Research Area: Cellular & Molecular Neuroscience

​synapses, vesicles, electrophysiology, protein complexes, signaling, small molecules, disease-linked genes, dendrites, cell death, brain disorders, neurodevelopment

Margaret Livingstone awarded the 2024 Scolnick Prize in Neuroscience

by Jill Crittenden |

Today the McGovern Institute at MIT announces that the 2024 Edward M. Scolnick Prize in Neuroscience will be awarded to Margaret Livingstone, Takeda Professor of Neurobiology at Harvard Medical School. The Scolnick Prize is awarded annually by the McGovern Institute, for outstanding achievements in neuroscience.

“Margaret Livingstone’s driven curiosity and original experimental approaches have led to fundamental advances in our understanding of visual perception,” says Robert Desimone, director of the McGovern Institute and chair of the selection committee. “In particular, she has made major advances in resolving a long-standing debate over whether the brain domains and neurons that are specifically tuned to detect facial features are present from birth or arise from experience. Her developmental research shows that the cerebral cortex already contains topographic sensory maps at birth but that domain-specific maps, for example to recognize facial-features, require experience and sensory input to develop normally.”

“Margaret Livingstone’s driven curiosity and original experimental approaches have led to fundamental advances in our understanding of visual perception.” — Robert Desimone

Livingstone received a BS from MIT in 1972 and, under the mentorship of Edward Kravitz, a PhD in neurobiology from Harvard University in 1981. Her doctoral research in lobsters showed that the biogenic amines serotonin and octopamine control context-dependent behaviors such as offensive versus defensive postures. She followed up on this discovery as a postdoctoral fellow by researching biogenic amine signaling in learning and memory, with Prof. William Quinn at Princeton University. Using learning and memory mutants created in the fruit fly model she identified defects in dopamine-synthesizing enzymes and calcium-dependent enzymes that produce cAMP. Her results supported the then burgeoning idea that biogenic amines signal through second messengers enable behavioral plasticity.

To test whether biogenic amines also control neuronal function in mammals, Livingstone moved back to Harvard Medical School in 1983 to study the effects of sleep on visual processing with David Hubel, who was studying neuronal activity in the nonhuman primate visual cortex. Over the course of a 20-year collaboration, Livingstone and Hubel showed that the visual system is functionally and anatomically divided into parallel pathways that detect and process the distinct visual features of color, motion, and orientation.

Livingstone quickly rose through the academic ranks at Harvard to be appointed as an instructor and then assistant professor in 1983, associate professor in 1986 and full professor in 1988. With her own laboratory, Livingstone began to explore the organization of face-perception domains in the inferotemporal cortex of nonhuman primates. By combining single-cell recording and fMRI brain imaging data from the same animal, her then graduate student Doris Tsao, in collaboration with Winrich Freiwald, showed that an abundance of individual neurons within the face-recognition domain are tuned to a combination of facial features. These results helped to explain the long-standing question of how individual neurons show such exquisite selectivity to specific faces.

Three images of Mona Lisa, side by side, each with a different filter slightly obscuring the face.
Mona Lisa’s smile has been described as mysterious and fleeting because it seems to disappear when viewers look directly at it. Livingstone showed that Mona Lisa’s smile is more apparent in our peripheral vision than our central (or foveal) vision because our peripheral vision is more sensitive to low spatial frequencies, or shadows and shadings of black and white. These shadows make her lips seem to turn upward into a subtle smile. The three images above show the painting filtered to reveal very low spatial frequency features (left, with the smile more apparent) to high spatial frequency features (right, with the smile being less visible). Image: Margaret Livingstone

In researching face patches, Livingstone became fascinated with the question of whether face-perception domains are present from birth, as many scientists thought at the time. Livingstone and her postdoc Michael Arcaro carried out experiments that showed that the development of face patches requires visual exposure to faces in the early postnatal period. Moreover, they showed that entirely unnatural symbol-specific domains can form in animals that experienced intensive visual exposure to symbols early in development. Thus, experience is both necessary and sufficient for the formation of feature-specific domains in the inferotemporal cortex. Livingtone’s results support a consistent principle for the development of higher-level cortex, from a hard-wired sensory topographic map present at birth to the formation of experience-dependent domains that detect combined, stimulus-specific features.

Livingstone is also known for her scientifically based exploration of the visual arts. Her book “Vision and Art: The Biology of Seeing,” which has sold more than 40,000 copies to date, explores how both the techniques artists use and our anatomy and physiology influence our perception of art. Livingstone has presented this work to audiences around the country, from Pixar Studios, MicroSoft and IBM to The Metropolitan Museum of Art, The National Gallery and The Hirshhorn Museum.

In 2014, Livingstone was awarded the Takeda Professorship of Neurobiology at Harvard Medical School. She was awarded the Mika Salpeter Lifetime Achievement Award from the Society for Neuroscience in 2011, the Grossman Award from the Society of Neurological Surgeons in 2013 and the Roberts Prize for Best Paper in Physics in Medicine and Biology in 2013 and 2016. Livingstone was elected fellow of the American Academy of Arts and Sciences in 2018 and of the National Academy of Science in 2020. She will be awarded the Scolnick Prize in the spring of 2024.

Calling neurons to attention

by Jennifer Michalowski |

The world assaults our senses, exposing us to more noise and color and scents and sensations than we can fully comprehend. Our brains keep us tuned in to what’s important, letting less relevant sights and sounds fade into the background while we focus on the most salient features of our surroundings. Now, scientists at MIT’s McGovern Institute have a better understanding of how the brain manages this critical task of directing our attention.

In the January 15, 2023, issue of the journal Neuron, a team led by Diego Mendoza-Halliday, a research scientist in McGovern Institute Director Robert Desimone’s lab, reports on a group of neurons in the brain’s prefrontal cortex that are critical for directing an animal’s visual attention. Their findings not only demonstrate this brain region’s important role in guiding attention, but also help establish attention as a function that is distinct from other cognitive functions, such as short-term memory, in the brain.

Attention and working memory

Mendoza-Halliday, who is now an assistant professor at the University of Pittsburgh, explains that attention has a close relationship to working memory, which the brain uses to temporarily store information after our senses take it in. The two brain functions strongly influence one another: We’re more likely to remember something if we pay attention to it, and paying attention to certain features of our environment may involve representing those features in our working memory. For example, he explains, both attention and working memory are called on when searching for a triangular red keychain on a cluttered desk: “What my brain does is it remembers that my keyholder is red and it’s a triangle, and then builds a working memory representation and uses it as a search template. So now everything that is red and everything that is a triangle receives preferential processing, or is attended to.”

Working memory and attention are so closely associated that some neuroscientists have proposed that the brain calls on the same neural mechanisms to create them. “This has led to the belief that maybe attention and working memory are just two sides of the same coin—that they’re basically the same function in different modes,” Mendoza-Halliday says. His team’s findings, however, say otherwise.

Circuit manipulation

To study the origins of attention in the brain, Mendoza-Halliday and colleagues trained monkeys to focus their attention on a visual feature that matches a cue they have seen before. After seeing a set of dots move across the screen, they must call on their working memory to remember the direction of that movement for a few seconds while the screen goes blank. Then the experimenters present the animals with more moving dots, this time traveling in multiple directions. By focusing on the dots moving in the same direction as the first set they saw, the monkeys are able to recognize when those dots briefly accelerate. Reporting on the speed change earns the animals a reward.

While the monkeys performed this task, the researchers monitored cells in several brain regions, including the prefrontal cortex, which Desimone’s team has proposed plays a role in directing attention. The activity patterns they recorded suggested that distinct groups of cells participated in the attention and working memory aspects of the task.

To better understand those cells’ roles, the researchers manipulated their activity. They used optogenetics, an approach in which a light-sensitive protein is introduced into neurons so that they can be switched on or off with a pulse of light. Desimone’s lab, in collaboration with Edward Boyden, the Y. Eva Tan Professor in Neurotechnology at MIT and a member of the McGovern Institute, pioneered the use of optogenetics in primates. “Optogenetics allows us to distinguish between correlation and causality in neural circuits,” says Desimone, the Doris and Don Berkey Professor of Neuroscience at MIT.  “If we turn off a circuit using optogenetics, and the animal can no longer perform the task, that is good evidence for a causal role of the circuit,” says Desimone, who is also a professor of brain and cognitive sciences at MIT.

Using this optogenetic method, they switched off neurons in a specific portion of the brain’s lateral prefrontal cortex for a few hundred milliseconds at a time as the monkeys performed their dot-tracking task. The researchers found that they could switch off signaling from the lateral prefrontal cortex early, when the monkeys needed their working memory but had no dots to attend to, without interfering with the animals’ ability to complete the task. But when they blocked signaling when the monkeys needed to focus their attention, the animals performed poorly.

The team also monitored activity in the brain visual’s cortex during the moving-dot task. When the lateral prefrontal cortex was shut off, neurons in connected visual areas showed less heightened reactivity to movement in the direction the monkey was attending to. Mendoza-Halliday says this suggests that cells in the lateral prefrontal cortex are important for telling sensory-processing circuits what visual features to pay attention to.

The discovery that at least part of the brain’s lateral prefrontal cortex is critical for attention but not for working memory offers a new view of the relationship between the two. “It is a physiological demonstration that working memory and attention cannot be the same function, since they rely on partially separate neuronal populations and neural mechanisms,” Mendoza-Halliday says.

Mapping healthy cells’ connections in the brain

by Jennifer Michalowski |

Portrait of scientist in a suit and tie.
McGovern Institute Principal Research Scientist Ian Wickersham. Photo: Caitlin Cunningham

A new tool developed by researchers at MIT’s McGovern Institute gives neuroscientists the power to find connected neurons within the brain’s tangled network of cells, and then follow or manipulate those neurons over a prolonged period. Its development, led by Principal Research Scientist Ian Wickersham, transforms a powerful tool for exploring the anatomy of the brain into a sophisticated system for studying brain function.

Wickersham and colleagues have designed their system to enable long-term analysis and experiments on groups of neurons that reach through the brain to signal to select groups of cells. It is described in the January 11, 2024, issue of the journal Nature Neuroscience. “This second-generation system will allow imaging, recording, and control of identified networks of synaptically-connected neurons in the context of behavioral studies and other experimental designs lasting weeks, months, or years,” Wickersham says.

The system builds on an approach to anatomical tracing that Wickersham developed in 2007, as a graduate student in Edward Callaway’s lab at the Salk Institute for Biological Studies. Its key is a modified version of a rabies virus, whose natural—and deadly—life cycle involves traveling through the brain’s neural network.

Viral tracing

The rabies virus is useful for tracing neuronal connections because once it has infected the nervous system, it spreads through the neural network by co-opting the very junctions that neurons use to communicate with one another. Hopping across those junctions, or synapses, the virus can pass from cell to cell. Traveling in the opposite direction of neuronal signals, it reaches the brain, where it continues to spread.

Labeled illustration of rabies virus
Simplified illustration of rabies virus. Image: istockphoto

To use the rabies virus to identify specific connections within the brain, Wickersham modified it to limit its spread. His original tracing system uses a rabies virus that lacks an essential gene. When researchers deliver the modified virus to the neurons whose connections they want to map, they also instruct those neurons to make the protein encoded by the virus’s missing gene. That allows the virus to replicate and travel across the synapses that link an infected cell to others in the network. Once it is inside a new cell, the virus is deprived of the critical protein and can go no farther.

Under a microscope, a fluorescent protein delivered by the modified virus lights up, exposing infected cells: those to which the virus was originally delivered as well as any neurons that send it direct inputs. Because the virus crosses only one synapse after leaving the cell it originally infected, the technique is known as monosynaptic tracing.

Labs around the world now use this method to identify which brain cells send signals to a particular set of neurons. But while the virus used in the original system can’t spread through the brain like a natural rabies virus, it still sickens the cells it does infect. Infected cells usually die in about two weeks, and that has limited scientists’ ability to conduct further studies of the cells whose connections they trace. “If you want to then go on to manipulate those connected populations of cells, you have a very short time window,” Wickersham says.

Reducing toxicity

To keep cells healthy after monosynaptic tracing, Wickersham, postdoctoral researcher Lei Jin, and colleagues devised a new approach. They began by deleting a second gene from the modified virus they use to label cells. That gene encodes an enzyme the rabies virus needs to produce the proteins encoded in its own genome. As with the original system, neurons are instructed to create the virus’s missing proteins, equipping the virus to replicate inside those cells. In this case, this is done in mice that have been genetically modified to produce the second deleted viral gene in specific sets of neurons.

Brightly colored neurons under a microscope.
The initially-infected “starter cells” at the injection site in the substantia nigra, pars compacta. Blue: tyrosine hydroxylase immunostaining, showing dopaminergic cells; green: enhanced green fluorescent protein showing neurons able to be initially infected with the rabies virus; red: the red fluorescent protein tdTomato, reporting the presence of the second-generation rabies virus. Image: Ian Wickersham, Lei Jin

To limit toxicity, Wickersham and his team built in a control that allows researchers to switch off cells’ production of viral proteins once the virus has had time to replicate and begin its spread to connected neurons. With those proteins no longer available to support the viral life cycle, the tracing tool is rendered virtually harmless. After following mice for up to 10 weeks, the researchers detected minimal toxicity in neurons where monosynaptic tracing was initiated. And, Wickersham says, “as far as we can tell, the trans-synaptically labeled cells are completely unscathed.”

Neurons illuminated in red under a microscope
Transsynaptically labeled cells in the striatum, which provides input to the dopaminergic cells of the substantia nigra. These cells show no morphological abnormalities or any other indication of toxicity five weeks after the rabies virus injection. Image: Ian Wickersham, Lei Jin

That means neuroscientists can now pair monosynaptic tracing with many of neuroscience’s most powerful tools for functional studies. To facilitate those experiments, Wickersham’s team encoded enzymes called recombinases into their connection-tracing rabies virus, which enables the introduction of genetically encoded research tools to targeted cells. After tracing cells’ connections, researchers will be able to manipulate those neurons, follow their activity, and explore their contributions to animal behavior. Such experiments will deepen scientists’ understanding of the inputs select groups of neurons receive from elsewhere in the brain, as well as the cells that are sending those signals.

Jin, who is now a principal investigator at Lingang Laboratory in Shanghai, says colleagues are already eager to begin working with the new non-toxic tracing system. Meanwhile, Wickersham’s group has already started experimenting with a third-generation system, which they hope will improve efficiency and be even more powerful.

K. Lisa Yang Postbaccalaureate Program names new scholars

by Julie Pryor |

Funded by philanthropist Lisa Yang, the K. Lisa Yang Postbaccalaureate Scholar Program provides two years of paid laboratory experience, mentorship, and education to recent college graduates from backgrounds underrepresented in neuroscience. This year, two young researchers in McGovern Institute labs, Joseph Itiat and Sam Merrow, are the recipients of the Yang postbac program.

Itiat moved to the United States from Nigeria in 2019 to pursue a degree in psychology and cognitive neuroscience at Temple University. Today, he is a Yang postbac in John Gabrieli’s lab studying the relationship between learning and value processes and their influence on future-oriented decision-making. Ultimately, Itiat hopes to develop models that map the underlying mechanisms driving these processes.

“Being African, with limited research experience and little representation in the domain of neuroscience research,” Itiat says, “I chose to pursue a postbaccalaureate
research program to prepare me for a top graduate school and a career in cognitive neuroscience.”

Merrow first fell in love with science while working at the Barrow Neurological Institute in Arizona during high school. After graduating from Simmons University in Boston, Massachusetts, Merrow joined Guoping Feng’s lab as a Yang postbac to pursue research on glial cells and brain disorders. “As a queer, nonbinary, LatinX person, I have not met anyone like me in my field, nor have I had role models that hold a similar identity to myself,” says Merrow.

“My dream is to one day become a professor, where I will be able to show others that science is for anyone.”

Previous Yang postbacs include Alex Negron, Zoe Pearce, Ajani Stewart, and Maya Taliaferro.

A new way to see the activity inside a living cell

Anne Trafton |

Living cells are bombarded with many kinds of incoming molecular signal that influence their behavior. Being able to measure those signals and how cells respond to them through downstream molecular signaling networks could help scientists learn much more about how cells work, including what happens as they age or become diseased.

Right now, this kind of comprehensive study is not possible because current techniques for imaging cells are limited to just a handful of different molecule types within a cell at one time. However, MIT researchers have developed an alternative method that allows them to observe up to seven different molecules at a time, and potentially even more than that.

“There are many examples in biology where an event triggers a long downstream cascade of events, which then causes a specific cellular function,” says Edward Boyden, the Y. Eva Tan Professor in Neurotechnology. “How does that occur? It’s arguably one of the fundamental problems of biology, and so we wondered, could you simply watch it happen?”

It’s arguably one of the fundamental problems of biology, and so we wondered, could you simply watch it happen? – Ed Boyden

The new approach makes use of green or red fluorescent molecules that flicker on and off at different rates. By imaging a cell over several seconds, minutes, or hours, and then extracting each of the fluorescent signals using a computational algorithm, the amount of each target protein can be tracked as it changes over time.

Boyden, who is also a professor of biological engineering and of brain and cognitive sciences at MIT, a Howard Hughes Medical Institute investigator, and a member of MIT’s McGovern Institute for Brain Research and Koch Institute for Integrative Cancer Research, as well as the co-director of the K. Lisa Yang Center for Bionics, is the senior author of the study, which appears today in Cell. MIT postdoc Yong Qian is the lead author of the paper.

Fluorescent signals

Labeling molecules inside cells with fluorescent proteins has allowed researchers to learn a great deal about the functions of many cellular molecules. This type of study is often done with green fluorescent protein (GFP), which was first deployed for imaging in the 1990s. Since then, several fluorescent proteins that glow in other colors have been developed for experimental use.

However, a typical light microscope can only distinguish two or three of these colors, allowing researchers only a tiny glimpse of the overall activity that is happening inside a cell. If they could track a greater number of labeled molecules, researchers could measure a brain cell’s response to different neurotransmitters during learning, for example, or investigate the signals that prompt a cancer cell to metastasize.

“Ideally, you would be able to watch the signals in a cell as they fluctuate in real time, and then you could understand how they relate to each other. That would tell you how the cell computes,” Boyden says. “The problem is that you can’t watch very many things at the same time.”

In 2020, Boyden’s lab developed a way to simultaneously image up to five different molecules within a cell, by targeting glowing reporters to distinct locations inside the cell. This approach, known as “spatial multiplexing,” allows researchers to distinguish signals for different molecules even though they may all be fluorescing the same color.

In the new study, the researchers took a different approach: Instead of distinguishing signals based on their physical location, they created fluorescent signals that vary over time. The technique relies on “switchable fluorophores” — fluorescent proteins that turn on and off at a specific rate. For this study, Boyden and his group members identified four green switchable fluorophores, and then engineered two more, all of which turn on and off at different rates. They also identified two red fluorescent proteins that switch at different rates, and engineered one additional red fluorophore.

Using four switchable fluorophores, MIT researchers were able to label and image four different kinases inside these cells (top four rows). In the bottom row, the cell nuclei are labeled in blue.
Image: Courtesy of the researchers

Each of these switchable fluorophores can be used to label a different type of molecule within a living cell, such an enzyme, signaling protein, or part of the cell cytoskeleton. After imaging the cell for several minutes, hours, or even days, the researchers use a computational algorithm to pick out the specific signal from each fluorophore, analogous to how the human ear can pick out different frequencies of sound.

“In a symphony orchestra, you have high-pitched instruments, like the flute, and low-pitched instruments, like a tuba. And in the middle are instruments like the trumpet. They all have different sounds, and our ear sorts them out,” Boyden says.

The mathematical technique that the researchers used to analyze the fluorophore signals is known as linear unmixing. This method can extract different fluorophore signals, similar to how the human ear uses a mathematical model known as a Fourier transform to extract different pitches from a piece of music.

Once this analysis is complete, the researchers can see when and where each of the fluorescently labeled molecules were found in the cell during the entire imaging period. The imaging itself can be done with a simple light microscope, with no specialized equipment required.

Biological phenomena

In this study, the researchers demonstrated their approach by labeling six different molecules involved in the cell division cycle, in mammalian cells. This allowed them to identify patterns in how the levels of enzymes called cyclin-dependent kinases change as a cell progresses through the cell cycle.

The researchers also showed that they could label other types of kinases, which are involved in nearly every aspect of cell signaling, as well as cell structures and organelles such as the cytoskeleton and mitochondria. In addition to their experiments using mammalian cells grown in a lab dish, the researchers showed that this technique could work in the brains of zebrafish larvae.

This method could be useful for observing how cells respond to any kind of input, such as nutrients, immune system factors, hormones, or neurotransmitters, according to the researchers. It could also be used to study how cells respond to changes in gene expression or genetic mutations. All of these factors play important roles in biological phenomena such as growth, aging, cancer, neurodegeneration, and memory formation.

“You could consider all of these phenomena to represent a general class of biological problem, where some short-term event — like eating a nutrient, learning something, or getting an infection — generates a long-term change,” Boyden says.

In addition to pursuing those types of studies, Boyden’s lab is also working on expanding the repertoire of switchable fluorophores so that they can study even more signals within a cell. They also hope to adapt the system so that it could be used in mouse models.

The research was funded by an Alana Fellowship, K. Lisa Yang, John Doerr, Jed McCaleb, James Fickel, Ashar Aziz, the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT, the Howard Hughes Medical Institute, and the National Institutes of Health.

Ariel Furst and Fan Wang receive 2023 National Institutes of Health awards

by MIT School of Science |

The National Institutes of Health (NIH) has awarded grants to MIT’s Ariel Furst and Fan Wang, through its High-Risk, High-Reward Research program. The NIH High-Risk, High-Reward Research program awarded 85 new research grants to support exceptionally creative scientists pursuing highly innovative behavioral and biomedical research projects.

Ariel Furst was selected as the recipient of the NIH Director’s New Innovator Award, which has supported unusually innovative research since 2007. Recipients are early-career investigators who are within 10 years of their final degree or clinical residency and have not yet received a research project grant or equivalent NIH grant.

Furst, the Paul M. Cook Career Development Assistant Professor of Chemical Engineering at MIT, invents technologies to improve human and environmental health by increasing equitable access to resources. Her lab develops transformative technologies to solve problems related to health care and sustainability by harnessing the inherent capabilities of biological molecules and cells. She is passionate about STEM outreach and increasing the participation of underrepresented groups in engineering.

After completing her PhD at Caltech, where she developed noninvasive diagnostics for colorectal cancer, Furst became an A. O. Beckman Postdoctoral Fellow at the University of California at Berkeley. There she developed sensors to monitor environmental pollutants. In 2022, Furst was awarded the MIT UROP Outstanding Faculty Mentor Award for her work with undergraduate researchers. She is a now a 2023 Marion Milligan Mason Awardee, a CIFAR Azrieli Global Scholar for Bio-Inspired Solar Energy, and an ARO Early Career Grantee. She is also a co-founder of the regenerative agriculture company, Seia Bio.

Fan Wang received the Pioneer Award, which has been challenging researchers at all career levels to pursue new directions and develop groundbreaking, high impact approaches to a broad area of biomedical and behavioral sciences since 2004.

Wang, a professor in the Department of Brain and Cognitive Sciences and an investigator in the McGovern Institute for Brain Research, is uncovering the neural circuit mechanisms that govern bodily sensations, like touch, pain, and posture, as well as the mechanisms that control sensorimotor behaviors. Researchers in the Wang lab aim to generate an integrated understanding of the sensation-perception-action process, hoping to find better treatments for diseases like chronic pain, addiction, and movement disorders. Wang’s lab uses genetic, viral, in vivo large-scale electrophysiology and imaging techniques to gain traction in these pursuits.

Wang obtained her PhD at Columbia University, working with Professor Richard Axel. She conducted her postdoctoral work at Stanford University with Mark Tessier-Lavigne, and then subsequently joined Duke University as faculty in 2003. Wang was later appointed as the Morris N. Broad Distinguished Professor of Neurobiology at the Duke University School of Medicine. In January 2023, she joined the faculty of the MIT School of Science and the McGovern Institute.

The High-Risk, High-Reward Research program is funded through the NIH Common Fund, which supports a series of exceptionally high-impact programs that cross NIH Institutes and Centers.

“The HRHR program is a pillar for innovation here at NIH, providing support to transformational research, with advances in biomedical and behavioral science,” says Robert W. Eisinger, acting director of the Division of Program Coordination, Planning, and Strategic Initiatives, which oversees the NIH Common Fund. “These awards align with the Common Fund’s mandate to support science expected to have exceptionally high and broadly applicable impact.”

NIH issued eight Pioneer Awards, 58 New Innovator Awards, six Transformative Research Awards, and 13 Early Independence Awards in 2023. Funding for the awards comes from the NIH Common Fund; the National Institute of General Medical Sciences; the National Institute of Mental Health; the National Library of Medicine; the National Institute on Aging; the National Heart, Lung, and Blood Institute; and the Office of Dietary Supplements.

New cellular census maps the complexity of a primate brain

by Jennifer Michalowski |

A new atlas developed by researchers at MIT’s McGovern Institute and Harvard Medical School catalogs a diverse array of brain cells throughout the marmoset brain. The atlas helps establish marmosets—small monkeys whose brains share many functional and structural features with the human brain—as a valuable model for neuroscience research.

Data from more than two million brain cells are included in the atlas, which spans 18 regions of the marmoset brain. A research team led by Guoping Feng, associate director of the McGovern Institute and member of the Broad Institute of Harvard and MIT, Harvard biologist and member of the Broad Institute of Harvard and MIT Steven McCarroll, and Princeton neurobiologist Fenna Krienen classified each cell according to its particular pattern of genetic activity, providing an important reference for studies of the marmoset brain. The team’s analysis, reported October 13, 2023, in the journal Science Advances, also reveals the profound influence of a cell’s developmental origin on its identity in the primate brain.

Regional variation in neocortical cell types and expression patterns. Image courtesy of the researchers.

Cellular diversity

Brains are made up of a tremendous diversity of cells. Neurons with dramatically different gene expression, shapes, and activities work together to process information and drive behavior, supported by an assortment of immune cells and other cell types. Scientists have only recently begun to catalog this cellular diversity—first in mice, and now in primates.

The marmoset is a quick-breeding monkey whose small brain has many of features similar to those that enable higher cognitive processes in humans. Feng says neuroscientists have begun turning to marmosets as a research model in recent years because new gene editing technology has made it easier to modify the animal’s DNA, so scientists can now study the genetic factors that shape marmosets’ brains and behavior. Feng, McCarroll, Krienen and others hope these animals will offer insights into how primate brains handle complex decision-making, social interactions, and other higher brain functions that are difficult to study in mice. Likewise, Feng says, the monkeys will help scientists investigate the impact of genetic mutations associated with brain disorders and explore potential therapeutic strategies.

To make marmosets a practical model for neuroscience, scientists need to understand the fundamental composition of their brains. Feng and McCarroll’s team have begun that characterization with their cell census, which was supported by the National Institutes of Health’s Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative’s Cell Census Network (BICCN), as part a larger effort to map cellular features in the brains of mice, non-human primates, and humans. It is an essential first step in the creation of a comprehensive atlas charting the molecular, anatomical, and functional features of cells in the marmoset brain.

“Hopefully, when the BRAIN Initiative is complete, we will have a very complete map of these cells: where they are located, their abundance, their functional properties,” says Feng. “This not only gives you knowledge of the normal brain, but you can also look at what aspects change in diseases of the brain. So it’s a really powerful database.”

To catalog the diversity of cells in the marmoset brain, the researchers undertook an expansive analysis of the molecular contents of 2.4 million brain cells from adult marmosets. For each of these cells, they analyzed the complete set of RNA copies of its genes that the cell had produced, known as the cell’s transcriptome. Because the transcriptome captures patterns of genetic activity inside a cell, it is an indication of the cell’s function and can be used to assess cellular identity.

Gene expression across neural populations. Image courtesy of the researchers.

The team’s analysis is one of the first to compare patterns of gene activity in cells from disparate regions of the marmoset brain. Doing so yielded surprising insights into the factors that shape brain cells’ transcriptomic identities. “What we found is that the cell’s transcriptome contains breadcrumbs that link back to the developmental origin of that cell type,” says Krienen, who led the cellular census as a postdoctoral researcher in McCarroll’s lab. That suggests that comparing cells’ transcriptomes can help scientists figure out how primate brains are assembled, which might lead to insights into neurodevelopmental disorders, she says.

The team also learned that a cell’s location in the brain was critical to shaping its transcriptomic identity. For example, Krienen says, “it turns out that an inhibitory neuron in the cortex doesn’t look very anything like an inhibitory neuron in the thalamus, probably because they have distinct embryonic origins.”

Expanding the cell census

This new picture of cellular diversity in the marmoset brain will help researchers understand how genetic perturbations affect different brain cells and interpret the results of future experiments. Importantly, Krienen says, it could help researchers pinpoint exactly which cells are affected in brain disorders, and how the effects of a disease might localize to specific brain regions.

Krienen, McCarroll, and Feng went beyond their initial survey of cellular diversity with analyses of specific subsets of cells, charting the spatial distribution of interneurons in a key region of the prefrontal cortex and visualizing the shapes of several molecularly-defined cell types. Now, they have begun expanding their cell census beyond the 18 brain structures represented in the reported work. As part of the BRAIN Initiative’s Brain Cell Atlas Network (BICAN), the team will profile cells throughout the entire adult marmoset brain, including multiple data types in their analysis. Building on cell census data, NIH BRAIN Initiative has also launched BRAIN CONNECTS projects to map cellular connectivity in the brain.

This work was supported by the National Institutes of Health, the National Science Foundation, MathWorks, MIT, Harvard Medical School, the Broad Institute’s Stanley Center for Psychiatric Research, the Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT, the Poitras Center for Psychiatric Disorders Research at MIT, and the McGovern Institute for Brain Research at MIT.

Twelve with MIT ties elected to the National Academy of Medicine for 2023

by MIT School of Science |

The National Academy of Medicine announced the election of 100 new members to join their esteemed ranks in 2023, among them five MIT faculty members and seven additional affiliates.

MIT professors Daniel Anderson, Regina Barzilay, Guoping Feng, Darrell Irvine, and Morgen Shen were among the new members. Justin Hanes PhD ’96, Said Ibrahim MBA ’16, and Jennifer West ’92, along with three former students in the Harvard-MIT Program in Health Sciences and Technology (HST) — Michael Chiang, Siddhartha Mukherjee, and Robert Vonderheide — were also elected, as was Yi Zhang, an associate member of The Broad Institute of MIT and Harvard.

Election to the academy is considered one of the highest honors in the fields of health and medicine and recognizes individuals who have demonstrated outstanding professional achievement and commitment to service, the academy noted in announcing the election of its new members.

MIT faculty

Daniel G. Anderson, professor in the Department of Chemical Engineering and the Institute for Medical Engineering and Science, was elected “for pioneering the area of non-viral gene therapy and cellular delivery. His work has resulted in fundamental scientific advances; over 500 papers, patents, and patent applications; and the creation of companies, products, and technologies that are now in the clinic.” Anderson is an affiliate of the Broad Institute of MIT and Harvard and of the Ragon Institute at MGH, MIT and Harvard.

Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health within the Department of Electrical Engineering and Computer Science at MIT, was elected “for the development of machine learning tools that have been transformational for breast cancer screening and risk assessment, and for the development of molecular design tools broadly utilized for drug discovery.” Barzilay is the AI faculty lead within the MIT Abdul Latif Jameel Clinic for Machine Learning in Health and an affiliate of the Computer Science and Artificial Intelligence Laboratory and Institute for Medical Engineering and Science.

Guoping Feng, the associate director of the McGovern Institute for Brain Research, James W. (1963) and Patricia T. Professor of Neuroscience in MIT’s Department of Brain and Cognitive Sciences, and an affiliate of the Broad Institute of MIT and Harvard, was elected “for his breakthrough discoveries regarding the pathological mechanisms of neurodevelopmental and psychiatric disorders, providing foundational knowledges and molecular targets for developing effective therapeutics for mental illness such as OCD, ASD, and ADHD.”

Darrell J. Irvine ’00, the Underwood-Prescott Professor of Biological Engineering and Materials Science at MIT and a member of the Koch Institute for Integrative Cancer Research, was elected “for the development of novel methods for delivery of immunotherapies and vaccines for cancer and infectious diseases.”

Morgan Sheng, professor of neuroscience in the Department of Brain and Cognitive Sciences, with affiliations in the McGovern Institute and The Picower Institute for Learning and Memory at MIT, as well as the Broad Institute of MIT and Harvard, was elected “for transforming the understanding of excitatory synapses. He revealed the postsynaptic density as a protein network controlling synaptic signaling and morphology; established the paradigm of signaling complexes organized by PDZ scaffolds; and pioneered the concept of localized regulation of mitochondria, apoptosis, and complement for targeted synapse elimination.”

Additional MIT affiliates

Michael F. Chiang, a former student in the Harvard-MIT Program in Health Sciences and Technology (HST) who is now director of the National Eye Institute of the National Institutes of Health, was honored “for pioneering applications of biomedical informatics to ophthalmology in artificial intelligence, telehealth, pediatric retinal disease, electronic health records, and data science, including methodological and diagnostic advances in AI for pediatric retinopathy of prematurity, and for contributions to developing and implementing the largest ambulatory care registry in the United States.”

Justin Hanes PhD ’96, who earned his PhD from the MIT Department of Chemical Engineering and is now a professor at Johns Hopkins University, was honored “for pioneering discoveries and inventions of innovative drug delivery technologies, especially mucosal, ocular, and central nervous system drug delivery systems; and for international leadership in research and education at the interface of engineering, medicine, and entrepreneurship, leading to clinical translation of drug delivery technologies.”

Said Ibrahim MBA ’16, a graduate of the MIT Sloan School of Management who is now a senior vice president and chair, department of medicine at the Zucker School of Medicine at Hofstra/Northwell, was honored for influential “health services research on racial disparities in elective joint replacement that has provided a national model for advancing health equity research beyond the identification of inequities and toward their remediation, and for his research that has been leveraged to engage diverse and innovative emerging scholars.”

Siddhartha Mukherjee, a former student in HST who is now an associate professor of medicine at Columbia University School of Medicine, was honored “for contributing important research in the immunotherapy of myeloid malignancies, such as acute myeloid leukemia, for establishing international centers for immunotherapy for childhood cancers, and for the discovery of tissue-resident stem cells.”

Robert H. Vonderheide, a former student in HST who is now a professor and vice dean at the Perelman School of Medicine and vice president of cancer programs at the University of Pennsylvania Health System, was honored “for developing immune combination therapies for patients with pancreatic cancer by driving proof-of-concept from lab to clinic, then leading national, randomized clinical trials for therapy, maintenance, and interception; and for improving access of minority individuals to clinical trials while directing an NCI comprehensive cancer center.”

Jennifer West ’92, a graduate of the MIT Department of Chemical Engineering who is now a professor of biomedical engineering and dean of the School of Engineering and Applied Science at the University of Virginia at Charlottesville, was honored “for the invention, development, and translation of novel biomaterials including bioactive, photopolymerizable hydrogels and theranostic nanoparticles.”

Yi Zhang, associate member of the Broad Institute, was honored “for making fundamental contributions to the epigenetics field through systematic identification and characterization of chromatin modifying enzymes, including EZH2, JmjC, and Tet. His proof-of-principle work on EZH2 inhibitors led to the founding of Epizyme and eventual making of tazemetostat, a drug approved for epithelioid sarcoma and follicular lymphoma.”

“It is my honor to welcome this truly exceptional class of new members to the National Academy of Medicine,” said NAM President Victor J. Dzau. “Their contributions to health and medicine are unparalleled, and their leadership and expertise will be essential to helping the NAM tackle today’s urgent health challenges, inform the future of health care, and ensure health equity for the benefit of all around the globe.”

Four McGovern Investigators receive NIH BRAIN Initiative grants

by Julie Pryor |

In the human brain, 86 billion neurons form more than 100 trillion connections with other neurons at junctions called synapses. Scientists at the McGovern Institute are working with their collaborators to develop technologies to map these connections across the brain, from mice to humans.

Today, the National Institutes of Health (NIH) announced a new program to support research projects that have the potential to reveal an unprecedented and dynamic picture of the connected networks in the brain. Four of these NIH-funded research projects will take place in McGovern labs.

BRAIN Initiative

In 2013, the Obama administration announced the Brain Research Through Advancing Innovative Neurotechnologies® (BRAIN) Initiative, a public-private research effort to support the development and application of new technologies to understand brain function.

Today, the NIH announced its third project supported by the BRAIN Initiative, called BRAIN Initiative Connectivity Across Scales (BRAIN CONNECTS). The new project complements two previous large-scale projects, which together aim to transform neuroscience research by generating wiring diagrams that can span entire brains across multiple species. These detailed wiring diagrams can help uncover the logic of the brain’s neural code, leading to a better understanding of how this circuitry makes us who we are and how it could be rewired to treat brain diseases.

BRAIN CONNECTS at McGovern

The initial round of BRAIN CONNECTS awards will support researchers at more than 40 university and research institutions across the globe with 11 grants totaling $150 million over five years. Four of these grants have been awarded to McGovern researchers Guoping Feng, Ila Fiete, Satra Ghosh, and Ian Wickersham, whose projects are outlined below:

BRAIN CONNECTS: Comprehensive regional projection map of marmoset with single axon and cell type resolution
Team: Guoping Feng (McGovern Institute, MIT), Partha Mitra (Cold Spring Harbor Laboratory), Xiao Wang (Broad Institute), Ian Wickersham (McGovern Institute, MIT)

Summary: This project will establish an integrated experimental-computational platform to create the first comprehensive brain-wide mesoscale connectivity map in a non-human primate (NHP), the common marmoset (Callithrix jacchus). It will do so by tracing axonal projections of RNA barcode-identified neurons brain-wide in the marmoset, utilizing a sequencing-based imaging method that also permits simultaneous transcriptomic cell typing of the identified neurons. This work will help bridge the gap between brain-wide mesoscale connectivity data available for the mouse from a decade of mapping efforts using modern techniques and the absence of comparable data in humans and NHPs.

BRAIN CONNECTS: A center for high-throughput integrative mouse connectomics
Team: Jeff Lichtman (Harvard University), Ila Fiete (McGovern Institute, MIT), Sebastian Seung (Princeton University), David Tank (Princeton University), Hongkui Zeng (Allen Institute), Viren Jain (Google), Greg Jeffries (Oxford University)

Summary: This project aims to produce a large-scale synapse-level brain map (connectome) that includes all the main areas of the mouse hippocampus. This region is of clinical interest because it is an essential part of the circuit underlying spatial navigation and memory and the earliest impairments and degeneration related to Alzheimer’s disease.

BRAIN CONNECTS: The center for Large-scale Imaging of Neural Circuits (LINC)
Team: Anastasia Yendiki (MGH), Satra Ghosh (McGovern, MIT), Suzanne Haber (University of Rochester), Elizabeth Hillman (Columbia University)

Summary: This project will generate connectional diagrams of the monkey and human brain at unprecedented resolutions. These diagrams will be linked both to the neuroanatomic literature and to in vivo neuroimaging techniques, bridging between the rigor of the former and the clinical relevance of the latter. The data to be generated by this project will advance our understanding of brain circuits that are implicated in motor and psychiatric disorders, and that are targeted by deep-brain stimulation to treat these disorders.

BRAIN CONNECTS: Mapping brain-wide connectivity of neuronal types using barcoded connectomics
Team: Xiaoyin Chen (Allen Institute), Ian Wickersham (McGovern Institute, MIT), and Justus Kebschull of JHU

Summary: This project aims to optimize and develop barcode sequencing-based neuroanatomical techniques to achieve brain-wide, high-throughput, highly multiplexed mapping of axonal projections and synaptic connectivity of neuronal types at cellular resolution in primate brains. The team will work together to apply these techniques to generate an unprecedented multi-resolution map of brain-wide projections and synaptic inputs of neurons in the macaque visual cortex at cellular resolution.

 

Study decodes surprising approach mice take in learning

by David Orenstein |

Neuroscience discoveries ranging from the nature of memory to treatments for disease have depended on reading the minds of mice, so researchers need to truly understand what the rodents’ behavior is telling them during experiments. In a new study that examines learning from reward, MIT researchers deciphered some initially mystifying mouse behavior, yielding new ideas about how mice think and a mathematical tool to aid future research.

The task the mice were supposed to master is simple: Turn a wheel left or right to get a reward and then recognize when the reward direction switches. When neurotypical people play such “reversal learning” games they quickly infer the optimal approach: stick with the direction that works until it doesn’t and then switch right away. Notably, people with schizophrenia struggle with the task. In the new study in PLOS Computational Biology, mice surprised scientists by showing that while they were capable of learning the “win-stay, lose-shift” strategy, they nonetheless refused to fully adopt it.

“It is not that mice cannot form an inference-based model of this environment—they can,” said corresponding author Mriganka Sur, Newton Professor in The Picower Institute for Learning and Memory and MIT’s Department of Brain and Cognitive Sciences (BCS). “The surprising thing is that they don’t persist with it. Even in a single block of the game where you know the reward is 100 percent on one side, every so often they will try the other side.”

While the mouse motif of departing from the optimal strategy could be due to a failure to hold it in memory, said lead author and Sur Lab graduate student Nhat Le, another possibility is that mice don’t commit to the “win-stay, lose-shift” approach because they don’t trust that their circumstances will remain stable or predictable. Instead, they might deviate from the optimal regime to test whether the rules have changed. Natural settings, after all, are rarely stable or predictable.

“I’d like to think mice are smarter than we give them credit for,” Le said.

But regardless of which reason may cause the mice to mix strategies, added co-senior author Mehrdad Jazayeri, Associate Professor in BCS and the McGovern Institute for Brain Research, it is important for researchers to recognize that they do and to be able to tell when and how they are choosing one strategy or another.

“This study highlights the fact that, unlike the accepted wisdom, mice doing lab tasks do not necessarily adopt a stationary strategy and it offers a computationally rigorous approach to detect and quantify such non-stationarities,” he said. “This ability is important because when researchers record the neural activity, their interpretation of the underlying algorithms and mechanisms may be invalid when they do not take the animals’ shifting strategies into account.”

Tracking thinking

The research team, which also includes co-author Murat Yildirim, a former Sur lab postdoc who is now an assistant professor at the Cleveland Clinic Lerner Research Institute, initially expected that the mice might adopt one strategy or the other. They simulated the results they’d expect to see if the mice either adopted the optimal strategy of inferring a rule about the task, or more randomly surveying whether left or right turns were being rewarded. Mouse behavior on the task, even after days, varied widely but it never resembled the results simulated by just one strategy.

To differing, individual extents, mouse performance on the task reflected variance along three parameters: how quickly they switched directions after the rule switched, how long it took them to transition to the new direction, and how loyal they remained to the new direction. Across 21 mice, the raw data represented a surprising diversity of outcomes on a task that neurotypical humans uniformly optimize. But the mice clearly weren’t helpless. Their average performance significantly improved over time, even though it plateaued below the optimal level.

In the task, the rewarded side switched every 15-25 turns. The team realized the mice were using more than one strategy in each such “block” of the game, rather than just inferring the simple rule and optimizing based on that inference. To disentangle when the mice were employing that strategy or another, the team harnessed an analytical framework called a Hidden Markov Model (HMM), which can computationally tease out when one unseen state is producing a result vs. another unseen state. Le likens it to what a judge on a cooking show might do: inferring which chef contestant made which version of a dish based on patterns in each plate of food before them.

Before the team could use an HMM to decipher their mouse performance results, however, they had to adapt it. A typical HMM might apply to individual mouse choices, but here the team modified it to explain choice transitions over the course of whole blocks. They dubbed their modified model the blockHMM. Computational simulations of task performance using the blockHMM showed that the algorithm is able to infer the true hidden states of an artificial agent. The authors then used this technique to show the mice were persistently blending multiple strategies, achieving varied levels of performance.

“We verified that each animal executes a mixture of behavior from multiple regimes instead of a behavior in a single domain,” Le and his co-authors wrote. “Indeed 17/21 mice used a combination of low, medium and high-performance behavior modes.”

Further analysis revealed that the strategies afoot were indeed the “correct” rule inference strategy and a more exploratory strategy consistent with randomly testing options to get turn-by-turn feedback.

Now that the researchers have decoded the peculiar approach mice take to reversal learning, they are planning to look more deeply into the brain to understand which brain regions and circuits are involved. By watching brain cell activity during the task, they hope to discern what underlies the decisions the mice make to switch strategies.

By examining reversal learning circuits in detail, Sur said, it’s possible the team will gain insights that could help explain why people with schizophrenia show diminished performance on reversal learning tasks. Sur added that some people with autism spectrum disorders also persist with newly unrewarded behaviors longer than neurotypical people, so his lab will also have that phenomenon in mind as they investigate.

Yildirim, too, is interested in examining potential clinical connections.

“This reversal learning paradigm fascinates me since I want to use it in my lab with various preclinical models of neurological disorders,” he said. “The next step for us is to determine the brain mechanisms underlying these differences in behavioral strategies and whether we can manipulate these strategies.”

Funding for the study came from The National Institutes of Health, the Army Research Office, a Paul and Lilah Newton Brain Science Research Award, the Massachusetts Life Sciences Initiative, The Picower Institute for Learning and Memory and The JPB Foundation.