics.

Two scientists in the lab.
“This could lead to a minimally invasive strategy that would change the game in terms of clinical care for persons suffering from limb pathology,” Hugh Herr says, pictured on left next to Herrera-Arcos.

Using mice as an animal model, the researchers compared the amount of muscle force they could generate using the traditional FES approach with forces generated by their optogenetic method. For the optogenetic studies, they used mice that had already been genetically engineered to express a light-sensitive protein called channelrhodopsin-2. They implanted a small light source near the tibial nerve, which controls muscles of the lower leg.

The researchers measured muscle force as they gradually increased the amount of light stimulation, and found that, unlike FES stimulation, optogenetic control produced a steady, gradual increase in contraction of the muscle.

“As we change the optical stimulation that we deliver to the nerve, we can proportionally, in an almost linear way, control the force of the muscle. This is similar to how the signals from our brain control our muscles. Because of this, it becomes easier to control the muscle compared with electrical stimulation,” Herrera-Arcos says.

Fatigue resistance

Using data from those experiments, the researchers created a mathematical model of optogenetic muscle control. This model relates the amount of light going into the system to the output of the muscle (how much force is generated).

This mathematical model allowed the researchers to design a closed-loop controller. In this type of system, the controller delivers a stimulatory signal, and after the muscle contracts, a sensor can detect how much force the muscle is exerting. This information is sent back to the controller, which calculates if, and how much, the light stimulation needs to be adjusted to reach the desired force.

Using this type of control, the researchers found that muscles could be stimulated for more than an hour before fatiguing, while muscles became fatigued after only 15 minutes using FES stimulation.

One hurdle the researchers are now working to overcome is how to safely deliver light-sensitive proteins into human tissue. Several years ago, Herr’s lab reported that in rats, these proteins can trigger an immune response that inactivates the proteins and could also lead to muscle atrophy and cell death.

“A key objective of the K. Lisa Yang Center for Bionics is to solve that problem,” Herr says. “A multipronged effort is underway to design new light-sensitive proteins, and strategies to deliver them, without triggering an immune response.”

As additional steps toward reaching human patients, Herr’s lab is also working on new sensors that can be used to measure muscle force and length, as well as new ways to implant the light source. If successful, the researchers hope their strategy could benefit people who have experienced strokes, limb amputation, and spinal cord injuries, as well as others who have impaired ability to control their limbs.

“This could lead to a minimally invasive strategy that would change the game in terms of clinical care for persons suffering from limb pathology,” Herr says.

The research was funded by the K. Lisa Yang Center for Bionics at MIT.

Five MIT faculty elected to the National Academy of Sciences for 2024

by School of Science, MIT |

The National Academy of Sciences has elected 120 members and 24 international members, including five faculty members from MIT. Guoping Feng, Piotr Indyk, Daniel J. Kleitman, Daniela Rus, and Senthil Todadri were elected in recognition of their “distinguished and continuing achievements in original research.” Membership to the National Academy of Sciences is one of the highest honors a scientist can receive in their career.

Among the new members added this year are also nine MIT alumni, including Zvi Bern ’82; Harold Hwang ’93, SM ’93; Leonard Kleinrock SM ’59, PhD ’63; Jeffrey C. Lagarias ’71, SM ’72, PhD ’74; Ann Pearson PhD ’00; Robin Pemantle PhD ’88; Jonas C. Peters PhD ’98; Lynn Talley PhD ’82; and Peter T. Wolczanski ’76. Those elected this year bring the total number of active members to 2,617, with 537 international members.

The National Academy of Sciences is a private, nonprofit institution that was established under a congressional charter signed by President Abraham Lincoln in 1863. It recognizes achievement in science by election to membership, and — with the National Academy of Engineering and the National Academy of Medicine — provides science, engineering, and health policy advice to the federal government and other organizations.

Guoping Feng

Guoping Feng is the James W. (1963) and Patricia T. Poitras Professor in the Department of Brain and Cognitive Sciences. He is also associate director and investigator in the McGovern Institute for Brain Research, a member of the Broad Institute of MIT and Harvard, and director of the Hock E. Tan and K. Lisa Yang Center for Autism Research.

His research focuses on understanding the molecular mechanisms that regulate the development and function of synapses, the places in the brain where neurons connect and communicate. He’s interested in how defects in the synapses can contribute to psychiatric and neurodevelopmental disorders. By understanding the fundamental mechanisms behind these disorders, he’s producing foundational knowledge that may guide the development of new treatments for conditions like obsessive-compulsive disorder and schizophrenia.

Feng received his medical training at Zhejiang University Medical School in Hangzhou, China, and his PhD in molecular genetics from the State University of New York at Buffalo. He did his postdoctoral training at Washington University at St. Louis and was on the faculty at Duke University School of Medicine before coming to MIT in 2010. He is a member of the American Academy of Arts and Sciences, a fellow of the American Association for the Advancement of Science, and was elected to the National Academy of Medicine in 2023.

Piotr Indyk

Piotr Indyk is the Thomas D. and Virginia W. Cabot Professor of Electrical Engineering and Computer Science. He received his magister degree from the University of Warsaw and his PhD from Stanford University before coming to MIT in 2000.

Indyk’s research focuses on building efficient, sublinear, and streaming algorithms. He’s developed, for example, algorithms that can use limited time and space to navigate massive data streams, that can separate signals into individual frequencies faster than other methods, and can address the “nearest neighbor” problem by finding highly similar data points without needing to scan an entire database. His work has applications on everything from machine learning to data mining.

He has been named a Simons Investigator and a fellow of the Association for Computer Machinery. In 2023, he was elected to the American Academy of Arts and Sciences.

Daniel J. Kleitman

Daniel Kleitman, a professor emeritus of applied mathematics, has been at MIT since 1966. He received his undergraduate degree from Cornell University and his master’s and PhD in physics from Harvard University before doing postdoctoral work at Harvard and the Niels Bohr Institute in Copenhagen, Denmark.

Kleitman’s research interests include operations research, genomics, graph theory, and combinatorics, the area of math concerned with counting. He was actually a professor of physics at Brandeis University before changing his field to math, encouraged by the prolific mathematician Paul Erdős. In fact, Kleitman has the rare distinction of having an Erdős number of just one. The number is a measure of the “collaborative distance” between a mathematician and Erdős in terms of authorship of papers, and studies have shown that leading mathematicians have particularly low numbers.

He’s a member of the American Academy of Arts and Sciences and has made important contributions to the MIT community throughout his career. He was head of the Department of Mathematics and served on a number of committees, including the Applied Mathematics Committee. He also helped create web-based technology and an online textbook for several of the department’s core undergraduate courses. He was even a math advisor for the MIT-based film “Good Will Hunting.”

Daniela Rus

Daniela Rus, the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science, is the director of the Computer Science and Artificial Intelligence Laboratory (CSAIL). She also serves as director of the Toyota-CSAIL Joint Research Center.

Her research on robotics, artificial intelligence, and data science is geared toward understanding the science and engineering of autonomy. Her ultimate goal is to create a future where machines are seamlessly integrated into daily life to support people with cognitive and physical tasks, and deployed in way that ensures they benefit humanity. She’s working to increase the ability of machines to reason, learn, and adapt to complex tasks in human-centered environments with applications for agriculture, manufacturing, medicine, construction, and other industries. She’s also interested in creating new tools for designing and fabricating robots and in improving the interfaces between robots and people, and she’s done collaborative projects at the intersection of technology and artistic performance.

Rus received her undergraduate degree from the University of Iowa and her PhD in computer science from Cornell University. She was a professor of computer science at Dartmouth College before coming to MIT in 2004. She is part of the Class of 2002 MacArthur Fellows; was elected to the National Academy of Engineering and the American Academy of Arts and Sciences; and is a fellow of the Association for Computer Machinery, the Institute of Electrical and Electronics Engineers, and the Association for the Advancement of Artificial Intelligence.

Senthil Todadri

Senthil Todadri, a professor of physics, came to MIT in 2001. He received his undergraduate degree from the Indian Institute of Technology in Kanpur and his PhD from Yale University before working as a postdoc at the Kavli Institute for Theoretical Physics in Santa Barbara, California.

Todadri’s research focuses on condensed matter theory. He’s interested in novel phases and phase transitions of quantum matter that expand beyond existing paradigms. Combining modeling experiments and abstract methods, he’s working to develop a theoretical framework for describing the physics of these systems. Much of that work involves understanding the phenomena that arise because of impurities or strong interactions between electrons in solids that don’t conform with conventional physical theories. He also pioneered the theory of deconfined quantum criticality, which describes a class of phase transitions, and he discovered the dualities of quantum field theories in two dimensional superconducting states, which has important applications to many problems in the field.

Todadri has been named a Simons Investigator, a Sloan Research Fellow, and a fellow of the American Physical Society. In 2023, he was elected to the American Academy of Arts and Sciences

Using MRI, engineers have found a way to detect light deep in the brain

Anne Trafton |

Scientists often label cells with proteins that glow, allowing them to track the growth of a tumor, or measure changes in gene expression that occur as cells differentiate.

A man stands with his arms crossed in front of a board with mathematical equations written on it.
Alan Jasanoff, associate member of the McGovern Institute, and a professor of brain and cognitive sciences, biological engineering, and nuclear science and engineering at MIT. Photo: Justin Knight

While this technique works well in cells and some tissues of the body, it has been difficult to apply this technique to image structures deep within the brain, because the light scatters too much before it can be detected.

MIT engineers have now come up with a novel way to detect this type of light, known as bioluminescence, in the brain: They engineered blood vessels of the brain to express a protein that causes them to dilate in the presence of light. That dilation can then be observed with magnetic resonance imaging (MRI), allowing researchers to pinpoint the source of light.

“A well-known problem that we face in neuroscience, as well as other fields, is that it’s very difficult to use optical tools in deep tissue. One of the core objectives of our study was to come up with a way to image bioluminescent molecules in deep tissue with reasonably high resolution,” says Alan Jasanoff, an MIT professor of biological engineering, brain and cognitive sciences, and nuclear science and engineering.

The new technique developed by Jasanoff and his colleagues could enable researchers to explore the inner workings of the brain in more detail than has previously been possible.

Jasanoff, who is also an associate investigator at MIT’s McGovern Institute for Brain Research, is the senior author of the study, which appears today in Nature Biomedical Engineering. Former MIT postdocs Robert Ohlendorf and Nan Li are the lead authors of the paper.

Detecting light

Bioluminescent proteins are found in many organisms, including jellyfish and fireflies. Scientists use these proteins to label specific proteins or cells, whose glow can be detected by a luminometer. One of the proteins often used for this purpose is luciferase, which comes in a variety of forms that glow in different colors.

Jasanoff’s lab, which specializes in developing new ways to image the brain using MRI, wanted to find a way to detect luciferase deep within the brain. To achieve that, they came up with a method for transforming the blood vessels of the brain into light detectors. A popular form of MRI works by imaging changes in blood flow in the brain, so the researchers engineered the blood vessels themselves to respond to light by dilating.

“Blood vessels are a dominant source of imaging contrast in functional MRI and other non-invasive imaging techniques, so we thought we could convert the intrinsic ability of these techniques to image blood vessels into a means for imaging light, by photosensitizing the blood vessels themselves,” Jasanoff says.

“We essentially turn the vasculature of the brain into a three-dimensional camera.” – Alan Jasanoff

To make the blood vessels sensitive to light, the researcher engineered them to express a bacterial protein called Beggiatoa photoactivated adenylate cyclase (bPAC). When exposed to light, this enzyme produces a molecule called cAMP, which causes blood vessels to dilate. When blood vessels dilate, it alters the balance of oxygenated and deoxygenated hemoglobin, which have different magnetic properties. This shift in magnetic properties can be detected by MRI.

BPAC responds specifically to blue light, which has a short wavelength, so it detects light generated within close range. The researchers used a viral vector to deliver the gene for bPAC specifically to the smooth muscle cells that make up blood vessels. When this vector was injected in rats, blood vessels throughout a large area of the brain became light-sensitive.

“Blood vessels form a network in the brain that is extremely dense. Every cell in the brain is within a couple dozen microns of a blood vessel,” Jasanoff says. “The way I like to describe our approach is that we essentially turn the vasculature of the brain into a three-dimensional camera.”

Once the blood vessels were sensitized to light, the researchers implanted cells that had been engineered to express luciferase if a substrate called CZT is present. In the rats, the researchers were able to detect luciferase by imaging the brain with MRI, which revealed dilated blood vessels.

Tracking changes in the brain

The researchers then tested whether their technique could detect light produced by the brain’s own cells, if they were engineered to express luciferase. They delivered the gene for a type of luciferase called GLuc to cells in a deep brain region known as the striatum. When the CZT substrate was injected into the animals, MRI imaging revealed the sites where light had been emitted.

This technique, which the researchers dubbed bioluminescence imaging using hemodynamics, or BLUsH, could be used in a variety of ways to help scientists learn more about the brain, Jasanoff says.

For one, it could be used to map changes in gene expression, by linking the expression of luciferase to a specific gene. This could help researchers observe how gene expression changes during embryonic development and cell differentiation, or when new memories form. Luciferase could also be used to map anatomical connections between cells or to reveal how cells communicate with each other.

The researchers now plan to explore some of those applications, as well as adapting the technique for use in mice and other animal models.

The research was funded by the U.S. National Institutes of Health, the G. Harold and Leila Y. Mathers Foundation, Lore Harp McGovern, Gardner Hendrie, a fellowship from the German Research Foundation, a Marie Sklodowska-Curie Fellowship from the European Union, and a Y. Eva Tan Fellowship and a J. Douglas Tan Fellowship, both from the McGovern Institute for Brain Research.