Author: MIT News

Study reveals a universal pattern of brain wave frequencies

Anne Trafton |

Throughout the brain’s cortex, neurons are arranged in six distinctive layers, which can be readily seen with a microscope. A team of MIT and Vanderbilt University neuroscientists has now found that these layers also show distinct patterns of electrical activity, which are consistent over many brain regions and across several animal species, including humans.

The researchers found that in the topmost layers, neuron activity is dominated by rapid oscillations known as gamma waves. In the deeper layers, slower oscillations called alpha and beta waves predominate. The universality of these patterns suggests that these oscillations are likely playing an important role across the brain, the researchers say.

“When you see something that consistent and ubiquitous across cortex, it’s playing a very fundamental role in what the cortex does,” says Earl Miller, the Picower Professor of Neuroscience, a member of MIT’s Picower Institute for Learning and Memory, and one of the senior authors of the new study.

Imbalances in how these oscillations interact with each other may be involved in brain disorders such as attention deficit hyperactivity disorder, the researchers say.

“Overly synchronous neural activity is known to play a role in epilepsy, and now we suspect that different pathologies of synchrony may contribute to many brain disorders, including disorders of perception, attention, memory, and motor control. In an orchestra, one instrument played out of synchrony with the rest can disrupt the coherence of the entire piece of music,” says Robert Desimone, director of MIT’s McGovern Institute for Brain Research and one of the senior authors of the study.

André Bastos, an assistant professor of psychology at Vanderbilt University, is also a senior author of the open-access paper, which appears today in Nature Neuroscience. The lead authors of the paper are MIT research scientist Diego Mendoza-Halliday and MIT postdoc Alex Major.

Layers of activity

The human brain contains billions of neurons, each of which has its own electrical firing patterns. Together, groups of neurons with similar patterns generate oscillations of electrical activity, or brain waves, which can have different frequencies. Miller’s lab has previously shown that high-frequency gamma rhythms are associated with encoding and retrieving sensory information, while low-frequency beta rhythms act as a control mechanism that determines which information is read out from working memory.

His lab has also found that in certain parts of the prefrontal cortex, different brain layers show distinctive patterns of oscillation: faster oscillation at the surface and slower oscillation in the deep layers. One study, led by Bastos when he was a postdoc in Miller’s lab, showed that as animals performed working memory tasks, lower-frequency rhythms generated in deeper layers regulated the higher-frequency gamma rhythms generated in the superficial layers.

In addition to working memory, the brain’s cortex also is the seat of thought, planning, and high-level processing of emotion and sensory information. Throughout the regions involved in these functions, neurons are arranged in six layers, and each layer has its own distinctive combination of cell types and connections with other brain areas.

“The cortex is organized anatomically into six layers, no matter whether you look at mice or humans or any mammalian species, and this pattern is present in all cortical areas within each species,” Mendoza-Halliday says. “Unfortunately, a lot of studies of brain activity have been ignoring those layers because when you record the activity of neurons, it’s been difficult to understand where they are in the context of those layers.”

In the new paper, the researchers wanted to explore whether the layered oscillation pattern they had seen in the prefrontal cortex is more widespread, occurring across different parts of the cortex and across species.

Using a combination of data acquired in Miller’s lab, Desimone’s lab, and labs from collaborators at Vanderbilt, the Netherlands Institute for Neuroscience, and the University of Western Ontario, the researchers were able to analyze 14 different areas of the cortex, from four mammalian species. This data included recordings of electrical activity from three human patients who had electrodes inserted in the brain as part of a surgical procedure they were undergoing.

Recording from individual cortical layers has been difficult in the past, because each layer is less than a millimeter thick, so it’s hard to know which layer an electrode is recording from. For this study, electrical activity was recorded using special electrodes that record from all of the layers at once, then feed the data into a new computational algorithm the authors designed, termed FLIP (frequency-based layer identification procedure). This algorithm can determine which layer each signal came from.

“More recent technology allows recording of all layers of cortex simultaneously. This paints a broader perspective of microcircuitry and allowed us to observe this layered pattern,” Major says. “This work is exciting because it is both informative of a fundamental microcircuit pattern and provides a robust new technique for studying the brain. It doesn’t matter if the brain is performing a task or at rest and can be observed in as little as five to 10 seconds.”

Across all species, in each region studied, the researchers found the same layered activity pattern.

“We did a mass analysis of all the data to see if we could find the same pattern in all areas of the cortex, and voilà, it was everywhere. That was a real indication that what had previously been seen in a couple of areas was representing a fundamental mechanism across the cortex,” Mendoza-Halliday says.

Maintaining balance

The findings support a model that Miller’s lab has previously put forth, which proposes that the brain’s spatial organization helps it to incorporate new information, which carried by high-frequency oscillations, into existing memories and brain processes, which are maintained by low-frequency oscillations. As information passes from layer to layer, input can be incorporated as needed to help the brain perform particular tasks such as baking a new cookie recipe or remembering a phone number.

“The consequence of a laminar separation of these frequencies, as we observed, may be to allow superficial layers to represent external sensory information with faster frequencies, and for deep layers to represent internal cognitive states with slower frequencies,” Bastos says. “The high-level implication is that the cortex has multiple mechanisms involving both anatomy and oscillations to separate ‘external’ from ‘internal’ information.”

Under this theory, imbalances between high- and low-frequency oscillations can lead to either attention deficits such as ADHD, when the higher frequencies dominate and too much sensory information gets in, or delusional disorders such as schizophrenia, when the low frequency oscillations are too strong and not enough sensory information gets in.

“The proper balance between the top-down control signals and the bottom-up sensory signals is important for everything the cortex does,” Miller says. “When the balance goes awry, you get a wide variety of neuropsychiatric disorders.”

The researchers are now exploring whether measuring these oscillations could help to diagnose these types of disorders. They are also investigating whether rebalancing the oscillations could alter behavior — an approach that could one day be used to treat attention deficits or other neurological disorders, the researchers say.

The researchers also hope to work with other labs to characterize the layered oscillation patterns in more detail across different brain regions.

“Our hope is that with enough of that standardized reporting, we will start to see common patterns of activity across different areas or functions that might reveal a common mechanism for computation that can be used for motor outputs, for vision, for memory and attention, et cetera,” Mendoza-Halliday says.

The research was funded by the U.S. Office of Naval Research, the U.S. National Institutes of Health, the U.S. National Eye Institute, the U.S. National Institute of Mental Health, the Picower Institute, a Simons Center for the Social Brain Postdoctoral Fellowship, and a Canadian Institutes of Health Postdoctoral Fellowship.

Complex, unfamiliar sentences make the brain’s language network work harder

Anne Trafton |

With help from an artificial language network, MIT neuroscientists have discovered what kind of sentences are most likely to fire up the brain’s key language processing centers.

The new study reveals that sentences that are more complex, either because of unusual grammar or unexpected meaning, generate stronger responses in these language processing centers. Sentences that are very straightforward barely engage these regions, and nonsensical sequences of words don’t do much for them either.

For example, the researchers found this brain network was most active when reading unusual sentences such as “Buy sell signals remains a particular,” taken from a publicly available language dataset called C4. However, it went quiet when reading something very straightforward, such as “We were sitting on the couch.”

“The input has to be language-like enough to engage the system,” says Evelina Fedorenko, Associate Professor of Neuroscience at MIT and a member of MIT’s McGovern Institute for Brain Research. “And then within that space, if things are really easy to process, then you don’t have much of a response. But if things get difficult, or surprising, if there’s an unusual construction or an unusual set of words that you’re maybe not very familiar with, then the network has to work harder.”

Fedorenko is the senior author of the study, which appears today in Nature Human Behavior. MIT graduate student Greta Tuckute is the lead author of the paper.

Processing language

In this study, the researchers focused on language-processing regions found in the left hemisphere of the brain, which includes Broca’s area as well as other parts of the left frontal and temporal lobes of the brain.

“This language network is highly selective to language, but it’s been harder to actually figure out what is going on in these language regions,” Tuckute says. “We wanted to discover what kinds of sentences, what kinds of linguistic input, drive the left hemisphere language network.”

The researchers began by compiling a set of 1,000 sentences taken from a wide variety of sources — fiction, transcriptions of spoken words, web text, and scientific articles, among many others.

Five human participants read each of the sentences while the researchers measured their language network activity using functional magnetic resonance imaging (fMRI). The researchers then fed those same 1,000 sentences into a large language model — a model similar to ChatGPT, which learns to generate and understand language from predicting the next word in huge amounts of text — and measured the activation patterns of the model in response to each sentence.

Once they had all of those data, the researchers trained a mapping model, known as an “encoding model,” which relates the activation patterns seen in the human brain with those observed in the artificial language model. Once trained, the model could predict how the human language network would respond to any new sentence based on how the artificial language network responded to these 1,000 sentences.

The researchers then used the encoding model to identify 500 new sentences that would generate maximal activity in the human brain (the “drive” sentences), as well as sentences that would elicit minimal activity in the brain’s language network (the “suppress” sentences).

In a group of three new human participants, the researchers found these new sentences did indeed drive and suppress brain activity as predicted.

“This ‘closed-loop’ modulation of brain activity during language processing is novel,” Tuckute says. “Our study shows that the model we’re using (that maps between language-model activations and brain responses) is accurate enough to do this. This is the first demonstration of this approach in brain areas implicated in higher-level cognition, such as the language network.”

Linguistic complexity

To figure out what made certain sentences drive activity more than others, the researchers analyzed the sentences based on 11 different linguistic properties, including grammaticality, plausibility, emotional valence (positive or negative), and how easy it is to visualize the sentence content.

For each of those properties, the researchers asked participants from crowd-sourcing platforms to rate the sentences. They also used a computational technique to quantify each sentence’s “surprisal,” or how uncommon it is compared to other sentences.

This analysis revealed that sentences with higher surprisal generate higher responses in the brain. This is consistent with previous studies showing people have more difficulty processing sentences with higher surprisal, the researchers say.

Another linguistic property that correlated with the language network’s responses was linguistic complexity, which is measured by how much a sentence adheres to the rules of English grammar and how plausible it is, meaning how much sense the content makes, apart from the grammar.

Sentences at either end of the spectrum — either extremely simple, or so complex that they make no sense at all — evoked very little activation in the language network. The largest responses came from sentences that make some sense but require work to figure them out, such as “Jiffy Lube of — of therapies, yes,” which came from the Corpus of Contemporary American English dataset.

“We found that the sentences that elicit the highest brain response have a weird grammatical thing and/or a weird meaning,” Fedorenko says. “There’s something slightly unusual about these sentences.”

The researchers now plan to see if they can extend these findings in speakers of languages other than English. They also hope to explore what type of stimuli may activate language processing regions in the brain’s right hemisphere.

The research was funded by an Amazon Fellowship from the Science Hub, an International Doctoral Fellowship from the American Association of University Women, the MIT-IBM Watson AI Lab, the National Institutes of Health, the McGovern Institute, the Simons Center for the Social Brain, and MIT’s Department of Brain and Cognitive Sciences.

Deep neural networks show promise as models of human hearing

Anne Trafton |

Computational models that mimic the structure and function of the human auditory system could help researchers design better hearing aids, cochlear implants, and brain-machine interfaces. A new study from MIT has found that modern computational models derived from machine learning are moving closer to this goal.

In the largest study yet of deep neural networks that have been trained to perform auditory tasks, the MIT team showed that most of these models generate internal representations that share properties of representations seen in the human brain when people are listening to the same sounds.

The study also offers insight into how to best train this type of model: The researchers found that models trained on auditory input including background noise more closely mimic the activation patterns of the human auditory cortex.

“What sets this study apart is it is the most comprehensive comparison of these kinds of models to the auditory system so far. The study suggests that models that are derived from machine learning are a step in the right direction, and it gives us some clues as to what tends to make them better models of the brain,” says Josh McDermott, an associate professor of brain and cognitive sciences at MIT, a member of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds, and Machines, and the senior author of the study.

MIT graduate student Greta Tuckute and Jenelle Feather PhD ’22 are the lead authors of the open-access paper, which appears today in PLOS Biology.

Models of hearing

Deep neural networks are computational models that consists of many layers of information-processing units that can be trained on huge volumes of data to perform specific tasks. This type of model has become widely used in many applications, and neuroscientists have begun to explore the possibility that these systems can also be used to describe how the human brain performs certain tasks.

“These models that are built with machine learning are able to mediate behaviors on a scale that really wasn’t possible with previous types of models, and that has led to interest in whether or not the representations in the models might capture things that are happening in the brain,” Tuckute says.

When a neural network is performing a task, its processing units generate activation patterns in response to each audio input it receives, such as a word or other type of sound. Those model representations of the input can be compared to the activation patterns seen in fMRI brain scans of people listening to the same input.

In 2018, McDermott and then-graduate student Alexander Kell reported that when they trained a neural network to perform auditory tasks (such as recognizing words from an audio signal), the internal representations generated by the model showed similarity to those seen in fMRI scans of people listening to the same sounds.

Since then, these types of models have become widely used, so McDermott’s research group set out to evaluate a larger set of models, to see if the ability to approximate the neural representations seen in the human brain is a general trait of these models.

For this study, the researchers analyzed nine publicly available deep neural network models that had been trained to perform auditory tasks, and they also created 14 models of their own, based on two different architectures. Most of these models were trained to perform a single task — recognizing words, identifying the speaker, recognizing environmental sounds, and identifying musical genre — while two of them were trained to perform multiple tasks.

When the researchers presented these models with natural sounds that had been used as stimuli in human fMRI experiments, they found that the internal model representations tended to exhibit similarity with those generated by the human brain. The models whose representations were most similar to those seen in the brain were models that had been trained on more than one task and had been trained on auditory input that included background noise.

“If you train models in noise, they give better brain predictions than if you don’t, which is intuitively reasonable because a lot of real-world hearing involves hearing in noise, and that’s plausibly something the auditory system is adapted to,” Feather says.

Hierarchical processing

The new study also supports the idea that the human auditory cortex has some degree of hierarchical organization, in which processing is divided into stages that support distinct computational functions. As in the 2018 study, the researchers found that representations generated in earlier stages of the model most closely resemble those seen in the primary auditory cortex, while representations generated in later model stages more closely resemble those generated in brain regions beyond the primary cortex.

Additionally, the researchers found that models that had been trained on different tasks were better at replicating different aspects of audition. For example, models trained on a speech-related task more closely resembled speech-selective areas.

“Even though the model has seen the exact same training data and the architecture is the same, when you optimize for one particular task, you can see that it selectively explains specific tuning properties in the brain,” Tuckute says.

McDermott’s lab now plans to make use of their findings to try to develop models that are even more successful at reproducing human brain responses. In addition to helping scientists learn more about how the brain may be organized, such models could also be used to help develop better hearing aids, cochlear implants, and brain-machine interfaces.

“A goal of our field is to end up with a computer model that can predict brain responses and behavior. We think that if we are successful in reaching that goal, it will open a lot of doors,” McDermott says.

The research was funded by the National Institutes of Health, an Amazon Fellowship from the Science Hub, an International Doctoral Fellowship from the American Association of University Women, an MIT Friends of McGovern Institute Fellowship, a fellowship from the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT, and a Department of Energy Computational Science Graduate Fellowship.

What does the future hold for generative AI?

by Adam Zewe |

Speaking at the “Generative AI: Shaping the Future” symposium on Nov. 28, the kickoff event of MIT’s Generative AI Week, keynote speaker and iRobot co-founder Rodney Brooks warned attendees against uncritically overestimating the capabilities of this emerging technology, which underpins increasingly powerful tools like OpenAI’s ChatGPT and Google’s Bard.

“Hype leads to hubris, and hubris leads to conceit, and conceit leads to failure,” cautioned Brooks, who is also a professor emeritus at MIT, a former director of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and founder of Robust.AI.

“No one technology has ever surpassed everything else,” he added.

The symposium, which drew hundreds of attendees from academia and industry to the Institute’s Kresge Auditorium, was laced with messages of hope about the opportunities generative AI offers for making the world a better place, including through art and creativity, interspersed with cautionary tales about what could go wrong if these AI tools are not developed responsibly.

Generative AI is a term to describe machine-learning models that learn to generate new material that looks like the data they were trained on. These models have exhibited some incredible capabilities, such as the ability to produce human-like creative writing, translate languages, generate functional computer code, or craft realistic images from text prompts.

In her opening remarks to launch the symposium, MIT President Sally Kornbluth highlighted several projects faculty and students have undertaken to use generative AI to make a positive impact in the world. For example, the work of the Axim Collaborative, an online education initiative launched by MIT and Harvard, includes exploring the educational aspects of generative AI to help underserved students.

The Institute also recently announced seed grants for 27 interdisciplinary faculty research projects centered on how AI will transform people’s lives across society.

In hosting Generative AI Week, MIT hopes to not only showcase this type of innovation, but also generate “collaborative collisions” among attendees, Kornbluth said.

Collaboration involving academics, policymakers, and industry will be critical if we are to safely integrate a rapidly evolving technology like generative AI in ways that are humane and help humans solve problems, she told the audience.

“I honestly cannot think of a challenge more closely aligned with MIT’s mission. It is a profound responsibility, but I have every confidence that we can face it, if we face it head on and if we face it as a community,” she said.

While generative AI holds the potential to help solve some of the planet’s most pressing problems, the emergence of these powerful machine learning models has blurred the distinction between science fiction and reality, said CSAIL Director Daniela Rus in her opening remarks. It is no longer a question of whether we can make machines that produce new content, she said, but how we can use these tools to enhance businesses and ensure sustainability. 

“Today, we will discuss the possibility of a future where generative AI does not just exist as a technological marvel, but stands as a source of hope and a force for good,” said Rus, who is also the Andrew and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science.

But before the discussion dove deeply into the capabilities of generative AI, attendees were first asked to ponder their humanity, as MIT Professor Joshua Bennett read an original poem.

Bennett, a professor in the MIT Literature Section and Distinguished Chair of the Humanities, was asked to write a poem about what it means to be human, and drew inspiration from his daughter, who was born three weeks ago.

The poem told of his experiences as a boy watching Star Trek with his father and touched on the importance of passing traditions down to the next generation.

In his keynote remarks, Brooks set out to unpack some of the deep, scientific questions surrounding generative AI, as well as explore what the technology can tell us about ourselves.

To begin, he sought to dispel some of the mystery swirling around generative AI tools like ChatGPT by explaining the basics of how this large language model works. ChatGPT, for instance, generates text one word at a time by determining what the next word should be in the context of what it has already written. While a human might write a story by thinking about entire phrases, ChatGPT only focuses on the next word, Brooks explained.

ChatGPT 3.5 is built on a machine-learning model that has 175 billion parameters and has been exposed to billions of pages of text on the web during training. (The newest iteration, ChatGPT 4, is even larger.) It learns correlations between words in this massive corpus of text and uses this knowledge to propose what word might come next when given a prompt.

The model has demonstrated some incredible capabilities, such as the ability to write a sonnet about robots in the style of Shakespeare’s famous Sonnet 18. During his talk, Brooks showcased the sonnet he asked ChatGPT to write side-by-side with his own sonnet.

But while researchers still don’t fully understand exactly how these models work, Brooks assured the audience that generative AI’s seemingly incredible capabilities are not magic, and it doesn’t mean these models can do anything.

His biggest fears about generative AI don’t revolve around models that could someday surpass human intelligence. Rather, he is most worried about researchers who may throw away decades of excellent work that was nearing a breakthrough, just to jump on shiny new advancements in generative AI; venture capital firms that blindly swarm toward technologies that can yield the highest margins; or the possibility that a whole generation of engineers will forget about other forms of software and AI.

At the end of the day, those who believe generative AI can solve the world’s problems and those who believe it will only generate new problems have at least one thing in common: Both groups tend to overestimate the technology, he said.

“What is the conceit with generative AI? The conceit is that it is somehow going to lead to artificial general intelligence. By itself, it is not,” Brooks said.

Following Brooks’ presentation, a group of MIT faculty spoke about their work using generative AI and participated in a panel discussion about future advances, important but underexplored research topics, and the challenges of AI regulation and policy.

The panel consisted of Jacob Andreas, an associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS) and a member of CSAIL; Antonio Torralba, the Delta Electronics Professor of EECS and a member of CSAIL; Ev Fedorenko, an associate professor of brain and cognitive sciences and an investigator at the McGovern Institute for Brain Research at MIT; and Armando Solar-Lezama, a Distinguished Professor of Computing and associate director of CSAIL. It was moderated by William T. Freeman, the Thomas and Gerd Perkins Professor of EECS and a member of CSAIL.

The panelists discussed several potential future research directions around generative AI, including the possibility of integrating perceptual systems, drawing on human senses like touch and smell, rather than focusing primarily on language and images. The researchers also spoke about the importance of engaging with policymakers and the public to ensure generative AI tools are produced and deployed responsibly.

“One of the big risks with generative AI today is the risk of digital snake oil. There is a big risk of a lot of products going out that claim to do miraculous things but in the long run could be very harmful,” Solar-Lezama said.

The morning session concluded with an excerpt from the 1925 science fiction novel “Metropolis,” read by senior Joy Ma, a physics and theater arts major, followed by a roundtable discussion on the future of generative AI. The discussion included Joshua Tenenbaum, a professor in the Department of Brain and Cognitive Sciences and a member of CSAIL; Dina Katabi, the Thuan and Nicole Pham Professor in EECS and a principal investigator in CSAIL and the MIT Jameel Clinic; and Max Tegmark, professor of physics; and was moderated by Daniela Rus.

One focus of the discussion was the possibility of developing generative AI models that can go beyond what we can do as humans, such as tools that can sense someone’s emotions by using electromagnetic signals to understand how a person’s breathing and heart rate are changing.

But one key to integrating AI like this into the real world safely is to ensure that we can trust it, Tegmark said. If we know an AI tool will meet the specifications we insist on, then “we no longer have to be afraid of building really powerful systems that go out and do things for us in the world,” he said.

A new way to see the activity inside a living cell

Anne Trafton |

Living cells are bombarded with many kinds of incoming molecular signal that influence their behavior. Being able to measure those signals and how cells respond to them through downstream molecular signaling networks could help scientists learn much more about how cells work, including what happens as they age or become diseased.

Right now, this kind of comprehensive study is not possible because current techniques for imaging cells are limited to just a handful of different molecule types within a cell at one time. However, MIT researchers have developed an alternative method that allows them to observe up to seven different molecules at a time, and potentially even more than that.

“There are many examples in biology where an event triggers a long downstream cascade of events, which then causes a specific cellular function,” says Edward Boyden, the Y. Eva Tan Professor in Neurotechnology. “How does that occur? It’s arguably one of the fundamental problems of biology, and so we wondered, could you simply watch it happen?”

It’s arguably one of the fundamental problems of biology, and so we wondered, could you simply watch it happen? – Ed Boyden

The new approach makes use of green or red fluorescent molecules that flicker on and off at different rates. By imaging a cell over several seconds, minutes, or hours, and then extracting each of the fluorescent signals using a computational algorithm, the amount of each target protein can be tracked as it changes over time.

Boyden, who is also a professor of biological engineering and of brain and cognitive sciences at MIT, a Howard Hughes Medical Institute investigator, and a member of MIT’s McGovern Institute for Brain Research and Koch Institute for Integrative Cancer Research, as well as the co-director of the K. Lisa Yang Center for Bionics, is the senior author of the study, which appears today in Cell. MIT postdoc Yong Qian is the lead author of the paper.

Fluorescent signals

Labeling molecules inside cells with fluorescent proteins has allowed researchers to learn a great deal about the functions of many cellular molecules. This type of study is often done with green fluorescent protein (GFP), which was first deployed for imaging in the 1990s. Since then, several fluorescent proteins that glow in other colors have been developed for experimental use.

However, a typical light microscope can only distinguish two or three of these colors, allowing researchers only a tiny glimpse of the overall activity that is happening inside a cell. If they could track a greater number of labeled molecules, researchers could measure a brain cell’s response to different neurotransmitters during learning, for example, or investigate the signals that prompt a cancer cell to metastasize.

“Ideally, you would be able to watch the signals in a cell as they fluctuate in real time, and then you could understand how they relate to each other. That would tell you how the cell computes,” Boyden says. “The problem is that you can’t watch very many things at the same time.”

In 2020, Boyden’s lab developed a way to simultaneously image up to five different molecules within a cell, by targeting glowing reporters to distinct locations inside the cell. This approach, known as “spatial multiplexing,” allows researchers to distinguish signals for different molecules even though they may all be fluorescing the same color.

In the new study, the researchers took a different approach: Instead of distinguishing signals based on their physical location, they created fluorescent signals that vary over time. The technique relies on “switchable fluorophores” — fluorescent proteins that turn on and off at a specific rate. For this study, Boyden and his group members identified four green switchable fluorophores, and then engineered two more, all of which turn on and off at different rates. They also identified two red fluorescent proteins that switch at different rates, and engineered one additional red fluorophore.

Using four switchable fluorophores, MIT researchers were able to label and image four different kinases inside these cells (top four rows). In the bottom row, the cell nuclei are labeled in blue.
Image: Courtesy of the researchers

Each of these switchable fluorophores can be used to label a different type of molecule within a living cell, such an enzyme, signaling protein, or part of the cell cytoskeleton. After imaging the cell for several minutes, hours, or even days, the researchers use a computational algorithm to pick out the specific signal from each fluorophore, analogous to how the human ear can pick out different frequencies of sound.

“In a symphony orchestra, you have high-pitched instruments, like the flute, and low-pitched instruments, like a tuba. And in the middle are instruments like the trumpet. They all have different sounds, and our ear sorts them out,” Boyden says.

The mathematical technique that the researchers used to analyze the fluorophore signals is known as linear unmixing. This method can extract different fluorophore signals, similar to how the human ear uses a mathematical model known as a Fourier transform to extract different pitches from a piece of music.

Once this analysis is complete, the researchers can see when and where each of the fluorescently labeled molecules were found in the cell during the entire imaging period. The imaging itself can be done with a simple light microscope, with no specialized equipment required.

Biological phenomena

In this study, the researchers demonstrated their approach by labeling six different molecules involved in the cell division cycle, in mammalian cells. This allowed them to identify patterns in how the levels of enzymes called cyclin-dependent kinases change as a cell progresses through the cell cycle.

The researchers also showed that they could label other types of kinases, which are involved in nearly every aspect of cell signaling, as well as cell structures and organelles such as the cytoskeleton and mitochondria. In addition to their experiments using mammalian cells grown in a lab dish, the researchers showed that this technique could work in the brains of zebrafish larvae.

This method could be useful for observing how cells respond to any kind of input, such as nutrients, immune system factors, hormones, or neurotransmitters, according to the researchers. It could also be used to study how cells respond to changes in gene expression or genetic mutations. All of these factors play important roles in biological phenomena such as growth, aging, cancer, neurodegeneration, and memory formation.

“You could consider all of these phenomena to represent a general class of biological problem, where some short-term event — like eating a nutrient, learning something, or getting an infection — generates a long-term change,” Boyden says.

In addition to pursuing those types of studies, Boyden’s lab is also working on expanding the repertoire of switchable fluorophores so that they can study even more signals within a cell. They also hope to adapt the system so that it could be used in mouse models.

The research was funded by an Alana Fellowship, K. Lisa Yang, John Doerr, Jed McCaleb, James Fickel, Ashar Aziz, the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT, the Howard Hughes Medical Institute, and the National Institutes of Health.

The brain may learn about the world the same way some computational models do

Anne Trafton |

To make our way through the world, our brain must develop an intuitive understanding of the physical world around us, which we then use to interpret sensory information coming into the brain.

How does the brain develop that intuitive understanding? Many scientists believe that it may use a process similar to what’s known as “self-supervised learning.” This type of machine learning, originally developed as a way to create more efficient models for computer vision, allows computational models to learn about visual scenes based solely on the similarities and differences between them, with no labels or other information.

A pair of studies from researchers at the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT offers new evidence supporting this hypothesis. The researchers found that when they trained models known as neural networks using a particular type of self-supervised learning, the resulting models generated activity patterns very similar to those seen in the brains of animals that were performing the same tasks as the models.

The findings suggest that these models are able to learn representations of the physical world that they can use to make accurate predictions about what will happen in that world, and that the mammalian brain may be using the same strategy, the researchers say.

“The theme of our work is that AI designed to help build better robots ends up also being a framework to better understand the brain more generally,” says Aran Nayebi, a postdoc in the ICoN Center. “We can’t say if it’s the whole brain yet, but across scales and disparate brain areas, our results seem to be suggestive of an organizing principle.”

Nayebi is the lead author of one of the studies, co-authored with Rishi Rajalingham, a former MIT postdoc now at Meta Reality Labs, and senior authors Mehrdad Jazayeri, an associate professor of brain and cognitive sciences and a member of the McGovern Institute for Brain Research; and Robert Yang, an assistant professor of brain and cognitive sciences and an associate member of the McGovern Institute. Ila Fiete, director of the ICoN Center, a professor of brain and cognitive sciences, and an associate member of the McGovern Institute, is the senior author of the other study, which was co-led by Mikail Khona, an MIT graduate student, and Rylan Schaeffer, a former senior research associate at MIT.

Both studies will be presented at the 2023 Conference on Neural Information Processing Systems (NeurIPS) in December.

Modeling the physical world

Early models of computer vision mainly relied on supervised learning. Using this approach, models are trained to classify images that are each labeled with a name — cat, car, etc. The resulting models work well, but this type of training requires a great deal of human-labeled data.

To create a more efficient alternative, in recent years researchers have turned to models built through a technique known as contrastive self-supervised learning. This type of learning allows an algorithm to learn to classify objects based on how similar they are to each other, with no external labels provided.

“This is a very powerful method because you can now leverage very large modern data sets, especially videos, and really unlock their potential,” Nayebi says. “A lot of the modern AI that you see now, especially in the last couple years with ChatGPT and GPT-4, is a result of training a self-supervised objective function on a large-scale dataset to obtain a very flexible representation.”

These types of models, also called neural networks, consist of thousands or millions of processing units connected to each other. Each node has connections of varying strengths to other nodes in the network. As the network analyzes huge amounts of data, the strengths of those connections change as the network learns to perform the desired task.

As the model performs a particular task, the activity patterns of different units within the network can be measured. Each unit’s activity can be represented as a firing pattern, similar to the firing patterns of neurons in the brain. Previous work from Nayebi and others has shown that self-supervised models of vision generate activity similar to that seen in the visual processing system of mammalian brains.

In both of the new NeurIPS studies, the researchers set out to explore whether self-supervised computational models of other cognitive functions might also show similarities to the mammalian brain. In the study led by Nayebi, the researchers trained self-supervised models to predict the future state of their environment across hundreds of thousands of naturalistic videos depicting everyday scenarios.

“For the last decade or so, the dominant method to build neural network models in cognitive neuroscience is to train these networks on individual cognitive tasks. But models trained this way rarely generalize to other tasks,” Yang says. “Here we test whether we can build models for some aspect of cognition by first training on naturalistic data using self-supervised learning, then evaluating in lab settings.”

Once the model was trained, the researchers had it generalize to a task they call “Mental-Pong.” This is similar to the video game Pong, where a player moves a paddle to hit a ball traveling across the screen. In the Mental-Pong version, the ball disappears shortly before hitting the paddle, so the player has to estimate its trajectory in order to hit the ball.

The researchers found that the model was able to track the hidden ball’s trajectory with accuracy similar to that of neurons in the mammalian brain, which had been shown in a previous study by Rajalingham and Jazayeri to simulate its trajectory — a cognitive phenomenon known as “mental simulation.” Furthermore, the neural activation patterns seen within the model were similar to those seen in the brains of animals as they played the game — specifically, in a part of the brain called the dorsomedial frontal cortex. No other class of computational model has been able to match the biological data as closely as this one, the researchers say.

“There are many efforts in the machine learning community to create artificial intelligence,” Jazayeri says. “The relevance of these models to neurobiology hinges on their ability to additionally capture the inner workings of the brain. The fact that Aran’s model predicts neural data is really important as it suggests that we may be getting closer to building artificial systems that emulate natural intelligence.”

Navigating the world

The study led by Khona, Schaeffer, and Fiete focused on a type of specialized neurons known as grid cells. These cells, located in the entorhinal cortex, help animals to navigate, working together with place cells located in the hippocampus.

While place cells fire whenever an animal is in a specific location, grid cells fire only when the animal is at one of the vertices of a triangular lattice. Groups of grid cells create overlapping lattices of different sizes, which allows them to encode a large number of positions using a relatively small number of cells.

In recent studies, researchers have trained supervised neural networks to mimic grid cell function by predicting an animal’s next location based on its starting point and velocity, a task known as path integration. However, these models hinged on access to privileged information about absolute space at all times — information that the animal does not have.

Inspired by the striking coding properties of the multiperiodic grid-cell code for space, the MIT team trained a contrastive self-supervised model to both perform this same path integration task and represent space efficiently while doing so. For the training data, they used sequences of velocity inputs. The model learned to distinguish positions based on whether they were similar or different — nearby positions generated similar codes, but further positions generated more different codes.

“It’s similar to training models on images, where if two images are both heads of cats, their codes should be similar, but if one is the head of a cat and one is a truck, then you want their codes to repel,” Khona says. “We’re taking that same idea but applying it to spatial trajectories.”

Once the model was trained, the researchers found that the activation patterns of the nodes within the model formed several lattice patterns with different periods, very similar to those formed by grid cells in the brain.

“What excites me about this work is that it makes connections between mathematical work on the striking information-theoretic properties of the grid cell code and the computation of path integration,” Fiete says. “While the mathematical work was analytic — what properties does the grid cell code possess? — the approach of optimizing coding efficiency through self-supervised learning and obtaining grid-like tuning is synthetic: It shows what properties might be necessary and sufficient to explain why the brain has grid cells.”

The research was funded by the K. Lisa Yang ICoN Center, the National Institutes of Health, the Simons Foundation, the McKnight Foundation, the McGovern Institute, and the Helen Hay Whitney Foundation.

Soft optical fibers block pain while moving and stretching with the body

by Jennifer Chu |

Scientists have a new tool to precisely illuminate the roots of nerve pain.

Engineers at MIT have developed soft and implantable fibers that can deliver light to major nerves through the body. When these nerves are genetically manipulated to respond to light, the fibers can send pulses of light to the nerves to inhibit pain. The optical fibers are flexible and stretch with the body.

The new fibers are meant as an experimental tool that can be used by scientists to explore the causes and potential treatments for peripheral nerve disorders in animal models. Peripheral nerve pain can occur when nerves outside the brain and spinal cord are damaged, resulting in tingling, numbness, and pain in affected limbs. Peripheral neuropathy is estimated to affect more than 20 million people in the United States.

“Current devices used to study nerve disorders are made of stiff materials that constrain movement, so that we can’t really study spinal cord injury and recovery if pain is involved,” says Siyuan Rao, assistant professor of biomedical engineering at the University of Massachusetts at Amherst, who carried out part of the work as a postdoc at MIT. “Our fibers can adapt to natural motion and do their work while not limiting the motion of the subject. That can give us more precise information.”

“Now, people have a tool to study the diseases related to the peripheral nervous system, in very dynamic, natural, and unconstrained conditions,” adds Xinyue Liu PhD ’22, who is now an assistant professor at Michigan State University (MSU).

Details of their team’s new fibers are reported today in a study appearing in Nature Methods. Rao’s and Liu’s MIT co-authors include Atharva Sahasrabudhe, a graduate student in chemistry; Xuanhe Zhao, professor of mechanical engineering and civil and environmental engineering; and Polina Anikeeva, professor of materials science and engineering, along with others at MSU, UMass-Amherst, Harvard Medical School, and the National Institutes of Health.

Beyond the brain

The new study grew out of the team’s desire to expand the use of optogenetics beyond the brain. Optogenetics is a technique by which nerves are genetically engineered to respond to light. Exposure to that light can then either activate or inhibit the nerve, which can give scientists information about how the nerve works and interacts with its surroundings.

Neuroscientists have applied optogenetics in animals to precisely trace the neural pathways underlying a range of brain disorders, including addiction, Parkinson’s disease, and mood and sleep disorders — information that has led to targeted therapies for these conditions.

To date, optogenetics has been primarily employed in the brain, an area that lacks pain receptors, which allows for the relatively painless implantation of rigid devices. However, the rigid devices can still damage neural tissues. The MIT team wondered whether the technique could be expanded to nerves outside the brain. Just as with the brain and spinal cord, nerves in the peripheral system can experience a range of impairment, including sciatica, motor neuron disease, and general numbness and pain.

Optogenetics could help neuroscientists identify specific causes of peripheral nerve conditions as well as test therapies to alleviate them. But the main hurdle to implementing the technique beyond the brain is motion. Peripheral nerves experience constant pushing and pulling from the surrounding muscles and tissues. If rigid silicon devices were used in the periphery, they would constrain an animal’s natural movement and potentially cause tissue damage.

Crystals and light

The researchers looked to develop an alternative that could work and move with the body. Their new design is a soft, stretchable, transparent fiber made from hydrogel — a rubbery, biocompatible mix of polymers and water, the ratio of which they tuned to create tiny, nanoscale crystals of polymers scattered throughout a more Jell-O-like solution.

The fiber embodies two layers — a core and an outer shell or “cladding.” The team mixed the solutions of each layer to generate a specific crystal arrangement. This arrangement gave each layer a specific, different refractive index, and together the layers kept any light traveling through the fiber from escaping or scattering away.

The team tested the optical fibers in mice whose nerves were genetically modified to respond to blue light that would excite neural activity or yellow light that would inhibit their activity. They found that even with the implanted fiber in place, mice were able to run freely on a wheel. After two months of wheel exercises, amounting to some 30,000 cycles, the researchers found the fiber was still robust and resistant to fatigue, and could also transmit light efficiently to trigger muscle contraction.

The team then turned on a yellow laser and ran it through the implanted fiber. Using standard laboratory procedures for assessing pain inhibition, they observed that the mice were much less sensitive to pain than rodents that were not stimulated with light. The fibers were able to significantly inhibit sciatic pain in those light-stimulated mice.

The researchers see the fibers as a new tool that can help scientists identify the roots of pain and other peripheral nerve disorders.

“We are focusing on the fiber as a new neuroscience technology,” Liu says. “We hope to help dissect mechanisms underlying pain in the peripheral nervous system. With time, our technology may help identify novel mechanistic therapies for chronic pain and other debilitating conditions such as nerve degeneration or injury.”

This research was supported, in part, by the National Institutes of Health, the National Science Foundation, the U.S. Army Research Office, the McGovern Institute for Brain Research, the Hock E. Tan and K. Lisa Yang Center for Autism Research, the K. Lisa Yang Brain-Body Center, and the Brain and Behavior Research Foundation.

Ariel Furst and Fan Wang receive 2023 National Institutes of Health awards

by MIT School of Science |

The National Institutes of Health (NIH) has awarded grants to MIT’s Ariel Furst and Fan Wang, through its High-Risk, High-Reward Research program. The NIH High-Risk, High-Reward Research program awarded 85 new research grants to support exceptionally creative scientists pursuing highly innovative behavioral and biomedical research projects.

Ariel Furst was selected as the recipient of the NIH Director’s New Innovator Award, which has supported unusually innovative research since 2007. Recipients are early-career investigators who are within 10 years of their final degree or clinical residency and have not yet received a research project grant or equivalent NIH grant.

Furst, the Paul M. Cook Career Development Assistant Professor of Chemical Engineering at MIT, invents technologies to improve human and environmental health by increasing equitable access to resources. Her lab develops transformative technologies to solve problems related to health care and sustainability by harnessing the inherent capabilities of biological molecules and cells. She is passionate about STEM outreach and increasing the participation of underrepresented groups in engineering.

After completing her PhD at Caltech, where she developed noninvasive diagnostics for colorectal cancer, Furst became an A. O. Beckman Postdoctoral Fellow at the University of California at Berkeley. There she developed sensors to monitor environmental pollutants. In 2022, Furst was awarded the MIT UROP Outstanding Faculty Mentor Award for her work with undergraduate researchers. She is a now a 2023 Marion Milligan Mason Awardee, a CIFAR Azrieli Global Scholar for Bio-Inspired Solar Energy, and an ARO Early Career Grantee. She is also a co-founder of the regenerative agriculture company, Seia Bio.

Fan Wang received the Pioneer Award, which has been challenging researchers at all career levels to pursue new directions and develop groundbreaking, high impact approaches to a broad area of biomedical and behavioral sciences since 2004.

Wang, a professor in the Department of Brain and Cognitive Sciences and an investigator in the McGovern Institute for Brain Research, is uncovering the neural circuit mechanisms that govern bodily sensations, like touch, pain, and posture, as well as the mechanisms that control sensorimotor behaviors. Researchers in the Wang lab aim to generate an integrated understanding of the sensation-perception-action process, hoping to find better treatments for diseases like chronic pain, addiction, and movement disorders. Wang’s lab uses genetic, viral, in vivo large-scale electrophysiology and imaging techniques to gain traction in these pursuits.

Wang obtained her PhD at Columbia University, working with Professor Richard Axel. She conducted her postdoctoral work at Stanford University with Mark Tessier-Lavigne, and then subsequently joined Duke University as faculty in 2003. Wang was later appointed as the Morris N. Broad Distinguished Professor of Neurobiology at the Duke University School of Medicine. In January 2023, she joined the faculty of the MIT School of Science and the McGovern Institute.

The High-Risk, High-Reward Research program is funded through the NIH Common Fund, which supports a series of exceptionally high-impact programs that cross NIH Institutes and Centers.

“The HRHR program is a pillar for innovation here at NIH, providing support to transformational research, with advances in biomedical and behavioral science,” says Robert W. Eisinger, acting director of the Division of Program Coordination, Planning, and Strategic Initiatives, which oversees the NIH Common Fund. “These awards align with the Common Fund’s mandate to support science expected to have exceptionally high and broadly applicable impact.”

NIH issued eight Pioneer Awards, 58 New Innovator Awards, six Transformative Research Awards, and 13 Early Independence Awards in 2023. Funding for the awards comes from the NIH Common Fund; the National Institute of General Medical Sciences; the National Institute of Mental Health; the National Library of Medicine; the National Institute on Aging; the National Heart, Lung, and Blood Institute; and the Office of Dietary Supplements.

Study: Deep neural networks don’t see the world the way we do

Anne Trafton |

Human sensory systems are very good at recognizing objects that we see or words that we hear, even if the object is upside down or the word is spoken by a voice we’ve never heard.

Computational models known as deep neural networks can be trained to do the same thing, correctly identifying an image of a dog regardless of what color its fur is, or a word regardless of the pitch of the speaker’s voice. However, a new study from MIT neuroscientists has found that these models often also respond the same way to images or words that have no resemblance to the target.

When these neural networks were used to generate an image or a word that they responded to in the same way as a specific natural input, such as a picture of a bear, most of them generated images or sounds that were unrecognizable to human observers. This suggests that these models build up their own idiosyncratic “invariances” — meaning that they respond the same way to stimuli with very different features.

The findings offer a new way for researchers to evaluate how well these models mimic the organization of human sensory perception, 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.

“This paper shows that you can use these models to derive unnatural signals that end up being very diagnostic of the representations in the model,” says McDermott, who is the senior author of the study. “This test should become part of a battery of tests that we as a field are using to evaluate models.”

Jenelle Feather PhD ’22, who is now a research fellow at the Flatiron Institute Center for Computational Neuroscience, is the lead author of the open-access paper, which appears today in Nature Neuroscience. Guillaume Leclerc, an MIT graduate student, and Aleksander Mądry, the Cadence Design Systems Professor of Computing at MIT, are also authors of the paper.

Different perceptions

In recent years, researchers have trained deep neural networks that can analyze millions of inputs (sounds or images) and learn common features that allow them to classify a target word or object roughly as accurately as humans do. These models are currently regarded as the leading models of biological sensory systems.

It is believed that when the human sensory system performs this kind of classification, it learns to disregard features that aren’t relevant to an object’s core identity, such as how much light is shining on it or what angle it’s being viewed from. This is known as invariance, meaning that objects are perceived to be the same even if they show differences in those less important features.

“Classically, the way that we have thought about sensory systems is that they build up invariances to all those sources of variation that different examples of the same thing can have,” Feather says. “An organism has to recognize that they’re the same thing even though they show up as very different sensory signals.”

The researchers wondered if deep neural networks that are trained to perform classification tasks might develop similar invariances. To try to answer that question, they used these models to generate stimuli that produce the same kind of response within the model as an example stimulus given to the model by the researchers.

They term these stimuli “model metamers,” reviving an idea from classical perception research whereby stimuli that are indistinguishable to a system can be used to diagnose its invariances. The concept of metamers was originally developed in the study of human perception to describe colors that look identical even though they are made up of different wavelengths of light.

To their surprise, the researchers found that most of the images and sounds produced in this way looked and sounded nothing like the examples that the models were originally given. Most of the images were a jumble of random-looking pixels, and the sounds resembled unintelligible noise. When researchers showed the images to human observers, in most cases the humans did not classify the images synthesized by the models in the same category as the original target example.

“They’re really not recognizable at all by humans. They don’t look or sound natural and they don’t have interpretable features that a person could use to classify an object or word,” Feather says.

The findings suggest that the models have somehow developed their own invariances that are different from those found in human perceptual systems. This causes the models to perceive pairs of stimuli as being the same despite their being wildly different to a human.

Idiosyncratic invariances

The researchers found the same effect across many different vision and auditory models. However, each of these models appeared to develop their own unique invariances. When metamers from one model were shown to another model, the metamers were just as unrecognizable to the second model as they were to human observers.

“The key inference from that is that these models seem to have what we call idiosyncratic invariances,” McDermott says. “They have learned to be invariant to these particular dimensions in the stimulus space, and it’s model-specific, so other models don’t have those same invariances.”

The researchers also found that they could induce a model’s metamers to be more recognizable to humans by using an approach called adversarial training. This approach was originally developed to combat another limitation of object recognition models, which is that introducing tiny, almost imperceptible changes to an image can cause the model to misrecognize it.

The researchers found that adversarial training, which involves including some of these slightly altered images in the training data, yielded models whose metamers were more recognizable to humans, though they were still not as recognizable as the original stimuli. This improvement appears to be independent of the training’s effect on the models’ ability to resist adversarial attacks, the researchers say.

“This particular form of training has a big effect, but we don’t really know why it has that effect,” Feather says. “That’s an area for future research.”

Analyzing the metamers produced by computational models could be a useful tool to help evaluate how closely a computational model mimics the underlying organization of human sensory perception systems, the researchers say.

“This is a behavioral test that you can run on a given model to see whether the invariances are shared between the model and human observers,” Feather says. “It could also be used to evaluate how idiosyncratic the invariances are within a given model, which could help uncover potential ways to improve our models in the future.”

The research was funded by the National Science Foundation, the National Institutes of Health, a Department of Energy Computational Science Graduate Fellowship, and a Friends of the McGovern Institute Fellowship.

Practicing mindfulness with an app may improve children’s mental health

Anne Trafton |

Many studies have found that practicing mindfulness — defined as cultivating an open-minded attention to the present moment — has benefits for children. Children who receive mindfulness training at school have demonstrated improvements in attention and behavior, as well as greater mental health.

When the Covid-19 pandemic began in 2020, sending millions of students home from school, a group of MIT researchers wondered if remote, app-based mindfulness practices could offer similar benefits. In a study conducted during 2020 and 2021, they report that children who used a mindfulness app at home for 40 days showed improvements in several aspects of mental health, including reductions in stress and negative emotions such as loneliness and fear.

The findings suggest that remote, app-based mindfulness interventions, which could potentially reach a larger number of children than school-based approaches, could offer mental health benefits, the researchers say.

“There is growing and compelling scientific evidence that mindfulness can support mental well-being and promote mental health in diverse children and adults,” says John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology, a professor of brain and cognitive sciences at MIT, and the senior author of the study, which appears this week in the journal Mindfulness.

Researchers in Gabrieli’s lab also recently reported that children who showed higher levels of mindfulness were more emotionally resilient to the negative impacts of the Covid-19 pandemic.

“To some extent, the impact of Covid is out of your control as an individual, but your ability to respond to it and to interpret it may be something that mindfulness can help with,” says MIT graduate student Isaac Treves, who is the lead author of both studies.

Pandemic resilience

After the pandemic began in early 2020, Gabrieli’s lab decided to investigate the effects of mindfulness on children who had to leave school and isolate from friends. In a study that appeared in the journal PLOS One in July, the researchers explored whether mindfulness could boost children’s resilience to negative emotions that the pandemic generated, such as frustration and loneliness.

Working with students between 8 and 10 years old, the researchers measured the children’s mindfulness using a standardized assessment that captures their tendency to blame themselves, ruminate on negative thoughts, and suppress their feelings.

The researchers also asked the children questions about how much the pandemic had affected different aspects of their lives, as well as questions designed to assess their levels of anxiety, depression, stress, and negative emotions such as worry or fear.

Among children who showed the highest levels of mindfulness, there was no correlation between how much the pandemic impacted them and negative feelings. However, in children with lower levels of mindfulness, there was a strong correlation between Covid-19 impact and negative emotions.

The children in this study did not receive any kind of mindfulness training, so their responses reflect their tendency to be mindful at the time they answered the researchers’ questions. The findings suggest that children with higher levels of mindfulness were less likely to get caught up in negative emotions or blame themselves for the negative things they experienced during the pandemic.

“This paper was our best attempt to look at mindfulness specifically in the context of Covid and to think about what are the factors that may help children adapt to the changing circumstances,” Treves says. “The takeaway is not that we shouldn’t worry about pandemics because we can just help the kids with mindfulness. People are able to be resilient when they’re in systems that support them, and in families that support them.”

Remote interventions

The researchers then built on that study by exploring whether a remote, app-based intervention could effectively increase mindfulness and improve mental health. Researchers in Gabrieli’s lab have previously shown that students who received mindfulness training in middle school showed better academic performance, received fewer suspensions, and reported less stress than those who did not receive the training.

For the new study, reported today in Mindfulness, the researchers worked with the same children they had recruited for the PLOS One study and divided them into three groups of about 80 students each.

One group received mindfulness training through an app created by Inner Explorer, a nonprofit that also develops school-based meditation programs. Those children were instructed to engage in mindfulness training five days a week, including relaxation exercises, breathing exercises, and other forms of meditation.

For comparison purposes, the other two groups were asked to use an app for listening to audiobooks (not related to mindfulness). One group was simply given the audiobook app and encouraged to listen at their own pace, while the other group also had weekly one-on-one virtual meetings with a facilitator.

At the beginning and end of the study, the researchers evaluated each participant’s levels of mindfulness, along with measures of mental health such as anxiety, stress, and depression. They found that in all three groups, mental health improved over the course of the eight-week study, and each group also showed increases in mindfulness and prosociality (engaging in helpful behavior).

Additionally, children in the mindfulness group showed some improvements that the other groups didn’t, including a more significant decrease in stress. They also found that parents in the mindfulness group reported that their children experienced more significant decreases in negative emotions such as anger and sadness. Students who practiced the mindfulness exercises the most days showed the greatest benefits.

The researchers were surprised to see that there were no significant differences in measures of anxiety and depression between the mindfulness group and audiobook groups; they hypothesize that may be because students who interacted with a facilitator in one of the audiobook groups also experienced beneficial effects on their mental health.

Overall, the findings suggest that there is value in remote, app-based mindfulness training, especially if children engage with the exercises consistently and receive encouragement from parents, the researchers say. Apps also offer the ability to reach a larger number of children than school-based programs, which require more training and resources.

“There are a lot of great ways to incorporate mindfulness training into schools, but in general, it’s more resource-intensive than having people download an app. So, in terms of pure scalability and cost-effectiveness, apps are useful,” Treves says. “Another good thing about apps is that the kids can go at their own pace and repeat practices that they like, so there’s more freedom of choice.”

The research was funded by the Chan Zuckerberg Initiative as part of the Reach Every Reader Project, the National Institutes of Health, and the National Science Foundation.