Author: MIT News

Alumnus gives MIT $4.5 million to study effects of cannabis on the brain

by MIT School of Science |

The following news is adapted from a press release issued in conjunction with Harvard Medical School.

Charles R. Broderick, an alumnus of MIT and Harvard University, has made gifts to both alma maters to support fundamental research into the effects of cannabis on the brain and behavior.

The gifts, totaling $9 million, represent the largest donation to date to support independent research on the science of cannabinoids. The donation will allow experts in the fields of neuroscience and biomedicine at MIT and Harvard Medical School to conduct research that may ultimately help unravel the biology of cannabinoids, illuminate their effects on the human brain, catalyze treatments, and inform evidence-based clinical guidelines, societal policies, and regulation of cannabis.

Lagging behind legislation

With the increasing use of cannabis both for medicinal and recreational purposes, there is a growing concern about critical gaps in knowledge.

In 2017, the National Academies of Sciences, Engineering, and Medicine issued a report calling upon philanthropic organizations, private companies, public agencies and others to develop a “comprehensive evidence base” on the short- and long-term health effects — both beneficial and harmful — of cannabis use.

“Our desire is to fill the research void that currently exists in the science of cannabis,” says Broderick, who was an early investor in Canada’s medical marijuana market.

Broderick is the founder of Uji Capital LLC, a family office focused on quantitative opportunities in global equity capital markets. Identifying the growth of the Canadian legal cannabis market as a strategic investment opportunity, Broderick took equity positions in Tweed Marijuana Inc. and Aphria Inc., which have since grown into two of North America’s most successful cannabis companies. Subsequently, Broderick made a private investment in and served as a board member for Tokyo Smoke, a cannabis brand portfolio, which merged in 2017 to create Hiku Brands, where he served as chairman. Hiku Brands was acquired by Canopy Growth Corp. in 2018.

Through the Broderick gifts to Harvard Medical School and MIT’s School of Science through the Picower Institute for Learning and Memory and the McGovern Institute for Brain Research, the Broderick funds will support independent studies of the neurobiology of cannabis; its effects on brain development, various organ systems and overall health, including treatment and therapeutic contexts; and cognitive, behavioral and social ramifications.

“I want to destigmatize the conversation around cannabis — and, in part, that means providing facts to the medical community, as well as the general public,” says Broderick, who argues that independent research needs to form the basis for policy discussions, regardless of whether it is good for business. “Then we’re all working from the same information. We need to replace rhetoric with research.”

MIT: Focused on brain health and function

The gift to MIT from Broderick will provide $4.5 million over three years to support independent research for four scientists at the McGovern and Picower institutes.

Two of these researchers — John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology, a professor of brain and cognitive sciences, and a member of MIT’s McGovern Institute for Brain Research; and Myriam Heiman, the Latham Family Associate Professor of Neuroscience at the Picower Institute — will separately explore the relationship between cannabis and schizophrenia.

Gabrieli, who directs the Martinos Imaging Center at MIT, will monitor any potential therapeutic value of cannabis for adults with schizophrenia using fMRI scans and behavioral studies.

“The ultimate goal is to improve brain health and wellbeing,” says Gabrieli. “And we have to make informed decisions on the way to this goal, wherever the science leads us. We need more data.”

Heiman, who is a molecular neuroscientist, will study how chronic exposure to phytocannabinoid molecules THC and CBD may alter the developmental molecular trajectories of cell types implicated in schizophrenia.

“Our lab’s research may provide insight into why several emerging lines of evidence suggest that adolescent cannabis use can be associated with adverse outcomes not seen in adults,” says Heiman.

In addition to these studies, Gabrieli also hopes to investigate whether cannabis can have therapeutic value for autism spectrum disorders, and Heiman plans to look at whether cannabis can have therapeutic value for Huntington’s disease.

MIT Institute Professor Ann Graybiel has proposed to study the cannabinoid 1 (CB1) receptor, which mediates many of the effects of cannabinoids. Her team recently found that CB1 receptors are tightly linked to dopamine — a neurotransmitter that affects both mood and motivation. Graybiel, who is also a member of the McGovern Institute, will examine how CB1 receptors in the striatum, a deep brain structure implicated in learning and habit formation, may influence dopamine release in the brain. These findings will be important for understanding the effects of cannabis on casual users, as well as its relationship to addictive states and neuropsychiatric disorders.

Earl Miller, Picower Professor of Neuroscience at the Picower Institute, will study effects of cannabinoids on both attention and working memory. His lab has recently formulated a model of working memory and unlocked how anesthetics reduce consciousness, showing in both cases a key role in the brain’s frontal cortex for brain rhythms, or the synchronous firing of neurons. He will observe how these rhythms may be affected by cannabis use — findings that may be able to shed light on tasks like driving where maintenance of attention is especially crucial.

Harvard Medical School: Mobilizing basic scientists and clinicians to solve an acute biomedical challenge 

The Broderick gift provides $4.5 million to establish the Charles R. Broderick Phytocannabinoid Research Initiative at Harvard Medical School, funding basic, translational and clinical research across the HMS community to generate fundamental insights about the effects of cannabinoids on brain function, various organ systems, and overall health.

The research initiative will span basic science and clinical disciplines, ranging from neurobiology and immunology to psychiatry and neurology, taking advantage of the combined expertise of some 30 basic scientists and clinicians across the school and its affiliated hospitals.

The epicenter of these research efforts will be the Department of Neurobiology under the leadership of Bruce Bean and Wade Regehr.

“I am excited by Bob’s commitment to cannabinoid science,” says Regehr, professor of neurobiology in the Blavatnik Institute at Harvard Medical School. “The research efforts enabled by Bob’s vision set the stage for unraveling some of the most confounding mysteries of cannabinoids and their effects on the brain and various organ systems.”

Bean, Regehr, and fellow neurobiologists Rachel Wilson and Bernardo Sabatini, for example, focus on understanding the basic biology of the cannabinoid system, which includes hundreds of plant and synthetic compounds as well as naturally occurring cannabinoids made in the brain.

Cannabinoid compounds activate a variety of brain receptors, and the downstream biological effects of this activation are astoundingly complex, varying by age and sex, and complicated by a person’s physiologic condition and overall health. This complexity and high degree of variability in individual biology has hampered scientific understanding of the positive and negative effects of cannabis on the human body. Bean, Regehr, and colleagues have already made critical insights showing how cannabinoids influence cell-to-cell communication in the brain.

“Even though cannabis products are now widely available, and some used clinically, we still understand remarkably little about how they influence brain function and neuronal circuits in the brain,” says Bean, the Robert Winthrop Professor of Neurobiology in the Blavatnik Institute at HMS. “This gift will allow us to conduct critical research into the neurobiology of cannabinoids, which may ultimately inform new approaches for the treatment of pain, epilepsy, sleep and mood disorders, and more.”

To propel research findings from lab to clinic, basic scientists from HMS will partner with clinicians from Harvard-affiliated hospitals, bringing together clinicians and scientists from disciplines including cardiology, vascular medicine, neurology, and immunology in an effort to glean a deeper and more nuanced understanding of cannabinoids’ effects on various organ systems and the body as a whole, rather than just on isolated organs.

For example, Bean and colleague Gary Yellen, who are studying the mechanisms of action of antiepileptic drugs, have become interested in the effects of cannabinoids on epilepsy, an interest they share with Elizabeth Thiele, director of the pediatric epilepsy program at Massachusetts General Hospital. Thiele is a pioneer in the use of cannabidiol for the treatment of drug-resistant forms of epilepsy. Despite proven clinical efficacy and recent FDA approval for rare childhood epilepsies, researchers still do not know exactly how cannabidiol quiets the misfiring brain cells of patients with the seizure disorder. Understanding its mechanism of action could help in developing new agents for treating other forms of epilepsy and other neurologic disorders.

Guoping Feng elected to American Academy of Arts and Sciences

by MIT News |

Four MIT faculty members are among more than 200 leaders from academia, business, public affairs, the humanities, and the arts elected to the American Academy of Arts and Sciences, the academy announced today.

One of the nation’s most prestigious honorary societies, the academy is also a leading center for independent policy research. Members contribute to academy publications, as well as studies of science and technology policy, energy and global security, social policy and American institutions, the humanities and culture, and education.

Those elected from MIT this year are:

  • Dimitri A. Antoniadis, Ray and Maria Stata Professor of Electrical Engineering;
  • Anantha P. Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science;
  • Guoping Feng, the James W. (1963) and Patricia T. Poitras Professor of Brain and Cognitive Sciences; and
  • David R. Karger, professor of electrical engineering.

“We are pleased to recognize the excellence of our new members, celebrate their compelling accomplishments, and invite them to join the academy and contribute to its work,” said David W. Oxtoby, president of the American Academy of Arts and Sciences. “With the election of these members, the academy upholds the ideals of research and scholarship, creativity and imagination, intellectual exchange and civil discourse, and the relentless pursuit of knowledge in all its forms.”

The new class will be inducted at a ceremony in October in Cambridge, Massachusetts.

Since its founding in 1780, the academy has elected leading “thinkers and doers” from each generation, including George Washington and Benjamin Franklin in the 18th century, Maria Mitchell and Daniel Webster in the 19th century, and Toni Morrison and Albert Einstein in the 20th century. The current membership includes more than 200 Nobel laureates and 100 Pulitzer Prize winners.

Elephant or chair? How the brain IDs objects

by Anne Trafton |

As visual information flows into the brain through the retina, the visual cortex transforms the sensory input into coherent perceptions. Neuroscientists have long hypothesized that a part of the visual cortex called the inferotemporal (IT) cortex is necessary for the key task of recognizing individual objects, but the evidence has been inconclusive.

In a new study, MIT neuroscientists have found clear evidence that the IT cortex is indeed required for object recognition; they also found that subsets of this region are responsible for distinguishing different objects.

In addition, the researchers have developed computational models that describe how these neurons transform visual input into a mental representation of an object. They hope such models will eventually help guide the development of brain-machine interfaces (BMIs) that could be used for applications such as generating images in the mind of a blind person.

“We don’t know if that will be possible yet, but this is a step on the pathway toward those kinds of applications that we’re thinking about,” says James DiCarlo, the head of MIT’s Department of Brain and Cognitive Sciences, a member of the McGovern Institute for Brain Research, and the senior author of the new study.

Rishi Rajalingham, a postdoc at the McGovern Institute, is the lead author of the paper, which appears in the March 13 issue of Neuron.

Distinguishing objects

In addition to its hypothesized role in object recognition, the IT cortex also contains “patches” of neurons that respond preferentially to faces. Beginning in the 1960s, neuroscientists discovered that damage to the IT cortex could produce impairments in recognizing non-face objects, but it has been difficult to determine precisely how important the IT cortex is for this task.

The MIT team set out to find more definitive evidence for the IT cortex’s role in object recognition, by selectively shutting off neural activity in very small areas of the cortex and then measuring how the disruption affected an object discrimination task. In animals that had been trained to distinguish between objects such as elephants, bears, and chairs, they used a drug called muscimol to temporarily turn off subregions about 2 millimeters in diameter. Each of these subregions represents about 5 percent of the entire IT cortex.

These experiments, which represent the first time that researchers have been able to silence such small regions of IT cortex while measuring behavior over many object discriminations, revealed that the IT cortex is not only necessary for distinguishing between objects, but it is also divided into areas that handle different elements of object recognition.

The researchers found that silencing each of these tiny patches produced distinctive impairments in the animals’ ability to distinguish between certain objects. For example, one subregion might be involved in distinguishing chairs from cars, but not chairs from dogs. Each region was involved in 25 to 30 percent of the tasks that the researchers tested, and regions that were closer to each other tended to have more overlap between their functions, while regions far away from each other had little overlap.

“We might have thought of it as a sea of neurons that are completely mixed together, except for these islands of “face patches.” But what we’re finding, which many other studies had pointed to, is that there is large-scale organization over the entire region,” Rajalingham says.

The features that each of these regions are responding to are difficult to classify, the researchers say. The regions are not specific to objects such as dogs, nor easy-to-describe visual features such as curved lines.

“It would be incorrect to say that because we observed a deficit in distinguishing cars when a certain neuron was inhibited, this is a ‘car neuron,’” Rajalingham says. “Instead, the cell is responding to a feature that we can’t explain that is useful for car discriminations. There has been work in this lab and others that suggests that the neurons are responding to complicated nonlinear features of the input image. You can’t say it’s a curve, or a straight line, or a face, but it’s a visual feature that is especially helpful in supporting that particular task.”

Bevil Conway, a principal investigator at the National Eye Institute, says the new study makes significant progress toward answering the critical question of how neural activity in the IT cortex produces behavior.

“The paper makes a major step in advancing our understanding of this connection, by showing that blocking activity in different small local regions of IT has a different selective deficit on visual discrimination. This work advances our knowledge not only of the causal link between neural activity and behavior but also of the functional organization of IT: How this bit of brain is laid out,” says Conway, who was not involved in the research.

Brain-machine interface

The experimental results were consistent with computational models that DiCarlo, Rajalingham, and others in their lab have created to try to explain how IT cortex neuron activity produces specific behaviors.

“That is interesting not only because it says the models are good, but because it implies that we could intervene with these neurons and turn them on and off,” DiCarlo says. “With better tools, we could have very large perceptual effects and do real BMI in this space.”

The researchers plan to continue refining their models, incorporating new experimental data from even smaller populations of neurons, in hopes of developing ways to generate visual perception in a person’s brain by activating a specific sequence of neuronal activity. Technology to deliver this kind of input to a person’s brain could lead to new strategies to help blind people see certain objects.

“This is a step in that direction,” DiCarlo says. “It’s still a dream, but that dream someday will be supported by the models that are built up by this kind of work.”

The research was funded by the National Eye Institute, the Office of Naval Research, and the Simons Foundation.

MRI sensor images deep brain activity

by Anne Trafton |

Calcium is a critical signaling molecule for most cells, and it is especially important in neurons. Imaging calcium in brain cells can reveal how neurons communicate with each other; however, current imaging techniques can only penetrate a few millimeters into the brain.

MIT researchers have now devised a new way to image calcium activity that is based on magnetic resonance imaging (MRI) and allows them to peer much deeper into the brain. Using this technique, they can track signaling processes inside the neurons of living animals, enabling them to link neural activity with specific behaviors.

“This paper describes the first MRI-based detection of intracellular calcium signaling, which is directly analogous to powerful optical approaches used widely in neuroscience but now enables such measurements to be performed in vivo in deep tissue,” says Alan Jasanoff, an MIT professor of biological engineering, brain and cognitive sciences, and nuclear science and engineering, and an associate member of MIT’s McGovern Institute for Brain Research.

Jasanoff is the senior author of the paper, which appears in the Feb. 22 issue of Nature Communications. MIT postdocs Ali Barandov and Benjamin Bartelle are the paper’s lead authors. MIT senior Catherine Williamson, recent MIT graduate Emily Loucks, and Arthur Amos Noyes Professor Emeritus of Chemistry Stephen Lippard are also authors of the study.

Getting into cells

In their resting state, neurons have very low calcium levels. However, when they fire an electrical impulse, calcium floods into the cell. Over the past several decades, scientists have devised ways to image this activity by labeling calcium with fluorescent molecules. This can be done in cells grown in a lab dish, or in the brains of living animals, but this kind of microscopy imaging can only penetrate a few tenths of a millimeter into the tissue, limiting most studies to the surface of the brain.

“There are amazing things being done with these tools, but we wanted something that would allow ourselves and others to look deeper at cellular-level signaling,” Jasanoff says.

To achieve that, the MIT team turned to MRI, a noninvasive technique that works by detecting magnetic interactions between an injected contrast agent and water molecules inside cells.

Many scientists have been working on MRI-based calcium sensors, but the major obstacle has been developing a contrast agent that can get inside brain cells. Last year, Jasanoff’s lab developed an MRI sensor that can measure extracellular calcium concentrations, but these were based on nanoparticles that are too large to enter cells.

To create their new intracellular calcium sensors, the researchers used building blocks that can pass through the cell membrane. The contrast agent contains manganese, a metal that interacts weakly with magnetic fields, bound to an organic compound that can penetrate cell membranes. This complex also contains a calcium-binding arm called a chelator.

Once inside the cell, if calcium levels are low, the calcium chelator binds weakly to the manganese atom, shielding the manganese from MRI detection. When calcium flows into the cell, the chelator binds to the calcium and releases the manganese, which makes the contrast agent appear brighter in an MRI image.

“When neurons, or other brain cells called glia, become stimulated, they often experience more than tenfold increases in calcium concentration. Our sensor can detect those changes,” Jasanoff says.

Precise measurements

The researchers tested their sensor in rats by injecting it into the striatum, a region deep within the brain that is involved in planning movement and learning new behaviors. They then used potassium ions to stimulate electrical activity in neurons of the striatum, and were able to measure the calcium response in those cells.

Jasanoff hopes to use this technique to identify small clusters of neurons that are involved in specific behaviors or actions. Because this method directly measures signaling within cells, it can offer much more precise information about the location and timing of neuron activity than traditional functional MRI (fMRI), which measures blood flow in the brain.

“This could be useful for figuring out how different structures in the brain work together to process stimuli or coordinate behavior,” he says.

In addition, this technique could be used to image calcium as it performs many other roles, such as facilitating the activation of immune cells. With further modification, it could also one day be used to perform diagnostic imaging of the brain or other organs whose functions rely on calcium, such as the heart.

The research was funded by the National Institutes of Health and the MIT Simons Center for the Social Brain.

Mapping the brain at high resolution

Anne Trafton |

Researchers have developed a new way to image the brain with unprecedented resolution and speed. Using this approach, they can locate individual neurons, trace connections between them, and visualize organelles inside neurons, over large volumes of brain tissue.

The new technology combines a method for expanding brain tissue, making it possible to image at higher resolution, with a rapid 3-D microscopy technique known as lattice light-sheet microscopy. In a paper appearing in Science Jan. 17, the researchers showed that they could use these techniques to image the entire fruit fly brain, as well as large sections of the mouse brain, much faster than has previously been possible. The team includes researchers from MIT, the University of California at Berkeley, the Howard Hughes Medical Institute, and Harvard Medical School/Boston Children’s Hospital.

This technique allows researchers to map large-scale circuits within the brain while also offering unique insight into individual neurons’ functions, says Edward Boyden, the Y. Eva Tan Professor in Neurotechnology, an associate professor of biological engineering and of brain and cognitive sciences at MIT, and a member of MIT’s McGovern Institute for Brain Research, Media Lab, and Koch Institute for Integrative Cancer Research.

“A lot of problems in biology are multiscale,” Boyden says. “Using lattice light-sheet microscopy, along with the expansion microscopy process, we can now image at large scale without losing sight of the nanoscale configuration of biomolecules.”

Boyden is one of the study’s senior authors, along with Eric Betzig, a senior fellow at the Janelia Research Campus and a professor of physics and molecular and cell biology at UC Berkeley. The paper’s lead authors are MIT postdoc Ruixuan Gao, former MIT postdoc Shoh Asano, and Harvard Medical School Assistant Professor Srigokul Upadhyayula.

Large-scale imaging

In 2015, Boyden’s lab developed a way to generate very high-resolution images of brain tissue using an ordinary light microscope. Their technique relies on expanding tissue before imaging it, allowing them to image the tissue at a resolution of about 60 nanometers. Previously, this kind of imaging could be achieved only with very expensive high-resolution microscopes, known as super-resolution microscopes.

In the new study, Boyden teamed up with Betzig and his colleagues at HHMI’s Janelia Research Campus to combine expansion microscopy with lattice light-sheet microscopy. This technology, which Betzig developed several years ago, has some key traits that make it ideal to pair with expansion microscopy: It can image large samples rapidly, and it induces much less photodamage than other fluorescent microscopy techniques.

“The marrying of the lattice light-sheet microscope with expansion microscopy is essential to achieve the sensitivity, resolution, and scalability of the imaging that we’re doing,” Gao says.

Imaging expanded tissue samples generates huge amounts of data — up to tens of terabytes per sample — so the researchers also had to devise highly parallelized computational image-processing techniques that could break down the data into smaller chunks, analyze it, and stitch it back together into a coherent whole.

In the Science paper, the researchers demonstrated the power of their new technique by imaging layers of neurons in the somatosensory cortex of mice, after expanding the tissue volume fourfold. They focused on a type of neuron known as pyramidal cells, one of the most common excitatory neurons found in the nervous system. To locate synapses, or connections, between these neurons, they labeled proteins found in the presynaptic and postsynaptic regions of the cells. This also allowed them to compare the density of synapses in different parts of the cortex.

Using this technique, it is possible to analyze millions of synapses in just a few days.

“We counted clusters of postsynaptic markers across the cortex, and we saw differences in synaptic density in different layers of the cortex,” Gao says. “Using electron microscopy, this would have taken years to complete.”

The researchers also studied patterns of axon myelination in different neurons. Myelin is a fatty substance that insulates axons and whose disruption is a hallmark of multiple sclerosis. The researchers were able to compute the thickness of the myelin coating in different segments of axons, and they measured the gaps between stretches of myelin, which are important because they help conduct electrical signals. Previously, this kind of myelin tracing would have required months to years for human annotators to perform.

This technology can also be used to image tiny organelles inside neurons. In the new paper, the researchers identified mitochondria and lysosomes, and they also measured variations in the shapes of these organelles.

Circuit analysis

The researchers demonstrated that this technique could be used to analyze brain tissue from other organisms as well; they used it to image the entire brain of the fruit fly, which is the size of a poppy seed and contains about 100,000 neurons. In one set of experiments, they traced an olfactory circuit that extends across several brain regions, imaged all dopaminergic neurons, and counted all synapses across the brain. By comparing multiple animals, they also found differences in the numbers and arrangements of synaptic boutons within each animal’s olfactory circuit.

In future work, Boyden envisions that this technique could be used to trace circuits that control memory formation and recall, to study how sensory input leads to a specific behavior, or to analyze how emotions are coupled to decision-making.

“These are all questions at a scale that you can’t answer with classical technologies,” he says.

The system could also have applications beyond neuroscience, Boyden says. His lab is planning to work with other researchers to study how HIV evades the immune system, and the technology could also be adapted to study how cancer cells interact with surrounding cells, including immune cells.

The research was funded by John Doerr, K. Lisa Yang and Y. Eva Tan, the Open Philanthropy Project, the National Institutes of Health, the Howard Hughes Medical Institute, the HHMI-Simons Faculty Scholars Program, the U.S. Army Research Laboratory and Army Research Office, the US-Israel Binational Science Foundation, Biogen, and Ionis Pharmaceuticals.

Team invents method to shrink objects to the nanoscale

Anne Trafton |

MIT researchers have invented a way to fabricate nanoscale 3-D objects of nearly any shape. They can also pattern the objects with a variety of useful materials, including metals, quantum dots, and DNA.

“It’s a way of putting nearly any kind of material into a 3-D pattern with nanoscale precision,” says Edward Boyden, the Y. Eva Tan Professor in Neurotechnology and an associate professor of biological engineering and of brain and cognitive sciences at MIT.

Using the new technique, the researchers can create any shape and structure they want by patterning a polymer scaffold with a laser. After attaching other useful materials to the scaffold, they shrink it, generating structures one thousandth the volume of the original.

These tiny structures could have applications in many fields, from optics to medicine to robotics, the researchers say. The technique uses equipment that many biology and materials science labs already have, making it widely accessible for researchers who want to try it.

Boyden, who is also a member of MIT’s Media Lab, McGovern Institute for Brain Research, and Koch Institute for Integrative Cancer Research, is one of the senior authors of the paper, which appears in the Dec. 13 issue of Science. The other senior author is Adam Marblestone, a Media Lab research affiliate, and the paper’s lead authors are graduate students Daniel Oran and Samuel Rodriques.

Implosion fabrication

Existing techniques for creating nanostructures are limited in what they can accomplish. Etching patterns onto a surface with light can produce 2-D nanostructures but doesn’t work for 3-D structures. It is possible to make 3-D nanostructures by gradually adding layers on top of each other, but this process is slow and challenging. And, while methods exist that can directly 3-D print nanoscale objects, they are restricted to specialized materials like polymers and plastics, which lack the functional properties necessary for many applications. Furthermore, they can only generate self-supporting structures. (The technique can yield a solid pyramid, for example, but not a linked chain or a hollow sphere.)

To overcome these limitations, Boyden and his students decided to adapt a technique that his lab developed a few years ago for high-resolution imaging of brain tissue. This technique, known as expansion microscopy, involves embedding tissue into a hydrogel and then expanding it, allowing for high resolution imaging with a regular microscope. Hundreds of research groups in biology and medicine are now using expansion microscopy, since it enables 3-D visualization of cells and tissues with ordinary hardware.

By reversing this process, the researchers found that they could create large-scale objects embedded in expanded hydrogels and then shrink them to the nanoscale, an approach that they call “implosion fabrication.”

As they did for expansion microscopy, the researchers used a very absorbent material made of polyacrylate, commonly found in diapers, as the scaffold for their nanofabrication process. The scaffold is bathed in a solution that contains molecules of fluorescein, which attach to the scaffold when they are activated by laser light.

Using two-photon microscopy, which allows for precise targeting of points deep within a structure, the researchers attach fluorescein molecules to specific locations within the gel. The fluorescein molecules act as anchors that can bind to other types of molecules that the researchers add.

“You attach the anchors where you want with light, and later you can attach whatever you want to the anchors,” Boyden says. “It could be a quantum dot, it could be a piece of DNA, it could be a gold nanoparticle.”

“It’s a bit like film photography — a latent image is formed by exposing a sensitive material in a gel to light. Then, you can develop that latent image into a real image by attaching another material, silver, afterwards. In this way implosion fabrication can create all sorts of structures, including gradients, unconnected structures, and multimaterial patterns,” Oran says.

Once the desired molecules are attached in the right locations, the researchers shrink the entire structure by adding an acid. The acid blocks the negative charges in the polyacrylate gel so that they no longer repel each other, causing the gel to contract. Using this technique, the researchers can shrink the objects 10-fold in each dimension (for an overall 1,000-fold reduction in volume). This ability to shrink not only allows for increased resolution, but also makes it possible to assemble materials in a low-density scaffold. This enables easy access for modification, and later the material becomes a dense solid when it is shrunk.

“People have been trying to invent better equipment to make smaller nanomaterials for years, but we realized that if you just use existing systems and embed your materials in this gel, you can shrink them down to the nanoscale, without distorting the patterns,” Rodriques says.

Currently, the researchers can create objects that are around 1 cubic millimeter, patterned with a resolution of 50 nanometers. There is a tradeoff between size and resolution: If the researchers want to make larger objects, about 1 cubic centimeter, they can achieve a resolution of about 500 nanometers. However, that resolution could be improved with further refinement of the process, the researchers say.

Better optics

The MIT team is now exploring potential applications for this technology, and they anticipate that some of the earliest applications might be in optics — for example, making specialized lenses that could be used to study the fundamental properties of light. This technique might also allow for the fabrication of smaller, better lenses for applications such as cell phone cameras, microscopes, or endoscopes, the researchers say. Farther in the future, the researchers say that this approach could be used to build nanoscale electronics or robots.

“There are all kinds of things you can do with this,” Boyden says. “Democratizing nanofabrication could open up frontiers we can’t yet imagine.”

Many research labs are already stocked with the equipment required for this kind of fabrication. “With a laser you can already find in many biology labs, you can scan a pattern, then deposit metals, semiconductors, or DNA, and then shrink it down,” Boyden says.

The research was funded by the Kavli Dream Team Program, the HHMI-Simons Faculty Scholars Program, the Open Philanthropy Project, John Doerr, the Office of Naval Research, the National Institutes of Health, the New York Stem Cell Foundation-Robertson Award, the U.S. Army Research Office, K. Lisa Yang and Y. Eva Tan, and the MIT Media Lab.

How the brain switches between different sets of rules

Anne Trafton |

Cognitive flexibility — the brain’s ability to switch between different rules or action plans depending on the context — is key to many of our everyday activities. For example, imagine you’re driving on a highway at 65 miles per hour. When you exit onto a local street, you realize that the situation has changed and you need to slow down.

When we move between different contexts like this, our brain holds multiple sets of rules in mind so that it can switch to the appropriate one when necessary. These neural representations of task rules are maintained in the prefrontal cortex, the part of the brain responsible for planning action.

A new study from MIT has found that a region of the thalamus is key to the process of switching between the rules required for different contexts. This region, called the mediodorsal thalamus, suppresses representations that are not currently needed. That suppression also protects the representations as a short-term memory that can be reactivated when needed.

“It seems like a way to toggle between irrelevant and relevant contexts, and one advantage is that it protects the currently irrelevant representations from being overwritten,” says Michael Halassa, an assistant professor of brain and cognitive sciences and a member of MIT’s McGovern Institute for Brain Research.

Halassa is the senior author of the paper, which appears in the Nov. 19 issue of Nature Neuroscience. The paper’s first author is former MIT graduate student Rajeev Rikhye, who is now a postdoc in Halassa’s lab. Aditya Gilra, a postdoc at the University of Bonn, is also an author.

Changing the rules

Previous studies have found that the prefrontal cortex is essential for cognitive flexibility, and that a part of the thalamus called the mediodorsal thalamus also contributes to this ability. In a 2017 study published in Nature, Halassa and his colleagues showed that the mediodorsal thalamus helps the prefrontal cortex to keep a thought in mind by temporarily strengthening the neuronal connections in the prefrontal cortex that encode that particular thought.

In the new study, Halassa wanted to further investigate the relationship between the mediodorsal thalamus and the prefrontal cortex. To do that, he created a task in which mice learn to switch back and forth between two different contexts — one in which they must follow visual instructions and one in which they must follow auditory instructions.

In each trial, the mice are given both a visual target (flash of light to the right or left) and an auditory target (a tone that sweeps from high to low pitch, or vice versa). These targets offer conflicting instructions. One tells the mouse to go to the right to get a reward; the other tells it to go left. Before each trial begins, the mice are given a cue that tells them whether to follow the visual or auditory target.

“The only way for the animal to solve the task is to keep the cue in mind over the entire delay, until the targets are given,” Halassa says.

The researchers found that thalamic input is necessary for the mice to successfully switch from one context to another. When they suppressed the mediodorsal thalamus during the cuing period of a series of trials in which the context did not change, there was no effect on performance. However, if they suppressed the mediodorsal thalamus during the switch to a different context, it took the mice much longer to switch.

By recording from neurons of the prefrontal cortex, the researchers found that when the mediodorsal thalamus was suppressed, the representation of the old context in the prefrontal cortex could not be turned off, making it much harder to switch to the new context.

In addition to helping the brain switch between contexts, this process also appears to help maintain the neural representation of the context that is not currently being used, so that it doesn’t get overwritten, Halassa says. This allows it to be activated again when needed. The mice could maintain these representations over hundreds of trials, but the next day, they had to relearn the rules associated with each context.

Sabine Kastner, a professor of psychology at the Princeton Neuroscience Institute, described the study as a major leap forward in the field of cognitive neuroscience.

“This is a tour-de-force from beginning to end, starting with a sophisticated behavioral design, state-of-the-art methods including causal manipulations, exciting empirical results that point to cell-type specific differences and interactions in functionality between thalamus and cortex, and a computational approach that links the neuroscience results to the field of artificial intelligence,” says Kastner, who was not involved in the research.

Multitasking AI

The findings could help guide the development of better artificial intelligence algorithms, Halassa says. The human brain is very good at learning many different kinds of tasks — singing, walking, talking, etc. However, neural networks (a type of artificial intelligence based on interconnected nodes similar to neurons) usually are good at learning only one thing. These networks are subject to a phenomenon called “catastrophic forgetting” — when they try to learn a new task, previous tasks become overwritten.

Halassa and his colleagues now hope to apply their findings to improve neural networks’ ability to store previously learned tasks while learning to perform new ones.

The research was funded by the National Institutes of Health, the Brain and Behavior Foundation, the Klingenstein Foundation, the Pew Foundation, the Simons Foundation, the Human Frontiers Science Program, and the German Ministry of Education.

Brain activity pattern may be early sign of schizophrenia

Anne Trafton |

Schizophrenia, a brain disorder that produces hallucinations, delusions, and cognitive impairments, usually strikes during adolescence or young adulthood. While some signs can suggest that a person is at high risk for developing the disorder, there is no way to definitively diagnose it until the first psychotic episode occurs.

MIT neuroscientists working with researchers at Beth Israel Deaconess Medical Center, Brigham and Women’s Hospital, and the Shanghai Mental Health Center have now identified a pattern of brain activity correlated with development of schizophrenia, which they say could be used as a marker to diagnose the disease earlier.

“You can consider this pattern to be a risk factor. If we use these types of brain measurements, then maybe we can predict a little bit better who will end up developing psychosis, and that may also help tailor interventions,” says Guusje Collin, a visiting scientist at MIT’s McGovern Institute for Brain Research and the lead author of the paper.

The study, which appeared in the journal Molecular Psychiatry on Nov. 8, was performed at the Shanghai Mental Health Center. Susan Whitfield-Gabrieli, a visiting scientist at the McGovern Institute and a professor of psychology at Northeastern University, is one of the principal investigators for the study, along with Jijun Wang of the Shanghai Mental Health Center, William Stone of Beth Israel Deaconess Medical Center, the late Larry Seidman of Beth Israel Deaconess Medical Center, and Martha Shenton of Brigham and Women’s Hospital.

Abnormal connections

Before they experience a psychotic episode, characterized by sudden changes in behavior and a loss of touch with reality, patients can experience milder symptoms such as disordered thinking. This kind of thinking can lead to behaviors such as jumping from topic to topic at random, or giving answers unrelated to the original question. Previous studies have shown that about 25 percent of people who experience these early symptoms go on to develop schizophrenia.

The research team performed the study at the Shanghai Mental Health Center because the huge volume of patients who visit the hospital annually gave them a large enough sample of people at high risk of developing schizophrenia.

The researchers followed 158 people between the ages of 13 and 34 who were identified as high-risk because they had experienced early symptoms. The team also included 93 control subjects, who did not have any risk factors. At the beginning of the study, the researchers used functional magnetic resonance imaging (fMRI) to measure a type of brain activity involving “resting state networks.” Resting state networks consist of brain regions that preferentially connect with and communicate with each other when the brain is not performing any particular cognitive task.

“We were interested in looking at the intrinsic functional architecture of the brain to see if we could detect early aberrant brain connectivity or networks in individuals who are in the clinically high-risk phase of the disorder,” Whitfield-Gabrieli says.

One year after the initial scans, 23 of the high-risk patients had experienced a psychotic episode and were diagnosed with schizophrenia. In those patients’ scans, taken before their diagnosis, the researchers found a distinctive pattern of activity that was different from the healthy control subjects and the at-risk subjects who had not developed psychosis.

For example, in most people, a part of the brain known as the superior temporal gyrus, which is involved in auditory processing, is highly connected to brain regions involved in sensory perception and motor control. However, in patients who developed psychosis, the superior temporal gyrus became more connected to limbic regions, which are involved in processing emotions. This could help explain why patients with schizophrenia usually experience auditory hallucinations, the researchers say.

Meanwhile, the high-risk subjects who did not develop psychosis showed network connectivity nearly identical to that of the healthy subjects.

Early intervention

This type of distinctive brain activity could be useful as an early indicator of schizophrenia, especially since it is possible that it could be seen in even younger patients. The researchers are now performing similar studies with younger at-risk populations, including children with a family history of schizophrenia.

“That really gets at the heart of how we can translate this clinically, because we can get in earlier and earlier to identify aberrant networks in the hopes that we can do earlier interventions, and possibly even prevent psychiatric disorders,” Whitfield-Gabrieli says.

She and her colleagues are now testing early interventions that could help to combat the symptoms of schizophrenia, including cognitive behavioral therapy and neural feedback. The neural feedback approach involves training patients to use mindfulness meditation to reduce activity in the superior temporal gyrus, which tends to increase before and during auditory hallucinations.

The researchers also plan to continue following the patients in the current study, and they are now analyzing some additional data on the white matter connections in the brains of these patients, to see if those connections might yield additional differences that could also serve as early indicators of disease.

The research was funded by the National Institutes of Health, the Ministry of Science and Technology of China, and the Poitras Center for Psychiatric Disorders Research at MIT. Collin was supported by a Marie Curie Global Fellowship grant from the European Commission.

Machines that learn language more like kids do

by Rob Matheson |

Children learn language by observing their environment, listening to the people around them, and connecting the dots between what they see and hear. Among other things, this helps children establish their language’s word order, such as where subjects and verbs fall in a sentence.

In computing, learning language is the task of syntactic and semantic parsers. These systems are trained on sentences annotated by humans that describe the structure and meaning behind words. Parsers are becoming increasingly important for web searches, natural-language database querying, and voice-recognition systems such as Alexa and Siri. Soon, they may also be used for home robotics.

But gathering the annotation data can be time-consuming and difficult for less common languages. Additionally, humans don’t always agree on the annotations, and the annotations themselves may not accurately reflect how people naturally speak.

In a paper being presented at this week’s Empirical Methods in Natural Language Processing conference, MIT researchers describe a parser that learns through observation to more closely mimic a child’s language-acquisition process, which could greatly extend the parser’s capabilities. To learn the structure of language, the parser observes captioned videos, with no other information, and associates the words with recorded objects and actions. Given a new sentence, the parser can then use what it’s learned about the structure of the language to accurately predict a sentence’s meaning, without the video.

This “weakly supervised” approach — meaning it requires limited training data — mimics how children can observe the world around them and learn language, without anyone providing direct context. The approach could expand the types of data and reduce the effort needed for training parsers, according to the researchers. A few directly annotated sentences, for instance, could be combined with many captioned videos, which are easier to come by, to improve performance.

In the future, the parser could be used to improve natural interaction between humans and personal robots. A robot equipped with the parser, for instance, could constantly observe its environment to reinforce its understanding of spoken commands, including when the spoken sentences aren’t fully grammatical or clear. “People talk to each other in partial sentences, run-on thoughts, and jumbled language. You want a robot in your home that will adapt to their particular way of speaking … and still figure out what they mean,” says co-author Andrei Barbu, a researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds, and Machines (CBMM) within MIT’s McGovern Institute.

The parser could also help researchers better understand how young children learn language. “A child has access to redundant, complementary information from different modalities, including hearing parents and siblings talk about the world, as well as tactile information and visual information, [which help him or her] to understand the world,” says co-author Boris Katz, a principal research scientist and head of the InfoLab Group at CSAIL. “It’s an amazing puzzle, to process all this simultaneous sensory input. This work is part of bigger piece to understand how this kind of learning happens in the world.”

Co-authors on the paper are: first author Candace Ross, a graduate student in the Department of Electrical Engineering and Computer Science and CSAIL, and a researcher in CBMM; Yevgeni Berzak PhD ’17, a postdoc in the Computational Psycholinguistics Group in the Department of Brain and Cognitive Sciences; and CSAIL graduate student Battushig Myanganbayar.

Visual learner

For their work, the researchers combined a semantic parser with a computer-vision component trained in object, human, and activity recognition in video. Semantic parsers are generally trained on sentences annotated with code that ascribes meaning to each word and the relationships between the words. Some have been trained on still images or computer simulations.

The new parser is the first to be trained using video, Ross says. In part, videos are more useful in reducing ambiguity. If the parser is unsure about, say, an action or object in a sentence, it can reference the video to clear things up. “There are temporal components — objects interacting with each other and with people — and high-level properties you wouldn’t see in a still image or just in language,” Ross says.

The researchers compiled a dataset of about 400 videos depicting people carrying out a number of actions, including picking up an object or putting it down, and walking toward an object. Participants on the crowdsourcing platform Mechanical Turk then provided 1,200 captions for those videos. They set aside 840 video-caption examples for training and tuning, and used 360 for testing. One advantage of using vision-based parsing is “you don’t need nearly as much data — although if you had [the data], you could scale up to huge datasets,” Barbu says.

In training, the researchers gave the parser the objective of determining whether a sentence accurately describes a given video. They fed the parser a video and matching caption. The parser extracts possible meanings of the caption as logical mathematical expressions. The sentence, “The woman is picking up an apple,” for instance, may be expressed as: λxy. woman x, pick_up x y, apple y.

Those expressions and the video are inputted to the computer-vision algorithm, called “Sentence Tracker,” developed by Barbu and other researchers. The algorithm looks at each video frame to track how objects and people transform over time, to determine if actions are playing out as described. In this way, it determines if the meaning is possibly true of the video.

Connecting the dots

The expression with the most closely matching representations for objects, humans, and actions becomes the most likely meaning of the caption. The expression, initially, may refer to many different objects and actions in the video, but the set of possible meanings serves as a training signal that helps the parser continuously winnow down possibilities. “By assuming that all of the sentences must follow the same rules, that they all come from the same language, and seeing many captioned videos, you can narrow down the meanings further,” Barbu says.

In short, the parser learns through passive observation: To determine if a caption is true of a video, the parser by necessity must identify the highest probability meaning of the caption. “The only way to figure out if the sentence is true of a video [is] to go through this intermediate step of, ‘What does the sentence mean?’ Otherwise, you have no idea how to connect the two,” Barbu explains. “We don’t give the system the meaning for the sentence. We say, ‘There’s a sentence and a video. The sentence has to be true of the video. Figure out some intermediate representation that makes it true of the video.’”

The training produces a syntactic and semantic grammar for the words it’s learned. Given a new sentence, the parser no longer requires videos, but leverages its grammar and lexicon to determine sentence structure and meaning.

Ultimately, this process is learning “as if you’re a kid,” Barbu says. “You see world around you and hear people speaking to learn meaning. One day, I can give you a sentence and ask what it means and, even without a visual, you know the meaning.”

“This research is exactly the right direction for natural language processing,” says Stefanie Tellex, a professor of computer science at Brown University who focuses on helping robots use natural language to communicate with humans. “To interpret grounded language, we need semantic representations, but it is not practicable to make it available at training time. Instead, this work captures representations of compositional structure using context from captioned videos. This is the paper I have been waiting for!”

In future work, the researchers are interested in modeling interactions, not just passive observations. “Children interact with the environment as they’re learning. Our idea is to have a model that would also use perception to learn,” Ross says.

This work was supported, in part, by the CBMM, the National Science Foundation, a Ford Foundation Graduate Research Fellowship, the Toyota Research Institute, and the MIT-IBM Brain-Inspired Multimedia Comprehension project.

Study reveals how the brain overcomes its own limitations

Anne Trafton |

Imagine trying to write your name so that it can be read in a mirror. Your brain has all of the visual information you need, and you’re a pro at writing your own name. Still, this task is very difficult for most people. That’s because it requires the brain to perform a mental transformation that it’s not familiar with: using what it sees in the mirror to accurately guide your hand to write backward.

MIT neuroscientists have now discovered how the brain tries to compensate for its poor performance in tasks that require this kind of complicated transformation. As it also does in other types of situations where it has little confidence in its own judgments, the brain attempts to overcome its difficulties by relying on previous experiences.

“If you’re doing something that requires a harder mental transformation, and therefore creates more uncertainty and more variability, you rely on your prior beliefs and bias yourself toward what you know how to do well, in order to compensate for that variability,” says Mehrdad Jazayeri, the Robert A. Swanson Career Development Professor of Life Sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

This strategy actually improves overall performance, the researchers report in their study, which appears in the Oct. 24 issue of the journal Nature Communications. Evan Remington, a McGovern Institute postdoc, is the paper’s lead author, and technical assistant Tiffany Parks is also an author on the paper.

Noisy computations

Neuroscientists have known for many decades that the brain does not faithfully reproduce exactly what the eyes see or what the ears hear. Instead, there is a great deal of “noise” — random fluctuations of electrical activity in the brain, which can come from uncertainty or ambiguity about what we are seeing or hearing. This uncertainty also comes into play in social interactions, as we try to interpret the motivations of other people, or when recalling memories of past events.

Previous research has revealed many strategies that help the brain to compensate for this uncertainty. Using a framework known as Bayesian integration, the brain combines multiple, potentially conflicting pieces of information and values them according to their reliability. For example, if given information by two sources, we’ll rely more on the one that we believe to be more credible.

In other cases, such as making movements when we’re uncertain exactly how to proceed, the brain will rely on an average of its past experiences. For example, when reaching for a light switch in a dark, unfamiliar room, we’ll move our hand toward a certain height and close to the doorframe, where past experience suggests a light switch might be located.

All of these strategies have been previously shown to work together to increase bias toward a particular outcome, which makes our overall performance better because it reduces variability, Jazayeri says.

Noise can also occur in the mental conversion of sensory information into a motor plan. In many cases, this is a straightforward task in which noise plays a minimal role — for example, reaching for a mug that you can see on your desk. However, for other tasks, such as the mirror-writing exercise, this conversion is much more complicated.

“Your performance will be variable, and it’s not because you don’t know where your hand is, and it’s not because you don’t know where the image is,” Jazayeri says. “It involves an entirely different form of uncertainty, which has to do with processing information. The act of performing mental transformations of information clearly induces variability.”

That type of mental conversion is what the researchers set out to explore in the new study. To do that, they asked subjects to perform three different tasks. For each one, they compared subjects’ performance in a version of the task where mapping sensory information to motor commands was easy, and a version where an extra mental transformation was required.

In one example, the researchers first asked participants to draw a line the same length as a line they were shown, which was always between 5 and 10 centimeters. In the more difficult version, they were asked to draw a line 1.5 times longer than the original line.

The results from this set of experiments, as well as the other two tasks, showed that in the version that required difficult mental transformations, people altered their performance using the same strategies that they use to overcome noise in sensory perception and other realms. For example, in the line-drawing task, in which the participants had to draw lines ranging from 7.5 to 15 centimeters, depending on the length of the original line, they tended to draw lines that were closer to the average length of all the lines they had previously drawn. This made their responses overall less variable and also more accurate.

“This regression to the mean is a very common strategy for making performance better when there is uncertainty,” Jazayeri says.

Noise reduction

The new findings led the researchers to hypothesize that when people get very good at a task that requires complex computation, the noise will become smaller and less detrimental to overall performance. That is, people will trust their computations more and stop relying on averages.

“As it gets easier, our prediction is the bias will go away, because that computation is no longer a noisy computation,” Jazayeri says. “You believe in the computation; you know the computation is working well.”

The researchers now plan to further study whether people’s biases decrease as they learn to perform a complicated task better. In the experiments they performed for the Nature Communications study, they found some preliminary evidence that trained musicians performed better in a task that involved producing time intervals of a specific duration.

The research was funded by the Alfred P. Sloan Foundation, the Esther A. and Joseph Klingenstein Fund, the Simons Foundation, the McKnight Endowment Fund for Neuroscience, and the McGovern Institute.