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Why do we dream?

by Shafaq Zia |

As part of our Ask the Brain series, science writer Shafaq Zia answers the question, “Why do we dream?”

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One night, Albert Einstein dreamt that he was walking through a farm where he found a herd of cows against an electric fence. When the farmer switched on the fence, the cows suddenly jumped back, all at the same time. But to the farmer’s eyes, who was standing at the other end of the field, they seemed to have jumped one after another, in a wave formation. Einstein woke up and the Theory of Relativity was born.

Dreaming is one of the oldest biological phenomena; for as long as humans have slept, they’ve dreamt. But through most of our history, dreams have remained mystified, leaving scientists, philosophers, and artists alike searching for meaning.

In many aboriginal cultures, such as the Esa Eja community in Peruvian Amazon, dreaming is a sacred practice for gaining knowledge, or solving a problem, through the dream narrative. But in the last century or so, technological advancements have allowed neuroscientists to take up dreams as a matter of scientific inquiry in order to answer a much-pondered question — what is the purpose of dreaming?

Falling asleep

The human brain is a fascinating place. It is composed of approximately 80 billion neurons and it is their combined electrical chatter that generates oscillations known as brain waves. There are five types of brain waves —  alpha, beta, theta, delta, and gamma — that each indicate a different state between sleep and wakefulness.

Using EEG, a test that records electrical activity in the brain, scientists have identified that when we’re awake, our brain emits beta and gamma waves. These tend to have a stimulating effect and help us remain actively engaged in mental activities.

The differently named frequency bands of neural oscillations, or brainwaves: delta, theta, alpha, beta, and gamma.

But during the transition to sleep, the number of beta waves lowers significantly and the brain produces high levels of alpha waves. These waves regulate attention and help filter out distractions. A recent study led by McGovern Institute Director Robert Desimone, showed that people can actually enhance their attention by controlling their own alpha brain waves using neurofeedback training. It’s unknown how long these effects might last and whether this kind of control could be achieved with other types of brain waves, but the researchers are now planning additional studies to explore these questions.

Alpha waves are also produced when we daydream, meditate, or listen to the sound of rain. As our minds wander, many parts of the brain are engaged, including a specialized system called the “default mode network.” Disturbances in this network, explains Susan Whitfield-Gabrieli, a professor of psychology at Northeastern University and a McGovern Institute research affiliate, have been linked to various brain disorders including schizophrenia, depression and ADHD. By identifying the brain circuits associated with mind wandering, she says, we can begin to develop better treatment options for people suffering from these disorders.

Finally, as we enter a dreamlike state, the prefrontal cortex of the brain, responsible for keeping impulses in check, slowly grows less active. This is when there’s a spur in theta waves that leads to an unconstrained window of consciousness; there is little censorship from the mind, allowing for visceral dreams and creative thoughts.

The dreaming brain

“Every time you learn something, it happens so quickly,” said Dheeraj Roy, postdoctoral fellow in Guoping Feng’s lab at the McGovern Institute. “The brain is continuously recording information, but how do you take a break and then make sense of it all?”

This is where dreams come in, says Roy. During sleep, newly-formed memories are gradually stabilized into a more permanent form of long-term storage in the brain. Dreaming, he says, is influenced by the consolidation of these memories during sleep. Most dreams are made up of experiences, thoughts, emotion, places, and people we have already encountered in our lives. But, during dreaming, bits and pieces of these memories seem to be reorganized to create a particularly bizarre scenario: you’re talking to your sister when it suddenly begins to rain roses and you’re dancing at a New Year’s party.

This re-organization may not be so random; as the brain is processing memories, it pulls together the ones that are seemingly related to each other. Perhaps you dreamt of your sister because you were at a store recently where a candle smelt like her rose-scented perfume, which reminded you of the time you made a new year resolution to spend less money on flowers.

Some brain disorders, like Parkinson’s disease, have been associated with vivid, unpleasant dreams and erratic brain wave patterns. Researchers at the McGovern Institute hope that a better understanding of mechanics of the brain – including neural circuits and brain waves – will help people with Parkinson’s and other brain disorders.

So perhaps dreams aren’t instilled with meaning, symbolism, and wisdom in the way we’ve always imagined, and they simply reflect important biological processes taking place in our brain. But with all that science has uncovered about dreaming and the ways in which it links to creativity and memory, the magical essence of this universal human experience remains untainted.

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Do you have a question for The Brain? Ask it here.

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.

McGovern Fellows recognized with life sciences innovation award

by Julie Pryor |

McGovern Institute Fellows Omar Abudayyeh and Jonathan Gootenberg have been named the inaugural recipients of the Termeer Scholars Awards, which recognize “emerging biomedical researchers that represent the future of the biotechnology industry.” The Termeer Foundation is a nonprofit organization focused on connecting life science innovators and catalyzing the creation of new medicines.

“The Termeer Foundation is committed to championing emerging biotechnology leaders and finding people who want to solve the biggest problems in human health,” said Belinda Termeer, president of the Termeer Foundation. “By supporting researchers like Omar and Jonathan, we plant the seeds for future success in individuals who are preparing to make significant contributions in academia and industry.”

The Abudayyeh-Gootenberg lab is developing a suite of new tools to enable next-generation cellular engineering, with uses in basic research, therapeutics and diagnostics. Building off the revolutionary biology of natural biological systems, including mobile genetic elements and CRISPR systems, the team develops new approaches for understanding and manipulating genomes, transcriptomes and cellular fate. The technologies have broad applications, including in oncology, aging and genetic disease.

These tools have been adopted by researchers over the world and formed the basis for four companies that Abudayyeh and Gootenberg have co-founded. They will receive a $50,000 grant to support professional development, knowledge advancement and/or stakeholder engagement and will become part of The Termeer Foundation’s signature Network of Termeer Fellows (first-time CEOs and entrepreneurs) and Mentors (experienced industry leaders).

“The Termeer Foundation is working to improve the long odds of biotechnology by identifying and supporting future biotech leaders; if we help them succeed as leaders, we can help their innovations reach patients,” said Alan Waltws, co-founder of the Termeer Foundation. “While our Termeer Fellows program has supported first time CEOs and entrepreneurs for the past five years, our new Termeer Scholars program will provide much needed support to the researchers whose innovative ideas represent the future of the biotechnology industry – researchers like Omar and Jonathan.”

Abudayyeh and Gootenberg were honored at the Termeer Foundation’s annual dinner in Boston on June 16, 2022.

Artificial neural networks model face processing in autism

by Matthew Hutson |

Many of us easily recognize emotions expressed in others’ faces. A smile may mean happiness, while a frown may indicate anger. Autistic people often have a more difficult time with this task. It’s unclear why. But new research, published today in The Journal of Neuroscience, sheds light on the inner workings of the brain to suggest an answer. And it does so using a tool that opens new pathways to modeling the computation in our heads: artificial intelligence.

Researchers have primarily suggested two brain areas where the differences might lie. A region on the side of the primate (including human) brain called the inferior temporal (IT) cortex contributes to facial recognition. Meanwhile, a deeper region called the amygdala receives input from the IT cortex and other sources and helps process emotions.

Kohitij Kar, a research scientist in the lab of MIT Professor James DiCarlo, hoped to zero in on the answer. (DiCarlo, the Peter de Florez Professor in the Department of Brain and Cognitive Sciences, is a member of the McGovern Institute for Brain Research and director of MIT’s Quest for Intelligence.)

Kar began by looking at data provided by two other researchers: Shuo Wang, at Washington University in St. Louis, and Ralph Adolphs, at the California Institute of Technology. In one experiment, they showed images of faces to autistic adults and to neurotypical controls. The images had been generated by software to vary on a spectrum from fearful to happy, and the participants judged, quickly, whether the faces depicted happiness. Compared with controls, autistic adults required higher levels of happiness in the faces to report them as happy.

Modeling the brain

Kar, who is also a member of the Center for Brains, Minds and Machines, trained an artificial neural network, a complex mathematical function inspired by the brain’s architecture, to perform the same task. The network contained layers of units that roughly resemble biological neurons that process visual information. These layers process information as it passes from an input image to a final judgment indicating the probability that the face is happy. Kar found that the network’s behavior more closely matched the neurotypical controls than it did the autistic adults.

The network also served two more interesting functions. First, Kar could dissect it. He stripped off layers and retested its performance, measuring the difference between how well it matched controls and how well it matched autistic adults. This difference was greatest when the output was based on the last network layer. Previous work has shown that this layer in some ways mimics the IT cortex, which sits near the end of the primate brain’s ventral visual processing pipeline. Kar’s results implicate the IT cortex in differentiating neurotypical controls from autistic adults.

The other function is that the network can be used to select images that might be more efficient in autism diagnoses. If the difference between how closely the network matches neurotypical controls versus autistic adults is greater when judging one set of images versus another set of images, the first set could be used in the clinic to detect autistic behavioral traits. “These are promising results,” Kar says. Better models of the brain will come along, “but oftentimes in the clinic, we don’t need to wait for the absolute best product.”

Next, Kar evaluated the role of the amygdala. Again, he used data from Wang and colleagues. They had used electrodes to record the activity of neurons in the amygdala of people undergoing surgery for epilepsy as they performed the face task. The team found that they could predict a person’s judgment based on these neurons’ activity. Kar re-analyzed the data, this time controlling for the ability of the IT-cortex-like network layer to predict whether a face truly was happy. Now, the amygdala provided very little information of its own. Kar concludes that the IT cortex is the driving force behind the amygdala’s role in judging facial emotion.

Noisy networks

Finally, Kar trained separate neural networks to match the judgments of neurotypical controls and autistic adults. He looked at the strengths or “weights” of the connections between the final layers and the decision nodes. The weights in the network matching autistic adults, both the positive or “excitatory” and negative or “inhibitory” weights, were weaker than in the network matching neurotypical controls. This suggests that sensory neural connections in autistic adults might be noisy or inefficient.

To further test the noise hypothesis, which is popular in the field, Kar added various levels of fluctuation to the activity of the final layer in the network modeling autistic adults. Within a certain range, added noise greatly increased the similarity between its performance and that of the autistic adults. Adding noise to the control network did much less to improve its similarity to the control participants. This further suggest that sensory perception in autistic people may be the result of a so-called “noisy” brain.

Computational power

Looking forward, Kar sees several uses for computational models of visual processing. They can be further prodded, providing hypotheses that researchers might test in animal models. “I think facial emotion recognition is just the tip of the iceberg,” Kar says. They can also be used to select or even generate diagnostic content. Artificial intelligence could be used to generate content like movies and educational materials that optimally engages autistic children and adults. One might even tweak facial and other relevant pixels in what autistic people see in augmented reality goggles, work that Kar plans to pursue in the future.

Ultimately, Kar says, the work helps to validate the usefulness of computational models, especially image-processing neural networks. They formalize hypotheses and make them testable. Does one model or another better match behavioral data? “Even if these models are very far off from brains, they are falsifiable, rather than people just making up stories,” he says. “To me, that’s a more powerful version of science.”

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.

Convenience-sized RNA editing

by Sarah CP Williams |

Last year, researchers at MIT’s McGovern Institute discovered and characterized Cas7-11, the first CRISPR enzyme capable of making precise, guided cuts to strands of RNA without harming cells in the process. Now, working with collaborators at the University of Tokyo, the same team has revealed that Cas7-11 can be shrunk to a more compact version, making it an even more viable option for editing the RNA inside living cells. The new, compact Cas7-11 was described today in the journal Cell along with a detailed structural analysis of the original enzyme.

“When we looked at the structure, it was clear there were some pieces that weren’t needed which we could actually remove,” says McGovern Fellow Omar Abudayyeh, who led the new work with McGovern Fellow Jonathan Gootenberg and collaborator Hiroshi Nishimasu from the University of Tokyo. “This makes the enzyme small enough that it fits into a single viral vector for therapeutic applications.”

The authors, who also include postdoctoral researcher Nathan Zhou from the McGovern Institute and Kazuki Kato from the University Tokyo, see the new three-dimensional structure of Cas7-11 as a rich resource toanswer questions about the basic biology of the enzymes and reveal other ways to tweak its function in the future.

Targeting RNA

McGovern Fellows Jonathan Gootenberg and Omar Abudayyeh in their lab. Photo: Caitlin Cunningham

Over the past decade, the CRISPR-Cas9 genome editing technology has given researchers the ability to modify the genes inside human cells—a boon for both basic research and the development of therapeutics to reverse disease-causing genetic mutations. But CRISPR-Cas9 only works to alter DNA, and for some research and clinical purposes, editing RNA is more effective or useful.

A cell retains its DNA for life, and passes an identical copy to daughter cells as it duplicates, so any changes to DNA are relatively permanent. However, RNA is a more transient molecule, transcribed from DNA and degraded not long after.

“There are lots of positives about being able to permanently change DNA, especially when it comes to treating an inherited genetic disease,” Gootenberg says. “But for an infection, an injury or some other temporary disease, being able to temporarily modify a gene through RNA targeting makes more sense.”

Until Abudayyeh, Gootenberg and their colleagues discovered and characterized Cas7-11, the only enzyme that could target RNA had a messy side effect; when it recognized a particular gene, the enzyme—Cas13—began cutting up all the RNA around it. This property makes Cas13 effective for diagnostic tests, where it is used to detect the presence of a piece of RNA, but not very useful for therapeutics, where targeted cuts are required.

The discovery of Cas7-11 opened the doors to a more precise form of RNA editing, analogous to the Cas9 enzyme for DNA. However, the massive Cas7-11 protein was too big to fit inside a single viral vector—the empty shell of a virus that researchers typically use to deliver gene editing machinery into patient’s cells.

Structural insight

To determine the overall structure of Cas7-11, Abudayyeh, Gootenberg and Nishimasu used cryo-electron microscopy, which shines beams of electrons on frozen protein samples and measures how the beams are transmitted. The researchers knew that Cas7-11 was like an amalgamation of five separate Cas enzymes, fused into one single gene, but were not sure exactly how those parts folded and fit together.

“The really fascinating thing about Cas7-11, from a fundamental biology perspective, is that it should be all these separate pieces that come together, but instead you have a fusion into one gene,” Gootenberg says. “We really didn’t know what that would look like.”

The structure of Cas7-11, caught in the act of binding both its target tRNA strand and the guide RNA, which directs that binding, revealed how the pieces assembled and which parts of the protein were critical to recognizing and cutting RNA. This kind of structural insight is critical to figuring out how to make Cas7-11 carry out targeted jobs inside human cells.

The structure also illuminated a section of the protein that wasn’t serving any apparent functional role. This finding suggested the researchers could remove it, re-engineering Cas7-11 to make it smaller without taking away its ability to target RNA. Abudayyeh and Gootenberg tested the impact of removing different bits of this section, resulting in a new compact version of the protein, dubbed Cas7-11S. With Cas7-11S in hand, they packaged the system inside a single viral vector, delivered it into mammalian cells and efficiently targeted RNA.

The team is now planning future studies on other proteins that interact with Cas7-11 in the bacteria that it originates from, and also hopes to continue working towards the use of Cas7-11 for therapeutic applications.

“Imagine you could have an RNA gene therapy, and when you take it, it modifies your RNA, but when you stop taking it, that modification stops,” Abudayyeh says. “This is really just the beginning of enabling that tool set.”

This research was funded, in part, by the McGovern Institute Neurotechnology Program, K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience, G. Harold & Leila Y. Mathers Charitable Foundation, MIT John W. Jarve (1978) Seed Fund for Science Innovation, FastGrants, Basis for Supporting Innovative Drug Discovery and Life Science Research Program, JSPS KAKENHI, Takeda Medical Research Foundation, and Inamori Research Institute for Science.

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.”

A voice for change — in Spanish

by Jennifer Michalowski |

Jessica Chomik-Morales had a bicultural childhood. She was born in Boca Raton, Florida, where her parents had come seeking a better education for their daughter than she would have access to in Paraguay. But when she wasn’t in school, Chomik-Morales was back in that small, South American country with her family. One of the consequences of growing up in two cultures was an early interest in human behavior. “I was always in observer mode,” Chomik-Morales says, recalling how she would tune in to the nuances of social interactions in order to adapt and fit in.

Today, that fascination with human behavior is driving Chomik-Morales as she works with MIT professor of cognitive science Laura Schulz and Walter A. Rosenblith Professor of Cognitive Neuroscience and McGovern Institute for Brain Research investigator Nancy Kanwisher as a post-baccalaureate research scholar, using functional brain imaging to investigate how the brain recognizes and understands causal relationships. Since arriving at MIT last fall, she’s worked with study volunteers to collect functional MRI (fMRI) scans and used computational approaches to interpret the images. She’s also refined her own goals for the future.

Jessica Chomik-Morales (right) with postdoctoral associate Héctor De Jesús-Cortés. Photo: Steph Stevens

She plans to pursue a career in clinical neuropsychology, which will merge her curiosity about the biological basis of behavior with a strong desire to work directly with people. “I’d love to see what kind of questions I could answer about the neural mechanisms driving outlier behavior using fMRI coupled with cognitive assessment,” she says. And she’s confident that her experience in MIT’s two-year post-baccalaureate program will help her get there. “It’s given me the tools I need, and the techniques and methods and good scientific practice,” she says. “I’m learning that all here. And I think it’s going to make me a more successful scientist in grad school.”

The road to MIT

Chomik-Morales’s path to MIT was not a straightforward trajectory through the U.S. school system. When her mom, and later her dad, were unable to return to the U.S., she started eight grade in the capital city of Asunción. It did not go well. She spent nearly every afternoon in the principal’s office, and soon her father was encouraging her to return to the United States. “You are an American,” he told her. “You have a right to the educational system there.”

Back in Florida, Chomik-Morales became a dedicated student, even while she worked assorted jobs and shuffled between the homes of families who were willing to host her. “I had to grow up,” she says. “My parents are sacrificing everything just so I can have a chance to be somebody. People don’t get out of Paraguay often, because there aren’t opportunities and it’s a very poor country. I was given an opportunity, and if I waste that, then that is disrespect not only to my parents, but to my lineage, to my country.”

As she graduated from high school and went on to earn a degree in cognitive neuroscience at Florida Atlantic University, Chomik-Morales found herself experiencing things that were completely foreign to her family. Though she spoke daily with her mom via WhatsApp, it was hard to share what she was learning in school or what she was doing in the lab. And while they celebrated her academic achievements, Chomik-Morales knew they didn’t really understand them. “Neither of my parents went to college,” she says. “My mom told me that she never thought twice about learning about neuroscience. She had this misconception that it was something that she would never be able to digest.”

Chomik-Morales believes that the wonders of neuroscience are for everybody. But she also knows that Spanish speakers like her mom have few opportunities to hear the kinds of accessible, engaging stories that might draw them in. So she’s working to change that. With support from the McGovern Institute, the National Science Foundation funded Science and Technology Center for Brains, Minds, and Machines, Chomik-Morales is hosting and producing a weekly podcast called “Mi Última Neurona” (“My Last Neuron”), which brings conversations with neuroscientists to Spanish speakers around the world.

Listeners hear how researchers at MIT and other institutions are exploring big concepts like consciousness and neurodegeneration, and learn about the approaches they use to study the brain in humans, animals, and computational models. Chomik-Morales wants listeners to get to know neuroscientists on a personal level too, so she talks with her guests about their career paths, their lives outside the lab, and often, their experiences as immigrants in the United States.

After recording an interview with Chomik-Morales that delved into science, art, and the educational system in his home country of Peru, postdoc Arturo Deza thinks “Mi Última Neurona” has the potential to inspire Spanish speakers in Latin America, as well immigrants in other countries. “Even if you’re not a scientist, it’s really going to captivate you and you’re going to get something out of it,” he says. To that point, Chomik-Morales’s mother has quickly become an enthusiastic listener, and even begun seeking out resources to learn more about the brain on her own.

Chomik-Morales hopes the stories her guests share on “Mi Última Neurona” will inspire a future generation of Hispanic neuroscientists. She also wants listeners to know that a career in science doesn’t have to mean leaving their country behind. “Gain whatever you need to gain from outside, and then, if it’s what you desire, you’re able to go back and help your own community,” she says. With “Mi Última Neurona,” she adds, she feels she is giving back to her roots.

How do illusions trick the brain?

by Dana Boebinger | Jarrod Hicks |

As part of our Ask the Brain series, Jarrod Hicks, a graduate student in Josh McDermott‘s lab and Dana Boebinger, a postdoctoral researcher at the University of Rochester (and former graduate student in Josh McDermott’s lab), answer the question, “How do illusions trick the brain?”

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Graduate student Jarrod Hicks studies how the brain processes sound. Photo: M.E. Megan Hicks

Imagine you’re a detective. Your job is to visit a crime scene, observe some evidence, and figure out what happened. However, there are often multiple stories that could have produced the evidence you observe. Thus, to solve the crime, you can’t just rely on the evidence in front of you – you have to use your knowledge about the world to make your best guess about the most likely sequence of events. For example, if you discover cat hair at the crime scene, your prior knowledge about the world tells you it’s unlikely that a cat is the culprit. Instead, a more likely explanation is that the culprit might have a pet cat.

Although it might not seem like it, this kind of detective work is what your brain is doing all the time. As your senses send information to your brain about the world around you, your brain plays the role of detective, piecing together each bit of information to figure out what is happening in the world. The information from your senses usually paints a pretty good picture of things, but sometimes when this information is incomplete or unclear, your brain is left to fill in the missing pieces with its best guess of what should be there. This means that what you experience isn’t actually what’s out there in the world, but rather what your brain thinks is out there. The consequence of this is that your perception of the world can depend on your experience and assumptions.

Optical illusions

Optical illusions are a great way of showing how our expectations and assumptions affect what we perceive. For example, look at the squares labeled “A” and “B” in the image below.

Checkershadow illusion. Image: Edward H. Adelson

Is one of them lighter than the other? Although most people would agree that the square labeled “B” is much lighter than the one labeled “A,” the two squares are actually the exact same color. You perceive the squares differently because your brain knows, from experience, that shadows tend to make things appear darker than what they actually are. So, despite the squares being physically identical, your brain thinks “B” should be lighter.

Auditory illusions

Tricks of perception are not limited to optical illusions. There are also several dramatic examples of how our expectations influence what we hear. For example, listen to the mystery sound below. What do you hear?

Mystery sound

Because you’ve probably never heard a sound quite like this before, your brain has very little idea about what to expect. So, although you clearly hear something, it might be very difficult to make out exactly what that something is. This mystery sound is something called sine-wave speech, and what you’re hearing is essentially a very degraded sound of someone speaking.

Now listen to a “clean” version of this speech in the audio clip below:

Clean speech

You probably hear a person saying, “the floor was quite slippery.” Now listen to the mystery sound above again. After listening to the original audio, your brain has a strong expectation about what you should hear when you listen to the mystery sound again. Even though you’re hearing the exact same mystery sound as before, you experience it completely differently. (Audio clips courtesy of University of Sussex).

 

Dana Boebinger describes the science of illusions in this McGovern Minute.

Subjective perceptions

These illusions have been specifically designed by scientists to fool your brain and reveal principles of perception. However, there are plenty of real-life situations in which your perceptions strongly depend on expectations and assumptions. For example, imagine you’re watching TV when someone begins to speak to you from another room. Because the noise from the TV makes it difficult to hear the person speaking, your brain might have to fill in the gaps to understand what’s being said. In this case, different expectations about what is being said could cause you to hear completely different things.

Which phrase do you hear?

Listen to the clip below to hear a repeating loop of speech. As the sound plays, try to listen for one of the phrases listed in teal below.

Because the audio is somewhat ambiguous, the phrase you perceive depends on which phrase you listen for. So even though it’s the exact same audio each time, you can perceive something totally different! (Note: the original audio recording is from a football game in which the fans were chanting, “that is embarrassing!”)

Illusions like the ones above are great reminders of how subjective our perceptions can be. In order to make sense of the messy information coming in from our senses, our brains are constantly trying to fill in the blanks and with its best guess of what’s out there. Because of this guesswork, our perceptions depend on our experiences, leading each of us to perceive and interact with the world in a way that’s uniquely ours.

Jarrod Hicks is a PhD candidate in the Department of Brain and Cognitive Sciences at MIT working with Josh McDermott in the Laboratory for Computational Audition. He studies sound segregation, a key aspect of real-world hearing in which a sound source of interest is estimated amid a mixture of competing sources. He is broadly interested in teaching/outreach, psychophysics, computational approaches to represent stimulus spaces, and neural coding of high-level sensory representations.

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Do you have a question for The Brain? Ask it here.

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.