Research Area: Brain Imaging

brain mapping, functional connectivity, fMRI, EEG, MEG, predictive imaging, precision interventions, contrast agents, theory of mind, the developing brain, learning

Language development in the brain

by Jennifer Michalowski |

The brain’s capacity to use and understand language expands rapidly in the first years of life, as babies start to make sense of the words they hear and eventually begin to piece together sentences of their own. The language-processing parts of the brain that make this possible continue to evolve in older children, as they expand their vocabularies and learn to use language more flexibly.

Scientists at MIT’s McGovern Institute have captured snapshots of the developing language-processing network in brain scans of hundreds of children and adolescents. Their data, reported on May 16 in the journal Nature Communications, show that the network continues to mature, becoming better integrated and increasingly responsive until around age 16. But they also found that a key feature of the adult language network is established early on: its localization in the left side of the brain.

Language lateralization

It is well known that using language is mostly the job of the left hemisphere. As adults, we call on the language-processing regions there when we read, write, speak, or listen to others talk. But there was some question as to whether this left lateralization is established early in life, or might instead emerge as the language network matures, with both sides of the brain contributing to language in childhood.

To find out, researchers needed to see young brains in action—and several McGovern Institute labs had collected exactly the right kind of data. Groups led by Associate Professor of Brain and Cognitive Sciences Evelina Fedorenko, John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology, and Rebecca Saxe, the John W. Jarve (1978) Professor of Brain and Cognitive Sciences  teamed up to share brain scans from children, adolescents, and adults and compare how their brains responded to language.

In studies aimed at better understanding a variety of cognitive functions and developmental disorders, the three teams had all collected functional MRI data while subjects participated in “language localizer” tasks—an approach the Fedorenko lab developed to map the language-processing network in a person’s brain. By monitoring brain activity with functional MRI as people engage in both language tasks and non-linguistic tasks, researchers can identify parts of the brain that are exclusively dedicated to language processing, whose precise anatomic location varies across individuals.

To activate the language network, the researchers had children listen to stories inside the MRI scanner. Depending on their age, some heard excerpts of Alice in Wonderland, some listened to podcasts and TED talks, and others heard shorter, simpler stories. To watch their brains during a non-linguistic task, the researchers had the children listen to nonsense words.

Across the data from the three labs, which included children between the ages of four and 16, as well as adults for comparison, the team saw clear developmental changes in the brain’s response to language. “The integration of the system—how well different subregions of the system correlated with each other and worked together during language processing—was stronger in older children as compared to younger children,” says Ola Ozernov-Palchik, a research scientist in Gabrieli’s lab and a research assistant professor at Boston University. The system was also more strongly activated by language in older children, which may reflect their growing comprehension of what they hear.

But strikingly, almost all language processing happened on the left side of the brain, even in the youngest subjects. “From age four on, it seems just as lateralized as in an adult,” Gabrieli says.

Language and developmental disorders

The researchers say this finding has implications for understanding developmental conditions that impact language, including autism and dyslexia. The right side of the brain frequently gets more involved in language processing in people with these conditions than it does in typically developing children. “Almost every single developmental disorder that’s associated with language has a theory that’s related to language lateralization,” Ozernov-Palchik says.

The reason for more bilateral language processing in some disorders is debated. One idea has been that some people might use both sides of their brain for language processing because their brains are less mature. If the right side of the brain processes language early in life, scientists had reasoned, it might simply continue to do so for longer in people with autism or dyslexia than it does in neurotypical individuals. But if most people use the left side of their brains for language even when they are young, the difference can’t be attributed to a developmental delay. Other developmental differences might cause bilateral language processing instead.

The researchers don’t have the full picture yet; they still need to know what parts of the brain process language in children younger than four. Likewise, they would like to know what the brain areas that become the language network are doing in the first months of life, when infants aren’t using language yet. They are eager to find out, both to understand fundamentals of brain development and to shed light on developmental disorders. “I think understanding that normal trajectory is really critical for interpreting what a deviation from that trajectory is,” says Amanda O’Brien, a former graduate student in Gabrieli’s lab who is now a postdoctoral fellow at Harvard.

One reason people thought lateralization might develop gradually is because damage to the left hemisphere of the brain impacts language abilities differently, depending on when it occurs. “If you have damage to the left hemisphere as an adult, you’re very likely to end up with some form of aphasia, at least temporarily,” Fedorenko explains. “But a lot of the time, with early damage to the left hemisphere, you grow up and you’re totally fine. The language can just develop in the right hemisphere.”

Some scientists suspected that the right side of the brain was able to take over language processing in children who suffered early-life brain damage because it was already participating in this function at the time. But the team’s findings suggest the developing brain may be nimbler than that. “Our data tell you that this early plasticity apparently happens in spite of the fact that by age four, we see these very strongly lateralized responses already,” Fedorenko says.

Brighter MRI signals

by Jennifer Michalowski |

When doctors and scientists want to see inside a body, magnetic resonance imaging (MRI) is a powerful tool. MRI can noninvasively capture detailed images of the body’s muscles, organs, and bones. It can monitor blood flow to generate a map of brain activity. And with new sensors developed by bioengineers at MIT, MRI can track the kinds of molecules that make our brains and bodies work.

In the May 13, 2026, issue of the journal Nature Biomedical Engineering, a team led by Alan Jasanoff, the Eugene McDermott Professor in the Brain Sciences and Human Behavior at MIT reports on their new sensors, which can brighten or dim MRI signals in response to specific molecular targets. The probes are designed to amplify the effect that each target molecule has on MRI signal, dramatically improving sensitivity over previous small-molecule sensors. Jasanoff, who is also an associate investigator at the McGovern Institute for Brain Research, says the approach his team used should enable the development of MRI sensors that detect neurotransmitters and other important molecules in the brain.

“We want to be able to measure distinct chemical signals like neurotransmitters, neuropeptides, and metabolites as they fluctuate across the whole brain,” Jasanoff says.

“These chemicals are important ingredients in neural computations, and we want to use the types of probes that we developed to detect these signals dynamically.”

Engineered nanoparticles

Jasanoff explains that researchers have struggled to use MRI to sensitively detect small molecules in the brain because the amount of any given neurochemical is low. Sensors can be designed to change the brightness of an MRI signal in the presence of specific molecules—but it takes a lot of contrast agent to achieve this. If every molecule of contrast agent needs its own target molecule to activate it, low concentrations of the target molecule limit the sensors’ visibility in an MRI scan. “The signal change that you see in the imaging will be very modest,” Jasanoff says. “It won’t let us detect physiological events.”

The Jasanoff team’s new sensors, whose development was led by postdoctoral researcher Sayani Das and graduate student Jacob Cyert Simon, overcome this problem. To generate a greater signal change in response to target molecules, the researchers designed probes in which a single target molecule impacts not one contrast agent, but many.

To achieve this, Das and Simon packaged an MRI contrast agent inside tiny sacs called liposomal nanoparticles. Each nanoparticle is packed with many molecules of gadolinium, a magnetic material that brightens the MRI signal that arises from hydrogen atoms in water. Inside their protective sacs, gadolinium has no effect on MRI signal, unless water molecules can easily get in and out.

Das and Simon built water channels into the walls of their gadolinium-filled nanoparticles, engineering them so that their opening depends on the presence or absence of a target molecule. When the channels open, more water enters and the gadolinium brightens the local MRI signal, lighting up that spot in a scan.

LisNR architecture consisting of an MRI contrast agent (gadoteridol) enclosed in a liposomal membrane (grey) perforated by water permeable pores (orange). Image courtesy of the researchers.

The researchers call their target-responsive sensors liposomal nanoparticle reporters, or LisNRs (pronounced “listeners”). They designed LisNRs that let water in only in the presence of their target molecule. The water channels in these nanoparticles stay blocked until they encounter their target, which can knock aside a channel-blocking bit of protein. Once the channel blocker is displaced, water enters and MRI signal brightens. They also made LisNRs that dim the MRI signal in the presence of the molecule they are designed to detect. These have a channel that stays open until the target molecule comes along and blocks it, keeping water out. Jasanoff lab members Vinay Sharma, Samira Abozeid, and Gregory Thiabaud played key roles in understanding and optimizing these interactions, and collaborators in the laboratory of Masayuki Inoue at the University of Tokyo helped the group engineer channels with higher potency.

In experiments led by postdoctoral researcher Miranda Dawson, Jasanoff’s team used their LisNRs to detect a molecule called biotin in the brains and bodies of living rats, illustrating the probe’s amplifying effects. “We showed that we could detect micromolar-scale levels of biotin with about tenfold greater sensitivity than we would have if we’d used a more conventional, one-to-one type sensing approach,” Jasanoff says. He adds that the team’s modeling suggests that with further development, they may be able to achieve even greater sensitivity gains.

The group showed that the new sensors can be delivered systemically, reaching various organs and spreading throughout the brain. This makes them promising tools for brain-wide imaging, as well as imaging targets in the peripheral nervous system or other tissues.

A next step will be engineering LisNRs that respond to the specific neurochemicals that Jasanoff and his team hope to study. “There are something like 100 neurochemicals in the brain that we’d love to detect in principle,” he says. They’ll start with dopamine and glutamate—two important and relatively abundant molecules that mediate communications between neurons.

This research, including support for postdoctoral fellows and graduate students involved in the work, was funded in part by Lore Harp McGovern, Yang Tan Collective at MIT, K. Lisa Yang Brain-Body Center at MIT, Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT, and K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT.

A different reality

by Jennifer Michalowski |

This story also appears in our Spring 2026 BrainScan newsletter.

***

Schizophrenia, a complex and variable psychiatric disorder, changes people’s perceptions of reality. People with schizophrenia may hear, see, or sense things that aren’t there, and they often hold firm to mistaken ideas about the world despite strong evidence to the contrary. As if these changes aren’t disruptive enough, they are usually accompanied by cognitive difficulties and disorganized thinking.

Scientists at the McGovern Institute’s Poitras Center for Psychiatric Disorders Research are looking for clues into the origins of the disorder and its symptoms so they can help guide the development of new treatments. Encouragingly, they are beginning to uncover the brain changes that reshape reality for people with schizophrenia.

Genetic clues

Researchers who want to study the root causes of a disease often turn to genetics for clues—and the genetics of schizophrenia are complicated. Hundreds of different genes seem to shape people’s risk of developing the disorder, most of which nudge risk only slightly. For most people, it seems to be the cumulative effect of these genes and how they intersect with other risk factors, like stress and prenatal complications, that determine who develops schizophrenia and who does not.

Gene variants that substantially impact the risk of schizophrenia are expected to reveal more about the underlying biology of the disorder than genes whose individual impact is minor. But these variants are rare, and it took a massive study to find them. In 2022, scientists at the Broad Institute’s Stanley Center for Psychiatric Research reported that after analyzing the DNA of more than 24,000 people with schizophrenia, they had identified mutations in 10 genes that dramatically increased the risk of the disorder.

“I think this is exciting, because for the first time, you can actually have an animal model based onhuman genetics findings,” says McGovern Institute and Stanley Center Investigator Guoping Feng. “You can put these mutations in animal models to try to understand how this mutation affects brain development, circuit formation, circuit function, and behavior.” Feng is also the James W. (1963) and Patricia T. Poitras Professor of Brain and Cognitive Sciences at MIT.

Woman and man sit at desk looking at brain image on computer screen.
Guoping Feng (right) and his postdoctoral researcher Tinting Zhou (left) examine a mouse brain carrying a genetic mutation associated with schizophrenia. Photo: Steph Stevens

In work supported by the Poitras Center, the Stelling Family Research Fund, and the Yang Tan Collective at MIT, Feng’s lab has engineered three strains of mice that carry ultra-rare schizophrenia-associated mutations. Their first significant findings come from mice with a mutation in a gene called Grin2a. People who inherit a dysfunctional Grin2a gene, which neurons need to detect and respond to a signaling molecule called NMDA, are 20 times more likely to develop schizophrenia than people in whom Grin2a is intact.

Tingting Zhou, a postdoctoral researcher in Feng’s lab, says the team had to think carefully about how to assess mice for schizophrenia-like symptoms. You can’t ask mice about hallucinations or delusions. Instead, Zhou designed an experiment that tested how well mice use new information to update their beliefs about the world—a process that is thought to be impaired in people who experience delusions.

To illustrate how failure to update beliefs can skew someone’s ideas about reality, Zhou describes a situation in which a person watches a stranger reach for something in their pocket, fearing that person intends to harm them. Then, the stranger’s hand emerges with a lollipop. The new information should alleviate concern—but a person with schizophrenia might hold on to their original belief, convinced the lollipop-holding stranger is a threat.

In Zhou’s experiments testing animals’ belief-updating abilities, mice had to keep up with changing information to earn as many treats as possible. Those with the Grin2a mutation were slow to adapt when experimenters adjusted the relative values of their choices. “Once the animal learns something, it’s very hard for them to update the information,” Zhou explains.

Zhou and Feng linked this behavioral difference to abnormally low activity in a part of the brain called the mediodorsal thalamus. The mediodorsal thalamus acts like a switchboard in the brain, routing and coordinating information between different parts of the cortex to support thinking, decision-making, and flexible behavior. Studies with patients have implicated this region in schizophrenia as well, showing that it has fewer cells and is less active in people with the disorder than those without.

A slice of mouse brain dyed purple showing two pink blobs towards the center.
The mediodorsal thalamus (pink) is less active in people with schizophrenia and mouse models of the disease. Image: Guoping Feng, Tingting Zhou

Feng’s lab and others are now looking for belief-updating deficits in other genetic models of schizophrenia. “The goal is to look at whether this is a converging mechanism…then you can start to look at what other [brain] regions are involved,” he says.

In mice with Grin2a mutations, the researchers were able to restore normal belief updating by activating neurons in the mediodorsal thalamus, offering hope that manipulating the same circuitry might benefit patients. “It will not be easy,” Feng says, “but at least you have something you can work on. Previously, it was just very hard to imagine how to develop a new therapeutic for schizophrenia.”

Internal noise

It’s not just the genes associated with schizophrenia that differ across affected individuals. The symptoms of the disorder vary, too. People experience some combination of delusions, hallucinations, disorganized speech, and cognitive problems—but none of these are experienced by everyone with the disorder. This heterogeneity complicates the diagnosis, treatment, and study of schizophrenia. For this reason, some researchers are focusing their efforts on understanding its individual symptoms.

Evelina Fedorenko, a McGovern Investigator and associate professor of brain and cognitive sciences, specializes in understanding how the brain processes speech and language. But recently, her group has teamed up with physician-researcher Ann Shinn at McLean Hospital to begin exploring why some people hear voices when no one is speaking.

About three out of four people with schizophrenia experience auditory hallucinations, which most commonly involve voices.

These hallucinations can be distressing, sometimes involving threatening language or commands to cause harm. Some people with mood disorders or post-traumatic stress disorder also hear them.

Scientist portrait
Tamar Regev was the 2022–2024 Poitras Center Postdoctoral
Fellow in Evelina Fedorenko’s lab. Photo: Steph Stevens

To investigate, Tamar Regev, a research scientist in the Fedorenko lab, asked people who experience auditory hallucinations to listen to different kinds of sounds inside an MRI scanner, then compared how their brains responded versus the brains of people without auditory hallucinations. Her study included participants with schizophrenia and bipolar disorder, both with and without a history of auditory hallucinations, as well as healthy controls.

Inside the scanner, participants listened to three kinds of audio: spoken language, gibberish, and gibberish so scrambled that it barely resembled speech. Regev analyzed how these sounds impacted activity in areas the brain uses to process auditory input at different levels: a part of the auditory cortex that is sensitive to all sounds; a higher-level region within the auditory cortex that usually responds to anything that sounds like speech, even if its content is unclear; and the brain’s language-processing network, which is called on to understand the content of speech, as well as written or signed communications.

Regev found that in people with hallucinations, the part of the brain that usually responds only to language responded to meaningless speech as well. “In this pathway from auditory to speech to language processing, the stimuli that should be filtered out somewhere on the way are now passing to higher stations,” she explains. While auditory hallucinations don’t require external sounds, Fedorenko and Regev propose that the brain’s language areas might be similarly activated by “internal noise” in auditory circuits.

Scrambled language

In people who experience auditory hallucinations, the brain’s language regions respond to sounds that aren’t language–including scrambled meaningless gibberish. Below is a sample gibberish clip used in Fedorenko’s study.

Early identification

McGovern scientists have also used brain imaging to investigate what happens in the brain before people develop clear symptoms of schizophrenia. The disorder is usually diagnosed in adolescence or young adulthood, when patients exhibit the first signs of psychosis—but its origins in the brain likely take root years before that.

“One of the things we’re super interested in is, can you identify people at risk early on, before they have a big problem,” says McGovern Investigator John Gabrieli, whose work is also supported by the Poitras Center and the Stelling Family Research Fund. That might give clinicians an opportunity to intervene and lessen or prevent the disorder’s most devastating effects, he says.

Gabrieli and his colleagues have studied the brains of children who, because they have a parent or sibling with schizophrenia, have an elevated risk of developing the disorder themselves. They found that a system called the default mode network (DMN), which is overactive in adults with schizophrenia, is already working overtime when children in this high-risk group are seven- to 12-years-old.

Gabrieli explains that the DMN is active when people are not actively engaged in an activity or thinking about the external world. “It turns on when you think about your family, your values, your hopes for the future, or important events of your life. It’s almost like a system of who /you are,” he says. Hallucinations and delusions experienced by people with schizophrenia may be associated with overactivity in this network.

MRI images of two brains, one showing an active DMN and the other showing a healthy DMN.
The default mode network (DMN) is a large-scale brain network that is active when a person is not focused on the outside world and the brain is at wakeful rest. The DMN is often over-engaged in adolescents with depression and anxiety, as well as teens at risk for these and other disorders like schizophrenia (left). DMN activation and connectivity can be “tuned” to a healthier state through the practice of mindfulness (right).

“They’re kind of living in their internal world of beliefs, as opposed to the reality that most of us occupy,” Gabrieli explains.

He and his colleagues think overactivity in the DMN might make people vulnerable to schizophrenia—and their data show this atypical activity can be detected many years before the core symptoms of schizophrenia appear. With further validation, children with hyperactivity of the DMN might be candidates for early intervention.

With new and better interventions, the ability to identify people who may be on a path toward schizophrenia will be even more impactful—underscoring the need for continued research on multiple fronts. A recent gift of $8 million to the Poitras Center from Patricia and James Poitras is helping accelerate this work in labs at the McGovern Institute and beyond.

Language processing beyond the neocortex

by Jennifer Michalowski |

The ability to use language to communicate is one of things that makes us human. At MIT’s McGovern Institute, scientists led by Evelina Fedorenko have defined an entire network of areas within the brain dedicated to this ability, which work together when we speak, listen, read, write, or sign.

Much of the language network lies within the brain’s neocortex, where many of our most sophisticated cognitive functions are carried out. Now, Fedorenko’s lab, which is part of MIT’s Department of Brain and Cognitive Sciences, has identified language-processing regions within the cerebellum, extending the language network to a part of the brain better known for helping to coordinate the body’s movements. Their findings are reported January 21, 2026, in the journal Neuron.

“It’s like there’s this region in the cerebellum that we’ve been forgetting about for a long time,” says Colton Casto, a graduate student at Harvard and MIT who works in Fedorenko’s lab. “If you’re a language researcher, you should be paying attention to the cerebellum.”

Imaging the language network

There have been hints that the cerebellum makes important contributions to language. Some functional imaging studies detected activity in this area during language use, and people who suffer damage to the cerebellum sometimes experience language impairments. But no one had been able to pin down exactly which parts of the cerebellum were involved or tease out their roles in language processing.

To get some answers, Fedorenko’s lab took a systematic approach, using methods they have used to map the language network in the neocortex. For 15 years, the lab has captured functional brain imaging data as volunteers carried out various tasks inside an MRI scanner. By monitoring brain activity as people engaged in different kinds of language tasks, like reading sentences or listening to spoken words, as well as non-linguistic tasks, like listening to noise or memorizing spatial patterns, the team has been able identify parts of the brain that are exclusively dedicated to language processing.

Their work shows that everyone’s language network uses the same neocortical regions. The precise anatomical location of these regions varies, however, so to study the language network in any individual, Fedorenko and her team must map that person’s network inside an MRI scanner using their language-localizer tasks.

Satellite language network

While the Fedorenko lab has largely focused on how the neocortex contributes to language processing, their brain scans also capture activity in the cerebellum. So Casto revisited those scans, analyzing cerebellar activity from more than 800 people to look for regions involved in language processing. Fedorenko points out that teasing out the individual anatomy of the language network turned out to particularly vital in the cerebellum, where neurons are densely packed and areas with different functional specializations sit very close to one another. Ultimately, Casto was able to identify four cerebellar areas that consistently got involved during language use.

The cerebellum, highlighted in red. Image: Anatomography maintained by Life Science Databases(LSDB).

Three of these regions were clearly involved in language use, but also reliably became engaged during certain kinds of non-linguistic tasks. Casto says this was a surprise, because all the core language areas in the neocortex are dedicated exclusively to language processing. The researchers speculate that the cerebellum may be integrating information from different parts of the cortex—a function that could be important for many cognitive tasks.

“We’ve found that language is distinct from many, many other things—but at some point, complex cognition requires everything to work together,” Fedorenko says. “How do these different kinds of information get connected? Maybe parts of the cerebellum serve that function.”

The researchers also found a spot in the right posterior cerebellum with activity patterns that more closely echoed those of the language network in the neocortex. This region stayed silent during non-linguistic tasks, but became active during language use. For all of the linguistic activities that Casto analyzed, this region exhibited patterns of activity that were very similar to what the lab has seen in neocortical components of the language network. “Its contribution to language seems pretty similar,” Casto says. The team describes this area as a “cerebellar satellite” of the language network.

Still, the researchers think it’s unlikely that neurons in the cerebellum, which are organized very differently than those in the neocortex, replicate the precise function of other parts of the language network. Fedorenko’s team plans to explore the function of this satellite region more deeply, investigating whether it may participate in different kinds of tasks.

The researchers are also exploring the possibility that the cerebellum is particularly important for language learning—playing an outsized role during development or when people learn languages later in life.

Fedorenko says the discovery may also have implications for treating language impairments caused when an injury or disease damages the brain’s neocortical language network. “This area may provide a very interesting potential target to help recovery from aphasia,” Fedorenko says. Currently, researchers are exploring the possibility that non-invasively stimulating language-associated parts of the brain might promote language recovery. “This right cerebellar region may be just the right thing to potentially stimulate to up-regulate some of that function that’s lost,” Fedorenko says.

Sven Dorkenwald

Synapse-resolution connectomics

The synaptic connectivity of neurons, their connectome, is fundamental to how networks of neurons function. Sven Dorkenwald develops computational and collaborative tools to map, analyze, and interpret synapse-resolution connectomes. His work has led to large connectomic reconstructions of the fruit fly brain and parts of mammalian brains. He uses these connectomes to investigate how neuronal circuits are organized and how their structure supports complex computations.

Who discovered neurons?

by Rubina Veerakone |

A self-portrait of Santiago Ramón y Cajal looking through a microscope.
A self-portrait of Santiago Ramón y Cajal looking through a microscope. Image: CC 2.0

On this day, December 10th, nearly 120 years ago, Santiago Ramón y Cajal received a Nobel Prize for capturing and interpreting the very first images of the brain’s most essential components — neurons.

“Many scientists consider Cajal the progenitor of neuroscience because he was the first to really see the brain for what it was: a computational engine made up of individual units,” says Mark Harnett, an investigator at the McGovern Institute and an associate professor in the Department of Brain and Cognitive Sciences. His lab explores how the biophysical features of neurons enable them to perform complex computations that drive thought and behavior.

For Harnett, Cajal is one of the greatest scientific minds to have helped us understand ourselves and our place in the world. Cajal was the first to uncover what neurons look like and propose how they function — equipping the field to solve a slew of the mind’s mysteries. Scientists built on this framework to learn how these remarkable cells relay information — by zapping electrical signals to each other — so we can think, feel, move, communicate, and create.

From art to science and back again

Cajal was born on May 1, 1852, in a small village nestled in the Spanish countryside. It was there Cajal fell deeply and madly in love with … art. But his father was a physician, and urged him to trade his sketches for a scalpel. Begrudgingly, Cajal eventually did. After graduating from medical school in 1873, he worked as an army doctor, but around 1880, he turned his attention to studying the nervous system.

An illustration of a brain cell.
A Purkinje neuron from the human cerebellum. Image: Cajal Institute (CSIC), Madrid

Nineteenth-century scientists didn’t think of the brain as a network of cells but more as plumbing, like the blood vessels in the circulatory system — a series of hollow tubes through which information somehow flowed. Cajal and others were skeptical of this perspective, yet had no way of visualizing the brain at a detailed, cellular level to confirm their suspicions. Scientists at the time stained thin slices of tissue to make cells visible under a microscope, but even the most sophisticated methods stained all cells at once, leaving an indecipherable mass under the microscope’s lens.

This changed in 1887 when Cajal encountered a technique devised by Camillo Golgi that stained only some cells. “Rather than seeing all the cells simultaneously, you saw one at a time,” Harnett explains, making it easier to view a cell’s precise form (Golgi shared the 1906 Nobel Prize with Cajal for this method). If he could refine Golgi’s approach and apply it to neural tissue, Cajal thought, he might finally determine the brain’s architecture.

When he did, a remarkable landscape appeared — black bulbs with sprawling branches, each casting a stringy silhouette. The scene awakened a prior passion. While viewing brain slices under a microscope, Cajal drew what he saw, with surgical precision and an artist’s eye. He had captured — for the first time — the mind’s timberland of cells.

A new theory of the mind

Cajal’s illustrations revealed that brain cells did not form a singular plumbing network, but were distinctly separate, with small gaps between them. “This completely upended what people at the time thought about the brain,” Harnett explains. “It wasn’t made up of connected tubes, but individual cells,” which a few years later in 1891 would be called neurons. Over nearly five decades Cajal created around 2,900 drawings — a collage of neurons from humans and a menagerie of fauna: mice, pigeons, lizards, newts, and fish — spanning a host of cell types, from Purkinje cells to basket and chandelier interneurons.

“Part of Cajal’s genius was that he proposed what the incredible anatomical diversity among neurons meant. He reasoned that maybe one part of the cell could work like an antenna to take in signals, and another might be a cable to send signals out. Cajal was already thinking about input and output at neurons, and synapses as points of contact between them,” Harnett notes. “Each neuron becomes a very complex engine for computation, as opposed to tube-based things that can’t really compute.”

Cajal’s notion that the brain was a network of individual cells would come to be known as the neuron doctrine, a bedrock principle that underlies all of neuroscience today. In his autobiography, Cajal describes neurons as “the mysterious butterflies of the soul, the beating of whose wings may someday – who knows? – clarify the secret of mental life.” And in many ways, they have.

One of thousands of neuron illustrations created by Santiago Ramón y Cajal. Image: CC 2.0

One scientist’s enduring influence

Much of scientists’ current approach to studying the brain is guided by Cajal’s blueprint. This is certainly true for the Harnett lab. “As many in the field do, we share Cajal’s aspiration to apply cutting-edge imaging to reveal hidden aspects of the brain and hypothesize about their function,” Harnett says. “Thankfully, unlike Cajal, we now have the advantage of functional tests to try to validate our hypotheses.”

An ultra high resolution image of a neuron taken by the Harnett lab. Image: Mark Harnett

In a study published in 2022, the Harnett lab used a super-resolution imaging tool to find that filopodia — tiny structures that protrude from dendrites (the signal-receiving “antennas” of neurons) — were far more abundant in the brain than previously thought. Through a battery of tests, they found that these “silent synapses” can become active to facilitate new neural connections. Such pliable sites were believed to only be present very early in life, but the researchers observed filopodia in adult mice, suggesting that they support continuous learning and computational flexibility over the lifespan.

Harnett explains that Cajal’s impact extends beyond neuroscience. “Where does the power of artificial intelligence (AI) come from? It comes, originally, from Cajal.” It’s no wonder, he says, that AI uses neural networks — a mimicry of one of nature’s most powerful designs, first described by Cajal. “The idea that neurons are computational units is really critical to the power and complexity you can achieve within a network. Cajal even hypothesized that changing the strength of signaling between neurons was how learning worked, an idea that was later validated and became one of the critical insights for revolutionizing deep learning in AI.”

By unveiling what’s really happening beneath our skulls, Cajal’s work would both motivate and guide studies of the brain for over a hundred years to come. “Many of his early hypotheses have proven to be true decades and decades later,” Harnett says. “He has and continues to inspire generations of neuroscientists.”

 

 

Musicians’ enhanced attention

by Jennifer Michalowski |

In a world full of competing sounds, we often have to filter out a lot of noise to hear what’s most important. This critical skill may come more easily for people with musical training, according to scientists at MIT’s McGovern Institute who used brain imaging to follow what happens when people try to focus their attention on certain sounds.

When Cassia Low Manting, a postdoctoral researcher working in the labs of McGovern Institute Investigators John Gabrieli and Dimitrios Pantazis, asked people to focus on a particular melody while another melody played at the same time, individuals with musical backgrounds were, unsurprisingly, better able to follow the target tune. An analysis of study participants’ brain activity suggests this advantage arises because musical training sharpens neural mechanisms that amplify the sounds they want to listen to while turning down distractions. “This points to the idea that we can train this selective attention ability,” Manting says.

The research team, including senior author Daniel Lundqvist at the Karolinska Institute in Sweden, reported their findings September 17, 2025, in the journal Science Advances. Manting, who is now at the Karolinska Institute, notes that the research is part of an ongoing collaboration between the two institutions.

Overcoming challenges

Participants in the study had vastly difference backgrounds when it came to music. Some were professional musicians with deep training and experience, while others struggled to differentiate between the two tunes they were played, despite each one’s distinct pitch. This disparity allowed the researchers to explore how the brain’s capacity for attention might change with experience. “Musicians are very fun to study because their brains have been morphed in ways based on their training,” Manting says. “It’s a nice model to study these training effects.”

Still, the researchers had significant challenges to overcome. It has been hard to study how the brain manages auditory attention, because when researchers use neuroimaging to monitor brain activity, they see the brain’s response to all sounds: those that the listener cares most about, as well as those the listener is trying to ignore. It is usually difficult to figure out which brain signals were triggered by which sounds.

Manting and her colleagues overcame this challenge with a method called frequency tagging. Rather than playing the melodies in their experiments at a constant volume, the volume of each melody oscillated, rising and falling with a particular frequency. Each melody had its own frequency, creating detectable patterns in the brain signals that responded to it. “When you play these two sounds simultaneously to the subject and you record the brain signal, you can say, this 39-Hertz activity corresponds to the lower pitch sound and the 43-Hertz activity corresponds specifically to the higher pitch sound,” Manting explains. “It is very clean and very clear.”

When they paired frequency tagging with magnetoencephalography, a noninvasive method of monitoring brain activity, the team was able to track how their study participants’ brains responded to each of two melodies during their experiments. While the two tunes played, subjects were instructed to follow either the higher pitched or the lower pitched melody. When the music stopped, they were asked about the final notes of the target tune: did they rise or did they fall? The researchers could make this task harder by making the two tunes closer together in pitch, as well as by altering the timing of the notes.

Manting used a survey that asked about musical experience to score each participant’s musicality, and this measure had an obvious effect on task performance: The more musical a person was, the more successful they were at following the tune they had been asked to track.

To look for differences in brain activity that might explain this, the research team developed a new machine-learning approach to analyze their data. They used it to tease apart what was happening in the brain as participants focused on the target tune—even, in some cases, when the notes of the distracting tune played at the exact same time.

Top-down vs bottom-up attention

What they found was a clear separation of brain activity associated with two kinds of attention, known as top-down and bottom-up attention. Manting explains that top-down attention is goal-oriented, involving a conscious focus—the kind of attention listeners called on as they followed the target tune. Bottom-up attention, on the other hand, is triggered by the nature of the sound itself. A fire alarm would be expected to trigger this kind of attention, both with its volume and its suddenness. The distracting tune in the team’s experiments triggered activity associated with bottom-up attention—but more so in some people than in others.

“The more musical someone is, the better they are at focusing their top-down selective attention, and the less the effect of bottom-up attention is,” Manting explains.

Manting expects that musicians use their heightened capacity for top-down attention in other situations, as well. For example, they might be better than others at following a conversation in a room filled with background chatter. “I would put my bet on it that there is a high chance that they will be great at zooming into sounds,” she says.

She wonders, however, if one kind of distraction might actually be harder for a musician to filter out: the sound of their own instrument. Manting herself plays both the piano and the Chinese harp, and she says hearing those instruments is “like someone calling my name.” It’s one of many questions about how musical training affects cognition that she plans to explore in her future work.

New gift expands mental illness studies at Poitras Center for Psychiatric Disorders Research

by Julie Pryor |

One in every eight people—970 million globally—live with mental illness, according to the World Health Organization, with depression and anxiety being the most common mental health conditions worldwide. Existing therapies for complex psychiatric disorders like depression, anxiety, and schizophrenia have limitations, and federal funding to address these shortcomings is growing increasingly uncertain.

Jim and Pat Poitras
James and Patricia Poitras at an event co-hosted by the McGovern Institute and Autism Speaks. Photo: Justin Knight

Patricia and James Poitras ’63 have committed $8 million to the Poitras Center for Psychiatric Disorders Research to launch pioneering research initiatives aimed at uncovering the brain basis of major mental illness and accelerating the development of novel treatments.

“Federal funding rarely supports the kind of bold, early-stage research that has the potential to transform our understanding of psychiatric illness. Pat and I want to help fill that gap—giving researchers the freedom to follow their most promising leads, even when the path forward isn’t guaranteed,” says James Poitras, who is chair of the McGovern Institute Board.

Their latest gift builds upon their legacy of philanthropic support for psychiatric disorders research at MIT, which now exceeds $46 million.

“With deep gratitude for Jim and Pat’s visionary support, we are eager to launch a bold set of studies aimed at unraveling the neural and cognitive underpinnings of major mental illnesses,” says Robert Desimone, director of the McGovern Institute, home to the Poitras Center. “Together, these projects represent a powerful step toward transforming how we understand and treat mental illness.”

A legacy of support

Soon after joining the McGovern Institute Leadership Board in 2006, the Poitrases made a $20 million commitment to establish the Poitras Center for Psychiatric Disorders Research at MIT. The center’s goal, to improve human health by addressing the root causes of complex psychiatric disorders, is deeply personal to them both.

“We had decided many years ago that our philanthropic efforts would be directed towards psychiatric research. We could not have imagined then that this perfect synergy between research at MIT’s McGovern Institute and our own philanthropic goals would develop,” recalls Patricia.

The center supports research at the McGovern Institute and collaborative projects with institutions such as the Broad Institute, McLean Hospital, Mass General Brigham and other clinical research centers. Since its establishment in 2007, the center has enabled advances in psychiatric research including the development of a machine learning “risk calculator” for bipolar disorder, the use of brain imaging to predict treatment outcomes for anxiety, and studies demonstrating that mindfulness can improve mental health in adolescents.

A scientist speaks at a podium with an image of DNA on the wall behind him.
Feng Zhang, the James and Patricia Poitras Professor of Neuroscience at MIT, delivers a lecture at the Poitras Center’s 10th anniversary celebration in 2017. Photo: Justin Knight

For the past decade, the Poitrases have also fueled breakthroughs in McGovern Investigator Feng Zhang’s lab, backing the invention of powerful CRISPR systems and other molecular tools that are transforming biology and medicine. Their support has enabled the Zhang team to engineer new delivery vehicles for gene therapy, including vehicles capable of carrying genetic payloads that were once out of reach. The lab has also advanced innovative RNA-guided gene engineering tools such as NovaIscB, published in Nature Biotechnology in May 2025. These revolutionary genome editing and delivery technologies hold promise for the next generation of therapies needed for serious psychiatric illness.

In addition to fueling research in the center, the Poitras family has gifted two endowed professorships—the James and Patricia Poitras Professor of Neuroscience at MIT, currently held by Feng Zhang, and the James W. (1963) and Patricia T. Poitras Professor of Brain and Cognitive Sciences at MIT, held by Guoping Feng—and an annual postdoctoral fellowship at the McGovern Institute.

New initiatives at the Poitras Center

The Poitras family’s latest commitment to the Poitras Center will launch an ambitious set of new projects that bring together neuroscientists, clinicians, and computational experts to probe underpinnings of complex psychiatric disorders including schizophrenia, anxiety, and depression. These efforts reflect the center’s core mission: to speed scientific discovery and therapeutic innovation in the field of psychiatric brain disorders research.

McGovern cognitive neuroscientists Evelina Fedorenko PhD ‘07 and Nancy Kanwisher ’80, PhD ’86, the Walter A. Rosenblith Professor of Cognitive Neuroscience—in collaboration with psychiatrist Ann Shinn of McLean Hospital—will explore how altered inner speech and reasoning contribute to the symptoms of schizophrenia. They will collect functional MRI data from individuals diagnosed with schizophrenia and matched controls as they perform reasoning tasks. The goal is to identify the brain activity patterns that underlie impaired reasoning in schizophrenia, a core cognitive disruption in the disorder.

Three women wearing name tags smile for hte camera.
Patricia Poitras (center) with McGovern Investigators Nancy Kanwisher ’80, PhD ’86 (left) and Martha Constantine-Paton (right) at the Poitras Center’s 10th anniversary celebration in 2017. Photo: Justin Knight

A complementary line of investigation will focus on the role of inner speech—the “voice in our head” that shapes thought and self-awareness. The team will conduct a large-scale online behavioral study of neurotypical individuals to analyze how inner speech characteristics correlate with schizophrenia-spectrum traits. This will be followed by neuroimaging work comparing brain architecture among individuals with strong or weak inner voices and people with schizophrenia, with the aim of discovering neural markers linked to self-talk and disrupted cognition.

A different project led by McGovern neuroscientist Mark Harnett and 2024–2026 Poitras Center Postdoctoral Fellow Cynthia Rais focuses on how ketamine—an increasingly used antidepressant—alters brain circuits to produce rapid and sustained improvements in mood. Despite its clinical success, ketamine’s mechanisms of action remain poorly understood. The Harnett lab is using sophisticated tools to track how ketamine affects synaptic communication and large-scale brain network dynamics, particularly in models of treatment-resistant depression. By mapping these changes at both the cellular and systems levels, the team hopes to reveal how ketamine lifts mood so quickly—and inform the development of safer, longer-lasting antidepressants.

Guoping Feng is leveraging a new animal model of depression to uncover the brain circuits that drive major depressive disorder. The new animal model provides a powerful system for studying the intricacies of mood regulation. Feng’s team is using state-of-the-art molecular tools to identify the specific genes and cell types involved in this circuit, with the goal of developing targeted treatments that can fine-tune these emotional pathways.

“This is one of the most promising models we have for understanding depression at a mechanistic level,” says Feng, who is also associate director of the McGovern Institute. “It gives us a clear target for future therapies.”

Another novel approach to treating mood disorders comes from the lab of James DiCarlo, the Peter de Florez Professor of Neuroscience at MIT, who is exploring the brain’s visual-emotional interface as a therapeutic tool for anxiety. The amygdala, a key emotional center in the brain, is heavily influenced by visual input. DiCarlo’s lab is using advanced computational models to design visual scenes that may subtly shift emotional processing in the brain—essentially using sight to regulate mood. Unlike traditional therapies, this strategy could offer a noninvasive, drug-free option for individuals suffering from anxiety.

Together, these projects exemplify the kind of interdisciplinary, high-impact research that the Poitras Center was established to support.

“Mental illness affects not just individuals, but entire families who often struggle in silence and uncertainty,” adds Patricia. “Our hope is that Poitras Center scientists will continue to make important advancements and spark novel treatments for complex mental health disorders and most of all, give families living with these conditions a renewed sense of hope for the future.”

How the brain distinguishes oozing fluids from solid objects

Anne Trafton |

Imagine a ball bouncing down a flight of stairs. Now think about a cascade of water flowing down those same stairs. The ball and the water behave very differently, and it turns out that your brain has different regions for processing visual information about each type of physical matter.

In a new study, MIT neuroscientists have identified parts of the brain’s visual cortex that respond preferentially when you look at “things” — that is, rigid or deformable objects like a bouncing ball. Other brain regions are more activated when looking at “stuff” — liquids or granular substances such as sand.

This distinction, which has never been seen in the brain before, may help the brain plan how to interact with different kinds of physical materials, the researchers say.

“When you’re looking at some fluid or gooey stuff, you engage with it in different way than you do with a rigid object. With a rigid object, you might pick it up or grasp it, whereas with fluid or gooey stuff, you probably are going to have to use a tool to deal with it,” says Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience; a member of the McGovern Institute for Brain Research and MIT’s Center for Brains, Minds, and Machines; and the senior author of the study.

MIT postdoc Vivian Paulun, who is joining the faculty of the University of Wisconsin at Madison this fall, is the lead author of the paper, which appears today in the journal Current Biology. RT Pramod, an MIT postdoc, and Josh Tenenbaum, an MIT professor of brain and cognitive sciences, are also authors of the study.

Stuff vs. things

Decades of brain imaging studies, including early work by Kanwisher, have revealed regions in the brain’s ventral visual pathway that are involved in recognizing the shapes of 3D objects, including an area called the lateral occipital complex (LOC). A region in the brain’s dorsal visual pathway, known as the frontoparietal physics network (FPN), analyzes the physical properties of materials, such as mass or stability.

Although scientists have learned a great deal about how these pathways respond to different features of objects, the vast majority of these studies have been done with solid objects, or “things.”

“Nobody has asked how we perceive what we call ‘stuff’ — that is, liquids or sand, honey, water, all sorts of gooey things. And so we decided to study that,” Paulun says.

These gooey materials behave very differently from solids. They flow rather than bounce, and interacting with them usually requires containers and tools such as spoons. The researchers wondered if these physical features might require the brain to devote specialized regions to interpreting them.

To explore how the brain processes these materials, Paulun used a software program designed for visual effects artists to create more than 100 video clips showing different types of things or stuff interacting with the physical environment. In these videos, the materials could be seen sloshing or tumbling inside a transparent box, being dropped onto another object, or bouncing or flowing down a set of stairs.

The researchers used functional magnetic resonance imaging (fMRI) to scan the visual cortex of people as they watched the videos. They found that both the LOC and the FPN respond to “things” and “stuff,” but that each pathway has distinctive subregions that respond more strongly to one or the other.

“Both the ventral and the dorsal visual pathway seem to have this subdivision, with one part responding more strongly to ‘things,’ and the other responding more strongly to ‘stuff,’” Paulun says. “We haven’t seen this before because nobody has asked that before.”

Roland Fleming, a professor of experimental psychology at Justus Liebig University of Geissen, described the findings as a “major breakthrough in the scientific understanding of how our brains represent the physical properties of our surrounding world.”

“We’ve known the distinction exists for a long time psychologically, but this is the first time that it’s been really mapped onto separate cortical structures in the brain. Now we can investigate the different computations that the distinct brain regions use to process and represent objects and materials,” says Fleming, who was not involved in the study.

Physical interactions

The findings suggest that the brain may have different ways of representing these two categories of material, similar to the artificial physics engines that are used to create video game graphics. These engines usually represent a 3D object as a mesh, while fluids are represented as sets of particles that can be rearranged.

“The interesting hypothesis that we can draw from this is that maybe the brain, similar to artificial game engines, has separate computations for representing and simulating ‘stuff’ and ‘things.’ And that would be something to test in the future,” Paulun says.

Portrait of smiling woman wearing a grey sweater.
McGovern Institute postdoc Vivian Paulun, who is joining the faculty of the University of Wisconsin at Madison in the fall of 2025, is the lead author of the “things vs. stuff” paper, which appears today in the journal Current Biology. Photo: Steph Stevens

The researchers also hypothesize that these regions may have developed to help the brain understand important distinctions that allow it to plan how to interact with the physical world. To further explore this possibility, the researchers plan to study whether the areas involved in processing rigid objects are also active when a brain circuit involved in planning to grasp objects is active.

They also hope to look at whether any of the areas within the FPN correlate with the processing of more specific features of materials, such as the viscosity of liquids or the bounciness of objects. And in the LOC, they plan to study how the brain represents changes in the shape of fluids and deformable substances.

The research was funded by the German Research Foundation, the U.S. National Institutes of Health, and a U.S. National Science Foundation grant to the Center for Brains, Minds, and Machines.

 

Looking under the hood at the brain’s language system

Anne Trafton |

As a young girl growing up in the former Soviet Union, Evelina Fedorenko PhD ’07 studied several languages, including English, as her mother hoped that it would give her the chance to eventually move abroad for better opportunities.

Her language studies not only helped her establish a new life in the United States as an adult, but also led to a lifelong interest in linguistics and how the brain processes language. Now an associate professor of brain and cognitive sciences at MIT, Fedorenko studies the brain’s language-processing regions: how they arise, whether they are shared with other mental functions, and how each region contributes to language comprehension and production.

Fedorenko’s early work helped to identify the precise locations of the brain’s language-processing regions, and she has been building on that work to generate insight into how different neuronal populations in those regions implement linguistic computations.

“It took a while to develop the approach and figure out how to quickly and reliably find these regions in individual brains, given this standard problem of the brain being a little different across people,” she says. “Then we just kept going, asking questions like: Does language overlap with other functions that are similar to it? How is the system organized internally? Do different parts of this network do different things? There are dozens and dozens of questions you can ask, and many directions that we have pushed on.”

Among some of the more recent directions, she is exploring how the brain’s language-processing regions develop early in life, through studies of very young children, people with unusual brain architecture, and computational models known as large language models.

From Russia to MIT

Fedorenko grew up in the Russian city of Volgograd, which was then part of the Soviet Union. When the Soviet Union broke up in 1991, her mother, a mechanical engineer, lost her job, and the family struggled to make ends meet.

“It was a really intense and painful time,” Fedorenko recalls. “But one thing that was always very stable for me is that I always had a lot of love, from my parents, my grandparents, and my aunt and uncle. That was really important and gave me the confidence that if I worked hard and had a goal, that I could achieve whatever I dreamed about.”

Fedorenko did work hard in school, studying English, French, German, Polish, and Spanish, and she also participated in math competitions. As a 15-year-old, she spent a year attending high school in Alabama, as part of a program that placed students from the former Soviet Union with American families. She had been thinking about applying to universities in Europe but changed her plans when she realized the American higher education system offered more academic flexibility.

After being admitted to Harvard University with a full scholarship, she returned to the United States in 1998 and earned her bachelor’s degree in psychology and linguistics, while also working multiple jobs to send money home to help her family.

While at Harvard, she also took classes at MIT and ended up deciding to apply to the Institute for graduate school. For her PhD research at MIT, she worked with Ted Gibson, a professor of brain and cognitive sciences, and later, Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience. She began by using functional magnetic resonance imaging (fMRI) to study brain regions that appeared to respond preferentially to music, but she soon switched to studying brain responses to language.

She found that working with Kanwisher, who studies the functional organization of the human brain but hadn’t worked much on language before, helped Fedorenko to build a research program free of potential biases baked into some of the early work on language processing in the brain.

“We really kind of started from scratch,” Fedorenko says, “combining the knowledge of language processing I have gained by working with Gibson and the rigorous neuroscience approaches that Kanwisher had developed when studying the visual system.”

After finishing her PhD in 2007, Fedorenko stayed at MIT for a few years as a postdoc funded by the National Institutes of Health, continuing her research with Kanwisher. During that time, she and Kanwisher developed techniques to identify language-processing regions in different people, and discovered new evidence that certain parts of the brain respond selectively to language. Fedorenko then spent five years as a research faculty member at Massachusetts General Hospital, before receiving an offer to join the faculty at MIT in 2019.

How the brain processes language

Since starting her lab at MIT’s McGovern Institute for Brain Research, Fedorenko and her trainees have made several discoveries that have helped to refine neuroscientists’ understanding of the brain’s language-processing regions, which are spread across the left frontal and temporal lobes of the brain.

In a series of studies, her lab showed that these regions are highly selective for language and are not engaged by activities such as listening to music, reading computer code, or interpreting facial expressions, all of which have been argued to be share similarities with language processing.

“We’ve separated the language-processing machinery from various other systems, including the system for general fluid thinking, and the systems for social perception and reasoning, which support the processing of communicative signals, like facial expressions and gestures, and reasoning about others’ beliefs and desires,” Fedorenko says. “So that was a significant finding, that this system really is its own thing.”

More recently, Fedorenko has turned her attention to figuring out, in more detail, the functions of different parts of the language processing network. In one recent study, she identified distinct neuronal populations within these regions that appear to have different temporal windows for processing linguistic content, ranging from just one word up to six words.

She is also studying how language-processing circuits arise in the brain, with ongoing studies in which she and a postdoc in her lab are using fMRI to scan the brains of young children, observing how their language regions behave even before the children have fully learned to speak and understand language.

Large language models (similar to ChatGPT) can help with these types of developmental questions, as the researchers can better control the language inputs to the model and have continuous access to its abilities and representations at different stages of learning.

“You can train models in different ways, on different kinds of language, in different kind of regimens. For example, training on simpler language first and then more complex language, or on language combined with some visual inputs. Then you can look at the performance of these language models on different tasks, and also examine changes in their internal representations across the training trajectory, to test which model best captures the trajectory of human language learning,” Fedorenko says.

To gain another window into how the brain develops language ability, Fedorenko launched the Interesting Brains Project several years ago. Through this project, she is studying people who experienced some type of brain damage early in life, such as a prenatal stroke, or brain deformation as a result of a congenital cyst. In some of these individuals, their conditions destroyed or significantly deformed the brain’s typical language-processing areas, but all of these individuals are cognitively indistinguishable from individuals with typical brains: They still learned to speak and understand language normally, and in some cases, they didn’t even realize that their brains were in some way atypical until they were adults.

“That study is all about plasticity and redundancy in the brain, trying to figure out what brains can cope with, and how” Fedorenko says. “Are there many solutions to build a human mind, even when the neural infrastructure is so different-looking?”