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Aging Brain Initiative awards fund five new ideas to study, fight neurodegeneration

Anne Trafton |

Neurodegenerative diseases are defined by an increasingly widespread and debilitating death of nervous system cells, but they also share other grim characteristics: Their cause is rarely discernible and they have all eluded cures. To spur fresh, promising approaches and to encourage new experts and expertise to join the field, MIT’s Aging Brain Initiative (ABI) this month awarded five seed grants after a competition among labs across the Institute.

Founded in 2015 by nine MIT faculty members, the ABI promotes research, symposia, and related activities to advance fundamental insights that can lead to clinical progress against neurodegenerative conditions, such as Alzheimer’s disease, with an age-related onset. With an emphasis on spurring research at an early stage before it is established enough to earn more traditional funding, the ABI derives support from philanthropic gifts.

“Solving the mysteries of how health declines in the aging brain and turning that knowledge into effective tools, treatments, and technologies is of the utmost urgency given the millions of people around the world who suffer with no meaningful treatment options,” says ABI director and co-founder Li-Huei Tsai, the Picower Professor of Neuroscience in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences. “We were very pleased that many groups across MIT were eager to contribute their expertise and creativity to that goal. From here, five teams will be able to begin testing their innovative ideas and the impact they could have.”

To address the clinical challenge of accurately assessing cognitive decline during Alzheimer’s disease progression and healthy aging, a team led by Thomas Heldt, associate professor of electrical and biomedical engineering in the Department of Electrical Engineering and Computer Science (EECS) and the Institute for Medical Engineering and Science, proposes to use artificial intelligence tools to bring diagnostics based on eye movements during cognitive tasks to everyday consumer electronics such as smartphones and tablets. By moving these capabilities to common at-home platforms, the team, which also includes EECS Associate Professor Vivian Sze, hopes to increase monitoring beyond what can only be intermittently achieved with high-end specialized equipment and dedicated staffing in specialists’ offices. The team will pilot their technology in a small study at Boston Medical Center in collaboration with neurosurgeon James Holsapple.

Institute Professor Ann Graybiel’s lab in the Department of Brain and Cognitive Sciences (BCS) and the McGovern Institute for Brain Research will test the hypothesis that mutations on a specific gene may lead to the early emergence of Alzheimer’s disease (AD) pathology in the striatum. That’s a a brain region crucial for motivation and movement that is directly and severely impacted by other neurodegenerative disorders including Parkinson’s and Huntington’s diseases, but that has largely been unstudied in Alzheimer’s. By editing the mutations into normal and AD-modeling mice, Research Scientist Ayano Matsushima and Graybiel hope to determine whether and how pathology, such as the accumulation of amyloid proteins, may result. Determining that could provide new insight into the progression of disease and introduce a new biomarker in a region that virtually all other studies have overlooked.

Numerous recent studies have highlighted a potential role for immune inflammation in Alzheimer’s disease. A team led by Gloria Choi, the Mark Hyman Jr. Associate Professor in BCS and The Picower Institute for Learning and Memory, will track one potential source of such activity by determining whether the brain’s meninges, which envelop the brain, becomes a means for immune cells activated by gut bacteria to circulate near the brain, where they may release signaling molecules that promote Alzheimer’s pathology. Working in mice, Choi’s lab will test whether such activity is prone to increase in Alzheimer’s and whether it contributes to disease.

A collaboration led by Peter Dedon, the Singapore Professor in MIT’s Department of Biological Engineering, will explore whether Alzheimer’s pathology is driven by dysregulation of transfer RNAs (tRNAs) and the dozens of natural tRNA modifications in the epitranscriptome, which play a key role in the process by which proteins are assembled based on genetic instructions. With Benjamin Wolozin of Boston University, Sherif Rashad of Tohoku University in Japan, and Thomas Begley of the State University of New York at Albany, Dedon will assess how the tRNA pool and epitranscriptome may differ in Alzheimer’s model mice and whether genetic instructions mistranslated because of tRNA dysregulation play a role in Alzheimer’s disease.

With her seed grant, Ritu Raman, the d’Arbeloff Assistant Professor of Mechanical Engineering, is launching an investigation of possible disruption of intercellular messages in amyotrophic lateral sclerosis (ALS), a terminal condition in which motor neuron causes loss of muscle control. Equipped with a new tool to finely sample interstitial fluid within tissues, Raman’s team will be able to monitor and compare cell-cell signaling in models of the junction between nerve and muscle. These models will be engineered from stem cells derived from patients with ALS. By studying biochemical signaling at the junction the lab hopes to discover new targets that could be therapeutically modified.

Major support for the seed grants, which provide each lab with $100,000, came from generous gifts by David Emmes SM ’76; Kathleen SM ’77, PhD ’86 and Miguel Octavio; the Estate of Margaret A. Ridge-Pappis, wife of the late James Pappis ScD ’59; the Marc Haas Foundation; and the family of former MIT President Paul Gray ’54, SM ’55, ScD ‘60, with additional funding from many annual fund donors to the Aging Brain Initiative Fund.

What words can convey

by Jennifer Michalowski |

From search engines to voice assistants, computers are getting better at understanding what we mean. That’s thanks to language processing programs that make sense of a staggering number of words, without ever being told explicitly what those words mean. Such programs infer meaning instead through statistics—and a new study reveals that this computational approach can assign many kinds of information to a single word, just like the human brain.

The study, published April 14, 2022, in the journal Nature Human Behavior, was co-led by Gabriel Grand, a graduate student at MIT’s Computer Science and Artificial Intelligence Laboratory, and Idan Blank, an assistant professor at the University of California, Los Angeles, and supervised by McGovern Investigator Ev Fedorenko, a cognitive neuroscientist who studies how the human brain uses and understands language, and Francisco Pereira at the National Institute of Mental Health. Fedorenko says the rich knowledge her team was able to find within computational language models demonstrates just how much can be learned about the world through language alone.

Early language models

The research team began its analysis of statistics-based language processing models in 2015, when the approach was new. Such models derive meaning by analyzing how often pairs of words co-occur in texts and using those relationships to assess the similarities of words’ meanings. For example, such a program might conclude that “bread” and “apple” are more similar to one another than they are to “notebook,” because “bread” and “apple” are often found in proximity to words like “eat” or “snack,” whereas “notebook” is not.

The models were clearly good at measuring words’ overall similarity to one another. But most words carry many kinds of information, and their similarities depend on which qualities are being evaluated. “Humans can come up with all these different mental scales to help organize their understanding of words,” explains Grand, a former undergraduate researcher in the Fedorenko lab. For examples, he says, “dolphins and alligators might be similar in size, but one is much more dangerous than the other.”

Grand and Idan Blank, who was then a graduate student at the McGovern Institute, wanted to know whether the models captured that same nuance. And if they did, how was the information organized?

To learn how the information in such a model stacked up to humans’ understanding of words, the team first asked human volunteers to score words along many different scales: Were the concepts those words conveyed big or small, safe or dangerous, wet or dry? Then, having mapped where people position different words along these scales, they looked to see whether language processing models did the same.

Grand explains that distributional semantic models use co-occurrence statistics to organize words into a huge, multidimensional matrix. The more similar words are to one another, the closer they are within that space. The dimensions of the space are vast, and there is no inherent meaning built into its structure. “In these word embeddings, there are hundreds of dimensions, and we have no idea what any dimension means,” he says. “We’re really trying to peer into this black box and say, ‘is there structure in here?’”

Word-vectors in the category ‘animals’ (blue circles) are orthogonally projected (light-blue lines) onto the feature subspace for ‘size’ (red line), defined as the vector difference between large−→−− and small−→−− (red circles). The three dimensions in this figure are arbitrary and were chosen via principal component analysis to enhance visualization (the original GloVe word embedding has 300 dimensions, and projection happens in that space). Image: Fedorenko lab

Specifically, they asked whether the semantic scales they had asked their volunteers use were represented in the model. So they looked to see where words in the space lined up along vectors defined by the extremes of those scales. Where did dolphins and tigers fall on line from “big” to “small,” for example? And were they closer together along that line than they were on a line representing danger (“safe” to “dangerous”)?

Across more than 50 sets of world categories and semantic scales, they found that the model had organized words very much like the human volunteers. Dolphins and tigers were judged to be similar in terms of size, but far apart on scales measuring danger or wetness. The model had organized the words in a way that represented many kinds of meaning—and it had done so based entirely on the words’ co-occurrences.

That, Fedorenko says, tells us something about the power of language. “The fact that we can recover so much of this rich semantic information from just these simple word co-occurrence statistics suggests that this is one very powerful source of learning about things that you may not even have direct perceptual experience with.”

Three from MIT awarded 2022 Paul and Daisy Soros Fellowships for New Americans

by Julia Mongo | Office of Distinguished Fellowships |

MIT graduate student Fernanda De La Torre, alumna Trang Luu ’18, SM ’20, and senior Syamantak Payra are recipients of the 2022 Paul and Daisy Soros Fellowships for New Americans.

De La Torre, Luu, and Payra are among 30 New Americans selected from a pool of over 1,800 applicants. The fellowship honors the contributions of immigrants and children of immigrants by providing $90,000 in funding for graduate school.

Students interested in applying to the P.D. Soros Fellowship for future years may contact Kim Benard, associate dean of distinguished fellowships in Career Advising and Professional Development.

Fernanda De La Torre

Fernanda De La Torre is a PhD student in the Department of Brain and Cognitive Sciences. With Professor Josh McDermott, she studies how we integrate vision and sound, and with Professor Robert Yang, she develops computational models of imagination.

De La Torre spent her early childhood with her younger sister and grandmother in Guadalajara, Mexico. At age 12, she crossed the Mexican border to reunite with her mother in Kansas City, Missouri. Shortly after, an abusive home environment forced De La Torre to leave her family and support herself throughout her early teens.

Despite her difficult circumstances, De La Torre excelled academically in high school. By winning various scholarships that would discretely take applications from undocumented students, she was able to continue her studies in computer science and mathematics at Kansas State University. There, she became intrigued by the mysteries of the human mind. During college, De La Torre received invaluable mentorship from her former high school principal, Thomas Herrera, who helped her become documented through the Violence Against Women Act. Her college professor, William Hsu, supported her interests in artificial intelligence and encouraged her to pursue a scientific career.

After her undergraduate studies, De La Torre won a post-baccalaureate fellowship from the Department of Brain and Cognitive Sciences at MIT, where she worked with Professor Tomaso Poggio on the theory of deep learning. She then transitioned into the department’s PhD program. Beyond contributing to scientific knowledge, De La Torre plans to use science to create spaces where all people, including those from backgrounds like her own, can innovate and thrive.

She says: “Immigrants face many obstacles, but overcoming them gives us a unique strength: We learn to become resilient, while relying on friends and mentors. These experiences foster both the desire and the ability to pay it forward to our community.”

Trang Luu

Trang Luu graduated from MIT with a BS in mechanical engineering in 2018, and a master of engineering degree in 2020. Her Soros award will support her graduate studies at Harvard University in the MBA/MS engineering sciences program.

Born in Saigon, Vietnam, Luu was 3 when her family immigrated to Houston, Texas. Watching her parents’ efforts to make a living in a land where they did not understand the culture or speak the language well, Luu wanted to alleviate hardship for her family. She took full responsibility for her education and found mentors to help her navigate the American education system. At home, she assisted her family in making and repairing household items, which fueled her excitement for engineering.

As an MIT undergraduate, Luu focused on assistive technology projects, applying her engineering background to solve problems impeding daily living. These projects included a new adaptive socket liner for below-the-knee amputees in Kenya, Ethiopia, and Thailand; a walking stick adapter for wheelchairs; a computer head pointer for patients with limited arm mobility, a safer makeshift cook stove design for street vendors in South Africa; and a quicker method to test new drip irrigation designs. As a graduate student in MIT D-Lab under the direction of Professor Daniel Frey, Luu was awarded a National Science Foundation Graduate Research Fellowship. In her graduate studies, Luu researched methods to improve evaporative cooling devices for off-grid farmers to reduce rapid fruit and vegetable deterioration.

These projects strengthened Luu’s commitment to innovating new technology and devices for people struggling with basic daily tasks. During her senior year, Luu collaborated on developing a working prototype of a wearable device that noninvasively reduces hand tremors associated with Parkinson’s disease or essential tremor. Observing patients’ joy after their tremors stopped compelled Luu and three co-founders to continue developing the device after college. Four years later, Encora Therapeutics has accomplished major milestones, including Breakthrough Device designation by the U.S. Food and Drug Administration.

Syamantak Payra

Hailing from Houston, Texas, Syamantak Payra is a senior majoring in electrical engineering and computer science, with minors in public policy and entrepreneurship and innovation. He will be pursuing a PhD in engineering at Stanford University, with the goal of creating new biomedical devices that can help improve daily life for patients worldwide and enhance health care outcomes for decades to come.

Payra’s parents had emigrated from India, and he grew up immersed in his grandparents’ rich Bengali culture. As a high school student, he conducted projects with NASA engineers at Johnson Space Center, experimented at home with his scientist parents, and competed in spelling bees and science fairs across the United States. Through these avenues and activities, Syamantak not only gained perspectives on bridging gaps between people, but also found passions for language, scientific discovery, and teaching others.

After watching his grandmother struggle with asthma and chronic obstructive pulmonary disease and losing his baby brother to brain cancer, Payra devoted himself to trying to use technology to solve health-care challenges. Payra’s proudest accomplishments include building a robotic leg brace for his paralyzed teacher and conducting free literacy workshops and STEM outreach programs that reached nearly a thousand underprivileged students across the Greater Houston Area.

At MIT, Payra has worked in Professor Yoel Fink’s research laboratory, creating digital sensor fibers that have been woven into intelligent garments that can assist in diagnosing illnesses, and in Professor Joseph Paradiso’s research laboratory, where he contributed to next-generation spacesuit prototypes that better protect astronauts on spacewalks. Payra’s research has been published by multiple scientific journals, and he was inducted into the National Gallery of America’s Young Inventors.

Clinical trials bring first CRISPR-based therapies to patients

by Jennifer Michalowski |

Nearly ten years ago, Feng Zhang and other pioneering scientists developed CRISPR, a revolutionary technology that quickly became biologists’ preferred method of editing DNA. Biologists, computer scientists, and engineers in Zhang’s lab are continuing to explore natural CRISPR systems and expand researchers’ gene-editing toolkit. But for their long-term goal of using those tools to improve health, clinical collaboration is essential.

Clinical trials are rarely led by academic researchers; licensing agreements and partnerships with industry are usually essential to transform laboratory findings into advances that impact patients. Editas Medicine, a company co-founded by Zhang, aims to use CRISPR to correct disease-causing genetic errors inside patient cells—and two of Editas’s experimental CRISPR-based therapies have reached clinical trials.

One is a treatment for sickle cell anemia, a disorder in which a single genetic mutation disrupts the production of hemoglobin, creating misshapen red blood cells that can’t carry oxygen efficiently. With CRISPR, that mutation can be corrected in stem cells isolated from a patient’s blood. The CRISPR-modified cells are then returned to the patient, where they are expected to generate healthy red blood cells. The same strategy may also be effective for treating another inherited blood disorder, transfusion-dependent beta thalassemia.

Editas is pursuing a similar strategy to correct the mutation that causes Leber congenital amaurosis, an inherited form of blindness—but in that case, the CRISPR-based therapy is delivered directly to cells inside the body. The experimental treatment uses a viral vector to introduce CRISPR to the retina of the eye, where a gene mutation impairs the function of light-sensitive photoreceptors. Clinical trial participants received their first treatments in 2020, and in 2021, the company announced that some patients had experienced improvements to their vision.

Unexpected synergy

by Jennifer Michalowski |

This story originally appeared in the Spring 2022 issue of BrainScan.

***

Recent results from cognitive neuroscientist Nancy Kanwisher’s lab have left her pondering the role of music in human evolution. “Music is this big mystery,” she says. “Every human society that’s been studied has music. No other animals have music in the way that humans do. And nobody knows why humans have music at all. This has been a puzzle for centuries.”

MIT neuroscientist and McGovern Investigator Nancy Kanwisher. Photo: Jussi Puikkonen/KNAW

Some biologists and anthropologists have reasoned that since there’s no clear evolutionary advantage for humans’ unique ability to create and respond to music, these abilities must have emerged when humans began to repurpose other brain functions. To appreciate song, they’ve proposed, we draw on parts of the brain dedicated to speech and language. It makes sense, Kanwisher says: music and language are both complex, uniquely human ways of communicating. “It’s very sensible to think that there might be common machinery,” she says. “But there isn’t.”

That conclusion is based on her team’s 2015 discovery of neurons in the human brain that respond only to music. They first became clued in to these music-sensitive cells when they asked volunteers to listen to a diverse panel of sounds inside an MRI scanner. Functional brain imaging picked up signals suggesting that some neurons were specialized to detect only music — but the broad map of brain activity generated by an fMRI couldn’t pinpoint those cells.

Singing in the brain

Kanwisher’s team wanted to know more — but neuroscientists who study the human brain can’t always probe its circuitry with the exactitude of their colleagues who study the brains of mice or rats. They can’t insert electrodes into human brains to monitor the neurons they’re interested in. Neurosurgeons, however, sometimes do — and thus, collaborating with neurosurgeons has created unique opportunities for Kanwisher and other McGovern investigators to learn about the human brain.

Kanwisher’s team collaborated with clinicians at Albany Medical Center to work with patients who are undergoing monitoring prior to surgical treatment for epilepsy. Before operating, a neurosurgeon must identify the spot in their patient’s brain that is triggering seizures. This means inserting electrodes into the brain to monitor specific areas over a few days or weeks. The electrodes they implant pinpoint activity far more precisely, both spatially and temporally, than an MRI. And with patients’ permission, researchers like Kanwisher can take advantage of the information they collect.

“The intracranial recording from human brains that’s possible from collaboration with neurosurgeons is extremely precious to us,” Kanwisher says. “All of the research is kind of opportunistic, on whatever the surgeons are doing for clinical reasons. But sometimes we get really lucky and the electrodes are right in an area where we have long-standing scientific questions that those data can answer.”

Song-selective neural population (yellow) in the “inflated” human brain. Image: Sam Norman-Haignere

The unexpected discovery of song-specific neurons, led by postdoctoral researcher Sam Norman-Haignere, who is now an assistant professor at the University of Rochester Medical Center, emerged from such a collaboration. The team worked with patients at Albany Medical Center whose presurgical monitoring encompassed the auditory-processing part of the brain that they were curious about. Sure enough, certain electrodes picked up activity only when patients were listening to music. The data indicated that in some of those locations, it didn’t matter what kind of music was playing: the cells fired in response to a range of sounds that included flute solos, heavy metal, and rap. But other locations became active exclusively in response to vocal music. “We did not have that hypothesis at all, Kanwisher says. “It reallytook our breath away,” she says.

When that discovery is considered along with findings from McGovern colleague Ev Fedorenko, who has shown that the brain’s language-processing regions do not respond to music, Kanwisher says it’s now clear that music and language are segregated in the human brain. The origins of our unique appreciation for music, however, remain a mystery.

Clinical advantage

Clinical collaborations are also important to researchers in Ann Graybiel’s lab, who rely largely on model organisms like mice and rats to investigate the fine details of neural circuits. Working with clinicians helps keep them focused on answering questions that matter to patients.

In studying how the brain makes decisions, the Graybiel lab has zeroed in on connections that are vital for making choices that carry both positive and negative consequences. This is the kind of decision-making that you might call on when considering whether to accept a job that pays more but will be more demanding than your current position, for example. In experiments with rats, mice, and monkeys, they’ve identified different neurons dedicated to triggering opposing actions — “approach” or “avoid” — in these complex decision-making tasks. They’ve also found evidence that both age and stress change how the brain deals with these kinds of decisions.

In work led by former Graybiel lab research scientist Ken-ichi Amemori, they have worked with psychiatrist Diego Pizzagalli at McLean Hospital to learn what happens in the human brain when people make these complex decisions.

By monitoring brain activity as people made decisions inside an MRI scanner, the team identified regions that lit up when people chose to “approach” or “avoid.” They also found parallel activity patterns in monkeys that performed the same task, supporting the relevance of animal studies to understanding this circuitry.

In people diagnosed with major depression, however, the brain responded to approach-avoidance conflict somewhat differently. Certain areas were not activated as strongly as they were in people without depression, regardless of whether subjects ultimately chose to “approach” or “avoid.” The team suspects that some of these differences might reflect a stronger tendency toward avoidance, in which potential rewards are less influential for decision-making, while an individual is experiencing major depression.

The brain activity associated with approach-avoidance conflict in humans appears to align with what Graybiel’s team has seen in mice, although clinical imaging cannot reveal nearly as much detail about the involved circuits. Graybiel says that gives her confidence that what they are learning in the lab, where they can manipulate and study neural circuits with precision, is important. “I think there’s no doubt that this is relevant to humans,” she says. “I want to get as far into the mechanisms as possible, because maybe we’ll hit something that’s therapeutically valuable, or maybe we will really get an intuition about how parts of the brain work. I think that will help people.”

Developing brain needs cannabinoid receptors after birth

by Jennifer Michalowski |

Doctors warn that marijuana use during pregnancy may have harmful effects on the development of a fetus, in part because the cannabinoid receptors activated by the drug are known be critical for enabling a developing brain to wire up properly. Now, scientists at MIT’s McGovern Institute have learned that cannabinoid receptors’ critical role in brain development does not end at birth.

In today’s online issue of the journal eNeuro, scientists led by McGovern investigator Ann Graybiel report that mice need the cannabinoid receptor CB1R to establish connections within the brain’s dopamine system that take shape soon after birth. The finding raises concern that marijuana use by nursing moms, who pass the CB1R-activating compound THC to their infants when they breastfeed, might interfere with brain development by disrupting cannabinoid signaling.

“This is a real change to one of the truly important systems in the brain—a major controller of our dopamine,” Graybiel says. Dopamine exerts a powerful influence over our motivations and behavior, and changes to the dopamine system contribute to disorders from Parkinson’s disease to addiction. Thus, the researchers say, it is vital to understand whether postnatal drug exposure might put developing dopamine circuits at risk.

Brain bouquets

Cannabinoid receptors in the brain are important mediators of mood, memory, and pain. Graybiel’s lab became interested in CB1R due to their dysregulation in Huntington’s and Parkinson’s diseases, both of which impair the brain’s ability to control movement and other functions. While investigating the receptor’s distribution in the brain, they discovered that in the adult mice, CB1R is abundant within small compartments within the striatum called striosomes. The receptor was particularly concentrated within the neurons that connect striosomes to a dopamine-rich area of the brain called the substantia nigra, via structures that Graybiel’s team has dubbed striosome-dendron bouquets.

Striosome-dendron bouquets are easy to overlook within the densely connected network of the brain. But when the cells that make up the bouquets are labeled with a fluorescent protein, the bouquets become visible—and their appearance is striking, says Jill Crittenden, a research scientist in Graybiel’s lab.

Striosomal neurons form these bouquets by reaching into the substantia nigra, whose cells use dopamine to influence movement, motivation, learning, and habit formation. Clusters of dopamine-producing neurons form dendrites there that intertwine tightly with incoming axons from the striosomal neurons. The resulting structures, whose intimately associated cells resemble the bundled stems of a floral bouquet, establish so many connections that they give striosomal neurons potent control over dopamine signaling.

By tracking the bouquets’ emergence in newborn mice, Graybiel’s team found that they form in the first week after birth, a period during which striosomal neurons are ramping up production of CB1R. Mice genetically engineered to lack CB1R, however, can’t make these elaborate but orderly bouquets. Without the receptor, fibers from striosomes extend into the substantia nigra, but fail to form the tightly intertwined “bouquet stems” that facilitate extensive connections with their targets. This disorganized structure is apparent as soon as bouquets arise in the brains of young pups and persists into adulthood. “There aren’t those beautiful, strong fibers anymore,” Crittenden says. “This suggests that those very strong controllers over the dopamine system function abnormally when you interfere with cannabinoid signaling.”

The finding was a surprise. Without zeroing in on striosome-dendron bouquets, it would be easy to miss CB1R’s impact on the dopamine system, Crittenden says. Plus, she adds, prior studies of the receptor’s role in development largely focused on fetal development. The new findings reveal that the cannabinoid system continues to guide the formation of brain circuits after birth.

Graybiel notes that funds from generous donors, including the Broderick Fund for Phytocannabinoid Research at MIT, the Saks Kavanaugh Foundation, the Kristin R. Pressman and Jessica J. Pourian ‘13 Fund, Mr. Robert Buxton, and the William N. & Bernice E. Bumpus Foundation, enabled her team’s studies of CB1R’s role in shaping striosome-dendron bouquets.

Now that they have shown that CB1R is needed for postnatal brain development, it will be important to determine the consequences of disrupting cannabinoid signaling during this critical period—including whether passing THC to a nursing baby impacts the brain’s dopamine system.

Study finds neurons that encode the outcomes of actions

Anne Trafton |

When we make complex decisions, we have to take many factors into account. Some choices have a high payoff but carry potential risks; others are lower risk but may have a lower reward associated with them.

A new study from MIT sheds light on the part of the brain that helps us make these types of decisions. The research team found a group of neurons in the brain’s striatum that encodes information about the potential outcomes of different decisions. These cells become particularly active when a behavior leads a different outcome than what was expected, which the researchers believe helps the brain adapt to changing circumstances.

“A lot of this brain activity deals with surprising outcomes, because if an outcome is expected, there’s really nothing to be learned. What we see is that there’s a strong encoding of both unexpected rewards and unexpected negative outcomes,” says Bernard Bloem, a former MIT postdoc and one of the lead authors of the new study.

Impairments in this kind of decision-making are a hallmark of many neuropsychiatric disorders, especially anxiety and depression. The new findings suggest that slight disturbances in the activity of these striatal neurons could swing the brain into making impulsive decisions or becoming paralyzed with indecision, the researchers say.

Rafiq Huda, a former MIT postdoc, is also a lead author of the paper, which appears in Nature Communications. Ann Graybiel, an MIT Institute Professor and member of MIT’s McGovern Institute for Brain Research, is the senior author of the study.

Learning from experience

The striatum, located deep within the brain, is known to play a key role in making decisions that require evaluating outcomes of a particular action. In this study, the researchers wanted to learn more about the neural basis of how the brain makes cost-benefit decisions, in which a behavior can have a mixture of positive and negative outcomes.

Striosomes (red) appear and then disappear as the view moves deeper into the striatum. Video courtesy of the researchers

To study this kind of decision-making, the researchers trained mice to spin a wheel to the left or the right. With each turn, they would receive a combination of reward (sugary water) and negative outcome (a small puff of air). As the mice performed the task, they learned to maximize the delivery of rewards and to minimize the delivery of air puffs. However, over hundreds of trials, the researchers frequently changed the probabilities of getting the reward or the puff of air, so the mice would need to adjust their behavior.

As the mice learned to make these adjustments, the researchers recorded the activity of neurons in the striatum. They had expected to find neuronal activity that reflects which actions are good and need to be repeated, or bad and that need to be avoided. While some neurons did this, the researchers also found, to their surprise, that many neurons encoded details about the relationship between the actions and both types of outcomes.

The researchers found that these neurons responded more strongly when a behavior resulted in an unexpected outcome, that is, when turning the wheel in one direction produced the opposite outcome as it had in previous trials. These “error signals” for reward and penalty seem to help the brain figure out that it’s time to change tactics.

Most of the neurons that encode these error signals are found in the striosomes — clusters of neurons located in the striatum. Previous work has shown that striosomes send information to many other parts of the brain, including dopamine-producing regions and regions involved in planning movement.

“The striosomes seem to mostly keep track of what the actual outcomes are,” Bloem says. “The decision whether to do an action or not, which essentially requires integrating multiple outcomes, probably happens somewhere downstream in the brain.”

Making judgments

The findings could be relevant not only to mice learning a task, but also to many decisions that people have to make every day as they weigh the risks and benefits of each choice. Eating a big bowl of ice cream after dinner leads to immediate gratification, but it might contribute to weight gain or poor health. Deciding to have carrots instead will make you feel healthier, but you’ll miss out on the enjoyment of the sweet treat.

“From a value perspective, these can be considered equally good,” Bloem says. “What we find is that the striatum also knows why these are good, and it knows what are the benefits and the cost of each. In a way, the activity there reflects much more about the potential outcome than just how likely you are to choose it.”

This type of complex decision-making is often impaired in people with a variety of neuropsychiatric disorders, including anxiety, depression, schizophrenia, obsessive-compulsive disorder, and posttraumatic stress disorder. Drug abuse can also lead to impaired judgment and impulsivity.

“You can imagine that if things are set up this way, it wouldn’t be all that difficult to get mixed up about what is good and what is bad, because there are some neurons that fire when an outcome is good and they also fire when the outcome is bad,” Graybiel says. “Our ability to make our movements or our thoughts in what we call a normal way depends on those distinctions, and if they get blurred, it’s real trouble.”

The new findings suggest that behavioral therapy targeting the stage at which information about potential outcomes is encoded in the brain may help people who suffer from those disorders, the researchers say.

The research was funded by the National Institutes of Health/National Institute of Mental Health, the Saks Kavanaugh Foundation, the William N. and Bernice E. Bumpus Foundation, the Simons Foundation, the Nancy Lurie Marks Family Foundation, the National Eye Institute, the National Institute of Neurological Disease and Stroke, the National Science Foundation, the Simons Foundation Autism Research Initiative, and JSPS KAKENHI.

Setting carbon management in stone

by EAPS | MIT News |

Keeping global temperatures within limits deemed safe by the Intergovernmental Panel on Climate Change means doing more than slashing carbon emissions. It means reversing them.

“If we want to be anywhere near those limits [of 1.5 or 2 C], then we have to be carbon neutral by 2050, and then carbon negative after that,” says Matěj Peč, a geoscientist and the Victor P. Starr Career Development Assistant Professor in the Department of Earth, Atmospheric, and Planetary Sciences (EAPS).

Going negative will require finding ways to radically increase the world’s capacity to capture carbon from the atmosphere and put it somewhere where it will not leak back out. Carbon capture and storage projects already suck in tens of million metric tons of carbon each year. But putting a dent in emissions will mean capturing many billions of metric tons more. Today, people emit around 40 billion tons of carbon each year globally, mainly by burning fossil fuels.

Because of the need for new ideas when it comes to carbon storage, Peč has created a proposal for the MIT Climate Grand Challenges competition — a bold and sweeping effort by the Institute to support paradigm-shifting research and innovation to address the climate crisis. Called the Advanced Carbon Mineralization Initiative, his team’s proposal aims to bring geologists, chemists, and biologists together to make permanently storing carbon underground workable under different geological conditions. That means finding ways to speed-up the process by which carbon pumped underground is turned into rock, or mineralized.

“That’s what the geology has to offer,” says Peč, who is a lead on the project, along with Ed Boyden, the Y. Eva Tan professor of neurotechnology and Howard Hughes Medical Institute investigator at the McGovern Institute for Brain Research, and Yogesh Surendranath, the Paul M Cook Career Development associate professor of chemistry. “You look for the places where you can safely and permanently store these huge volumes of CO2.”

Peč‘s proposal is one of 27 finalists selected from a pool of almost 100 Climate Grand Challenge proposals submitted by collaborators from across the Institute. Each finalist team received $100,000 to further develop their research proposals. A subset of finalists will be announced in April, making up a portfolio of multiyear “flagship” projects receiving additional funding and support.

Building industries capable of going carbon negative presents huge technological, economic, environmental, and political challenges. For one, it’s expensive and energy-intensive to capture carbon from the air with existing technologies, which are “hellishly complicated,” says Peč. Much of the carbon capture underway today focuses on more concentrated sources like coal- or gas-burning power plants.

It’s also difficult to find geologically suitable sites for storage. To keep it in the ground after it has been captured, carbon must either be trapped in airtight reservoirs or turned to stone.

One of the best places for carbon capture and storage (CCS) is Iceland, where a number of CCS projects are up and running. The island’s volcanic geology helps speed up the mineralization process, as carbon pumped underground interacts with basalt rock at high temperatures. In that ideal setting, says Peč, 95 percent of carbon injected underground is mineralized after just two years — a geological flash.

But Iceland’s geology is unusual. Elsewhere requires deeper drilling to reach suitable rocks at suitable temperature, which adds costs to already expensive projects. Further, says Peč, there’s not a complete understanding of how different factors influence the speed of mineralization.

Peč‘s Climate Grand Challenge proposal would study how carbon mineralizes under different conditions, as well as explore ways to make mineralization happen more rapidly by mixing the carbon dioxide with different fluids before injecting it underground. Another idea — and the reason why there are biologists on the team — is to learn from various organisms adept at turning carbon into calcite shells, the same stuff that makes up limestone.

Two other carbon management proposals, led by EAPS Cecil and Ida Green Professor Bradford Hager, were also selected as Climate Grand Challenge finalists. They focus on both the technologies necessary for capturing and storing gigatons of carbon as well as the logistical challenges involved in such an enormous undertaking.

That involves everything from choosing suitable sites for storage, to regulatory and environmental issues, as well as how to bring disparate technologies together to improve the whole pipeline. The proposals emphasize CCS systems that can be powered by renewable sources, and can respond dynamically to the needs of different hard-to-decarbonize industries, like concrete and steel production.

“We need to have an industry that is on the scale of the current oil industry that will not be doing anything but pumping CO2 into storage reservoirs,” says Peč.

For a problem that involves capturing enormous amounts of gases from the atmosphere and storing it underground, it’s no surprise EAPS researchers are so involved. The Earth sciences have “everything” to offer, says Peč, including the good news that the Earth has more than enough places where carbon might be stored.

“Basically, the Earth is really, really large,” says Peč. “The reasonably accessible places, which are close to the continents, store somewhere on the order of tens of thousands to hundreds thousands of gigatons of carbon. That’s orders of magnitude more than we need to put back in.”

Q&A: Climate Grand Challenges finalists on accelerating reductions in global greenhouse gas emissions

by MIT News |

This is the second article in a four-part interview series highlighting the work of the 27 MIT Climate Grand Challenges finalists, which received a total of $2.7 million in startup funding to advance their projects. In April, the Institute will name a subset of the finalists as multiyear flagship projects.

Last month, the Intergovernmental Panel on Climate Change (IPCC), an expert body of the United Nations representing 195 governments, released its latest scientific report on the growing threats posed by climate change, and called for drastic reductions in greenhouse gas emissions to avert the most catastrophic outcomes for humanity and natural ecosystems.

Bringing the global economy to net-zero carbon dioxide emissions by midcentury is complex and demands new ideas and novel approaches. The first-ever MIT Climate Grand Challenges competition focuses on four problem areas including removing greenhouse gases from the atmosphere and identifying effective, economic solutions for managing and storing these gases. The other Climate Grand Challenges research themes address using data and science to forecast climate-related risk, decarbonizing complex industries and processes, and building equity and fairness into climate solutions.

In the following conversations prepared for MIT News, faculty from three of the teams working to solve “Removing, managing, and storing greenhouse gases” explain how they are drawing upon geological, biological, chemical, and oceanic processes to develop game-changing techniques for carbon removal, management, and storage. Their responses have been edited for length and clarity.

Directed evolution of biological carbon fixation

Agricultural demand is estimated to increase by 50 percent in the coming decades, while climate change is simultaneously projected to drastically reduce crop yield and predictability, requiring a dramatic acceleration of land clearing. Without immediate intervention, this will have dire impacts on wild habitat, rob the livelihoods of hundreds of millions of subsistence farmers, and create hundreds of gigatons of new emissions. Matthew Shoulders, associate professor in the Department of Chemistry, talks about the working group he is leading in partnership with Ed Boyden, the Y. Eva Tan Professor in Neurotechnology at MIT, Investigator at the Howard Hughes Medical Institute and the McGovern Institute for Brain Research, that aims to massively reduce carbon emissions from agriculture by relieving core biochemical bottlenecks in the photosynthetic process using the most sophisticated synthetic biology available to science.

Q: Describe the two pathways you have identified for improving agricultural productivity and climate resiliency.

A: First, cyanobacteria grow millions of times faster than plants and dozens of times faster

than microalgae. Engineering these cyanobacteria as a source of key food products using synthetic biology will enable food production using less land, in a fundamentally more climate-resilient manner. Second, carbon fixation, or the process by which carbon dioxide is incorporated into organic compounds, is the rate-limiting step of photosynthesis and becomes even less efficient under rising temperatures. Enhancements to Rubisco, the enzyme mediating this central process, will both improve crop yields and provide climate resilience to crops needed by 2050. Our team, led by Robbie Wilson and Max Schubert, has created new directed evolution methods tailored for both strategies, and we have already uncovered promising early results. Applying directed evolution to photosynthesis, carbon fixation, and food production has the potential to usher in a second green revolution.

Q: What partners will you need to accelerate the development of your solutions?

A: We have already partnered with leading agriculture institutes with deep experience in plant transformation and field trial capacity, enabling the integration of our improved carbon-dioxide-fixing enzymes into a wide range of crop plants. At the deployment stage, we will be positioned to partner with multiple industry groups to achieve improved agriculture at scale. Partnerships with major seed companies around the world will be key to leverage distribution channels in manufacturing supply chains and networks of farmers, agronomists, and licensed retailers. Support from local governments will also be critical where subsidies for seeds are necessary for farmers to earn a living, such as smallholder and subsistence farming communities. Additionally, our research provides an accessible platform that is capable of enabling and enhancing carbon dioxide sequestration in diverse organisms, extending our sphere of partnership to a wide range of companies interested in industrial microbial applications, including algal and cyanobacterial, and in carbon capture and storage.

Strategies to reduce atmospheric methane

One of the most potent greenhouse gases, methane is emitted by a range of human activities and natural processes that include agriculture and waste management, fossil fuel production, and changing land use practices — with no single dominant source. Together with a diverse group of faculty and researchers from the schools of Humanities, Arts, and Social Sciences; Architecture and Planning; Engineering; and Science; plus the MIT Schwarzman College of Computing, Desiree Plata, associate professor in the Department of Civil and Environmental Engineering, is spearheading the MIT Methane Network, an integrated approach to formulating scalable new technologies, business models, and policy solutions for driving down levels of atmospheric methane.

Q: What is the problem you are trying to solve and why is it a “grand challenge”?

A: Removing methane from the atmosphere, or stopping it from getting there in the first place, could change the rates of global warming in our lifetimes, saving as much as half a degree of warming by 2050. Methane sources are distributed in space and time and tend to be very dilute, making the removal of methane a challenge that pushes the boundaries of contemporary science and engineering capabilities. Because the primary sources of atmospheric methane are linked to our economy and culture — from clearing wetlands for cultivation to natural gas extraction and dairy and meat production — the social and economic implications of a fundamentally changed methane management system are far-reaching. Nevertheless, these problems are tractable and could significantly reduce the effects of climate change in the near term.

Q: What is known about the rapid rise in atmospheric methane and what questions remain unanswered?

A: Tracking atmospheric methane is a challenge in and of itself, but it has become clear that emissions are large, accelerated by human activity, and cause damage right away. While some progress has been made in satellite-based measurements of methane emissions, there is a need to translate that data into actionable solutions. Several key questions remain around improving sensor accuracy and sensor network design to optimize placement, improve response time, and stop leaks with autonomous controls on the ground. Additional questions involve deploying low-level methane oxidation systems and novel catalytic materials at coal mines, dairy barns, and other enriched sources; evaluating the policy strategies and the socioeconomic impacts of new technologies with an eye toward decarbonization pathways; and scaling technology with viable business models that stimulate the economy while reducing greenhouse gas emissions.

Deploying versatile carbon capture technologies and storage at scale

There is growing consensus that simply capturing current carbon dioxide emissions is no longer sufficient — it is equally important to target distributed sources such as the oceans and air where carbon dioxide has accumulated from past emissions. Betar Gallant, the American Bureau of Shipping Career Development Associate Professor of Mechanical Engineering, discusses her work with Bradford Hager, the Cecil and Ida Green Professor of Earth Sciences in the Department of Earth, Atmospheric and Planetary Sciences, and T. Alan Hatton, the Ralph Landau Professor of Chemical Engineering and director of the School of Chemical Engineering Practice, to dramatically advance the portfolio of technologies available for carbon capture and permanent storage at scale. (A team led by Assistant Professor Matěj Peč of EAPS is also addressing carbon capture and storage.)

Q: Carbon capture and storage processes have been around for several decades. What advances are you seeking to make through this project?

A: Today’s capture paradigms are costly, inefficient, and complex. We seek to address this challenge by developing a new generation of capture technologies that operate using renewable energy inputs, are sufficiently versatile to accommodate emerging industrial demands, are adaptive and responsive to varied societal needs, and can be readily deployed to a wider landscape.

New approaches will require the redesign of the entire capture process, necessitating basic science and engineering efforts that are broadly interdisciplinary in nature. At the same time, incumbent technologies have been optimized largely for integration with coal- or natural gas-burning power plants. Future applications must shift away from legacy emitters in the power sector towards hard-to-mitigate sectors such as cement, iron and steel, chemical, and hydrogen production. It will become equally important to develop and optimize systems targeted for much lower concentrations of carbon dioxide, such as in oceans or air. Our effort will expand basic science studies as well as human impacts of storage, including how public engagement and education can alter attitudes toward greater acceptance of carbon dioxide geologic storage.

Q: What are the expected impacts of your proposed solution, both positive and negative?

A: Renewable energy cannot be deployed rapidly enough everywhere, nor can it supplant all emissions sources, nor can it account for past emissions. Carbon capture and storage (CCS) provides a demonstrated method to address emissions that will undoubtedly occur before the transition to low-carbon energy is completed. CCS can succeed even if other strategies fail. It also allows for developing nations, which may need to adopt renewables over longer timescales, to see equitable economic development while avoiding the most harmful climate impacts. And, CCS enables the future viability of many core industries and transportation modes, many of which do not have clear alternatives before 2050, let alone 2040 or 2030.

The perceived risks of potential leakage and earthquakes associated with geologic storage can be minimized by choosing suitable geologic formations for storage. Despite CCS providing a well-understood pathway for removing enough of the carbon dioxide already emitted into the atmosphere, some environmentalists vigorously oppose it, fearing that CCS rewards oil companies and disincentivizes the transition away from fossil fuels. We believe that it is more important to keep in mind the necessity of meeting key climate targets for the sake of the planet, and welcome those who can help.

An optimized solution for face recognition

by Jennifer Michalowski |

The human brain seems to care a lot about faces. It’s dedicated a specific area to identifying them, and the neurons there are so good at their job that most of us can readily recognize thousands of individuals. With artificial intelligence, computers can now recognize faces with a similar efficiency—and neuroscientists at MIT’s McGovern Institute have found that a computational network trained to identify faces and other objects discovers a surprisingly brain-like strategy to sort them all out.

The finding, reported March 16, 2022, in Science Advances, suggests that the millions of years of evolution that have shaped circuits in the human brain have optimized our system for facial recognition.

“The human brain’s solution is to segregate the processing of faces from the processing of objects,” explains Katharina Dobs, who led the study as a postdoctoral researcher in McGovern investigator Nancy Kanwisher’s lab. The artificial network that she trained did the same. “And that’s the same solution that we hypothesize any system that’s trained to recognize faces and to categorize objects would find,” she adds.

“These two completely different systems have figured out what a—if not the—good solution is. And that feels very profound,” says Kanwisher.

Functionally specific brain regions

More than twenty years ago, Kanwisher’s team discovered a small spot in the brain’s temporal lobe that responds specifically to faces. This region, which they named the fusiform face area, is one of many brain regions Kanwisher and others have found that are dedicated to specific tasks, such as the detection of written words, the perception of vocal songs, and understanding language.

Kanwisher says that as she has explored how the human brain is organized, she has always been curious about the reasons for that organization. Does the brain really need special machinery for facial recognition and other functions? “‘Why questions’ are very difficult in science,” she says. But with a sophisticated type of machine learning called a deep neural network, her team could at least find out how a different system would handle a similar task.

Dobs, who is now a research group leader at Justus Liebig University Giessen in Germany, assembled hundreds of thousands of images with which to train a deep neural network in face and object recognition. The collection included the faces of more than 1,700 different people and hundreds of different kinds of objects, from chairs to cheeseburgers. All of these were presented to the network, with no clues about which was which. “We never told the system that some of those are faces, and some of those are objects. So it’s basically just one big task,” Dobs says. “It needs to recognize a face identity, as well as a bike or a pen.”

Visualization of the preferred stimulus for example face-ranked filters. While filters in early layers (e.g., Conv5) were maximally activated by simple features, filters responded to features that appear somewhat like face parts (e.g., nose and eyes) in mid-level layers (e.g., Conv9) and appear to represent faces in a more holistic manner in late convolutional layers. Image: Kanwisher lab

As the program learned to identify the objects and faces, it organized itself into an information-processing network with that included units specifically dedicated to face recognition. Like the brain, this specialization occurred during the later stages of image processing. In both the brain and the artificial network, early steps in facial recognition involve more general vision processing machinery, and final stages rely on face-dedicated components.

It’s not known how face-processing machinery arises in a developing brain, but based on their findings, Kanwisher and Dobs say networks don’t necessarily require an innate face-processing mechanism to acquire that specialization. “We didn’t build anything face-ish into our network,” Kanwisher says. “The networks managed to segregate themselves without being given a face-specific nudge.”

Kanwisher says it was thrilling seeing the deep neural network segregate itself into separate parts for face and object recognition. “That’s what we’ve been looking at in the brain for twenty-some years,” she says. “Why do we have a separate system for face recognition in the brain? This tells me it is because that is what an optimized solution looks like.”

Now, she is eager to use deep neural nets to ask similar questions about why other brain functions are organized the way they are. “We have a new way to ask why the brain is organized the way it is,” she says. “How much of the structure we see in human brains will arise spontaneously by training networks to do comparable tasks?”