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David Ginty named winner of 2022 Scolnick Prize

by Jill Crittenden |

Harvard neurobiologist David Ginty, winner of the 2022 Scolnick Prize.

The McGovern Institute for Brain Research announced today that Harvard neurobiologist David D. Ginty has been selected for the 2022 Edward M. Scolnick Prize in Neuroscience. Ginty, who is the Edward R. and Anne G. Lefler Professor of Neurobiology at Harvard Medical School, is being recognized for his fundamental discoveries into the neural mechanisms underlying the sense of touch. The Scolnick Prize is awarded annually by the McGovern Institute for outstanding advances in neuroscience.

“David Ginty has made seminal contributions in basic research that also have important translational implications,” says Robert Desimone, McGovern Institute Director and chair of the selection committee. “His rigorous research has led us to understand how the peripheral nervous system encodes the overall perception of touch, and how molecular mechanisms underlying this can fail in disease states.”

Ginty obtained his PhD in 1989 with Edward Seidel where he studied cell proliferation factors. He went on to a postdoctoral fellowship researching nerve growth factor with John Wagner at the Dana-Farber Cancer Institute and, upon Wagner’s departure to Cornell, transferred to Michael Greenberg’s lab at Harvard Medical School. There, he dissected intracellular signaling pathways for neuronal growth factors and neurotransmitters and developed key antibody reagents to detect activated forms of transcription factors. These antibody tools are now used by labs around the world in the research of neuronal plasticity and brain disorders, including Alzheimer’s disease and schizophrenia.

In 1995, Ginty started his own laboratory at Johns Hopkins University with a focus on the development and functional organization of the peripheral nervous system. Ginty’s group created and applied the latest genetic engineering techniques in mice to uncover how the peripheral nervous system develops and is organized at the molecular, cellular and circuit levels to perceive touch. Most notably, using gene targeting combined with electrophysiological, behavioral and anatomical analyses, the Ginty lab untangled properties and functions of the different types of touch neurons, termed low- and high-threshold mechanoreceptors, that convey distinct aspects of stimulus information from the skin to the central nervous system. Ginty and colleagues also discovered organizational principles of spinal cord and brainstem circuits dedicated to touch information processing, and that integration of signals from the different mechanoreceptor types begins within spinal cord networks before signal transmission to the brain.

In 2013, Ginty joined the faculty of Harvard Medical School where his team applied their genetic tools and techniques to probe the neural basis of touch sensitivity disorders. They discovered properties and functions of peripheral sensory neurons, spinal cord circuits, and ascending pathways that transmit noxious, painful stimuli from the periphery to the brain. They also asked whether abnormalities in peripheral nervous system function lead to touch over-reactivity in cases of autism or in neuropathic pain caused by nerve injury, chemotherapy, or diabetes, where even a soft touch can be aversive or painful. His team found that sensory abnormalities observed in several mouse models of autism spectrum disorder could be traced to peripheral mechanosensory neurons. They also found that reducing the activity of peripheral sensory neurons prevented tactile over-reactivity in these models and even, in some cases, lessened anxiety and abnormal social behaviors. These findings provided a plausible explanation for how sensory dysfunction may contribute to physiological and cognitive impairments in autism. Importantly, this laid the groundwork for a new approach and initiative to identify new potential therapies for disorders of touch and pain.

Ginty was named a Howard Hughes Medical Institute Investigator in 2000 and was elected to the American Academy of Arts and Sciences in 2015 and the National Academy of Sciences in 2017. He shared Columbia University’s Alden W. Spencer Prize with Ardem Patapoutian in 2017 and was awarded the Society for Neuroscience Julius Axelrod Prize in 2021. Ginty is also known for exceptional mentorship. He directed the neuroscience graduate program at Johns Hopkins from 2006 to 2013 and now serves as the associate director of Harvard’s neurobiology graduate program.

The McGovern Institute will award the Scolnick Prize to Ginty on Wednesday, June 1, 2022. At 4:00 pm he will deliver a lecture entitled “The sensory neurons of touch: beauty is skin deep,” to be followed by a reception at the McGovern Institute, 43 Vassar Street (building 46, room 3002) in Cambridge. The event is free and open to the public; registration is required.

Singing in the brain

Anne Trafton |

Press Mentions

Some neurons in your brain respond to singing...

Smithsonian Magazine

For the first time, MIT neuroscientists have identified a population of neurons in the human brain that lights up when we hear singing, but not other types of music.

These neurons, found in the auditory cortex, appear to respond to the specific combination of voice and music, but not to either regular speech or instrumental music. Exactly what they are doing is unknown and will require more work to uncover, the researchers say.

“The work provides evidence for relatively fine-grained segregation of function within the auditory cortex, in a way that aligns with an intuitive distinction within music,” says Sam Norman-Haignere, a former MIT postdoc who is now an assistant professor of neuroscience at the University of Rochester Medical Center.

The work builds on a 2015 study in which the same research team used functional magnetic resonance imaging (fMRI) to identify a population of neurons in the brain’s auditory cortex that responds specifically to music. In the new work, the researchers used recordings of electrical activity taken at the surface of the brain, which gave them much more precise information than fMRI.

“There’s one population of neurons that responds to singing, and then very nearby is another population of neurons that responds broadly to lots of music. At the scale of fMRI, they’re so close that you can’t disentangle them, but with intracranial recordings, we get additional resolution, and that’s what we believe allowed us to pick them apart,” says Norman-Haignere.

Norman-Haignere is the lead author of the study, which appears today in the journal Current Biology. Josh McDermott, an associate professor of brain and cognitive sciences, and Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience, both members of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds and Machines (CBMM), are the senior authors of the study.

Neural recordings

In their 2015 study, the researchers used fMRI to scan the brains of participants as they listened to a collection of 165 sounds, including different types of speech and music, as well as everyday sounds such as finger tapping or a dog barking. For that study, the researchers devised a novel method of analyzing the fMRI data, which allowed them to identify six neural populations with different response patterns, including the music-selective population and another population that responds selectively to speech.

In the new study, the researchers hoped to obtain higher-resolution data using a technique known as electrocorticography (ECoG), which allows electrical activity to be recorded by electrodes placed inside the skull. This offers a much more precise picture of electrical activity in the brain compared to fMRI, which measures blood flow in the brain as a proxy of neuron activity.

“With most of the methods in human cognitive neuroscience, you can’t see the neural representations,” Kanwisher says. “Most of the kind of data we can collect can tell us that here’s a piece of brain that does something, but that’s pretty limited. We want to know what’s represented in there.”

Electrocorticography cannot be typically be performed in humans because it is an invasive procedure, but it is often used to monitor patients with epilepsy who are about to undergo surgery to treat their seizures. Patients are monitored over several days so that doctors can determine where their seizures are originating before operating. During that time, if patients agree, they can participate in studies that involve measuring their brain activity while performing certain tasks. For this study, the MIT team was able to gather data from 15 participants over several years.

For those participants, the researchers played the same set of 165 sounds that they used in the earlier fMRI study. The location of each patient’s electrodes was determined by their surgeons, so some did not pick up any responses to auditory input, but many did. Using a novel statistical analysis that they developed, the researchers were able to infer the types of neural populations that produced the data that were recorded by each electrode.

“When we applied this method to this data set, this neural response pattern popped out that only responded to singing,” Norman-Haignere says. “This was a finding we really didn’t expect, so it very much justifies the whole point of the approach, which is to reveal potentially novel things you might not think to look for.”

That song-specific population of neurons had very weak responses to either speech or instrumental music, and therefore is distinct from the music- and speech-selective populations identified in their 2015 study.

Music in the brain

In the second part of their study, the researchers devised a mathematical method to combine the data from the intracranial recordings with the fMRI data from their 2015 study. Because fMRI can cover a much larger portion of the brain, this allowed them to determine more precisely the locations of the neural populations that respond to singing.

“This way of combining ECoG and fMRI is a significant methodological advance,” McDermott says. “A lot of people have been doing ECoG over the past 10 or 15 years, but it’s always been limited by this issue of the sparsity of the recordings. Sam is really the first person who figured out how to combine the improved resolution of the electrode recordings with fMRI data to get better localization of the overall responses.”

The song-specific hotspot that they found is located at the top of the temporal lobe, near regions that are selective for language and music. That location suggests that the song-specific population may be responding to features such as the perceived pitch, or the interaction between words and perceived pitch, before sending information to other parts of the brain for further processing, the researchers say.

The researchers now hope to learn more about what aspects of singing drive the responses of these neurons. They are also working with MIT Professor Rebecca Saxe’s lab to study whether infants have music-selective areas, in hopes of learning more about when and how these brain regions develop.

The research was funded by the National Institutes of Health, the U.S. Army Research Office, the National Science Foundation, the NSF Science and Technology Center for Brains, Minds, and Machines, the Fondazione Neurone, the Howard Hughes Medical Institute, and the Kristin R. Pressman and Jessica J. Pourian ’13 Fund at MIT.

On a mission to alleviate chronic pain

Anne Trafton |

About 50 million Americans suffer from chronic pain, which interferes with their daily life, social interactions, and ability to work. MIT Professor Fan Wang wants to develop new ways to help relieve that pain, by studying and potentially modifying the brain’s own pain control mechanisms.

Her recent work has identified an “off switch” for pain, located in the brain’s amygdala. She hopes that finding ways to control this switch could lead to new treatments for chronic pain.

“Chronic pain is a major societal issue,” Wang says. “By studying pain-suppression neurons in the brain’s central amygdala, I hope to create a new therapeutic approach for alleviating pain.”

Wang, who joined the MIT faculty in January 2021, is also the leader of a new initiative at the McGovern Institute for Brain Research that is studying drug addiction, with the goal of developing more effective treatments for addiction.

“Opioid prescription for chronic pain is a major contributor to the opioid epidemic. With the Covid pandemic, I think addiction and overdose are becoming worse. People are more anxious, and they seek drugs to alleviate such mental pain,” Wang says. “As scientists, it’s our duty to tackle this problem.”

Sensory circuits

Wang, who grew up in Beijing, describes herself as “a nerdy child” who loved books and math. In high school, she took part in science competitions, then went on to study biology at Tsinghua University. She arrived in the United States in 1993 to begin her PhD at Columbia University. There, she worked on tracing the connection patterns of olfactory receptor neurons in the lab of Richard Axel, who later won the Nobel Prize for his discoveries of odorant receptors and how the olfactory system is organized.

After finishing her PhD, Wang decided to switch gears. As a postdoc at the University of California at San Francisco and then Stanford University, she began studying how the brain perceives touch.

In 2003, Wang joined the faculty at Duke University School of Medicine. There, she began developing techniques to study the brain circuits that underlie the sense of touch, tracing circuits that carry sensory information from the whiskers of mice to the brain. She also studied how the brain integrates movements of touch organs with signals of sensory stimuli to generate perception (such as using stretching movements to sense elasticity).

As she pursued her sensory perception studies, Wang became interested in studying pain perception, but she felt she needed to develop new techniques to tackle it. While at Duke, she invented a technique called CANE (capturing activated neural ensembles), which can identify networks of neurons that are activated by a particular stimulus.

Using this approach in mice, she identified neurons that become active in response to pain, but so many neurons across the brain were activated that it didn’t offer much useful information. As a way to indirectly get at how the brain controls pain, she decided to use CANE to explore the effects of drugs used for general anesthesia. During general anesthesia, drugs render a patient unconscious, but Wang hypothesized that the drugs might also shut off pain perception.

“At that time, it was just a wild idea,” Wang recalls. “I thought there may be other mechanisms — that instead of just a loss of consciousness, anesthetics may do something to the brain that actually turns pain off.”

Support for the existence of an “off switch” for pain came from the observation that wounded soldiers on a battlefield can continue to fight, essentially blocking out pain despite their injuries.

In a study of mice treated with anesthesia drugs, Wang discovered that the brain does have this kind of switch, in an unexpected location: the amygdala, which is involved in regulating emotion. She showed that this cluster of neurons can turn off pain when activated, and when it is suppressed, mice become highly sensitive to ordinary gentle touch.

“There’s a baseline level of activity that makes the animals feel normal, and when you activate these neurons, they’ll feel less pain. When you silence them, they’ll feel more pain,” Wang says.

Turning off pain

That finding, which Wang reported in 2020, raised the possibility of somehow modulating that switch in humans to try to treat chronic pain. This is a long-term goal of Wang’s, but more work is required to achieve it, she says. Currently her lab is working on analyzing the RNA expression patterns of the neurons in the cluster she identified. They also are measuring the neurons’ electrical activity and how they interact with other neurons in the brain, in hopes of identifying circuits that could be targeted to tamp down the perception of pain.

One way of modulating these circuits could be to use deep brain stimulation, which involves implanting electrodes in certain areas of the brain. Focused ultrasound, which is still in early stages of development and does not require surgery, could be a less invasive alternative.

Another approach Wang is interested in exploring is pairing brain stimulation with a context such as looking at a smartphone app. This kind of pairing could help train the brain to shut off pain using the app, without the need for the original stimulation (deep brain stimulation or ultrasound).

“Maybe you don’t need to constantly stimulate the brain. You may just need to reactivate it with a context,” Wang says. “After a while you would probably need to be restimulated, or reconditioned, but at least you have a longer window where you don’t need to go to the hospital for stimulation, and you just need to use a context.”

Wang, who was drawn to MIT in part by its focus on fostering interdisciplinary collaborations, is now working with several other McGovern Institute members who are taking different angles to try to figure out how the brain generates the state of craving that occurs in drug addiction, including opioid addiction.

“We’re going to focus on trying to understand this craving state: how it’s created in the brain and how can we sort of erase that trace in the brain, or at least control it. And then you can neuromodulate it in real time, for example, and give people a chance to get back their control,” she says.

Dendrites may help neurons perform complicated calculations

Anne Trafton |

Within the human brain, neurons perform complex calculations on information they receive. Researchers at MIT have now demonstrated how dendrites — branch-like extensions that protrude from neurons — help to perform those computations.

The researchers found that within a single neuron, different types of dendrites receive input from distinct parts of the brain, and process it in different ways. These differences may help neurons to integrate a variety of inputs and generate an appropriate response, the researchers say.

In the neurons that the researchers examined in this study, it appears that this dendritic processing helps cells to take in visual information and combine it with motor feedback, in a circuit that is involved in navigation and planning movement.

“Our hypothesis is that these neurons have the ability to pick out specific features and landmarks in the visual environment, and combine them with information about running speed, where I’m going, and when I’m going to start, to move toward a goal position,” says Mark Harnett, an associate professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Mathieu Lafourcade, a former MIT postdoc, is the lead author of the paper, which appears today in Neuron.

Complex calculations

Any given neuron can have dozens of dendrites, which receive synaptic input from other neurons. Neuroscientists have hypothesized that these dendrites can act as compartments that perform their own computations on incoming information before sending the results to the body of the neuron, which integrates all these signals to generate an output.

Previous research has shown that dendrites can amplify incoming signals using specialized proteins called NMDA receptors. These are voltage-sensitive neurotransmitter receptors that are dependent on the activity of other receptors called AMPA receptors. When a dendrite receives many incoming signals through AMPA receptors at the same time, the threshold to activate nearby NMDA receptors is reached, creating an extra burst of current.

This phenomenon, known as supralinearity, is believed to help neurons distinguish between inputs that arrive close together or farther apart in time or space, Harnett says.

In the new study, the MIT researchers wanted to determine whether different types of inputs are targeted specifically to different types of dendrites, and if so, how that would affect the computations performed by those neurons. They focused on a population of neurons called pyramidal cells, the principal output neurons of the cortex, which have several different types of dendrites. Basal dendrites extend below the body of the neuron, apical oblique dendrites extend from a trunk that travels up from the body, and tuft dendrites are located at the top of the trunk.

Harnett and his colleagues chose a part of the brain called the retrosplenial cortex (RSC) for their studies because it is a good model for association cortex — the type of brain cortex used for complex functions such as planning, communication, and social cognition. The RSC integrates information from many parts of the brain to guide navigation, and pyramidal neurons play a key role in that function.

In a study of mice, the researchers first showed that three different types of input come into pyramidal neurons of the RSC: from the visual cortex into basal dendrites, from the motor cortex into apical oblique dendrites, and from the lateral nuclei of the thalamus, a visual processing area, into tuft dendrites.

“Until now, there hasn’t been much mapping of what inputs are going to those dendrites,” Harnett says. “We found that there are some sophisticated wiring rules here, with different inputs going to different dendrites.”

A range of responses

The researchers then measured electrical activity in each of those compartments. They expected that NMDA receptors would show supralinear activity, because this behavior has been demonstrated before in dendrites of pyramidal neurons in both the primary sensory cortex and the hippocampus.

In the basal dendrites, the researchers saw just what they expected: Input coming from the visual cortex provoked supralinear electrical spikes, generated by NMDA receptors. However, just 50 microns away, in the apical oblique dendrites of the same cells, the researchers found no signs of supralinear activity. Instead, input to those dendrites drives a steady linear response. Those dendrites also have a much lower density of NMDA receptors.

“That was shocking, because no one’s ever reported that before,” Harnett says. “What that means is the apical obliques don’t care about the pattern of input. Inputs can be separated in time, or together in time, and it doesn’t matter. It’s just a linear integrator that’s telling the cell how much input it’s getting, without doing any computation on it.”

Those linear inputs likely represent information such as running speed or destination, Harnett says, while the visual information coming into the basal dendrites represents landmarks or other features of the environment. The supralinearity of the basal dendrites allows them to perform more sophisticated types of computation on that visual input, which the researchers hypothesize allows the RSC to flexibly adapt to changes in the visual environment.

In the tuft dendrites, which receive input from the thalamus, it appears that NMDA spikes can be generated, but not very easily. Like the apical oblique dendrites, the tuft dendrites have a low density of NMDA receptors. Harnett’s lab is now studying what happens in all of these different types of dendrites as mice perform navigation tasks.

The research was funded by a Boehringer Ingelheim Fonds PhD Fellowship, the National Institutes of Health, the James W. and Patricia T. Poitras Fund, the Klingenstein-Simons Fellowship Program, a Vallee Scholar Award, and a McKnight Scholar Award.

Seven new faculty join the MIT School of Science

by School of Science |

This winter, seven new faculty members join the MIT School of Science in the departments of Biology and Brain and Cognitive Sciences.

Siniša Hrvatin studies how animals initiate, regulate, and survive states of stasis, such as torpor and hibernation. To survive extreme environments, many animals have evolved the ability to decrease metabolic rate and body temperature and enter dormant states. His long-term goal is to harness the potential of these biological adaptations to advance medicine. Previously, he identified the neurons that regulate mouse torpor and established a platform for the development of cell-type-specific viral drivers.
Hrvatin earned his bachelor’s degree in biochemical sciences in 2007 and his PhD in stem cell and regenerative medicine in 2013, both from Harvard University. He was then a postdoc in bioengineering at MIT and a postdoc in neurobiology at Harvard Medical School. Hrvatin returns to MIT as an assistant professor of biology and a member of the Whitehead Institute for Biomedical Research.

Sara Prescott investigates how sensory inputs from within the body control mammalian physiology and behavior. Specifically, she uses mammalian airways as a model system to explore how the cells that line the surface of the body communicate with parts of the nervous system. For example, what mechanisms elicit a reflexive cough? Prescott’s research considers the critical questions of how airway insults are detected, encoded, and adapted to mammalian airways with the ultimate goal of providing new ways to treat autonomic dysfunction.

Prescott earned her bachelor’s degree in molecular biology from Princeton University in 2008 followed by her PhD in developmental biology from Stanford University in 2016. Prior to joining MIT, she was a postdoc at Harvard Medical School and Howard Hughes Medical Institute. The Department of Biology welcomes Prescott as an assistant professor.
Alison Ringel is a T-cell immunologist with a background in biochemistry, biophysics, and structural biology. She investigates how environmental factors such as aging, metabolism, and diet impact tumor progress and the immune responses that cause tumor control. By mapping the environment around a tumor on a cellular level, she seeks to gain a molecular understanding of cancer risk factors.

Ringel received a bachelor’s degree in molecular biology, biochemistry, and physics from Wesleyan University, then a PhD in molecular biophysics from John Hopkins University School of Medicine. Previously, Ringel was a postdoc in the Department of Cell Biology at Harvard Medical School. She joins MIT as an assistant professor in the Department of Biology and a core member of the Ragon Institute of MGH, MIT and Harvard.

Francisco J. Sánchez-Rivera PhD ’16 investigates genetic variation with a focus on cancer. He integrates genome engineering technologies, genetically-engineered mouse models (GEMMs), and single cell lineage tracing and omics approaches in order to understand the mechanics of cancer development and evolution. With state-of-the-art technologies — including a CRISPR-based genome editing system he developed as a graduate student at MIT — he hopes to make discoveries in cancer genetics that will shed light on disease progression and pave the way for better therapeutic treatments.

Sánchez-Rivera received his bachelor’s degree in microbiology from the University of Puerto Rico at Mayagüez followed by a PhD in biology from MIT. He then pursued postdoctoral studies at Memorial Sloan Kettering Cancer Center supported by a HHMI Hanna Gray Fellowship. Sánchez-Rivera returns to MIT as an assistant professor in the Department of Biology and a member of the Koch Institute for Integrative Cancer Research at MIT.

Nidhi Seethapathi builds predictive models to help understand human movement with a combination of theory, computational modeling, and experiments. Her research focuses on understanding the objectives that govern movement decisions, the strategies used to execute movement, and how new movements are learned. By studying movement in real-world contexts using creative approaches, Seethapathi aims to make discoveries and develop tools that could improve neuromotor rehabilitation.

Seethapathi earned her bachelor’s degree in mechanical engineering from the Veermata Jijabai Technological Institute followed by her PhD in mechanical engineering from Ohio State University. In 2018, she continued to the University of Pennsylvania where she was a postdoc. She joins MIT as an assistant professor in the Department of Brain and Cognitive Sciences with a shared appointment in the Department of Electrical Engineering and Computer Science at the MIT Schwarzman College of Computing.

Hernandez Moura Silva researches how the immune system supports tissue physiology. Silva focuses on macrophages, a type of immune cell involved in tissue homeostasis. He plans to establish new strategies to explore the effects and mechanisms of such immune-related pathways, his research ultimately leading to the development of therapeutic approaches to treat human diseases.

Silva earned a bachelor’s degree in biological sciences and a master’s degree in molecular biology from the University of Brasilia. He continued to complete a PhD in immunology at the University of São Paulo School of Medicine: Heart Institute. Most recently, he acted as the Bernard Levine Postdoctoral Fellow in immunology and immuno-metabolism at the New York University School of Medicine: Skirball Institute of Biomolecular Medicine. Silva joins MIT as an assistant professor in the Department of Biology and a core member of the Ragon Institute.

Yadira Soto-Feliciano PhD ’16 studies chromatin — the complex of DNA and proteins that make up chromosomes. She combines cancer biology and epigenetics to understand how certain proteins affect gene expression and, in turn, how they impact the development of cancer and other diseases. In decoding the chemical language of chromatin, Soto-Feliciano pursues a basic understanding of gene regulation that could improve the clinical management of diseases associated with their dysfunction.

Soto-Feliciano received her bachelor’s degree in chemistry from the University of Puerto Rico at Mayagüez followed by a PhD in biology from MIT, where she was also a research fellow with the Koch Institute. Most recently, she was the Damon Runyon-Sohn Pediatric Cancer Postdoctoral Fellow at The Rockefeller University. Soto-Feliciano returns to MIT as an assistant professor in the Department of Biology and a member of the Koch Institute.

A new approach to curbing cocaine use

by Jennifer Michalowski |

Cocaine, opioids, and other drugs of abuse disrupt the brain’s reward system, often shifting users’ priorities to obtaining more drug above all else. For people battling addiction, this persistent craving is notoriously difficult to overcome—but new research from scientists at MIT’s McGovern Institute and collaborators points toward a therapeutic strategy that could help.

Researchers in MIT Institute Professor Ann Graybiel’s lab and collaborators at the University of Copenhagen and Vanderbilt University report in a January 25, 2022 online publication in the journal Addiction Biology that activating a signaling molecule in the brain known as muscarinic receptor 4 (M4) causes rodents to reduce cocaine self-administration and simultaneously choose a food treat over cocaine.

M4 receptors are found on the surface of neurons in the brain, where they alter signaling in response to the neurotransmitter acetylcholine. They are plentiful in the striatum, a brain region that Graybiel’s lab has shown is deeply involved in habit formation. They are of interest to addiction researchers because, along with a related receptor called M1, which is also abundant in the striatum, they often seem to act in opposition to the neurotransmitter dopamine.

Drugs of abuse stimulate the brain’s habit circuits by allowing dopamine to build up in the brain. With chronic use, that circuitry can become less sensitive to dopamine, so experiences that were once rewarding become less pleasurable and users are driven to seek higher doses of their drug. Attempts to directly block the dopamine system have not been found to be an effective way of treating addiction and can have unpleasant or dangerous side-effects, so researchers are seeking an alternative strategy to restore balance within the brain’s reward circuitry. “Another way to tweak that system is to activate these muscarinic receptors,” explains Jill Crittenden, a research scientist in the Graybiel lab.

New pathways to treatment

At the University of Copenhagen, neuroscientist Morgane Thomsen has found that activating the M1 receptor causes rodents to choose a food treat over cocaine. In the new work, she showed that a drug that selectively activates the M4 receptor has a similar effect.

When rats that have been trained to self-administer cocaine are given an M4-activating compound, they immediately reduce their drug use, actively choosing food instead. Thomsen found that this effect grew stronger over a seven-day course of treatment, with cocaine use declining day by day. When the M4-activating treatment was stopped, rats quickly resumed their prior cocaine-seeking behavior.

While Thomsen’s experiments have now shown that animals’ cocaine use can be reduced by activating either M1 or M4, it’s clear that the two muscarinic receptors don’t modulate cocaine use in the same way. M1 activation works on a different time scale, taking some time to kick in, but leaving some lasting effects even after the treatment has been discontinued.

Experiments with genetically modified mice developed in Graybiel’s lab confirm that the two receptors influence drug-seeking behavior via different molecular pathways. Previously, the team discovered that activating M1 has no effect on cocaine-seeking in mice that lack a signaling molecule called CalDAG-GEFI. M4 activation, however, reduces cocaine consumption regardless of whether CalDAG-GEFI is present. “The CalDAG-GEFI is completely essential for the M1 effect to happen, but doesn’t appear to play any role in the M4 effect,” Thomsen says. “So that really separates the pathways. In both the behavior and the neurobiology, it’s two different ways that we can modulate the cocaine effects.” The findings suggest that activating M4 could help people with substance abuse disorders overcome their addiction, and that such a strategy might be even more effective if combined with activation of the M1 receptor.

Graybiel’s lab first became interested in CalDAG-GEFI in the late 1990s, when they discovered that it was unusually abundant in the main compartment of the brain’s striatum. Their research revealed the protein to be important for controlling movement and even uncovered an essential role in blood clotting—but CalDAG-GEFI’s impacts on behavior remained elusive for a long time. Graybiel says it’s gratifying that this long-standing interest has now shed light on a potential therapeutic strategy for substance abuse disorder. Her lab will continue investigating the molecular pathways that underlie addiction as part of the McGovern Institute’s new addiction initiative.

Assessing connections in the brain’s reading network

by Jennifer Michalowski |

When we read, information zips between language processing centers in different parts of the brain, traveling along neural highways in the white matter. This coordinated activity allows us to decipher words and comprehend their meaning. Many neuroscientists suspect that variations in white matter may underlie differences in reading ability, and hope that by determining which white matter tracts are involved, they will be able to guide the development of more effective interventions for children who struggle with reading skills.

In a January 14, 2022, online publication in the journal NeuroImage, scientists at MIT’s McGovern Institute report on the largest brain imaging study to date to evaluate the relationship between white matter structure and reading ability. Their findings suggest that if white matter deficiencies are a significant cause of reading disability, new strategies will be needed to pin them down.

White matter is composed of bundles of insulated nerve fibers. It can be thought of as the internet of the brain, says senior author John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology at MIT. “It’s the connectivity: the way that the brain communicates at some distance to orchestrate higher-level thoughts, and abilities like reading,” explains Gabrieli, who is also a professor of brain and cognitive sciences and an investigator at the McGovern Institute.

The left inferior cerebellar peduncle, a white matter tract that connects the cerebellum to the brainstem and spinal cord. Image: Steven Meisler

Long-distance connections

To visualize white matter and study its structure, neuroscientists use an imaging technique called diffusion-weighted imaging (DWI). Images are collected in an MRI scanner by tracking the movements of water molecules in the brain. A key measure used to interpret these images is fractional anisotropy (FA), which varies with many physical features of nerve fibers, such as their density, diameter, and degree of insulation. Although FA does not measure any of these properties directly, it is considered an indicator of structural integrity within white matter tracts.

Several studies have found the FA of one or more white matter tracts to be lower in children with low reading scores or dyslexia than in children with stronger reading abilities. But those studies are small—usually involving only a few dozen children—and their findings are inconsistent. So it has been difficult to attribute reading problems to poor connections between specific parts of the brain.

Hoping to glean more conclusive results, Gabrieli and Steven Meisler, a graduate student in the Harvard Program in Speech and Hearing Bioscience and Technology who is completing his doctoral work in the Gabrieli lab, turned to a large collection of high-quality brain images available through the Child Mind Institute’s Healthy Brain Network. Using DWI images collected from 686 children and state-of-the-art methods of analysis, they assessed the FA of 20 white matter tracts that are thought to be important for reading.

The children represented in the dataset had diverse reading abilities, but surprisingly, when they compared children with and without reading disability, Meisler and Gabrieli found no significant differences in the FA of any of the 20 tracts. Nor did they find any correlation between white matter FA and children’s overall reading scores.

More detailed analysis did link reading ability to the FA of two particular white matter tracts. The researchers only detected the correlation when they narrowed their analysis to children older than eight, who are usually reading to learn, rather than learning to read. Within this group, they found two white matter tracts whose FA was lower in children who struggled with a specific reading skill: reading “pseudowords.” The ability to read nonsense words is used to assess knowledge of the relationship between letters and sounds, since real words can be recognized instead through experience and memory.

The right superior longitudinal fasciculus, a white matter tract that connects frontal brain regions to parietal areas. The research team found that fractional anisotropy (FA) of the right superior longitudinal fasciculus and the left inferior cerebellar peduncles (shown above) correlated positively with pseudoword reading ability among children ages 9 and older. Image: Steven Meisler

The first of these tracts connects language processing centers in the frontal and parietal brain regions. The other contains fibers that connect that the brainstem with the cerebellum, and may help control the eye movements needed to see and track words. The FA differences that Meisler and Gabrieli linked to reading scores were small, and it’s not yet clear what they mean. Since less cohesive structure in these two tracts was linked to lower pseudoword-reading scores only in older children, it may be a consequence of living with a reading disability rather than a cause, Meisler says.

The findings don’t rule out a role for white matter structure in reading disability, but they do suggest that researchers will need a different approach to find relevant features. “Our results suggest that FA does not relate to reading abilities as much as previously thought,” Meisler says. In future studies, he says, researchers will likely need to take advantage of more advanced methods of image analysis to assess features that more directly reflect white matter’s ability to serve as a conduit of information.

School of Science announces 2022 Infinite Expansion Awards

by MIT School of Science |

The MIT School of Science has announced eight postdocs and research scientists as recipients of the 2022 Infinite Expansion Award.

The award, formerly known as the Infinite Kilometer Award, was created in 2012 to highlight extraordinary members of the MIT science community. The awardees are nominated not only for their research, but for going above and beyond in mentoring junior colleagues, participating in educational programs, and contributing to their departments, labs, and research centers, the school, and the Institute.

The 2022 School of Science Infinite Expansion winners are:

  • Héctor de Jesús-Cortés, a postdoc in the Picower Institute for Learning and Memory, nominated by professor and Department of Brain and Cognitive Sciences (BCS) head Michale Fee, professor and McGovern Institute for Brain Research Director Robert Desimone, professor and Picower Institute Director Li-Huei Tsai, professor and associate BCS head Laura Schulz, associate professor and associate BCS head Joshua McDermott, and professor and BCS Postdoc Officer Mark Bear for his “awe-inspiring commitment of time and energy to research, outreach, education, mentorship, and community;”
  • Harold Erbin, a postdoc in the Laboratory for Nuclear Science’s Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), nominated by professor and IAIFI Director Jesse Thaler, associate professor and IAIFI Deputy Director Mike Williams, and associate professor and IAIFI Early Career and Equity Committee Chair Tracy Slatyer for “provid[ing] exemplary service on the IAIFI Early Career and Equity Committee” and being “actively involved in many other IAIFI community building efforts;”
  • Megan Hill, a postdoc in the Department of Chemistry, nominated by Professor Jeremiah Johnson for being an “outstanding scientist” who has “also made exceptional contributions to our community through her mentorship activities and participation in Women in Chemistry;”
  • Kevin Kuns, a postdoc in the Kavli Institute for Astrophysics and Space Research, nominated by Associate Professor Matthew Evans for “consistently go[ing] beyond expectations;”
  • Xingcheng Lin, a postdoc in the Department of Chemistry, nominated by Associate Professor Bin Zhang for being “very talented, extremely hardworking, and genuinely enthusiastic about science;”
  • Alexandra Pike, a postdoc in the Department of Biology, nominated by Professor Stephen Bell for “not only excel[ing] in the laboratory” but also being “an exemplary citizen in the biology department, contributing to teaching, community, and to improving diversity, equity, and inclusion in the department;”
  • Nora Shipp, a postdoc with the Kavli Institute for Astrophysics and Space Research, nominated by Assistant Professor Lina Necib for being “independent, efficient, with great leadership qualities” with “impeccable” research; and
  • Jakob Voigts, a research scientist in the McGovern Institute for Brain Research, nominated by Associate Professor Mark Harnett and his laboratory for “contribut[ing] to the growth and development of the lab and its members in numerous and irreplaceable ways.”

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

Where did that sound come from?

Anne Trafton |

The human brain is finely tuned not only to recognize particular sounds, but also to determine which direction they came from. By comparing differences in sounds that reach the right and left ear, the brain can estimate the location of a barking dog, wailing fire engine, or approaching car.

MIT neuroscientists have now developed a computer model that can also perform that complex task. The model, which consists of several convolutional neural networks, not only performs the task as well as humans do, it also struggles in the same ways that humans do.

“We now have a model that can actually localize sounds in the real world,” says Josh McDermott, an associate professor of brain and cognitive sciences and a member of MIT’s McGovern Institute for Brain Research. “And when we treated the model like a human experimental participant and simulated this large set of experiments that people had tested humans on in the past, what we found over and over again is it the model recapitulates the results that you see in humans.”

Findings from the new study also suggest that humans’ ability to perceive location is adapted to the specific challenges of our environment, says McDermott, who is also a member of MIT’s Center for Brains, Minds, and Machines.

McDermott is the senior author of the paper, which appears today in Nature Human Behavior. The paper’s lead author is MIT graduate student Andrew Francl.

Modeling localization

When we hear a sound such as a train whistle, the sound waves reach our right and left ears at slightly different times and intensities, depending on what direction the sound is coming from. Parts of the midbrain are specialized to compare these slight differences to help estimate what direction the sound came from, a task also known as localization.

This task becomes markedly more difficult under real-world conditions — where the environment produces echoes and many sounds are heard at once.

Scientists have long sought to build computer models that can perform the same kind of calculations that the brain uses to localize sounds. These models sometimes work well in idealized settings with no background noise, but never in real-world environments, with their noises and echoes.

To develop a more sophisticated model of localization, the MIT team turned to convolutional neural networks. This kind of computer modeling has been used extensively to model the human visual system, and more recently, McDermott and other scientists have begun applying it to audition as well.

Convolutional neural networks can be designed with many different architectures, so to help them find the ones that would work best for localization, the MIT team used a supercomputer that allowed them to train and test about 1,500 different models. That search identified 10 that seemed the best-suited for localization, which the researchers further trained and used for all of their subsequent studies.

To train the models, the researchers created a virtual world in which they can control the size of the room and the reflection properties of the walls of the room. All of the sounds fed to the models originated from somewhere in one of these virtual rooms. The set of more than 400 training sounds included human voices, animal sounds, machine sounds such as car engines, and natural sounds such as thunder.

The researchers also ensured the model started with the same information provided by human ears. The outer ear, or pinna, has many folds that reflect sound, altering the frequencies that enter the ear, and these reflections vary depending on where the sound comes from. The researchers simulated this effect by running each sound through a specialized mathematical function before it went into the computer model.

“This allows us to give the model the same kind of information that a person would have,” Francl says.

After training the models, the researchers tested them in a real-world environment. They placed a mannequin with microphones in its ears in an actual room and played sounds from different directions, then fed those recordings into the models. The models performed very similarly to humans when asked to localize these sounds.

“Although the model was trained in a virtual world, when we evaluated it, it could localize sounds in the real world,” Francl says.

Similar patterns

The researchers then subjected the models to a series of tests that scientists have used in the past to study humans’ localization abilities.

In addition to analyzing the difference in arrival time at the right and left ears, the human brain also bases its location judgments on differences in the intensity of sound that reaches each ear. Previous studies have shown that the success of both of these strategies varies depending on the frequency of the incoming sound. In the new study, the MIT team found that the models showed this same pattern of sensitivity to frequency.

“The model seems to use timing and level differences between the two ears in the same way that people do, in a way that’s frequency-dependent,” McDermott says.

The researchers also showed that when they made localization tasks more difficult, by adding multiple sound sources played at the same time, the computer models’ performance declined in a way that closely mimicked human failure patterns under the same circumstances.

“As you add more and more sources, you get a specific pattern of decline in humans’ ability to accurately judge the number of sources present, and their ability to localize those sources,” Francl says. “Humans seem to be limited to localizing about three sources at once, and when we ran the same test on the model, we saw a really similar pattern of behavior.”

Because the researchers used a virtual world to train their models, they were also able to explore what happens when their model learned to localize in different types of unnatural conditions. The researchers trained one set of models in a virtual world with no echoes, and another in a world where there was never more than one sound heard at a time. In a third, the models were only exposed to sounds with narrow frequency ranges, instead of naturally occurring sounds.

When the models trained in these unnatural worlds were evaluated on the same battery of behavioral tests, the models deviated from human behavior, and the ways in which they failed varied depending on the type of environment they had been trained in. These results support the idea that the localization abilities of the human brain are adapted to the environments in which humans evolved, the researchers say.

The researchers are now applying this type of modeling to other aspects of audition, such as pitch perception and speech recognition, and believe it could also be used to understand other cognitive phenomena, such as the limits on what a person can pay attention to or remember, McDermott says.

The research was funded by the National Science Foundation and the National Institute on Deafness and Other Communication Disorders.

Five MIT faculty elected 2021 AAAS Fellows

by MIT News Office |

Five MIT faculty members have been elected as fellows of the American Association for the Advancement of Science (AAAS).

The 2021 class of AAAS Fellows includes 564 scientists, engineers, and innovators spanning 24 scientific disciplines who are being recognized for their scientifically and socially distinguished achievements.

Mircea Dincă is the W. M. Keck Professor of Energy in the Department of Chemistry. His group’s research focuses on addressing challenges related to the storage and consumption of energy, and global environmental concerns. Central to these efforts are the synthesis of novel organic-inorganic hybrid materials and the manipulation of their electrochemical and photophysical properties, with a current emphasis on porous materials and extended one-dimensional van der Waals materials.

Guoping Feng is the James W. and Patricia T. Poitras Professor of Neuroscience in the Department of Brain and Cognitive Sciences, associate director of MIT’s McGovern Institute for Brain Research, director of Model Systems and Neurobiology at the Stanley Center for Psychiatric Research, and an institute member of the Broad Institute of MIT and Harvard. His research is devoted to understanding the development and function of synapses in the brain and how synaptic dysfunction may contribute to neurodevelopmental and psychiatric disorders. By understanding the molecular, cellular, and circuitry mechanisms of these disorders, Feng hopes his work will eventually lead to the development of new and effective treatments for the millions of people suffering from these devastating diseases.

David Shoemaker is a senior research scientist with the MIT Kavli Institute for Astrophysics and Space Research. His work is focused on gravitational-wave observation and includes developing technologies for the detectors (LIGO, LISA), developing proposals for new instruments (Cosmic Explorer), managing the teams to build them and the consortia which exploit the data (LIGO Scientific Collaboration, LISA Consortium), and supporting the overall growth of the field (Gravitational-Wave International Committee).

Ian Hunter is the Hatsopoulos Professor of Mechanical Engineering and runs the Bioinstrumentation Lab at MIT. His main areas of research are instrumentation, microrobotics, medical devices, and biomimetic materials. Over the years he and his students have developed many instruments and devices including: confocal laser microscopes, scanning tunneling electron microscopes, miniature mass spectrometers, new forms of Raman spectroscopy, needle-free drug delivery technologies, nano- and micro-robots, microsurgical robots, robotic endoscopes, high-performance Lorentz force motors, and microarray technologies for massively parallel chemical and biological assays.

Evelyn N. Wang is the Ford Professor of Engineering and head of the Department of Mechanical Engineering. Her research program combines fundamental studies of micro/nanoscale heat and mass transport processes with the development of novel engineered structures to create innovative solutions in thermal management, energy, and water harvesting systems. Her work in thermophotovoltaics was named to Technology Review’s lists of Biggest Clean Energy Advances, in 2016, and Ten Breakthrough Technologies, in 2017, and to the Department of Energy Frontiers Research Center’s Ten of Ten awards. Her work extracting water from air has won her the title of 2017 Foreign Policy’s Global ReThinker and the 2018 Eighth Prince Sultan bin Abdulaziz International Prize for Water.