Author: MIT News

Women in STEM — A celebration of excellence and curiosity

Anne Trafton |

What better way to commemorate Women’s History Month and International Women’s Day than to give  three of the world’s most accomplished scientists an opportunity to talk about their careers? On March 7, MindHandHeart invited professors Paula Hammond, Ann Graybiel, and Sangeeta Bhatia to share their career journeys, from the progress they have witnessed to the challenges they have faced as women in STEM. Their conversation was moderated by Mary Fuller, chair of the faculty and professor of literature.

Hammond, an Institute professor with appointments in the Department of Chemical Engineering and the Koch Institute for Integrative Cancer Research, reflected on the strides made by women faculty at MIT, while acknowledging ongoing challenges. “I think that we have advanced a great deal in the last few decades in terms of the numbers of women who are present, although we still have a long way to go,” Hammond noted in her opening. “We’ve seen a remarkable increase over the past couple of decades in our undergraduate population here at MIT, and now we’re beginning to see it in the graduate population, which is really exciting.” Hammond was recently appointed to the role of vice provost for faculty.

Ann Graybiel, also an Institute professor, who has appointments in the Department of Brain and Cognitive Sciences and the McGovern Institute for Brain Research, described growing up in the Deep South. “Girls can’t do science,” she remembers being told in school, and they “can’t do research.” Yet her father, a physician scientist, often took her with him to work and had her assist from a young age, eventually encouraging her directly to pursue a career in science. Graybiel, who first came to MIT in 1973, noted that she continued to face barriers and rejection throughout her career long after leaving the South, but that individual gestures of inspiration, generosity, or simple statements of “You can do it” from her peers helped her power through and continue in her scientific pursuits.

Sangeeta Bhatia, the John and Dorothy Wilson Professor of Health Sciences and Technology and Electrical Engineering and Computer Science, director of the Marble Center for Cancer Nanomedicine at the Koch Institute for Integrative Cancer Research, and a member of the Institute for Medical Engineering and Science, is also the mother of two teenage girls. She shared her perspective on balancing career and family life: “I wanted to pick up my kids from school and I wanted to know their friends. … I had a vision for the life that I wanted.” Setting boundaries at work, she noted, empowered her to achieve both personal and professional goals. Bhatia also described her collaboration with President Emerita Susan Hockfield and MIT Amgen Professor of Biology Emerita Nancy Hopkins to spearhead the Future Founders Initiative, which aims to boost the representation of female faculty members pursuing biotechnology ventures.

A video of the full panel discussion is available on the MindHandHeart YouTube channel.

From neurons to learning and memory

Anne Trafton |

Mark Harnett, an associate professor at MIT, still remembers the first time he saw electrical activity spiking from a living neuron.

He was a senior at Reed College and had spent weeks building a patch clamp rig — an experimental setup with an electrode that can be used to gently probe a neuron and measure its electrical activity.

“The first time I stuck one of these electrodes onto one of these cells and could see the electrical activity happening in real time on the oscilloscope, I thought, ‘Oh my God, this is what I’m going to do for the rest of my life. This is the coolest thing I’ve ever seen!’” Harnett says.

Harnett, who recently earned tenure in MIT’s Department of Brain and Cognitive Sciences, now studies the electrical properties of neurons and how these properties enable neural circuits to perform the computations that give rise to brain functions such as learning, memory, and sensory perception.

“My lab’s ultimate goal is to understand how the cortex works,” Harnett says. “What are the computations? How do the cells and the circuits and the synapses support those computations? What are the molecular and structural substrates of learning and memory? How do those things interact with circuit dynamics to produce flexible, context-dependent computation?”

“We go after that by looking at molecules, like synaptic receptors and ion channels, all the way up to animal behavior, and building theoretical models of neural circuits,” he adds.

Influence on the mind

Harnett’s interest in science was sparked in middle school, when he had a teacher who made the subject come to life. “It was middle school science, which was a lot of just mixing random things together. It wasn’t anything particularly advanced, but it was really fun,” he says. “Our teacher was just super encouraging and inspirational, and she really sparked what became my lifelong interest in science.”

When Harnett was 11, his father got a new job at a technology company in Minneapolis and the family moved from New Jersey to Minnesota, which proved to be a difficult adjustment. When choosing a college, Harnett decided to go far away, and ended up choosing Reed College, a school in Portland, Oregon, that encourages a great deal of independence in both academics and personal development.

“Reed was really free,” he recalls. “It let you grow into who you wanted to be, and try things, both for what you wanted to do academically or artistically, but also the kind of person you wanted to be.”

While in college, Harnett enjoyed both biology and English, especially Shakespeare. His English professors encouraged him to go into science, believing that the field needed scientists who could write and think creatively. He was interested in neuroscience, but Reed didn’t have a neuroscience department, so he took the closest subject he could find — a course in neuropharmacology.

“That class totally blew my mind. It was just fascinating to think about all these pharmacological agents, be they from plants or synthetic or whatever, influencing how your mind worked,” Harnett says. “That class really changed my whole way of thinking about what I wanted to do, and that’s when I decided I wanted to become a neuroscientist.”

For his senior research thesis, Harnett joined an electrophysiology lab at Oregon Health Sciences University (OHSU), working with Professor Larry Trussell, who studies synaptic transmission in the auditory system. That lab was where he first built and used a patch clamp rig to measure neuron activity.

After graduating from college, he spent a year as a research technician in a lab at the University of Minnesota, then returned to OHSU to work in a different research lab studying ion channels and synaptic physiology. Eventually he decided to go to graduate school, ending up at the University of Texas at Austin, where his future wife was studying public policy.

For his PhD research, he studied the neurons that release the neuromodulator dopamine and how they are affected by drugs of abuse and addiction. However, once he finished his degree, he decided to return to studying the biophysics of computation, which he pursued during a postdoc at the Howard Hughes Medical Institute Janelia Research Campus with Jeff Magee.

A broad approach

When he started his lab at MIT’s McGovern Institute in 2015, Harnett set out to expand his focus. While the physiology of ion channels and synapses forms the basis of much of his lab’s work, they connect these processes to neuronal computation, cortical circuit operation, and higher-level cognitive functions.

Electrical impulses that flow between neurons, allowing them to communicate with each other, are produced by ion channels that control the flow of ions such as potassium and sodium. In a 2021 study, Harnett and his students discovered that human neurons have a much smaller number of these channels than expected, compared to the neurons of other mammals.

This reduction in density may have evolved to help the brain operate more efficiently, allowing it to divert resources to other energy-intensive processes that are required to perform complex cognitive tasks. Harnett’s lab has also found that in human neurons, electrical signals weaken as they flow along dendrites, meaning that small sections of dendrites can form units that perform individual computations within a neuron.

Harnett’s lab also recently discovered, to their surprise, that the adult brain contains millions of “silent synapses” — immature connections that remain inactive until they’re recruited to help form new memories. The existence of these synapses offers a clue to how the adult brain is able to continually form new memories and learn new things without having to modify mature synapses.

Many of these projects fall into areas that Harnett didn’t necessarily envision himself working on when he began his faculty career, but they naturally grew out of the broad approach he wanted to take to studying the cortex. To that end, he sought to bring people to the lab who wanted to work at different levels — from molecular physiology up to behavior and computational modeling.

As a postdoc studying electrophysiology, Harnett spent most of his time working alone with his patch clamp device and two-photon microscope. While that type of work still goes on his lab, the overall atmosphere is much more collaborative and convivial, and as a mentor, he likes to give his students broad leeway to come up with their own projects that fit in with the lab’s overall mission.

“I have this incredible, dynamic group that has been really great to work with. We take a broad approach to studying the cortex, and I think that’s what makes it fun,” he says. “Working with the folks that I’ve been able to recruit — grad students, techs, undergrads, and postdocs — is probably the thing that really matters the most to me.”

A new computational technique could make it easier to engineer useful proteins

Anne Trafton |

To engineer proteins with useful functions, researchers usually begin with a natural protein that has a desirable function, such as emitting fluorescent light, and put it through many rounds of random mutation that eventually generate an optimized version of the protein.

This process has yielded optimized versions of many important proteins, including green fluorescent protein (GFP). However, for other proteins, it has proven difficult to generate an optimized version. MIT researchers have now developed a computational approach that makes it easier to predict mutations that will lead to better proteins, based on a relatively small amount of data.

Using this model, the researchers generated proteins with mutations that were predicted to lead to improved versions of GFP and a protein from adeno-associated virus (AAV), which is used to deliver DNA for gene therapy. They hope it could also be used to develop additional tools for neuroscience research and medical applications.

Woman gestures with her hand in front of a glass wall with equations written on it.
MIT Professor of Brain and Cognitive Sciences Ila Fiete in her lab at the McGovern Institute. Photo: Steph Stevens

“Protein design is a hard problem because the mapping from DNA sequence to protein structure and function is really complex. There might be a great protein 10 changes away in the sequence, but each intermediate change might correspond to a totally nonfunctional protein. It’s like trying to find your way to the river basin in a mountain range, when there are craggy peaks along the way that block your view. The current work tries to make the riverbed easier to find,” says Ila Fiete, a professor of brain and cognitive sciences at MIT, a member of MIT’s McGovern Institute for Brain Research, director of the K. Lisa Yang Integrative Computational Neuroscience Center, and one of the senior authors of the study.

Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health at MIT, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT, are also senior authors of an open-access paper on the work, which will be presented at the International Conference on Learning Representations in May. MIT graduate students Andrew Kirjner and Jason Yim are the lead authors of the study. Other authors include Shahar Bracha, an MIT postdoc, and Raman Samusevich, a graduate student at Czech Technical University.

Optimizing proteins

Many naturally occurring proteins have functions that could make them useful for research or medical applications, but they need a little extra engineering to optimize them. In this study, the researchers were originally interested in developing proteins that could be used in living cells as voltage indicators. These proteins, produced by some bacteria and algae, emit fluorescent light when an electric potential is detected. If engineered for use in mammalian cells, such proteins could allow researchers to measure neuron activity without using electrodes.

While decades of research have gone into engineering these proteins to produce a stronger fluorescent signal, on a faster timescale, they haven’t become effective enough for widespread use. Bracha, who works in Edward Boyden’s lab at the McGovern Institute, reached out to Fiete’s lab to see if they could work together on a computational approach that might help speed up the process of optimizing the proteins.

“This work exemplifies the human serendipity that characterizes so much science discovery,” Fiete says.

“This work grew out of the Yang Tan Collective retreat, a scientific meeting of researchers from multiple centers at MIT with distinct missions unified by the shared support of K. Lisa Yang. We learned that some of our interests and tools in modeling how brains learn and optimize could be applied in the totally different domain of protein design, as being practiced in the Boyden lab.”

For any given protein that researchers might want to optimize, there is a nearly infinite number of possible sequences that could generated by swapping in different amino acids at each point within the sequence. With so many possible variants, it is impossible to test all of them experimentally, so researchers have turned to computational modeling to try to predict which ones will work best.

In this study, the researchers set out to overcome those challenges, using data from GFP to develop and test a computational model that could predict better versions of the protein.

They began by training a type of model known as a convolutional neural network (CNN) on experimental data consisting of GFP sequences and their brightness — the feature that they wanted to optimize.

The model was able to create a “fitness landscape” — a three-dimensional map that depicts the fitness of a given protein and how much it differs from the original sequence — based on a relatively small amount of experimental data (from about 1,000 variants of GFP).

These landscapes contain peaks that represent fitter proteins and valleys that represent less fit proteins. Predicting the path that a protein needs to follow to reach the peaks of fitness can be difficult, because often a protein will need to undergo a mutation that makes it less fit before it reaches a nearby peak of higher fitness. To overcome this problem, the researchers used an existing computational technique to “smooth” the fitness landscape.

Once these small bumps in the landscape were smoothed, the researchers retrained the CNN model and found that it was able to reach greater fitness peaks more easily. The model was able to predict optimized GFP sequences that had as many as seven different amino acids from the protein sequence they started with, and the best of these proteins were estimated to be about 2.5 times fitter than the original.

“Once we have this landscape that represents what the model thinks is nearby, we smooth it out and then we retrain the model on the smoother version of the landscape,” Kirjner says. “Now there is a smooth path from your starting point to the top, which the model is now able to reach by iteratively making small improvements. The same is often impossible for unsmoothed landscapes.”

Proof-of-concept

The researchers also showed that this approach worked well in identifying new sequences for the viral capsid of adeno-associated virus (AAV), a viral vector that is commonly used to deliver DNA. In that case, they optimized the capsid for its ability to package a DNA payload.

“We used GFP and AAV as a proof-of-concept to show that this is a method that works on data sets that are very well-characterized, and because of that, it should be applicable to other protein engineering problems,” Bracha says.

The researchers now plan to use this computational technique on data that Bracha has been generating on voltage indicator proteins.

“Dozens of labs having been working on that for two decades, and still there isn’t anything better,” she says. “The hope is that now with generation of a smaller data set, we could train a model in silico and make predictions that could be better than the past two decades of manual testing.”

The research was funded, in part, by the U.S. National Science Foundation, the Machine Learning for Pharmaceutical Discovery and Synthesis consortium, the Abdul Latif Jameel Clinic for Machine Learning in Health, the DTRA Discovery of Medical Countermeasures Against New and Emerging threats program, the DARPA Accelerated Molecular Discovery program, the Sanofi Computational Antibody Design grant, the U.S. Office of Naval Research, the Howard Hughes Medical Institute, the National Institutes of Health, the K. Lisa Yang ICoN Center, and the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT.

For people who speak many languages, there’s something special about their native tongue

Anne Trafton |

A new study of people who speak many languages has found that there is something special about how the brain processes their native language.

In the brains of these polyglots — people who speak five or more languages — the same language regions light up when they listen to any of the languages that they speak. In general, this network responds more strongly to languages in which the speaker is more proficient, with one notable exception: the speaker’s native language. When listening to one’s native language, language network activity drops off significantly.

The findings suggest there is something unique about the first language one acquires, which allows the brain to process it with minimal effort, the researchers say.

“Something makes it a little bit easier to process — maybe it’s that you’ve spent more time using that language — and you get a dip in activity for the native language compared to other languages that you speak proficiently,” says Evelina Fedorenko, an associate professor of neuroscience at MIT, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Saima Malik-Moraleda, a graduate student in the Speech and Hearing Bioscience and Technology Program at Harvard University, and Olessia Jouravlev, a former MIT postdoc who is now an associate professor at Carleton University, are the lead authors of the paper, which appears today in the journal Cerebral Cortex.

Many languages, one network

McGovern Investivator Ev Fedorenko in the Martinos Imaging Center at MIT. Photo: Caitlin Cunningham

The brain’s language processing network, located primarily in the left hemisphere, includes regions in the frontal and temporal lobes. In a 2021 study, Fedorenko’s lab found that in the brains of polyglots, the language network was less active when listening to their native language than the language networks of people who speak only one language.

In the new study, the researchers wanted to expand on that finding and explore what happens in the brains of polyglots as they listen to languages in which they have varying levels of proficiency. Studying polyglots can help researchers learn more about the functions of the language network, and how languages learned later in life might be represented differently than a native language or languages.

“With polyglots, you can do all of the comparisons within one person. You have languages that vary along a continuum, and you can try to see how the brain modulates responses as a function of proficiency,” Fedorenko says.

For the study, the researchers recruited 34 polyglots, each of whom had at least some degree of proficiency in five or more languages but were not bilingual or multilingual from infancy. Sixteen of the participants spoke 10 or more languages, including one who spoke 54 languages with at least some proficiency.

Each participant was scanned with functional magnetic resonance imaging (fMRI) as they listened to passages read in eight different languages. These included their native language, a language they were highly proficient in, a language they were moderately proficient in, and a language in which they described themselves as having low proficiency.

They were also scanned while listening to four languages they didn’t speak at all. Two of these were languages from the same family (such as Romance languages) as a language they could speak, and two were languages completely unrelated to any languages they spoke.

The passages used for the study came from two different sources, which the researchers had previously developed for other language studies. One was a set of Bible stories recorded in many different languages, and the other consisted of passages from “Alice in Wonderland” translated into many languages.

Brain scans revealed that the language network lit up the most when participants listened to languages in which they were the most proficient. However, that did not hold true for the participants’ native languages, which activated the language network much less than non-native languages in which they had similar proficiency. This suggests that people are so proficient in their native language that the language network doesn’t need to work very hard to interpret it.

“As you increase proficiency, you can engage linguistic computations to a greater extent, so you get these progressively stronger responses. But then if you compare a really high-proficiency language and a native language, it may be that the native language is just a little bit easier, possibly because you’ve had more experience with it,” Fedorenko says.

Brain engagement

The researchers saw a similar phenomenon when polyglots listened to languages that they don’t speak: Their language network was more engaged when listening to languages related to a language that they could understand, than compared to listening to completely unfamiliar languages.

“Here we’re getting a hint that the response in the language network scales up with how much you understand from the input,” Malik-Moraleda says. “We didn’t quantify the level of understanding here, but in the future we’re planning to evaluate how much people are truly understanding the passages that they’re listening to, and then see how that relates to the activation.”

The researchers also found that a brain network known as the multiple demand network, which turns on whenever the brain is performing a cognitively demanding task, also becomes activated when listening to languages other than one’s native language.

“What we’re seeing here is that the language regions are engaged when we process all these languages, and then there’s this other network that comes in for non-native languages to help you out because it’s a harder task,” Malik-Moraleda says.

In this study, most of the polyglots began studying their non-native languages as teenagers or adults, but in future work, the researchers hope to study people who learned multiple languages from a very young age. They also plan to study people who learned one language from infancy but moved to the United States at a very young age and began speaking English as their dominant language, while becoming less proficient in their native language, to help disentangle the effects of proficiency versus age of acquisition on brain responses.

The research was funded by the McGovern Institute for Brain Research, MIT’s Department of Brain and Cognitive Sciences, and the Simons Center for the Social Brain.

How the brain coordinates speaking and breathing

Anne Trafton |

MIT researchers have discovered a brain circuit that drives vocalization and ensures that you talk only when you breathe out, and stop talking when you breathe in.

McGovern Investigator Fan Wang. Photo: Caitliin Cunningham

The newly discovered circuit controls two actions that are required for vocalization: narrowing of the larynx and exhaling air from the lungs. The researchers also found that this vocalization circuit is under the command of a brainstem region that regulates the breathing rhythm, which ensures that breathing remains dominant over speech.

“When you need to breathe in, you have to stop vocalization. We found that the neurons that control vocalization receive direct inhibitory input from the breathing rhythm generator,” says Fan Wang, an MIT professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Jaehong Park, a Duke University graduate student who is currently a visiting student at MIT, is the lead author of the study, which appears today in Science. Other authors of the paper include MIT technical associates Seonmi Choi and Andrew Harrahill, former MIT research scientist Jun Takatoh, and Duke University researchers Shengli Zhao and Bao-Xia Han.

Vocalization control

Located in the larynx, the vocal cords are two muscular bands that can open and close. When they are mostly closed, or adducted, air exhaled from the lungs generates sound as it passes through the cords.

The MIT team set out to study how the brain controls this vocalization process, using a mouse model. Mice communicate with each other using sounds known as ultrasonic vocalizations (USVs), which they produce using the unique whistling mechanism of exhaling air through a small hole between nearly closed vocal cords.

“We wanted to understand what are the neurons that control the vocal cord adduction, and then how do those neurons interact with the breathing circuit?” Wang says.

To figure that out, the researchers used a technique that allows them to map the synaptic connections between neurons. They knew that vocal cord adduction is controlled by laryngeal motor neurons, so they began by tracing backward to find the neurons that innervate those motor neurons.

This revealed that one major source of input is a group of premotor neurons found in the hindbrain region called the retroambiguus nucleus (RAm). Previous studies have shown that this area is involved in vocalization, but it wasn’t known exactly which part of the RAm was required or how it enabled sound production.

Image of green and magenta cells under a microscope.
Laryngeal premotor neurons (green) and Fos (magenta) labeling in the RAm. Image: Fan Wang

The researchers found that these synaptic tracing-labeled RAm neurons were strongly activated during USVs. This observation prompted the team to use an activity-dependent method to target these vocalization-specific RAm neurons, termed as RAmVOC. They used chemogenetics and optogenetics to explore what would happen if they silenced or stimulated their activity. When the researchers blocked the RAmVOC neurons, the mice were no longer able to produce USVs or any other kind of vocalization. Their vocal cords did not close, and their abdominal muscles did not contract, as they normally do during exhalation for vocalization.

Conversely, when the RAmVOC neurons were activated, the vocal cords closed, the mice exhaled, and USVs were produced. However, if the stimulation lasted two seconds or longer, these USVs would be interrupted by inhalations, suggesting that the process is under control of the same part of the brain that regulates breathing.

“Breathing is a survival need,” Wang says. “Even though these neurons are sufficient to elicit vocalization, they are under the control of breathing, which can override our optogenetic stimulation.”

Rhythm generation

Additional synaptic mapping revealed that neurons in a part of the brainstem called the pre-Bötzinger complex, which acts as a rhythm generator for inhalation, provide direct inhibitory input to the RAmVOC neurons.

“The pre-Bötzinger complex generates inhalation rhythms automatically and continuously, and the inhibitory neurons in that region project to these vocalization premotor neurons and essentially can shut them down,” Wang says.

This ensures that breathing remains dominant over speech production, and that we have to pause to breathe while speaking.

The researchers believe that although human speech production is more complex than mouse vocalization, the circuit they identified in mice plays the conserved role in speech production and breathing in humans.

“Even though the exact mechanism and complexity of vocalization in mice and humans is really different, the fundamental vocalization process, called phonation, which requires vocal cord closure and the exhalation of air, is shared in both the human and the mouse,” Park says.

The researchers now hope to study how other functions such as coughing and swallowing food may be affected by the brain circuits that control breathing and vocalization.

The research was funded by the National Institutes of Health.

School of Science announces 2024 Infinite Expansion Awards

Anne Trafton |

The MIT School of Science has announced nine postdocs and research scientists as recipients of the 2024 Infinite Expansion Award, which highlights extraordinary members of the MIT community.

The following are the 2024 School of Science Infinite Expansion winners:

  • Sarthak Chandra, a research scientist in the Department of Brain and Cognitive Sciences, was nominated by Professor Ila Fiete, who wrote, “He has expanded the research abilities of my group by being a versatile and brilliant scientist, by drawing connections with a different area that he was an expert in from his PhD training, and by being a highly involved and caring mentor.”
  • Michal Fux, a research scientist in the Department of Brain and Cognitive Sciences, was nominated by Professor Pawan Sinha, who wrote, “She is one of those figurative beams of light that not only brilliantly illuminate scientific questions, but also enliven a research team.”
  • Andrew Savinov, a postdoc in the Department of Biology, was nominated by Associate Professor Gene-Wei Li, who wrote, “Andrew is an extraordinarily creative and accomplished biophysicist, as well as an outstanding contributor to the broader MIT community.”
  • Ho Fung Cheng, a postdoc in the Department of Chemistry, was nominated by Professor Jeremiah Johnson, who wrote, “His impact on research and our departmental community during his time at MIT has been outstanding, and I believe that he will be a worldclass teacher and research group leader in his independent career next year.”
  • Gabi Wenzel, a postdoc in the Department of Chemistry, was nominated by Assistant Professor Brett McGuire, who wrote, “In the one year since Gabi joined our team, she has become an indispensable leader, demonstrating exceptional skill, innovation, and dedication in our challenging research environment.”
  • Yu-An Zhang, a postdoc in the Department of Chemistry, was nominated by Professor Alison Wendlandt, who wrote, “He is a creative, deep-thinking scientist and a superb organic chemist. But above all, he is an off-scale mentor and a cherished coworker.”
  • Wouter Van de Pontseele, a senior postdoc in the Laboratory for Nuclear Science, was nominated by Professor Joseph Formaggio, who wrote, “He is a talented scientist with an intense creativity, scholarship, and student mentorship record. In the time he has been with my group, he has led multiple facets of my experimental program and has been a wonderful citizen of the MIT community.”
  • Alexander Shvonski, a lecturer in the Department of Physics, was nominated by Assistant Professor Andrew Vanderburg, who wrote, “… I have been blown away by Alex’s knowledge of education research and best practices, his skills as a teacher and course content designer, and I have been extremely grateful for his assistance.”
  • David Stoppel, a research scientist in The Picower Institute for Learning and Memory, was nominated by Professor Mark Bear and his research group, who wrote, “As impressive as his research achievements might be, David’s most genuine qualification for this award is his incredible commitment to mentorship and the dissemination of knowledge.”

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

Exposure to different kinds of music influences how the brain interprets rhythm

Anne Trafton |

When listening to music, the human brain appears to be biased toward hearing and producing rhythms composed of simple integer ratios — for example, a series of four beats separated by equal time intervals (forming a 1:1:1 ratio).

However, the favored ratios can vary greatly between different societies, according to a large-scale study led by researchers at MIT and the Max Planck Institute for Empirical Aesthetics and carried out in 15 countries. The study included 39 groups of participants, many of whom came from societies whose traditional music contains distinctive patterns of rhythm not found in Western music.

“Our study provides the clearest evidence yet for some degree of universality in music perception and cognition, in the sense that every single group of participants that was tested exhibits biases for integer ratios. It also provides a glimpse of the variation that can occur across cultures, which can be quite substantial,” says Nori Jacoby, the study’s lead author and a former MIT postdoc, who is now a research group leader at the Max Planck Institute for Empirical Aesthetics in Frankfurt, Germany.

The brain’s bias toward simple integer ratios may have evolved as a natural error-correction system that makes it easier to maintain a consistent body of music, which human societies often use to transmit information.

“When people produce music, they often make small mistakes. Our results are consistent with the idea that our mental representation is somewhat robust to those mistakes, but it is robust in a way that pushes us toward our preexisting ideas of the structures that should be found in music,” says Josh McDermott, an associate professor of brain and cognitive sciences at MIT and a member of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds, and Machines.

McDermott is the senior author of the study, which appears today in Nature Human Behaviour. The research team also included scientists from more than two dozen institutions around the world.

A global approach

The new study grew out of a smaller analysis that Jacoby and McDermott published in 2017. In that paper, the researchers compared rhythm perception in groups of listeners from the United States and the Tsimane’, an Indigenous society located in the Bolivian Amazon rainforest.

pitch perception study
Nori Jacoby, a former MIT postdoc now at the Max Planck Institute for Empirical Aesthetics, runs an experiment with a member of the Tsimane’ tribe, who have had little exposure to Western music. Photo: Josh McDermott

To measure how people perceive rhythm, the researchers devised a task in which they play a randomly generated series of four beats and then ask the listener to tap back what they heard. The rhythm produced by the listener is then played back to the listener, and they tap it back again. Over several iterations, the tapped sequences became dominated by the listener’s internal biases, also known as priors.

“The initial stimulus pattern is random, but at each iteration the pattern is pushed by the listener’s biases, such that it tends to converge to a particular point in the space of possible rhythms,” McDermott says. “That can give you a picture of what we call the prior, which is the set of internal implicit expectations for rhythms that people have in their heads.”

When the researchers first did this experiment, with American college students as the test subjects, they found that people tended to produce time intervals that are related by simple integer ratios. Furthermore, most of the rhythms they produced, such as those with ratios of 1:1:2 and 2:3:3, are commonly found in Western music.

The researchers then went to Bolivia and asked members of the Tsimane’ society to perform the same task. They found that Tsimane’ also produced rhythms with simple integer ratios, but their preferred ratios were different and appeared to be consistent with those that have been documented in the few existing records of Tsimane’ music.

“At that point, it provided some evidence that there might be very widespread tendencies to favor these small integer ratios, and that there might be some degree of cross-cultural variation. But because we had just looked at this one other culture, it really wasn’t clear how this was going to look at a broader scale,” Jacoby says.

To try to get that broader picture, the MIT team began seeking collaborators around the world who could help them gather data on a more diverse set of populations. They ended up studying listeners from 39 groups, representing 15 countries on five continents — North America, South America, Europe, Africa, and Asia.

“This is really the first study of its kind in the sense that we did the same experiment in all these different places, with people who are on the ground in those locations,” McDermott says. “That hasn’t really been done before at anything close to this scale, and it gave us an opportunity to see the degree of variation that might exist around the world.”

A grid of nine different photos showing a researcher working with an individual at a table. The individuals are wearing headphones.
Example testing sites. a, Yaranda, Bolivia. b, Montevideo, Uruguay. c, Sagele, Mali. d, Spitzkoppe, Namibia. e, Pleven, Bulgaria. f, Bamako, Mali. g, D’Kar, Botswana. h, Stockholm, Sweden. i, Guizhou, China. j, Mumbai, India. Verbal informed consent was obtained from the individuals in each photo.

Cultural comparisons

Just as they had in their original 2017 study, the researchers found that in every group they tested, people tended to be biased toward simple integer ratios of rhythm. However, not every group showed the same biases. People from North America and Western Europe, who have likely been exposed to the same kinds of music, were more likely to generate rhythms with the same ratios. However, many groups, for example those in Turkey, Mali, Bulgaria, and Botswana showed a bias for other rhythms.

“There are certain cultures where there are particular rhythms that are prominent in their music, and those end up showing up in the mental representation of rhythm,” Jacoby says.

The researchers believe their findings reveal a mechanism that the brain uses to aid in the perception and production of music.

“When you hear somebody playing something and they have errors in their performance, you’re going to mentally correct for those by mapping them onto where you implicitly think they ought to be,” McDermott says. “If you didn’t have something like this, and you just faithfully represented what you heard, these errors might propagate and make it much harder to maintain a musical system.”

Among the groups that they studied, the researchers took care to include not only college students, who are easy to study in large numbers, but also people living in traditional societies, who are more difficult to reach. Participants from those more traditional groups showed significant differences from college students living in the same countries, and from people who live in those countries but performed the test online.

“What’s very clear from the paper is that if you just look at the results from undergraduate students around the world, you vastly underestimate the diversity that you see otherwise,” Jacoby says. “And the same was true of experiments where we tested groups of people online in Brazil and India, because you’re dealing with people who have internet access and presumably have more exposure to Western music.”

The researchers now hope to run additional studies of different aspects of music perception, taking this global approach.

“If you’re just testing college students around the world or people online, things look a lot more homogenous. I think it’s very important for the field to realize that you actually need to go out into communities and run experiments there, as opposed to taking the low-hanging fruit of running studies with people in a university or on the internet,” McDermott says.

The research was funded by the James S. McDonnell Foundation, the Canadian National Science and Engineering Research Council, the South African National Research Foundation, the United States National Science Foundation, the Chilean National Research and Development Agency, the Austrian Academy of Sciences, the Japan Society for the Promotion of Science, the Keio Global Research Institute, the United Kingdom Arts and Humanities Research Council, the Swedish Research Council, and the John Fell Fund.

Imaging method reveals new cells and structures in human brain tissue

Anne Trafton |

Using a novel microscopy technique, MIT and Brigham and Women’s Hospital/Harvard Medical School researchers have imaged human brain tissue in greater detail than ever before, revealing cells and structures that were not previously visible.

McGovern Institute Investigator Edward Boyden. Photo: Justin Knight

Among their findings, the researchers discovered that some “low-grade” brain tumors contain more putative aggressive tumor cells than expected, suggesting that some of these tumors may be more aggressive than previously thought.

The researchers hope that this technique could eventually be deployed to diagnose tumors, generate more accurate prognoses, and help doctors choose treatments.

“We’re starting to see how important the interactions of neurons and synapses with the surrounding brain are to the growth and progression of tumors. A lot of those things we really couldn’t see with conventional tools, but now we have a tool to look at those tissues at the nanoscale and try to understand these interactions,” says Pablo Valdes, a former MIT postdoc who is now an assistant professor of neuroscience at the University of Texas Medical Branch and the lead author of the study.

Edward Boyden, the Y. Eva Tan Professor in Neurotechnology at MIT; a professor of biological engineering, media arts and sciences, and brain and cognitive sciences; a Howard Hughes Medical Institute investigator; and a member of MIT’s McGovern Institute for Brain Research and Koch Institute for Integrative Cancer Research; and E. Antonio Chiocca, a professor of neurosurgery at Harvard Medical School and chair of neurosurgery at Brigham and Women’s Hospital, are the senior authors of the study, which appears today in Science Translational Medicine.

Making molecules visible

The new imaging method is based on expansion microscopy, a technique developed in Boyden’s lab in 2015 based on a simple premise: Instead of using powerful, expensive microscopes to obtain high-resolution images, the researchers devised a way to expand the tissue itself, allowing it to be imaged at very high resolution with a regular light microscope.

The technique works by embedding the tissue into a polymer that swells when water is added, and then softening up and breaking apart the proteins that normally hold tissue together. Then, adding water swells the polymer, pulling all the proteins apart from each other. This tissue enlargement allows researchers to obtain images with a resolution of around 70 nanometers, which was previously possible only with very specialized and expensive microscopes such as scanning electron microscopes.

In 2017, the Boyden lab developed a way to expand preserved human tissue specimens, but the chemical reagents that they used also destroyed the proteins that the researchers were interested in labeling. By labeling the proteins with fluorescent antibodies before expansion, the proteins’ location and identity could be visualized after the expansion process was complete. However, the antibodies typically used for this kind of labeling can’t easily squeeze through densely packed tissue before it’s expanded.

So, for this study, the authors devised a different tissue-softening protocol that breaks up the tissue but preserves proteins in the sample. After the tissue is expanded, proteins can be labelled with commercially available fluorescent antibodies. The researchers then can perform several rounds of imaging, with three or four different proteins labeled in each round. This labeling of proteins enables many more structures to be imaged, because once the tissue is expanded, antibodies can squeeze through and label proteins they couldn’t previously reach.

The technique works by embedding the tissue into a polymer that swells when water is added, and then softening up and breaking apart the proteins that normally hold tissue together.

“We open up the space between the proteins so that we can get antibodies into crowded spaces that we couldn’t otherwise,” Valdes says. “We saw that we could expand the tissue, we could decrowd the proteins, and we could image many, many proteins in the same tissue by doing multiple rounds of staining.”

Working with MIT Assistant Professor Deblina Sarkar, the researchers demonstrated a form of this “decrowding” in 2022 using mouse tissue.

The new study resulted in a decrowding technique for use with human brain tissue samples that are used in clinical settings for pathological diagnosis and to guide treatment decisions. These samples can be more difficult to work with because they are usually embedded in paraffin and treated with other chemicals that need to be broken down before the tissue can be expanded.

In this study, the researchers labeled up to 16 different molecules per tissue sample. The molecules they targeted include markers for a variety of structures, including axons and synapses, as well as markers that identify cell types such as astrocytes and cells that form blood vessels. They also labeled molecules linked to tumor aggressiveness and neurodegeneration.

Using this approach, the researchers analyzed healthy brain tissue, along with samples from patients with two types of glioma — high-grade glioblastoma, which is the most aggressive primary brain tumor, with a poor prognosis, and low-grade gliomas, which are considered less aggressive.

“We wanted to look at brain tumors so that we can understand them better at the nanoscale level, and by doing that, to be able to develop better treatments and diagnoses in the future. At this point, it was more developing a tool to be able to understand them better, because currently in neuro-oncology, people haven’t done much in terms of super-resolution imaging,” Valdes says.

A diagnostic tool

To identify aggressive tumor cells in gliomas they studied, the researchers labeled vimentin, a protein that is found in highly aggressive glioblastomas. To their surprise, they found many more vimentin-expressing tumor cells in low-grade gliomas than had been seen using any other method.

“This tells us something about the biology of these tumors, specifically, how some of them probably have a more aggressive nature than you would suspect by doing standard staining techniques,” Valdes says.

When glioma patients undergo surgery, tumor samples are preserved and analyzed using immunohistochemistry staining, which can reveal certain markers of aggressiveness, including some of the markers analyzed in this study.

“These are incurable brain cancers, and this type of discovery will allow us to figure out which cancer molecules to target so we can design better treatments. It also proves the profound impact of having clinicians like us at the Brigham and Women’s interacting with basic scientists such as Ed Boyden at MIT to discover new technologies that can improve patient lives,” Chiocca says.

The researchers hope their expansion microscopy technique could allow doctors to learn much more about patients’ tumors, helping them to determine how aggressive the tumor is and guiding treatment choices. Valdes now plans to do a larger study of tumor types to try to establish diagnostic guidelines based on the tumor traits that can be revealed using this technique.

“Our hope is that this is going to be a diagnostic tool to pick up marker cells, interactions, and so on, that we couldn’t before,” he says. “It’s a practical tool that will help the clinical world of neuro-oncology and neuropathology look at neurological diseases at the nanoscale like never before, because fundamentally it’s a very simple tool to use.”

Boyden’s lab also plans to use this technique to study other aspects of brain function, in healthy and diseased tissue.

“Being able to do nanoimaging is important because biology is about nanoscale things — genes, gene products, biomolecules — and they interact over nanoscale distances,” Boyden says. “We can study all sorts of nanoscale interactions, including synaptic changes, immune interactions, and changes that occur during cancer and aging.”

The research was funded by K. Lisa Yang, the Howard Hughes Medical Institute, John Doerr, Open Philanthropy, the Bill and Melinda Gates Foundation, the Koch Institute Frontier Research Program, the National Institutes of Health, and the Neurosurgery Research and Education Foundation.

Simons Center’s collaborative approach propels autism research, at MIT and beyond

by David Orenstein |

The secret to the success of MIT’s Simons Center for the Social Brain is in the name. With a founding philosophy of “collaboration and community” that has supported scores of scientists across more than a dozen Boston-area research institutions, the SCSB advances research by being inherently social.

SCSB’s mission is “to understand the neural mechanisms underlying social cognition and behavior and to translate this knowledge into better diagnosis and treatment of autism spectrum disorders.” When Director Mriganka Sur founded the center in 2012 in partnership with the Simons Foundation Autism Research Initiative (SFARI) of Jim and Marilyn Simons, he envisioned a different way to achieve urgently needed research progress than the traditional approach of funding isolated projects in individual labs. Sur wanted SCSB’s contribution to go beyond papers, though it has generated about 350 and counting. He sought the creation of a sustained, engaged autism research community at MIT and beyond.

“When you have a really big problem that spans so many issues — a clinical presentation, a gene, and everything in between — you have to grapple with multiple scales of inquiry,” says Sur, the Newton Professor of Neuroscience in MIT’s Department of Brain and Cognitive Sciences (BCS) and The Picower Institute for Learning and Memory. “This cannot be solved by one person or one lab. We need to span multiple labs and multiple ways of thinking. That was our vision.”

In parallel with a rich calendar of public colloquia, lunches, and special events, SCSB catalyzes multiperspective, multiscale research collaborations in two programmatic ways. Targeted projects fund multidisciplinary teams of scientists with complementary expertise to collectively tackle a pressing scientific question. Meanwhile, the center supports postdoctoral Simons Fellows with not one, but two mentors, ensuring a further cross-pollination of ideas and methods.

Complementary collaboration

In 11 years, SCSB has funded nine targeted projects. Each one, by design, involves a deep and multifaceted exploration of a major question with both fundamental importance and clinical relevance. The first project, back in 2013, for example, marshaled three labs spanning BCS, the Department of Biology, and The Whitehead Institute for Biomedical Research to advance understanding of how mutation of the Shank3 gene leads to the pathophysiology of Phelan-McDermid Syndrome by working across scales ranging from individual neural connections to whole neurons to circuits and behavior.

Other past projects have applied similarly integrated, multiscale approaches to topics ranging from how 16p11.2 gene deletion alters the development of brain circuits and cognition to the critical role of the thalamic reticular nucleus in information flow during sleep and wakefulness. Two others produced deep examinations of cognitive functions: how we go from hearing a string of words to understanding a sentence’s intended meaning, and the neural and behavioral correlates of deficits in making predictions about social and sensory stimuli. Yet another project laid the groundwork for developing a new animal model for autism research.

SFARI is especially excited by SCSB’s team science approach, says Kelsey Martin, executive vice president of autism and neuroscience at the Simons Foundation. “I’m delighted by the collaborative spirit of the SCSB,” Martin says. “It’s wonderful to see and learn about the multidisciplinary team-centered collaborations sponsored by the center.”

New projects

In the last year, SCSB has launched three new targeted projects. One team is investigating why many people with autism experience sensory overload and is testing potential interventions to help. The scientists hypothesize that patients experience a deficit in filtering out the mundane stimuli that neurotypical people predict are safe to ignore. Studies suggest the predictive filter relies on relatively low-frequency “alpha/beta” brain rhythms from deep layers of the cortex moderating the higher frequency “gamma” rhythms in superficial layers that process sensory information.

Together, the labs of Charles Nelson, professor of pediatrics at Boston Children’s Hospital (BCH), and BCS faculty members Bob Desimone, the Doris and Don Berkey Professor of Neuroscience at MIT and director of the McGovern Institute, and Earl K. Miller, the Picower Professor, are testing the hypothesis in two different animal models at MIT and in human volunteers at BCH. In the animals they’ll also try out a new real-time feedback system invented in Miller’s lab that can potentially correct the balance of these rhythms in the brain. And in an animal model engineered with a Shank3 mutation, Desimone’s lab will test a gene therapy, too.

“None of us could do all aspects of this project on our own,” says Miller, an investigator in the Picower Institute. “It could only come about because the three of us are working together, using different approaches.”

Right from the start, Desimone says, close collaboration with Nelson’s group at BCH has been essential. To ensure his and Miller’s measurements in the animals and Nelson’s measurements in the humans are as comparable as possible, they have tightly coordinated their research protocols.

“If we hadn’t had this joint grant we would have chosen a completely different, random set of parameters than Chuck, and the results therefore wouldn’t have been comparable. It would be hard to relate them,” says Desimone, who also directs MIT’s McGovern Institute for Brain Research. “This is a project that could not be accomplished by one lab operating in isolation.”

Another targeted project brings together a coalition of seven labs — six based in BCS (professors Evelina Fedorenko, Edward Gibson, Nancy Kanwisher, Roger Levy, Rebecca Saxe, and Joshua Tenenbaum) and one at Dartmouth College (Caroline Robertson) — for a synergistic study of the cognitive, neural, and computational underpinnings of conversational exchanges. The study will integrate the linguistic and non-linguistic aspects of conversational ability in neurotypical adults and children and those with autism.

Fedorenko said the project builds on advances and collaborations from the earlier language Targeted Project she led with Kanwisher.

“Many directions that we started to pursue continue to be active directions in our labs. But most importantly, it was really fun and allowed the PIs [principal investigators] to interact much more than we normally would and to explore exciting interdisciplinary questions,” Fedorenko says. “When Mriganka approached me a few years after the project’s completion asking about a possible new targeted project, I jumped at the opportunity.”

Gibson and Robertson are studying how people align their dialogue, not only in the content and form of their utterances, but using eye contact. Fedorenko and Kanwisher will employ fMRI to discover key components of a conversation network in the cortex. Saxe will examine the development of conversational ability in toddlers using novel MRI techniques. Levy and Tenenbaum will complement these efforts to improve computational models of language processing and conversation.

The newest Targeted Project posits that the immune system can be harnessed to help treat behavioral symptoms of autism. Four labs — three in BCS and one at Harvard Medical School (HMS) — will study mechanisms by which peripheral immune cells can deliver a potentially therapeutic cytokine to the brain. A study by two of the collaborators, MIT associate professor Gloria Choi and HMS associate professor Jun Huh, showed that when IL-17a reaches excitatory neurons in a region of the mouse cortex, it can calm hyperactivity in circuits associated with social and repetitive behavior symptoms. Huh, an immunologist, will examine how IL-17a can get from the periphery to the brain, while Choi will examine how it has its neurological effects. Sur and MIT associate professor Myriam Heiman will conduct studies of cell types that bridge neural circuits with brain circulatory systems.

“It is quite amazing that we have a core of scientists working on very different things coming together to tackle this one common goal,” Choi says. “I really value that.”

Multiple mentors

While SCSB Targeted Projects unify labs around research, the center’s Simons Fellowships unify labs around young researchers, providing not only funding, but a pair of mentors and free-flowing interactions between their labs. Fellows also gain opportunities to inform and inspire their fundamental research by visiting with patients with autism, Sur says.

“The SCSB postdoctoral program serves a critical role in ensuring that a diversity of outstanding scientists are exposed to autism research during their training, providing a pipeline of new talent and creativity for the field,” adds Martin, of the Simons Foundation.

Simons Fellows praise the extra opportunities afforded by additional mentoring. Postdoc Alex Major was a Simons Fellow in Miller’s lab and that of Nancy Kopell, a mathematics professor at Boston University renowned for her modeling of the brain wave phenomena that the Miller lab studies experimentally.

“The dual mentorship structure is a very useful aspect of the fellowship” Major says. “It is both a chance to network with another PI and provides experience in a different neuroscience sub-field.”

Miller says co-mentoring expands the horizons and capabilities of not only the mentees but also the mentors and their labs. “Collaboration is 21st century neuroscience,” Miller says. “Some our studies of the brain have gotten too big and comprehensive to be encapsulated in just one laboratory. Some of these big questions require multiple approaches and multiple techniques.”

Desimone, who recently co-mentored Seng Bum (Michael Yoo) along with BCS and McGovern colleague Mehrdad Jazayeri in a project studying how animals learn from observing others, agrees.

“We hear from postdocs all the time that they wish they had two mentors, just in general to get another point of view,” Desimone says. “This is a really good thing and it’s a way for faculty members to learn about what other faculty members and their postdocs are doing.”

Indeed, the Simons Center model suggests that research can be very successful when it’s collaborative and social.

How the brain responds to reward is linked to socioeconomic background

Anne Trafton |

MIT neuroscientists have found that the brain’s sensitivity to rewarding experiences — a critical factor in motivation and attention — can be shaped by socioeconomic conditions.

In a study of 12 to 14-year-olds whose socioeconomic status (SES) varied widely, the researchers found that children from lower SES backgrounds showed less sensitivity to reward than those from more affluent backgrounds.

Using functional magnetic resonance imaging (fMRI), the research team measured brain activity as the children played a guessing game in which they earned extra money for each correct guess. When participants from higher SES backgrounds guessed correctly, a part of the brain called the striatum, which is linked to reward, lit up much more than in children from lower SES backgrounds.

The brain imaging results also coincided with behavioral differences in how participants from lower and higher SES backgrounds responded to correct guesses. The findings suggest that lower SES circumstances may prompt the brain to adapt to the environment by dampening its response to rewards, which are often scarcer in low SES environments.

“If you’re in a highly resourced environment, with many rewards available, your brain gets tuned in a certain way. If you’re in an environment in which rewards are more scarce, then your brain accommodates the environment in which you live. Instead of being overresponsive to rewards, it seems like these brains, on average, are less responsive, because probably their environment has been less consistent in the availability of rewards,” says John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology, a professor of brain and cognitive sciences, and a member of MIT’s McGovern Institute for Brain Research.

Gabrieli and Rachel Romeo, a former MIT postdoc who is now an assistant professor in the Department of Human Development and Quantitative Methodology at the University of Maryland, are the senior authors of the study. MIT postdoc Alexandra Decker is the lead author of the paper, which appears today in the Journal of Neuroscience.

Reward response

Previous research has shown that children from lower SES backgrounds tend to perform worse on tests of attention and memory, and they are more likely to experience depression and anxiety. However, until now, few studies have looked at the possible association between SES and reward sensitivity.

In the new study, the researchers focused on a part of the brain called the striatum, which plays a significant role in reward response and decision-making. Studies in people and animal models have shown that this region becomes highly active during rewarding experiences.

To investigate potential links between reward sensitivity, the striatum, and socioeconomic status, the researchers recruited more than 100 adolescents from a range of SES backgrounds, as measured by household income and how much education their parents received.

Each of the participants underwent fMRI scanning while they played a guessing game. The participants were shown a series of numbers between 1 and 9, and before each trial, they were asked to guess whether the next number would be greater than or less than 5. They were told that for each correct guess, they would earn an extra dollar, and for each incorrect guess, they would lose 50 cents.

Unbeknownst to the participants, the game was set up to control whether the guess would be correct or incorrect. This allowed the researchers to ensure that each participant had a similar experience, which included periods of abundant rewards or few rewards. In the end, everyone ended up winning the same amount of money (in addition to a stipend that each participant received for participating in the study).

Previous work has shown that the brain appears to track the rate of rewards available. When rewards are abundant, people or animals tend to respond more quickly because they don’t want to miss out on the many available rewards. The researchers saw that in this study as well: When participants were in a period when most of their responses were correct, they tended to respond more quickly.

“If your brain is telling you there’s a really high chance that you’re going to receive a reward in this environment, it’s going to motivate you to collect rewards, because if you don’t act, you’re missing out on a lot of rewards,” Decker says.

Brain scans showed that the degree of activation in the striatum appeared to track fluctuations in the rate of rewards across time, which the researchers think could act as a motivational signal that there are many rewards to collect. The striatum lit up more during periods in which rewards were abundant and less during periods in which rewards were scarce. However, this effect was less pronounced in the children from lower SES backgrounds, suggesting their brains were less attuned to fluctuations in the rate of reward over time.

The researchers also found that during periods of scarce rewards, participants tended to take longer to respond after a correct guess, another phenomenon that has been shown before. It’s unknown exactly why this happens, but two possible explanations are that people are savoring their reward or that they are pausing to update the reward rate. However, once again, this effect was less pronounced in the children from lower SES backgrounds — that is, they did not pause as long after a correct guess during the scarce-reward periods.

“There was a reduced response to reward, which is really striking. It may be that if you’re from a lower SES environment, you’re not as hopeful that the next response will gain similar benefits, because you may have a less reliable environment for earning rewards,” Gabrieli says. “It just points out the power of the environment. In these adolescents, it’s shaping their psychological and brain response to reward opportunity.”

Environmental effects

The fMRI scans performed during the study also revealed that children from lower SES backgrounds showed less activation in the striatum when they guessed correctly, suggesting that their brains have a dampened response to reward.

The researchers hypothesize that these differences in reward sensitivity may have evolved over time, in response to the children’s environments.

“Socioeconomic status is associated with the degree to which you experience rewards over the course of your lifetime,” Decker says. “So, it’s possible that receiving a lot of rewards perhaps reinforces behaviors that make you receive more rewards, and somehow this tunes the brain to be more responsive to rewards. Whereas if you are in an environment where you receive fewer rewards, your brain might become, over time, less attuned to them.”

The study also points out the value of recruiting study subjects from a range of SES backgrounds, which takes more effort but yields important results, the researchers say.

“Historically, many studies have involved the easiest people to recruit, who tend to be people who come from advantaged environments. If we don’t make efforts to recruit diverse pools of participants, we almost always end up with children and adults who come from high-income, high-education environments,” Gabrieli says. “Until recently, we did not realize that principles of brain development vary in relation to the environment in which one grows up, and there was very little evidence about the influence of SES.”

The research was funded by the William and Flora Hewlett Foundation and a Natural Sciences and Engineering Research Council of Canada Postdoctoral Fellowship.