This is your brain. This is your brain on code

Functional magnetic resonance imaging (fMRI), which measures changes in blood flow throughout the brain, has been used over the past couple of decades for a variety of applications, including “functional anatomy” — a way of determining which brain areas are switched on when a person carries out a particular task. fMRI has been used to look at people’s brains while they’re doing all sorts of things — working out math problems, learning foreign languages, playing chess, improvising on the piano, doing crossword puzzles, and even watching TV shows like “Curb Your Enthusiasm.”

One pursuit that’s received little attention is computer programming — both the chore of writing code and the equally confounding task of trying to understand a piece of already-written code. “Given the importance that computer programs have assumed in our everyday lives,” says Shashank Srikant, a PhD student in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), “that’s surely worth looking into. So many people are dealing with code these days — reading, writing, designing, debugging — but no one really knows what’s going on in their heads when that happens.” Fortunately, he has made some “headway” in that direction in a paper — written with MIT colleagues Benjamin Lipkin (the paper’s other lead author, along with Srikant), Anna Ivanova, Evelina Fedorenko, and Una-May O’Reilly — that was presented earlier this month at the Neural Information Processing Systems Conference held in New Orleans.

The new paper built on a 2020 study, written by many of the same authors, which used fMRI to monitor the brains of programmers as they “comprehended” small pieces, or snippets, of code. (Comprehension, in this case, means looking at a snippet and correctly determining the result of the computation performed by the snippet.) The 2020 work showed that code comprehension did not consistently activate the language system, brain regions that handle language processing, explains Fedorenko, a brain and cognitive sciences (BCS) professor and a coauthor of the earlier study. “Instead, the multiple demand network — a brain system that is linked to general reasoning and supports domains like mathematical and logical thinking — was strongly active.” The current work, which also utilizes MRI scans of programmers, takes “a deeper dive,” she says, seeking to obtain more fine-grained information.

Whereas the previous study looked at 20 to 30 people to determine which brain systems, on average, are relied upon to comprehend code, the new research looks at the brain activity of individual programmers as they process specific elements of a computer program. Suppose, for instance, that there’s a one-line piece of code that involves word manipulation and a separate piece of code that entails a mathematical operation. “Can I go from the activity we see in the brains, the actual brain signals, to try to reverse-engineer and figure out what, specifically, the programmer was looking at?” Srikant asks. “This would reveal what information pertaining to programs is uniquely encoded in our brains.” To neuroscientists, he notes, a physical property is considered “encoded” if they can infer that property by looking at someone’s brain signals.

Take, for instance, a loop — an instruction within a program to repeat a specific operation until the desired result is achieved — or a branch, a different type of programming instruction than can cause the computer to switch from one operation to another. Based on the patterns of brain activity that were observed, the group could tell whether someone was evaluating a piece of code involving a loop or a branch. The researchers could also tell whether the code related to words or mathematical symbols, and whether someone was reading actual code or merely a written description of that code.

That addressed a first question that an investigator might ask as to whether something is, in fact, encoded. If the answer is yes, the next question might be: where is it encoded? In the above-cited cases — loops or branches, words or math, code or a description thereof — brain activation levels were found to be comparable in both the language system and the multiple demand network.

A noticeable difference was observed, however, when it came to code properties related to what’s called dynamic analysis.

Programs can have “static” properties — such as the number of numerals in a sequence — that do not change over time. “But programs can also have a dynamic aspect, such as the number of times a loop runs,” Srikant says. “I can’t always read a piece of code and know, in advance, what the run time of that program will be.” The MIT researchers found that for dynamic analysis, information is encoded much better in the multiple demand network than it is in the language processing center. That finding was one clue in their quest to see how code comprehension is distributed throughout the brain — which parts are involved and which ones assume a bigger role in certain aspects of that task.

The team carried out a second set of experiments, which incorporated machine learning models called neural networks that were specifically trained on computer programs. These models have been successful, in recent years, in helping programmers complete pieces of code. What the group wanted to find out was whether the brain signals seen in their study when participants were examining pieces of code resembled the patterns of activation observed when neural networks analyzed the same piece of code. And the answer they arrived at was a qualified yes.

“If you put a piece of code into the neural network, it produces a list of numbers that tells you, in some way, what the program is all about,” Srikant says. Brain scans of people studying computer programs similarly produce a list of numbers. When a program is dominated by branching, for example, “you see a distinct pattern of brain activity,” he adds, “and you see a similar pattern when the machine learning model tries to understand that same snippet.”

Mariya Toneva of the Max Planck Institute for Software Systems considers findings like this “particularly exciting. They raise the possibility of using computational models of code to better understand what happens in our brains as we read programs,” she says.

The MIT scientists are definitely intrigued by the connections they’ve uncovered, which shed light on how discrete pieces of computer programs are encoded in the brain. But they don’t yet know what these recently-gleaned insights can tell us about how people carry out more elaborate plans in the real world. Completing tasks of this sort — such as going to the movies, which requires checking showtimes, arranging for transportation, purchasing tickets, and so forth — could not be handled by a single unit of code and just a single algorithm. Successful execution of such a plan would instead require “composition” — stringing together various snippets and algorithms into a sensible sequence that leads to something new, just like assembling individual bars of music in order to make a song or even a symphony. Creating models of code composition, says O’Reilly, a principal research scientist at CSAIL, “is beyond our grasp at the moment.”

Lipkin, a BCS PhD student, considers this the next logical step — figuring out how to “combine simple operations to build complex programs and use those strategies to effectively address general reasoning tasks.” He further believes that some of the progress toward that goal achieved by the team so far owes to its interdisciplinary makeup. “We were able to draw from individual experiences with program analysis and neural signal processing, as well as combined work on machine learning and natural language processing,” Lipkin says. “These types of collaborations are becoming increasingly common as neuro- and computer scientists join forces on the quest towards understanding and building general intelligence.”

This project was funded by grants from the MIT-IBM Watson AI lab, MIT Quest Initiative, National Science Foundation, National Institutes of Health, McGovern Institute of Brain Research, MIT Department of Brain and Cognitive Sciences, and the Simons Center for the Social Brain.

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.

Brains on conlangs

For a few days in November, the McGovern Institute hummed with invented languages. Strangers greeted one another in Esperanto; trivia games were played in High Valyrian; Klingon and Na’vi were heard inside MRI scanners. Creators and users of these constructed languages (conlangs) had gathered at MIT in the name of neuroscience. McGovern Institute investigator Evelina Fedorenko and her team wanted to know what happened in their brains when they heard and understood these “foreign” tongues.

The constructed languages spoken by attendees had all been created for specific purposes. Most, like the Na’vi language spoken in the movie Avatar, had given identity and voice to the inhabitants of fictional worlds, while Esperanto was created to reduce barriers to international communication. But despite their distinct origins, a familiar pattern of activity emerged when researchers scanned speakers’ brains. The brain, they found, processes constructed languages with the same network of areas it uses for languages that evolved naturally over millions of years.

The meaning of language

“There’s all these things that people call language,” Fedorenko says. “Music is a kind of language and math is a kind of language.” But the brain processes these metaphorical languages differently than it does the languages humans use to communicate broadly about the world. To neuroscientists like Fedorenko, they can’t legitimately be considered languages at all. In contrast, she says, “these constructed languages seem really quite like natural languages.”

The “Brains on Conlangs” event that Fedorenko’s team hosted was part of its ongoing effort to understand the way language is generated and understood by the brain. Her lab and others have identified specific brain regions involved in linguistic processing, but it’s not yet clear how universal the language network is. Most studies of language cognition have focused on languages widely spoken in well-resourced parts of the world—primarily English, German, and Dutch. There are thousands of languages—spoken or signed—that have not been included.

Brain activation in a Klingon speaker while listening to English (left) and Klingon (right). Image: Saima Malik Moraleda

Fedorenko and her team are deliberately taking a broader approach. “If we’re making claims about language as a whole, it’s kind of weird to make it based on a handful of languages,” she says. “So we’re trying to create tools and collect some data on as many languages as possible.”

So far, they have found that the language networks used by native speakers of dozens of different languages do share key architectural similarities. And by including a more diverse set of languages in their research, Fedorenko and her team can begin to explore how the brain makes sense of linguistic features that are not part of English or other well studied languages. The Brains on Conlangs event was a chance to expand their studies even further.

Connecting conlangs

Nearly 50 speakers of Esperanto, Klingon, High Valyrian, Dothraki, and Na’vi attended Brains on Conlangs, drawn by the opportunity to connect with other speakers, hear from language creators, and contribute to the science. Graduate student Saima Malik-Moraleda and postbac research assistant Maya Taliaferro, along with other members of both the Fedorenko lab and brain and cognitive sciences professor Ted Gibson’s lab, and with help from Steve Shannon, Operations Manager of the Martinos Imaging Center, worked tirelessly to collect data from all participants. Two MRI scanners ran nearly continuously as speakers listened to passages in their chosen languages and researchers captured images of the brain’s response. To enable the research team to find the language-specific network in each person’s brain, participants also performed other tasks inside the scanner, including a memory task and listening to muffled audio in which the constructed languages were spoken, but unintelligible. They performed language tasks in English, as well.

To understand how the brain processes constructed languages (conlangs), McGovern Investigator Ev Fedorenko (center) gathered with conlang creators/speakers Marc Okrand (Klingon), Paul Frommer (Na’vi), Damian Blasi, Jessie Sams (méníshè), David Peterson (High Valyrian and Dothraki) and Aroka Okrent at the McGovern Institute for the “Brains on Colangs” event in November 2022. Photo: Elise Malvicini

Prior to the study, Fedorenko says, she had suspected constructed languages would activate the brain’s natural language-processing network, but she couldn’t be sure. Another possibility was that languages like Klingon and Esperanto would be handled instead by a problem-solving network known to be used when people work with some other so-called “languages,” like mathematics or computer programming. But once the data was in, the answer was clear. The five constructed languages included in the study all activated the brain’s language network.

That makes sense, Fedorenko says, because like natural languages, constructed languages enable people to communicate by associating words or signs with objects and ideas. Any language is essentially a way of mapping forms to meanings, she says. “You can construe it as a set of memories of how a particular sequence of sounds corresponds to some meaning. You’re learning meanings of words and constructions, and how to put them together to get more complex meanings. And it seems like the brain’s language system is very well suited for that set of computations.”

The ways we move

This story originally appeared in the Winter 2023 issue of BrainScan.

Many people barely consider how their bodies move — at least not until movement becomes more difficult due to injury or disease. But the McGovern scientists who are working to understand human movement and restore it after it has been lost know that the way we move is an engineering marvel.
Muscles, bones, brain, and nerves work together to navigate and interact with an ever-changing environment, making constant but often imperceptible adjustments to carry out our goals. It’s an efficient and highly adaptable system, and the way it’s put together is not at all intuitive, says Hugh Herr, a new associate investigator at the Institute.

That’s why Herr, who also co-directs MIT’s new K. Lisa Yang Center for Bionics, looks to biology to guide the development of artificial limbs that aim to give people the same agency, control, and comfort of natural limbs. McGovern Associate Investigator Nidhi Seethapathi, who like Herr joined the Institute in September, is also interested in understanding human movement in all its complexity. She is coming at the problem from a different direction, using computational modeling to predict how and why we move the way we do.

Moving through change

The computational models that Seethapathi builds in her lab aim to predict how humans will move under different conditions. If a person is placed in an unfamiliar environment and asked to navigate a course under time pressure, what path will they take? How will they move their limbs, and what forces will they exert? How will their movements change as they become more comfortable on the terrain?

McGovern Associate Investigator Nidhi Seethapathi with lab members (from left to right) Inseung Kang, Nikasha Patel, Antoine De Comite, Eric Wang, and Crista Falk. Photo: Steph Stevens

Seethapathi uses the principles of robotics to build models that answer these questions, then tests them by placing real people in the same scenarios and monitoring their movements. So far, that has mostly meant inviting study subjects to her lab, but as she expands her models to predict more complex movements, she will begin monitoring people’s activity in the real world, over longer time periods than laboratory experiments typically allow.

Seethapathi’s hope is that her findings will inform the way doctors, therapists, and engineers help patients regain control over their movements after an injury or stroke, or learn to live with movement disorders like Parkinson’s disease. To make a real difference, she stresses, it’s important to bring studies of human movement out of the lab, where subjects are often limited to simple tasks like walking on a treadmill, into more natural settings. “When we’re talking about doing physical therapy, neuromotor rehabilitation, robotic exoskeletons — any way of helping people move better — we want to do it in the real world, for everyday, complex tasks,” she says.

When we’re talking about helping people move better — we want to do it in the real world, for everyday, complex tasks,” says Seethapathi.

Seethapathi’s work is already revealing how the brain directs movement in the face of competing priorities. For example, she has found that when people are given a time constraint for traveling a particular distance, they walk faster than their usual, comfortable pace — so much so that they often expend more energy than necessary and arrive at their destination a bit early. Her models suggest that people pick up their pace more than they need to because humans’ internal estimations of time are imprecise.

Her team is also learning how movements change as a person becomes familiar with an environment or task. She says people find an efficient way to move through a lot of practice. “If you’re walking in a straight line for a very long time, then you seem to pick the movement that is optimal for that long-distance walk,” she explains. But in the real world, things are always changing — both in the body and in the environment. So Seethapathi models how people behave when they must move in a new way or navigate a new environment. “In these kinds of conditions, people eventually wind up on an energy-optimal solution,” she says. “But initially, they pick something that prevents them from falling down.”

To capture the complexity of human movement, Seethapathi and her team are devising new tools that will let them monitor people’s movements outside the lab. They are also drawing on data from other fields, from architecture to physical therapy, and even from studies of other animals. “If I have general principles, they should be able to tell me how modifications in the body or in how the brain is connected to the body would lead to different movements,” she says. “I’m really excited about generalizing these principles across timescales and species.”

Building new bodies

In Herr’s lab, a deepening understanding of human movement is helping drive the development of increasingly sophisticated artificial limbs and other wearable robots. The team designs devices that interface directly with a user’s nervous system, so they are not only guided by the brain’s motor control systems, but also send information back to the brain.

Herr, a double amputee with two artificial legs of his own, says prosthetic devices are getting better at replicating natural movements, guided by signals from the brain. Mimicking the design and neural signals found in biology can even give those devices much of the extraordinary adaptability of natural human movement. As an example, Herr notes that his legs effortlessly navigate varied terrain. “There’s adaptive, stabilizing features, and the machine doesn’t have to detect every pothole and pebble and banana peel on the ground, because the morphology and the nervous system control is so inherently adaptive,” he says.

McGovern Associate Investigator Hugh Herr at work in the K. Lisa Yang Center for Bionics at MIT. Photo: Jimmy Day/Media Lab

But, he notes, the field of bionics is in its infancy, and there’s lots of room for improvement. “It’s only a matter of time before a robotic knee, for example, can be as good as the biological knee or better,” he says. “But the problem is the human attached to that knee won’t feel it’s their knee until they can feel it, and until their central nervous system has complete agency over that knee,” he says. “So if you want to actually build new bodies and not just more and more powerful tools for humans, you have to link to the brain bidirectionally.”

Herr’s team has found that surgically restoring natural connections between pairs of muscles that normally work in opposition to move a limb, such as the arm’s biceps and triceps, gives the central nervous system signals about how that limb is moving, even when a natural limb is gone. The idea takes a cue from the work of McGovern Emeritus Investigator Emilio Bizzi, who found that the coordinated activation of groups of muscles by the nervous system, called muscle synergies, is important for motor control.

“It’s only a matter of time before a robotic knee can be as good as the biological knee or better,” says Herr.

“When a person thinks and moves their phantom limb, those muscle pairings move dynamically, so they feel, in a natural way, the limb moving — even though the limb is not there,” Herr explains. He adds that when those proprioceptive signals communicate instead how an artificial limb is moving, a person experiences “great agency and ownership” of that limb. Now, his group is working to develop sensors that detect and relay information usually processed by sensory neurons in the skin, so prosthetic devices can also perceive pressure and touch.

At the same time, they’re working to improve the mechanical interface between wearable robots and the body to optimize comfort and fit — whether that’s by using detailed anatomical imaging to guide the design of an individual’s device or by engineering devices that integrate directly with a person’s skeleton. There’s no “average” human, Herr says, and effective technologies must meet individual needs, not just for fit, but also for function. At that same time, he says it’s important to plan for cost-effective, mass production, because the need for these technologies is so great.

“The amount of human suffering caused by the lack of technology to address disability is really beyond comprehension,” he says. He expects tremendous progress in the growing field of bionics in the coming decades, but he’s impatient. “I think in 50 years, when scientists look back to this era, it’ll be laughable,” he says. “I’m always anxiously wanting to be in the future.”

Machine learning can predict bipolar disorder in children and teens

Bipolar disorder often begins in childhood or adolescence, triggering dramatic mood shifts and intense emotions that cause problems at home and school. But the condition is often overlooked or misdiagnosed until patients are older. New research suggests that machine learning, a type of artificial intelligence, could help by identifying children who are at risk of bipolar disorder so doctors are better prepared to recognize the condition if it develops.

On October 13, 2022, researchers led by McGovern Institute investigator John Gabrieli and collaborators at Massachusetts General Hospital reported in the Journal of Psychiatric Research that when presented with clinical data on nearly 500 children and teenagers, a machine learning model was able to identify about 75 percent of those who were later diagnosed with bipolar disorder. The approach performs better than any other method of predicting bipolar disorder, and could be used to develop a simple risk calculator for health care providers.

Gabrieli says such a tool would be particularly valuable because bipolar disorder is less common in children than conditions like major depression, with which it shares symptoms, and attention-deficit/ hyperactivity disorder (ADHD), with which it often co-occurs. “Humans are not well tuned to watch out for rare events,” he says. “If you have a decent measure, it’s so much easier for a machine to identify than humans. And in this particular case, [the machine learning prediction] was surprisingly robust.”

Detecting bipolar disorder

Mai Uchida, Director of Massachusetts General Hospital’s Child Depression Program, says that nearly two percent of youth worldwide are estimated to have bipolar disorder, but diagnosing pediatric bipolar disorder can be challenging. A certain amount of emotional turmoil is to be expected in children and teenagers, and even when moods become seriously disruptive, children with bipolar disorder are often initially diagnosed with major depression or ADHD. That’s a problem, because the medications used to treat those conditions often worsen the symptoms of bipolar disorder. Tailoring treatment to a diagnosis of bipolar disorder, in contrast, can lead to significant improvements for patients and their families. “When we can give them a little bit of ease and give them a little bit of control over themselves, it really goes a long way,” Uchida says.

In fact, a poor response to antidepressants or ADHD medications can help point a psychiatrist toward a diagnosis of bipolar disorder. So too can a child’s family history, in addition to their own behavior and psychiatric history. But, Uchida says, “it’s kind of up to the individual clinician to pick up on these things.”

Uchida and Gabrieli wondered whether machine learning, which can find patterns in large, complex datasets, could focus in on the most relevant features to identify individuals with bipolar disorder. To find out, they turned to data from a study that began in the 1990s. The study, headed by Joseph Biederman, Chief of the Clinical and Research Programs in Pediatric Psychopharmacology and Adult ADHD at Massachusetts General Hospital, had collected extensive psychiatric assessments of hundreds of children with and without ADHD, then followed those individuals for ten years.

To explore whether machine learning could find predictors of bipolar disorder within that data, Gabrieli, Uchida, and colleagues focused on 492 children and teenagers without ADHD, who were recruited to the study as controls. Over the ten years of the study, 45 of those individuals developed bipolar disorder.

Within the data collected at the study’s outset, the machine learning model was able to find patterns that associated with a later diagnosis of bipolar disorder. A few behavioral measures turned out to be particularly relevant to the model’s predictions: children and teens with combined problems with attention, aggression, and anxiety were most likely to later be diagnosed with bipolar disorder. These indicators were all picked up by a standard assessment tool called the Child Behavior Checklist.

Uchida and Gabrieli say the machine learning model could be integrated into the medical record system to help pediatricians and child psychiatrists catch early warning signs of bipolar disorder. “The information that’s collected could alert a clinician to the possibility of a bipolar disorder developing,” Uchida says. “Then at least they’re aware of the risk, and they may be able to maybe pick up on some of the deterioration when it’s happening and think about either referring them or treating it themselves.”

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.

New CRISPR-based tool inserts large DNA sequences at desired sites in cells

Building on the CRISPR gene-editing system, MIT researchers have designed a new tool that can snip out faulty genes and replace them with new ones, in a safer and more efficient way.

Using this system, the researchers showed that they could deliver genes as long as 36,000 DNA base pairs to several types of human cells, as well as to liver cells in mice. The new technique, known as PASTE, could hold promise for treating diseases that are caused by defective genes with a large number of mutations, such as cystic fibrosis.

“It’s a new genetic way of potentially targeting these really hard to treat diseases,” says Omar Abudayyeh, a McGovern Fellow at MIT’s McGovern Institute for Brain Research. “We wanted to work toward what gene therapy was supposed to do at its original inception, which is to replace genes, not just correct individual mutations.”

The new tool combines the precise targeting of CRISPR-Cas9, a set of molecules originally derived from bacterial defense systems, with enzymes called integrases, which viruses use to insert their own genetic material into a bacterial genome.

“Just like CRISPR, these integrases come from the ongoing battle between bacteria and the viruses that infect them,” says Jonathan Gootenberg, also a McGovern Fellow. “It speaks to how we can keep finding an abundance of interesting and useful new tools from these natural systems.”

Gootenberg and Abudayyeh are the senior authors of the new study, which appears today in Nature Biotechnology. The lead authors of the study are MIT technical associates Matthew Yarnall and Rohan Krajeski, former MIT graduate student Eleonora Ioannidi, and MIT graduate student Cian Schmitt-Ulms.

DNA insertion

The CRISPR-Cas9 gene editing system consists of a DNA-cutting enzyme called Cas9 and a short RNA strand that guides the enzyme to a specific area of the genome, directing Cas9 where to make its cut. When Cas9 and the guide RNA targeting a disease gene are delivered into cells, a specific cut is made in the genome, and the cells’ DNA repair processes glue the cut back together, often deleting a small portion of the genome.

If a DNA template is also delivered, the cells can incorporate a corrected copy into their genomes during the repair process. However, this process requires cells to make double-stranded breaks in their DNA, which can cause chromosomal deletions or rearrangements that are harmful to cells. Another limitation is that it only works in cells that are dividing, as nondividing cells don’t have active DNA repair processes.

The MIT team wanted to develop a tool that could cut out a defective gene and replace it with a new one without inducing any double-stranded DNA breaks. To achieve this goal, they turned to a family of enzymes called integrases, which viruses called bacteriophages use to insert themselves into bacterial genomes.

For this study, the researchers focused on serine integrases, which can insert huge chunks of DNA, as large as 50,000 base pairs. These enzymes target specific genome sequences known as attachment sites, which function as “landing pads.” When they find the correct landing pad in the host genome, they bind to it and integrate their DNA payload.

In past work, scientists have found it challenging to develop these enzymes for human therapy because the landing pads are very specific, and it’s difficult to reprogram integrases to target other sites. The MIT team realized that combining these enzymes with a CRISPR-Cas9 system that inserts the correct landing site would enable easy reprogramming of the powerful insertion system.

The new tool, PASTE (Programmable Addition via Site-specific Targeting Elements), includes a Cas9 enzyme that cuts at a specific genomic site, guided by a strand of RNA that binds to that site. This allows them to target any site in the genome for insertion of the landing site, which contains 46 DNA base pairs. This insertion can be done without introducing any double-stranded breaks by adding one DNA strand first via a fused reverse transcriptase, then its complementary strand.

Once the landing site is incorporated, the integrase can come along and insert its much larger DNA payload into the genome at that site.

“We think that this is a large step toward achieving the dream of programmable insertion of DNA,” Gootenberg says. “It’s a technique that can be easily tailored both to the site that we want to integrate as well as the cargo.”

Gene replacement

In this study, the researchers showed that they could use PASTE to insert genes into several types of human cells, including liver cells, T cells, and lymphoblasts (immature white blood cells). They tested the delivery system with 13 different payload genes, including some that could be therapeutically useful, and were able to insert them into nine different locations in the genome.

In these cells, the researchers were able to insert genes with a success rate ranging from 5 to 60 percent. This approach also yielded very few unwanted “indels” (insertions or deletions) at the sites of gene integration.

“We see very few indels, and because we’re not making double-stranded breaks, you don’t have to worry about chromosomal rearrangements or large-scale chromosome arm deletions,” Abudayyeh says.

The researchers also demonstrated that they could insert genes in “humanized” livers in mice. Livers in these mice consist of about 70 percent human hepatocytes, and PASTE successfully integrated new genes into about 2.5 percent of these cells.

The DNA sequences that the researchers inserted in this study were up to 36,000 base pairs long, but they believe even longer sequences could also be used. A human gene can range from a few hundred to more than 2 million base pairs, although for therapeutic purposes only the coding sequence of the protein needs to be used, drastically reducing the size of the DNA segment that needs to be inserted into the genome.

“The ability to site-specifically make large genomic integrations is of huge value to both basic science and biotechnology studies. This toolset will, I anticipate, be very enabling for the research community,” says Prashant Mali, a professor of bioengineering at the University of California at San Diego, who was not involved in the study.

The researchers are now further exploring the possibility of using this tool as a possible way to replace the defective cystic fibrosis gene. This technique could also be useful for treating blood diseases caused by faulty genes, such as hemophilia and G6PD deficiency, or Huntington’s disease, a neurological disorder caused by a defective gene that has too many gene repeats.

The researchers have also made their genetic constructs available online for other scientists to use.

“One of the fantastic things about engineering these molecular technologies is that people can build on them, develop and apply them in ways that maybe we didn’t think of or hadn’t considered,” Gootenberg says. “It’s really great to be part of that emerging community.”

The research was funded by a Swiss National Science Foundation Postdoc Mobility Fellowship, the U.S. National Institutes of Health, the McGovern Institute Neurotechnology Program, the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience, the G. Harold and Leila Y. Mathers Charitable Foundation, the MIT John W. Jarve Seed Fund for Science Innovation, Impetus Grants, a Cystic Fibrosis Foundation Pioneer Grant, Google Ventures, Fast Grants, the Harvey Family Foundation, and the McGovern Institute.

Ila Fiete wins Swartz Prize for Theoretical and Computational Neuroscience

The Society for Neuroscience (SfN) has awarded the Swartz Prize for Theoretical and Computational Neuroscience to Ila Fiete, professor in the Department of Brain and Cognitive Sciences, associate member of the McGovern Institute for Brain Research, and director of the K. Lisa Yang Integrative Computational Neuroscience Center. The SfN, the world’s largest neuroscience organization, announced that Fiete received the prize for her breakthrough research modeling hippocampal grid cells, a component of the navigational system of the mammalian brain.

“Fiete’s body of work has already significantly shaped the field of neuroscience and will continue to do so for the foreseeable future,” states the announcement from SfN.

“Fiete is considered one of the strongest theorists of her generation who has conducted highly influential work demonstrating that grid cell networks have attractor-like dynamics,” says Hollis Cline, a professor at the Scripps Research Institute of California and head of the Swartz Prize selection committee.

Grid cells are found in the cortex of all mammals. Their unique firing properties, creating a neural representation of our surroundings, allow us to navigate the world. Fiete and collaborators developed computational models showing how interactions between neurons can lead to the formation of periodic lattice-like firing patterns of grid cells and stabilize these patterns to create spatial memory. They showed that as we move around in space, these neural patterns can integrate velocity signals to provide a constantly updated estimate of our position, as well as detect and correct errors in the estimated position.

Fiete also proposed that multiple copies of these patterns at different spatial scales enabled efficient and high-capacity representation. Next, Fiete and colleagues worked with multiple collaborators to design experimental tests and establish rare evidence that these pattern-forming mechanisms underlie the function of memory pattern dynamics in the brain.

“I’m truly honored to receive the Swartz Prize,” says Fiete. “This prize recognizes my group’s efforts to decipher the circuit-level mechanisms of cognitive functions involving navigation, integration, and memory. It also recognizes, in its focus, the bearing-of-fruit of dynamical circuit models from my group and others that explain how individually simple elements combine to generate the longer-lasting memory states and complex computations of the brain. I am proud to be able to represent, in some measure, the work of my incredible students, postdocs, collaborators, and intellectual mentors. I am indebted to them and grateful for the chance to work together.”

According to the SfN announcement, Fiete has contributed to the field in many other ways, including modeling “how entorhinal cortex could interact with the hippocampus to efficiently and robustly store large numbers of memories and developed a remarkable method to discern the structure of intrinsic dynamics in neuronal circuits.” This modeling led to the discovery of an internal compass that tracks the direction of one’s head, even in the absence of external sensory input.

“Recently, Fiete’s group has explored the emergence of modular organization, a line of work that elucidates how grid cell modularity and general cortical modules might self-organize from smooth genetic gradients,” states the SfN announcement. Fiete and her research group have shown that even if the biophysical properties underlying grid cells of different scale are mostly similar, continuous variations in these properties can result in discrete groupings of grid cells, each with a different function.

Fiete was recognized with the Swartz Prize, which includes a $30,000 award, during the SfN annual meeting in San Diego.

Other recent MIT winners of the Swartz Prize include Professor Emery Brown (2020) and Professor Tomaso Poggio (2014).

How touch dampens the brain’s response to painful stimuli

McGovern Investigator Fan Wang. Photo: Caitliin Cunningham

When we press our temples to soothe an aching head or rub an elbow after an unexpected blow, it often brings some relief. It is believed that pain-responsive cells in the brain quiet down when these neurons also receive touch inputs, say scientists at MIT’s McGovern Institute, who for the first time have watched this phenomenon play out in the brains of mice.

The team’s discovery, reported November 16, 2022, in the journal Science Advances, offers researchers a deeper understanding of the complicated relationship between pain and touch and could offer some insights into chronic pain in humans. “We’re interested in this because it’s a common human experience,” says McGovern Investigator Fan Wang. “When some part of your body hurts, you rub it, right? We know touch can alleviate pain in this way.” But, she says, the phenomenon has been very difficult for neuroscientists to study.

Modeling pain relief

Touch-mediated pain relief may begin in the spinal cord, where prior studies have found pain-responsive neurons whose signals are dampened in response to touch. But there have been hints that the brain was involved too. Wang says this aspect of the response has been largely unexplored, because it can be hard to monitor the brain’s response to painful stimuli amidst all the other neural activity happening there—particularly when an animal moves.

So while her team knew that mice respond to a potentially painful stimulus on the cheek by wiping their faces with their paws, they couldn’t follow the specific pain response in the animals’ brains to see if that rubbing helped settle it down. “If you look at the brain when an animal is rubbing the face, movement and touch signals completely overwhelm any possible pain signal,” Wang explains.

She and her colleagues have found a way around this obstacle. Instead of studying the effects of face-rubbing, they have focused their attention on a subtler form of touch: the gentle vibrations produced by the movement of the animals’ whiskers. Mice use their whiskers to explore, moving them back and forth in a rhythmic motion known as whisking to feel out their environment. This motion activates touch receptors in the face and sends information to the brain in the form of vibrotactile signals. The human brain receives the same kind of touch signals when a person shakes their hand as they pull it back from a painfully hot pan—another way we seek touch-mediate pain relief.

If you look at the brain when an animal is rubbing the face, movement and touch signals completely overwhelm any possible pain signal, says Wang.

Wang and her colleagues found that this whisker movement alters the way mice respond to bothersome heat or a poke on the face—both of which usually lead to face rubbing. “When the unpleasant stimuli were applied in the presence of their self-generated vibrotactile whisking…they respond much less,” she says. Sometimes, she says, whisking animals entirely ignore these painful stimuli.

In the brain’s somatosensory cortex, where touch and pain signals are processed, the team found signaling changes that seem to underlie this effect. “The cells that preferentially respond to heat and poking are less frequently activated when the mice are whisking,” Wang says. “They’re less likely to show responses to painful stimuli.” Even when whisking animals did rub their faces in response to painful stimuli, the team found that neurons in the brain took more time to adopt the firing patterns associated with that rubbing movement. “When there is a pain stimulation, usually the trajectory the population dynamics quickly moved to wiping. But if you already have whisking, that takes much longer,” Wang says.

Wang notes that even in the fraction of a second before provoked mice begin rubbing their faces, when the animals are relatively still, it can be difficult to sort out which brain signals are related to perceiving heat and poking and which are involved in whisker movement. Her team developed computational tools to disentangle these, and are hoping other neuroscientists will use the new algorithms to make sense of their own data.

Whisking’s effects on pain signaling seem to depend on dedicated touch-processing circuitry that sends tactile information to the somatosensory cortex from a brain region called the ventral posterior thalamus. When the researchers blocked that pathway, whisking no longer dampened the animals’ response to painful stimuli. Now, Wang says, she and her team are eager to learn how this circuitry works with other parts of the brain to modulate the perception and response to painful stimuli.

Wang says the new findings might shed light on a condition called thalamic pain syndrome, a chronic pain disorder that can develop in patients after a stroke that affects the brain’s thalamus. “Such strokes may impair the functions of thalamic circuits that normally relay pure touch signals and dampen painful signals to the cortex,” she says.

Not every reader’s struggle is the same

Many children struggle to learn to read, and studies have shown that students from a lower socioeconomic status (SES) background are more likely to have difficulty than those from a higher SES background.

MIT neuroscientists have now discovered that the types of difficulties that lower-SES students have with reading, and the underlying brain signatures, are, on average, different from those of higher-SES students who struggle with reading.

In a new study, which included brain scans of more than 150 children as they performed tasks related to reading, researchers found that when students from higher SES backgrounds struggled with reading, it could usually be explained by differences in their ability to piece sounds together into words, a skill known as phonological processing.

However, when students from lower SES backgrounds struggled, it was best explained by differences in their ability to rapidly name words or letters, a task associated with orthographic processing, or visual interpretation of words and letters. This pattern was further confirmed by brain activation during phonological and orthographic processing.

These differences suggest that different types of interventions may needed for different groups of children, the researchers say. The study also highlights the importance of including a wide range of SES levels in studies of reading or other types of academic learning.

“Within the neuroscience realm, we tend to rely on convenience samples of participants, so a lot of our understanding of the neuroscience components of reading in general, and reading disabilities in particular, tends to be based on higher-SES families,” says Rachel Romeo, a former graduate student in the Harvard-MIT Program in Health Sciences and Technology and the lead author of the study. “If we only look at these nonrepresentative samples, we can come away with a relatively biased view of how the brain works.”

Romeo is now an assistant professor in the Department of Human Development and Quantitative Methodology at the University of Maryland. John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology and a professor of brain and cognitive sciences at MIT, is the senior author of the paper, which appears today in the journal Developmental Cognitive Neuroscience.

Components of reading

For many years, researchers have known that children’s scores on standardized assessments of reading are correlated with socioeconomic factors such as school spending per student or the number of children at the school who qualify for free or reduced-price lunches.

Studies of children who struggle with reading, mostly done in higher-SES environments, have shown that the aspect of reading they struggle with most is phonological awareness: the understanding of how sounds combine to make a word, and how sounds can be split up and swapped in or out to make new words.

“That’s a key component of reading, and difficulty with phonological processing is often one of the hallmarks of dyslexia or other reading disorders,” Romeo says.

In the new study, the MIT team wanted to explore how SES might affect phonological processing as well as another key aspect of reading, orthographic processing. This relates more to the visual components of reading, including the ability to identify letters and read words.

To do the study, the researchers recruited first and second grade students from the Boston area, making an effort to include a range of SES levels. For the purposes of this study, SES was assessed by parents’ total years of formal education, which is commonly used as a measure of the family’s SES.

“We went into this not necessarily with any hypothesis about how SES might relate to the two types of processing, but just trying to understand whether SES might be impacting one or the other more, or if it affects both types the same,” Romeo says.

The researchers first gave each child a series of standardized tests designed to measure either phonological processing or orthographic processing. Then, they performed fMRI scans of each child while they carried out additional phonological or orthographic tasks.

The initial series of tests allowed the researchers to determine each child’s abilities for both types of processing, and the brain scans allowed them to measure brain activity in parts of the brain linked with each type of processing.

The results showed that at the higher end of the SES spectrum, differences in phonological processing ability accounted for most of the differences between good readers and struggling readers. This is consistent with the findings of previous studies of reading difficulty. In those children, the researchers also found greater differences in activity in the parts of the brain responsible for phonological processing.

However, the outcomes were different when the researchers analyzed the lower end of the SES spectrum. There, the researchers found that variance in orthographic processing ability accounted for most of the differences between good readers and struggling readers. MRI scans of these children revealed greater differences in brain activity in parts of the brain that are involved in orthographic processing.

Optimizing interventions

There are many possible reasons why a lower SES background might lead to difficulties in orthographic processing, the researchers say. It might be less exposure to books at home, or limited access to libraries and other resources that promote literacy. For children from this background who struggle with reading, different types of interventions might benefit them more than the ones typically used for children who have difficulty with phonological processing.

In a 2017 study, Gabrieli, Romeo, and others found that a summer reading intervention that focused on helping students develop the sensory and cognitive processing necessary for reading was more beneficial for students from lower-SES backgrounds than children from higher-SES backgrounds. Those findings also support the idea that tailored interventions may be necessary for individual students, they say.

“There are two major reasons we understand that cause children to struggle as they learn to read in these early grades. One of them is learning differences, most prominently dyslexia, and the other one is socioeconomic disadvantage,” Gabrieli says. “In my mind, schools have to help all these kinds of kids become the best readers they can, so recognizing the source or sources of reading difficulty ought to inform practices and policies that are sensitive to these differences and optimize supportive interventions.”

Gabrieli and Romeo are now working with researchers at the Harvard University Graduate School of Education to evaluate language and reading interventions that could better prepare preschool children from lower SES backgrounds to learn to read. In her new lab at the University of Maryland, Romeo also plans to further delve into how different aspects of low SES contribute to different areas of language and literacy development.

“No matter why a child is struggling with reading, they need the education and the attention to support them. Studies that try to tease out the underlying factors can help us in tailoring educational interventions to what a child needs,” she says.

The research was funded by the Ellison Medical Foundation, the Halis Family Foundation, and the National Institutes of Health.