Author: MIT News

How the brain generates rhythmic behavior

Anne Trafton |

Many of our bodily functions, such as walking, breathing, and chewing, are controlled by brain circuits called central oscillators, which generate rhythmic firing patterns that regulate these behaviors.

MIT neuroscientists have now discovered the neuronal identity and mechanism underlying one of these circuits: an oscillator that controls the rhythmic back-and-forth sweeping of tactile whiskers, or whisking, in mice. This is the first time that any such oscillator has been fully characterized in mammals.

The MIT team found that the whisking oscillator consists of a population of inhibitory neurons in the brainstem that fires rhythmic bursts during whisking. As each neuron fires, it also inhibits some of the other neurons in the network, allowing the overall population to generate a synchronous rhythm that retracts the whiskers from their protracted positions.

“We have defined a mammalian oscillator molecularly, electrophysiologically, functionally, and mechanistically,” says Fan Wang, an MIT professor of brain and cognitive sciences and a member of MIT’s McGovern Institute for Brain Research. “It’s very exciting to see a clearly defined circuit and mechanism of how rhythm is generated in a mammal.”

Wang is the senior author of the study, which appears today in Nature. The lead authors of the paper are MIT research scientists Jun Takatoh and Vincent Prevosto.

Rhythmic behavior

Most of the research that clearly identified central oscillator circuits has been done in invertebrates. For example, Eve Marder’s lab at Brandeis University found cells in the stomatogastric ganglion in lobsters and crabs that generate oscillatory activity to control rhythmic motion of the digestive tract.

Characterizing oscillators in mammals, especially in awake behaving animals, has proven to be highly challenging. The oscillator that controls walking is believed to be distributed throughout the spinal cord, making it difficult to precisely identify the neurons and circuits involved. The oscillator that generates rhythmic breathing is located in a part of the brain stem called the pre-Bötzinger complex, but the exact identity of the oscillator neurons is not fully understood.

“There haven’t been detailed studies in awake behaving animals, where one can record from molecularly identified oscillator cells and manipulate them in a precise way,” Wang says.

Whisking is a prominent rhythmic exploratory behavior in many mammals, which use their tactile whiskers to detect objects and sense textures. In mice, whiskers extend and retract at a frequency of about 12 cycles per second. Several years ago, Wang’s lab set out try to identify the cells and the mechanism that control this oscillation.

To find the location of the whisking oscillator, the researchers traced back from the motor neurons that innervate whisker muscles. Using a modified rabies virus that infects axons, the researchers were able to label a group of cells presynaptic to these motor neurons in a part of the brainstem called the vibrissa intermediate reticular nucleus (vIRt). This finding was consistent with previous studies showing that damage to this part of the brain eliminates whisking.

The researchers then found that about half of these vIRt neurons express a protein called parvalbumin, and that this subpopulation of cells drives the rhythmic motion of the whiskers. When these neurons are silenced, whisking activity is abolished.

Next, the researchers recorded electrical activity from these parvalbumin-expressing vIRt neurons in brainstem in awake mice, a technically challenging task, and found that these neurons indeed have bursts of activity only during the whisker retraction period. Because these neurons provide inhibitory synaptic inputs to whisker motor neurons, it follows that rhythmic whisking is generated by a constant motor neuron protraction signal interrupted by the rhythmic retraction signal from these oscillator cells.

“That was a super satisfying and rewarding moment, to see that these cells are indeed the oscillator cells, because they fire rhythmically, they fire in the retraction phase, and they’re inhibitory neurons,” Wang says.

A maximum projection image showing tracked whiskers on the mouse muzzle. The right (control) side shows the back-and-forth rhythmic sweeping of the whiskers, while the experimental side where the whisking oscillator neurons are silenced, the whiskers move very little. Image: Wang Lab

“New principles”

The oscillatory bursting pattern of vIRt cells is initiated at the start of whisking. When the whiskers are not moving, these neurons fire continuously. When the researchers blocked vIRt neurons from inhibiting each other, the rhythm disappeared, and instead the oscillator neurons simply increased their rate of continuous firing.

This type of network, known as recurrent inhibitory network, differs from the types of oscillators that have been seen in the stomatogastric neurons in lobsters, in which neurons intrinsically generate their own rhythm.

“Now we have found a mammalian network oscillator that is formed by all inhibitory neurons,” Wang says.

The MIT scientists also collaborated with a team of theorists led by David Golomb at Ben-Gurion University, Israel, and David Kleinfeld at the University of California at San Diego. The theorists created a detailed computational model outlining how whisking is controlled, which fits well with all experimental data. A paper describing that model is appearing in an upcoming issue of Neuron.

Wang’s lab now plans to investigate other types of oscillatory circuits in mice, including those that control chewing and licking.

“We are very excited to find oscillators of these feeding behaviors and compare and contrast to the whisking oscillator, because they are all in the brain stem, and we want to know whether there’s some common theme or if there are many different ways to generate oscillators,” she says.

The research was funded by the National Institutes of Health.

Microscopy technique reveals hidden nanostructures in cells and tissues

Anne Trafton |

Press Mentions

Making the invisible visible

The Economist

Inside a living cell, proteins and other molecules are often tightly packed together. These dense clusters can be difficult to image because the fluorescent labels used to make them visible can’t wedge themselves in between the molecules.

MIT researchers have now developed a novel way to overcome this limitation and make those “invisible” molecules visible. Their technique allows them to “de-crowd” the molecules by expanding a cell or tissue sample before labeling the molecules, which makes the molecules more accessible to fluorescent tags.

This method, which builds on a widely used technique known as expansion microscopy previously developed at MIT, should allow scientists to visualize molecules and cellular structures that have never been seen before.

“It’s becoming clear that the expansion process will reveal many new biological discoveries. If biologists and clinicians have been studying a protein in the brain or another biological specimen, and they’re labeling it the regular way, they might be missing entire categories of phenomena,” says Edward Boyden, the Y. Eva Tan Professor in Neurotechnology, a professor of biological engineering and brain and cognitive sciences at MIT, a Howard Hughes Medical Institute investigator, and a member of MIT’s McGovern Institute for Brain Research and Koch Institute for Integrative Cancer Research.

Using this technique, Boyden and his colleagues showed that they could image a nanostructure found in the synapses of neurons. They also imaged the structure of Alzheimer’s-linked amyloid beta plaques in greater detail than has been possible before.

“Our technology, which we named expansion revealing, enables visualization of these nanostructures, which previously remained hidden, using hardware easily available in academic labs,” says Deblina Sarkar, an assistant professor in the Media Lab and one of the lead authors of the study.

The senior authors of the study are Boyden; Li-Huei Tsai, director of MIT’s Picower Institute for Learning and Memory; and Thomas Blanpied, a professor of physiology at the University of Maryland. Other lead authors include Jinyoung Kang, an MIT postdoc, and Asmamaw Wassie, a recent MIT PhD recipient. The study appears today in Nature Biomedical Engineering.

De-crowding

Imaging a specific protein or other molecule inside a cell requires labeling it with a fluorescent tag carried by an antibody that binds to the target. Antibodies are about 10 nanometers long, while typical cellular proteins are usually about 2 to 5 nanometers in diameter, so if the target proteins are too densely packed, the antibodies can’t get to them.

This has been an obstacle to traditional imaging and also to the original version of expansion microscopy, which Boyden first developed in 2015. In the original version of expansion microscopy, researchers attached fluorescent labels to molecules of interest before they expanded the tissue. The labeling was done first, in part because the researchers had to use an enzyme to chop up proteins in the sample so the tissue could be expanded. This meant that the proteins couldn’t be labeled after the tissue was expanded.

To overcome that obstacle, the researchers had to find a way to expand the tissue while leaving the proteins intact. They used heat instead of enzymes to soften the tissue, allowing the tissue to expand 20-fold without being destroyed. Then, the separated proteins could be labeled with fluorescent tags after expansion.

With so many more proteins accessible for labeling, the researchers were able to identify tiny cellular structures within synapses, the connections between neurons that are densely packed with proteins. They labeled and imaged seven different synaptic proteins, which allowed them to visualize, in detail, “nanocolumns” consisting of calcium channels aligned with other synaptic proteins. These nanocolumns, which are believed to help make synaptic communication more efficient, were first discovered by Blanpied’s lab in 2016.

“This technology can be used to answer a lot of biological questions about dysfunction in synaptic proteins, which are involved in neurodegenerative diseases,” Kang says. “Until now there has been no tool to visualize synapses very well.”

New patterns

The researchers also used their new technique to image beta amyloid, a peptide that forms plaques in the brains of Alzheimer’s patients. Using brain tissue from mice, the researchers found that amyloid beta forms periodic nanoclusters, which had not been seen before. These clusters of amyloid beta also include potassium channels. The researchers also found amyloid beta molecules that formed helical structures along axons.

“In this paper, we don’t speculate as to what that biology might mean, but we show that it exists. That is just one example of the new patterns that we can see,” says Margaret Schroeder, an MIT graduate student who is also an author of the paper.

Sarkar says that she is fascinated by the nanoscale biomolecular patterns that this technology unveils. “With a background in nanoelectronics, I have developed electronic chips that require extremely precise alignment, in the nanofab. But when I see that in our brain Mother Nature has arranged biomolecules with such nanoscale precision, that really blows my mind,” she says.

Boyden and his group members are now working with other labs to study cellular structures such as protein aggregates linked to Parkinson’s and other diseases. In other projects, they are studying pathogens that infect cells and molecules that are involved in aging in the brain. Preliminary results from these studies have also revealed novel structures, Boyden says.

“Time and time again, you see things that are truly shocking,” he says. “It shows us how much we are missing with classical unexpanded staining.”

The researchers are also working on modifying the technique so they can image up to 20 proteins at a time. They are also working on adapting their process so that it can be used on human tissue samples.

Sarkar and her team, on the other hand, are developing tiny wirelessly powered nanoelectronic devices which could be distributed in the brain. They plan to integrate these devices with expansion revealing. “This can combine the intelligence of nanoelectronics with the nanoscopy prowess of expansion technology, for an integrated functional and structural understanding of the brain,” Sarkar says.

The research was funded by the National Institutes of Health, the National Science Foundation, the Ludwig Family Foundation, the JPB Foundation, the Open Philanthropy Project, John Doerr, Lisa Yang and the Tan-Yang Center for Autism Research at MIT, the U.S. Army Research Office, Charles Hieken, Tom Stocky, Kathleen Octavio, Lore McGovern, Good Ventures, and HHMI.

These neurons have food on the brain

Anne Trafton |

A gooey slice of pizza. A pile of crispy French fries. Ice cream dripping down a cone on a hot summer day. When you look at any of these foods, a specialized part of your visual cortex lights up, according to a new study from MIT neuroscientists.

This newly discovered population of food-responsive neurons is located in the ventral visual stream, alongside populations that respond specifically to faces, bodies, places, and words. The unexpected finding may reflect the special significance of food in human culture, the researchers say.

“Food is central to human social interactions and cultural practices. It’s not just sustenance,” says Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience and a member of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds, and Machines. “Food is core to so many elements of our cultural identity, religious practice, and social interactions, and many other things that humans do.”

The findings, based on an analysis of a large public database of human brain responses to a set of 10,000 images, raise many additional questions about how and why this neural population develops. In future studies, the researchers hope to explore how people’s responses to certain foods might differ depending on their likes and dislikes, or their familiarity with certain types of food.

MIT postdoc Meenakshi Khosla is the lead author of the paper, along with MIT research scientist N. Apurva Ratan Murty. The study appears today in the journal Current Biology.

Visual categories

More than 20 years ago, while studying the ventral visual stream, the part of the brain that recognizes objects, Kanwisher discovered cortical regions that respond selectively to faces. Later, she and other scientists discovered other regions that respond selectively to places, bodies, or words. Most of those areas were discovered when researchers specifically set out to look for them. However, that hypothesis-driven approach can limit what you end up finding, Kanwisher says.

“There could be other things that we might not think to look for,” she says. “And even when we find something, how do we know that that’s actually part of the basic dominant structure of that pathway, and not something we found just because we were looking for it?”

To try to uncover the fundamental structure of the ventral visual stream, Kanwisher and Khosla decided to analyze a large, publicly available dataset of full-brain functional magnetic resonance imaging (fMRI) responses from eight human subjects as they viewed thousands of images.

“We wanted to see when we apply a data-driven, hypothesis-free strategy, what kinds of selectivities pop up, and whether those are consistent with what had been discovered before. A second goal was to see if we could discover novel selectivities that either haven’t been hypothesized before, or that have remained hidden due to the lower spatial resolution of fMRI data,” Khosla says.

To do that, the researchers applied a mathematical method that allows them to discover neural populations that can’t be identified from traditional fMRI data. An fMRI image is made up of many voxels — three-dimensional units that represent a cube of brain tissue. Each voxel contains hundreds of thousands of neurons, and if some of those neurons belong to smaller populations that respond to one type of visual input, their responses may be drowned out by other populations within the same voxel.

The new analytical method, which Kanwisher’s lab has previously used on fMRI data from the auditory cortex, can tease out responses of neural populations within each voxel of fMRI data.

Using this approach, the researchers found four populations that corresponded to previously identified clusters that respond to faces, places, bodies, and words. “That tells us that this method works, and it tells us that the things that we found before are not just obscure properties of that pathway, but major, dominant properties,” Kanwisher says.

Intriguingly, a fifth population also emerged, and this one appeared to be selective for images of food.

“We were first quite puzzled by this because food is not a visually homogenous category,” Khosla says. “Things like apples and corn and pasta all look so unlike each other, yet we found a single population that responds similarly to all these diverse food items.”

The food-specific population, which the researchers call the ventral food component (VFC), appears to be spread across two clusters of neurons, located on either side of the FFA. The fact that the food-specific populations are spread out between other category-specific populations may help explain why they have not been seen before, the researchers say.

“We think that food selectivity had been harder to characterize before because the populations that are selective for food are intermingled with other nearby populations that have distinct responses to other stimulus attributes. The low spatial resolution of fMRI prevents us from seeing this selectivity because the responses of different neural population get mixed in a voxel,” Khosla says.

“The technique which the researchers used to identify category-sensitive cells or areas is impressive, and it recovered known category-sensitive systems, making the food category findings most impressive,” says Paul Rozin, a professor of psychology at the University of Pennsylvania, who was not involved in the study. “I can’t imagine a way for the brain to reliably identify the diversity of foods based on sensory features. That makes this all the more fascinating, and likely to clue us in about something really new.”

Food vs non-food

The researchers also used the data to train a computational model of the VFC, based on previous models Murty had developed for the brain’s face and place recognition areas. This allowed the researchers to run additional experiments and predict the responses of the VFC. In one experiment, they fed the model matched images of food and non-food items that looked very similar — for example, a banana and a yellow crescent moon.

“Those matched stimuli have very similar visual properties, but the main attribute in which they differ is edible versus inedible,” Khosla says. “We could feed those arbitrary stimuli through the predictive model and see whether it would still respond more to food than non-food, without having to collect the fMRI data.”

They could also use the computational model to analyze much larger datasets, consisting of millions of images. Those simulations helped to confirm that the VFC is highly selective for images of food.

From their analysis of the human fMRI data, the researchers found that in some subjects, the VFC responded slightly more to processed foods such as pizza than unprocessed foods like apples. In the future they hope to explore how factors such as familiarity and like or dislike of a particular food might affect individuals’ responses to that food.

They also hope to study when and how this region becomes specialized during early childhood, and what other parts of the brain it communicates with. Another question is whether this food-selective population will be seen in other animals such as monkeys, who do not attach the cultural significance to food that humans do.

The research was funded by the National Institutes of Health, the National Eye Institute, and the National Science Foundation through the MIT Center for Brains, Minds, and Machines.

Whether speaking Turkish or Norwegian, the brain’s language network looks the same

Anne Trafton |

Over several decades, neuroscientists have created a well-defined map of the brain’s “language network,” or the regions of the brain that are specialized for processing language. Found primarily in the left hemisphere, this network includes regions within Broca’s area, as well as in other parts of the frontal and temporal lobes.

However, the vast majority of those mapping studies have been done in English speakers as they listened to or read English texts. MIT neuroscientists have now performed brain imaging studies of speakers of 45 different languages. The results show that the speakers’ language networks appear to be essentially the same as those of native English speakers.

The findings, while not surprising, establish that the location and key properties of the language network appear to be universal. The work also lays the groundwork for future studies of linguistic elements that would be difficult or impossible to study in English speakers because English doesn’t have those features.

“This study is very foundational, extending some findings from English to a broad range of languages,” says Evelina Fedorenko, the Frederick A. and Carole J. Middleton Career Development Associate Professor of Neuroscience at MIT and a member of MIT’s McGovern Institute for Brain Research. “The hope is that now that we see that the basic properties seem to be general across languages, we can ask about potential differences between languages and language families in how they are implemented in the brain, and we can study phenomena that don’t really exist in English.”

Fedorenko is the senior author of the study, which appears today in Nature Neuroscience. Saima Malik-Moraleda, a PhD student in the Speech and Hearing Bioscience and Technology program at Harvard University, and Dima Ayyash, a former research assistant, are the lead authors of the paper.

Mapping language networks

The precise locations and shapes of language areas differ across individuals, so to find the language network, researchers ask each person to perform a language task while scanning their brains with functional magnetic resonance imaging (fMRI). Listening to or reading sentences in one’s native language should activate the language network. To distinguish this network from other brain regions, researchers also ask participants to perform tasks that should not activate it, such as listening to an unfamiliar language or solving math problems.

Several years ago, Fedorenko began designing these “localizer” tasks for speakers of languages other than English. While most studies of the language network have used English speakers as subjects, English does not include many features commonly seen in other languages. For example, in English, word order tends to be fixed, while in other languages there is more flexibility in how words are ordered. Many of those languages instead use the addition of morphemes, or segments of words, to convey additional meaning and relationships between words.

“There has been growing awareness for many years of the need to look at more languages, if you want make claims about how language works, as opposed to how English works,” Fedorenko says. “We thought it would be useful to develop tools to allow people to rigorously study language processing in the brain in other parts of the world. There’s now access to brain imaging technologies in many countries, but the basic paradigms that you would need to find the language-responsive areas in a person are just not there.”

For the new study, the researchers performed brain imaging of two speakers of 45 different languages, representing 12 different language families. Their goal was to see if key properties of the language network, such as location, left lateralization, and selectivity, were the same in those participants as in people whose native language is English.

The researchers decided to use “Alice in Wonderland” as the text that everyone would listen to, because it is one of the most widely translated works of fiction in the world. They selected 24 short passages and three long passages, each of which was recorded by a native speaker of the language. Each participant also heard nonsensical passages, which should not activate the language network, and was asked to do a variety of other cognitive tasks that should not activate it.

The team found that the language networks of participants in this study were found in approximately the same brain regions, and had the same selectivity, as those of native speakers of English.

“Language areas are selective,” Malik-Moraleda says. “They shouldn’t be responding during other tasks such as a spatial working memory task, and that was what we found across the speakers of 45 languages that we tested.”

Additionally, language regions that are typically activated together in English speakers, such as the frontal language areas and temporal language areas, were similarly synchronized in speakers of other languages.

The researchers also showed that among all of the subjects, the small amount of variation they saw between individuals who speak different languages was the same as the amount of variation that would typically be seen between native English speakers.

Similarities and differences

While the findings suggest that the overall architecture of the language network is similar across speakers of different languages, that doesn’t mean that there are no differences at all, Fedorenko says. As one example, researchers could now look for differences in speakers of languages that predominantly use morphemes, rather than word order, to help determine the meaning of a sentence.

“There are all sorts of interesting questions you can ask about morphological processing that don’t really make sense to ask in English, because it has much less morphology,” Fedorenko says.

Another possibility is studying whether speakers of languages that use differences in tone to convey different word meanings would have a language network with stronger links to auditory brain regions that encode pitch.

Right now, Fedorenko’s lab is working on a study in which they are comparing the ‘temporal receptive fields’ of speakers of six typologically different languages, including Turkish, Mandarin, and Finnish. The temporal receptive field is a measure of how many words the language processing system can handle at a time, and for English, it has been shown to be six to eight words long.

“The language system seems to be working on chunks of just a few words long, and we’re trying to see if this constraint is universal across these other languages that we’re testing,” Fedorenko says.

The researchers are also working on creating language localizer tasks and finding study participants representing additional languages beyond the 45 from this study.

The research was funded by the National Institutes of Health and research funds from MIT’s Department of Brain and Cognitive Sciences, the McGovern Institute, and the Simons Center for the Social Brain. Malik-Moraleda was funded by a la Caixa Fellowship and a Friends of McGovern fellowship.

Three distinct brain circuits in the thalamus contribute to Parkinson’s symptoms

Anne Trafton |

Parkinson’s disease is best-known as a disorder of movement. Patients often experience tremors, loss of balance, and difficulty initiating movement. The disease also has lesser-known symptoms that are nonmotor, including depression.

In a study of a small region of the thalamus, MIT neuroscientists have now identified three distinct circuits that influence the development of both motor and nonmotor symptoms of Parkinson’s. Furthermore, they found that by manipulating these circuits, they could reverse Parkinson’s symptoms in mice.

The findings suggest that those circuits could be good targets for new drugs that could help combat many of the symptoms of Parkinson’s disease, the researchers say.

“We know that the thalamus is important in Parkinson’s disease, but a key question is how can you put together a circuit that that can explain many different things happening in Parkinson’s disease. Understanding different symptoms at a circuit level can help guide us in the development of better therapeutics,” says Guoping Feng, the James W. and Patricia T. Poitras Professor in Brain and Cognitive Sciences at MIT, a member of the Broad Institute of Harvard and MIT, and the associate director of the McGovern Institute for Brain Research at MIT.

Feng is the senior author of the study, which appears today in Nature. Ying Zhang, a J. Douglas Tan Postdoctoral Fellow at the McGovern Institute, and Dheeraj Roy, a NIH K99 Awardee and a McGovern Fellow at the Broad Institute, are the lead authors of the paper.

Tracing circuits

The thalamus consists of several different regions that perform a variety of functions. Many of these, including the parafascicular (PF) thalamus, help to control movement. Degeneration of these structures is often seen in patients with Parkinson’s disease, which is thought to contribute to their motor symptoms.

In this study, the MIT team set out to try to trace how the PF thalamus is connected to other brain regions, in hopes of learning more about its functions. They found that neurons of the PF thalamus project to three different parts of the basal ganglia, a cluster of structures involved in motor control and other functions: the caudate putamen (CPu), the subthalamic nucleus (STN), and the nucleus accumbens (NAc).

“We started with showing these different circuits, and we demonstrated that they’re mostly nonoverlapping, which strongly suggests that they have distinct functions,” Roy says.

Further studies revealed those functions. The circuit that projects to the CPu appears to be involved in general locomotion, and functions to dampen movement. When the researchers inhibited this circuit, mice spent more time moving around the cage they were in.

The circuit that extends into the STN, on the other hand, is important for motor learning — the ability to learn a new motor skill through practice. The researchers found that this circuit is necessary for a task in which the mice learn to balance on a rod that spins with increasing speed.

Lastly, the researchers found that, unlike the others, the circuit that connects the PF thalamus to the NAc is not involved in motor activity. Instead, it appears to be linked to motivation. Inhibiting this circuit generates depression-like behaviors in healthy mice, and they will no longer seek a reward such as sugar water.

Druggable targets

Once the researchers established the functions of these three circuits, they decided to explore how they might be affected in Parkinson’s disease. To do that, they used a mouse model of Parkinson’s, in which dopamine-producing neurons in the midbrain are lost.

They found that in this Parkinson’s model, the connection between the PF thalamus and the CPu was enhanced, and that this led to a decrease in overall movement. Additionally, the connections from the PF thalamus to the STN were weakened, which made it more difficult for the mice to learn the accelerating rod task.

Lastly, the researchers showed that in the Parkinson’s model, connections from the PF thalamus to the NAc were also interrupted, and that this led to depression-like symptoms in the mice, including loss of motivation.

Using chemogenetics or optogenetics, which allows them to control neuronal activity with a drug or light, the researchers found that they could manipulate each of these three circuits and in doing so, reverse each set of Parkinson’s symptoms. Then, they decided to look for molecular targets that might be “druggable,” and found that each of the three PF thalamus regions have cells that express different types of cholinergic receptors, which are activated by the neurotransmitter acetylcholine. By blocking or activating those receptors, depending on the circuit, they were also able to reverse the Parkinson’s symptoms.

“We found three distinct cholinergic receptors that can be expressed in these three different PF circuits, and if we use antagonists or agonists to modulate these three different PF populations, we can rescue movement, motor learning, and also depression-like behavior in PD mice,” Zhang says.

Parkinson’s patients are usually treated with L-dopa, a precursor of dopamine. While this drug helps patients regain motor control, it doesn’t help with motor learning or any nonmotor symptoms, and over time, patients become resistant to it.

The researchers hope that the circuits they characterized in this study could be targets for new Parkinson’s therapies. The types of neurons that they identified in the circuits of the mouse brain are also found in the nonhuman primate brain, and the researchers are now using RNA sequencing to find genes that are expressed specifically in those cells.

“RNA-sequencing technology will allow us to do a much more detailed molecular analysis in a cell-type specific way,” Feng says. “There may be better druggable targets in these cells, and once you know the specific cell types you want to modulate, you can identify all kinds of potential targets in them.”

The research was funded, in part, by the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience at MIT, the Stanley Center for Psychiatric Research at the Broad Institute, the James and Patricia Poitras Center for Psychiatric Disorders Research at MIT, the National Institutes of Health BRAIN Initiative, and the National Institute of Mental Health.

New research center focused on brain-body relationship established at MIT

by Julie Pryor |

The inextricable link between our brains and our bodies has been gaining increasing recognition among researchers and clinicians over recent years. Studies have shown that the brain-body pathway is bidirectional — meaning that our mental state can influence our physical health and vice versa. But exactly how the two interact is less clear.

A new research center at MIT, funded by a $38 million gift to the McGovern Institute for Brain Research from philanthropist K. Lisa Yang, aims to unlock this mystery by creating and applying novel tools to explore the multidirectional, multilevel interplay between the brain and other body organ systems. This gift expands Yang’s exceptional philanthropic support of human health and basic science research at MIT over the past five years.

“Lisa Yang’s visionary gift enables MIT scientists and engineers to pioneer revolutionary technologies and undertake rigorous investigations into the brain’s complex relationship with other organ systems,” says MIT President L. Rafael Reif.  “Lisa’s tremendous generosity empowers MIT scientists to make pivotal breakthroughs in brain and biomedical research and, collectively, improve human health on a grand scale.”

The K. Lisa Yang Brain-Body Center will be directed by Polina Anikeeva, professor of materials science and engineering and brain and cognitive sciences at MIT and an associate investigator at the McGovern Institute. The center will harness the power of MIT’s collaborative, interdisciplinary life sciences research and engineering community to focus on complex conditions and diseases affecting both the body and brain, with a goal of unearthing knowledge of biological mechanisms that will lead to promising therapeutic options.

“Under Professor Anikeeva’s brilliant leadership, this wellspring of resources will encourage the very best work of MIT faculty, graduate fellows, and research — and ultimately make a real impact on the lives of many,” Reif adds.

microscope image of gut
Mouse small intestine stained to reveal cell nucleii (blue) and peripheral nerve fibers (red).
Image: Polina Anikeeva, Marie Manthey, Kareena Villalobos

Center goals  

Initial projects in the center will focus on four major lines of research:

  • Gut-Brain: Anikeeva’s group will expand a toolbox of new technologies and apply these tools to examine major neurobiological questions about gut-brain pathways and connections in the context of autism spectrum disorders, Parkinson’s disease, and affective disorders.
  • Aging: CRISPR pioneer Feng Zhang, the James and Patricia Poitras Professor of Neuroscience at MIT and investigator at the McGovern Institute, will lead a group in developing molecular tools for precision epigenomic editing and erasing accumulated “errors” of time, injury, or disease in various types of cells and tissues.
  • Pain: The lab of Fan Wang, investigator at the McGovern Institute and professor of brain and cognitive sciences, will design new tools and imaging methods to study autonomic responses, sympathetic-parasympathetic system balance, and brain-autonomic nervous system interactions, including how pain influences these interactions.
  • Acupuncture: Wang will also collaborate with Hilda (“Scooter”) Holcombe, a veterinarian in MIT’s Division of Comparative Medicine, to advance techniques for documenting changes in brain and peripheral tissues induced by acupuncture in mouse models. If successful, these techniques could lay the groundwork for deeper understandings of the mechanisms of acupuncture, specifically how the treatment stimulates the nervous system and restores function.

A key component of the K. Lisa Yang Brain-Body Center will be a focus on educating and training the brightest young minds who aspire to make true breakthroughs for individuals living with complex and often devastating diseases. A portion of center funding will endow the new K. Lisa Yang Brain-Body Fellows Program, which will support four annual fellowships for MIT graduate students and postdocs working to advance understanding of conditions that affect both the body and brain.

Mens sana in corpore sano

“A phrase I remember reading in secondary school has always stuck with me: ‘mens sana in corpore sano’ — ‘a healthy mind in a healthy body,’” says Lisa Yang, a former investment banker committed to advocacy for individuals with visible and invisible disabilities. “When we look at how stress, nutrition, pain, immunity, and other complex factors impact our health, we truly see how inextricably linked our brains and bodies are. I am eager to help MIT scientists and engineers decode these links and make real headway in creating therapeutic strategies that result in longer, healthier lives.”

“This center marks a once-in-a-lifetime opportunity for labs like mine to conduct bold and risky studies into the complexities of brain-body connections,” says Anikeeva, who works at the intersection of materials science, electronics, and neurobiology. “The K. Lisa Yang Brain-Body Center will offer a pathbreaking, holistic approach that bridges multiple fields of study. I have no doubt that the center will result in revolutionary strides in our understanding of the inextricable bonds between the brain and the body’s peripheral organ systems, and a bold new way of thinking in how we approach human health overall.”

Lindsay Case and Guangyu Robert Yang named 2022 Searle Scholars

Anne Trafton |

MIT cell biologist Lindsay Case and computational neuroscientist Guangyu Robert Yang have been named 2022 Searle Scholars, an award given annually to 15 outstanding U.S. assistant professors who have high potential for ongoing innovative research contributions in medicine, chemistry, or the biological sciences.

Case is an assistant professor of biology, while Yang is an assistant professor of brain and cognitive sciences and electrical engineering and computer science, and an associate investigator at the McGovern Institute for Brain Research. They will each receive $300,000 in flexible funding to support their high-risk, high-reward work over the next three years.

Lindsay Case

Case arrived at MIT in 2021, after completing a postdoc at the University of Texas Southwestern Medical Center in the lab of Michael Rosen. Prior to that, she earned her PhD from the University of North Carolina at Chapel Hill, working in the lab of Clare Waterman at the National Heart Lung and Blood Institute.

Situated in MIT’s Building 68, Case’s lab studies how molecules within cells organize themselves, and how such organization begets cellular function. Oftentimes, molecules will assemble at the cell’s plasma membrane — a complex signaling platform where hundreds of receptors sense information from outside the cell and initiate cellular changes in response. Through her experiments, Case has found that molecules at the plasma membrane can undergo a process known as phase separation, condensing to form liquid-like droplets.

As a Searle Scholar, Case is investigating the role that phase separation plays in regulating a specific class of signaling molecules called kinases. Her team will take a multidisciplinary approach to probe what happens when kinases phase separate into signaling clusters, and what cellular changes occur as a result. Because phase separation is emerging as a promising new target for small molecule therapies, this work will help identify kinases that are strong candidates for new therapeutic interventions to treat diseases such as cancer.

“I am honored to be recognized by the Searle Scholars Program, and thrilled to join such an incredible community of scientists,” Case says. “This support will enable my group to broaden our research efforts and take our preliminary findings in exciting new directions. I look forward to better understanding how phase separation impacts cellular function.”

Guangyu Robert Yang

Before coming to MIT in 2021, Yang trained in physics at Peking University, obtained a PhD in computational neuroscience at New York University with Xiao-Jing Wang, and further trained as a postdoc at the Center for Theoretical Neuroscience of Columbia University, as an intern at Google Brain, and as a junior fellow at the Simons Society of Fellows.

His research team at MIT, the MetaConscious Group, develops models of mental functions by incorporating multiple interacting modules. They are designing pipelines to process and compare large-scale experimental datasets that span modalities ranging from behavioral data to neural activity data to molecular data. These datasets are then be integrated to train individual computational modules based on the experimental tasks that were evaluated such as vision, memory, or movement.

Ultimately, Yang seeks to combine these modules into a “network of networks” that models higher-level brain functions such as the ability to flexibly and rapidly learn a variety of tasks. Such integrative models are rare because, until recently, it was not possible to acquire data that spans modalities and brain regions in real time as animals perform tasks. The time is finally right for integrative network models. Computational models that incorporate such multisystem, multilevel datasets will allow scientists to make new predictions about the neural basis of cognition and open a window to a mathematical understanding the mind.

“This is a new research direction for me, and I think for the field too. It comes with many exciting opportunities as well as challenges. Having this recognition from the Searle Scholars program really gives me extra courage to take on the uncertainties and challenges,” says Yang.

Since 1981, 647 scientists have been named Searle Scholars. Including this year, the program has awarded more than $147 million. Eighty-five Searle Scholars have been inducted into the National Academy of Sciences. Twenty scholars have been recognized with a MacArthur Fellowship, known as the “genius grant,” and two Searle Scholars have been awarded the Nobel Prize in Chemistry. The Searle Scholars Program is funded through the Searle Funds at The Chicago Community Trust and administered by Kinship Foundation.

A brain circuit in the thalamus helps us hold information in mind

Anne Trafton |

As people age, their working memory often declines, making it more difficult to perform everyday tasks. One key brain region linked to this type of memory is the anterior thalamus, which is primarily involved in spatial memory — memory of our surroundings and how to navigate them.

In a study of mice, MIT researchers have identified a circuit in the anterior thalamus that is necessary for remembering how to navigate a maze. The researchers also found that this circuit is weakened in older mice, but enhancing its activity greatly improves their ability to run the maze correctly.

This region could offer a promising target for treatments that could help reverse memory loss in older people, without affecting other parts of the brain, the researchers say.

“By understanding how the thalamus controls cortical output, hopefully we could find more specific and druggable targets in this area, instead of generally modulating the prefrontal cortex, which has many different functions,” says Guoping Feng, the James W. and Patricia T. Poitras Professor in Brain and Cognitive Sciences at MIT, a member of the Broad Institute of Harvard and MIT, and the associate director of the McGovern Institute for Brain Research at MIT.

Feng is the senior author of the study, which appears today in the Proceedings of the National Academy of Sciences. Dheeraj Roy, a NIH K99 Awardee and a McGovern Fellow at the Broad Institute, and Ying Zhang, a J. Douglas Tan Postdoctoral Fellow at the McGovern Institute, are the lead authors of the paper.

Spatial memory

The thalamus, a small structure located near the center of the brain, contributes to working memory and many other executive functions, such as planning and attention. Feng’s lab has recently been investigating a region of the thalamus known as the anterior thalamus, which has important roles in memory and spatial navigation.

Previous studies in mice have shown that damage to the anterior thalamus leads to impairments in spatial working memory. In humans, studies have revealed age-related decline in anterior thalamus activity, which is correlated with lower performance on spatial memory tasks.

The anterior thalamus is divided into three sections: ventral, dorsal, and medial. In a study published last year, Feng, Roy and Zhang studied the role of the anterodorsal (AD) thalamus and anteroventral (AV) thalamus in memory formation. They found that the AD thalamus is involved in creating mental maps of physical spaces, while the AV thalamus helps the brain to distinguish these memories from other memories of similar spaces.

In their new study, the researchers wanted to look more deeply at the AV thalamus, exploring its role in a spatial working memory task. To do that, they trained mice to run a simple T-shaped maze. At the beginning of each trial, the mice ran until they reached the T. One arm was blocked off, forcing them to run down the other arm. Then, the mice were placed in the maze again, with both arms open. The mice were rewarded if they chose the opposite arm from the first run. This meant that in order to make the correct decision, they had to remember which way they had turned on the previous run.

As the mice performed the task, the researchers used optogenetics to inhibit activity of either AV or AD neurons during three different parts of the task: the sample phase, which occurs during the first run; the delay phase, while they are waiting for the second run to begin; and the choice phase, when the mice make their decision which way to turn during the second run.

The researchers found that inhibiting AV neurons during the sample or choice phases had no effect on the mice’s performance, but when they suppressed AV activity during the delay phase, which lasted 10 seconds or longer, the mice performed much worse on the task.

This suggests that the AV neurons are most important for keeping information in mind while it is needed for a task. In contrast, inhibiting the AD neurons disrupted performance during the sample phase but had little effect during the delay phase. This finding was consistent with the research team’s earlier study showing that AD neurons are involved in forming memories of a physical space.

“The anterior thalamus in general is a spatial learning region, but the ventral neurons seem to be needed in this maintenance period, during this short delay,” Roy says. “Now we have two subdivisions within the anterior thalamus: one that seems to help with contextual learning and the other that actually helps with holding this information.”

Age-related decline

The researchers then tested the effects of age on this circuit. They found that older mice (14 months) performed worse on the T-maze task and their AV neurons were less excitable. However, when the researchers artificially stimulated those neurons, the mice’s performance on the task dramatically improved.

Another way to enhance performance in this memory task is to stimulate the prefrontal cortex, which also undergoes age-related decline. However, activating the prefrontal cortex also increases measures of anxiety in the mice, the researchers found.

“If we directly activate neurons in medial prefrontal cortex, it will also elicit anxiety-related behavior, but this will not happen during AV activation,” Zhang says. “That is an advantage of activating AV compared to prefrontal cortex.”

If a noninvasive or minimally invasive technology could be used to stimulate those neurons in the human brain, it could offer a way to help prevent age-related memory decline, the researchers say. They are now planning to perform single-cell RNA sequencing of neurons of the anterior thalamus to find genetic signatures that could be used to identify cells that would make the best targets.

The research was funded, in part, by the Stanley Center for Psychiatric Research at the Broad Institute, the Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT, and the James and Patricia Poitras Center for Psychiatric Disorders Research at MIT.

Circuit that focuses attention brings in wide array of inputs

by David Orenstein | Picower Institute |

In a new brain-wide circuit tracing study, scientists at MIT’s Picower Institute for Learning and Memory focused selective attention on a circuit that governs, fittingly enough, selective attention. The comprehensive maps they produced illustrate how broadly the mammalian brain incorporates and integrates information to focus its sensory resources on its goals.

Working in mice, the team traced thousands of inputs into the circuit, a communication loop between the anterior cingulate cortex (ACC) and the lateral posterior (LP) thalamus. In primates the LP is called the pulvinar. Studies in humans and nonhuman primates have indicated that the byplay of these two regions is critical for brain functions like being able to focus on an object of interest in a crowded scene, says study co-lead author Yi Ning Leow, a graduate student in the lab of senior author Mriganka Sur, the Newton Professor in MIT’s Department of Brain and Cognitive Sciences. Research has implicated dysfunction in the circuit in attention-affecting disorders such as autism and attention deficit/hyperactivity disorder.

The new study in the Journal of Comparative Neurology extends what’s known about the circuit by detailing it in mice, Leow says, importantly showing that the mouse circuit is closely analogous to the primate version even if the LP is proportionately smaller and less evolved than the pulvinar.

“In these rodent models we were able to find very similar circuits,” Leow says. “So we can possibly study these higher-order functions in mice as well. We have a lot more genetic tools in mice so we are better able to look at this circuit.”

The study, also co-led by former MIT undergraduate Blake Zhou, therefore provides a detailed roadmap in the experimentally accessible mouse model for understanding how the ACC and LP cooperate to produce selective attention. For instance, now that Leow and Zhou have located all the inputs that are wired into the circuit, Leow is tapping into those feeds to eavesdrop on the information they are carrying. Meanwhile, she is correlating that information flow with behavior.

“This study lays the groundwork for understanding one of the most important, yet most elusive, components of brain function, namely our ability to selectively attend to one thing out of several, as well as switch attention,” Sur says.

Using virally mediated circuit-tracing techniques pioneered by co-author Ian Wickersham, principal research scientist in brain and cognitive sciences and the McGovern Institute for Brain Research at MIT, the team found distinct sources of input for the ACC and the LP. Generally speaking, the detailed study finds that the majority of inputs to the ACC were from frontal cortex areas that typically govern goal-directed planning, and from higher visual areas. The bulk of inputs to the LP, meanwhile, were from deeper regions capable of providing context such as the mouse’s needs, location and spatial cues, information about movement, and general information from a mix of senses.

So even though focusing attention might seem like a matter of controlling the senses, Leow says, the circuit pulls in a lot of other information as well.

“We’re seeing that it’s not just sensory — there are so many inputs that are coming from non-sensory areas as well, both sub-cortically and cortically,” she says. “It seems to be integrating a lot of different aspects that might relate to the behavioral state of the animal at a given time. It provides a way to provide a lot of internal and special context for that sensory information.”

Given the distinct sets of inputs to each region, the ACC may be tasked with focusing attention on a desired object, while the LP is modulating how the ACC goes about making those computations, accounting for what’s going on both inside and outside the animal. Decoding just what that incoming contextual information is, and what the LP tells the ACC, are the key next steps, Leow says. Another clear set of questions the study raises are what are the circuit’s outputs. In other words, after it integrates all this information, what does it do with it?

The paper’s other authors are Heather Sullivan and Alexandria Barlowe.

A National Science Scholarship, the National Institutes of Health, and the JPB Foundation provided support for the study.

Approaching human cognition from many angles

by Alli Armijo | MIT News correspondent |

In January, as the Charles River was starting to freeze over, Keith Murray and the other members of MIT’s men’s heavyweight crew team took to erging on the indoor rowing machine. For 80 minutes at a time, Murray endured one of the most grueling workouts of his college experience. To distract himself from the pain, he would talk with his teammates, covering everything from great philosophical ideas to personal coffee preferences.

For Murray, virtually any conversation is an opportunity to explore how people think and why they think in certain ways. Currently a senior double majoring in computation and cognition, and linguistics and philosophy, Murray tries to understand the human experience based on knowledge from all of these fields.

“I’m trying to blend different approaches together to understand the complexities of human cognition,” he says. “For example, from a physiological perspective, the brain is just billions of neurons firing all at once, but this hardly scratches the surface of cognition.”

Murray grew up in Corydon, Indiana, where he attended the Indiana Academy for Science, Mathematics, and Humanities during his junior year of high school. He was exposed to philosophy there, learning the ideas of Plato, Socrates, and Thomas Aquinas, to name a few. When looking at colleges, Murray became interested in MIT because he wanted to learn about human thought processes from different perspectives. “Coming to MIT, I knew I wanted to do something philosophical. But I wanted to also be on the more technical side of things,” he says.

Once on campus, Murray immediately pursued an opportunity through the Undergraduate Research Opportunity Program (UROP) in the Digital Humanities Lab. There he worked with language-processing technology to analyze gendered language in various novels, with the end goal of displaying the data for an online audience. He learned about the basic mathematical models used for analyzing and presenting data online, to study the social implications of linguistic phrases and expressions.

Murray also joined the Concourse learning community, which brought together different perspectives from the humanities, sciences, and math in a weekly seminar. “I was exposed to some excellent examples of how to do interdisciplinary work,” he recalls.

In the summer before his sophomore year, Murray took a position as a researcher in the Harnett Lab, where instead of working with novels, he was working with mice. Alongside postdoc Lucas Fisher, Murray trained mice to do navigational tasks using virtual reality equipment. His goal was to explore neural encoding in navigation, understanding why the mice behaved in certain ways after being shown certain stimuli on the screens. Spending time in the lab, Murray became increasingly interested in neuroscience and the biological components behind human thought processes.

He sought out other neuroscience-related research experiences, which led him to explore a SuperUROP project in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). Working under Professor Nancy Lynch, he designed theoretical models of the retina using machine learning. Murray was excited to apply the techniques he learned in 9.40 (Introduction to Neural Computation) to address complex neurological problems. Murray considers this one of his most challenging research experiences, as the experience was entirely online.

“It was during the pandemic, so I had to learn a lot on my own; I couldn’t exactly do research in a lab. It was a big challenge, but at the end, I learned a lot and ended up getting a publication out of it,” he reflects.

This past semester, Murray has worked in the lab of Professor Ila Fiete in the McGovern Institute for Brain Research, constructing deep-learning models of animals performing navigational tasks. Through this UROP, which builds on his final project from Fiete’s class 9.49 (Neural Circuits for Cognition), Murray has been working to incorporate existing theoretical models of the hippocampus to investigate the intersection between artificial intelligence and neuroscience.

Reflecting on his varied research experiences, Murray says they have shown him new ways to explore the human brain from multiple perspectives, something he finds helpful as he tries to understand the complexity of human behavior.

Outside of his academic pursuits, Murray has continued to row with the crew team, where he walked on his first year. He sees rowing as a way to build up his strength, both physically and mentally. “When I’m doing my class work or I’m thinking about projects, I am using the same mental toughness that I developed during rowing,” he says. “That’s something I learned at MIT, to cultivate the dedication you put toward something. It’s all the same mental toughness whether you apply it to physical activities like rowing, or research projects.”

Looking ahead, Murray hopes to pursue a PhD in neuroscience, looking to find ways to incorporate his love of philosophy and human thought into his cognitive research. “I think there’s a lot more to do with neuroscience, especially with artificial intelligence. There are so many new technological developments happening right now,” he says.