Category: Featured News

How is the McGovern Institute using AI?

by Jennifer Michalowski |

This story also appears in our Fall 2026 BrainScan newsletter.

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The tools of artificial intelligence (AI) are enabling McGovern researchers to ask new kinds of questions and solve more complex problems — and that’s leading to new insights into how the brain works and how mental illness might be more effectively managed.

“I can’t think of any facet [of brain research] where there isn’t an AI-based tool or technology that helps us,” says McGovern Institute Senior Research Scientist Satrajit Ghosh. Ghosh thinks today’s AI technology is particularly powerful in the early stages of research, when researchers are figuring out which questions are important and how to answer them. Historically, this has meant deep reading of the scientific literature — but existing knowledge can be overlooked when people don’t know where to look for it.

“The entire scientific workflow — from ideation to research to execution to dissemination — now has AI tools and technologies that can help.”
– Satra Ghosh

Large language models like Gemini or ChatGPT can pull information and ideas from sources across the internet, connecting users with resources they might not consider accessing on their own. “We get caught in our tracks and grooves that are hard to step outside of,” Ghosh says. “There are still biases in these tools, but I think overall they’re in a much more flexible space than humans typically are.”

McGovern Investigator Feng Zhang adds that by accelerating this vital phase of doing science, AI tools are broadening the scope of what an individual lab can explore. “We’ve never been short on good questions — what’s new is that we can chase them the moment they occur to us, and follow them as far as the data goes,” says Zhang, who is the James and Patricia Poitras Professor of Neuroscience at MIT.

Testing ideas faster

Once a plan is in place, researchers can often go from idea to execution much faster with the help of AI. McGovern Investigators Nancy Kanwisher and Josh McDermott say they have begun using AI models to pilot experiments, refining their approach before working with human study participants. They even use AI to generate images or sounds that their subjects will hear or see during their studies, designing stimuli to uncover specific aspects of the brain’s response.

The models they use are far more reliable representations of human perception than researchers were able to build just a few years ago. And with major advances in AI’s ability to generate computer code, models that took months or years to piece together can now be generated in days.

McDermott, who is the Uncas (1923) and Helen Whitaker Professor of Brain and Cognitive Sciences at MIT, uses an array of models to investigate different aspects of auditory processing in the brain. His team has models that recognize speech, models that localize sounds, and models that detect changes in pitch. “I fantasized about doing these things when I was a grad student, and it just wasn’t possible,” he says. “We were always trying to build working models of things, and it never worked. Now it’s really possible, and it’s just opening huge numbers of doors.”

Using a computational model of the auditory system, Josh McDermott has shown how the brain can selectively focus attention on one voice among many in a noisy environment — shedding light on a longstanding neuroscientific phenomenon known as the “cocktail party problem.” Photo: Steph Stevens

For McDermott, one priority is finding ways to develop better devices to help people hear. Computational models can facilitate that by enabling experimentation on a scale that is not possible with human subjects, allowing researchers to explore thousands of different conditions or rapidly test the consequences of modifying a hearing aid algorithm in different ways.

AI models also allow researchers to explore conditions they can’t test in the real world. McDermott’s lab, for example, has studied how hearing might work in a world where sounds behave differently, helping reveal which aspects of perception were shaped by the environment in which our ancestors evolved. And Kanwisher says models help overcome the limitations of experiments with humans, where the tools for monitoring and manipulating brain activity are imprecise and researchers have no control over their subjects’ life experiences.

That’s particularly valuable for studying cognitive functions that are uniquely human. “If you want to study things like language, or mathematical cognition, or theory of mind, it’s pretty hard to do in a mouse or even a monkey,” says McGovern Investigator and Associate Professor of Brain and Cognitive Sciences Evelina Fedorenko, who studies how the brain creates and understands language. AI models, on the other hand, can be trained to mimic these abilities. And what scientists like Fedorenko are learning is that models that behave similarly to humans often process information in ways that are similar to the human brain.

“If you’re in the business of studying sensory systems, like I am, it’s totally changed the way that we do science.” – Josh McDermott

Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience at MIT, and her colleagues have used AI to explore how and why brains have networks specialized for the processing of certain kinds of objects. Her team and others have tasked AI models with processing visual information, training them with an assortment of images without further information or instruction. Remarkably, such models develop specific responses for certain categories of images: faces, places, bodies, and words — all of which Kanwisher’s team has shown are preferentially processed by specific regions in the human brain.

Kanwisher says that was surprising, because cognitive scientists have reasoned that human brains likely evolved these selective regions to optimize the way perceptual information is later used by the brain. But that doesn’t explain why AI responds to these categories with the same selectivity.

“The AI models don’t use face images to engage social cognition or place images to engage navigation,” she says. “They don’t do a thing with them, and yet they pull out the same categories as important.” That doesn’t rule out previous ideas about human brains — but, Kanwisher says, “It’s proof that you can have these shockingly similar things for very different reasons.” Her group is now exploring how the models’ training experience shapes the development of selective regions — something that cannot be done in humans.

Building better tools

AI is also helping McGovern scientists build physical tools, both to allow researchers to study the brain in new ways and to get better treatments to patients.

“Molecular syringes” (above), engineered by the AI tool AlphaFold in the Zhang lab, can deliver therapeutic cargo into specific cells in the body. These nanosyringes may lead to safer and more effective treatments for a variety of conditions, including cancer. Image: Joseph Kreitz, Feng Zhang

In Feng Zhang and Ed Boyden’s labs, researchers are using an AI tool called AlphaFold to design proteins with specific properties. Zhang’s lab is searching for proteins that can deliver therapies to specific brain cells, while Boyden’s team is developing tools for monitoring signaling in the brain.

AlphaFold predicts the structure of biomolecules, such as how a sequence of amino acids will fold into a three-dimensional protein. This eliminates much of the trial-and-error stage of protein engineering, speeding scientists’ ability to find molecules that interact with biological systems in the ways that they want.

Boyden, who is the Y. Eva Tan Professor in Neurotechnology at MIT, envisions another powerful AI tool: one that predicts how a brain works from its structure. The idea, he says, is to use machine learning to associate detailed brain maps with physiology and circuit function.

Building such a model would require vast amounts of data, but Boyden says the tools and tissue samples needed for large-scale structural analyses are available.

He envisions a kind of “compiler” built from molecular maps of brain cells and tissues — one that could help neuroscientists understand circuit function and eventually predict the effects of medications and other therapies.

Decoding data

Once an experiment is complete, data analysis begins — and AI is enabling scientists to find insights in datasets that, until recently, were too large and complex to make sense of.

AI tools have been invaluable, for instance, in creating detailed wiring diagrams, or connectomes, which many neuroscientists see as critical guides for brain research. That work, which involves tracing neurons across millions of microscope images, relies heavily on computer vision.

Without AI, piecing together the circuitry of an entire brain would take lifetimes. Remarkably, scientists now have access to a fully mapped connectome for the brain of a fruit fly, whose neurons make more than 50 million connections with one another.

A consortium of scientists, including McGovern Investigator Sven Dorkenwald, created a complete connectome of the fly brain (above) using computer vision algorithms and hundreds of human proofreaders. It is the largest, most complete neuronal map ever produced by scientists. Image: FlyWire Consortium, Sven Dorkenwald

McGovern Investigator Tomaso Poggio and his team are using AI to interact with that connectome by running simulations of circuit function, testing for a motion-detecting circuit that Poggio first proposed in theoretical work he did in the 1980s. Until recently, Poggio, who is the Eugene McDermott Professor Emeritus of Brain and Cognitive Sciences at MIT, had no way to find it.

Personalizing mental health

Other McGovern researchers are deploying AI to find ways to improve the diagnosis and treatment of brain disorders. They are finding that AI can pick up on signals that can be difficult for clinicians to detect — sometimes years before diagnosis.

Working with Satra Ghosh, John Gabrieli’s team found that a machine learning model could predict from clinical data which children and teenagers would later be diagnosed with bipolar disorder. With other collaborators, Ghosh has found that AI can recognize autism-related behaviors in parents’ videos of their toddlers, and he trained an AI model to detect changes in voice and speech patterns associated with conditions like Alzheimer’s and Parkinson’s disease. With further development, tools like these could facilitate earlier diagnosis and intervention for people who need it.

Researchers in Gabrieli’s lab think AI can also enable a more personalized approach to treating mental illness. It’s difficult to predict who will respond to a particular medication or therapy, leaving doctors and patients with a trial-and-error approach to treatment. Gabrieli’s team is betting that machine learning algorithms can find patterns hidden within complex clinical datasets that can enable more informed treatment decisions.

By merging machine-learning approaches with clinical and neuroimaging data, Gun Ahn, a graduate student in John Gabrieli’s lab, is pioneering precision-based methods that could help heal mental illness more quickly and effectively than current strategies.

In a recent success, Gun Ahn, a graduate student on Gabrieli’s team, followed up on a study in which the group found that a daily mindfulness practice meaningfully reduced anxiety for about 50 percent of autistic adults. No single factor separated those who benefitted from those who did not, so the team used machine learning to analyze demographic data and patient questionnaires. Their model was able to predict, with 80 percent accuracy, who responded to the practice.

This finding could help patients decide whether mindfulness is worth trying, particularly if it can one day be paired with predictions about alternative treatments. Gabrieli’s lab is taking a similar approach to predict patients’ response to medications for attention-deficit/hyperactivity disorder, hoping to help people bypass ineffective options and quickly find what works for them.

Tailoring interventions to a patient’s personal biology and experience has long been a goal in psychiatry — but the knowledge clinicians need to achieve this has been elusive. “For about 10 to 15 years, people have been talking about the incredible value that precision psychiatry would have,” says Gabrieli, who is the Grover Hermann Professor of Health Sciences and Technology at MIT. “This is, to me, the most promising scientific approach that could be widely usable.”

AI could even help clinicians move beyond formal diagnoses to understanding and treating people more holistically. Ahn imagines a model trained broadly, with data from a wide swath of patients.

Moving the field forward

Science isn’t done once the data has been analyzed: sharing knowledge is essential, too. Poggio, for example, integrated AI into the writing of a new book, “Brains Minds Machines: The Mystery of Human Intelligence, The Enigmas of the Artificial,” even crediting Gemini and ChatGPT as coauthors. He says the tools helped him organize his thinking and offload tedious tasks.

Looking ahead, most McGovern scientists say AI technologies are advancing so rapidly, their future impacts on neuroscience are difficult to predict. Could AI one day replace human researchers altogether? Some think it’s possible — at least in some areas.

“The scary prospect is that AI is going to put humans out of business. We’re definitely not close to that now, but nobody can predict the future,” says McDermott. “Physically doing experiments may be something that continues to be the domain of humans. But deciding what experiments to run and what they mean, and analyzing all the data? It’s unclear whether humans are still going to be the most efficient way to do that.”

Others are more skeptical. “It’s hard for AI to generate truly creative new ideas,” Boyden says. “I haven’t seen that happen yet.”

For now at least, today’s tools need oversight and guidance. To get the most out of them, users need experience and expertise. “They still have hallucinations from time to time,” Poggio warns. “One has to be careful.”

Nonetheless, today’s tools are tremendously empowering. They’re not just getting more powerful; they’re also getting more accessible. “These models are becoming exciting because they’re becoming usable by everyone, not just specialists,” Zhang says. “And when more people can do more, the whole field moves faster.”

How visual learning happens in the brain

by Jennifer Michalowski |

The wiring and rewiring of the brain never ends. Neural pathways are constantly being reshaped as we interact with the world and learn new things. At MIT’s McGovern Institute and York University in Toronto, scientists are combining detailed analysis of brain activity with computational modeling to better understand that change.

McGovern Institute postdoctoral fellow Lynn Sörensen, Investigator James DiCarlo, and York University assistant professor Kohitij Kar worked together to compare what happened when monkeys and an artificial neural network with brain-like architecture were trained to visually identify the same objects. As the model’s performance improved, it reorganized itself in ways that closely paralleled changes the team detected in the brains of monkeys.

Their work, reported today in the journal Nature Communications, shows how changes in visual processing support animals’ ability to learn to discriminate new kinds of objects. By modeling these changes, the researchers hope to better predict how training reshapes perception, which could one day inform educational strategies for a wide range of learners.

Subtle changes

Learning about a new object calls on many parts of the brain. Visual-processing areas work together to make sense of information taken in through the eyes, then communicate with other brain areas to give the visual information meaning and guide behavior. Multiple parts of this system likely change during learning, and the research team wanted a clearer understanding of how that change is distributed.

Neuroscientists have debated how much change occurs in the brain’s visual-processing areas when an animal learns to recognize new objects. Some suspected that visual-processing pathways remain largely unchanged during learning to avoid broadly disrupting visual perception, but others have reported changes in activity within dedicated visual-processing areas with this kind of learning in humans and other primates.  

To take a closer look, the team focused on neural activity in a key component of the brain’s visual object-processing network, the inferior temporal (IT) cortex. By the time visual information reaches the IT cortex, key object features are clearly represented—so much so that it’s possible to “decode” what object a monkey is seeing and even predict what errors it’s likely to make in identifying it, simply by analyzing patterns of neural activity there.

The team recorded neural activity in the IT cortex from two groups of monkeys as the animals looked at and identified images of objects. Some of the monkeys were untrained, so the images they saw had little meaning to them. Others had already learned to identify similar objects, so they could usually discriminate between elephants, chairs, and other select objects, even when those objects were presented at different sizes, from different angles, or against different backgrounds than the ones they had seen before.

The broad pattern of activity in the IT cortex was largely similar in trained and untrained monkeys, suggesting that learning had not dramatically rewritten this high-level visual representation. Still, the group found subtle but reliable differences in the way neurons in the IT cortex responded to images in monkeys that had learned to recognize the kinds of objects they were shown, compared to the untrained monkeys.

Modeling learning

The group turned to computational models to investigate how those modest changes might contribute to learning. Sörensen trained a suite of artificial neural networks whose internal components had been mapped to monkey IT cortex to identify the same categories of objects the monkeys had seen. The models were designed to learn using gradient descent, meaning they continually improved their accuracy by adjusting their parameters in response to errors.

Only some of the primate-like models showed learning behavior that matched that of the monkeys. In those that did, the IT-like stage changed in ways that resembled the learning-related changes the researchers had observed in the IT cortex of trained monkeys.

While gradient descent is commonly used to train artificial intelligence, it is generally considered biologically implausible as a direct model of how the brain learns. The researchers say the strong match in learning effects between the monkeys and their model demonstrates that these kinds of artificial neural networks can offer insights into biological learning at a useful level of abstraction, even if the brain does not learn in the same way.

“This shows that you can actually build in silico versions of future experiments,” Sörensen says. “I think that gives us this playground of asking ‘what if’ questions—and potentially predicting new things that go beyond the experimenter’s intuition.”

Most of the changes that allowed for learning in the model occurred outside of the IT cortex. “This tells us that there is a lot between the area we recorded from and the final behavioral readout that needs to change during this process,” Kar says. He adds that the team’s model will be useful as researchers look more deeply into how downstream brain areas contribute to learning.

The researchers stress that their study allowed more granular measurements of brain activity than would be possible in humans, and because monkeys’ brains are organized similarly to our own, their experiments have direct relevance to human learning. They say understanding the impact of plasticity in monkeys’ IT cortex could help researchers design new learning strategies for humans.

“Our prior conceptual working model of you—or a monkey—learning new objects was that your brain makes changes to synaptic connections that are largely downstream of your visual system, so you don’t destroy your visual system,” says DiCarlo, who is also the Peter de Florez Professor of Brain and Cognitive Sciences and Director of the MIT Siegel Family Quest for Intelligence. “You wouldn’t want your whole visual system to become an elephant detector [just because you’ve learned to identify an elephant]. But this study went beyond that to say actually, when you learn ‘elephant,’ your IT does change a little bit to make it a little more relevant to elephants.”

That likely has consequences for recognizing other visual features, too. Subtle changes in the IT cortex that support elephant recognition might also make you better at identifying things other than elephants, DiCarlo says. Likewise, the same changes might make it a little harder to identify something else.

These kinds of consequences may be difficult to predict intuitively, but become obvious with computational modeling. For instance, the team’s models revealed that after learning to recognize new objects, the IT cortex contained more information about objects’ locations. By providing insights like these, models could aid the design of more effective training strategies for visual tasks, including for people with altered sensory processing, who may learn from visual information in atypical ways.

 

 

Unpacking social intelligence

by Jennifer Michalowski |

Experience is a powerful teacher—and not every experience has to be our own to help us understand the world. What happens to others is instructive, too. That’s true for humans as well as for other social animals. New research from scientists at the McGovern Institute shows what happens in the brains of monkeys as they integrate their observations of others with knowledge gleaned from their own experience.

“The study shows how you use observation to update your assumptions about the world,” explains McGovern Institute Investigator Mehrdad Jazayeri, who led the research. His team’s findings, published in the January 7 issue of the journal Nature, also help explain why we tend to weigh information gleaned from observation and direct experience differently when we make decisions. Jazayeri is also a professor of brain and cognitive sciences at MIT and an investigator at the Howard Hughes Medical Institute.

“As humans, we do a large part of our learning through observing other people’s experiences and what they go through and what decisions they make,” says Setayesh Radkani, a graduate student in Jazayeri’s lab. For example, she says, if you get sick after eating out, you might wonder if the food at the restaurant was to blame. As you consider whether it’s safe to return, you’ll likely take into account whether the friends you’d dined with got sick too. Your experiences as well as those of your friends will inform your understanding of what happened.

The research team wanted to know how this works: When we make decisions that draw on both direct experience and observation, how does the brain combine the two kinds of evidence? Are the two kinds of information handled differently?

Social experiment

It is hard to tease out the factors that influence social learning. “When you’re trying to compare experiential learning versus observational learning, there are a ton of things that can be different,” Radkani says. For example, people may draw different conclusions about someone else’s experiences than their own, because they know less about that person’s motivations and beliefs. Factors like social status, individual differences, and emotional states can further complicate these situations and be hard to control for, even in a lab.

To create a carefully controlled scenario in which they could focus on how observation changes our understanding of the world, Radkani and postdoctoral fellow Michael Yoo devised a computer game that would allow two players to learn from one another through their experiences. They taught this game to both humans and monkeys.

Their approach, Jazayeri says, goes far beyond the kinds of tasks that are typically studied in a neuroscience lab. “I think it might be one of the most sophisticated tasks monkeys have been trained to perform in a lab,” he says.

Both monkeys and humans played the game in pairs. The object was to collect enough tokens to earn a reward. Players could choose to enter either of two virtual arenas to play—but in one of the two arenas, tokens had no value. In that arena, no matter how many tokens a player collected, they could not win. Players were not told which arena was which, and the winnable and unwinnable arenas sometimes swapped without warning.

Only one individual played at a time, but regardless of who was playing, both individuals watched all of the games. So as either player collected tokens and either did or did not receive a reward, both the player and the observer got the same information. They could use that information to decide which arena to choose in their next round.

Experience outweighs observation

Humans and monkeys have sophisticated social intelligence and both clearly took their partners’ experiences into account as they played the game. But the researchers found that the outcomes of a player’s own games had a stronger influence on each individual’s choice of arena than the outcomes of their partner’s games. “They seem to learn less efficiently from observation, suggesting they tend to devalue the observational evidence,” Radkani says. That distinction was reflected in the patterns of neural activity that the team detected in the brains of the monkeys.

Postdoctoral fellow Ruidong Chen and research assistant Neelima Valluru recorded signals from a part of the brain’s frontal lobe called the anterior cingulate cortex (ACC) as the monkeys played the game. The ACC is known to be involved in social processing. It also integrates information gained through multiple experiences, and seems to use this to update an animal’s beliefs about the world. Prior to the Jazayeri lab’s experiments, this integrative function had only been linked to animals’ direct experiences—not their observations of others.

Consistent with earlier studies, neurons in the ACC changed their activity patterns both when the monkeys played the game and when they watched their partner take a turn. But these signals were complex and variable, making it hard to discern the underlying logic. To tackle this challenge, Chen recorded neural activity from large groups of neurons in both animals across dozens of experiments. “We also had to devise new analysis methods to crack the code and tease out the logic of the computation,” Chen says.

One of the researchers’ central questions was how information about self and other makes its way to the ACC. The team reasoned that there were two possibilities: either the ACC receives a single input on each trial specifying who is acting, or it receives separate input streams for self and other. To test these alternatives, they built artificial neural network models organized both ways and analyzed how well each model matched their neural data. The results suggested that the ACC receives two distinct inputs, one reflecting evidence acquired through direct experience and one reflecting evidence acquired through observation.

The team also found a tantalizing clue about why the brain tends to trust firsthand experiences more than observations. Their analysis showed that the integration process in the ACC was biased toward direct experience. As a result, both humans and monkeys cared more about their own experiences than the experiences of their partner.

Jazayeri says the study paves the way to deeper investigations of how the brain drives social behavior. Now that his team has examined one of the most fundamental features of social learning, they plan to add additional nuance to their studies, potentially exploring how different abilities or the social relationships between animals influence learning.

“Under the broad umbrella of social cognition, this is like step zero,” he says. “But it’s a really important step, because it begins to provide a basis for understanding how the brain represents and uses social information in shaping the mind.”

This research was supported in part by the Yang Tan Collective at MIT.

New study suggests a way to rejuvenate the immune system

by Anne Trafton |

As people age, their immune system function declines. T cell populations become smaller and can’t react to pathogens as quickly, making people more susceptible to a variety of infections.

To try to overcome that decline, researchers at MIT and the Broad Institute have found a way to temporarily program cells in the liver to improve T-cell function. This reprogramming can compensate for the age-related decline of the thymus, where T cell maturation normally occurs.

Using mRNA to deliver three key factors that usually promote T-cell survival, the researchers were able to rejuvenate the immune systems of mice. Aged mice that received the treatment showed much larger and more diverse T cell populations in response to vaccination, and they also responded better to cancer immunotherapy treatments. Their findings are published in the December 17 issue of the journal Nature.

If developed for use in patients, this type of treatment could help people lead healthier lives as they age, the researchers say.

“If we can restore something essential like the immune system, hopefully we can help people stay free of disease for a longer span of their life,” says Feng Zhang, the James and Patricia Poitras Professor of Neuroscience at MIT, who has joint appointments in the departments of Brain and Cognitive Sciences and Biological Engineering.

Zhang, who is also an investigator at the McGovern Institute for Brain Research at MIT, a core institute member at the Broad Institute of MIT and Harvard, an investigator in the Howard Hughes Medical Institute, and co-director of the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT, is the senior author of the new study. Former MIT postdoc Mirco Friedrich is the lead author of the paper, which appears today in Nature.

A temporary factory

The thymus, a small organ located in front of the heart, plays a critical role in T-cell development. Within the thymus, immature T cells go through a checkpoint process that ensures a diverse repertoire of T cells. The thymus also secretes cytokines and growth factors that help T cells to survive.

However, starting in early adulthood, the thymus begins to shrink. This process, known as thymic involution, leads to a decline in the production of new T cells. By the age of approximately 75, the thymus is greatly reduced.

“As we get older, the immune system begins to decline. We wanted to think about how can we maintain this kind of immune protection for a longer period of time, and that’s what led us to think about what we can do to boost immunity,” Friedrich says.

Previous work on rejuvenating the immune system has focused on delivering T cell growth factors into the bloodstream, but that can have harmful side effects. Researchers are also exploring the possibility of using transplanted stem cells to help regrow functional tissue in the thymus.

The MIT team took a different approach: They wanted to see if they could create a temporary “factory” in the body that would generate the T-cell-stimulating signals that are normally produced by the thymus.

“Our approach is more of a synthetic approach,” Zhang says. “We’re engineering the body to mimic thymic factor secretion.”

For their factory location, they settled on the liver, for several reasons. First, the liver has a high capacity for producing proteins, even in old age. Also, it’s easier to deliver mRNA to the liver than to most other organs of the body. The liver was also an appealing target because all of the body’s circulating blood has to flow through it, including T cells.

To create their factory, the researchers identified three immune cues that are important for T-cell maturation. They encoded these three factors into mRNA sequences that could be delivered by lipid nanoparticles. When injected into the bloodstream, these particles accumulate in the liver and the mRNA is taken up by hepatocytes, which begin to manufacture the proteins encoded by the mRNA.

The factors that the researchers delivered are DLL1, FLT-3, and IL-7, which help immature progenitor T cells mature into fully differentiated T cells.

Immune rejuvenation

Tests in mice revealed a variety of beneficial effects. First, the researchers injected the mRNA particles into 18-month-old mice, equivalent to humans in their 50s. Because mRNA is short-lived, the researchers gave the mice multiple injections over four weeks to maintain a steady production by the liver.

After this treatment, T cell populations showed significant increases in size and function.

The researchers then tested whether the treatment could enhance the animals’ response to vaccination. They vaccinated the mice with ovalbumin, a protein found in egg whites that is commonly used to study how the immune system responds to a specific antigen. In 18-month-old mice that received the mRNA treatment before vaccination, the researchers found that the population of cytotoxic T-cells specific to ovalbumin doubled, compared to mice of the same age that did not receive the mRNA treatment.

The mRNA treatment can also boost the immune system’s response to cancer immunotherapy, the researchers found. They delivered the mRNA treatment to 18-month-old mice, who were then implanted with tumors and treated with a checkpoint inhibitor drug. This drug, which targets the protein PD-L1, is designed to help take the brakes off the immune system and stimulate T cells to attack tumor cells.

Mice that received the treatment showed much higher survival rates and longer lifespan that those that received the checkpoint inhibitor drug but not the mRNA treatment.

The researchers found that all three factors were necessary to induce this immune enhancement; none could achieve all aspects of it on their own. They now plan to study the treatment in other animal models and to identify additional signaling factors that may further enhance immune system function. They also hope to study how the treatment affects other immune cells, including B cells.

Other authors of the paper include Julie Pham, Jiakun Tian, Hongyu Chen, Jiahao Huang, Niklas Kehl, Sophia Liu, Blake Lash, Fei Chen, Xiao Wang, and Rhiannon Macrae.

The research was funded, in part, by the Howard Hughes Medical Institute, the K. Lisa Yang Brain-Body Center, part of the Yang Tan Collective at MIT, Broad Institute Programmable Therapeutics Gift Donors, the Pershing Square Foundation, J. and P. Poitras, and an EMBO Postdoctoral Fellowship.

New MIT initiative seeks to transform rare brain disorders research

by Rubina Veerakone |

More than 300 million people worldwide are living with rare disorders — many of which have a genetic cause and affect the brain and nervous system — yet the vast majority of these conditions lack an approved therapy. Because each rare disorder affects fewer than 65 out of every 100,000 people, studying these disorders and creating new treatments for them is especially challenging.

Thanks to a generous philanthropic gift from Ana Méndez ’91 and Rajeev Jayavant ’86, EE ’88, SM ’88, MIT is now poised to fill the gaps in this research landscape. By establishing the Rare Brain Disorders Nexus — or RareNet — at MIT’s McGovern Institute, the alumni aim to convene leaders in neuroscience research, clinical medicine, patient advocacy, and industry to streamline the lab-to-clinic pipeline for rare brain disorder treatments.

“Ana and Rajeev’s commitment to MIT will form crucial partnerships to propel the translation of scientific discoveries into promising therapeutics and expand the Institute’s impact on the rare brain disorders community,” says MIT President Sally Kornbluth. “We are deeply grateful for their pivotal role in advancing such critical science and bringing attention to conditions that have long been overlooked.”

Building new coalitions

Several hurdles have slowed the lab-to-clinic pipeline for rare brain disorder research. It is difficult to secure a sufficient number of patients per study, and current research efforts are fragmented since each study typically focuses on a single disorder (there are more than 7,000 known rare disorders, according to the World Health Organization). Pharmaceutical companies are often reluctant to invest in emerging treatments due to a limited market size and the high costs associated with preparing drugs for commercialization.

Méndez and Jayavant envision that RareNet will finally break down these barriers. “Our hope is that RareNet will allow leaders in the field to come together under a shared framework and ignite scientific breakthroughs across multiple conditions. A discovery for one rare brain disorder could unlock new insights that are relevant to another,” says Jayavant. “By congregating the best minds in the field, we are confident that MIT will create the right scientific climate to produce drug candidates that may benefit a spectrum of uncommon conditions.”

Guoping Feng, the James W. (1963) and Patricia T. Poitras Professor in Neuroscience and associate director of the McGovern Institute for Brain Research at MIT, will serve as RareNet’s inaugural faculty director. Feng holds a strong record of advancing studies on therapies for neurodevelopmental disorders, including autism spectrum disorders, Williams syndrome, and uncommon forms of epilepsy. His team’s gene therapy for Phelan-McDermid syndrome, a rare and profound autism spectrum disorder, has been licensed to Jaguar Gene Therapy and is currently undergoing clinical trials. “RareNet pioneers a unique model for biomedical research — one that is reimagining the role academia can play in developing therapeutics,” says Feng.

Image of SHANK3 therapy correctly finding its way to dendrites. Image: Guoping Feng
An early version of a gene therapy for SHANK3 mutations — linked to a rare brain disorder called Phelan-McDermid syndrome — correctly finds its way to neurons. Image: Feng lab

RareNet plans to deploy two major initiatives: a global consortium and a therapeutic pipeline accelerator. The consortium will form an international network of researchers, clinicians, and patient groups from the outset. It seeks to connect siloed research efforts, secure more patient samples, promote data sharing, and drive a strong sense of trust and goal alignment across the RareNet community. Partnerships within the consortium will support the aim of the therapeutic pipeline accelerator: to de-risk early lab discoveries and expedite their translation to clinic. By fostering more targeted collaborations — especially between academia and industry — the accelerator will prepare potential treatments for clinical use as efficiently as possible.

MIT labs are focusing on four uncommon conditions in the first wave of RareNet projects: Rett syndrome, prion disease, disorders linked to SYNGAP1 mutations, and Sturge-Weber syndrome. The teams are working to develop novel therapies that can slow, halt, or reverse dysfunctions in the brain and nervous system.

These efforts will build new bridges to connect key stakeholders across the rare brain disorders community and disrupt conventional research approaches. “Rajeev and I are motivated to seed powerful collaborations between MIT researchers, clinicians, patients, and industry,” says Méndez. “Guoping Feng clearly understands our goal to create an environment where foundational studies can thrive and seamlessly move toward clinical impact.”

“Patient and caregiver experiences, and our foreseeable impact on their lives, will guide us and remain at the forefront of our work,” Feng adds. “For far too long the rare brain disorders community has been deprived of life-changing treatments — and, importantly, hope. RareNet gives us the opportunity to transform how we study these conditions and to do so at a moment when it’s needed more than ever.”

 

MIT cognitive scientists reveal why some sentences stand out from others

by Anne Trafton |

Press Mentions

Why do some sentences stay in your head...

Brighter Side of News

“You still had to prove yourself.”

“Every cloud has a blue lining!”

Which of those sentences are you most likely to remember a few minutes from now? If you guessed the second, you’re probably correct.

According to a new study from MIT cognitive scientists, sentences that stick in your mind longer are those that have distinctive meanings, making them stand out from sentences you’ve previously seen. They found that meaning, not any other trait, is the most important feature when it comes to memorability.

Greta Tuckute, a former graduate student in the Fedorenko lab. Photo: Caitlin Cunningham

“One might have thought that when you remember sentences, maybe it’s all about the visual features of the sentence, but we found that that was not the case. A big contribution of this paper is pinning down that it is the meaning-related space that makes sentences memorable,” says Greta Tuckute PhD ’25, who is now a research fellow at Harvard University’s Kempner Institute.

The findings support the hypothesis that sentences with distinctive meanings — like “Does olive oil work for tanning?” — are stored in brain space that is not cluttered with sentences that mean almost the same thing. Sentences with similar meanings end up densely packed together and are therefore more difficult to recognize confidently later on, the researchers believe.

“When you encode sentences that have a similar meaning, there’s feature overlap in that space. Therefore, a particular sentence you’ve encoded is not linked to a unique set of features, but rather to a whole bunch of features that may overlap with other sentences,” says Evelina Fedorenko, an MIT associate professor of brain and cognitive sciences (BCS), a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Tuckute and Thomas Clark, an MIT graduate student, are the lead authors of the paper, which appears in the Journal of Memory and Language. MIT graduate student Bryan Medina is also an author.

Distinctive sentences

What makes certain things more memorable than others is a longstanding question in cognitive science and neuroscience. In a 2011 study, Aude Oliva, now a senior research scientist at MIT and MIT director of the MIT-IBM Watson AI Lab, showed that not all items are created equal: Some types of images are much easier to remember than others, and people are remarkably consistent in what images they remember best.

In that study, Oliva and her colleagues found that, in general, images with people in them are the most memorable, followed by images of human-scale space and close-ups of objects. Least memorable are natural landscapes.

As a follow-up to that study, Fedorenko and Oliva, along with Ted Gibson, another faculty member in BCS, teamed up to determine if words also vary in their memorability. In a study published earlier this year, co-led by Tuckute and Kyle Mahowald, a former PhD student in BCS, the researchers found that the most memorable words are those that have the most distinctive meanings.

Words are categorized as being more distinctive if they have a single meaning, and few or no synonyms — for example, words like “pineapple” or “avalanche” which were found to be very memorable. On the other hand, words that can have multiple meanings, such as “light,” or words that have many synonyms, like “happy,” were more difficult for people to recognize accurately.

In the new study, the researchers expanded their scope to analyze the memorability of sentences. Just like words, some sentences have very distinctive meanings, while others communicate similar information in slightly different ways.

To do the study, the researchers assembled a collection of 2,500 sentences drawn from publicly available databases that compile text from novels, news articles, movie dialogues, and other sources. Each sentence that they chose contained exactly six words.

The researchers then presented a random selection of about 1,000 of these sentences to each study participant, including repeats of some sentences. Each of the 500 participants in the study was asked to press a button when they saw a sentence that they remembered seeing earlier.

The most memorable sentences — the ones where participants accurately and quickly indicated that they had seen them before — included strings such as “Homer Simpson is hungry, very hungry,” and “These mosquitoes are — well, guinea pigs.”

Those memorable sentences overlapped significantly with sentences that were determined as having distinctive meanings as estimated through the high-dimensional vector space of a large language model (LLM) known as Sentence BERT. That model is able to generate sentence-level representations of sentences, which can be used for tasks like judging meaning similarity between sentences. This model provided researchers with a distinctness score for each sentence based on its semantic similarity to other sentences.

The researchers also evaluated the sentences using a model that predicts memorability based on the average memorability of the individual words in the sentence. This model performed fairly well at predicting overall sentence memorability, but not as well as Sentence BERT. This suggests that the meaning of a sentence as a whole — above and beyond the contributions from individual words — determines how memorable it will be, the researchers say.

Noisy memories

While cognitive scientists have long hypothesized that the brain’s memory banks have a limited capacity, the findings of the new study support an alternative hypothesis that would help to explain how the brain can continue forming new memories without losing old ones.

This alternative, known as the noisy representation hypothesis, says that when the brain encodes a new memory, be it an image, a word, or a sentence, it is represented in a noisy way — that is, this representation is not identical to the stimulus, and some information is lost. For example, for an image, you may not encode the exact viewing angle at which an object is shown, and for a sentence, you may not remember the exact construction used.

Under this theory, a new sentence would be encoded in a similar part of the memory space as sentences that carry a similar meanings, whether they were encountered recently or sometime across a lifetime of language experience. This jumbling of similar meanings together increases the amount of noise and can make it much harder, later on, to remember the exact sentence you have seen before.

“The representation is gradually going to accumulate some noise. As a result, when you see an image or a sentence for a second time, your accuracy at judging whether you’ve seen it before will be affected, and it’ll be less than 100 percent in most cases,” Clark says.

However, if a sentence has a unique meaning that is encoded in a less densely crowded space, it will be easier to pick out later on.

“Your memory may still be noisy, but your ability to make judgments based on the representations is less affected by that noise because the representation is so distinctive to begin with,” Clark says.

The researchers now plan to study whether other features of sentences, such as more vivid and descriptive language, might also contribute to making them more memorable, and how the language system may interact with the hippocampal memory structures during the encoding and retrieval of memories.

The research was funded, in part, by the National Institutes of Health, the McGovern Institute, the Department of Brain and Cognitive Sciences, the Simons Center for the Social Brain, and the MIT Quest Initiative for Intelligence.

International neuroscience collaboration unveils comprehensive cellular-resolution map of brain activity

by Julie Pryor |

The first comprehensive map of mouse brain activity has been unveiled by a large international collaboration of neuroscientists. Researchers from the International Brain Laboratory (IBL), including McGovern Investigator Ila Fiete, published their findings today in two papers in Nature, revealing insights into how decision-making unfolds across the entire brain in mice at single-cell resolution. This brain-wide activity map challenges the traditional hierarchical view of information processing in the brain and shows that decision-making is distributed across many regions in a highly coordinated way.

“This is the first time anyone has produced a full, brain-wide map of the activity of single neurons during decision-making,” explains Co-Founder of IBL Alexandre Pouget. “The scale is unprecedented as we recorded from over half a million neurons across mice in 12 labs, covering 279 brain areas, which together represent 95% of the mouse brain volume. The decision-making activity, and particularly reward, lit up the brain like a Christmas tree,” adds Pouget, who is also a Group Leader at the University of Geneva.

Brain-wide map showing 75,000 analyzed neurons lighting up during different stages of decision-making. At the beginning of the trial, the activity is quiet. Then it builds up in the visual areas at the back of the brain, followed by a rise in activity spreading across the brain as evidence accumulates towards a decision. Next, motor areas light up as there is movement onset and finally there is a spike in activity everywhere in the brain as the animal is rewarded.

Modeling decision-making

The brain map was made possible by a major international collaboration of neuroscientists from multiple universities, including MIT. Researchers across 12 labs used state-of-the-art silicon electrodes, called Neuropixels probes,  for simultaneous neural recordings to measure brain activity while mice were carrying out a decision-making task.

McGovern Associate Investigator Ila Fiete. Photo: Caitlin Cunningham

“Participating in the International Brain Laboratory has added new ways for our group to contribute to science,” says Fiete, who is also a professor of brain and cognitive sciences director of the K. Lisa Yang ICoN Center at MIT. “Our lab has helped standardize methods to analyze and generate robust conclusions from data. As computational neuroscientists interested in building models of how the brain works, access to brainwide recordings is incredible: the traditional approach of recording from one or a few brain areas limited our ability to build and test theories, resulting in fragmented models. Now we have the delightful but formidable task to make sense of how all parts of the brain coordinate to perform a behavior. Surprisingly, having a full view of the brain leads to simplifications in the models of decision making.”

The labs collected data from mice performing a decision-making task with sensory, motor, and cognitive components. In the task, a mouse sits in front of a screen and a light appears on the left or right side. If the mouse then responds by moving a small wheel in the correct direction, it receives a reward.

In some trials, the light is so faint that the animal must guess which way to turn the wheel, for which it can use prior knowledge: the light tends to appear more frequently on one side for a number of trials, before the high-frequency side switches. Well-trained mice learn to use this information to help them make correct guesses. These challenging trials therefore allowed the researchers to study how prior expectations influence perception and decision-making.

Brain-wide results

The first paper, “A brain-wide map of neural activity during complex behaviour,” showed that decision-making signals are surprisingly distributed across the brain, not localized to specific regions. This adds brain-wide evidence to a growing number of studies that challenge the traditional hierarchical model of brain function and emphasizes that there is constant communication across brain areas during decision-making, movement onset, and even reward. This means that neuroscientists will need to take a more holistic, brain-wide approach when studying complex behaviors in future.

Flat maps of the mouse brain showing which areas have significant changes in activity during each of three task intervals. Credit: Michael Schartner & International Brain Laboratory

“The unprecedented breadth of our recordings pulls back the curtain on how the entire brain performs the whole arc of sensory processing, cognitive decision-making, and movement generation,” says Fiete. “Structuring a collaboration that collects a large standardized dataset which single labs could not assemble is a revolutionary new direction for systems neuroscience, initiating the field into the hyper-collaborative mode that has contributed to leaps forward in particle physics and human genetics. Beyond our own conclusions, the dataset and associated technologies, which were released much earlier as part of the IBL mission, have already become a massively used resource for the entire neuroscience community.”

The second paper, “Brain-wide representations of prior information,” showed that prior expectations, our beliefs about what is likely to happen based on our recent experience, are encoded throughout the brain. Surprisingly, these expectations are not only found in cognitive areas, but also brain areas that process sensory information and control actions. For example, expectations are even encoded in early sensory areas such as the thalamus, the brain’s first relay for visual input from the eye. This supports the view that the brain acts as a prediction machine, but with expectations encoded across multiple brain structures playing a central role in guiding behavior responses. These findings could have implications for understanding conditions such as schizophrenia and autism, which are thought to be caused by differences in the way expectations are updated in the brain.

“Much remains to be unpacked: if it is possible to find a signal in a brain area, does it mean that this area is generating the signal, or simply reflecting a signal generated somewhere else? How strongly is our perception of the world is shaped by our expectations? Now we can generate some quantitative answers and begin the next phase experiments to learn about the origins of the expectation signals by intervening to modulate their activity,” says Fiete.

Looking ahead, the team at IBL plan to expand beyond their initial focus on decision-making to explore a broader range of neuroscience questions. With renewed funding in hand, IBL aims to expand its research scope and continue to support large-scale, standardized experiments.

New model of collaborative neuroscience

Officially launched in 2017, IBL introduced a new model of collaboration in neuroscience that uses a standardized set of tools and data processing pipelines shared across multiple labs, enabling the collection of massive datasets while ensuring data alignment and reproducibility. This approach to democratize and accelerate science draws inspiration from large-scale collaborations in physics and biology, such as CERN and the Human Genome Project.

All data from these studies, along with detailed specifications of the tools and protocols used for data collection, are openly accessible to the global scientific community for further analysis and research. Summaries of these resources can be viewed and downloaded on the IBL website under the sections: Data, Tools, Protocols.

This research was supported by grants from Wellcome (209558 and 216324), the Simons Foundation, The National Institutes of Health (NIH U19NS12371601), the National Science Foundation (NSF 1707398), the Gatsby Charitable Foundation (GAT3708), andby the Max Planck Society and the Humboldt Foundation.

 

Searching for self

by Jennifer Michalowski |

This story also appears in the Fall 2025 issue of BrainScan. 

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The question of how we know ourselves might seem the subject of philosophers, but it is just as much a matter of biology. As modern neuroscientists obtain an increasingly sophisticated understanding of how the brain generates emotions, responds to the external world, and learns from experience, some researchers are returning to a central question: How do we know our experiences, emotions, and physical sensations belong to us?

Curiosity about how the brain generates our sense of self has been a driving force for the research of McGovern Investigator Fan Wang. Following that curiosity has drawn Wang into diverse studies, exploring the origins of pain and the mechanisms we use to control our movements.

“We cannot pinpoint a set of active neurons and say that’s the sense of self. That still remains a mystery,” says Wang, who is also a professor of brain and cognitive sciences and co-director of the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT. But she and other neuroscientists are drilling down into different functions of the brain that together might generate our awareness of ourselves.

Woman wearing blue blazer smiles and gestures off camera with man in white lab coat seated next to her.
McGovern Investigator Fan Wang (right) with research scientist Vincent Prevosto, who studies brain regions implicated in whisker movement. Photo: Steph Stevens

Wang, who teaches the undergraduate course, “Neurobiology of Self,” explains that there are lots of ways to think about our sense of self, which are probably deeply integrated in the brain. Some are mostly about our physical bodies: How do we experience touch? How do we understand
where we are in space, or recognize the boundary between ourselves and rest of the world? Some consider more internal sensations, like how we experience pain or hunger. Emotion is also key to our sense of self: How do we know that anger or joy are our own, and why do these states change the way our bodies feel?

Wang can trace her initial interest in the brain’s sense of self to work she did as a graduate student in Richard Axel’s lab at Columbia University. The lab had identified receptors expressed by sensory neurons in the nose that detect odorous substances. Wang and others discovered the pathways that information about these smells takes to the brain, and how the brain distinguishes one smell from another.

Who is the “knower” of this information? “The answer,” Wang says, “is ‘I’ or ‘me.’ But understanding where I get the sense of self and how that is constructed, is what drives me to do neuroscience.”

Mechanisms of movement

In her lab at the McGovern Institute, Wang is studying how the brain controls the body’s movements, which she sees as closely tied to the awareness of our physical selves. “The reason I think I am in my body is because I can control my movement. I generate the movement. I cannot control your movement,” says Wang. “Volitional movement gives us a sense of agency, and this sense of agency resembles the sense of self.” For the mice that the group studies, one crucial type of movement comes from the whiskers, which the animals depend on as they explore their environments. Wang’s group has traced the neural circuity that controls whiskers’ rhythmic back-and-forth, which is initiated in the brainstem, where many of the body’s most vital functions are controlled. Wang describes the simple circuit as an oscillator, or a self-generated loop.

A maximum projection image showing tracked whiskers on the mouse muzzle. The right (control) side shows the back-and-forth rhythmic sweeping of the whiskers, while the experimental side where the whisking oscillator neurons are silenced, the whiskers move very little. Image: Wang Lab

Once it’s started, “the movement can go on unless some other signals stop it,” she says. The movement the circuit generates is simple but voluntary, and can be fine-tuned based on the sensory feedback the whiskers relay back to the brain. They’ve also been investigating how mice move the larynx to generate the squeaks and calls they use to communicate. These intentional movements must be coordinated with the ongoing cycles of respiration since we produce normal sounds only during expiration. Wang’s team has found neurons in the brainstem that generate vocalization-specific movements, and also discovered how respiration-controlling neural circuits can override them, ensuring that breathing is prioritized.

Wang says understanding the circuitry that controls these simple movements sets the stage for figuring out how the brain modifies activity in those circuits to create more complex, intentional movements. “That brings me closer to understanding where this volition is generated — and closer to this sense of self,” she says.

Emotional pain

Still, she knows that volitional movements — even those generated in response to perceptions of the environment — do not, on their own, define a sense of self. As a counterexample, she looks to self-driving cars: “There’s sensory information coming into the central computer, which then generates a motor output — where to drive, where to turn, where to stop. But none of us think a Waymo taxi has a sense of self.”

Wang says when she pondered the ways in which AI-powered cars lack a sense of self, she began thinking about emotions and pain. “If the self-driving Waymo crashes, it will not feel pain,” she says. “But if we hurt ourselves, we will feel pain. And we will hate that, and then we’ll learn.” So her lab is also exploring how the nervous system generates pain perception, including the emotional response that it evokes.

Ensembles of neurons in the amygdala activated by general anesthesia. Image: Fan Wang

In both humans and mice, pain causes emotional suffering that can be recognized and measured through changes in body functions like heart rate and blood pressure. With funding from the K. Lisa Yang Brain-Body Center at MIT, Wang’s lab is carefully tracking these involuntary, or autonomic, functions to gain a more complete understanding of pain’s emotional impact. This approach has helped clarify the role of pain-suppressing neurons in the brain’s amygdala — an important emotion-processing center — that Wang’s team discovered in 2020. When researchers selectively activate those cells in mice, the animals’ behavior makes it clear that the neurons are suppressing pain. Now, the group has learned that activating these neurons suppresses the autonomic response to pain.

Wang says there’s hope that modulating pain’s emotional response might be a way to treat chronic pain in patients. She explains that some patients with damage to another one of the brain’s emotional centers, the cingulate cortex, feel painful stimuli, but experience them as merely intense sensations. That suggests that it might be possible to modulate the emotional response to pain to eliminate patients’ suffering, without blocking the protective information that pain can provide.

The team has also been focusing on another set of anesthesia-activated neurons, which they have found suppress anxiety. When anxiety-suppressing neurons are activated in mice, the animals’ heart rates slow and they become more willing to explore bright, open spaces. Another anxiety-associated measure — heart rate variability — increases. Wang explains that this change is particularly significant: “If you have persistent low heart rate variability, especially in veterans, that is a very good predictor for anxiety developing into depression in the future,” she says.

The team’s findings, which suggest that changes in autonomic functions may themselves relieve anxiety, point toward potential new targets for anti-anxiety therapies. And by highlighting the connection between emotion and bodily responses, they offer more clues about our sense of self. “These neurons are now changing some high-level concept about anxiety,” Wang points out.

That link between emotion and body seems to Wang to be key to the sense of self. The big questions remain unanswered, but that simply stokes her curiosity. “I can be aware of my bodily responses: I am aware of ‘I am anxious’ or ‘I am in pain.’ I can see the pathways from which stimuli go into these nervous systems and come back down to the body and control the response. But I still don’t know who is the person — the knower,” she says. “I haven’t found it, so I’m going to keep looking.”

Polina Anikeeva named 2024 Blavatnik Award Finalist

by Julie Pryor |

The Blavatnik Family Foundation and New York Academy of Sciences has announced the honorees of the 2024 Blavatnik National Awards, and McGovern Investigator Polina Anikeeva is among five finalists in the category of physical sciences and engineering.

Anikeeva, the Matoula S. Salapatas Professor in Materials Science and Engineering at MIT, works at the intersection of materials science, electronics, and neurobiology to improve our understanding of brain-body communication. She is head of MIT’s Materials Science and Engineering Department, and is also a professor of brain and cognitive sciences, director of the K. Lisa Yang Brain-Body Center, and associate director of the Research Laboratory of Electronics. Anikeeva’s lab has developed ultrathin, flexible fibers that probe the flow of information between the brain and peripheral organs in the body. Her ultimate goal is to develop novel technologies to achieve healthy minds in healthy bodies.

The Blavatnik National Awards for Young Scientists is the largest unrestricted scientific prize offered to America’s most promising, faculty-level scientific researchers under 42. The 2024 Blavatnik National Awards received 331 nominations from 172 institutions in 43 US states and selected three women scientists as laureates (Cigall Kadoch, Dana Farber Cancer Institute; Markita del Carpio Landry, UC Berkeley; and Britney Schmidt, Cornell University). An additional 15 finalists, including two from MIT: Anikeeva and Yogesh Surendranath will also receive monetary prizes.

“On behalf of the Blavatnik Family Foundation, I congratulate this year’s outstanding laureates and finalists for their exceptional research. They are among the preeminent leaders of the next generation of scientific innovation and discovery,” said Len Blavatnik, founder of Access Industries and the Blavatnik Family Foundation and a member of the President’s Council of The New York Academy of Sciences.

The Blavatnik National Awards for Young Scientists will celebrate the 2024 laureates and finalists in a gala ceremony on October 1, 2024, at the American Museum of Natural History in New York.

Scientists find neurons that process language on different timescales

by Anne Trafton |

Using functional magnetic resonance imaging (fMRI), neuroscientists have identified several regions of the brain that are responsible for processing language. However, discovering the specific functions of neurons in those regions has proven difficult because fMRI, which measures changes in blood flow, doesn’t have high enough resolution to reveal what small populations of neurons are doing.

Now, using a more precise technique that involves recording electrical activity directly from the brain, MIT neuroscientists have identified different clusters of neurons that appear to process different amounts of linguistic context. These “temporal windows” range from just one word up to about six words.

The temporal windows may reflect different functions for each population, the researchers say. Populations with shorter windows may analyze the meanings of individual words, while those with longer windows may interpret more complex meanings created when words are strung together.

“This is the first time we see clear heterogeneity within the language network,” says Evelina Fedorenko, an associate professor of neuroscience at MIT. “Across dozens of fMRI experiments, these brain areas all seem to do the same thing, but it’s a large, distributed network, so there’s got to be some structure there. This is the first clear demonstration that there is structure, but the different neural populations are spatially interleaved so we can’t see these distinctions with fMRI.”

Fedorenko, who is also a member of MIT’s McGovern Institute for Brain Research, is the senior author of the study, which appears today in Nature Human Behavior. MIT postdoc Tamar Regev and Harvard University graduate student Colton Casto are the lead authors of the paper.

Temporal windows

Functional MRI, which has helped scientists learn a great deal about the roles of different parts of the brain, works by measuring changes in blood flow in the brain. These measurements act as a proxy of neural activity during a particular task. However, each “voxel,” or three-dimensional chunk, of an fMRI image represents hundreds of thousands to millions of neurons and sums up activity across about two seconds, so it can’t reveal fine-grained detail about what those neurons are doing.

One way to get more detailed information about neural function is to record electrical activity using electrodes implanted in the brain. These data are hard to come by because this procedure is done only in patients who are already undergoing surgery for a neurological condition such as severe epilepsy.

“It can take a few years to get enough data for a task because these patients are relatively rare, and in a given patient electrodes are implanted in idiosyncratic locations based on clinical needs, so it takes a while to assemble a dataset with sufficient coverage of some target part of the cortex. But these data, of course, are the best kind of data we can get from human brains: You know exactly where you are spatially and you have very fine-grained temporal information,” Fedorenko says.

In a 2016 study, Fedorenko reported using this approach to study the language processing regions of six people. Electrical activity was recorded while the participants read four different types of language stimuli: complete sentences, lists of words, lists of non-words, and “jabberwocky” sentences — sentences that have grammatical structure but are made of nonsense words.

Those data showed that in some neural populations in language processing regions, activity would gradually build up over a period of several words, when the participants were reading sentences. However, this did not happen when they read lists of words, lists of nonwords, of Jabberwocky sentences.

In the new study, Regev and Casto went back to those data and analyzed the temporal response profiles in greater detail. In their original dataset, they had recordings of electrical activity from 177 language-responsive electrodes across the six patients. Conservative estimates suggest that each electrode represents an average of activity from about 200,000 neurons. They also obtained new data from a second set of 16 patients, which included recordings from another 362 language-responsive electrodes.

When the researchers analyzed these data, they found that in some of the neural populations, activity would fluctuate up and down with each word. In others, however, activity would build up over multiple words before falling again, and yet others would show a steady buildup of neural activity over longer spans of words.

By comparing their data with predictions made by a computational model that the researchers designed to process stimuli with different temporal windows, the researchers found that neural populations from language processing areas could be divided into three clusters. These clusters represent temporal windows of either one, four, or six words.

“It really looks like these neural populations integrate information across different timescales along the sentence,” Regev says.

Processing words and meaning

These differences in temporal window size would have been impossible to see using fMRI, the researchers say.

“At the resolution of fMRI, we don’t see much heterogeneity within language-responsive regions. If you localize in individual participants the voxels in their brain that are most responsive to language, you find that their responses to sentences, word lists, jabberwocky sentences and non-word lists are highly similar,” Casto says.

The researchers were also able to determine the anatomical locations where these clusters were found. Neural populations with the shortest temporal window were found predominantly in the posterior temporal lobe, though some were also found in the frontal or anterior temporal lobes. Neural populations from the two other clusters, with longer temporal windows, were spread more evenly throughout the temporal and frontal lobes.

Fedorenko’s lab now plans to study whether these timescales correspond to different functions. One possibility is that the shortest timescale populations may be processing the meanings of a single word, while those with longer timescales interpret the meanings represented by multiple words.

“We already know that in the language network, there is sensitivity to how words go together and to the meanings of individual words,” Regev says. “So that could potentially map to what we’re finding, where the longest timescale is sensitive to things like syntax or relationships between words, and maybe the shortest timescale is more sensitive to features of single words or parts of them.”

The research was funded by the Zuckerman-CHE STEM Leadership Program, the Poitras Center for Psychiatric Disorders Research, the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, the U.S. National Institutes of Health, an American Epilepsy Society Research and Training Fellowship, the McDonnell Center for Systems Neuroscience, Fondazione Neurone, the McGovern Institute, MIT’s Department of Brain and Cognitive Sciences, and the Simons Center for the Social Brain.