Five MIT faculty elected to the National Academy of Sciences for 2024

The National Academy of Sciences has elected 120 members and 24 international members, including five faculty members from MIT. Guoping Feng, Piotr Indyk, Daniel J. Kleitman, Daniela Rus, and Senthil Todadri were elected in recognition of their “distinguished and continuing achievements in original research.” Membership to the National Academy of Sciences is one of the highest honors a scientist can receive in their career.

Among the new members added this year are also nine MIT alumni, including Zvi Bern ’82; Harold Hwang ’93, SM ’93; Leonard Kleinrock SM ’59, PhD ’63; Jeffrey C. Lagarias ’71, SM ’72, PhD ’74; Ann Pearson PhD ’00; Robin Pemantle PhD ’88; Jonas C. Peters PhD ’98; Lynn Talley PhD ’82; and Peter T. Wolczanski ’76. Those elected this year bring the total number of active members to 2,617, with 537 international members.

The National Academy of Sciences is a private, nonprofit institution that was established under a congressional charter signed by President Abraham Lincoln in 1863. It recognizes achievement in science by election to membership, and — with the National Academy of Engineering and the National Academy of Medicine — provides science, engineering, and health policy advice to the federal government and other organizations.

Guoping Feng

Guoping Feng is the James W. (1963) and Patricia T. Poitras Professor in the Department of Brain and Cognitive Sciences. He is also associate director and investigator in the McGovern Institute for Brain Research, a member of the Broad Institute of MIT and Harvard, and director of the Hock E. Tan and K. Lisa Yang Center for Autism Research.

His research focuses on understanding the molecular mechanisms that regulate the development and function of synapses, the places in the brain where neurons connect and communicate. He’s interested in how defects in the synapses can contribute to psychiatric and neurodevelopmental disorders. By understanding the fundamental mechanisms behind these disorders, he’s producing foundational knowledge that may guide the development of new treatments for conditions like obsessive-compulsive disorder and schizophrenia.

Feng received his medical training at Zhejiang University Medical School in Hangzhou, China, and his PhD in molecular genetics from the State University of New York at Buffalo. He did his postdoctoral training at Washington University at St. Louis and was on the faculty at Duke University School of Medicine before coming to MIT in 2010. He is a member of the American Academy of Arts and Sciences, a fellow of the American Association for the Advancement of Science, and was elected to the National Academy of Medicine in 2023.

Piotr Indyk

Piotr Indyk is the Thomas D. and Virginia W. Cabot Professor of Electrical Engineering and Computer Science. He received his magister degree from the University of Warsaw and his PhD from Stanford University before coming to MIT in 2000.

Indyk’s research focuses on building efficient, sublinear, and streaming algorithms. He’s developed, for example, algorithms that can use limited time and space to navigate massive data streams, that can separate signals into individual frequencies faster than other methods, and can address the “nearest neighbor” problem by finding highly similar data points without needing to scan an entire database. His work has applications on everything from machine learning to data mining.

He has been named a Simons Investigator and a fellow of the Association for Computer Machinery. In 2023, he was elected to the American Academy of Arts and Sciences.

Daniel J. Kleitman

Daniel Kleitman, a professor emeritus of applied mathematics, has been at MIT since 1966. He received his undergraduate degree from Cornell University and his master’s and PhD in physics from Harvard University before doing postdoctoral work at Harvard and the Niels Bohr Institute in Copenhagen, Denmark.

Kleitman’s research interests include operations research, genomics, graph theory, and combinatorics, the area of math concerned with counting. He was actually a professor of physics at Brandeis University before changing his field to math, encouraged by the prolific mathematician Paul Erdős. In fact, Kleitman has the rare distinction of having an Erdős number of just one. The number is a measure of the “collaborative distance” between a mathematician and Erdős in terms of authorship of papers, and studies have shown that leading mathematicians have particularly low numbers.

He’s a member of the American Academy of Arts and Sciences and has made important contributions to the MIT community throughout his career. He was head of the Department of Mathematics and served on a number of committees, including the Applied Mathematics Committee. He also helped create web-based technology and an online textbook for several of the department’s core undergraduate courses. He was even a math advisor for the MIT-based film “Good Will Hunting.”

Daniela Rus

Daniela Rus, the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science, is the director of the Computer Science and Artificial Intelligence Laboratory (CSAIL). She also serves as director of the Toyota-CSAIL Joint Research Center.

Her research on robotics, artificial intelligence, and data science is geared toward understanding the science and engineering of autonomy. Her ultimate goal is to create a future where machines are seamlessly integrated into daily life to support people with cognitive and physical tasks, and deployed in way that ensures they benefit humanity. She’s working to increase the ability of machines to reason, learn, and adapt to complex tasks in human-centered environments with applications for agriculture, manufacturing, medicine, construction, and other industries. She’s also interested in creating new tools for designing and fabricating robots and in improving the interfaces between robots and people, and she’s done collaborative projects at the intersection of technology and artistic performance.

Rus received her undergraduate degree from the University of Iowa and her PhD in computer science from Cornell University. She was a professor of computer science at Dartmouth College before coming to MIT in 2004. She is part of the Class of 2002 MacArthur Fellows; was elected to the National Academy of Engineering and the American Academy of Arts and Sciences; and is a fellow of the Association for Computer Machinery, the Institute of Electrical and Electronics Engineers, and the Association for the Advancement of Artificial Intelligence.

Senthil Todadri

Senthil Todadri, a professor of physics, came to MIT in 2001. He received his undergraduate degree from the Indian Institute of Technology in Kanpur and his PhD from Yale University before working as a postdoc at the Kavli Institute for Theoretical Physics in Santa Barbara, California.

Todadri’s research focuses on condensed matter theory. He’s interested in novel phases and phase transitions of quantum matter that expand beyond existing paradigms. Combining modeling experiments and abstract methods, he’s working to develop a theoretical framework for describing the physics of these systems. Much of that work involves understanding the phenomena that arise because of impurities or strong interactions between electrons in solids that don’t conform with conventional physical theories. He also pioneered the theory of deconfined quantum criticality, which describes a class of phase transitions, and he discovered the dualities of quantum field theories in two dimensional superconducting states, which has important applications to many problems in the field.

Todadri has been named a Simons Investigator, a Sloan Research Fellow, and a fellow of the American Physical Society. In 2023, he was elected to the American Academy of Arts and Sciences

A new computational technique could make it easier to engineer useful proteins

To engineer proteins with useful functions, researchers usually begin with a natural protein that has a desirable function, such as emitting fluorescent light, and put it through many rounds of random mutation that eventually generate an optimized version of the protein.

This process has yielded optimized versions of many important proteins, including green fluorescent protein (GFP). However, for other proteins, it has proven difficult to generate an optimized version. MIT researchers have now developed a computational approach that makes it easier to predict mutations that will lead to better proteins, based on a relatively small amount of data.

Using this model, the researchers generated proteins with mutations that were predicted to lead to improved versions of GFP and a protein from adeno-associated virus (AAV), which is used to deliver DNA for gene therapy. They hope it could also be used to develop additional tools for neuroscience research and medical applications.

Woman gestures with her hand in front of a glass wall with equations written on it.
MIT Professor of Brain and Cognitive Sciences Ila Fiete in her lab at the McGovern Institute. Photo: Steph Stevens

“Protein design is a hard problem because the mapping from DNA sequence to protein structure and function is really complex. There might be a great protein 10 changes away in the sequence, but each intermediate change might correspond to a totally nonfunctional protein. It’s like trying to find your way to the river basin in a mountain range, when there are craggy peaks along the way that block your view. The current work tries to make the riverbed easier to find,” says Ila Fiete, a professor of brain and cognitive sciences at MIT, a member of MIT’s McGovern Institute for Brain Research, director of the K. Lisa Yang Integrative Computational Neuroscience Center, and one of the senior authors of the study.

Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health at MIT, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT, are also senior authors of an open-access paper on the work, which will be presented at the International Conference on Learning Representations in May. MIT graduate students Andrew Kirjner and Jason Yim are the lead authors of the study. Other authors include Shahar Bracha, an MIT postdoc, and Raman Samusevich, a graduate student at Czech Technical University.

Optimizing proteins

Many naturally occurring proteins have functions that could make them useful for research or medical applications, but they need a little extra engineering to optimize them. In this study, the researchers were originally interested in developing proteins that could be used in living cells as voltage indicators. These proteins, produced by some bacteria and algae, emit fluorescent light when an electric potential is detected. If engineered for use in mammalian cells, such proteins could allow researchers to measure neuron activity without using electrodes.

While decades of research have gone into engineering these proteins to produce a stronger fluorescent signal, on a faster timescale, they haven’t become effective enough for widespread use. Bracha, who works in Edward Boyden’s lab at the McGovern Institute, reached out to Fiete’s lab to see if they could work together on a computational approach that might help speed up the process of optimizing the proteins.

“This work exemplifies the human serendipity that characterizes so much science discovery,” Fiete says.

“This work grew out of the Yang Tan Collective retreat, a scientific meeting of researchers from multiple centers at MIT with distinct missions unified by the shared support of K. Lisa Yang. We learned that some of our interests and tools in modeling how brains learn and optimize could be applied in the totally different domain of protein design, as being practiced in the Boyden lab.”

For any given protein that researchers might want to optimize, there is a nearly infinite number of possible sequences that could generated by swapping in different amino acids at each point within the sequence. With so many possible variants, it is impossible to test all of them experimentally, so researchers have turned to computational modeling to try to predict which ones will work best.

In this study, the researchers set out to overcome those challenges, using data from GFP to develop and test a computational model that could predict better versions of the protein.

They began by training a type of model known as a convolutional neural network (CNN) on experimental data consisting of GFP sequences and their brightness — the feature that they wanted to optimize.

The model was able to create a “fitness landscape” — a three-dimensional map that depicts the fitness of a given protein and how much it differs from the original sequence — based on a relatively small amount of experimental data (from about 1,000 variants of GFP).

These landscapes contain peaks that represent fitter proteins and valleys that represent less fit proteins. Predicting the path that a protein needs to follow to reach the peaks of fitness can be difficult, because often a protein will need to undergo a mutation that makes it less fit before it reaches a nearby peak of higher fitness. To overcome this problem, the researchers used an existing computational technique to “smooth” the fitness landscape.

Once these small bumps in the landscape were smoothed, the researchers retrained the CNN model and found that it was able to reach greater fitness peaks more easily. The model was able to predict optimized GFP sequences that had as many as seven different amino acids from the protein sequence they started with, and the best of these proteins were estimated to be about 2.5 times fitter than the original.

“Once we have this landscape that represents what the model thinks is nearby, we smooth it out and then we retrain the model on the smoother version of the landscape,” Kirjner says. “Now there is a smooth path from your starting point to the top, which the model is now able to reach by iteratively making small improvements. The same is often impossible for unsmoothed landscapes.”


The researchers also showed that this approach worked well in identifying new sequences for the viral capsid of adeno-associated virus (AAV), a viral vector that is commonly used to deliver DNA. In that case, they optimized the capsid for its ability to package a DNA payload.

“We used GFP and AAV as a proof-of-concept to show that this is a method that works on data sets that are very well-characterized, and because of that, it should be applicable to other protein engineering problems,” Bracha says.

The researchers now plan to use this computational technique on data that Bracha has been generating on voltage indicator proteins.

“Dozens of labs having been working on that for two decades, and still there isn’t anything better,” she says. “The hope is that now with generation of a smaller data set, we could train a model in silico and make predictions that could be better than the past two decades of manual testing.”

The research was funded, in part, by the U.S. National Science Foundation, the Machine Learning for Pharmaceutical Discovery and Synthesis consortium, the Abdul Latif Jameel Clinic for Machine Learning in Health, the DTRA Discovery of Medical Countermeasures Against New and Emerging threats program, the DARPA Accelerated Molecular Discovery program, the Sanofi Computational Antibody Design grant, the U.S. Office of Naval Research, the Howard Hughes Medical Institute, the National Institutes of Health, the K. Lisa Yang ICoN Center, and the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT.

Study reveals a universal pattern of brain wave frequencies

Throughout the brain’s cortex, neurons are arranged in six distinctive layers, which can be readily seen with a microscope. A team of MIT and Vanderbilt University neuroscientists has now found that these layers also show distinct patterns of electrical activity, which are consistent over many brain regions and across several animal species, including humans.

The researchers found that in the topmost layers, neuron activity is dominated by rapid oscillations known as gamma waves. In the deeper layers, slower oscillations called alpha and beta waves predominate. The universality of these patterns suggests that these oscillations are likely playing an important role across the brain, the researchers say.

“When you see something that consistent and ubiquitous across cortex, it’s playing a very fundamental role in what the cortex does,” says Earl Miller, the Picower Professor of Neuroscience, a member of MIT’s Picower Institute for Learning and Memory, and one of the senior authors of the new study.

Imbalances in how these oscillations interact with each other may be involved in brain disorders such as attention deficit hyperactivity disorder, the researchers say.

“Overly synchronous neural activity is known to play a role in epilepsy, and now we suspect that different pathologies of synchrony may contribute to many brain disorders, including disorders of perception, attention, memory, and motor control. In an orchestra, one instrument played out of synchrony with the rest can disrupt the coherence of the entire piece of music,” says Robert Desimone, director of MIT’s McGovern Institute for Brain Research and one of the senior authors of the study.

André Bastos, an assistant professor of psychology at Vanderbilt University, is also a senior author of the open-access paper, which appears today in Nature Neuroscience. The lead authors of the paper are MIT research scientist Diego Mendoza-Halliday and MIT postdoc Alex Major.

Layers of activity

The human brain contains billions of neurons, each of which has its own electrical firing patterns. Together, groups of neurons with similar patterns generate oscillations of electrical activity, or brain waves, which can have different frequencies. Miller’s lab has previously shown that high-frequency gamma rhythms are associated with encoding and retrieving sensory information, while low-frequency beta rhythms act as a control mechanism that determines which information is read out from working memory.

His lab has also found that in certain parts of the prefrontal cortex, different brain layers show distinctive patterns of oscillation: faster oscillation at the surface and slower oscillation in the deep layers. One study, led by Bastos when he was a postdoc in Miller’s lab, showed that as animals performed working memory tasks, lower-frequency rhythms generated in deeper layers regulated the higher-frequency gamma rhythms generated in the superficial layers.

In addition to working memory, the brain’s cortex also is the seat of thought, planning, and high-level processing of emotion and sensory information. Throughout the regions involved in these functions, neurons are arranged in six layers, and each layer has its own distinctive combination of cell types and connections with other brain areas.

“The cortex is organized anatomically into six layers, no matter whether you look at mice or humans or any mammalian species, and this pattern is present in all cortical areas within each species,” Mendoza-Halliday says. “Unfortunately, a lot of studies of brain activity have been ignoring those layers because when you record the activity of neurons, it’s been difficult to understand where they are in the context of those layers.”

In the new paper, the researchers wanted to explore whether the layered oscillation pattern they had seen in the prefrontal cortex is more widespread, occurring across different parts of the cortex and across species.

Using a combination of data acquired in Miller’s lab, Desimone’s lab, and labs from collaborators at Vanderbilt, the Netherlands Institute for Neuroscience, and the University of Western Ontario, the researchers were able to analyze 14 different areas of the cortex, from four mammalian species. This data included recordings of electrical activity from three human patients who had electrodes inserted in the brain as part of a surgical procedure they were undergoing.

Recording from individual cortical layers has been difficult in the past, because each layer is less than a millimeter thick, so it’s hard to know which layer an electrode is recording from. For this study, electrical activity was recorded using special electrodes that record from all of the layers at once, then feed the data into a new computational algorithm the authors designed, termed FLIP (frequency-based layer identification procedure). This algorithm can determine which layer each signal came from.

“More recent technology allows recording of all layers of cortex simultaneously. This paints a broader perspective of microcircuitry and allowed us to observe this layered pattern,” Major says. “This work is exciting because it is both informative of a fundamental microcircuit pattern and provides a robust new technique for studying the brain. It doesn’t matter if the brain is performing a task or at rest and can be observed in as little as five to 10 seconds.”

Across all species, in each region studied, the researchers found the same layered activity pattern.

“We did a mass analysis of all the data to see if we could find the same pattern in all areas of the cortex, and voilà, it was everywhere. That was a real indication that what had previously been seen in a couple of areas was representing a fundamental mechanism across the cortex,” Mendoza-Halliday says.

Maintaining balance

The findings support a model that Miller’s lab has previously put forth, which proposes that the brain’s spatial organization helps it to incorporate new information, which carried by high-frequency oscillations, into existing memories and brain processes, which are maintained by low-frequency oscillations. As information passes from layer to layer, input can be incorporated as needed to help the brain perform particular tasks such as baking a new cookie recipe or remembering a phone number.

“The consequence of a laminar separation of these frequencies, as we observed, may be to allow superficial layers to represent external sensory information with faster frequencies, and for deep layers to represent internal cognitive states with slower frequencies,” Bastos says. “The high-level implication is that the cortex has multiple mechanisms involving both anatomy and oscillations to separate ‘external’ from ‘internal’ information.”

Under this theory, imbalances between high- and low-frequency oscillations can lead to either attention deficits such as ADHD, when the higher frequencies dominate and too much sensory information gets in, or delusional disorders such as schizophrenia, when the low frequency oscillations are too strong and not enough sensory information gets in.

“The proper balance between the top-down control signals and the bottom-up sensory signals is important for everything the cortex does,” Miller says. “When the balance goes awry, you get a wide variety of neuropsychiatric disorders.”

The researchers are now exploring whether measuring these oscillations could help to diagnose these types of disorders. They are also investigating whether rebalancing the oscillations could alter behavior — an approach that could one day be used to treat attention deficits or other neurological disorders, the researchers say.

The researchers also hope to work with other labs to characterize the layered oscillation patterns in more detail across different brain regions.

“Our hope is that with enough of that standardized reporting, we will start to see common patterns of activity across different areas or functions that might reveal a common mechanism for computation that can be used for motor outputs, for vision, for memory and attention, et cetera,” Mendoza-Halliday says.

The research was funded by the U.S. Office of Naval Research, the U.S. National Institutes of Health, the U.S. National Eye Institute, the U.S. National Institute of Mental Health, the Picower Institute, a Simons Center for the Social Brain Postdoctoral Fellowship, and a Canadian Institutes of Health Postdoctoral Fellowship.

Calling neurons to attention

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

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.

A new way to see the activity inside a living cell

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

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.

One scientist’s journey from the Middle East to MIT

Smiling man holidng paper in a room.
Ubadah Sabbagh, soon after receiving his US citizenship papers, in April 2023. Photo: Ubadah Sabbagh

“I recently exhaled a breath I’ve been holding in for nearly half my life. After applying over a decade ago, I’m finally an American. This means so many things to me. Foremost, it means I can go back to the the Middle East, and see my mama and the family, for the first time in 14 years.” — McGovern Institute Postdoctoral Associate Ubadah Sabbagh, X (formerly Twitter) post, April 27, 2023

The words sit atop a photo of Ubadah Sabbagh, who joined the lab of Guoping Feng, James W. (1963) and Patricia T. Poitras Professor at MIT, as a postdoctoral associate in 2021. Sabbagh, a Syrian national, is dressed in a charcoal grey jacket, a keffiyeh loose around his neck, and holding his US citizenship papers, which he began applying for when he was 19 and an undergraduate at the University of Missouri-Kansas City (UMKC) studying biology and bioinformatics.

In the photo he is 29.

A clarity of vision

Sabbagh’s journey from the Middle East to his research position at MIT has been marked by determination and courage, a multifaceted curiosity, and a role as a scientist-writer/scientist-advocate.  He is particularly committed to the importance of humanity in science.

“For me, a scientist is a person who is not only in the lab but also has a unique perspective to contribute to society,” he says. “The scientific method is an idea, and that can be objective. But the process of doing science is a human endeavor, and like all human endeavors, it is inherently both social and political.”

At just 30 years of age, some of Sabbagh’s ideas have disrupted conventional thinking about how science is done in the United States. He believes nations should do science not primarily to compete, for example, but to be aspirational.

“It is our job to make our work accessible to the public, to educate and inform, and to help ground policy,” he says. “In our technologically advanced society, we need to raise the baseline for public scientific intuition so that people are empowered and better equipped to separate truth from myth.”

Two men sitting at a booth wearing headphones.
Ubadah Sabbagh is interviewed for Max Planck Forida’s Neurotransmissions podcast at the 2023 Society for Neuroscience conference in San Diego. Photo: Max Planck Florida

His research and advocacy work have won him accolades, including the 2023 Young Arab Pioneers Award from the Arab Youth Center and the 2020 Young Investigator Award from the American Society of Neurochemistry. He was also named to the 2021 Forbes “30 under 30” list, the first Syrian to be selected in the Science category.

A path to knowledge

Sabbagh’s path to that knowledge began when, living on his own at age 16, he attended Longview Community College, in Kansas City, often juggling multiple jobs. It continued at UMKC, where he fell in love with biology and had his first research experience with bioinformatician Gerald Wyckoff at the same time the civil war in Syria escalated, with his family still in the Middle East. “That was a rough time for me,” he says. “I had a lot of survivor’s guilt: I am here, I have all of this stability and security compared to what they have, and while they had suffocation, I had opportunity. I need to make this mean something positive, not just for me, but in as broad a way as possible for other people.”

Child smiles in front of scientific poster.
Ubadah Sabbagh, age 9, presents his first scientific poster. Photo: Ubadah Sabbagh

The war also sparked Sabbagh’s interest in human behavior—“where it originates, what motivates people to do things, but in a biological, not a psychological way,” he says. “What circuitry is engaged? What is the infrastructure of the brain that leads to X, Y, Z?”

His passion for neuroscience blossomed as a graduate student at Virginia Tech, where he earned his PhD in translational biology, medicine, and health. There, he received a six-year NIH F99/K00 Award, and under the mentorship of neuroscientist at the Fralin Biomedical Research Institute he researched the connections between the eye and the brain, specifically, mapping the architecture of the principle neurons in a region of the thalamus essential to visual processing.

“The retina, and the entire visual system, struck me as elegant, with beautiful layers of diverse cells found at every node,” says Sabbagh, his own eyes lighting up.

His research earned him a coveted spot on the Forbes “30 under 30” list, generating enormous visibility, including in the Arab world, adding visitors to his already robust X (formerly Twitter) account, which has more than 9,200 followers. “The increased visibility lets me use my voice to advocate for the things I care about,” he says.

“I need to make this mean something positive, not just for me, but in as broad a way as possible for other people.” — Ubadah Sabbagh

Those causes range from promoting equity and inclusion in science to transforming the American system of doing science for the betterment of science and the scientists themselves. He cofounded the nonprofit Black in Neuro to celebrate and empower Black scholars in neuroscience, and he continues to serve on the board. He is the chair of an advisory committee for the Society for Neuroscience (SfN), recommending ways SfN can better address the needs of its young members, and a member of the Advisory Committee to the National Institutes of Health (NIH) Director working group charged with re-envisioning postdoctoral training. He serves on the advisory board of Community for Rigor, a new NIH initiative that aims to teach scientific rigor at national scale and, in his spare time, he writes articles about the relationship of science and policy for publications including Scientific American and the Washington Post.

Still, there have been obstacles. The same year Sabbagh received the NIH F99/K00 Award, he faced major setbacks in his application to become a citizen. He would not try again until 2021, when he had his PhD in hand and had joined the McGovern Institute.

An MIT postdoc and citizenship

Sabbagh dove into his research in Guoping Feng’s lab with the same vigor and outside-the-box thinking that characterized his previous work. He continues to investigate the thalamus, but in a region that is less involved in processing pure sensory signals, such as light and sound, and more focused on cognitive functions of the brain. He aims to understand how thalamic brain areas orchestrate complex functions we carry out every day, including working memory and cognitive flexibility.

“This is important to understand because when this orchestra goes out of tune it can lead to a range of neurological disorders, including autism spectrum disorder and schizophrenia,” he says. He is also developing new tools for studying the brain using genome editing and viral engineering to expand the toolkit available to neuroscientists.

Microscopic image of mouse brain
Neurons in a transgenic mouse brain labeled by Sabbagh using genome editing technology in the Feng lab. Image: Ubadah Sabbagh

The environment at the McGovern Institute is also a source of inspiration for Sabbagh’s research. “The scale and scope of work being done at McGovern is remarkable. It’s an exciting place for me to be as a neuroscientist,” said Sabbagh. “Besides being intellectually enriching, I’ve found great community here – something that’s important to me wherever I work.”

Returning to the Middle East

Profile of scientist Ubadah Sabbagh speaking at a table.
McGovern postdoc Ubadah Sabbagh at the 2023 Young Arab Pioneers Award ceremony in Abu Dhabi. Photo: Arab Youth Center

While at an advisory meeting at the NIH, Sabbagh learned he had been selected as a Young Arab Pioneer by the Arab Youth Center and was flown the next day to Abu Dhabi for a ceremony overseen by Her Excellency Shamma Al Mazrui, Cabinet Member and Minister of Community Development in the United Arab Emirates. The ceremony recognized 20 Arab youth from around the world in sectors ranging from scientific research to entrepreneurship and community development. Sabbagh’s research “presented a unique portrayal of creative Arab youth and an admirable representation of the values of youth beyond the Arab world,” said Sadeq Jarrar, executive director of the center.

“There I was, among other young Arab leaders, learning firsthand about their efforts, aspirations, and their outlook for the future,” says Sabbagh, who was deeply inspired by the experience.

Just a month earlier, his passport finally secured, Sabbagh had reunited with his family in the Middle East after more than a decade in the United States. “I had been away for so long,” he said, describing the experience as a “cultural reawakening.”

Woman hands man an award on stage.
Ubadah Sabbagh receives a Young Arab Pioneer Award by Her Excellency Shamma Al Mazrui, Cabinet Member and Minister of Community Development in the United Arab Emirates. Photo: Arab Youth Center

Sabbagh saw a gaping need he had not been aware of when he left 14 years earlier, as a teen. “The Middle East had such a glorious intellectual past,” he says. “But for years people have been leaving to get their advanced scientific training, and there is no adequate infrastructure to support them if they want to go back.” He wondered: What if there were a scientific renaissance in the region? How would we build infrastructure to cultivate local minds and local talent? What if the next chapter of the Middle East included being a new nexus of global scientific advancements?

“I felt so inspired,” he says. “I have a longing, someday, to meaningfully give back.”

Season’s Greetings from the McGovern Institute

This year’s holiday video (shown above) was inspired by Ev Fedorenko’s July 2022 Nature Neuroscience paper, which found similar patterns of brain activation and language selectivity across speakers of 45 different languages.

Universal language network

Ev Fedorenko uses the widely translated book “Alice in Wonderland” to test brain responses to different languages. Photo: Caitlin Cunningham

Over several decades, neuroscientists have created a well-defined map of the brain’s “language network,” or the regions of the brain that are specialized for processing language. Found primarily in the left hemisphere, this network includes regions within Broca’s area, as well as in other parts of the frontal and temporal lobes. Although roughly 7,000 languages are currently spoken and signed across the globe, the vast majority of those mapping studies have been done in English speakers as they listened to or read English texts.

To truly understand the cognitive and neural mechanisms that allow us to learn and process such diverse languages, Fedorenko and her team scanned the brains of speakers of 45 different languages while they listened to Alice in Wonderland in their native language. The results show that the speakers’ language networks appear to be essentially the same as those of native English speakers — which suggests that the location and key properties of the language network appear to be universal.

The many languages of McGovern

English may be the primary language used by McGovern researchers, but more than 35 other languages are spoken by scientists and engineers at the McGovern Institute. Our holiday video features 30 of these researchers saying Happy New Year in their native (or learned) language. Below is the complete list of languages included in our video. Expand each accordion to learn more about the speaker of that particular language and the meaning behind their new year’s greeting.

Silent synapses are abundant in the adult brain

MIT neuroscientists have discovered that the adult brain contains millions of “silent synapses” — immature connections between neurons that remain inactive until they’re recruited to help form new memories.

Until now, it was believed that silent synapses were present only during early development, when they help the brain learn the new information that it’s exposed to early in life. However, the new MIT study revealed that in adult mice, about 30 percent of all synapses in the brain’s cortex are silent.

The existence of these silent synapses may help to explain how the adult brain is able to continually form new memories and learn new things without having to modify existing conventional synapses, the researchers say.

“These silent synapses are looking for new connections, and when important new information is presented, connections between the relevant neurons are strengthened. This lets the brain create new memories without overwriting the important memories stored in mature synapses, which are harder to change,” says Dimitra Vardalaki, an MIT graduate student and the lead author of the new study.

Mark Harnett, an associate professor of brain and cognitive sciences and an investigator at the McGovern Institute for Brain Research, is the senior author of the paper, which appears today in Nature. Kwanghun Chung, an associate professor of chemical engineering at MIT, is also an author.

A surprising discovery

When scientists first discovered silent synapses decades ago, they were seen primarily in the brains of young mice and other animals. During early development, these synapses are believed to help the brain acquire the massive amounts of information that babies need to learn about their environment and how to interact with it. In mice, these synapses were believed to disappear by about 12 days of age (equivalent to the first months of human life).

However, some neuroscientists have proposed that silent synapses may persist into adulthood and help with the formation of new memories. Evidence for this has been seen in animal models of addiction, which is thought to be largely a disorder of aberrant learning.

Theoretical work in the field from Stefano Fusi and Larry Abbott of Columbia University has also proposed that neurons must display a wide range of different plasticity mechanisms to explain how brains can both efficiently learn new things and retain them in long-term memory. In this scenario, some synapses must be established or modified easily, to form the new memories, while others must remain much more stable, to preserve long-term memories.

In the new study, the MIT team did not set out specifically to look for silent synapses. Instead, they were following up on an intriguing finding from a previous study in Harnett’s lab. In that paper, the researchers showed that within a single neuron, dendrites — antenna-like extensions that protrude from neurons — can process synaptic input in different ways, depending on their location.

As part of that study, the researchers tried to measure neurotransmitter receptors in different dendritic branches, to see if that would help to account for the differences in their behavior. To do that, they used a technique called eMAP (epitope-preserving Magnified Analysis of the Proteome), developed by Chung. Using this technique, researchers can physically expand a tissue sample and then label specific proteins in the sample, making it possible to obtain super-high-resolution images.

The first thing we saw, which was super bizarre and we didn’t expect, was that there were filopodia everywhere.

While they were doing that imaging, they made a surprising discovery. “The first thing we saw, which was super bizarre and we didn’t expect, was that there were filopodia everywhere,” Harnett says.

Filopodia, thin membrane protrusions that extend from dendrites, have been seen before, but neuroscientists didn’t know exactly what they do. That’s partly because filopodia are so tiny that they are difficult to see using traditional imaging techniques.

After making this observation, the MIT team set out to try to find filopodia in other parts of the adult brain, using the eMAP technique. To their surprise, they found filopodia in the mouse visual cortex and other parts of the brain, at a level 10 times higher than previously seen. They also found that filopodia had neurotransmitter receptors called NMDA receptors, but no AMPA receptors.

A typical active synapse has both of these types of receptors, which bind the neurotransmitter glutamate. NMDA receptors normally require cooperation with AMPA receptors to pass signals because NMDA receptors are blocked by magnesium ions at the normal resting potential of neurons. Thus, when AMPA receptors are not present, synapses that have only NMDA receptors cannot pass along an electric current and are referred to as “silent.”

Unsilencing synapses

To investigate whether these filopodia might be silent synapses, the researchers used a modified version of an experimental technique known as patch clamping. This allowed them to monitor the electrical activity generated at individual filopodia as they tried to stimulate them by mimicking the release of the neurotransmitter glutamate from a neighboring neuron.

Using this technique, the researchers found that glutamate would not generate any electrical signal in the filopodium receiving the input, unless the NMDA receptors were experimentally unblocked. This offers strong support for the theory the filopodia represent silent synapses within the brain, the researchers say.

The researchers also showed that they could “unsilence” these synapses by combining glutamate release with an electrical current coming from the body of the neuron. This combined stimulation leads to accumulation of AMPA receptors in the silent synapse, allowing it to form a strong connection with the nearby axon that is releasing glutamate.

The researchers found that converting silent synapses into active synapses was much easier than altering mature synapses.

“If you start with an already functional synapse, that plasticity protocol doesn’t work,” Harnett says. “The synapses in the adult brain have a much higher threshold, presumably because you want those memories to be pretty resilient. You don’t want them constantly being overwritten. Filopodia, on the other hand, can be captured to form new memories.”

“Flexible and robust”

The findings offer support for the theory proposed by Abbott and Fusi that the adult brain includes highly plastic synapses that can be recruited to form new memories, the researchers say.

“This paper is, as far as I know, the first real evidence that this is how it actually works in a mammalian brain,” Harnett says. “Filopodia allow a memory system to be both flexible and robust. You need flexibility to acquire new information, but you also need stability to retain the important information.”

The researchers are now looking for evidence of these silent synapses in human brain tissue. They also hope to study whether the number or function of these synapses is affected by factors such as aging or neurodegenerative disease.

“It’s entirely possible that by changing the amount of flexibility you’ve got in a memory system, it could become much harder to change your behaviors and habits or incorporate new information,” Harnett says. “You could also imagine finding some of the molecular players that are involved in filopodia and trying to manipulate some of those things to try to restore flexible memory as we age.”

The research was funded by the Boehringer Ingelheim Fonds, the National Institutes of Health, the James W. and Patricia T. Poitras Fund at MIT, a Klingenstein-Simons Fellowship, and Vallee Foundation Scholarship, and a McKnight Scholarship.