Research Area: Systems Neuroscience

​neural circuits, attention, brain disorders, executive function, sensory processing, behavior, timing, plasticity, learning, memory, emotion, habits

Study helps explain why motivation to learn declines with age

Anne Trafton |

As people age, they often lose their motivation to learn new things or engage in everyday activities. In a study of mice, MIT neuroscientists have now identified a brain circuit that is critical for maintaining this kind of motivation.

This circuit is particularly important for learning to make decisions that require evaluating the cost and reward that come with a particular action. The researchers showed that they could boost older mice’s motivation to engage in this type of learning by reactivating this circuit, and they could also decrease motivation by suppressing the circuit.

“As we age, it’s harder to have a get-up-and-go attitude toward things,” says Ann Graybiel, an Institute Professor at MIT and member of the McGovern Institute for Brain Research. “This get-up-and-go, or engagement, is important for our social well-being and for learning — it’s tough to learn if you aren’t attending and engaged.”

Graybiel is the senior author of the study, which appears today in Cell. The paper’s lead authors are Alexander Friedman, a former MIT research scientist who is now an assistant professor at the University of Texas at El Paso, and Emily Hueske, an MIT research scientist.

Evaluating cost and benefit

The striatum is part of the basal ganglia — a collection of brain centers linked to habit formation, control of voluntary movement, emotion, and addiction. For several decades, Graybiel’s lab has been studying clusters of cells called striosomes, which are distributed throughout the striatum. Graybiel discovered striosomes many years ago, but their function had remained mysterious, in part because they are so small and deep within the brain that it is difficult to image them with functional magnetic resonance imaging (fMRI).

In recent years, Friedman, Graybiel, and colleagues including MIT research fellow Ken-ichi Amemori have discovered that striosomes play an important role in a type of decision-making known as approach-avoidance conflict. These decisions involve choosing whether to take the good with the bad — or to avoid both — when given options that have both positive and negative elements. An example of this kind of decision is having to choose whether to take a job that pays more but forces a move away from family and friends. Such decisions often provoke great anxiety.

In a related study, Graybiel’s lab found that striosomes connect to cells of the substantia nigra, one of the brain’s major dopamine-producing centers. These studies led the researchers to hypothesize that striosomes may be acting as a gatekeeper that absorbs sensory and emotional information coming from the cortex and integrates it to produce a decision on how to act. These actions can then be invigorated by the dopamine-producing cells.

The researchers later discovered that chronic stress has a major impact on this circuit and on this kind of emotional decision-making. In a 2017 study performed in rats and mice, they showed that stressed animals were far more likely to choose high-risk, high-payoff options, but that they could block this effect by manipulating the circuit.

In the new Cell study, the researchers set out to investigate what happens in striosomes as mice learn how to make these kinds of decisions. To do that, they measured and analyzed the activity of striosomes as mice learned to choose between positive and negative outcomes.

During the experiments, the mice heard two different tones, one of which was accompanied by a reward (sugar water), and another that was paired with a mildly aversive stimulus (bright light). The mice gradually learned that if they licked a spout more when they heard the first tone, they would get more of the sugar water, and if they licked less during the second, the light would not be as bright.

Learning to perform this kind of task requires assigning value to each cost and each reward. The researchers found that as the mice learned the task, striosomes showed higher activity than other parts of the striatum, and that this activity correlated with the mice’s behavioral responses to both of the tones. This suggests that striosomes could be critical for assigning subjective value to a particular outcome.

“In order to survive, in order to do whatever you are doing, you constantly need to be able to learn. You need to learn what is good for you, and what is bad for you,” Friedman says.

“A person, or this case a mouse, may value a reward so highly that the risk of experiencing a possible cost is overwhelmed, while another may wish to avoid the cost to the exclusion of all rewards. And these may result in reward-driven learning in some and cost-driven learning in others,” Hueske says.

The researchers found that inhibitory neurons that relay signals from the prefrontal cortex help striosomes to enhance their signal-to-noise ratio, which helps to generate the strong signals that are seen when the mice evaluate a high-cost or high-reward option.

Loss of motivation

Next, the researchers found that in older mice (between 13 and 21 months, roughly equivalent to people in their 60s and older), the mice’s engagement in learning this type of cost-benefit analysis went down. At the same time, their striosomal activity declined compared to that of younger mice. The researchers found a similar loss of motivation in a mouse model of Huntington’s disease, a neurodegenerative disorder that affects the striatum and its striosomes.

When the researchers used genetically targeted drugs to boost activity in the striosomes, they found that the mice became more engaged in performance of the task. Conversely, suppressing striosomal activity led to disengagement.

In addition to normal age-related decline, many mental health disorders can skew the ability to evaluate the costs and rewards of an action, from anxiety and depression to conditions such as PTSD. For example, a depressed person may undervalue potentially rewarding experiences, while someone suffering from addiction may overvalue drugs but undervalue things like their job or their family.

The researchers are now working on possible drug treatments that could stimulate this circuit, and they suggest that training patients to enhance activity in this circuit through biofeedback could offer another potential way to improve their cost-benefit evaluations.

“If you could pinpoint a mechanism which is underlying the subjective evaluation of reward and cost, and use a modern technique that could manipulate it, either psychiatrically or with biofeedback, patients may be able to activate their circuits correctly,” Friedman says.

The research was funded by the CHDI Foundation, the Saks Kavanaugh Foundation, the National Institutes of Health, the Nancy Lurie Marks Family Foundation, the Bachmann-Strauss Dystonia and Parkinson’s Foundation, the William N. and Bernice E. Bumpus Foundation, the Simons Center for the Social Brain, the Kristin R. Pressman and Jessica J. Pourian ’13 Fund, Michael Stiefel, and Robert Buxton.

Researchers ID crucial brain pathway involved in object recognition

by Alison Gold |

MIT researchers have identified a brain pathway critical in enabling primates to effortlessly identify objects in their field of vision. The findings enrich existing models of the neural circuitry involved in visual perception and help to further unravel the computational code for solving object recognition in the primate brain.

Led by Kohitij Kar, a postdoctoral associate at the McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, the study looked at an area called the ventrolateral prefrontal cortex (vlPFC), which sends feedback signals to the inferior temporal (IT) cortex via a network of neurons. The main goal of this study was to test how the back and forth information processing of this circuitry, that is, this recurrent neural network, is essential to rapid object identification in primates.

The current study, published in Neuron and available today via open access, is a follow-up to prior work published by Kar and James DiCarlo, Peter de Florez Professor of Neuroscience, the head of MIT’s Department of Brain and Cognitive Sciences, and an investigator in the McGovern Institute for Brain Research and the Center for Brains, Minds, and Machines.

Monkey versus machine

In 2019, Kar, DiCarlo, and colleagues identified that primates must use some recurrent circuits during rapid object recognition. Monkey subjects in that study were able to identify objects more accurately than engineered “feedforward” computational models, called deep convolutional neural networks, that lacked recurrent circuitry.

Interestingly, specific images for which models performed poorly compared to monkeys in object identification, also took longer to be solved in the monkeys’ brains — suggesting that the additional time might be due to recurrent processing in the brain. Based on the 2019 study, it remained unclear though exactly which recurrent circuits were responsible for the delayed information boost in the IT cortex. That’s where the current study picks up.

“In this new study, we wanted to find out: Where are these recurrent signals in IT coming from?” Kar said. “Which areas reciprocally connected to IT, are functionally the most critical part of this recurrent circuit?”

To determine this, researchers used a pharmacological agent to temporarily block the activity in parts of the vlPFC in macaques while they engaged in an object discrimination task. During these tasks, monkeys viewed images that contained an object, such as an apple, a car, or a dog; then, researchers used eye tracking to determine if the monkeys could correctly indicate what object they had previously viewed when given two object choices.

“We observed that if you use pharmacological agents to partially inactivate the vlPFC, then both the monkeys’ behavior and IT cortex activity deteriorates but more so for certain specific images. These images were the same ones we identified in the previous study — ones that were poorly solved by ‘feedforward’ models and took longer to be solved in the monkey’s IT cortex,” said Kar.

MIT researchers used an object recognition task (e.g., recognizing that there is a “bird” and not an “elephant” in the shown image) in studying the role of feedback from primate ventrolateral prefrontal cortex (vlPFC) to the inferior temporal (IT) cortex via a network of neurons. In primate brains, temporally blocking the vlPFC (green shaded area) disrupts the recurrent neural network comprising vlPFC and IT inducing specific deficits, implicating its role in rapid object identification. Image: Kohitij Kar, brain image adapted from SciDraw

“These results provide evidence that this recurrently connected network is critical for rapid object recognition, the behavior we’re studying. Now, we have a better understanding of how the full circuit is laid out, and what are the key underlying neural components of this behavior.”

The full study, entitled “Fast recurrent processing via ventrolateral prefrontal cortex is needed by the primate ventral stream for robust core visual object recognition,” will run in print January 6, 2021.

“This study demonstrates the importance of pre-frontal cortical circuits in automatically boosting object recognition performance in a very particular way,” DiCarlo said. “These results were obtained in nonhuman primates and thus are highly likely to also be relevant to human vision.”

The present study makes clear the integral role of the recurrent connections between the vlPFC and the primate ventral visual cortex during rapid object recognition. The results will be helpful to researchers designing future studies that aim to develop accurate models of the brain, and to researchers who seek to develop more human-like artificial intelligence.

Tool developed in Graybiel lab reveals new clues about Parkinson’s disease

by Jennifer Michalowski |

As the brain processes information, electrical charges zip through its circuits and neurotransmitters pass molecular messages from cell to cell. Both forms of communication are vital, but because they are usually studied separately, little is known about how they work together to control our actions, regulate mood, and perform the other functions of a healthy brain.

Neuroscientists in Ann Graybiel’s laboratory at MIT’s McGovern Institute are taking a closer look at the relationship between these electrical and chemical signals. “Considering electrical signals side by side with chemical signals is really important to understand how the brain works,” says Helen Schwerdt, a postdoctoral researcher in Graybiel’s lab. Understanding that relationship is also crucial for developing better ways to diagnose and treat nervous system disorders and mental illness, she says, noting that the drugs used to treat these conditions typically aim to modulate the brain’s chemical signaling, yet studies of brain activity are more likely to focus on electrical signals, which are easier to measure.

Schwerdt and colleagues in Graybiel’s lab have developed new tools so that chemical and electrical signals can, for the first time, be measured simultaneously in the brains of primates. In a study published September 25, 2020, in Science Advances, they used those tools to reveal an unexpectedly complex relationship between two types of signals that are disrupted in patients with Parkinson’s disease—dopamine signaling and coordinated waves of electrical activity known as beta-band oscillations.

Complicated relationship

Graybiel’s team focused its attention on beta-band activity and dopamine signaling because studies of patients with Parkinson’s disease had suggested a straightforward inverse relationship between the two. The tremors, slowness of movement, and other symptoms associated with the disease develop and progress as the brain’s production of the neurotransmitter dopamine declines, and at the same time, beta-band oscillations surge to abnormal levels. Beta-band oscillations are normally observed in parts of the brain that control movement when a person is paying attention or planning to move. It’s not clear what they do or why they are disrupted in patients with Parkinson’s disease. But because patients’ symptoms tend to be worst when beta activity is high—and because beta activity can be measured in real time with sensors placed on the scalp or with a deep-brain stimulation device that has been implanted for treatment, researchers have been hopeful that it might be useful for monitoring the disease’s progression and patients’ response to treatment. In fact, clinical trials are already underway to explore the effectiveness of modulating deep-brain stimulation treatment based on beta activity.

When Schwerdt and colleagues examined these two types of signals in the brains of rhesus macaques, they discovered that the relationship between beta activity and dopamine is more complicated than previously thought.

Their new tools allowed them to simultaneously monitor both signals with extraordinary precision, targeting specific parts of the striatum—a region deep within the brain involved in controlling movement, where dopamine is particularly abundant—and taking measurements on the millisecond time scale to capture neurons’ rapid-fire communications.

They took these measurements as the monkeys performed a simple task, directing their gaze in a particular direction in anticipation of a reward. This allowed the researchers to track chemical and electrical signaling during the active, motivated movement of the animals’ eyes. They found that beta activity did increase as dopamine signaling declined—but only in certain parts of the striatum and during certain tasks. The reward value of a task, an animal’s past experiences, and the particular movement the animal performed all impacted the relationship between the two types of signals.

Multi-modal systems allow subsecond recording of chemical and electrical neural signals in the form of dopamine molecular concentrations and beta-band local field potentials (beta LFPs), respectively. Online measurements of dopamine and beta LFP (time-dependent traces displayed in box on right) were made in the primate striatum (caudate nucleus and putamen colored in green and purple, respectively, in the left brain image) as the animal was performing a task in which eye movements were made to cues displayed on the left (purple event marker line) and right (green event) of a screen in order to receive large or small amounts of food reward (red and blue events). Dopamine and beta LFP neural signals are centrally implicated in Parkinson’s disease and other brain disorders. Image: Helen Schwerdt

“What we expected is there in the overall view, but if we just look at a different level of resolution, all of a sudden the rules don’t hold,” says Graybiel, who is also an MIT Institute Professor. “It doesn’t destroy the likelihood that one would want to have a treatment related to this presumed opposite relationship, but it does say there’s something more here that we haven’t known about.”

The researchers say it’s important to investigate this more nuanced relationship between dopamine signaling and beta activity, and that understanding it more deeply might lead to better treatments for patients with Parkinson’s disease and related disorders. While they plan to continue to examine how the two types of signals relate to one another across different parts of the brain and under different behavioral conditions, they hope that other teams will also take advantage of the tools they have developed. “As these methods in neuroscience become more and more precise and dazzling in their power, we’re bound to discover new things,” says Graybiel.

This study was supported by the National Institute of Biomedical Imaging and Bioengineering, the National Institute of Neurological Disorders and Stroke, the Army Research Office, the Saks Kavanaugh Foundation, the National Science Foundation, Kristin R. Pressman and Jessica J. Pourian ’13 Fund, and Robert Buxton.

How general anesthesia reduces pain

by Sabbi Lall |

General anesthesia is medication that suppresses pain and renders patients unconscious during surgery, but whether pain suppression is simply a side effect of loss of consciousness has been unclear. Fan Wang and colleagues have now identified the circuits linked to pain suppression under anesthesia in mouse models, showing that this effect is separable from the unconscious state itself.

“Existing literature suggests that the brain may contain a switch that can turn off pain perception,” explains Fan Wang, a professor at Duke University and lead author of the study. “I had always wanted to find this switch, and it occurred to me that general anesthetics may activate this switch to produce analgesia.”

Wang, who will join the McGovern Institute in January 2021, set out to test this idea with her student, Thuy Hua, and postdoc, Bin Chen.

Pain suppressor

Loss of pain, or analgesia, is an important property of anesthetics that helps to make surgical and invasive medical procedures humane and bearable. In spite of their long use in the medical world, there is still very little understanding of how anesthetics work. It has generally been assumed that a side effect of loss of consciousness is analgesia, but several recent observations have brought this idea into question, and suggest that changes in consciousness might be separable from pain suppression.

A key clue that analgesia is separable from general anesthesia comes from the accounts of patients that regain consciousness during surgery. After surgery, these patients can recount conversations between staff or events that occurred in the operating room, despite not feeling any pain. In addition, some general anesthetics, such as ketamine, can be deployed at low concentrations for pain suppression without loss of consciousness.

Following up on these leads, Wang and colleagues set out to uncover which neural circuits might be involved in suppressing pain during exposure to general anesthetics. Using CANE, a procedure developed by Wang that can detect which neurons activate in response to an event, Wang discovered a new population of GABAergic neurons activated by general anesthetic in the mouse central amygdala.

These neurons become activated in response to different anesthetics, including ketamine, dexmedetomidine, and isoflurane. Using optogenetics to manipulate the activity state of these neurons, Wang and her lab found that they led to marked changes in behavioral responses to painful stimuli.

“The first time we used optogenetics to turn on these cells, a mouse that was in the middle of taking care of an injury simply stopped and started walked around with no sign of pain,” Wang explains.

Specifically, activating these cells blocks pain in multiple models and tests, whereas inhibiting these neurons rendered mice aversive to gentle touch — suggesting that they are involved in a newly uncovered central pain circuit.

The study has implications for both anesthesia and pain. It shows that general anesthetics have complex, multi-faceted effects and that the brain may contain a central pain suppression system.

“We want to figure out how diverse general anesthetics activate these neurons,” explains Wang. “That way we can find compounds that can specifically activate these pain-suppressing neurons without sedation. We’re now also testing whether placebo analgesia works by activating these same central neurons.”

The study also has implications for addiction as it may point to an alternative system for central pain suppression that could be a target of drugs that do not have the devastating side effects of opioids.

Fan Wang

Sensing the World

Why do we feel pain? What causes us to have intense cravings? How do we manage move so effortlessly through the world?

Fan Wang’s research focuses on the neural circuits governing the bidirectional interactions between the brain and body. She is specifically interested in the circuits that control the sensory and emotional aspects of pain and addiction, as well as the sensory and motor circuits that work together to execute behaviors such as eating, drinking, and moving. She has explored how anesthesia suppresses pain, how brain circuits generate rhythmic behaviors, how the brain coordinates speaking and breathing, and how drugs of abuse influence brain circuits that drive addiction. Wang’s lab deploys a range of techniques to gain traction in these studies, including genetic and viral methods, in vivo electrophysiology, in vivo imaging, and behavioral and autonomic response tracking. Her research has profound implications for real-world problems, including chronic pain and addiction.

Mapping the brain’s sensory gatekeeper

Anne Trafton |

Many people with autism experience sensory hypersensitivity, attention deficits, and sleep disruption. One brain region that has been implicated in these symptoms is the thalamic reticular nucleus (TRN), which is believed to act as a gatekeeper for sensory information flowing to the cortex.

A team of researchers from MIT and the Broad Institute of MIT and Harvard has now mapped the TRN in unprecedented detail, revealing that the region contains two distinct subnetworks of neurons with different functions. The findings could offer researchers more specific targets for designing drugs that could alleviate some of the sensory, sleep, and attention symptoms of autism, says Guoping Feng, one of the leaders of the research team.

These cross-sections of the thalamic reticular nucleus (TRN) show two distinct populations of neurons, labeled in purple and green. A team of researchers from MIT and the Broad Institute of MIT and Harvard has now mapped the TRN in unprecedented detail.
Image: courtesy of the researchers

“The idea is that you could very specifically target one group of neurons, without affecting the whole brain and other cognitive functions,” says Feng, the James W. and Patricia Poitras Professor of Neuroscience at MIT and a member of MIT’s McGovern Institute for Brain Research.

Feng; Zhanyan Fu, associate director of neurobiology at the Broad Institute’s Stanley Center for Psychiatric Research; and Joshua Levin, a senior group leader at the Broad Institute, are the senior authors of the study, which appears today in Nature. The paper’s lead authors are former MIT postdoc Yinqing Li, former Broad Institute postdoc Violeta Lopez-Huerta, and Broad Institute research scientist Xian Adiconis.

Distinct populations

When sensory input from the eyes, ears, or other sensory organs arrives in our brains, it goes first to the thalamus, which then relays it to the cortex for higher-level processing. Impairments of these thalamo-cortical circuits can lead to attention deficits, hypersensitivity to noise and other stimuli, and sleep problems.

One of the major pathways that controls information flow between the thalamus and the cortex is the TRN, which is responsible for blocking out distracting sensory input. In 2016, Feng and MIT Assistant Professor Michael Halassa, who is also an author of the new Nature paper, discovered that loss of a gene called Ptchd1 significantly affects TRN function. In boys, loss of this gene, which is carried on the X chromosome, can lead to attention deficits, hyperactivity, aggression, intellectual disability, and autism spectrum disorders.

In that study, the researchers found that when the Ptchd1 gene was knocked out in mice, the animals showed many of the same behavioral defects seen in human patients. When it was knocked out only in the TRN, the mice showed only hyperactivity, attention deficits, and sleep disruption, suggesting that the TRN is responsible for those symptoms.

In the new study, the researchers wanted to try to learn more about the specific types of neurons found in the TRN, in hopes of finding new ways to treat hyperactivity and attention deficits. Currently, those symptoms are most often treated with stimulant drugs such as Ritalin, which have widespread effects throughout the brain.

“Our goal was to find some specific ways to modulate the function of thalamo-cortical output and relate it to neurodevelopmental disorders,” Feng says. “We decided to try using single-cell technology to dissect out what cell types are there, and what genes are expressed. Are there specific genes that are druggable as a target?”

To explore that possibility, the researchers sequenced the messenger RNA molecules found in neurons of the TRN, which reveals genes that are being expressed in those cells. This allowed them to identify hundreds of genes that could be used to differentiate the cells into two subpopulations, based on how strongly they express those particular genes.

They found that one of these cell populations is located in the core of the TRN, while the other forms a very thin layer surrounding the core. These two populations also form connections to different parts of the thalamus, the researchers found. Based on those connections, the researchers hypothesize that cells in the core are involved in relaying sensory information to the brain’s cortex, while cells in the outer layer appear to help coordinate information that comes in through different senses, such as vision and hearing.

“Druggable targets”

The researchers now plan to study the varying roles that these two populations of neurons may have in a variety of neurological symptoms, including attention deficits, hypersensitivity, and sleep disruption. Using genetic and optogenetic techniques, they hope to determine the effects of activating or inhibiting different TRN cell types, or genes expressed in those cells.

“That can help us in the future really develop specific druggable targets that can potentially modulate different functions,” Feng says. “Thalamo-cortical circuits control many different things, such as sensory perception, sleep, attention, and cognition, and it may be that these can be targeted more specifically.”

This approach could also be useful for treating attention or hypersensitivity disorders even when they aren’t caused by defects in TRN function, the researchers say.

“TRN is a target where if you enhance its function, you might be able to correct problems caused by impairments of the thalamo-cortical circuits,” Feng says. “Of course we are far away from the development of any kind of treatment, but the potential is that we can use single-cell technology to not only understand how the brain organizes itself, but also how brain functions can be segregated, allowing you to identify much more specific targets that modulate specific functions.”

The research was funded by the Simons Center for the Social Brain at MIT, the Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT, the James and Patricia Poitras Center for Psychiatric Disorders Research at MIT, the Stanley Center for Psychiatric Research at the Broad Institute, the National Institutes of Health/National Institute for Mental Health, the Klarman Cell Observatory at the Broad Institute, the Pew Foundation, and the Human Frontiers Science Program.

Nine MIT School of Science professors receive tenure for 2020

by School of Science |

Beginning July 1, nine faculty members in the MIT School of Science have been granted tenure by MIT. They are appointed in the departments of Brain and Cognitive Sciences, Chemistry, Mathematics, and Physics.

Physicist Ibrahim Cisse investigates living cells to reveal and study collective behaviors and biomolecular phase transitions at the resolution of single molecules. The results of his work help determine how disruptions in genes can cause diseases like cancer. Cisse joined the Department of Physics in 2014 and now holds a joint appointment with the Department of Biology. His education includes a bachelor’s degree in physics from North Carolina Central University, concluded in 2004, and a doctoral degree in physics from the University of Illinois at Urbana-Champaign, achieved in 2009. He followed his PhD with a postdoc at the École Normale Supérieure of Paris and a research specialist appointment at the Howard Hughes Medical Institute’s Janelia Research Campus.

Jörn Dunkel is a physical applied mathematician. His research focuses on the mathematical description of complex nonlinear phenomena in a variety of fields, especially biophysics. The models he develops help predict dynamical behaviors and structure formation processes in developmental biology, fluid dynamics, and even knot strengths for sailing, rock climbing and construction. He joined the Department of Mathematics in 2013 after completing postdoctoral appointments at Oxford University and Cambridge University. He received diplomas in physics and mathematics from Humboldt University of Berlin in 2004 and 2005, respectively. The University of Augsburg awarded Dunkel a PhD in statistical physics in 2008.

A cognitive neuroscientist, Mehrdad Jazayeri studies the neurobiological underpinnings of mental functions such as planning, inference, and learning by analyzing brain signals in the lab and using theoretical and computational models, including artificial neural networks. He joined the Department of Brain and Cognitive Sciences in 2013. He achieved a BS in electrical engineering from the Sharif University of Technology in 1994, an MS in physiology at the University of Toronto in 2001, and a PhD in neuroscience from New York University in 2007. Prior to joining MIT, he was a postdoc at the University of Washington. Jazayeri is also an investigator at the McGovern Institute for Brain Research.

Yen-Jie Lee is an experimental particle physicist in the field of proton-proton and heavy-ion physics. Utilizing the Large Hadron Colliders, Lee explores matter in extreme conditions, providing new insight into strong interactions and what might have existed and occurred at the beginning of the universe and in distant star cores. His work on jets and heavy flavor particle production in nuclei collisions improves understanding of the quark-gluon plasma, predicted by quantum chromodynamics (QCD) calculations, and the structure of heavy nuclei. He also pioneered studies of high-density QCD with electron-position annihilation data. Lee joined the Department of Physics in 2013 after a fellowship at CERN and postdoc research at the Laboratory for Nuclear Science at MIT. His bachelor’s and master’s degrees were awarded by the National Taiwan University in 2002 and 2004, respectively, and his doctoral degree by MIT in 2011. Lee is a member of the Laboratory for Nuclear Science.

Josh McDermott investigates the sense of hearing. His research addresses both human and machine audition using tools from experimental psychology, engineering, and neuroscience. McDermott hopes to better understand the neural computation underlying human hearing, to improve devices to assist hearing impaired, and to enhance machine interpretation of sounds. Prior to joining MIT’s Department of Brain and Cognitive Sciences, he was awarded a BA in 1998 in brain and cognitive sciences by Harvard University, a master’s degree in computational neuroscience in 2000 by University College London, and a PhD in brain and cognitive sciences in 2006 by MIT. Between his doctoral time at MIT and returning as a faculty member, he was a postdoc at the University of Minnesota and New York University, and a visiting scientist at Oxford University. McDermott is also an associate investigator at the McGovern Institute for Brain Research and an investigator in the Center for Brains, Minds and Machines.

Solving environmental challenges by studying and manipulating chemical reactions is the focus of Yogesh Surendranath’s research. Using chemistry, he works at the molecular level to understand how to efficiently interconvert chemical and electrical energy. His fundamental studies aim to improve energy storage technologies, such as batteries, fuel cells, and electrolyzers, that can be used to meet future energy demand with reduced carbon emissions. Surendranath joined the Department of Chemistry in 2013 after a postdoc at the University of California at Berkeley. His PhD was completed in 2011 at MIT, and BS in 2006 at the University of Virginia. Suendranath is also a collaborator in the MIT Energy Initiative.

A theoretical astrophysicist, Mark Vogelsberger is interested in large-scale structures of the universe, such as galaxy formation. He combines observational data, theoretical models, and simulations that require high-performance supercomputers to improve and develop detailed models that simulate galaxy diversity, clustering, and their properties, including a plethora of physical effects like magnetic fields, cosmic dust, and thermal conduction. Vogelsberger also uses simulations to generate scenarios involving alternative forms of dark matter. He joined the Department of Physics in 2014 after a postdoc at the Harvard-Smithsonian Center for Astrophysics. Vogelsberger is a 2006 graduate of the University of Mainz undergraduate program in physics, and a 2010 doctoral graduate of the University of Munich and the Max Plank Institute for Astrophysics. He is also a principal investigator in the MIT Kavli Institute for Astrophysics and Space Research.

Adam Willard is a theoretical chemist with research interests that fall across molecular biology, renewable energy, and material science. He uses theory, modeling, and molecular simulation to study the disorder that is inherent to systems over nanometer-length scales. His recent work has highlighted the fundamental and unexpected role that such disorder plays in phenomena such as microscopic energy transport in semiconducting plastics, ion transport in batteries, and protein hydration. Joining the Department of Chemistry in 2013, Willard was formerly a postdoc at Lawrence Berkeley National Laboratory and then the University of Texas at Austin. He holds a PhD in chemistry from the University of California at Berkeley, achieved in 2009, and a BS in chemistry and mathematics from the University of Puget Sound, granted in 2003.

Lindley Winslow seeks to understand the fundamental particles shaped the evolution of our universe. As an experimental particle and nuclear physicist, she develops novel detection technology to search for axion dark matter and a proposed nuclear decay that makes more matter than antimatter. She started her faculty position in the Department of Physics in 2015 following a postdoc at MIT and a subsequent faculty position at the University of California at Los Angeles. Winslow achieved her BA in physics and astronomy in 2001 and PhD in physics in 2008, both at the University of California at Berkeley. She is also a member of the Laboratory for Nuclear Science.

COMMANDing drug delivery

by Sabbi Lall |

While we are starting to get a handle on drugs and therapeutics that might to help alleviate brain disorders, efficient delivery remains a roadblock to tackling these devastating diseases. Research from the Graybiel, Cima, and Langer labs now uses a computational approach, one that accounts for the irregular shape of the target brain region, to deliver drugs effectively and specifically.

“Identifying therapeutic molecules that can treat neural disorders is just the first step,” says McGovern Investigator Ann Graybiel.

“There is still a formidable challenge when it comes to precisely delivering the therapeutic to the cells most affected in the disorder,” explains Graybiel, an MIT Institute Professor and a senior author on the paper. “Because the brain is so structurally complex, and subregions are irregular in shape, new delivery approaches are urgently needed.”

Fine targeting

Brain disorders often arise from dysfunction in specific regions. Parkinson’s disease, for example, arise from loss of neurons in a specific forebrain region, the striatum. Targeting such structures is a major therapeutic goal, and demands both overcoming the blood brain barrier, while also being specific to the structures affected by the disorder.

Such targeted therapy can potentially be achieved using intracerebral catheters. While this is a more specific form of delivery compared to systemic administration of a drug through the bloodstream, many brain regions are irregular in shape. This means that delivery throughout a specific brain region using a single catheter, while also limiting the spread of a given drug beyond the targeted area, is difficult. Indeed, intracerebral delivery of promising therapeutics has not led to the desired long-term alleviation of disorders.

“Accurate delivery of drugs to reach these targets is really important to ensure optimal efficacy and avoid off-target adverse effects. Our new system, called COMMAND, determines how best to dose targets,” says Michael Cima, senior author on the study and the David H. Koch Professor of Engineering in the Department of Materials Science and Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research.

3D renderings of simulated multi-bolus delivery to various brain structures (striatum, amygdala, substantia nigra, and hippocampus) with one to four boluses.

COMMAND response

In the case of Parkinson’s disease, implants are available that limit symptoms, but these are only effective in a subset of patients. There are, however, a number of promising potential therapeutic treatments, such as GDNF administration, where long-term, precise delivery is needed to move the therapy forward.

The Graybiel, Cima, and Langer labs developed COMMAND (computational mapping algorithms for neural drug delivery) that helps to target a drug to a specific brain region at multiple sites (multi-bolus delivery).

“Many clinical trials are believed to have failed due to poor drug distribution following intracerebral injection,” explained Khalil Ramadi, PhD ’19, one of the lead researchers on the paper, and a postdoctoral fellow at the Koch and McGovern Institute. “We rationalized that both research experiments and clinical therapies would benefit from computationally optimized infusion, to enable greater consistency across groups and studies, as well as more efficacious therapeutic delivery.”

The COMMAND system finds balance between the twin challenges of drug delivery by maximizing on-target and minimizing off-target delivery. COMMAND is essentially an algorithm that minimizes an error that reflects leakage beyond the bounds of a specific target area, in this case the striatum. A second error is also minimized, and this encapsulates the need to target across this irregularly shaped brain region. The strategy to overcome this is to deliver multiple “boluses” to different areas of the striatum to target this region precisely, yet completely.

“COMMAND applies a simple principle when determining where to place the drug: Maximize the amount of drug falling within the target brain structure and minimize tissues exposed beyond the target region,” explains Ashvin Bashyam, PhD ’19, co-lead author and a former graduate student with Michael Cima at MIT. “This balance is specified based drug properties such as minimum effective therapeutic concentration, toxicity, and diffusivity within brain tissue.”

The number of drug sites applied is kept as low as possible, keeping surgery simple while still providing enough flexibility to cover the target region. In computational simulations, the researchers were able to deliver drugs to compact brain structures, such as the striatum and amygdala, but also broader and more irregular regions, such as hippocampus.

To examine the spatiotemporal dynamics of actual delivery, the researchers used positron emission tomography (PET) and a ‘labeled’ solution, Cu-64, that allowed them to image and follow an infused bolus after delivery with a microprobe. Using this system, the researchers successfully used PET to validate the accuracy of multi-bolus delivery to the rat striatum and its coverage as guided by COMMAND.

“We anticipate that COMMAND can improve researchers’ ability to precisely target brain structures to better understand their function, and become a platform to standardize methods across neuroscience experiments,” explains Graybiel. “Beyond the lab, we hope COMMAND will lay the foundation to help bring multifocal, chronic drug delivery to patients.”

Protecting healthcare workers during the COVID-19 pandemic

by Jill Crittenden |

“When the COVID-19 crisis hit the US this March, my biggest concern was the shortage of face masks, which are a key weapon for healthcare providers, frontline service workers, and the public to protect against respiratory transmission of COVID-19. In mid-March I kicked off a gofundme campaign for simple masks to protect frontline service workers but, when it was first announced that frontline healthcare providers were short, I completed the campaign and joined groups of scientists and physicians working on N95 mask reuse in Boston (MGB Center for COVID Innovation) and nation-wide (N95DECON). The N95DECON team and used zoom to connect volunteer scientists, engineers, clinicians and students from across the US to address this problem.

I am deeply committed to helping conserve and decontaminate the N95 masks that are essential for our healthcare workers to most safely treat COVID-19 patients.

I personally love zoom meetings from home for many reasons. For one thing, you can meet people instantaneously from all over the world, no need to travel at all. Also, it is less hierarchical than a typical conference because people all have the same place at the table, rather than some people being relegated to ‘the back of the room.’

McGovern research scientist Jill Crittenden (top left) in a zoom meeting with the Boston-based COVID-19 Innovation Center N95 Reuse team. Photo: Jill Crittenden

For two weeks, we met online daily and exchanged information, suggestions and ideas in a free, open, and transparent way. We reviewed a large body of the information on N95 decontamination and deliberated different methods based on evidence from scientific literature and available data. Our discussions followed the same principles I use in my own work in the Graybiel lab; exploring whether data is convincing, definitive, complete, and reproducible. I am so proud of our resulting report, which provides a summary of this critical information.

I am deeply committed to helping conserve and decontaminate the N95 masks that are essential for our healthcare workers to most safely treat COVID-19 patients. I know physicians personally who are very grateful that teams of scientists are doing the in-depth data analysis so that they can feel confident in what is best for their own health.”

Jill Crittenden is a research scientist in Ann Graybiel‘s lab at the McGovern Institute. She studies neural microcircuits in the basal ganglia that are relevant to Huntington’s and Parkinson’s diseases, dystonia, drug addiction, and repetitive movement disorders such as autism and obsessive-compulsive disorder. Read more about her N95DECON project on our news site.

Jill has also developed a set of helpful guidelines for face masks (either purchased or DIY). She discussed these guidelines, among other COVID-19 related topics on the podcast Dear Discreet Guide.

#WeAreMcGovern

How the brain encodes landmarks that help us navigate

Anne Trafton |

When we move through the streets of our neighborhood, we often use familiar landmarks to help us navigate. And as we think to ourselves, “OK, now make a left at the coffee shop,” a part of the brain called the retrosplenial cortex (RSC) lights up.

While many studies have linked this brain region with landmark-based navigation, exactly how it helps us find our way is not well-understood. A new study from MIT neuroscientists now reveals how neurons in the RSC use both visual and spatial information to encode specific landmarks.

“There’s a synthesis of some of these signals — visual inputs and body motion — to represent concepts like landmarks,” says Mark Harnett, an assistant professor of brain and cognitive sciences and a member of MIT’s McGovern Institute for Brain Research. “What we went after in this study is the neuron-level and population-level representation of these different aspects of spatial navigation.”

In a study of mice, the researchers found that this brain region creates a “landmark code” by combining visual information about the surrounding environment with spatial feedback of the mice’s own position along a track. Integrating these two sources of information allowed the mice to learn where to find a reward, based on landmarks that they saw.

“We believe that this code that we found, which is really locked to the landmarks, and also gives the animals a way to discriminate between landmarks, contributes to the animals’ ability to use those landmarks to find rewards,” says Lukas Fischer, an MIT postdoc and the lead author of the study.

Harnett is the senior author of the study, which appears today in the journal eLife. Other authors are graduate student Raul Mojica Soto-Albors and recent MIT graduate Friederike Buck.

Encoding landmarks

Previous studies have found that people with damage to the RSC have trouble finding their way from one place to another, even though they can still recognize their surroundings. The RSC is also one of the first areas affected in Alzheimer’s patients, who often have trouble navigating.

The RSC is wedged between the primary visual cortex and the motor cortex, and it receives input from both of those areas. It also appears to be involved in combining two types of representations of space — allocentric, meaning the relationship of objects to each other, and egocentric, meaning the relationship of objects to the viewer.

“The evidence suggests that RSC is really a place where you have a fusion of these different frames of reference,” Harnett says. “Things look different when I move around in the room, but that’s because my vantage point has changed. They’re not changing with respect to one another.”

In this study, the MIT team set out to analyze the behavior of individual RSC neurons in mice, including how they integrate multiple inputs that help with navigation. To do that, they created a virtual reality environment for the mice by allowing them to run on a treadmill while they watch a video screen that makes it appear they are running along a track. The speed of the video is determined by how fast the mice run.

At specific points along the track, landmarks appear, signaling that there’s a reward available a certain distance beyond the landmark. The mice had to learn to distinguish between two different landmarks, and to learn how far beyond each one they had to run to get the reward.

Once the mice learned the task, the researchers recorded neural activity in the RSC as the animals ran along the virtual track. They were able to record from a few hundred neurons at a time, and found that most of them anchored their activity to a specific aspect of the task.

There were three primary anchoring points: the beginning of the trial, the landmark, and the reward point. The majority of the neurons were anchored to the landmarks, meaning that their activity would consistently peak at a specific point relative to the landmark, say 50 centimeters before it or 20 centimeters after it.

Most of those neurons responded to both of the landmarks, but a small subset responded to only one or the other. The researchers hypothesize that those strongly selective neurons help the mice to distinguish between the landmarks and run the correct distance to get the reward.

When the researchers used optogenetics (a tool that can turn off neuron activity) to block activity in the RSC, the mice’s performance on the task became much worse.

Combining inputs

The researchers also did an experiment in which the mice could choose to run or not while the video played at a constant speed, unrelated to the mice’s movement. The mice could still see the landmarks, but the location of the landmarks was no longer linked to a reward or to the animals’ own behavior. In that situation, RSC neurons did respond to the landmarks, but not as strongly as they did when the mice were using them for navigation.

Further experiments allowed the researchers to tease out just how much neuron activation is produced by visual input (seeing the landmarks) and by feedback on the mouse’s own movement. However, simply adding those two numbers yielded totals much lower than the neuron activity seen when the mice were actively navigating the track.

“We believe that is evidence for a mechanism of nonlinear integration of these inputs, where they get combined in a way that creates a larger response than what you would get if you just added up those two inputs in a linear fashion,” Fischer says.

The researchers now plan to analyze data that they have already collected on how neuron activity evolves over time as the mice learn the task. They also hope to perform further experiments in which they could try to separately measure visual and spatial inputs into different locations within RSC neurons.

The research was funded by the National Institutes of Health, the McGovern Institute, the NEC Corporation Fund for Research in Computers and Communications at MIT, and the Klingenstein-Simons Fellowship in Neuroscience.