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A connectome for cognition

by Jennifer Michalowski |

The lateral prefrontal cortex is a particularly well-connected part of the brain. Neurons there communicate with processing centers throughout the rest of the brain, gathering information and sending commands to implement executive control over behavior. Now, scientists at MIT’s McGovern Institute have mapped these connections and revealed an unexpected order within them: The lateral prefrontal cortex, they’ve found, contains maps of other major parts of the brain’s cortex.

The researchers, led by postdoctoral researcher Rui Xu and McGovern Institute Director Robert Desimone, report that the lateral prefrontal cortex contains a set of maps that represent the major processing centers in the other parts of the cortex, including the temporal and parietal lobes. Their organization likely supports the lateral prefrontal cortex’s roles managing complex functions such as attention and working memory, which require integrating information from multiple sources and coordinating activity elsewhere in the brain. The findings are published November 4, 2021, in the journal Neuron.

Topographic maps

The layout of the maps, which allows certain regions of the lateral prefrontal cortex to directly interact with multiple areas across the brain, indicates that this part of the brain is particularly well positioned for its role. “This function of integrating and then sending back control signals to appropriate levels in the processing hierarchies of the brain is clearly one of the reasons that prefrontal cortex is so important for cognition and executive control,” says Desimone.

In many parts of the brain, neurons’ physical organization has been found to reflect the information represented there. For example, individual neurons’ positions within the visual cortex mirror the layout of the cells in the retina from which they receive input, such that the spatial pattern of neuronal activity in this part of the brain provides an approximate view of the image seen by the eyes. For example, if you fixate on the first letter of a word, the next letters in the word will map to sequential locations in the visual cortex. Likewise, the arm and hand are mapped to adjacent locations in the somatic cortex, where the brain receives sensory information from the skin.

Topographic maps such as these, which have been found primarily in brain regions involved in sensory and motor processing, offer clues about how information is stored and processed in the brain. Neuroscientists have hoped that topographic maps within the lateral prefrontal cortex will provide insight into the complex cognitive processes that are carried out there—but such maps have been elusive.

Previous anatomical studies had given little indication how different parts of the brain communicate preferentially to specific locations within the prefrontal cortex to give rise to regional specialization of cognitive functions. Recently, however, the Desimone lab identified two areas within the lateral prefrontal cortex of monkeys with specific roles in focusing an animal’s visual attention. Knowing that some spots within the lateral prefrontal cortex were wired for specific functions, they wondered if others were, too. They decided they needed a detailed map of the connections emanating from this part of the brain, and devised a plan to plot connectivity from hundreds of points within the lateral prefrontal cortex.

Cortical connectome

To generate a wiring diagram, or connectome, Xu used functional MRI to monitor activity throughout a monkey’s brain as he stimulated specific points within its lateral prefrontal cortex. He moved systematically through the brain region, stimulating points spaced as close as one millimeter apart, and noting which parts of the brain lit up in response. Ultimately, the team collected data from about 100 sites for each of two monkeys.

As the data accumulated, clear patterns emerged. Different regions within the lateral prefrontal cortex formed orderly connections with each of five processing centers throughout the brain. Points within each of these maps connected to sites with the same relative positions in the distant processing centers. Because some parts of the lateral prefrontal cortex are wired to interact with more than one processing centers, these maps overlap, positioning the prefrontal cortex to integrate information from different sources.

The team found significant overlap, for example, between the maps of the temporal cortex, a part of the brain that uses visual information to recognize objects, and the parietal cortex, which computes the spatial relationships between objects. “It is mapping objects and space together in a way that would integrate the two systems,” explains Desimone. “And then on top of that, it has other maps of other brain systems that are partially overlapping with that—so they’re all sort of coming together.”

Desimone and Xu say the new connectome will help guide further investigations of how the prefrontal cortex orchestrates complex cognitive processes. “I think this really gives us a direction for the future, because we now need to understand the cognitive concepts that are mapped there,” Desimone says.

Already, they say, the connectome offers encouragement that a deeper understanding of complex cognition is within reach. “This topographic connectivity gives the lateral prefrontal some specific advantage to serve its function,” says Xu. “This suggests that lateral prefrontal cortex has a fine organization, just like the more studied parts of the brain, so the approaches that have been used to study these other regions may also benefit the studies of high-level cognition.”

Artificial intelligence sheds light on how the brain processes language

Anne Trafton |

In the past few years, artificial intelligence models of language have become very good at certain tasks. Most notably, they excel at predicting the next word in a string of text; this technology helps search engines and texting apps predict the next word you are going to type.

The most recent generation of predictive language models also appears to learn something about the underlying meaning of language. These models can not only predict the word that comes next, but also perform tasks that seem to require some degree of genuine understanding, such as question answering, document summarization, and story completion.

Such models were designed to optimize performance for the specific function of predicting text, without attempting to mimic anything about how the human brain performs this task or understands language. But a new study from MIT neuroscientists suggests the underlying function of these models resembles the function of language-processing centers in the human brain.

Computer models that perform well on other types of language tasks do not show this similarity to the human brain, offering evidence that the human brain may use next-word prediction to drive language processing.

“The better the model is at predicting the next word, the more closely it fits the human brain,” says Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience, a member of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds, and Machines (CBMM), and an author of the new study. “It’s amazing that the models fit so well, and it very indirectly suggests that maybe what the human language system is doing is predicting what’s going to happen next.”

Joshua Tenenbaum, a professor of computational cognitive science at MIT and a member of CBMM and MIT’s Artificial Intelligence Laboratory (CSAIL); and Evelina Fedorenko, the Frederick A. and Carole J. Middleton Career Development Associate Professor of Neuroscience and a member of the McGovern Institute, are the senior authors of the study, which appears this week in the Proceedings of the National Academy of Sciences.

Martin Schrimpf, an MIT graduate student who works in CBMM, is the first author of the paper.

Making predictions

The new, high-performing next-word prediction models belong to a class of models called deep neural networks. These networks contain computational “nodes” that form connections of varying strength, and layers that pass information between each other in prescribed ways.

Over the past decade, scientists have used deep neural networks to create models of vision that can recognize objects as well as the primate brain does. Research at MIT has also shown that the underlying function of visual object recognition models matches the organization of the primate visual cortex, even though those computer models were not specifically designed to mimic the brain.

In the new study, the MIT team used a similar approach to compare language-processing centers in the human brain with language-processing models. The researchers analyzed 43 different language models, including several that are optimized for next-word prediction. These include a model called GPT-3 (Generative Pre-trained Transformer 3), which, given a prompt, can generate text similar to what a human would produce. Other models were designed to perform different language tasks, such as filling in a blank in a sentence.

As each model was presented with a string of words, the researchers measured the activity of the nodes that make up the network. They then compared these patterns to activity in the human brain, measured in subjects performing three language tasks: listening to stories, reading sentences one at a time, and reading sentences in which one word is revealed at a time. These human datasets included functional magnetic resonance (fMRI) data and intracranial electrocorticographic measurements taken in people undergoing brain surgery for epilepsy.

They found that the best-performing next-word prediction models had activity patterns that very closely resembled those seen in the human brain. Activity in those same models was also highly correlated with measures of human behavioral measures such as how fast people were able to read the text.

“We found that the models that predict the neural responses well also tend to best predict human behavior responses, in the form of reading times. And then both of these are explained by the model performance on next-word prediction. This triangle really connects everything together,” Schrimpf says.

“A key takeaway from this work is that language processing is a highly constrained problem: The best solutions to it that AI engineers have created end up being similar, as this paper shows, to the solutions found by the evolutionary process that created the human brain. Since the AI network didn’t seek to mimic the brain directly — but does end up looking brain-like — this suggests that, in a sense, a kind of convergent evolution has occurred between AI and nature,” says Daniel Yamins, an assistant professor of psychology and computer science at Stanford University, who was not involved in the study.

Game changer

One of the key computational features of predictive models such as GPT-3 is an element known as a forward one-way predictive transformer. This kind of transformer is able to make predictions of what is going to come next, based on previous sequences. A significant feature of this transformer is that it can make predictions based on a very long prior context (hundreds of words), not just the last few words.

Scientists have not found any brain circuits or learning mechanisms that correspond to this type of processing, Tenenbaum says. However, the new findings are consistent with hypotheses that have been previously proposed that prediction is one of the key functions in language processing, he says.

“One of the challenges of language processing is the real-time aspect of it,” he says. “Language comes in, and you have to keep up with it and be able to make sense of it in real time.”

The researchers now plan to build variants of these language processing models to see how small changes in their architecture affect their performance and their ability to fit human neural data.

“For me, this result has been a game changer,” Fedorenko says. “It’s totally transforming my research program, because I would not have predicted that in my lifetime we would get to these computationally explicit models that capture enough about the brain so that we can actually leverage them in understanding how the brain works.”

The researchers also plan to try to combine these high-performing language models with some computer models Tenenbaum’s lab has previously developed that can perform other kinds of tasks such as constructing perceptual representations of the physical world.

“If we’re able to understand what these language models do and how they can connect to models which do things that are more like perceiving and thinking, then that can give us more integrative models of how things work in the brain,” Tenenbaum says. “This could take us toward better artificial intelligence models, as well as giving us better models of how more of the brain works and how general intelligence emerges, than we’ve had in the past.”

The research was funded by a Takeda Fellowship; the MIT Shoemaker Fellowship; the Semiconductor Research Corporation; the MIT Media Lab Consortia; the MIT Singleton Fellowship; the MIT Presidential Graduate Fellowship; the Friends of the McGovern Institute Fellowship; the MIT Center for Brains, Minds, and Machines, through the National Science Foundation; the National Institutes of Health; MIT’s Department of Brain and Cognitive Sciences; and the McGovern Institute.

Other authors of the paper are Idan Blank PhD ’16 and graduate students Greta Tuckute, Carina Kauf, and Eghbal Hosseini.

Five with MIT ties elected to the National Academy of Medicine for 2021

by Julie Pryor |

The National Academy of Medicine (NAM) has announced the election of 100 new members for 2021, including two MIT faculty members and three additional Institute affiliates.

Faculty honorees include Linda G. Griffith, a professor in the MIT departments of Biological Engineering and Mechanical Engineering; and Feng Zhang, a professor in the MIT departments of Brain and Cognitive Sciences and Biological Engineering. Guillermo Antonio Ameer SCD ’99, a professor of biomedical engineering and surgery at Northwestern University; Darrell Gaskin SM ’87, a professor of health policy and management at Johns Hopkins University; and Vamsi Mootha, an institute member of the Broad Institute of MIT and Harvard and former student in the Harvard-MIT Program in Health Sciences and Technology, were also honored.

The new inductees were elected through a process that recognizes individuals who have made major contributions to the advancement of the medical sciences, health care, and public health. Election to the academy is considered one of the highest honors in the fields of health and medicine and recognizes individuals who have demonstrated outstanding professional achievement and commitment to service.

Griffith, the School of Engineering Professor of Teaching Innovation and director of the Center for Gynepathology Research at MIT, is credited for her longstanding leadership in research, education, and medical translation. Specifically, the NAM recognizes her pioneering work in tissue engineering, biomaterials, and systems biology, including the development of the first “liver chip” technology. Griffith is also recognized for inventing 3D biomaterials printing and organotypic models for systems gynopathology, and for the establishment of the biological engineering department at MIT.

The academy recognizes Zhang, the Patricia and James Poitras ’63 Professor in Neuroscience at MIT, for revolutionizing molecular biology and powering transformative leaps forward in our ability to study and treat human diseases. Zhang, who also is an investigator at the Howard Hughes Medical Institute and the McGovern Institute for Brain Research, and a core member of the Broad Institute of MIT and Harvard, is specifically credited for the discovery of novel microbial enzymes and their development as molecular technologies, including optogenetics and CRISPR-mediated genome editing. The academy also commends Zhang for his outstanding mentoring and professional services.

Ameer, the Daniel Hale Williams Professor of Biomedical Engineering and Surgery at the Northwestern University Feinberg School of Medicine, earned his Doctor of Science degree from the MIT Department of Chemical Engineering in 1999. A professor of biomedical engineering and of surgery who is also the director of the Center for Advanced Regenerative Engineering, he is cited by the NAM “For pioneering contributions to regenerative engineering and medicine through the development, dissemination, and translation of citrate-based biomaterials, a new class of biodegradable polymers that enabled the commercialization of innovative medical devices approved by the U.S. Food and Drug Administration for use in a variety of surgical procedures.”

Gaskin, the William C. and Nancy F. Richardson Professor in Health Policy and Management, Bloomberg School of Public Health at Johns Hopkins University, earned his Master of Science degree from the MIT Department of Economics in 1987. A health economist who advances community, neighborhood, and market-level policies and programs that reduce health disparities, he is cited by the NAM “For his work as a leading health economist and health services researcher who has advanced fundamental understanding of the role of place as a driver in racial and ethnic health disparities.”

Mootha, the founding co-director of the Broad Institute’s Metabolism Program, is a professor of systems biology and medicine at Harvard Medical School and a professor in the Department of Molecular Biology at Massachusetts General Hospital. An alumnus of the Harvard-MIT Program in Health Sciences and Technology and former postdoc with the Whitehead Institute for Biomedical Research, Mootha is an expert in the mitochondrion, the “powerhouse of the cell,” and its role in human disease. The NAM cites Mootha “For transforming the field of mitochondrial biology by creatively combining modern genomics with classical bioenergetics.”

Established in 1970 by the National Academy of Sciences, the NAM addresses critical issues in health, science, medicine, and related policy and inspires positive actions across sectors. NAM works alongside the National Academy of Sciences and National Academy of Engineering to provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. The National Academies of Sciences, Engineering, and Medicine also encourage education and research, recognize outstanding contributions to knowledge, and increase public understanding of STEMM. With their election, NAM members make a commitment to volunteer their service in National Academies activities.

Data transformed

by Jennifer Michalowski |

With the tools of modern neuroscience, data accumulates quickly. Recording devices listen in on the electrical conversations between neurons, picking up the voices of hundreds of cells at a time. Microscopes zoom in to illuminate the brain’s circuitry, capturing thousands of images of cells’ elaborately branched paths. Functional MRIs detect changes in blood flow to map activity within a person’s brain, generating a complete picture by compiling hundreds of scans.

“When I entered neuroscience about 20 years ago, data were extremely precious, and ideas, as the expression went, were cheap. That’s no longer true,” says McGovern Associate Investigator Ila Fiete. “We have an embarrassment of wealth in the data but lack sufficient conceptual and mathematical scaffolds to understand it.”

Fiete will lead the McGovern Institute’s new K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center, whose scientists will create mathematical models and other computational tools to confront the current deluge of data and advance our understanding of the brain and mental health. The center, funded by a $24 million donation from philanthropist Lisa Yang, will take a uniquely collaborative approach to computational neuroscience, integrating data from MIT labs to explain brain function at every level, from the molecular to the behavioral.

“Driven by technologies that generate massive amounts of data, we are entering a new era of translational neuroscience research,” says Yang, whose philanthropic investment in MIT research now exceeds $130 million. “I am confident that the multidisciplinary expertise convened by this center will revolutionize how we synthesize this data and ultimately understand the brain in health and disease.”

Data integration

Fiete says computation is particularly crucial to neuroscience because the brain is so staggeringly complex. Its billions of neurons, which are themselves complicated and diverse, interact with one other through trillions of connections.

“Conceptually, it’s clear that all these interactions are going to lead to pretty complex things. And these are not going to be things that we can explain in stories that we tell,” Fiete says. “We really will need mathematical models. They will allow us to ask about what changes when we perturb one or several components — greatly accelerating the rate of discovery relative to doing those experiments in real brains.”

By representing the interactions between the components of a neural circuit, a model gives researchers the power to explore those interactions, manipulate them, and predict the circuit’s behavior under different conditions.

“You can observe these neurons in the same way that you would observe real neurons. But you can do even more, because you have access to all the neurons and you have access to all the connections and everything in the network,” explains computational neuroscientist and McGovern Associate Investigator Guangyu Robert Yang (no relation to Lisa Yang), who joined MIT as a junior faculty member in July 2021.

Many neuroscience models represent specific functions or parts of the brain. But with advances in computation and machine learning, along with the widespread availability of experimental data with which to test and refine models, “there’s no reason that we should be limited to that,” he says.

Robert Yang’s team at the McGovern Institute is working to develop models that integrate multiple brain areas and functions. “The brain is not just about vision, just about cognition, just about motor control,” he says. “It’s about all of these things. And all these areas, they talk to one another.” Likewise, he notes, it’s impossible to separate the molecules in the brain from their effects on behavior – although those aspects of neuroscience have traditionally been studied independently, by researchers with vastly different expertise.

The ICoN Center will eliminate the divides, bringing together neuroscientists and software engineers to deal with all types of data about the brain. To foster interdisciplinary collaboration, every postdoctoral fellow and engineer at the center will work with multiple faculty mentors. Working in three closely interacting scientific cores, fellows will develop computational technologies for analyzing molecular data, neural circuits, and behavior, such as tools to identify pat-terns in neural recordings or automate the analysis of human behavior to aid psychiatric diagnoses. These technologies will also help researchers model neural circuits, ultimately transforming data into knowledge and understanding.

“Lisa is focused on helping the scientific community realize its goals in translational research,” says Nergis Mavalvala, dean of the School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “With her generous support, we can accelerate the pace of research by connecting the data to the delivery of tangible results.”

Computational modeling

In its first five years, the ICoN Center will prioritize four areas of investigation: episodic memory and exploration, including functions like navigation and spatial memory; complex or stereotypical behavior, such as the perseverative behaviors associated with autism and obsessive-compulsive disorder; cognition and attention; and sleep. The goal, Fiete says, is to model the neuronal interactions that underlie these functions so that researchers can predict what will happen when something changes — when certain neurons become more active or when a genetic mutation is introduced, for example. When paired with experimental data from MIT labs, the center’s models will help explain not just how these circuits work, but also how they are altered by genes, the environment, aging, and disease.

These focus areas encompass circuits and behaviors often affected by psychiatric disorders and neurodegeneration, and models will give researchers new opportunities to explore their origins and potential treatment strategies. “I really think that the future of treating disorders of the mind is going to run through computational modeling,” says McGovern Associate Investigator Josh McDermott.

In McDermott’s lab, researchers are modeling the brain’s auditory circuits. “If we had a perfect model of the auditory system, we would be able to understand why when somebody loses their hearing, auditory abilities degrade in the very particular ways in which they degrade,” he says. Then, he says, that model could be used to optimize hearing aids by predicting how the brain would interpret sound altered in various ways by the device.

Similar opportunities will arise as researchers model other brain systems, McDermott says, noting that computational models help researchers grapple with a dauntingly vast realm of possibilities. “There’s lots of different ways the brain can be set up, and lots of different potential treatments, but there is a limit to the number of neuroscience or behavioral experiments you can run,” he says. “Doing experiments on a computational system is cheap, so you can explore the dynamics of the system in a very thorough way.”

The ICoN Center will speed the development of the computational tools that neuroscientists need, both for basic understanding of the brain and clinical advances. But Fiete hopes for a culture shift within neuroscience, as well. “There are a lot of brilliant students and postdocs who have skills that are mathematics and computational and modeling based,” she says. “I think once they know that there are these possibilities to collaborate to solve problems related to psychiatric disorders and how we think, they will see that this is an exciting place to apply their skills, and we can bring them in.”

New integrative computational neuroscience center established at MIT’s McGovern Institute

by Jennifer Michalowski |

With the tools of modern neuroscience, researchers can peer into the brain with unprecedented accuracy. Recording devices listen in on the electrical conversations between neurons, picking up the voices of hundreds of cells at a time. Genetic tools allow us to focus on specific types of neurons based on their molecular signatures. Microscopes zoom in to illuminate the brain’s circuitry, capturing thousands of images of elaborately branched dendrites. Functional MRIs detect changes in blood flow to map activity within a person’s brain, generating a complete picture by compiling hundreds of scans.

This deluge of data provides insights into brain function and dynamics at different levels – molecules, cells, circuits, and behavior — but the insights often remain compartmentalized in separate research silos. An innovative new center at MIT’s McGovern Institute aims to leverage them into powerful revelations of the brain’s inner workings.

The K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center will create advanced mathematical models and computational tools to synthesize the deluge of data across scales and advance our understanding of the brain and mental health.

The center, funded by a $24 million donation from philanthropist Lisa Yang and led by McGovern Institute Associate Investigator Ila Fiete, will take a collaborative approach to computational neuroscience, integrating cutting-edge modeling techniques and data from MIT labs to explain brain function at every level, from the molecular to the behavioral.

“Our goal is that sophisticated, truly integrated computational models of the brain will make it possible to identify how ‘control knobs’ such as genes, proteins, chemicals, and environment drive thoughts and behavior, and to make inroads toward urgent unmet needs in understanding and treating brain disorders,” says Fiete, who is also a brain and cognitive sciences professor at MIT.

“Driven by technologies that generate massive amounts of data, we are entering a new era of translational neuroscience research,” says Yang, whose philanthropic investment in MIT research now exceeds $130 million. “I am confident that the multidisciplinary expertise convened by the ICoN center will revolutionize how we synthesize this data and ultimately understand the brain in health and disease.”

Connecting the data

It is impossible to separate the molecules in the brain from their effects on behavior – although those aspects of neuroscience have traditionally been studied independently, by researchers with vastly different expertise. The ICoN Center will eliminate the divides, bringing together neuroscientists and software engineers to deal with all types of data about the brain.

“The center’s highly collaborative structure, which is essential for unifying multiple levels of understanding, will enable us to recruit talented young scientists eager to revolutionize the field of computational neuroscience,” says Robert Desimone, director of the McGovern Institute. “It is our hope that the ICoN Center’s unique research environment will truly demonstrate a new academic research structure that catalyzes bold, creative research.”

To foster interdisciplinary collaboration, every postdoctoral fellow and engineer at the center will work with multiple faculty mentors. In order to attract young scientists and engineers to the field of computational neuroscience, the center will also provide four graduate fellowships to MIT students each year in perpetuity. Interacting closely with three scientific cores, engineers and fellows will develop computational models and technologies for analyzing molecular data, neural circuits, and behavior, such as tools to identify patterns in neural recordings or automate the analysis of human behavior to aid psychiatric diagnoses. These technologies and models will be instrumental in synthesizing data into knowledge and understanding.

Center priorities

In its first five years, the ICoN Center will prioritize four areas of investigation: episodic memory and exploration, including functions like navigation and spatial memory; complex or stereotypical behavior, such as the perseverative behaviors associated with autism and obsessive-compulsive disorder; cognition and attention; and sleep. Models of complex behavior will be created in collaboration with clinicians and researchers at Children’s Hospital of Philadelphia.

The goal, Fiete says, is to model the neuronal interactions that underlie these functions so that researchers can predict what will happen when something changes — when certain neurons become more active or when a genetic mutation is introduced, for example. When paired with experimental data from MIT labs, the center’s models will help explain not just how these circuits work, but also how they are altered by genes, the environment, aging, and disease. These focus areas encompass circuits and behaviors often affected by psychiatric disorders and neurodegeneration, and models will give researchers new opportunities to explore their origins and potential treatment strategies.

“Lisa Yang is focused on helping the scientific community realize its goals in translational research,” says Nergis Mavalvala, dean of the School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “With her generous support, we can accelerate the pace of research by connecting the data to the delivery of tangible results.”

 

Seven from MIT receive National Institutes of Health awards

by MIT School of Science |

On Oct. 5, the National Institutes of Health announced the names of 106 scientists who have been awarded grants through the High-Risk, High-Reward Research program to advance highly innovative biomedical and behavioral research. Seven of the recipients are MIT faculty members.

The High-Risk, High-Reward Research program catalyzes scientific discovery by supporting research proposals that, due to their inherent risk, may struggle in the traditional peer-review process despite their transformative potential. Program applicants are encouraged to pursue trailblazing ideas in any area of research relevant to the NIH’s mission to advance knowledge and enhance health.

“The science put forward by this cohort is exceptionally novel and creative and is sure to push at the boundaries of what is known,” says NIH Director Francis S. Collins. “These visionary investigators come from a wide breadth of career stages and show that groundbreaking science can happen at any career level given the right opportunity.”

New innovators

Four MIT researchers received New Innovator Awards, which recognize “unusually innovative research from early career investigators.” They are:

  • Pulin Li is a member at the Whitehead Institute for Biomedical Research and an assistant professor in the Department of Biology. Li combines approaches from synthetic biology, developmental biology, biophysics and systems biology to quantitatively understand the genetic circuits underlying cell-cell communication that creates multicellular behaviors.
  • Seychelle Vos, the Robert A. Swanson (1969) Career Development Professor of Life Sciences in the Department of Biology, studies the interplay of gene expression and genome organization. Her work focuses on understanding how large molecular machineries involved in genome organization and gene transcription regulate each others’ function to ultimately determine cell fate and identity.
  • Xiao Wang, the Thomas D. and Virginia Cabot Assistant Professor of Chemistry and a member of the Broad Institute of MIT and Harvard, aims to develop high-resolution and highly-multiplexed molecular imaging methods across multiple scales toward understanding the physical and chemical basis of brain wiring and function.
  • Alison Wendlandt is a Cecil and Ida Green Career Development Assistant Professor of Chemistry. Wendlandt focuses on the development of selective, catalytic reactions using the tools of organic and organometallic synthesis and physical organic chemistry. Mechanistic study plays a central role in the development of these new transformations.

Transformative researchers

Two MIT researchers have received Transformative Research Awards, which “promote cross-cutting, interdisciplinary approaches that could potentially create or challenge existing paradigms.” The recipients are:

  • Manolis Kellis is a professor of computer science at MIT in the area of computational biology, an associate member of the Broad Institute, and a principal investigator with MIT’s Computer Science and Artificial Intelligence Laboratory. He aims to further our understanding of the human genome by computational integration of large-scale functional and comparative genomics datasets.
  • Myriam Heiman is the Latham Family Career Development Associate Professor of Neuroscience in the Department of Brain and Cognitive Sciences and an investigator in the Picower Institute for Learning and Memory. Heiman studies the selective vulnerability and pathophysiology seen in two neurodegenerative diseases of the basal ganglia, Huntington’s disease, and Parkinson’s disease.

Together, Heiman, Kellis and colleagues will launch a five-year investigation to pinpoint what may be going wrong in specific brain cells and to help identify new treatment approaches for amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration with motor neuron disease (FTLD/MND). The project will bring together four labs, including Heiman and Kellis’ labs at MIT, to apply innovative techniques ranging from computational, genomic, and epigenomic analyses of cells from a rich sample of central nervous system tissue, to precision genetic engineering of stem cells and animal models.

Pioneering researchers

  • Polina Anikeeva received a Pioneer Award, which “challenges investigators at all career levels to pursue new research directions and develop groundbreaking, high-impact approaches to a broad area of biomedical, behavioral, or social science.” Anikeeva is an MIT professor of materials science and engineering, a professor of brain and cognitive sciences, and a McGovern Institute for Brain Research associate investigator. She has established a research program that uniquely combines materials synthesis, device fabrication, neurophysiology, and animal models of behavior. Her group carries out projects that understand, invent, and design materials from the level of atoms to functional devices with applications in fundamental neuroscience.

The program is supported by the NIH Common Fund, which oversees programs that pursue major opportunities and gaps throughout the research enterprise that are of great importance to NIH and require collaboration across the agency to succeed. It issues four awards each year: the Pioneer Award, the New Innovator Award, the Transformative Research Award, and the Early Independence Award.

This year, NIH issued 10 Pioneer awards, 64 New Innovator awards, 19 Transformative Research awards (10 general, four ALS-related, and five Covid-19-related), and 13 Early Independence awards for 2021. Funding for the awards comes from the NIH Common Fund, the National Institute of General Medical Sciences, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke.

Artificial networks learn to smell like the brain

by Jennifer Michalowski |

Using machine learning, a computer model can teach itself to smell in just a few minutes. When it does, researchers have found, it builds a neural network that closely mimics the olfactory circuits that animal brains use to process odors.

Animals from fruit flies to humans all use essentially the same strategy to process olfactory information in the brain. But neuroscientists who trained an artificial neural network to take on a simple odor classification task were surprised to see it replicate biology’s strategy so faithfully.

“The algorithm we use has no resemblance to the actual process of evolution,” says Guangyu Robert Yang, an associate investigator at MIT’s McGovern Institute, who led the work as a postdoctoral fellow at Columbia University. The similarities between the artificial and biological systems suggest that the brain’s olfactory network is optimally suited to its task.

Yang and his collaborators, who reported their findings October 6, 2021, in the journal Neuron, say their artificial network will help researchers learn more about the brain’s olfactory circuits. The work also helps demonstrate artificial neural networks’ relevance to neuroscience. “By showing that we can match the architecture [of the biological system] very precisely, I think that gives more confidence that these neural networks can continue to be useful tools for modeling the brain,” says Yang, who is also an assistant professor in MIT’s Departments of Brain and Cognitive Sciences and Electrical Engineering and Computer Science and a member of the Center for Brains, Minds and Machines.

Mapping natural olfactory circuits

For fruit flies, the organism in which the brain’s olfactory circuitry has been best mapped, smell begins in the antennae. Sensory neurons there, each equipped with odor receptors specialized to detect specific scents, transform the binding of odor molecules into electrical activity. When an odor is detected, these neurons, which make up the first layer of the olfactory network, signal to the second-layer: a set of neurons that reside in a part of the brain called the antennal lobe. In the antennal lobe, sensory neurons that share the same receptor converge onto the same second-layer neuron. “They’re very choosy,” Yang says. “They don’t receive any input from neurons expressing other receptors.” Because it has fewer neurons than the first layer, this part of the network is considered a compression layer. These second-layer neurons, in turn, signal to a larger set of neurons in the third layer. Puzzlingly, those connections appear to be random.

For Yang, a computational neuroscientist, and Columbia University graduate student Peter Yiliu Wang, this knowledge of the fly’s olfactory system represented a unique opportunity. Few parts of the brain have been mapped as comprehensively, and that has made it difficult to evaluate how well certain computational models represent the true architecture of neural circuits, they say.

Building an artificial smell network

Neural networks, in which artificial neurons rewire themselves to perform specific tasks, are computational tools inspired by the brain. They can be trained to pick out patterns within complex datasets, making them valuable for speech and image recognition and other forms of artificial intelligence. There are hints that the neural networks that do this best replicate the activity of the nervous system. But, says Wang, who is now a postdoctoral researcher at Stanford University, differently structured networks could generate similar results, and neuroscientists still need to know whether artificial neural networks reflect the actual structure of biological circuits. With comprehensive anatomical data about fruit fly olfactory circuits, he says: “We’re able to ask this question: Can artificial neural networks truly be used to study the brain?”

Collaborating closely with Columbia neuroscientists Richard Axel and Larry Abbott, Yang and Wang constructed a network of artificial neurons comprising an input layer, a compression layer, and an expansion layer—just like the fruit fly olfactory system. They gave it the same number of neurons as the fruit fly system, but no inherent structure: connections between neurons would be rewired as the model learned to classify odors.

The scientists asked the network to assign data representing different odors to categories, and to correctly categorize not just single odors, but also mixtures of odors. This is something that the brain’s olfactory system is uniquely good at, Yang says. If you combine the scents of two different apples, he explains, the brain still smells apple. In contrast, if two photographs of cats are blended pixel by pixel, the brain no longer sees a cat. This ability is just one feature of the brain’s odor-processing circuits, but captures the essence of the system, Yang says.

It took the artificial network only minutes to organize itself. The structure that emerged was stunningly similar to that found in the fruit fly brain. Each neuron in the compression layer received inputs from a particular type of input neuron and connected, seemingly randomly, to multiple neurons in the expansion layer. What’s more, each neuron in the expansion layer receives connections, on average, from six compression-layer neurons—exactly as occurs in the fruit fly brain.

“It could have been one, it could have been 50. It could have been anywhere in between,” Yang says. “Biology finds six, and our network finds about six as well.” Evolution found this organization through random mutation and natural selection; the artificial network found it through standard machine learning algorithms.

The surprising convergence provides strong support that the brain circuits that interpret olfactory information are optimally organized for their task, he says. Now, researchers can use the model to further explore that structure, exploring how the network evolves under different conditions and manipulating the circuitry in ways that cannot be done experimentally.

Dealing with uncertainty

by Jennifer Michalowski |

As we interact with the world, we are constantly presented with information that is unreliable or incomplete – from jumbled voices in a crowded room to solicitous strangers with unknown motivations. Fortunately, our brains are well equipped to evaluate the quality of the evidence we use to make decisions, usually allowing us to act deliberately, without jumping to conclusions.

Now, neuroscientists at MIT’s McGovern Institute have homed in on key brain circuits that help guide decision-making under conditions of uncertainty. By studying how mice interpret ambiguous sensory cues, they’ve found neurons that stop the brain from using unreliable information.

“One area cares about the content of the message—that’s the prefrontal cortex—and the thalamus seems to care about how certain the input is.” – Michael Halassa

The findings, published October 6, 2021, in the journal Nature, could help researchers develop treatments for schizophrenia and related conditions, whose symptoms may be at least partly due to affected individuals’ inability to effectively gauge uncertainty.

Decoding ambiguity

“A lot of cognition is really about handling different types of uncertainty,” says McGovern Associate Investigator Michael Halassa, explaining that we all must use ambiguous information to make inferences about what’s happening in the world. Part of dealing with this ambiguity involves recognizing how confident we can be in our conclusions. And when this process fails, it can dramatically skew our interpretation of the world around us.

“In my mind, schizophrenia spectrum disorders are really disorders of appropriately inferring the causes of events in the world and what other people think,” says Halassa, who is a practicing psychiatrist. Patients with these disorders often develop strong beliefs based on events or signals most people would dismiss as meaningless or irrelevant, he says. They may assume hidden messages are embedded in a garbled audio recording, or worry that laughing strangers are plotting against them. Such things are not impossible—but delusions arise when patients fail to recognize that they are highly unlikely.

Halassa and postdoctoral researcher Arghya Mukherjee wanted to know how healthy brains handle uncertainty, and recent research from other labs provided some clues. Functional brain imaging had shown that when people are asked to study a scene but they aren’t sure what to pay attention to, a part of the brain called the mediodorsal thalamus becomes active. The less guidance people are given for this task, the harder the mediodorsal thalamus works.

The thalamus is a sort of crossroads within the brain, made up of cells that connect distant brain regions to one another. Its mediodorsal region sends signals to the prefrontal cortex, where sensory information is integrated with our goals, desires, and knowledge to guide behavior. Previous work in the Halassa lab showed that the mediodorsal thalamus helps the prefrontal cortex tune in to the right signals during decision-making, adjusting signaling as needed when circumstances change. Intriguingly, this brain region has been found to be less active in people with schizophrenia than it is in others.

group photo of study authors
Study authors (from left to right) Michael Halassa, Arghya Mukherjee, Norman Lam and Ralf Wimmer.

Working with postdoctoral researcher Norman Lam and research scientist Ralf Wimmer, Halassa and Mukherjee designed a set of animal experiments to examine the mediodorsal thalamus’s role in handling uncertainty. Mice were trained to respond to sensory signals according to audio cues that alerted them whether to focus on either light or sound. When the animals were given conflicting cues, it was up to them animal to figure out which one was represented most prominently and act accordingly. The experimenters varied the uncertainty of this task by manipulating the numbers and ratio of the cues.

Division of labor

By manipulating and recording activity in the animals’ brains, the researchers found that the prefrontal cortex got involved every time mice completed this task, but the mediodorsal thalamus was only needed when the animals were given signals that left them uncertain how to behave. There was a simple division of labor within the brain, Halassa says. “One area cares about the content of the message—that’s the prefrontal cortex—and the thalamus seems to care about how certain the input is.”

Within the mediodorsal thalamus, Halassa and Mukherjee found a subset of cells that were especially active when the animals were presented with conflicting sound cues. These neurons, which connect directly to the prefrontal cortex, are inhibitory neurons, capable of dampening downstream signaling. So when they fire, Halassa says, they effectively stop the brain from acting on unreliable information. Cells of a different type were focused on the uncertainty that arises when signaling is sparse. “There’s a dedicated circuitry to integrate evidence across time to extract meaning out of this kind of assessment,” Mukherjee explains.

As Halassa and Mukherjee investigate these circuits more deeply, a priority will be determining whether they are disrupted in people with schizophrenia. To that end, they are now exploring the circuitry in animal models of the disorder. The hope, Mukherjee says, is to eventually target dysfunctional circuits in patients, using noninvasive, focused drug delivery methods currently under development. “We have the genetic identity of these circuits. We know they express specific types of receptors, so we can find drugs that target these receptors,” he says. “Then you can specifically release these drugs in the mediodorsal thalamus to modulate the circuits as a potential therapeutic strategy.”

This work was funded by grants from the National Institute of Mental Health (R01MH107680-05 and R01MH120118-02).

New bionics center established at MIT with $24 million gift

by Jennifer Michalowski |

A deepening understanding of the brain has created unprecedented opportunities to alleviate the challenges posed by disability. Scientists and engineers are taking design cues from biology itself to create revolutionary technologies that restore the function of bodies affected by injury, aging, or disease – from prosthetic limbs that effortlessly navigate tricky terrain to digital nervous systems that move the body after a spinal cord injury.

With the establishment of the new K. Lisa Yang Center for Bionics, MIT is pushing forward the development and deployment of enabling technologies that communicate directly with the nervous system to mitigate a broad range of disabilities. The center’s scientists, clinicians, and engineers will work together to create, test, and disseminate bionic technologies that integrate with both the body and mind.

The center is funded by a $24 million gift to MIT’s McGovern Institute for Brain Research from philanthropist Lisa Yang, a former investment banker committed to advocacy for individuals with visible and invisible disabilities.

Portait of philanthropist Lisa Yang.
Philanthropist Lisa Yang is committed to advocacy for individuals with visible and invisible disabilities. Photo: Caitlin Cunningham

Her previous gifts to MIT have also enabled the establishment of the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience, Hock E. Tan and K. Lisa Yang Center for Autism Research, Y. Eva Tan Professorship in Neurotechnology, and the endowed K. Lisa Yang Post-Baccalaureate Program.

“The K. Lisa Yang Center for Bionics will provide a dynamic hub for scientists, engineers and designers across MIT to work together on revolutionary answers to the challenges of disability,” says MIT President L. Rafael Reif. “With this visionary gift, Lisa Yang is unleashing a powerful collaborative strategy that will have broad impact across a large spectrum of human conditions – and she is sending a bright signal to the world that the lives of individuals who experience disability matter deeply.”

An interdisciplinary approach

To develop prosthetic limbs that move as the brain commands or optical devices that bypass an injured spinal cord to stimulate muscles, bionic developers must integrate knowledge from a diverse array of fields—from robotics and artificial intelligence to surgery, biomechanics, and design. The K. Lisa Yang Center for Bionics will be deeply interdisciplinary, uniting experts from three MIT schools: Science, Engineering, and Architecture and Planning. With clinical and surgical collaborators at Harvard Medical School, the center will ensure that research advances are tested rapidly and reach people in need, including those in traditionally underserved communities.

To support ongoing efforts to move toward a future without disability, the center will also provide four endowed fellowships for MIT graduate students working in bionics or other research areas focused on improving the lives of individuals who experience disability.

“I am thrilled to support MIT on this major research effort to enable powerful new solutions that improve the quality of life for individuals who experience disability,” says Yang. “This new commitment extends my philanthropic investment into the realm of physical disabilities, and I look forward to the center’s positive impact on countless lives, here in the US and abroad.”

The center will be led by Hugh Herr, a professor of media arts and sciences at MIT’s Media Lab, and Ed Boyden, the Y. Eva Tan Professor of Neurotechnology at MIT, a professor of biological engineering, brain and cognitive sciences, and media arts and sciences, and an investigator at MIT’s McGovern Institute and the Howard Hughes Medical Institute.

A double amputee himself, Herr is a pioneer in the development of bionic limbs to improve mobility for those with physical disabilities. “The world profoundly needs relief from the disabilities imposed by today’s nonexistent or broken technologies. We must continually strive towards a technological future in which disability is no longer a common life experience,” says Herr. “I am thrilled that the Yang Center for Bionics will help to measurably improve the human experience for so many.”

Boyden, who is a renowned creator of tools to analyze and control the brain, will play a key role in merging bionics technologies with the nervous system. “The Yang Center for Bionics will be a research center unlike any other in the world,” he says. “A deep understanding of complex biological systems, coupled with rapid advances in human-machine bionic interfaces, mean we will soon have the capability to offer entirely new strategies for individuals who experience disability. It is an honor to be part of the center’s founding team.”

Center priorities

In its first four years, the K. Lisa Yang Center for Bionics will focus on developing and testing three bionic technologies:

  • Digital nervous system: to eliminate movement disorders caused by spinal cord injuries, using computer-controlled muscle activations to control limb movements while simultaneously stimulating spinal cord repair
  • Brain-controlled limb exoskeletons: to assist weak muscles and enable natural movement for people affected by stroke or musculoskeletal disorders
  • Bionic limb reconstruction: to restore natural, brain-controlled movements as well as the sensation of touch and proprioception (awareness of position and movement) from bionic limbs

A fourth priority will be developing a mobile delivery system to ensure patients in medically underserved communities have access to prosthetic limb services. Investigators will field test a system that uses a mobile clinic to conduct the medical imaging needed to design personalized, comfortable prosthetic limbs and to fit the prostheses to patients where they live. Investigators plan to initially bring this mobile delivery system to Sierra Leone, where thousands of people suffered amputations during the country’s 11-year civil war. While the population of persons with amputation continues to increase each year in Sierra Leone, today less than 10% of persons in need benefit from functional prostheses. Through the mobile delivery system, a key center objective is to scale up production and access of functional limb prostheses for Sierra Leoneans in dire need.

Portrait of Lisa Yang, Hugh Herr, Julius Maada Bio, and David Moinina Sengeh (from left to right).
Philanthropist Lisa Yang (far left) and MIT bionics researcher Hugh Herr (second from left) met with Sierra Leone’s President Julius Maada Bio (second from right) and Chief Innovation Officer for the Directorate of Science, Technology and Innovation, David Moinina Sengeh, to discuss the mobile clinic component of the new K. Lisa Yang Center for Bionics at MIT. Photo: David Moinina Sengeh

“The mobile prosthetics service fueled by the K. Lisa Yang Center for Bionics at MIT is an innovative solution to a global problem,” said Julius Maada Bio, President of Sierra Leone. “I am proud that Sierra Leone will be the first site for deploying this state-of-the-art digital design and fabrication process. As leader of a government that promotes innovative technologies and prioritizes human capital development, I am overjoyed that this pilot project will give Sierra Leoneans (especially in rural areas) access to quality limb prostheses and thus improve their quality of life.”

Together, Herr and Boyden will launch research at the bionics center with three other MIT faculty: Assistant Professor of Media Arts and Sciences Canan Dagdeviren, Walter A. Rosenblith Professor of Cognitive Neuroscience Nancy Kanwisher, and David H. Koch (1962) Institute Professor Robert Langer. They will work closely with three clinical collaborators at Harvard Medical School: orthopedic surgeon Marco Ferrone, plastic surgeon Matthew Carty, and Nancy Oriol, Faculty Associate Dean for Community Engagement in Medical Education.

“Lisa Yang and I share a vision for a future in which each and every person in the world has the right to live without a debilitating disability if they so choose,” adds Herr. “The Yang Center will be a potent catalyst for true innovation and impact in the bionics space, and I am overjoyed to work with my colleagues at MIT, and our accomplished clinical partners at Harvard, to make important steps forward to help realize this vision.”

Tracking time in the brain

by Jennifer Michalowski |

By studying how primates mentally measure time, scientists at MIT’s McGovern Institute have discovered that the brain runs an internal clock whose speed is set by prior experience. In new experiences, the brain closely tracks how elapsed time intervals differ from its preset expectation—indicating that for the brain, time is relative.

The findings, reported September 15, 2021, in the journal Neuron, help explain how the brain uses past experience to make predictions—a powerful strategy for navigating a complex and ever-changing world. The research was led by McGovern Investigator Mehrdad Jazayeri, who is working to understand how the brain forms internal models of the world.

Internal clock

Sensory information tells us a lot about our environment, but the brain needs more than data, Jazayeri says. Internal models are vital for understanding the relationships between things, making generalizations, and interpreting and acting on our perceptions. They help us focus on what’s most important and make predictions about our surroundings, as well as the consequences of our actions. “To be efficient in learning about the world and interacting with the world, we need those predictions,” Jazayeri says. When we enter a new grocery store, for example, we don’t have to check every aisle for the peanut butter, because we know it is likely to be near the jam. Likewise, an experienced racquetball player knows how the ball will move when her paddle hits it a certain way.

Jazayeri’s team was interested in how the brain might make predictions about time. Previously, his team showed how neurons in the frontal cortex—a part of the brain involved in planning—can tick off the passage of time like a metronome. By training monkeys to use an eye movement to indicate the duration of time that separated two flashes of light, they found that cells that track time during this task cooperate to form an adjustable internal clock. Those cells generate a pattern of activity that can be drawn out to measure long time intervals or compressed to track shorter ones. The changes in these signal dynamics reflect elapsed time so precisely that by monitoring the right neurons, Jazayeri’s team can determine exactly how fast a monkey’s internal clock is running.

Predictive processing

Nicolas Meirhaeghe, a graduate student in Mehrdad Jazayeri’s lab, studies how we plan and perform movements in the face of uncertainty. He is pictured here as part of the McGovern Institute 20th anniversary “Rising Stars” photo series. Photo: Michael Spencer

For their most recent experiments, graduate student Nicolas Meirhaeghe designed a series of experiments in which the delay between the two flashes of light changed as the monkeys repeated the task. Sometimes the flashes were separated by just a fraction of a second, sometimes the delay was a bit longer. He found that the time-keeping activity pattern in the frontal cortex occurred over different time scales as the monkeys came to expect delays of different durations. As the duration of the delay fluctuated, the brain appeared to take all prior experience into account, setting the clock to measure the average of those times in anticipation of the next interval.

The behavior of the neurons told the researchers that as a monkey waited for a new set of light cues, it already had an expectation about how long the delay would be. To make such a prediction, Meirhaeghe says, “the brain has no choice but to use all the different values that you perceive from your experience, average those out, and use this as the expectation.”

By analyzing neuronal behavior during their experiments, Jazayeri and Meirhaeghe determined that the brain’s signals were not encoding the full time elapsed between light cues, but instead how that time differed from the predicted time. Calculating this prediction error enabled the monkeys to report back how much time had elapsed.

Neuroscientists have suspected that this strategy, known as predictive processing, is widely used by the brain—although until now there has been little evidence of it outside early sensory areas. “You have a lot of stimuli that are coming from the environment, but lots of stimuli are actually predictable,” Meirhaeghe says. “The idea is that your brain is learning through experience patterns in the environment, and is subtracting your expectation from the incoming signal. What the brain actually processes in the end is the result of this subtraction.”

Finally, the researchers investigated the brain’s ability to update its expectations about time. After presenting monkeys with delays within a particular time range, they switched without warning to times that fluctuated within a new range. The brain responded quickly, updating its internal clock. “If you look inside the brain, after about 100 trials the monkeys have already figured out that these statistics have changed,” says Jazayeri.

It took longer, however—as many as 1,000 trials—for the monkeys to change their behavior in response to the change. “It seems like this prediction, and updating the internal model about the statistics of the world, is way faster than our muscles are able to implement,” Jazayeri says. “Our motor system is kind of lagging behind what our cognitive abilities tell us.” This makes sense, he says, because not every change in the environment merits a change in behavior. “You don’t want to be distracted by every small thing that deviates from your prediction. You want to pay attention to things that have a certain level of consistency.”