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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.”

Can (and should) consciousness be uploaded to a machine?

by Julie Pryor |

Consciousness transfer, the hypothetical process of moving a human mind to a digital medium, has been a cornerstone of science fiction movies and books for decades. But as AI advances, questions about whether human consciousness could be transferred to a machine have moved from science fiction to serious academic debate.

The human brain is widely considered the most complex structure in the known universe, with tens of billions of neurons and more than 100 trillion points of connection. The theoretical process of uploading the mind, or “whole brain emulation,” would require mapping all of these circuits and understanding their unique function – a feat that many neuroscientists say exceeds current technologies and computing power. Mapping even the poppy seed-sized brain of a fly required an unprecedented collaboration of hundreds of scientists, various AI tools, and a global community of human proofreaders six years to complete. Researchers estimate it will take another 10 to 15 years to do the same in the mouse.

These technical hurdles aside, consciousness transfer also raises profound philosophical questions about identity, mortality, and what it means to exist. Even if we could upload our minds to a machine, should we?

To explore these questions, we asked members of the McGovern community to weigh in on the debate.

Jeffrey Brown II

Jeffrey Brown II is an EECS PhD student in Edward Boyden’s lab at the McGovern Institute. He builds AI agents to help construct a complete and accurate wiring diagram of the brain.“

Working on this problem can be extremely stimulative for neuroscience because, if you approach it seriously, you will have to deal with the some of the field’s most challenging problems.” – Jeffrey Brown

Jeffrey Brown II is an EECS graduate student using AI tools in the Boyden lab to construct a complete wiring diagram of the brain.

No one one really knows how the brain works, says Brown, and it’s unclear how much we need to know about the human brain in order to build an AI model of it.

“We don’t even have a list of all the circuits in the human brain,” says Brown, adding that he hopes to scale this up as part of his doctoral thesis in the Boyden lab. “We know many pieces of the brain, but we don’t really know how they all interact across massive scales of space and time,” he explains.

Some researchers believe functional data suffices; others insist on biophysical accuracy; still others use different approaches entirely. There’s no scientific consensus on what “good enough” actually means, Brown says.

Brown emphasizes that serious work in this space could be tremendously valuable for neuroscience, pushing researchers to grapple with some of the field’s most fundamental challenges. “On the other hand, if you don’t do the science carefully, I think people could hurt themselves, most likely by ceding their health (physical and mental) or agency to building a digital model that is not really “them.””

Davy Deng

Davy Deng is a Harvard-MIT HST PhD student in Ed Boyden’s lab at the McGovern Institute. His research goal is to build a digital brain using high-resolution multimodal data from the nervous systems of small animals.

“The most scientifically grounded and promising path toward whole brain emulation involves studying the nervous system of a small worm.” – Davy Deng

Davy Deng is a Harvard-MIT HST PhD student in Ed Boyden’s developing technologies toward reconstruction of the mind in machines.

In his recent TEDxMIT talk, Deng highlights a striking geographic divide: “When I mention whole brain emulation in Silicon Valley, people lean forward… But if I mention this in Boston, I tend to get some suspicious looks.”

This hype and skepticism has clouded real progress, he says, and a rigorous scientific foundation is needed to make headway. He argues that C. elegans, a 1-millimeter transparent worm, offers a unique opportunity to do this. The complete wiring diagram of the worm is well established, yet scientists have been unsuccessful in emulating its brain activity in a computer.

“This tells us that a wiring diagram alone does not contain sufficient information to simulate a brain,” he says.

His solution is to incorporate two additional layers of information – the chemical signatures of each connection and whole-organism imaging during behavior – creating what he calls the “ground truth” necessary for brain emulation.

Ila Fiete

Ila Fiete is an associate investigator at the McGovern Institute, a professor of brain and cognitive sciences, and the director of the K. Lisa Yang ICoN Center at MIT.

“I do think consciousness transfer could be possible!” – Ila Fiete

McGovern Institute Associate Investigator Ila Fiete.

Initial proofs of concept based on the connectome show some promise, Fiete says. (Nature, Sept 2024; Nature, Oct 2024; NeurIPS 2025) These studies combine supervised learning with existing circuit knowledge to infer the function of individual neurons.

“The challenge will be to show whether it is possible to do this for the whole brain, where we do not have knowledge of what most of the circuits exactly do, or how they do it.”

Rather than declaring consciousness transfer inherently good or bad, Fiete recognizes that there are profound philosophical concerns that deserve careful attention from ethicists.

“I think once people have an idea, it’s impossible to stop it from being pursued,” she says. “That’s a pathology of our species stemming from our curiosity and drive for improvement.”

“Fortunately, I do think and hope that we are capable of building guardrails around how these things are done.”

Alan Jasanoff

Alan Jasanoff is an associate investigator at the McGovern Institute, the Eugene McDermott Professor in the Brain Sciences and Human Behavior at MIT. He is also the author of the book, “The Biological Mind: How Brain, Body, and Environment Collaborate to Make Us Who We Are.” 

“Given our current level of knowledge, it is hard to imagine how this could be done.
But never say never!” – Alan Jasanoff

Even if we mapped the billions of neurons and their interactions in the human brain, says Jasanoff, we still can’t easily explain the “phenomenal” aspect of consciousness: why we feel and experience things the way we do.

McGovern Institute Associate Investigator Alan Jasanoff.

“This aspect of consciousness doesn’t seem reducible to more fundamental characteristics the way that biological phenomena like disease, digestion, or even life itself seem to be,” he says.

“And if phenomenal consciousness were somehow recreated, consider what kind of existence awaits,” Jasanoff reflects.

“What would give that life satisfaction or a sense of purpose? The virtual consciousness might achieve freedom from biological suffering and death, but how could it escape virtual equivalents of those in the machine environment?”

Qiyao (Catherine) Liang

Catherine Liang is an EECS PhD student in Ila Fiete’s lab at the McGovern Institute. She studies how intelligence emerges in brains and artificial systems. 

“Consciousness uploading could be brilliant in principle, but terrible if developed or used without a clear understanding of its consequence.” – Catherine Liang

Catherine Liang is an EECS PhD student in the Fiete lab studying how intelligence emerges in biological and artificial systems.

“It may one day be possible to create models that imitate a person’s memories, personality, and behavior closely enough to function as a digital descendant,” says Liang. “Even whole-brain emulation may become a reality if consciousness depends mainly on the brain’s functional organization.”

“But that is assuming that consciousness is an emergent property of the physical brain, and precisely what level of details we need to emulate is the open question,” she says.

Consciousness uploading could theoretically overcome aging, disease, and biological limitations, says Liang, but it poses catastrophic risks if the process merely copies consciousness while the original person dies.

Profound ethical concerns would emerge around access, consent, ownership, and the legal rights of digital minds, she cautions.

Caitlin Lienkaemper

Caitlin Lienkaemper is the Swartz Foundation Postdoctoral Fellow for Theory in Neuroscience in Ila Fiete’s lab where she uses mathematical tools to study how the brain encodes and processes information.

“If I only existed as thoughts on a computer, I don’t think I’d be alive in a meaningful sense; I am my body as much as I am my thoughts.” – Caitlin Lienkaemper

Caitlin Lienkaemper is the Swartz Foundation Postdoctoral Fellow for Theory in Neuroscience in Ila Fiete’s lab.

While Lienkaemper acknowledges that a highly detailed simulation of a human brain could theoretically be conscious, she remains skeptical—in part because there’s no scientific consensus on how to measure consciousness in the first place.

“We don’t have a fully fleshed out theory of consciousness that would give us reason to trust any measurement,” she explains. “And even if we did, it’s unclear whether predictions valid for human brains would hold for machines.”

Beyond the technical barriers, though, Lienkaemper questions whether consciousness transfer is even desirable. She sees the pursuit as a “secular repackaging” of the concept of an immortal soul—and worries it fundamentally misses what makes life meaningful.

“I don’t think my thoughts are the only important thing about me,” she says. “I’m my body as much as I am my thoughts, and I value my connection to the natural world and to other people. Investing our energy in digital immortality rather than preserving Earth’s ecosystems seems both impractical and selfish.”

 

RareNet Symposium 2026

by Julie Pryor |

On June 9, the McGovern Institute convened leaders in science, biotechnology, and patient advocacy for RareNet 2026, a first-of-its-kind symposium aimed at dismantling the barriers between laboratory discovery and life-changing treatments for rare brain disorders.

Over 300 million people worldwide live with rare disorders—most affecting the brain and nervous system. Yet the vast majority lack an approved therapy. The Rare Brain Disorders Nexus (RareNet) was established at the McGovern Institute in 2025 by MIT alums Ana Méndez ’91 and Rajeev Jayavant ’86, (EE ’88, SM ’88) to address this need. Led by Guoping Feng, the James W. (1963) and Patricia T. Poitras Professor of Neuroscience at MIT, RareNet draws together expertise from the MIT community and beyond to expedite the path from lab to clinic.

MIT President Sally Kornbluth set the tone at the inaugural RareNet symposium, thanking founders Méndez and Jayavant for helping MIT focus on this important challenge: “I look forward to watching RareNet dissolve needless barriers, accelerate timelines, and bring new hope to millions of patients and their families for whom hope is long overdue.”

MIT President Sally Kornbluth (third from left) with RareNet founders Rajeev Jayavant (far left) and Ana Méndez (center) at the June 9 symposium at the McGovern Institute. Also pictured are the founders’ son Neal (second from left), RareNet Director Guoping Feng, RareNet Scientific Advisor Xian Gao, and McGovern Institute Director Robert Desimone. Photo: Steph Stevens

RareNet’s collaborative vision came into focus at the symposium, where more than a dozen leading neuroscientists, biotech innovators, and patient advocates shared the podium. The scientific program spanned the full translational pathway, from fundamental discovery to clinical development.

Feng Zhang (McGovern Institute, MIT; HHMI; Broad Institute), Katherine High (RhyGaze AG; Rockefeller University), Kiran Musunuru (University of Pennsylvania), and Timothy Yu (Boston Children’s Hospital; Harvard Medical School) discussed emerging genetic medicines–including genome editing, gene therapy, and individualized therapeutic strategies–and the challenges involved in bringing them safely to patients.

Kevin Bender (University of California San Francisco), Christopher Walsh (Boston Children’s Hospital; Harvard Medical School), Sonia Vallabh (Broad Institute; MGH; Harvard Medical School), and Joseph Buxbaum (Icahn School of Medicine at Mount Sinai) explored how insights into disease mechanisms, human genetics, and patient-derived data are advancing research in neurodevelopmental disorders, autism, and prion disease.

Representatives from the Sturge-Weber and FOXP1 communities–including Karen Ball, Matt Shirley, and Samit Dasgupta–demonstrated how patient foundations can help define research priorities, build essential resources, and drive promising discoveries toward meaningful treatments.

Also among the day’s speakers was Monica Coenraads, who transformed her child’s rare disease into a powerful research initiative. When her daughter Chelsea was diagnosed with Rett syndrome in 1998 at age two, Coenraads faced an uncertain future. Today, as founder and CEO of the Rett Syndrome Research Trust, she is helping to rewrite that story for other families. In a compelling talk with John Sinnamon, Director of Research at RSRT, Coenraads shared both the scientific breakthroughs reshaping Rett syndrome treatment and the deeply personal journey that sparked it all.

“Monica and John’s talks capture the vital connection between patients and families, cutting-edge research, and translational innovation,” says RareNet Executive Director Xian Gao. “It’s a powerful reminder that behind every research breakthrough is a human story demanding progress.”

Language skills stay strong in older adults, even while other cognitive abilities decline

Anne Trafton |

As people age, many cognitive functions tend to decline. Brain scanning studies have revealed corresponding changes in the function of a brain network that is involved in many of these cognitive functions, including working memory and problem-solving.

When it comes to language skills, however, the picture is different. Unless impaired by a stroke or dementia, most older people retain their language skills and may even improve them as they steadily gain vocabulary throughout their lives.

A new brain imaging study from an MIT-Boston University collaboration now reveals the neural activity underlying this observation. The researchers found that in older adults, activity of the language processing network is nearly identical to that seen in the brains of younger adults during language tasks.

In contrast, the researchers found that activation patterns in the multiple demand network, a brain system involved in executive control tasks such as decision-making, were very different in older and younger adults.

“In the language network, we couldn’t find any differences between older and younger groups. In contrast, the executive system showed decline across almost all of the measures,” says Anne Billot.

“The network synchronization declined in older adults, the extent of activation was reduced, and the magnitude of activation was reduced as well,” adds Billot, one of the lead authors of the new study, who carried out this work while doing her PhD at BU and is now a postdoc at Harvard University.

The findings suggest that parts of the brain that are specialized for specific functions, such as language processing, may be more resilient to aging than the multiple demand network, a more general-purpose network that has greater flexibility in its function, the researchers say.

Former MIT research assistant Niharika Jhingan is also a lead author of the study, which appears today in Nature Communications. Evelina Fedorenko, an MIT associate professor of brain and cognitive sciences and member of MIT’s McGovern Institute for Brain Research, and Swathi Kiran, the James and Cecilia Tse Ying Professor in Neurorehabilitation at BU, are the paper’s senior co-authors.

The resilience of language

To study the effects of aging on the brain, the researchers looked at two groups of people, ages 17-39 and 41-80. Based on previous studies, they expected that the multiple demand network, which includes several regions in the frontal and parietal lobes of the brain, would look different in the brains of older people.

“It’s well known that executive functions, such as attention, working memory, and cognitive control, tend to decline with age. And it’s also known that in opposition to that, language skills typically tend to remain quite stable or even improve with age,” Billot says. “These two types of functions really go in opposite directions in healthy aging. In terms of behavior, that’s quite well-established, and we wanted to see if that was also the case in the brain.”

In previous studies of the multiple demand network, scientists have found that as people age network activity becomes less synchronized. Some neuroscientists have hypothesized that this may also happen in the language network, but studies haven’t found definitive evidence for this.

The MIT researchers were able to look at both networks by designing tasks that elicit responses primarily in either the language network or the multiple demand network. This allowed them to identify, for each participant, the brain areas that belong to each network.

Neural responses in young adults and older adults are compared across four neural measures. Across these measures, the language network shows preserved function with age, whereas the multiple demand network shows consistent decline. Image courtesy of the researchers.

During a spatial memory task — remembering the location of squares in a grid — the researchers confirmed that the multiple demand network showed altered activity in older adults. Compared to the younger subjects, their networks were smaller and less well-synchronized, and the overall activation level was weaker.

To identify the language network, the researchers had participants listen to stories and read sentences. They found that in both groups, brain activity in response to language showed similar levels and spatial distribution across the network.

They also found that younger and older subjects showed similar brain responses when they encountered an unfamiliar word or an unusual grammatical construction.

“We have previously used similar kinds of materials to show that young adults show strong sensitivity to these points of linguistic difficulty: activity in the language areas goes up. Here we found that in older adults, you also see this sensitivity, which suggests that there’s nothing fundamentally different about how they process language,” Fedorenko says.

A language boost

The researchers also showed that in older people, the language network did not show any signs of becoming less synchronized. Additionally, the network did not show signs that it was blurring together with the multiple demand network, as some neuroscientists have hypothesized might happen.

While this study did not evaluate language ability, other studies have shown that not only do language skills not decline with age, for some people, their language processing improves in older age. This might be because vocabulary and reading skill can continually grow over time, the researchers say.

“Vocabulary keeps increasing as long as people have been measuring, which makes sense. People get exposed to more and more language, and older people sometimes start reading more, so they get an extra boost — it’s like a large language model trained on increasingly more data,” Fedorenko says.

Given these findings, one possible generalization is that parts of the brain that are specialized for particular functions such as language may be less susceptible to age-related decline than the multiple demand network. That network is unique in its ability to give the human brain the flexibility to learn new skills and adapt to new situations.

“The multiple demand network is a different system in the sense that it’s not accumulating knowledge over time. It’s more like a flexible resource that you can deploy in all sorts of ways. And somehow that’s the thing that is more vulnerable to aging,” Fedorenko says. “Why it’s so vulnerable — that is a very good question.”

The research was funded by the National Institute on Deafness and Other Communication Disorders, as well as MIT’s McGovern Institute, Simons Center for the Social Brain, Poitras Center for Psychiatric Disorders Research, and Quest for Intelligence.

Tackling rare genetic disorders with patient-focused science

by Stefanie Koperniak | Office of Graduate Education |

Shannon Knight attributes her interest in neuroscience to an experience she had in high school. She and her sister attended a medical day for students at the nearby University of Illinois Chicago. As they were on their way out of the event, they walked past a room with a person holding a brain.

“We stopped and backpedaled into the room, and I was so fascinated,” says Knight. “I was able to hold the brain of a patient who had passed away of Alzheimer’s. The brain holds so much emotion, decision-making — everything. I realized that this man’s entire memory was in my hands, and something clicked for me. I decided that I really wanted to learn much more about this organ.”

Now in her sixth year of doctoral studies at MIT’s McGovern Institute for Brain Research, Knight is working on developing a novel gene therapy for childhood-onset epilepsy, specifically SYNGAP1 haploinsufficiency. This rare genetic disorder is caused by a mutation in the SYNGAP1 gene, rendering one of the two copies of the gene nonfunctional.

SYNGAP1 is important for brain development and neuronal communication, and the disorder leads to seizures in children starting as young as 4 months old. Other symptoms include intellectual disabilities, challenges with eating and sleeping, and difficulties with movement.

While there are currently methods to address the symptoms of the disorder, such as anti-seizure medications and dietary restrictions, as the child ages, the seizures often become resistant to medications. Knight is working to develop a therapeutic using CRISPR, a biotechnology tool used to edit genes. This therapeutic aims to address the root cause of this medication resistance by focusing on the gene itself.

“The idea of leading science with empathy is something that I feel very deeply,” she says. “I hope my efforts in the lab work toward the benefit of the people affected, rather than just for the benefit of my own science.”

Researching gene therapies

Knight’s interest in the brain flourished as a neuroscience major at Bowdoin College, working with Professor Hadley Horch. While she had originally planned to be pre-med, Knight ultimately decided that it wasn’t the best fit. She enjoyed the research she did as part of her honors thesis, exploring the regeneration of neurons in the auditory system of crickets, and decided that she wanted to pursue more research in molecular neuroscience, as well as genetics.

After graduating, Knight worked at the Perrimon Lab at Harvard University, where she first learned about CRISPR, applying it in a fruit fly model. She worked for two years in the lab, co-authoring a few papers and applying to graduate schools.

She ultimately landed in the lab of MIT Professor Guoping Feng, studying the potential of utilizing CRISPR to develop a gene therapy treatment for Phelan-McDermid Syndrome, a rare genetic disorder caused by a deletion or mutation on the 22nd chromosome.

“Many of our graduate students are passionate about making a positive impact to society through cutting-edge research, and Shannon is a perfect example,” says Feng, the James W. and Patricia T. Poitras Professor and associate director at the McGovern Institute. “She is developing gene therapy technologies that have the potential to help many kids with devastating neurodevelopmental disorders.”

Building off of the gene therapy research around Phelan-McDermid syndrome, which is now in clinical trials in patients, Knight is now in the early phases of testing gene therapy for SYNGAP1 disorder. The goal is to go through the same process for the SYNGAP1 gene therapy as for the Phelan-McDermid gene therapy — eventually obtaining U.S. Food and Drug Administration approval and beginning clinical trials.

The testing of the gene therapy on mice with a version of SYNGAP1 disorder has alleviated seizures and all of the behavioral phenotypes. This promising work is being accelerated by the Rare Brain Disorders Nexus, an MIT initiative that launched in the fall of 2025.

“Something I think about a lot is the idea of who ‘deserves’ the attention of a gene therapy. I feel that, regardless of how rare a genetic disorder might be, it still deserves care,” says Knight. “SYNGAP1 disorder is extremely rare, only impacting one to four out of every 10,000 children. I am very fortunate to be at an institution like MIT that has so many labs and brilliant researchers working on diseases that impact large portions of society, and it was really important to me to spend my PhD years helping a small, often unseen population. Although I don’t actually have a relationship with someone who has SYNGAP1 disorder, I know so many people who feel invisible in systems, and it is really important to me to be able to focus on people who feel unseen and give them hope.”

Inspiring others in the lab

In addition to her passion for neuroscience and genetic research, Knight has also developed a love of teaching. She has been a teaching assistant for class 9.12 (Experimental Molecular Neurobiology), leading the lab portion of the course. She has enjoyed working closely with small classes of students, introducing them to the fundamentals of neuroscience lab research.

“We walked through the process of looking at a specific protein in neurons, and talked about how you can go from cell culture all the way up to a mouse brain — and all the steps in between,” she says. “It was so important to me to be able to teach the students and help them to consider all of the different types of experiments they could do.”

Knight received the Goodwin Medal in 2025 in recognition of her commitment to excellent teaching.

“I’ve talked to many of the students since then,” she says, “and many said it was one of their favorite classes.”

High-speed microscopy reveals electrical activity across the brain

by Anne Trafton |

Within the brain, neurons compute by generating electrical impulses. These signals travel throughout neurons, which are in turn connected in vast networks that control brain functions such as sensory perception, memory formation, and control of movement.

McGovern Institute Investigator Edward Boyden. Photo: Justin Knight

In an advance that could help neuroscientists map those neural networks, leading to a better understanding of how neural activity underlies behavior and other brain functions, MIT engineers have invented a new microscope that can image electrical activity in neurons distributed across the brain of an entire organism, the experimental model Danio rerio (zebrafish).

Using a microscope that they adapted for fast, high-volumetric rate imaging, the researchers were able to track electrical activity across the brain on the scale of milliseconds. This method revealed patterns of neural activity from neurons throughout the brain that were activated in response to ultraviolet light.

“All of the parts of the brain are connected together, so if you want to truly understand the brain, you have to understand how all the neurons work together as an emergent whole,” says Ed Boyden, the Y. Eva Tan Professor in Neurotechnology at MIT; a professor of biological engineering, media arts and sciences, and brain and cognitive sciences; and a member of MIT’s McGovern Institute for Brain Research, Yang Tan Collective, and the Koch Institute for Integrative Cancer Research.

Boyden is the senior author of the study, which appears today in Nature Methods. Former J. Douglas Tan Postdoctoral Fellow Zeguan Wang ’PhD 24 and former MIT research scientist Jie Zhang are the lead authors of the paper. Other authors include former MIT postdoc Panagiotis Symvoulidis, Picower Institute research scientist Wei Guo, graduate students Davy Deng and Lige Zhang, Koch Institute research scientist Adam Amsterdam, Picower Institute research scientist Takato Honda, Boston College undergraduate Steven Roche, and Matthew Wilson, the Sherman Fairchild Professor of Neuroscience at MIT and a member of the Picower Institute.

High-speed imaging

One technique often used to measure neuron activity in the brain is calcium imaging. Calcium flows into neurons after they fire an electrical impulse, so measuring calcium levels in the cells can serve as a proxy for neural activity. However, this type of imaging isn’t fast enough to capture single spikes of activity.

“Calcium imaging inherently is very slow, so you’re talking about imaging activity on the order of seconds or even minutes. Typically that is too slow for us to be able to see a lot of these high-speed neural activities,” Zhang says. “Neurons compute using electrical activity, so with voltage imaging, you can get direct observation of that.”

To enable direct imaging of voltage, researchers have developed proteins called genetically encoded voltage indicators — fluorescent proteins that can be genetically expressed in neurons. When a neuron fires an impulse, the protein fluoresces, which can be detected with a fluorescence microscope.

“All of the parts of the brain are connected together, so if you want to truly understand the brain, you have to understand how all the neurons work together as an emergent whole.” – Ed Boyden

In previous work, researchers have used these proteins to image small populations of neurons, usually focusing on one localized part of the brain. Until now, there hasn’t been a way to image a large volume, such as the entire brain, with the millisecond-scale resolution needed to see electrical impulses from individual neurons.

To achieve that, the MIT team decided to modify a commonly used microscope known as a light sheet microscope. This type of microscope uses a sheet of laser light to illuminate a thin slice of a sample. By imaging many layers in sequence, this technique can generate 3D images of a large volume. However, with previous microscopes, the scanning of an entire volume would take too long to be able to capture neuronal impulses across the volume at single cell resolution.

“Different groups of neurons that are distributed across the brain coordinate together at millisecond timescales to generate a lot of behaviors and brain computations,” Wang says. “To understand the principles, we need the technology to observe their activity at the same time, across the whole brain, so we are not missing any important participant neurons.”

To make the imaging process fast enough to image millisecond-scale activity, the researchers increased the image acquisition speed of the microscope’s camera, and they also boosted the scanning speed of the microscope using a technique called remote refocusing.

Using this approach, the researchers showed that they could scan the entire zebrafish brain 200 times per second, or once every five milliseconds.

Mapping brain activity

To test the new microscope, the researchers engineered neurons in larval zebrafish to express a voltage indicator called Positron2-Kv. Although they had hoped that the indicator would end up in every neuron, it produced signals in neurons distributed throughout the brain, with about one quarter of the neurons exhibiting acceptable signals. This was enough, however, to observe patterns of activity across the brain. The researchers imaged the brain as the fish were resting, and they were able to observe single voltage spikes from neurons, as well as rapid bursts of spikes.

Additionally, this technique revealed patterns in how the brain is activated following a stimulus such as ultraviolet light. Immediately following the stimulus, activity was seen in the optic tectum, which receives and processes visual input from the retina. This activity propagated from one side of a part of the brain called the tectum to the other. Stimulus-independent activity also occurred in sequences across sets of neurons in the cerebellum and hindbrain.

The new microscope scans the entire zebrafish brain 200 times per second, revealing brain-wide patterns of electrical activity. Each flashing spot represents an active neuron. Image: Zeguan Wang

The researchers now hope to increase the percentage of neurons that they can image across the brain, as well as the microscope’s speed and resolution. They are also working on expanding the use of this technique to other experimental models, including mice.

This approach, they say, could offer neuroscientists a new way to generate hypotheses about what happens in the brain when it engages in specific behaviors, or about how brain activity is linked to states of mind such as daydreaming.

“A big question is simply to understand how neurons work together as a network. And this might be the first time that you could do that, because you can image the voltage of neurons distributed throughout the network,” Boyden says.

The research was funded by the National Institutes of Health, the BRAIN Initiative, the Picower Institute Innovation Fund, K. Lisa Yang, Ashar Aziz, the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience at MIT, the Hock E. Tan and K. Lisa Yang Center for Autism Research, the Alana Down Syndrome Center, John Doerr, Jed McCaleb, James Fickel, and the Howard Hughes Medical Institute.

 

 

DNA shaper steers nervous system development

by Jennifer Michalowski |

Portrait of Robert Horvitz at a computer.
McGovern Investigator Robert Horvitz shared the 2002 Nobel Prize in Medicine with colleagues Sydney Brenner and John Sulston for discoveries that helped explain how genes regulate programmed cell death and organ development. Photo: AP Images/Aynsley Floyd

A functional nervous system depends on the cooperation of many kinds of cells. So as developing organisms build their nervous systems, their neurons must take on different forms and functions to fulfill their designated roles. That carefully orchestrated process gives rise to thousands of different cell types in the human brain.

In the tiny worm known as C. elegans, the nervous system is far simpler, comprising a mere 118 classes of neurons.

At MIT, scientists in H. Robert Horvitz’s lab are studying the worms to learn about how nervous systems develop. Horvitz is the David H. Koch Professor of Biology at MIT, an Investigator at the McGovern Institute for Brain Research at MIT, and an Investigator at the Howard Hughes Medical Institute. His team has just discovered that a protein complex called cohesin, which helps shape the three-dimensional structure of the genome in both worms and humans, is critical for establishing some neurons’ identities as development unfolds.

The findings, reported July 31, 2026, in the journal Science Advances, could help scientists find a way to treat a rare developmental disorder called Cornelia de Lange syndrome, which is caused by mutations that interrupt the cohesin complex.

Model organism

Postdoctoral researcher Dongyeop Lee explains that C. elegans is a powerful model for studying neurodevelopment not just because its nervous system has been comprehensively mapped, but also because of the ease and speed with which scientists can study the function of its genes.

Dongyeop Lee is a postdoctoral researcher in the Horvitz lab and the first author of the Science Advances paper.

Because many of the worm’s genes have been retained through evolution, findings from studies of C. elegans often reveal important aspects of human biology. The current study began with worms that, because of a genetic mutation, make too many neurons of a certain type.

Adrenergic neurons, named for the kind of neurotransmitter they use to communicate with other neurons, are vital for enabling worms to respond to both their environment and their own internal state. Normally, C. elegans has just two pairs of adrenergic neurons: two RIM neurons and two RIC neurons. But the worms Lee studied had extras of both.

Takashi Hirose, a former member of the Horvitz lab, first observed this change in 2007.

Lee later continued the study and discovered that worms carrying a mutation in a gene called coh-1 have extra adrenergic neurons. The coh-1 gene encodes one part of the cohesin complex.

When Lee tested other mutations that disrupt cohesin, he found the same effect: Worms without fully functional cohesin had too many RIM neurons and too many RIC neurons.

Molecular switch

With a series of experiments designed to tease apart how cohesin impacts neurons’ identities, Lee discovered that cohesin cooperates with a gene-regulating protein called EOR-1 (known in humans as PLZF) to direct some neurons to develop into neurons that communicate with the inhibitory neurotransmitter GABA.

By reorganizing the structure of the genome, cohesin can change the way gene regulators like EOR-1 interact with DNA. Lee’s experiments showed that when either cohesin or EOR-1 couldn’t do its job, cells that should have become GABA-producing neurons become adrenergic neurons instead.

“What we found is that there are two alternative possible fates of certain neurons, and cohesin acts as a molecular switch that decides one of the possible neuronal fates,” Lee explains. “This means the structure of genomic DNA in the nucleus is important for neuronal fate determination.”

Disease connection

Lee adds that extra adrenergic neurons were not the only abnormality he observed in worms with cohesin mutations. Cohesin is important for shaping cells and tissues throughout the body. “The mutants have severe developmental defects,” Lee says. “They grow slowly. They don’t move well, and they also have defects in reproduction.”

Notably, the problems Lee saw in the worms echo aspects of Cornelia de Lange syndrome, a rare genetic disorder that impacts physical, cognitive, and behavioral development. Cornelia de Lange syndrome can be caused by mutations in cohesin genes, and Lee says that the discovery of how cohesin mutations affect worm development and behavior opens new opportunities to study the disease and search for potential therapeutic targets in C. elegans.

The Horvitz lab already has some promising leads. Taking advantage of the quick genetic screens that are possible in worms, Lee has found additional mutations that can counteract impaired cohesin, improving the health of worms with cohesin mutations. The team is now working to identify the genes where these suppressor mutations occur, so they can investigate whether they might make good therapeutic targets in humans.

Meanwhile, the team is also exploring a potential role for cohesin in shaping the fates of other neuron types, as well as searching broadly for additional molecules that work with cohesin to guide development. “We expect we have opened up a new biology,” Lee says. “This paper is just the beginning.”

MIT honors employees with 2026 Excellence Awards, Collier Medal, and Staff Award for Distinction in Service

Anne Trafton |

On June 4, colleagues held homemade signs, waved pompoms, and cheered loudly for award recipients in Kresge Auditorium. Twenty individuals and three teams received MIT Excellence Awards — the Institute’s highest honor for staff. Additional honors included the Collier Medal, the Staff Award for Distinction in Service, and the Gordon Y. Billard Award.

The Collier Medal honors the memory of MIT police officer Sean Collier, who gave his life in service to the MIT community. Recipients embody a deep commitment to community and approach their work with compassion for others. The Staff Award for Distinction in Service is presented to an individual who approaches their work with kindness, empathy, and approachability, and serves as a trusted adviser at the Institute. The Gordon Y. Billard Award is given to staff or faculty members, or MIT-affiliated individuals, who make “significant and lasting contributions to the MIT community.”

The 2026 MIT Excellence Award recipients and their categories are:

Bringing Out the Best 

  • Robin Elices
  • Kate McCarthy
  • Jim Mitchell

Embracing Inclusion 

  • Allison Chang
  • Mandana Sassanfar

Innovative Solutions 

  • Kayla S. (KB) Burt
  • Amanda Jarvis
  • Julie Uva
  • Sustainability Team (Yu Cheng, Brian Goldberg, Susy Jones, Steve Lanou, Ellie McLane, and Julie Newman)

Outstanding Contributor 

  • Barry Pugatch
  • Emma Shortall
  • Chao Li
  • Catherine Nunziata
  • Trinidad Carney
  • Gang Liu
  • Laura von Bosau
  • James Daley
  • Craig Rowe
  • MIT Health Housekeeping Team (Michael Batista, Maria Coelho, Mae Evans, Maria Fatima Rosario, Selam Stefanos, Claudia Teixeira, and Claudia Vidal)

Serving Our Community 

  • Olivia Cheo
  • Clayton Hainsworth
  • Atsushi Takahashi
  • MIT Health Ambulatory Safety Net and Population Management Team (Michele M. A. David, Solanlly Mendez, Pamela Mensah, Nicole Napier, Lucus David Sensius, and Stephanie Shaprio)

The 2026 Collier Medal recipient was Michael Grenier, pub manager, dining, Division of Student Life. At the Muddy Charles and the Thirsty Ear pubs, Grenier creates spaces where MIT community members can relax, meet friends, and find unconditional support. His acts of kindness are woven into the atmosphere he creates, and alumni regard him as someone who shaped an important part of their early adult lives.

This year’s winner of the Staff Award for Distinction in Service was Christina Couch, associate director and lecturer, MIT Graduate Program in Science Writing, Comparative Media Studies, School of Humanities, Arts and Social Sciences (SHASS). Couch has been an invaluable member of the SHASS community — as a student, alumna, and staff member. Through her work, she has created opportunities for students to interact and build relationships with professional journalists, and underlying everything she does is her compassion and deep belief in student potential.

Three community members were honored with a 2026 Gordon Y. Billard Award.

  • Cullen R. Buie, professor of mechanical and biological engineering, associate department head of mechanical engineering, and head of house, Maseeh Hall
  • Traci Swartz, assistant director, Community Services Office, Institute Affairs, Office of the President
  • David L. Verrill, executive director, Initiative on the Digital Economy, MIT Sloan School of Management

Presenters included Provost Anantha Chandrakasan; MIT Chief of Police John DiFava and Captain Andrew Turco; Executive Vice President and Treasurer Glen Shor; Associate Provost Maria Yang; Dean of the School of Science Nergis Mavalvala; Lincoln Laboratory Assistant Director Justin Brooke; Vice President for Human Resources Ramona Allen; and Chancellor Melissa Nobles.

Visit the MIT Human Resources website for more information about the award recipients, categories, and to view photos and video of the event.

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.

 

 

Separating logic and language

by Jennifer Michalowski |

Portrait of a smiling scientist in a blue v-neck blouse.
Hope Kean, a postdoctoral researcher and former ICoN graduate fellow in Evelina Fedorenko’s lab. Photo by Caitlin Cunningham Photography.

Some people find it useful to talk through their problems—but language isn’t necessary for logical reasoning, cognitive neuroscientists at MIT’s McGovern Institute say. In research published today in the journal PNAS, researchers led by McGovern Institute investigator Evelina Fedorenko have shown that people can perform well on tasks that require logical reasoning even if their language abilities are severely impaired. What’s more, brain imaging shows that language-processing parts of the brain are not called on for logical reasoning. Fedorenko is also an associate professor of brain and cognitive sciences at MIT.

Philosophers, linguists, and cognitive scientists have debated the relationship between language and thought for thousands of years, with many arguing that we use language to think.  There are good reasons to suspect a close relationship between logic and language, acknowledges Hope Kean, a postdoctoral researcher and former ICoN graduate fellow in Fedorenko’s lab. “Abstract thinking has properties that look a lot like language,” she says, pointing to structural similarities: “You can decompose a thought into subcomponents, like little atoms of logical propositions, and you can combine them in a hierarchical manner to make more complex structured rules, very akin to language.”

But she and Fedorenko suspected that while we largely depend on language to communicate about logical reasoning—from presenting a problem to explaining how we have arrived at conclusions—the brain might use a separate system for the reasoning itself. “There are aspects of thinking that seem to go beyond some of the limitations of language,” Kean explains. Logical reasoning demands precision that language often lacks. And language is linear, progressing one word at a time, whereas evaluating available information to reach logical conclusions can require thinking in less linear ways.

Logical reasoning

These observations left Kean curious about how the brain handles logical reasoning. It’s a particularly difficult question to answer scientifically, because it’s hard to take language out of the equation when working with human study participants. But Fedorenko’s team did just that by collaborating with Rosemary Varley, a neuroscientist at University College London who studies acquired language disorders, and her team.

Together, the scientists worked with two patients who had experienced stroke that damaged language-processing parts of their brains, leaving them with severe impairments in both understanding and producing language. They designed language-free logic games in which participants were asked to infer relationships between sets of numbers. Given two lists, they had to figure out the hidden rule that turned one list into the other, such as reversing the digits or removing numbers above a certain value. Once they thought they’d discovered the rule, they had to apply it to new examples. In a second game, participants were presented a set of geometric patterns and asked to identify another pattern to complete the matrix.

As participants solved increasingly difficult puzzles, it became clear that people don’t need language for this kind of reasoning. Patients with language impairments solved the problems as well as a control group and were even able to communicate the rules they inferred using gestures or with a sketch. “It really upends a theory that says that symbolic rule induction is not possible without linguistic capacities,” says Kean.

Four brain scans with the bottom two scans showing activation areas in red.
Anatomical scans of two participants with severe aphasia (top row) and a projection of the probabilistic atlas for the language network into the MRI image of one of the participants. Image: Hope Kean

Alongside this part of the study, Kean and colleagues also used functional brain imaging to study what happens in the brains of healthy adults when they are engaged in logical reasoning. Participants in this part of the study visited MIT for a series of MRI scans, which captured images of their brain activity during an array of tasks. In addition to completing different kinds of logic games inside the scanner, participants were asked to engage in tasks designed to map the language-processing parts of their brain. Another set of tasks was used to map each person’s Multiple Demand network—a distributed brain system that supports complex problem solving.

These neurotypical participants completed logic games similar to those used with the language-impaired patients. They were also presented with problems that required syllogistic reasoning, using “if-then” statements such as “If the ball is red, then it is big. The ball is red. Is the ball big?” The team varied the difficulty of the logic puzzles so they could see which brain areas became more active when the need for logical reasoning intensified. Likewise, they looked for changes in brain activity when participants had to infer a hidden rule versus simply applying a rule they’d been given.

Here too, a separation between language and logic was clear: The MRI scans showed the brain’s language system is not engaged for either inductive reasoning (when participants identified hidden rules) or deductive reasoning (when they assessed the validity of syllogistic conclusions). Surprisingly, the Multiple Demand network, which many scientists had suspected was important for logical reasoning, was engaged during inductive reasoning but didn’t seem to get involved in deductive reasoning—a finding Kean is building on in her ongoing work.

For Fedorenko and Kean, the findings are strong support for a separation of logic and language in the brain. They add to previous findings from Fedorenko’s lab showing that other types of thinking, such as object categorization and social reasoning, also do not rely on language.

Acquired language impairments and AI

The researchers say these findings have important implications for how we think about acquired language impairments, or aphasia. Specialists who work with people with aphasia have long recognized that loss of language does not mean loss of intelligence. People with aphasia can continue to enjoy playing chess, solving sudoku puzzles, or being in charge of the family’s finances. But it is common for others to confuse their communicative difficulties with thinking difficulties.

“This research adds to a growing body of work establishing that even severely aphasic individuals can preserve their ability for abstract logical thought—a defining feature of our species,” Fedorenko says. “We should continue to educate the public that linguistic difficulties—in aphasia, but also in those with developmental language conditions, such as stuttering, or those who do not speak English natively—are not indicative of how smart or capable someone is.”

There could be implications for artificial intelligence, too. Large language models like ChatGPT and Claude are trained entirely on text and use text as their output—yet they convincingly simulate some kinds of human reasoning. Exploring the differences between these models and the human brain, where language and abstract logical thought are distinct, might offer useful insights to inform future models, Kean says.

When it comes to understanding how the human brain reasons, Kean calls this a new frontier in the geography of thought—and she says it’s one she is eager to explore.