Author: Julie Pryor

For healthy hearing, timing matters

by Jennifer Michalowski |

When soundwaves reach the inner ear, neurons there pick up the vibrations and alert the brain. Encoded in their signals is a wealth of information that enables us to follow conversations, recognize familiar voices, appreciate music, and quickly locate a ringing phone or crying baby.

Seated man, smiling at camera
McGovern Institute Associate Investigator Josh McDermott. Photo: Justin Knight

Neurons send signals by emitting spikes—brief changes in voltage that propagate along nerve fibers, also known as action potentials. Remarkably, auditory neurons can fire hundreds of spikes per second, and time their spikes with exquisite precision to match the oscillations of incoming soundwaves.

With powerful new models of human hearing, scientists at MIT’s McGovern Institute have determined that this precise timing is vital for some of the most important ways we make sense of auditory information, including recognizing voices and localizing sounds.

The findings, reported December 4, 2024, in the journal Nature Communications, show how machine learning can help neuroscientists understand how the brain uses auditory information in the real world. McGovern Investigator Josh McDermott, who led the research, explains that his team’s models better equip researchers to study the consequences of different types of hearing impairment and devise more effective interventions.

Science of sound

The nervous system’s auditory signals are timed so precisely, researchers have long suspected that timing is important to our perception of sound. Soundwaves oscillate at rates that determine their pitch: low-pitched sounds travel in slow waves, whereas high-pitched sound waves oscillate more frequently. The auditory nerve that relays information from sound-detecting hair cells in the ear to the brain generates electrical spikes that corresponds to the frequency of these oscillations. “The action potentials in an auditory nerve get fired at very particular points in time relative to the peaks in the stimulus waveform,” explains McDermott, who is also an associate professor of brain and cognitive sciences at MIT.

This relationship, known as phase-locking, requires neurons to time their spikes with sub-millisecond precision. But scientists haven’t really known how informative these temporal patterns are to the brain. Beyond being scientifically intriguing, McDermott says, the question has important clinical implications: “If you want to design a prosthesis that provides electrical signals to the brain to reproduce the function of the ear, it’s arguably pretty important to know what kinds of information in the normal ear actually matter,” he says.

This has been difficult to study experimentally: Animal models can’t offer much insight into how the human brain extracts structure in language or music, and the auditory nerve is inaccessible for study in humans. So McDermott and graduate student Mark Saddler turned to artificial neural networks.

Artificial hearing

Neuroscientists have long used computational models to explore how sensory information might be decoded by the brain, but until recent advances in computing power and machine learning methods, these models were limited to simulating simple tasks. “One of the problems with these prior models is that they’re often way too good,” says Saddler, who is now at the Technical University of Denmark. For example, a computational model tasked with identifying the higher pitch in a pair of simple tones is likely to perform better than people who are asked to do the same thing. “This is not the kind of task that we do every day in hearing,” Saddler points out. “The brain is not optimized to solve this very artificial task.” This mismatch limited the insights that could be drawn from this prior generation of models.

To better understand the brain, Saddler and McDermott wanted to challenge a hearing model to do things that people use their hearing for in the real world, like recognizing words and voices. That meant developing an artificial neural network to simulate the parts of the brain that receive input from the ear. The network was given input from some 32,000 simulated sound-detecting sensory neurons and then optimized for various real-world tasks.

The researchers showed that their model replicated human hearing well—better than any previous model of auditory behavior, McDermott says. In one test, the artificial neural network was asked to recognize words and voices within dozens of types of background noise, from the hum of an airplane cabin to enthusiastic applause. Under every condition, the model performed very similarly to humans.

“The ability to link patterns of firing in the auditory nerve with behavior opens a lot of doors.” – Josh McDermott

When the team degraded the timing of the spikes in the simulated ear, however, their model could no longer match humans’ ability to recognize voices or identify the locations of sounds. For example, while McDermott’s team had previously shown that people use pitch to help them identify people’s voices, the model revealed that that this ability is lost without precisely timed signals. “You need quite precise spike timing in order to both account for human behavior and to perform well on the task,” Saddler says. That suggests that the brain uses precisely timed auditory signals because they aid these practical aspects of hearing.

The team’s findings demonstrate how artificial neural networks can help neuroscientists understand how the information extracted by the ear influences our perception of the world, both when hearing is intact and when it is impaired. “The ability to link patterns of firing in the auditory nerve with behavior opens a lot of doors,” McDermott says.

“Now that we have these models that link neural responses in the ear to auditory behavior, we can ask, ‘If we simulate different types of hearing loss, what effect is that going to have on our auditory abilities?’” McDermott says. “That will help us better diagnose hearing loss, and we think there are also extensions of that to help us design better hearing aids or cochlear implants.” For example, he says, “The cochlear implant is limited in various ways—it can do some things and not others. What’s the best way to set up that cochlear implant to enable you to mediate behaviors? You can, in principle, use the models to tell you that.”

Personal interests can influence how children’s brains respond to language

by Rubina Veerakone |

A new study from the McGovern Institute shows how interests can modulate language processing in children’s brains and paves the way for personalized brain research.

The paper, which appears in Imaging Neuroscience, was conducted in the lab of McGovern Institute Investigator John Gabrieli, and led by senior author Anila D’Mello, a former McGovern postdoctoral fellow and current assistant professor at the University of Texas Southwestern Medical Center and the University of Texas at Dallas.

“Traditional studies give subjects identical stimuli to avoid confounding the results,” says Gabrieli, who is also the Grover Hermann Professor of Health Sciences and Technology and a professor of brain and cognitive sciences at MIT.

“However, our research tailored stimuli to each child’s interest, eliciting stronger—and more consistent—activity patterns in the brain’s language regions across individuals.” – John Gabrieli

Funded by the Hock E. Tan and K. Lisa Yang Center for Autism Research in MIT’s Yang Tan Collective, this work unveils a new paradigm that challenges current methods and shows how personalization can be a powerful strategy in neuroscience. The paper’s co-first authors are Halie Olson, a postdoctoral associate at the McGovern Institute, and Kristina Johnson, an assistant professor at Northeastern University and former doctoral student at the MIT Media Lab. “Our research integrates participants’ lived experiences into the study design,” says Johnson. “This approach not only enhances the validity of our findings but also captures the diversity of individual perspectives, often overlooked in traditional research.”

Taking interest into account

When it comes to language, our interests are like operators behind the switchboard. They guide what we talk about and who we talk to. Research suggests that interests are also potent motivators and can help improve language skills. For instance, children score higher on reading tests when the material covers topics that are interesting to them.

But neuroscience has shied away from using personal interests to study the brain, especially in the realm of language. This is mainly because interests, which vary between people, could throw a wrench into experimental control—a core principle that drives scientists to limit factors that can muddle the results.

Gabrieli, D’Mello, Olson, and Johnson ventured into this unexplored territory. The team wondered if tailoring language stimuli to children’s interests might lead to higher responses in language regions of the brain. “Our study is unique in its approach to control the kind of brain activity our experiments yield, rather than control the stimuli we give subjects,” says D’Mello. “This stands in stark contrast to most neuroimaging studies that control the stimuli but might introduce differences in each subject’s level of interest in the material.”

Three women posing for photo with brain images in background.
Researchers Halie Olson (left), Kristina Johnson (center), and Anila D’Mello (right). Photo: Caitlin Cunningham

In their recent study, the authors recruited a cohort of 20 children to investigate how personal interests affected the way the brain processes language. Caregivers described their child’s interests to the researchers, spanning baseball, train lines, Minecraft, and musicals. During the study, children listened to audio stories tuned to their unique interests. They were also presented with audio stories about nature (this was not an interest among the children) for comparison. To capture brain activity patterns, the team used functional magnetic resonance imaging (fMRI), which measures changes in blood flow caused by underlying neural activity.

New insights into the brain

“We found that, when children listened to stories about topics they were really interested in, they showed stronger neural responses in language areas than when they listened to generic stories that weren’t tailored to their interests,” says Olson. “Not only does this tell us how interests affect the brain, but it also shows that personalizing our experimental stimuli can have a profound impact on neuroimaging results.”

The researchers noticed a particularly striking result. “Even though the children listened to completely different stories, their brain activation patterns were more overlapping with their peers when they listened to idiosyncratic stories compared to when they listened to the same generic stories about nature,” says D’Mello. This, she notes, points to how interests can boost both the magnitude and consistency of signals in language regions across subjects without changing how these areas communicate with each other.

 

Individual activation maps from three participants showing increased engagement of language regions for personally interesting versus generic narratives. Image courtesy of the researchers.

Gabrieli noted another finding: “In addition to the stronger engagement of language regions for content of interest, there was also stronger activation in brain regions associated with reward and also with self-reflection.” Personal interests are individually relevant and can be rewarding, potentially driving higher activation in these regions during personalized stories.

These personalized paradigms might be particularly well-suited to studies of the brain in unique or neurodivergent populations. Indeed, the team is already applying these methods to study language in the brains of autistic children.

This study breaks new ground in neuroscience and serves as a prototype for future work that personalizes research to unearth further knowledge of the brain. In doing so, scientists can compile a more complete understanding of the type of information that is processed by specific brain circuits and more fully grasp complex functions such as language.

Season’s Greetings from the McGovern Institute

by Julie Pryor |

For this year’s holiday greeting, we asked the McGovern Institute community what comes to mind when they think of the winter holidays. More than 100 words were submitted for the project. The words were fed into ChatGPT to generate our holiday “prediction.” And a text-to-music generator (Udio) converted the words into a holiday song.

With special thanks to Jarrod Hicks and Jamal Williams from the McDermott lab for the inspiration…and to AI for pushing the boundaries of science and imagination.

Video credits:
Jacob Pryor (animation)
JR Narrows, Space Lute (sound design)

Revisiting reinforcement learning

by Jennifer Michalowski |

MIT Institute Professor Ann Graybiel. Photo: Justin Knight

Dopamine is a powerful signal in the brain, influencing our moods, motivations, movements, and more. The neurotransmitter is crucial for reward-based learning, a function that may be disrupted in a number of psychiatric conditions, from mood disorders to addiction. Now, researchers led by Ann Graybiel, an investigator at MIT’s McGovern Institute, have found surprising patterns of dopamine signaling that suggest neuroscientists may need to refine their model of how reinforcement learning occurs in the brain. The team’s findings were published October 14, 2024, in the journal Nature Communications.

Dopamine plays a critical role in teaching people and other animals about the cues and behaviors that portend both positive and negative outcomes; the classic example of this type of learning is the dog that Ivan Pavlov trained to anticipate food at the sound of bell. Graybiel explains that according to the standard model of reinforcement learning, when an animal is exposed to a cue paired with a reward, dopamine-producing cells initially fire in response to the reward. As animals learn the association between the cue and the reward, the timing of dopamine release shifts, so it becomes associated with the cue instead of the reward itself.

But with new tools enabling more detailed analyses of when and where dopamine is released in the brain, Graybiel’s team is finding that this model doesn’t completely hold up. The group started picking up clues that the field’s model of reinforcement learning was incomplete more than ten years ago, when Mark Howe, a graduate student in the lab, noticed that the dopamine signals associated with reward were released not in a sudden burst the moment a reward was obtained, but instead before that, building gradually as a rat got closer to its treat. Dopamine might actually be communicating to the rest of the brain the proximity of the reward, they reasoned. “That didn’t fit at all with the standard, canonical model,” Graybiel says.

Dopamine dynamics

As other neuroscientists considered how a model of reinforcement learning could take those findings into account, Graybiel and postdoctoral researcher Min Jung Kim decided it was time to take a closer look at dopamine dynamics.

“We thought, let’s go back to the most basic kind of experiment and start all over again,” Graybiel says.

That meant using sensitive new dopamine sensors to track the neurotransmitter’s release in the brains of mice as they learned to associated a blue light with a satisfying sip of water. The team focused its attention on the striatum, a region within the brain’s basal ganglia, where neurons use dopamine to influence neural circuits involved in a variety of processes, including reward-based learning.

The researchers found that the timing of dopamine release varied in different parts of the striatum. But nowhere did Graybiel’s team find a transition in dopamine release timing from the time of the reward to the time to the cue—the key transition predicted by the standard model of reinforcement learning model.

In the team’s simplest experiments, where every time a mouse saw a light it was paired with a reward, the lateral part of the striatum reliably released dopamine when animals were given their water. This strong response to the reward never diminished, even as the mice learned to expect the reward when they saw a light. In the medial part of the striatum, in contrast, dopamine was never released at the time of the reward. Cells there always fired when a mouse saw the light, even early in the learning process. This was puzzling, Graybiel says, because at the beginning of learning, dopamine would have been predicted to respond to the reward itself.

The patterns of dopamine release became even more unexpected when Graybiel’s team introduced a second light into its experimental setup. The new light, in a different position than the first, did not signal a reward. Mice watched as either light was given as the cue, one at a time, with water accompanying only the original cue.

In these experiments, when the mice saw the reward-associated light, dopamine release went up in the centromedial striatum and surprisingly, stayed up until the reward was delivered. In the lateral part of the region, dopamine also involved a sustained period where signaling plateaued.

Graybiel says she was surprised to see how much dopamine responses changed when the experimenters introduce the second light. The responses to the rewarded light were different when the other light could be shown in other trials, even though the mice saw only one light at a time. “There must be a cognitive aspect to this that comes into play,” she says. “The brain wants to hold onto the information that the cue has come on for a while.” Cells in the striatum seem to achieve this through the sustained dopamine release that continued during the brief delay between the light and the reward in the team’s experiments. Indeed, Graybiel said, while this kind of sustained dopamine release has not previously been linked to reinforcement learning, it is reminiscent of sustained signaling that has been tied to working memory in other parts of the brain.

Reinforcement learning, reconsidered

Ultimately, Graybiel says, “many of our results didn’t fit reinforcement learning models as traditionally—and by now canonically—considered.” That suggests neuroscientists’ understanding of this process will need to evolve as part of the field’s deepening understanding of the brain. “But this is just one step to help us all refine our understanding and to have reformulations of the models of how basal ganglia influence movement and thought and emotion. These reformulations will have to include surprises about the reinforcement learning system vis-á-vis these plateaus, but they could possibly give us insight into how a single experience can linger in this reinforcement-related part of our brains,” she says.

This study was funded by the National Institutes of Health, the William N. & Bernice E. Bumpus Foundation, the Saks Kavanaugh Foundation, the CHDI Foundation, Joan and Jim Schattinger, and Lisa Yang.

Illuminating the architecture of the mind

by Jennifer Michalowski |

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

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McGovern investigator Nancy Kanwisher and her team have big questions about the nature of the human mind. Energized by Kanwisher’s enthusiasm for finding out how and why the brain works as it does, her team collaborates broadly and embraces various tools of neuroscience. But their core discoveries tend to emerge from pictures of the brain in action. For Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience at MIT, “there’s nothing like looking inside.”

Kanwisher and her colleagues have scanned the brains of hundreds of volunteers using functional magnetic resonance imaging (fMRI). With each scan, they collect a piece of insight into how the brain is organized.

Male and female researchers sitting in an imaging center with an MRI in the background.
Nancy Kanwisher (right), whose unfaltering support for students and trainees has earned her awards for outstanding teaching and mentorship, is now working with research scientist RT Pramod to find the brain’s “physics network.” Photo: Steph Stevens

Recognizing faces

By visualizing the parts of the brain that get involved in various mental activities — and, importantly, which do not — they’ve discovered that certain parts of the brain specialize in surprisingly specific tasks. Earlier this year Kanwisher was awarded the prestigious Kavli Prize in Neuroscience for the discovery of one of these hyper-specific regions: a small spot within the brain’s neocortex that recognizes faces.

Kanwisher found that this region, which she named the fusiform face area (FFA), is highly sensitive to images of faces and appears to be largely uninterested in other objects. Without the FFA, the brain struggles with facial recognition — an impairment seen in patients who have experienced damage to this part of the brain.

Beyond the FFA

Not everything in the brain is so specialized. Many areas participate in a range of cognitive processes, and even the most specialized modules, like the FFA, must work with other brain regions to process and use information. Plus, Kanwisher and her team have tracked brain activity during many functions without finding regions devoted exclusively to those tasks. (There doesn’t appear to be a part of the brain dedicated to recognizing snakes, for example).

Still, work in the Kanwisher lab demonstrates that as a specialized functional module within the brain, the FFA is not unique. In collaboration with McGovern colleagues Josh McDermott and Evelina Fedorenko, the group has found areas devoted to perceiving music and using language. There’s even a region dedicated to thinking about other people’s thoughts, identified by Rebecca Saxe in work she started as a graduate student in Kanwisher’s lab.

Brain with colored blobs.
Kanwisher’s team has found several hyperspecific regions of the brain, including those dedicated to using language (red-orange), perceiving music (yellow), thinking about other people’s thoughts (blue), recognizing bodies (green), and our intuitive sense of physics (teal). (This is an artistic adaptation of Kanwisher’s data.)

Having established these regions’ roles, Kanwisher and her collaborators are now looking at how and why they become so specialized. Meanwhile, the group has also turned its attention to a more complex function that seems to largely take place within a defined network: our intuitive sense of physics.

The brain’s game engine

Early in life, we begin to understand the nature of objects and materials, such as the fact that objects can support but not move through each other. Later, we intuitively understand how it feels to move on a slippery floor, what happens when moving objects collide, and where a tossed ball will fall. “You can’t do anything at all in the world without some understanding of the physics of the world you’re acting on,” Kanwisher says.

Kanwisher says MIT colleague Josh Tenenbaum first sparked her interest in intuitive physical reasoning. Tenenbaum and his students had been arguing that humans understand
the physical world using a simulation system, much like the physics engines that video games use to generate realistic movement and interactions within virtual environments. Kanwisher decided to team up with Tenenbaum to test whether there really is a game engine in the head, and if so, what it computes and represents.

An unstable column of blue and yellow blocks piled on top of a table that is half red, half green.
By asking subjects in an MRI scanner to predict which way this block tower might fall, Kanwisher’s team is zeroing in on the location of the brain’s “physics network.” Image: RT Pramod, Nancy Kanwisher

To find out, Kanwisher and her team have asked volunteers to evaluate various scenarios while in an MRI scanner — some that require physical reasoning and some that do not. They found sizable parts of the brain that participate in physical reasoning tasks but stay quiet during other kinds of thinking.

Research scientist RT Pramod says he was initially skeptical the brain would dedicate special circuitry to the diverse tasks involved in our intuitive sense of physics — but he’s been convinced by the data he’s found. “I see consistent evidence that if you’re reasoning, if you’re thinking, or even if you’re looking at anything sort of “physics-y” about the world, you will see activations in these regions and only in these regions — not anywhere else,” he says.

Pramod’s experiments also show that these regions are called on to make predictions about the physical world. When volunteers watch videos of objects whose trajectories portend a crash — but do not actually depict that crash — it is the physics network that signals what is about to happen. “Only these regions have this information, suggesting that maybe there is some truth to the physics engine hypothesis,” Pramod says.

Kanwisher says she doesn’t expect physical reasoning, which her group has tied to sizable swaths of the brain’s frontal and parietal cortex, to be executed by a module as distinct as the FFA. “It’s not going to be like one hyper-specific region and that’s all that happens there,” she says. “I think ultimately it’s much more interesting than that.”

To figure out what these regions can and cannot do, Kanwisher’s team has broadened the ways in which they ask volunteers to think about physics inside the MRI scanner. So far, Kanwisher says, the group’s tests have focused on rigid objects. But what about soft, squishy ones, or liquids?

A red liquid sloshes inside a clear container.
Kanwisher’s team is exploring whether non-rigid materials, like the liquid in this image, engage the brain’s “physics network” in the same way as rigid objects. Image: Vivian Paulun

Vivian Paulun, a postdoc working jointly with Kanwisher and Tenenbaum, is investigating whether our innate expectations about these kinds of materials occur within the network that they have linked to physical reasoning about rigid objects. Another set of experiments will explore whether we use sounds, like that of a bouncing ball or a screeching car, to predict physics physical events with the same network that interprets visual cues.

Meanwhile, she is also excited about an opportunity to find out what happens when the brain’s physics network is damaged. With collaborators in England, the group plans to find out whether patients in which stroke has affected this part of the brain have specific deficits in physical reasoning.

Probing these questions could reveal fundamental truths about the human mind and intelligence. Pramod points out that it could also help advance artificial intelligence, which so far has been unable to match humans when it comes to physical reasoning. “Inferences that are sort of easy for us are still really difficult for even state-of-the art computer vision,” he says. “If we want to get to a stage where we have really good machine learning algorithms that can interact with the world the way we do, I think we should first understand how the brain does it.”

A cell protector collaborates with a killer

by Jennifer Michalowski |

From early development to old age, cell death is a part of life. Without enough of a critical type of cell death known as apoptosis, animals wind up with too many cells, which can set the stage for cancer or autoimmune disease. But careful control is essential, because when apoptosis eliminates the wrong cells, the effects can be just as dire, helping to drive many kinds of neurodegenerative disease.

Portrait of a scientist
McGovern Investigator Robert Horvitz poses for a photo in his laboratory. Photo: AP Images/Aynsley Floyd

By studying the microscopic roundworm Caenorhabditis elegans—which was honored with its fourth Nobel Prize last month—scientists at MIT’s McGovern Institute have begun to unravel a longstanding mystery about the factors that control apoptosis: how a protein capable of preventing programmed cell death can also promote it. Their study, led by McGovern Investigator Robert Horvitz and reported October 9, 2024, in the journal Science Advances, sheds light on the process of cell death in both health and disease.

“These findings, by graduate student Nolan Tucker and former graduate student, now MIT faculty colleague, Peter Reddien, have revealed that a protein interaction long thought to block apoptosis in C. elegans, likely instead has the opposite effect,” says Horvitz, who shared the 2002 Nobel Prize for discovering and characterizing the genes controlling cell death in C. elegans.

Mechanisms of cell death

Horvitz, Tucker, Reddien and colleagues have provided foundational insights in the field of apoptosis by using C. elegans to analyze the mechanisms that drive apoptosis as well as the mechanisms that determine how cells ensure apoptosis happens when and where it should. Unlike humans and other mammals, which depend on dozens of proteins to control apoptosis, these worms use just a few. And when things go awry, it’s easy to tell: When there’s not enough apoptosis, researchers can see that there are too many cells inside the worms’ translucent bodies. And when there’s too much, the worms lack certain biological functions or, in more extreme cases, can’t reproduce or die during embryonic development.

black and white microscopic image of worms
The nematode worm Caenorhabditis elegans has provided answers to many fundamental questions in biology. Image: Robert Horvitz

Work in the Horvitz lab defined the roles of many of the genes and proteins that control apoptosis in worms. These regulators proved to have counterparts in human cells, and for that reason studies of worms have helped reveal how human cells govern cell death and pointed toward potential targets for treating disease.

A protein’s dual role

Three of C. elegans’ primary regulators of apoptosis actively promote cell death, whereas just one, CED-9, reins in the apoptosis-promoting proteins to keep cells alive. As early as the 1990s, however, Horvitz and colleagues recognized that CED-9 was not exclusively a protector of cells. Their experiments indicated that the protector protein also plays a role in promoting cell death. But while researchers thought they knew how CED-9 protected against apoptosis, its pro-apoptotic role was more puzzling.

CED-9’s dual role means that mutations in the gene that encode it can impact apoptosis in multiple ways. Most ced-9 mutations interfere with the protein’s ability to protect against cell death and result in excess cell death. Conversely, mutations that abnormally activate ced-9 cause too little cell death, just like mutations that inactivate any of the three killer genes.

An atypical ced-9 mutation, identified by Reddien when he was a PhD student in Horvitz’s lab, hinted at how CED-9 promotes cell death. That mutation altered the part of the CED-9 protein that interacts with the protein CED-4, which is proapoptotic. Since the mutation specifically leads to a reduction in apoptosis, this suggested that CED-9 might need to interact with CED-4 to promote cell death.

The idea was particularly intriguing because researchers had long thought that CED-9’s interaction with CED-4 had exactly the opposite effect: In the canonical model, CED-9 anchors CED-4 to cells’ mitochondria, sequestering the CED-4 killer protein and preventing it from associating with and activating another key killer, the CED-3 protein —thereby preventing apoptosis.

To test the hypothesis that CED-9’s interactions with the killer CED-4 protein enhance apoptosis, the team needed more evidence. So graduate student Nolan Tucker used CRISPR gene editing tools to create more worms with mutations in CED-9, each one targeting a different spot in the CED-4-binding region. Then he examined the worms. “What I saw with this particular class of mutations was extra cells and viability,” he says—clear signs that the altered CED-9 was still protecting against cell death, but could no longer promote it. “Those observations strongly supported the hypothesis that the ability to bind CED-4 is needed for the pro-apoptotic function of CED-9,” Tucker explains. Their observations also suggested that, contrary to earlier thinking, CED-9 doesn’t need to bind with CED-4 to protect against apoptosis.

When he looked inside the cells of the mutant worms, Tucker found additional evidence that these mutations prevented CED-9’s ability to interact with CED-4. When both CED-9 and CED-4 are intact, CED-4 appears associated with cells’ mitochondria. But in the presence of these mutations, CED-4 was instead at the edge of the cell nucleus. CED-9’s ability to bind CED-4 to mitochondria appeared to be necessary to promote apoptosis, not to protect against it.

In wild-type worms CED-4 is localized to mitochondria. However, the introduction of CED-9-CED-4 binding mutations such as ced-4(n6703) or ced-9(n6704), causes CED-4 protein to localize to the outer edge of the nucleus. Image: Nolan Tucker, Robert Horvitz

Looking ahead

While the team’s findings begin to explain a long-unanswered question about one of the primary regulators of apoptosis, they raise new ones, as well. “I think that this main pathway of apoptosis has been seen by a lot of people as more or less settled science. Our findings should change that view,” Tucker says.

The researchers see important parallels between their findings from this study of worms and what’s known about cell death pathways in mammals. The mammalian counterpart to CED-9 is a protein called BCL-2, mutations in which can lead to cancer.  BCL-2, like CED-9, can both promote and protect against apoptosis. As with CED-9, the pro-apoptotic function of BCL-2 has been mysterious. In mammals, too, mitochondria play a key role in activating apoptosis. The Horvitz lab’s discovery opens opportunities to better understand how apoptosis is regulated not only in worms but also in humans, and how dysregulation of apoptosis in humans can lead to such disorders as cancer, autoimmune disease and neurodegeneration.

Adults’ brain activity appears unchanged after a year of medical use of cannabis

by Jennifer Michalowski |

In a study of adults who use cannabis because they are seeking relief from pain, depression, anxiety, or insomnia, scientists at MIT and Harvard found no changes in brain activity after one year of self-directed use. The study, reported September 18, 2024, in JAMA Network Open, is among the first to investigate how the real-world ways people use cannabis to treat medical symptoms might impact the brain in lasting ways.

While some studies have linked chronic cannabis use to changes in the brain’s structure and function, outcomes vary depending, in part, on how and when people use the substance. People who begin using cannabis during adolescence, while the brain is still developing, may be particularly vulnerable to brain changes. The potency of the products they use and how often they use them matter, too.

Participants in the research, who obtained medical cannabis cards at the outset of the study, tended to choose lower potency products and use them less than daily. This may be why the researchers’ analysis—which focused on the brain activity associated with three kinds of cognitive processes—showed no changes after a year of use.

“For most older adults, occasional cannabis will not dramatically affect brain activation,” says Harvard neuroscientist Jodi Gilman, who led the study. “However, there are some individuals who may be vulnerable to negative effects of cannabis on cognitive function, particularly those using higher potency products more frequently.”

Gilman cautions that in another study of the same medical cannabis users, her team found that the drug failed to alleviate patients’ pain, depression, or anxiety. “So it didn’t help their symptoms—but it wasn’t associated with significant changes in brain activation,” she says. She also cautioned that some adults in the study did develop problems with cannabis use, including cannabis use disorder.

Medical cannabis programs are currently established in 38 U.S. states and Washington, D.C., increasing access to a substance that many people hope might help them relieve distressing medical symptoms. But little is known about how this type of cannabis use affects neural circuits in the brain. “Cannabis has been legalized through ballot initiatives and by legislatures. Dispensary cannabis has not been tested through large, randomized, double-blind clinical trials,” Gilman says. With McGovern Principal Research Scientist Satrajit Ghosh and MD-PhD student Debbie Burdinski, she set out to see what neuroimaging data would reveal about the impacts of this type of cannabis use.

Participants in their study were all adults seeking relief from depression, anxiety, pain, or insomnia who, prior to obtaining their medical cannabis cards, had never used cannabis at high frequencies. The researchers wanted their study to reflect the ways people really use cannabis, so participants were free choose which types of products they used, as well as how much and how often. “We told people, “Get what you want, use it you as you wish, and we’re going to look at how it may affect the brain,” Gilman explains.

Participants reported using a variety of products, but generally, they tended to choose low-potency products. Their frequency of use also varied, from less than once a month to once or more each day. Fewer than 20 percent of participants were daily users.

At the start of the study and again one year later, the research team used functional MRI scans to watch what happened in the brain while participants used three key cognitive skills: working memory, inhibitory control, and reward processing. The activity revealed on the scans showed the researchers which parts of the brain were working to perform these tasks.

Alterations in activity patterns could indicate changes in brains function. But in the 54 participants who underwent both brain scans, Gilman, Ghosh, and Burdinski found that after one year of cannabis use, brain activity during these three cognitive tasks was unchanged. Burdinski notes that many facets of cognition were not followed in the study, so some changes to brain activity could have occurred without being evident in the team’s data.

The researchers acknowledge that their study cohort, whose members were mostly female, middle-aged, and well educated, was less diverse than the population of people who use cannabis for medical symptoms. In fact, Gilman says, groups that are most vulnerable to negative consequences of cannabis may not have been well represented in the study, and it’s possible that a study of a different subgroup would have found different results.

Ghosh points out that there is still a lot to learn about the impact of cannabis, and larger studies are needed to understand its effects on the brain, including how it impacts different populations. For some individuals, he stresses, its use can have severe, debilitating effects, including symptoms of psychosis, delusions, or cannabinoid hyperemesis syndrome.

Larger studies are needed to understand cannabis’s effects on the brain and how it impacts different populations, Ghosh says. “Science can help us understand how we should be thinking about the impact of various substances or various interventions on the brain, instead of just anecdotal considerations of how they work,” he says. “Maybe there are people for whom there are changes. Now we can start teasing apart those details.”

Polina Anikeeva named 2024 Blavatnik Award Finalist

by Julie Pryor |

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

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

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

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

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

Finding some stability in adaptable brains

by Jennifer Michalowski |

One of the brain’s most celebrated qualities is its adaptability. Changes to neural circuits, whose connections are continually adjusted as we experience and interact with the world, are key to how we learn. But to keep knowledge and memories intact, some parts of the circuitry must be resistant to this constant change.

“Brains have figured out how to navigate this landscape of balancing between stability and flexibility, so that you can have new learning and you can have lifelong memory,” says neuroscientist Mark Harnett, an investigator at MIT’s McGovern Institute.

In the August 27, 2024 of the journal Cell Reports, Harnett and his team show how individual neurons can contribute to both parts of this vital duality. By studying the synapses through which pyramidal neurons in the brain’s sensory cortex communicate, they have learned how the cells preserve their understanding of some of the world’s most fundamental features, while also maintaining the flexibility they need to adapt to a changing world.

McGovern Institute Investigator Mark Harnett. Photo: Adam Glanzman

Visual connections

Pyramidal neurons receive input from other neurons via thousands of connection points. Early in life, these synapses are extremely malleable; their strength can shift as a young animal takes in visual information and learns to interpret it. Most remain adaptable into adulthood, but Harnett’s team discovered that some of the cells’ synapses lose their flexibility when the animals are less than a month old. Having both stable and flexible synapses means these neurons can combine input from different sources to use visual information in flexible ways.

Microscopic image of a mouse brain.
A confocal image of a mouse brain showing dLGN neurons in pink. Image: Courtney Yaeger, Mark Harnett.

Postdoctoral fellow Courtney Yaeger took a close look at these unusually stable synapses, which cluster together along a narrow region of the elaborately branched pyramidal cells. She was interested in the connections through which the cells receive primary visual information, so she traced their connections with neurons in a vision-processing center of the brain’s thalamus called the dorsal lateral geniculate nucleus (dLGN).

The long extensions through which a neuron receives signals from other cells are called dendrites, and they branch of from the main body of the cell into a tree-like structure. Spiny protrusions along the dendrites form the synapses that connect pyramidal neurons to other cells. Yaeger’s experiments showed that connections from the dLGN all led to a defined region of the pyramidal cells—a tight band within what she describes as the trunk of the dendritic tree.

Yaeger found several ways in which synapses in this region— formally known as the apical oblique dendrite domain—differ from other synapses on the same cells. “They’re not actually that far away from each other, but they have completely different properties,” she says.

Stable synapses

In one set of experiments, Yaeger activated synapses on the pyramidal neurons and measured the effect on the cells’ electrical potential. Changes to a neuron’s electrical potential generate the impulses the cells use to communicate with one another. It is common for a synapse’s electrical effects to amplify when synapses nearby are also activated. But when signals were delivered to the apical oblique dendrite domain, each one had the same effect, no matter how many synapses were stimulated. Synapses there don’t interact with one another at all, Harnett says. “They just do what they do. No matter what their neighbors are doing, they all just do kind of the same thing.”

Two rows of seven confocal microscope images of dendrites.
Representative oblique (top) and basal (bottom) dendrites from the same Layer 5 pyramidal neuron imaged across 7 days. Transient spines are labeled with yellow arrowheads the day before disappearance. Image: Courtney Yaeger, Mark Harnett.

The team was also able to visualize the molecular contents of individual synapses. This revealed a surprising lack of a certain kind of neurotransmitter receptor, called NMDA receptors, in the apical oblique dendrites. That was notable because of NMDA receptors’ role in mediating changes in the brain. “Generally when we think about any kind of learning and memory and plasticity, it’s NMDA receptors that do it,” Harnett says. “That is the by far most common substrate of learning and memory in all brains.”

When Yaeger stimulated the apical oblique synapses with electricity, generating patterns of activity that would strengthen most synapses, the team discovered a consequence of the limited presence of NMDA receptors. The synapses’ strength did not change. “There’s no activity-dependent plasticity going on there, as far as we have tested,” Yaeger says.

That makes sense, the researchers say, because the cells’ connections from the thalamus relay primary visual information detected by the eyes. It is through these connections that the brain learns to recognize basic visual features like shapes and lines.

“These synapses are basically a robust, high fidelity readout of this visual information,” Harnett explains. “That’s what they’re conveying, and it’s not context sensitive. So it doesn’t matter how many other synapses are active, they just do exactly what they’re going to do, and you can’t modify them up and down based on activity. So they’re very, very stable.”

“You actually don’t want those to be plastic,” adds Yaeger.

“Can you imagine going to sleep and then forgetting what a vertical line looks like? That would be disastrous.” – Courtney Yaeger

By conducting the same experiments in mice of different ages, the researchers determined that the synapses that connect pyramidal neurons to the thalamus become stable a few weeks after young mice first open their eyes. By that point, Harnett says, they have learned everything they need to learn. On the other hand, if mice spend the first weeks of their lives in the dark, the synapses never stabilize—further evidence that the transition depends on visual experience.

The team’s findings not only help explain how the brain balances flexibility and stability, they could help researchers teach artificial intelligence how to do the same thing. Harnett says artificial neural networks are notoriously bad at this: When an artificial neural network that does something well is trained to do something new, it almost always experiences “catastrophic forgetting” and can no longer perform its original task. Harnett’s team is exploring how they can use what they’ve learned about real brains to overcome this problem in artificial networks.

Harnessing the power of placebo for pain relief

by Jennifer Michalowski |

Placebos are inert treatments, generally not expected to impact biological pathways or improve a person’s physical health. But time and again, some patients report that they feel better after taking a placebo. Increasingly, doctors and scientists are recognizing that rather than dismissing placebos as mere trickery, they may be able to help patients by harnessing their power.

To maximize the impact of the placebo effect and design reliable therapeutic strategies, researchers need a better understanding of how it works. Now, with a new animal model developed by scientists at the McGovern Institute, they will be able to investigate the neural circuits that underlie placebos’ ability to elicit pain relief.

“The brain and body interaction has a lot of potential, in a way that we don’t fully understand,” says McGovern investigator Fan Wang. “I really think there needs to be more of a push to understand placebo effect, in pain and probably in many other conditions. Now we have a strong model to probe the circuit mechanism.”

Context-dependent placebo effect

McGovern Investigator Fan Wang. Photo: Caitliin Cunningham

In the September 5, 2024, issue of the journal Current Biology, Wang and her team report that they have elicited strong placebo pain relief in mice by activating pain-suppressing neurons in the brain while the mice are in a specific environment—thereby teaching the animals that they feel better when they are in that context. Following their training, placing the mice in that environment alone is enough to suppress pain. The team’s experiments, which were funded by the National Institutes of Health, the K. Lisa Yang Brain-Body Center and the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics within MIT’s Yang Tan Collective show that this context-dependent placebo effect relieves both acute and chronic pain.

Context is critical for the placebo effect. While a pill can help a patient feel better when they expect it to, even if it is made only of sugar or starch, it seems to be not just the pill that sets up those expectations, but the entire scenario in which the pill is taken. For example, being in a hospital and interacting with doctors can contribute to a patient’s perception of care, and these social and environmental factors can make a placebo effect more probable.

Postdoctoral fellows Bin Chen and Nitsan Goldstein used visual and textural cues to define a specific place. Then they activated pain-suppressing neurons in the brain while the animals were in this “pain-relief box.” Those pain-suppressing neurons, which Wang’s lab discovered a few years ago, are located in an emotion-processing center of the brain called the central amygdala. By expressing light-sensitive channels in these neurons, the researchers were able to suppress pain with light in the pain-relief box and leave the neurons inactive when mice were in a control box.

Animals learned to prefer the pain-relief box to other environments. And when the researchers tested their response to potentially painful stimuli after they had made that association, they found the mice were less sensitive while they were there. “Just by being in the context that they had associated with pain suppression, we saw that reduced pain—even though we weren’t actually activating those [pain-suppressing] neurons,” Goldstein explains.

Acute and chronic pain relief

Some scientists have been able to elicit placebo pain relief in rodents by treating the animals with morphine, linking environmental cues to the pain suppression caused by the drugs similar to the way Wang’s team did by directly activating pain-suppressing neurons. This drug-based approach works best for setting up expectations of relief for acute pain; its placebo effect is short-lived and mostly ineffective against chronic pain. So Wang, Chen, and Goldstein were particularly pleased to find that their engineered placebo effect was effective for relieving both acute and chronic pain.

In their experiments, animals experiencing a chemotherapy-induced hypersensitivity to touch exhibited a preference for the pain relief box as much as animals who were exposed to a chemical that induces acute pain, days after their initial conditioning. Once there, their chemotherapy-induced pain sensitivity was eliminated; they exhibited no more sensitivity to painful stimuli than they had prior to receiving chemotherapy.

One of the biggest surprises came when the researchers turned their attention back to the pain-suppressing neurons in the central amygdala that they had used to trigger pain relief. They suspected that those neurons might be reactivated when mice returned to the pain-relief box. Instead, they found that after the initial conditioning period, those neurons remained quiet. “These neurons are not reactivated, yet the mice appear to be no longer in pain,” Wang says. “So it suggests this memory of feeling well is transferred somewhere else.”

Goldstein adds that there must be a pain-suppressing neural circuit somewhere that is activated by pain-relief-associated contexts—and the team’s new placebo model sets researchers up to investigate those pathways. A deeper understanding of that circuitry could enable clinicians to deploy the placebo effect—alone or in combination with active treatments—to better manage patients’ pain in the future.