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

Anne Trafton |

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.”

Proof-of-concept

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.

Reevaluating an approach to functional brain imaging

by Jennifer Michalowski |

A new way of imaging the brain with magnetic resonance imaging (MRI) does not directly detect neural activity as originally reported, according to scientists at MIT’s McGovern Institute. The method, first described in 2022, generated excitement within the neuroscience community as a potentially transformative approach. But a study from the lab of McGovern Associate Investigator Alan Jasanoff, reported March 27, 2024, in the journal Science Advances, demonstrates that MRI signals produced by the new method are generated in large part by the imaging process itself, not neuronal activity.

A man stands with his arms crossed in front of a board with mathematical equations written on it.
Alan Jasanoff, associate member of the McGovern Institute, and a professor of brain and cognitive sciences, biological engineering, and nuclear science and engineering at MIT. Photo: Justin Knight

Jasanoff explains that having a noninvasive means of seeing neuronal activity in the brain is a long-sought goal for neuroscientists. The functional MRI methods that researchers currently use to monitor brain activity don’t actually detect neural signaling. Instead, they use blood flow changes triggered by brain activity as a proxy. This reveals which parts of the brain are engaged during imaging, but it cannot pinpoint neural activity to precise locations, and it is too slow to truly track neurons’ rapid-fire communications.

So when a team of scientists reported in Science a new MRI method called DIANA, for “direct imaging of neuronal activity,” neuroscientists paid attention. The authors claimed that DIANA detected MRI signals in the brain that corresponded to the electrical signals of neurons, and that it acquired signals far faster than the methods now used for functional MRI.

“Everyone wants this,” Jasanoff says. “If we could look at the whole brain and follow its activity with millisecond precision and know that all the signals that we’re seeing have to do with cellular activity, this would be just wonderful. It could tell us all kinds of things about how the brain works and what goes wrong in disease.”

Jasanoff adds that from the initial report, it was not clear what brain changes DIANA was detecting to produce such a rapid readout of neural activity. Curious, he and his team began to experiment with the method. “We wanted to reproduce it, and we wanted to understand how it worked,” he says.

Decoding DIANA

Recreating the MRI procedure reported by DIANA’s developers, postdoctoral researcher Valerie Doan Phi Van imaged the brain of a rat as an electric stimulus was delivered to one paw. Phi Van says she was excited to see an MRI signal appear in the brain’s sensory cortex, exactly when and where neurons were expected to respond to the sensation on the paw. “I was able to reproduce it,” she says. “I could see the signal.”

With further tests of the system, however, her enthusiasm waned. To investigate the source of the signal, she disconnected the device used to stimulate the animal’s paw, then repeated the imaging. Again, signals showed up in the sensory processing part of the brain. But this time, there was no reason for neurons in that area to be activated. In fact, Phi Van found, the MRI produced the same kinds of signals when the animal inside the scanner was replaced with a tube of water. It was clear DIANA’s functional signals were not arising from neural activity.

Phi Van traced the source of the specious signals to the pulse program that directs DIANA’s imaging process, detailing the sequence of steps the MRI scanner uses to collect data. Embedded within DIANA’s pulse program was a trigger for the device that delivers sensory input to the animal inside the scanner. That synchronizes the two processes, so the stimulation occurs at a precise moment during data acquisition. That trigger appeared to be causing signals that DIANA’s developers had concluded indicated neural activity.

It was clear DIANA’s functional signals were not arising from neural activity.

Phi Van altered the pulse program, changing the way the stimulator was triggered. Using the updated program, the MRI scanner detected no functional signal in the brain in response to the same paw stimulation that had produced a signal before. “If you take this part of the code out, then the signal will also be gone. So that means the signal we see is an artifact of the trigger,” she says.

Jasanoff and Phi Van went on to find reasons why other researchers have struggled to reproduce the results of the original DIANA report, noting that the trigger-generated signals can disappear with slight variations in the imaging process. With their postdoctoral colleague Sajal Sen, they also found evidence that cellular changes that DIANA’s developers had proposed might give rise to a functional MRI signal were not related to neuronal activity.

Jasanoff and Phi Van say it was important to share their findings with the research community, particularly as efforts continue to develop new neuroimaging methods. “If people want to try to repeat any part of the study or implement any kind of approach like this, they have to avoid falling into these pits,” Jasanoff says. He adds that they admire the authors of the original study for their ambition: “The community needs scientists who are willing to take risks to move the field ahead.”

Beyond the brain

by Jennifer Michalowski |

This story also appears in the Spring 2024 issue of BrainScan.

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Like many people, graduate student Guillermo Herrera-Arcos found himself working from home in the spring of 2020. Surrounded by equipment he’d hastily borrowed from the lab, he began testing electrical components he would need to control muscles in a new way. If it worked, he and colleagues in Hugh Herr’s lab might have found a promising strategy for restoring movement when signals from the brain fail to reach the muscles, such as after a spinal cord injury or stroke.

Man holds a fiber that is illuminated with blue light at its tip.
Guillermo Herrera-Arcos, a graduate student in Hugh Herr’s lab, is developing an optical technology with the potential to restore movement in people with spinal cord injury or stroke. Photo: Steph Stevens

Herrera-Arcos and Herr’s work is one way McGovern neuroscientists are working at the interface of brain and machine. Such work aims to enable better ways of understanding and treating injury and disease, offering scientists tools to manipulate neural signaling as well as to replace its function when it is lost.

Restoring movement

The system Herrera-Arcos and Herr were developing wouldn’t be the first to bypass the brain to move muscles. Neuroprosthetic devices that use electricity to stimulate muscle-activating motor neurons are sometimes used during rehabilitation from an injury, helping patients maintain muscle mass when they can’t use their muscles on their own. But existing neuroprostheses lack the precision of the body’s natural movement system. They send all-or-nothing signals that quickly tire muscles out.

TWo men looking at a computer screen, one points to the image on the screen.
Hugh Herr (left) and graduate student Guillermo Herrera-Arco at work in the lab. Photo: Steph Stevens

Researchers attribute that fatigue to an unnatural recruitment of neurons and muscle fibers. Electrical signals go straight to the largest, most powerful components of the system, even when smaller units could do the job. “You turn up the stimulus and you get no force, and then suddenly, you get too much force. And then fatigue, a lack of controllability, and so on,” Herr explains. The nervous system, in contrast, calls first on small motor units and recruits larger ones only when needed to generate more force.

Optical solution

In hopes of recreating this strategic pattern of muscle activation, Herr and Herrera-Arcos turned to a technique pioneered by McGovern Investigator Edward Boyden that has become common research: controlling neural activity with light. To put neurons under their control, researchers equip them with light-sensitive proteins. The cells can then be switched on or off within milliseconds using an optic fiber.

When a return to the lab enabled Herr and Herrera-Arcos to test their idea, they were thrilled with the results. Using light to switch on motor neurons and stimulate a single muscle in mice, they recreated the nervous system’s natural muscle activation pattern. Consequently, fatigue did not set in nearly as quickly as it would with an electrically-activated system. Herrera-Arcos says he set out to measure the force generated by the muscle and how long it took to fatigue, and he had to keep extending his experiments: After an hour of light stimulation, it was still going strong.

To optimize the force generated by the system, the researchers used feedback from the muscle to modulate the intensity of the neuron-activating light. Their success suggests this type of closed-loop system could enable fatigue-resistant neuroprostheses for muscle control.

“The field has been struggling for many decades with the challenge of how to control living muscle tissue,” Herr says. “So the idea that this could be solved is very, very exciting.”

There’s work to be done to translate what the team has learned into practical neuroprosthetics for people who need them. To use light to stimulate human motor neurons, light-sensitive proteins will need to be delivered to those cells. Figuring out how to do that safely is a high priority at the K. Lisa Yang Center for Bionics, which Herr co-directs with Boyden, and might lead to better ways of obtaining tactile and proprioceptive feedback from prosthetic limbs, as well as to control muscles for the restoration of natural movements after spinal cord injury. “It would be a game changer for a number of conditions,” Herr says.

Gut-brain connection

While Herr’s team works where the nervous system meets the muscle, researchers in Polina Anikeeva’s lab are exploring the brain’s relationship with an often-overlooked part of the nervous system — the hundreds of millions of neurons in the gut.

“Classically, when we think of brain function in neuroscience, it is always studied in the framework of how the brain interacts with the surrounding environment and how it integrates different stimuli,” says Atharva Sahasrabudhe, a graduate student in the group. “But the brain does not function in a vacuum. It’s constantly getting and integrating signals from the peripheral organs.”

Man smiles at camera while holding up tiny devices.
Atharva Sahasrabudhe holds some of the fiber technology he developed in the Anikeeva lab. Photo: Steph Stevens

The nervous system has a particularly pronounced presence in the gut. Neurons embedded within the walls of the gastrointestinal (GI) tract monitor local conditions and relay information to the brain. This mind-body connection may help explain the GI symptoms associated with some brain-related conditions, including Parkinson’s disease, mood disorders, and autism. Researchers have yet to untangle whether GI symptoms help drive these conditions, are a consequence of them, or are coincidental. Either way, Anikeeva says, “if there is a GI connection, maybe we can tap into this connection to improve the quality of life of affected individuals.”

Flexible fibers

At the K. Lisa Yang Brain-Body Center that Anikeeva directs, studying how the gut communicates with the brain is a high priority. But most of neuroscientists’ tools are designed specifically to investigate the brain. To explore new territory, Sahasrabudhe devised a device that is compatible with the long and twisty GI tract of a mouse.

The new tool is a slender, flexible fiber equipped with light emitters for activating subsets of cells and tiny channels for delivering nutrients or drugs. To access neurons dispersed throughout the GI tract, its wirelessly controlled components are embedded along its length. A more rigid probe at one end of the device is designed to monitor and manipulate neural activity in the brain, so researchers can follow the nervous system’s swift communications across the gut-brain axis.

Scientists on Anikeeva’s team are deploying the device to investigate how gut-brain communications contribute to several conditions. Postdoctoral researcher Sharmelee Selvaraji is focused on Parkinson’s disease. Like many scientists, she wonders whether the neurodegenerative movement disorder might actually start in the gut. There’s a molecular link: the misshapen protein that sickens brain cells in patients with Parkinson’s disease has been found aggregating in the gut, too. And the constipation and other GI problems that are common complaints for people with Parkinson’s disease usually start decades before the onset of motor symptoms. She hopes that by investigating gut-brain communications in a mouse model of the disease, she will uncover important clues about its origins and progression.

“We’re trying to observe the effects of Parkinson’s in the gut, and then eventually, we may be able to intervene at an earlier stage to slow down the disease progression, or even cure it,” says Selvaraji.

Meanwhile, colleagues in the lab are exploring related questions about gut-brain communications in mouse models of autism, anxiety disorders, and addiction. Others continue to focus on technology development, adding new capabilities to the gut-brain probe or applying similar engineering principles to new problems.

“We are realizing that the brain is very much connected to the rest of the body,” Anikeeva says. “There is now a lot of effort in the lab to create technology suitable for a variety of really interesting organs that will help us study brain-body connections.”

Researchers reveal roadmap for AI innovation in brain and language learning

by Georgia Tech |

One of the hallmarks of humanity is language, but now, powerful new artificial intelligence tools also compose poetry, write songs, and have extensive conversations with human users. Tools like ChatGPT and Gemini are widely available at the tap of a button — but just how smart are these AIs?

A new multidisciplinary research effort co-led by Anna (Anya) Ivanova, assistant professor in the School of Psychology at Georgia Tech, alongside Kyle Mahowald, an assistant professor in the Department of Linguistics at the University of Texas at Austin, is working to uncover just that.

Their results could lead to innovative AIs that are more similar to the human brain than ever before — and also help neuroscientists and psychologists who are unearthing the secrets of our own minds.

The study, “Dissociating Language and Thought in Large Language Models,” is published this week in the scientific journal Trends in Cognitive Sciences. The work is already making waves in the scientific community: an earlier preprint of the paper, released in January 2023, has already been cited more than 150 times by fellow researchers. The research team has continued to refine the research for this final journal publication.

“ChatGPT became available while we were finalizing the preprint,” explains Ivanova, who conducted the research while a postdoctoral researcher at MIT’s McGovern Institute. “Over the past year, we’ve had an opportunity to update our arguments in light of this newer generation of models, now including ChatGPT.”

Form versus function

The study focuses on large language models (LLMs), which include AIs like ChatGPT. LLMs are text prediction models, and create writing by predicting which word comes next in a sentence — just like how a cell phone or email service like Gmail might suggest what next word you might want to write. However, while this type of language learning is extremely effective at creating coherent sentences, that doesn’t necessarily signify intelligence.

Ivanova’s team argues that formal competence — creating a well-structured, grammatically correct sentence — should be differentiated from functional competence — answering the right question, communicating the correct information, or appropriately communicating. They also found that while LLMs trained on text prediction are often very good at formal skills, they still struggle with functional skills.

“We humans have the tendency to conflate language and thought,” Ivanova says. “I think that’s an important thing to keep in mind as we’re trying to figure out what these models are capable of, because using that ability to be good at language, to be good at formal competence, leads many people to assume that AIs are also good at thinking — even when that’s not the case.

It’s a heuristic that we developed when interacting with other humans over thousands of years of evolution, but now in some respects, that heuristic is broken,” Ivanova explains.

The distinction between formal and functional competence is also vital in rigorously testing an AI’s capabilities, Ivanova adds. Evaluations often don’t distinguish formal and functional competence, making it difficult to assess what factors are determining a model’s success or failure. The need to develop distinct tests is one of the team’s more widely accepted findings, and one that some researchers in the field have already begun to implement.

Creating a modular system

While the human tendency to conflate functional and formal competence may have hindered understanding of LLMs in the past, our human brains could also be the key to unlocking more powerful AIs.

Leveraging the tools of cognitive neuroscience while a postdoctoral associate at Massachusetts Institute of Technology (MIT), Ivanova and her team studied brain activity in neurotypical individuals via fMRI, and used behavioral assessments of individuals with brain damage to test the causal role of brain regions in language and cognition — both conducting new research and drawing on previous studies. The team’s results showed that human brains use different regions for functional and formal competence, further supporting this distinction in AIs.

“Our research shows that in the brain, there is a language processing module and separate modules for reasoning,” Ivanova says. This modularity could also serve as a blueprint for how to develop future AIs.

“Building on insights from human brains — where the language processing system is sharply distinct from the systems that support our ability to think — we argue that the language-thought distinction is conceptually important for thinking about, evaluating, and improving large language models, especially given recent efforts to imbue these models with human-like intelligence,” says Ivanova’s former advisor and study co-author Evelina Fedorenko, a professor of brain and cognitive sciences at MIT and a member of the McGovern Institute for Brain Research.

Developing AIs in the pattern of the human brain could help create more powerful systems — while also helping them dovetail more naturally with human users. “Generally, differences in a mechanism’s internal structure affect behavior,” Ivanova says. “Building a system that has a broad macroscopic organization similar to that of the human brain could help ensure that it might be more aligned with humans down the road.”

In the rapidly developing world of AI, these systems are ripe for experimentation. After the team’s preprint was published, OpenAI announced their intention to add plug-ins to their GPT models.

“That plug-in system is actually very similar to what we suggest,” Ivanova adds. “It takes a modularity approach where the language model can be an interface to another specialized module within a system.”

While the OpenAI plug-in system will include features like booking flights and ordering food, rather than cognitively inspired features, it demonstrates that “the approach has a lot of potential,” Ivanova says.

The future of AI — and what it can tell us about ourselves

While our own brains might be the key to unlocking better, more powerful AIs, these AIs might also help us better understand ourselves. “When researchers try to study the brain and cognition, it’s often useful to have some smaller system where you can actually go in and poke around and see what’s going on before you get to the immense complexity,” Ivanova explains.

However, since human language is unique, model or animal systems are more difficult to relate. That’s where LLMs come in.

“There are lots of surprising similarities between how one would approach the study of the brain and the study of an artificial neural network” like a large language model, she adds. “They are both information processing systems that have biological or artificial neurons to perform computations.”

In many ways, the human brain is still a black box, but openly available AIs offer a unique opportunity to see the synthetic system’s inner workings and modify variables, and explore these corresponding systems like never before.

“It’s a really wonderful model that we have a lot of control over,” Ivanova says. “Neural networks — they are amazing.”

Along with Anna (Anya) Ivanova, Kyle Mahowald, and Evelina Fedorenko, the research team also includes Idan Blank (University of California, Los Angeles), as well as Nancy Kanwisher and Joshua Tenenbaum (Massachusetts Institute of Technology).

Honoring a visionary

by McGovern Institute |

Today marks the 10th anniversary of the passing of Pat McGovern, an extraordinary visionary and philanthropist whose legacy continues to inspire and impact the world. As the founder of International Data Group (IDG)—a premier information technology organization—McGovern was not just a pioneering figure in the technology media world, but also a passionate advocate for using technology for the greater good.

Under McGovern’s leadership, IDG became a global powerhouse, launching iconic publications such as Computerworld, Macworld, and PCWorld. His foresight also led to the creation of IDG Ventures, a network of venture funds around the world, including the notable IDG Capital in Beijing.

Beyond his remarkable business acumen, McGovern, with his wife, Lore, co-founded the McGovern Institute for Brain Research at MIT in 2000. This institute has been at the forefront of neuroscience research, contributing to groundbreaking advancements in perception, attention, memory, and artificial intelligence (AI), as well as discoveries with direct translational impact, such as CRISPR technology. CRISPR discoveries made at the McGovern Institute are now licensed for the first clinical application of genome editing in sickle cell disease.

Pat McGovern’s commitment to bettering humanity is further evidenced by the Patrick J. McGovern Foundation, which works in partnership with public, private, and social institutions to drive progress on our most pressing challenges through the use of artificial intelligence, data science, and key emerging technologies.

Remembering Pat McGovern

On this solemn anniversary, we reflect on Pat McGovern’s enduring influence through the words of those who knew him best.

Lore Harp McGovern
Co-founder and board member of the McGovern Institute for Brain Research

“Technology was Pat’s medium, the platform on which he built his amazing company 60 years ago. But it was people who truly motivated Pat, and he empowered and encouraged them to reach for the stars. He lived by the motto, ‘let’s try it,’ and believed that nothing was out bounds. His goal was to help create a more just and peaceful world, and establishing the McGovern Institute was our way to give back meaningfully to this world. I know he would be so proud of what has been achieved and what is yet to come.”

Robert Desimone
Director of the McGovern Institute for Brain Research

“Pat McGovern had a vision for an international community of scientists and students drawn together to collaborate on understanding the brain.  This vision has been realized in the McGovern Institute, and we are now seeing the profound advances in our understanding of the brain and even clinical applications that Pat predicted would follow.”

Hugo Shong
Chairman of IDG Capital

“Pat’s impact on technology, science and research is immeasurable. A man of tremendous vision, he grew IDG out of Massachusetts and made it into one of the world’s most recognized brands in its space, forging partnerships and winning friends wherever he went. He applied that very same vision and energy to the McGovern Institute and the Patrick J. McGovern Foundation, in support of their impressive and necessary causes. I know he would be extremely proud of what both organizations have achieved thus far, and particularly how their work has broken technological frontiers and bettered the lives of millions.”

Vilas Dhar
President of the Patrick J. McGovern Foundation

“Patrick J. McGovern was more than a tech mogul; he was a visionary who believed in the power of information to empower people and improve societies. His work has had a profound effect on public policy and education, laying the groundwork for a more informed and connected world and guiding our work to ensure that artificial intelligence is used to sustain a human-centered world that creates economic and social opportunity for all.  On a personal level, Pat’s leadership was characterized by a genuine care for his employees and a belief in their potential. He created a culture of curiosity, encouraging humanity to explore, innovate, and dream big. His spirit lives on in every philanthropic activity we undertake.”

Genevieve Juillard
CEO of IDG 

“The legacy of Pat McGovern is felt not just in Boston, but around the world—by the thousands of IDG customers and by people like me who have the privilege to work at IDG, 60 years after he founded it. His innovative spirit and unwavering commitment to excellence continue to inspire and guide us.”

Sudhir Sethi
Founder and Chairman of Chiratae Ventures (formally IDG Ventures)

“Pat McGovern was a visionary who foresaw the potential of technology in India and nurtured the ecosystem as an active participant. Pat enabled a launchpad for Chiratae Ventures, empowering our journey to become the leading home-grown venture capital fund in India today. Pat is a role model to entrepreneurs worldwide, and we honor his legacy with our annual ‘Chiratae Ventures Patrick J. McGovern Awards’ that celebrate courage and the spirit of entrepreneurship.”

Marc Benioff
Founder and CEO of Salesforce wrote in the book “Future Forward that “Pat McGovern was a gift to us all, a trailblazing visionary who showed an entire generation of entrepreneurs what it means to be a principle-based leader and how to lead with higher values.”

Pat McGovern’s memory lives on not just in the institutions and innovations he fostered, but in the countless lives he touched and transformed. Today, we celebrate a man who saw the future and helped us all move towards it with hope and determination.

For people who speak many languages, there’s something special about their native tongue

Anne Trafton |

A new study of people who speak many languages has found that there is something special about how the brain processes their native language.

In the brains of these polyglots — people who speak five or more languages — the same language regions light up when they listen to any of the languages that they speak. In general, this network responds more strongly to languages in which the speaker is more proficient, with one notable exception: the speaker’s native language. When listening to one’s native language, language network activity drops off significantly.

The findings suggest there is something unique about the first language one acquires, which allows the brain to process it with minimal effort, the researchers say.

“Something makes it a little bit easier to process — maybe it’s that you’ve spent more time using that language — and you get a dip in activity for the native language compared to other languages that you speak proficiently,” says Evelina Fedorenko, an associate professor of neuroscience at MIT, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Saima Malik-Moraleda, a graduate student in the Speech and Hearing Bioscience and Technology Program at Harvard University, and Olessia Jouravlev, a former MIT postdoc who is now an associate professor at Carleton University, are the lead authors of the paper, which appears today in the journal Cerebral Cortex.

Many languages, one network

McGovern Investivator Ev Fedorenko in the Martinos Imaging Center at MIT. Photo: Caitlin Cunningham

The brain’s language processing network, located primarily in the left hemisphere, includes regions in the frontal and temporal lobes. In a 2021 study, Fedorenko’s lab found that in the brains of polyglots, the language network was less active when listening to their native language than the language networks of people who speak only one language.

In the new study, the researchers wanted to expand on that finding and explore what happens in the brains of polyglots as they listen to languages in which they have varying levels of proficiency. Studying polyglots can help researchers learn more about the functions of the language network, and how languages learned later in life might be represented differently than a native language or languages.

“With polyglots, you can do all of the comparisons within one person. You have languages that vary along a continuum, and you can try to see how the brain modulates responses as a function of proficiency,” Fedorenko says.

For the study, the researchers recruited 34 polyglots, each of whom had at least some degree of proficiency in five or more languages but were not bilingual or multilingual from infancy. Sixteen of the participants spoke 10 or more languages, including one who spoke 54 languages with at least some proficiency.

Each participant was scanned with functional magnetic resonance imaging (fMRI) as they listened to passages read in eight different languages. These included their native language, a language they were highly proficient in, a language they were moderately proficient in, and a language in which they described themselves as having low proficiency.

They were also scanned while listening to four languages they didn’t speak at all. Two of these were languages from the same family (such as Romance languages) as a language they could speak, and two were languages completely unrelated to any languages they spoke.

The passages used for the study came from two different sources, which the researchers had previously developed for other language studies. One was a set of Bible stories recorded in many different languages, and the other consisted of passages from “Alice in Wonderland” translated into many languages.

Brain scans revealed that the language network lit up the most when participants listened to languages in which they were the most proficient. However, that did not hold true for the participants’ native languages, which activated the language network much less than non-native languages in which they had similar proficiency. This suggests that people are so proficient in their native language that the language network doesn’t need to work very hard to interpret it.

“As you increase proficiency, you can engage linguistic computations to a greater extent, so you get these progressively stronger responses. But then if you compare a really high-proficiency language and a native language, it may be that the native language is just a little bit easier, possibly because you’ve had more experience with it,” Fedorenko says.

Brain engagement

The researchers saw a similar phenomenon when polyglots listened to languages that they don’t speak: Their language network was more engaged when listening to languages related to a language that they could understand, than compared to listening to completely unfamiliar languages.

“Here we’re getting a hint that the response in the language network scales up with how much you understand from the input,” Malik-Moraleda says. “We didn’t quantify the level of understanding here, but in the future we’re planning to evaluate how much people are truly understanding the passages that they’re listening to, and then see how that relates to the activation.”

The researchers also found that a brain network known as the multiple demand network, which turns on whenever the brain is performing a cognitively demanding task, also becomes activated when listening to languages other than one’s native language.

“What we’re seeing here is that the language regions are engaged when we process all these languages, and then there’s this other network that comes in for non-native languages to help you out because it’s a harder task,” Malik-Moraleda says.

In this study, most of the polyglots began studying their non-native languages as teenagers or adults, but in future work, the researchers hope to study people who learned multiple languages from a very young age. They also plan to study people who learned one language from infancy but moved to the United States at a very young age and began speaking English as their dominant language, while becoming less proficient in their native language, to help disentangle the effects of proficiency versus age of acquisition on brain responses.

The research was funded by the McGovern Institute for Brain Research, MIT’s Department of Brain and Cognitive Sciences, and the Simons Center for the Social Brain.

How the brain coordinates speaking and breathing

Anne Trafton |

MIT researchers have discovered a brain circuit that drives vocalization and ensures that you talk only when you breathe out, and stop talking when you breathe in.

McGovern Investigator Fan Wang. Photo: Caitliin Cunningham

The newly discovered circuit controls two actions that are required for vocalization: narrowing of the larynx and exhaling air from the lungs. The researchers also found that this vocalization circuit is under the command of a brainstem region that regulates the breathing rhythm, which ensures that breathing remains dominant over speech.

“When you need to breathe in, you have to stop vocalization. We found that the neurons that control vocalization receive direct inhibitory input from the breathing rhythm generator,” says Fan Wang, an MIT professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Jaehong Park, a Duke University graduate student who is currently a visiting student at MIT, is the lead author of the study, which appears today in Science. Other authors of the paper include MIT technical associates Seonmi Choi and Andrew Harrahill, former MIT research scientist Jun Takatoh, and Duke University researchers Shengli Zhao and Bao-Xia Han.

Vocalization control

Located in the larynx, the vocal cords are two muscular bands that can open and close. When they are mostly closed, or adducted, air exhaled from the lungs generates sound as it passes through the cords.

The MIT team set out to study how the brain controls this vocalization process, using a mouse model. Mice communicate with each other using sounds known as ultrasonic vocalizations (USVs), which they produce using the unique whistling mechanism of exhaling air through a small hole between nearly closed vocal cords.

“We wanted to understand what are the neurons that control the vocal cord adduction, and then how do those neurons interact with the breathing circuit?” Wang says.

To figure that out, the researchers used a technique that allows them to map the synaptic connections between neurons. They knew that vocal cord adduction is controlled by laryngeal motor neurons, so they began by tracing backward to find the neurons that innervate those motor neurons.

This revealed that one major source of input is a group of premotor neurons found in the hindbrain region called the retroambiguus nucleus (RAm). Previous studies have shown that this area is involved in vocalization, but it wasn’t known exactly which part of the RAm was required or how it enabled sound production.

Image of green and magenta cells under a microscope.
Laryngeal premotor neurons (green) and Fos (magenta) labeling in the RAm. Image: Fan Wang

The researchers found that these synaptic tracing-labeled RAm neurons were strongly activated during USVs. This observation prompted the team to use an activity-dependent method to target these vocalization-specific RAm neurons, termed as RAmVOC. They used chemogenetics and optogenetics to explore what would happen if they silenced or stimulated their activity. When the researchers blocked the RAmVOC neurons, the mice were no longer able to produce USVs or any other kind of vocalization. Their vocal cords did not close, and their abdominal muscles did not contract, as they normally do during exhalation for vocalization.

Conversely, when the RAmVOC neurons were activated, the vocal cords closed, the mice exhaled, and USVs were produced. However, if the stimulation lasted two seconds or longer, these USVs would be interrupted by inhalations, suggesting that the process is under control of the same part of the brain that regulates breathing.

“Breathing is a survival need,” Wang says. “Even though these neurons are sufficient to elicit vocalization, they are under the control of breathing, which can override our optogenetic stimulation.”

Rhythm generation

Additional synaptic mapping revealed that neurons in a part of the brainstem called the pre-Bötzinger complex, which acts as a rhythm generator for inhalation, provide direct inhibitory input to the RAmVOC neurons.

“The pre-Bötzinger complex generates inhalation rhythms automatically and continuously, and the inhibitory neurons in that region project to these vocalization premotor neurons and essentially can shut them down,” Wang says.

This ensures that breathing remains dominant over speech production, and that we have to pause to breathe while speaking.

The researchers believe that although human speech production is more complex than mouse vocalization, the circuit they identified in mice plays the conserved role in speech production and breathing in humans.

“Even though the exact mechanism and complexity of vocalization in mice and humans is really different, the fundamental vocalization process, called phonation, which requires vocal cord closure and the exhalation of air, is shared in both the human and the mouse,” Park says.

The researchers now hope to study how other functions such as coughing and swallowing food may be affected by the brain circuits that control breathing and vocalization.

The research was funded by the National Institutes of Health.

School of Science announces 2024 Infinite Expansion Awards

Anne Trafton |

The MIT School of Science has announced nine postdocs and research scientists as recipients of the 2024 Infinite Expansion Award, which highlights extraordinary members of the MIT community.

The following are the 2024 School of Science Infinite Expansion winners:

  • Sarthak Chandra, a research scientist in the Department of Brain and Cognitive Sciences, was nominated by Professor Ila Fiete, who wrote, “He has expanded the research abilities of my group by being a versatile and brilliant scientist, by drawing connections with a different area that he was an expert in from his PhD training, and by being a highly involved and caring mentor.”
  • Michal Fux, a research scientist in the Department of Brain and Cognitive Sciences, was nominated by Professor Pawan Sinha, who wrote, “She is one of those figurative beams of light that not only brilliantly illuminate scientific questions, but also enliven a research team.”
  • Andrew Savinov, a postdoc in the Department of Biology, was nominated by Associate Professor Gene-Wei Li, who wrote, “Andrew is an extraordinarily creative and accomplished biophysicist, as well as an outstanding contributor to the broader MIT community.”
  • Ho Fung Cheng, a postdoc in the Department of Chemistry, was nominated by Professor Jeremiah Johnson, who wrote, “His impact on research and our departmental community during his time at MIT has been outstanding, and I believe that he will be a worldclass teacher and research group leader in his independent career next year.”
  • Gabi Wenzel, a postdoc in the Department of Chemistry, was nominated by Assistant Professor Brett McGuire, who wrote, “In the one year since Gabi joined our team, she has become an indispensable leader, demonstrating exceptional skill, innovation, and dedication in our challenging research environment.”
  • Yu-An Zhang, a postdoc in the Department of Chemistry, was nominated by Professor Alison Wendlandt, who wrote, “He is a creative, deep-thinking scientist and a superb organic chemist. But above all, he is an off-scale mentor and a cherished coworker.”
  • Wouter Van de Pontseele, a senior postdoc in the Laboratory for Nuclear Science, was nominated by Professor Joseph Formaggio, who wrote, “He is a talented scientist with an intense creativity, scholarship, and student mentorship record. In the time he has been with my group, he has led multiple facets of my experimental program and has been a wonderful citizen of the MIT community.”
  • Alexander Shvonski, a lecturer in the Department of Physics, was nominated by Assistant Professor Andrew Vanderburg, who wrote, “… I have been blown away by Alex’s knowledge of education research and best practices, his skills as a teacher and course content designer, and I have been extremely grateful for his assistance.”
  • David Stoppel, a research scientist in The Picower Institute for Learning and Memory, was nominated by Professor Mark Bear and his research group, who wrote, “As impressive as his research achievements might be, David’s most genuine qualification for this award is his incredible commitment to mentorship and the dissemination of knowledge.”

Winners are honored with a monetary award and will be celebrated with family, friends, and nominators at a later date, along with recipients of the Infinite Mile Award.

Imaging method reveals new cells and structures in human brain tissue

Anne Trafton |

Using a novel microscopy technique, MIT and Brigham and Women’s Hospital/Harvard Medical School researchers have imaged human brain tissue in greater detail than ever before, revealing cells and structures that were not previously visible.

McGovern Institute Investigator Edward Boyden. Photo: Justin Knight

Among their findings, the researchers discovered that some “low-grade” brain tumors contain more putative aggressive tumor cells than expected, suggesting that some of these tumors may be more aggressive than previously thought.

The researchers hope that this technique could eventually be deployed to diagnose tumors, generate more accurate prognoses, and help doctors choose treatments.

“We’re starting to see how important the interactions of neurons and synapses with the surrounding brain are to the growth and progression of tumors. A lot of those things we really couldn’t see with conventional tools, but now we have a tool to look at those tissues at the nanoscale and try to understand these interactions,” says Pablo Valdes, a former MIT postdoc who is now an assistant professor of neuroscience at the University of Texas Medical Branch and the lead author of the study.

Edward Boyden, the Y. Eva Tan Professor in Neurotechnology at MIT; a professor of biological engineering, media arts and sciences, and brain and cognitive sciences; a Howard Hughes Medical Institute investigator; and a member of MIT’s McGovern Institute for Brain Research and Koch Institute for Integrative Cancer Research; and E. Antonio Chiocca, a professor of neurosurgery at Harvard Medical School and chair of neurosurgery at Brigham and Women’s Hospital, are the senior authors of the study, which appears today in Science Translational Medicine.

Making molecules visible

The new imaging method is based on expansion microscopy, a technique developed in Boyden’s lab in 2015 based on a simple premise: Instead of using powerful, expensive microscopes to obtain high-resolution images, the researchers devised a way to expand the tissue itself, allowing it to be imaged at very high resolution with a regular light microscope.

The technique works by embedding the tissue into a polymer that swells when water is added, and then softening up and breaking apart the proteins that normally hold tissue together. Then, adding water swells the polymer, pulling all the proteins apart from each other. This tissue enlargement allows researchers to obtain images with a resolution of around 70 nanometers, which was previously possible only with very specialized and expensive microscopes such as scanning electron microscopes.

In 2017, the Boyden lab developed a way to expand preserved human tissue specimens, but the chemical reagents that they used also destroyed the proteins that the researchers were interested in labeling. By labeling the proteins with fluorescent antibodies before expansion, the proteins’ location and identity could be visualized after the expansion process was complete. However, the antibodies typically used for this kind of labeling can’t easily squeeze through densely packed tissue before it’s expanded.

So, for this study, the authors devised a different tissue-softening protocol that breaks up the tissue but preserves proteins in the sample. After the tissue is expanded, proteins can be labelled with commercially available fluorescent antibodies. The researchers then can perform several rounds of imaging, with three or four different proteins labeled in each round. This labeling of proteins enables many more structures to be imaged, because once the tissue is expanded, antibodies can squeeze through and label proteins they couldn’t previously reach.

The technique works by embedding the tissue into a polymer that swells when water is added, and then softening up and breaking apart the proteins that normally hold tissue together.

“We open up the space between the proteins so that we can get antibodies into crowded spaces that we couldn’t otherwise,” Valdes says. “We saw that we could expand the tissue, we could decrowd the proteins, and we could image many, many proteins in the same tissue by doing multiple rounds of staining.”

Working with MIT Assistant Professor Deblina Sarkar, the researchers demonstrated a form of this “decrowding” in 2022 using mouse tissue.

The new study resulted in a decrowding technique for use with human brain tissue samples that are used in clinical settings for pathological diagnosis and to guide treatment decisions. These samples can be more difficult to work with because they are usually embedded in paraffin and treated with other chemicals that need to be broken down before the tissue can be expanded.

In this study, the researchers labeled up to 16 different molecules per tissue sample. The molecules they targeted include markers for a variety of structures, including axons and synapses, as well as markers that identify cell types such as astrocytes and cells that form blood vessels. They also labeled molecules linked to tumor aggressiveness and neurodegeneration.

Using this approach, the researchers analyzed healthy brain tissue, along with samples from patients with two types of glioma — high-grade glioblastoma, which is the most aggressive primary brain tumor, with a poor prognosis, and low-grade gliomas, which are considered less aggressive.

“We wanted to look at brain tumors so that we can understand them better at the nanoscale level, and by doing that, to be able to develop better treatments and diagnoses in the future. At this point, it was more developing a tool to be able to understand them better, because currently in neuro-oncology, people haven’t done much in terms of super-resolution imaging,” Valdes says.

A diagnostic tool

To identify aggressive tumor cells in gliomas they studied, the researchers labeled vimentin, a protein that is found in highly aggressive glioblastomas. To their surprise, they found many more vimentin-expressing tumor cells in low-grade gliomas than had been seen using any other method.

“This tells us something about the biology of these tumors, specifically, how some of them probably have a more aggressive nature than you would suspect by doing standard staining techniques,” Valdes says.

When glioma patients undergo surgery, tumor samples are preserved and analyzed using immunohistochemistry staining, which can reveal certain markers of aggressiveness, including some of the markers analyzed in this study.

“These are incurable brain cancers, and this type of discovery will allow us to figure out which cancer molecules to target so we can design better treatments. It also proves the profound impact of having clinicians like us at the Brigham and Women’s interacting with basic scientists such as Ed Boyden at MIT to discover new technologies that can improve patient lives,” Chiocca says.

The researchers hope their expansion microscopy technique could allow doctors to learn much more about patients’ tumors, helping them to determine how aggressive the tumor is and guiding treatment choices. Valdes now plans to do a larger study of tumor types to try to establish diagnostic guidelines based on the tumor traits that can be revealed using this technique.

“Our hope is that this is going to be a diagnostic tool to pick up marker cells, interactions, and so on, that we couldn’t before,” he says. “It’s a practical tool that will help the clinical world of neuro-oncology and neuropathology look at neurological diseases at the nanoscale like never before, because fundamentally it’s a very simple tool to use.”

Boyden’s lab also plans to use this technique to study other aspects of brain function, in healthy and diseased tissue.

“Being able to do nanoimaging is important because biology is about nanoscale things — genes, gene products, biomolecules — and they interact over nanoscale distances,” Boyden says. “We can study all sorts of nanoscale interactions, including synaptic changes, immune interactions, and changes that occur during cancer and aging.”

The research was funded by K. Lisa Yang, the Howard Hughes Medical Institute, John Doerr, Open Philanthropy, the Bill and Melinda Gates Foundation, the Koch Institute Frontier Research Program, the National Institutes of Health, and the Neurosurgery Research and Education Foundation.

Simons Center’s collaborative approach propels autism research, at MIT and beyond

by David Orenstein |

The secret to the success of MIT’s Simons Center for the Social Brain is in the name. With a founding philosophy of “collaboration and community” that has supported scores of scientists across more than a dozen Boston-area research institutions, the SCSB advances research by being inherently social.

SCSB’s mission is “to understand the neural mechanisms underlying social cognition and behavior and to translate this knowledge into better diagnosis and treatment of autism spectrum disorders.” When Director Mriganka Sur founded the center in 2012 in partnership with the Simons Foundation Autism Research Initiative (SFARI) of Jim and Marilyn Simons, he envisioned a different way to achieve urgently needed research progress than the traditional approach of funding isolated projects in individual labs. Sur wanted SCSB’s contribution to go beyond papers, though it has generated about 350 and counting. He sought the creation of a sustained, engaged autism research community at MIT and beyond.

“When you have a really big problem that spans so many issues — a clinical presentation, a gene, and everything in between — you have to grapple with multiple scales of inquiry,” says Sur, the Newton Professor of Neuroscience in MIT’s Department of Brain and Cognitive Sciences (BCS) and The Picower Institute for Learning and Memory. “This cannot be solved by one person or one lab. We need to span multiple labs and multiple ways of thinking. That was our vision.”

In parallel with a rich calendar of public colloquia, lunches, and special events, SCSB catalyzes multiperspective, multiscale research collaborations in two programmatic ways. Targeted projects fund multidisciplinary teams of scientists with complementary expertise to collectively tackle a pressing scientific question. Meanwhile, the center supports postdoctoral Simons Fellows with not one, but two mentors, ensuring a further cross-pollination of ideas and methods.

Complementary collaboration

In 11 years, SCSB has funded nine targeted projects. Each one, by design, involves a deep and multifaceted exploration of a major question with both fundamental importance and clinical relevance. The first project, back in 2013, for example, marshaled three labs spanning BCS, the Department of Biology, and The Whitehead Institute for Biomedical Research to advance understanding of how mutation of the Shank3 gene leads to the pathophysiology of Phelan-McDermid Syndrome by working across scales ranging from individual neural connections to whole neurons to circuits and behavior.

Other past projects have applied similarly integrated, multiscale approaches to topics ranging from how 16p11.2 gene deletion alters the development of brain circuits and cognition to the critical role of the thalamic reticular nucleus in information flow during sleep and wakefulness. Two others produced deep examinations of cognitive functions: how we go from hearing a string of words to understanding a sentence’s intended meaning, and the neural and behavioral correlates of deficits in making predictions about social and sensory stimuli. Yet another project laid the groundwork for developing a new animal model for autism research.

SFARI is especially excited by SCSB’s team science approach, says Kelsey Martin, executive vice president of autism and neuroscience at the Simons Foundation. “I’m delighted by the collaborative spirit of the SCSB,” Martin says. “It’s wonderful to see and learn about the multidisciplinary team-centered collaborations sponsored by the center.”

New projects

In the last year, SCSB has launched three new targeted projects. One team is investigating why many people with autism experience sensory overload and is testing potential interventions to help. The scientists hypothesize that patients experience a deficit in filtering out the mundane stimuli that neurotypical people predict are safe to ignore. Studies suggest the predictive filter relies on relatively low-frequency “alpha/beta” brain rhythms from deep layers of the cortex moderating the higher frequency “gamma” rhythms in superficial layers that process sensory information.

Together, the labs of Charles Nelson, professor of pediatrics at Boston Children’s Hospital (BCH), and BCS faculty members Bob Desimone, the Doris and Don Berkey Professor of Neuroscience at MIT and director of the McGovern Institute, and Earl K. Miller, the Picower Professor, are testing the hypothesis in two different animal models at MIT and in human volunteers at BCH. In the animals they’ll also try out a new real-time feedback system invented in Miller’s lab that can potentially correct the balance of these rhythms in the brain. And in an animal model engineered with a Shank3 mutation, Desimone’s lab will test a gene therapy, too.

“None of us could do all aspects of this project on our own,” says Miller, an investigator in the Picower Institute. “It could only come about because the three of us are working together, using different approaches.”

Right from the start, Desimone says, close collaboration with Nelson’s group at BCH has been essential. To ensure his and Miller’s measurements in the animals and Nelson’s measurements in the humans are as comparable as possible, they have tightly coordinated their research protocols.

“If we hadn’t had this joint grant we would have chosen a completely different, random set of parameters than Chuck, and the results therefore wouldn’t have been comparable. It would be hard to relate them,” says Desimone, who also directs MIT’s McGovern Institute for Brain Research. “This is a project that could not be accomplished by one lab operating in isolation.”

Another targeted project brings together a coalition of seven labs — six based in BCS (professors Evelina Fedorenko, Edward Gibson, Nancy Kanwisher, Roger Levy, Rebecca Saxe, and Joshua Tenenbaum) and one at Dartmouth College (Caroline Robertson) — for a synergistic study of the cognitive, neural, and computational underpinnings of conversational exchanges. The study will integrate the linguistic and non-linguistic aspects of conversational ability in neurotypical adults and children and those with autism.

Fedorenko said the project builds on advances and collaborations from the earlier language Targeted Project she led with Kanwisher.

“Many directions that we started to pursue continue to be active directions in our labs. But most importantly, it was really fun and allowed the PIs [principal investigators] to interact much more than we normally would and to explore exciting interdisciplinary questions,” Fedorenko says. “When Mriganka approached me a few years after the project’s completion asking about a possible new targeted project, I jumped at the opportunity.”

Gibson and Robertson are studying how people align their dialogue, not only in the content and form of their utterances, but using eye contact. Fedorenko and Kanwisher will employ fMRI to discover key components of a conversation network in the cortex. Saxe will examine the development of conversational ability in toddlers using novel MRI techniques. Levy and Tenenbaum will complement these efforts to improve computational models of language processing and conversation.

The newest Targeted Project posits that the immune system can be harnessed to help treat behavioral symptoms of autism. Four labs — three in BCS and one at Harvard Medical School (HMS) — will study mechanisms by which peripheral immune cells can deliver a potentially therapeutic cytokine to the brain. A study by two of the collaborators, MIT associate professor Gloria Choi and HMS associate professor Jun Huh, showed that when IL-17a reaches excitatory neurons in a region of the mouse cortex, it can calm hyperactivity in circuits associated with social and repetitive behavior symptoms. Huh, an immunologist, will examine how IL-17a can get from the periphery to the brain, while Choi will examine how it has its neurological effects. Sur and MIT associate professor Myriam Heiman will conduct studies of cell types that bridge neural circuits with brain circulatory systems.

“It is quite amazing that we have a core of scientists working on very different things coming together to tackle this one common goal,” Choi says. “I really value that.”

Multiple mentors

While SCSB Targeted Projects unify labs around research, the center’s Simons Fellowships unify labs around young researchers, providing not only funding, but a pair of mentors and free-flowing interactions between their labs. Fellows also gain opportunities to inform and inspire their fundamental research by visiting with patients with autism, Sur says.

“The SCSB postdoctoral program serves a critical role in ensuring that a diversity of outstanding scientists are exposed to autism research during their training, providing a pipeline of new talent and creativity for the field,” adds Martin, of the Simons Foundation.

Simons Fellows praise the extra opportunities afforded by additional mentoring. Postdoc Alex Major was a Simons Fellow in Miller’s lab and that of Nancy Kopell, a mathematics professor at Boston University renowned for her modeling of the brain wave phenomena that the Miller lab studies experimentally.

“The dual mentorship structure is a very useful aspect of the fellowship” Major says. “It is both a chance to network with another PI and provides experience in a different neuroscience sub-field.”

Miller says co-mentoring expands the horizons and capabilities of not only the mentees but also the mentors and their labs. “Collaboration is 21st century neuroscience,” Miller says. “Some our studies of the brain have gotten too big and comprehensive to be encapsulated in just one laboratory. Some of these big questions require multiple approaches and multiple techniques.”

Desimone, who recently co-mentored Seng Bum (Michael Yoo) along with BCS and McGovern colleague Mehrdad Jazayeri in a project studying how animals learn from observing others, agrees.

“We hear from postdocs all the time that they wish they had two mentors, just in general to get another point of view,” Desimone says. “This is a really good thing and it’s a way for faculty members to learn about what other faculty members and their postdocs are doing.”

Indeed, the Simons Center model suggests that research can be very successful when it’s collaborative and social.