Research Area: Brain Imaging

brain mapping, functional connectivity, fMRI, EEG, MEG, predictive imaging, precision interventions, contrast agents, theory of mind, the developing brain, learning

Embracing neurodiversity to better understand autism

by Sabbi Lall |

Researchers often approach autism spectrum disorder (ASD) through the lens of what might “break down.” While this approach has value, autism is an extremely heterogeneous condition, and diagnosed individuals have a broad range of abilities.

The Gabrieli lab is embracing this diversity and leveraging the strengths of diagnosed individuals by researching their specific “affinities.”

Affinities involve a strong passion for specific topics, ranging from insects to video game characters, and can include impressive feats of knowledge and focus.

The biological basis of these affinities and associated abilities remains unclear, which is intriguing to John Gabrieli and his lab.

“A striking aspect of autism is the great variation from individual to individual,” explains McGovern Investigator John Gabrieli. “Understanding what motivates an individual child may inform how to best help that child reach his or her communicative potential.”

Doug Tan is an artist on the autism spectrum who has a particular interest in Herbie, the fictional Volkswagen Beetle. Nearly all of Tan’s works include a visual reference to his “affinity” (shown here in black). Image: Doug Tan

Affinities have traditionally been seen as a distraction “interfering” with conventional teaching and learning. This mindset was upended by the 2014 book Life Animated by Ron Suskind, whose autistic son Owen seemingly lost his ability to speak around age three. Despite this setback, Owen maintained a deep affinity for Disney movies and characters. Rather than extinguishing this passion, the Suskinds embraced it as a path to connection.

Reframing such affinities as a strength not a frustration, and a path to communication rather than a roadblock, caught the attention of Kristy Johnson, a PhD student at the MIT Media Lab, who also has a non-verbal child with autism.

“My interest is in empowering and understanding populations that have traditionally been hard to study, including those with non-verbal and minimally verbal autism,” explains Johnson. “One way to do that is through affinities.”

But even identifying affinities is difficult. An interest in “trains” might mean 18th-century smokestacks to one child, and the purple line of the MBTA commuter rail to another. Serendipitously, she mentioned her interest to Gabrieli one day. He slammed his hands on the table, jumped up, and ran to find lab members Anila D’Mello and Halie Olson, who were gearing up to pursue the neural basis of affinities in autism. A collaboration was born.

Scientific challenge

What followed was six months of intense discussion. How can an affinity be accurately defined? How can individually tailored experiments be adequately controlled? What makes a robust comparison group? How can task-related performance differences between individuals with autism be accounted for?

The handful of studies that had used fMRI neuroimaging to examine affinities in autism had focused on the brain’s reward circuitry. D’Mello and Olson wanted to examine the language network of the brain — a well-defined network of brain regions whose activation can be measured by fMRI. Affinities trigger communication in some individuals with autism (Suskind’s family were using Disney characters to engage and communicate, not simply as a reward). Was the language network being engaged by affinities? Could these results point to a way of tailoring learning for all types of development?

“The language network involves lots of regions across the brain, including temporal, parietal, frontal, and subcortical areas, which play specific roles in different aspects of language processing” explains Olson. “We were interested in a task that used affinities to tap the language network.”

fMRI reveals regions of the brain that show increased activity for stories related to affinities versus neutral stories; these include regions important for language processing. Image: Anila D’Mello

By studying this network, the team is testing whether affinities can elicit “typical” activation in regions of the brain that are sometimes assumed to not be engaged in autism. The approach may help develop better paradigms for studying other tasks with individuals with autism. Regardless of whether there are differences between the group diagnosed with autism and typically developing children, insight will likely be gained into how personalized special interests influence engagement of the language network.

The resulting study is task-free, removing the variable of differing motor or cognitive skill sets. Kids watch videos of their individual affinity in the fMRI scanner, and then listen to stories based on that affinity. They also watch and listen to “neutral” videos and stories about nature that are consistent across all children. Identifying affinities robustly so that the right stimulus can be presented is critical. Rather than an interest in bugs, affinities are often very specific (bugs that eat other bugs). But identifying and cross-checking affinities is something the group is becoming adept at. The results are emerging, but the effects that the team are seeing are significant, and preliminary data suggest that affinities engage networks beyond reward circuits.

“We have a small sample right now, but across the sample, there seems to be a difference in activation in the brain’s language network when listening to affinity stories compared to neutral stories,” explains D’Mello. “The biggest surprise is that the differences are evident in single subjects.”

Future forward

The work is already raising exciting new questions. Are there other brain regions engaged by affinities? How would such information inform education and intervention paradigms? In addition, the team is showing it’s possible to derive information from individualized, naturalistic experimental paradigms, a message for brain imaging and behavioral studies in general. The researchers also hope the results inspire parents, teachers, and psychologists to perceive and engage with an individual’s affinities in new ways.

“This could really help teach us to communicate with and motivate very young and non-verbal kids on the spectrum in a way that is interesting and meaningful to them,” D’Mello explains.

By studying the strengths of individuals with autism, these researchers are showing that, through embracing neurodiversity, we can enhance science, our understanding of the brain, and perhaps even our understanding of ourselves.

Learn about autism studies at MIT

Nancy Kanwisher to receive George A. Miller Prize in Cognitive Neuroscience

by Julie Pryor |

Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience at MIT, has been named this year’s winner of the George A. Miller Prize in Cognitive Neuroscience. The award, given annually by the Cognitive Neuroscience Society (CNS), recognizes individuals “whose distinguished research is at the cutting-edge of their discipline with realized or future potential, to revolutionize cognitive neuroscience.”

Kanwisher studies the functional organization of the human mind and, over the last 20 years, her lab has played a central role in the identification of several dozen regions of the cortex in humans that are engaged in particular components of perception and cognition. She is perhaps best known for identifying brain regions specialized for recognizing faces.

Kanwisher will deliver her prize lecture, “Functional imaging of the human brain: A window into the architecture of the mind” at the 2020 CNS annual meeting in Boston this March.

Brain biomarkers predict mood and attention symptoms

by Sabbi Lall |

Mood and attentional disorders amongst teens are an increasing concern, for parents, society, and for peers. A recent Pew research center survey found conditions such as depression and anxiety to be the number one concern that young students had about their friends, ranking above drugs or bullying.

“We’re seeing an epidemic in teen anxiety and depression,” explains McGovern Research Affiliate Susan Whitfield-Gabrieli.

“Scientists are finding a huge increase in suicide ideation and attempts, something that hit home for me as a mother of teens. Emergency rooms in hospitals now have guards posted outside doors of these teenagers that attempted suicide—this is a pressing issue,” explains Whitfield-Gabrieli who is also director of the Northeastern University Biomedical Imaging Center and a member of the Poitras Center for Psychiatric Disorders Research.

Finding new methods for discovering early biomarkers for risk of psychiatric disorders would allow early interventions and avoid reaching points of crisis such as suicide ideation or attempts. In research published recently in JAMA Psychiatry, Whitfield-Gabrieli and colleagues found that signatures predicting future development of depression and attentional symptoms can be detected in children as young as seven years old.

Long-term view

While previous work had suggested that there may be biomarkers that predict development of mood and attentional disorders, identifying early biomarkers prior to an onset of illness requires following a cohort of pre-teens from a young age, and monitoring them across years. This effort to have a proactive, rather than reactive, approach to the development of symptoms associated with mental disorders is exactly the route Whitfield-Gabrieli and colleagues took.

“One of the exciting aspects of this study is that the cohort is not pre-selected for already having symptoms of psychiatric disorders themselves or even in their family,” explained Whitfield-Gabrieli. “It’s an unbiased cohort that we followed over time.”

McGovern research affiliate Susan Whitfield-Gabrieli has discovered early brain biomarkers linked to psychiatric disorders.

In some past studies, children were pre-selected, for example a major depressive disorder diagnosis in the parents, but Whitfield-Gabrieli and colleagues, Silvia Bunge from Berkeley and Laurie Cutting from Vanderbilt, recruited a range of children without preconditions, and examined them at age 7, then again 4 years later. The researchers examined resting state functional connectivity, and compared this to scores on the child behavioral checklist (CBCL), allowing them to relate differences in the brain to a standardized analysis of behavior that can be linked to psychiatric disorders. The CBCL is used both in research and in the clinic and his highly predictive of disorders including ADHD, so that changes in the brain could be related to changes in a widely used clinical scoring system.

“Over the four years, some people got worse, some got better, and some stayed the same according the CBCL. We could relate this directly to differences in brain networks, and could identify at age 7 who would get worse,” explained Whitfield-Gabrieli.

Brain network changes

The authors analyzed differences in resting state network connectivity, regions across the brain that rise and fall in activity level together, as visualized using fMRI. Reduced connectivity between these regions may allow us to get a handle on reduced “top-down” control of neural circuits. The dorsolateral prefrontal region is linked to executive function, external attention, and emotional control. Increased connection with the medial prefrontal cortex is known to be present in attention deficit hyperactivity disorder (ADHD), while a reduced connection to a different brain region, the sgACC, is seen in major depressive disorder. The question remained as to whether these changes can be seen prior to the onset of diagnosable attentional or mood disorders.

Whitfield-Gabrieli and colleagues found that these resting state networks varied in the brains of children that would later develop anxiety/depression and ADHD symptoms. Weaker scores in connectivity between the dorsolateral and medial prefrontal cortical regions tended to be seen in children whose attention scores went on to improve. Analysis of the resting state networks above could differentiate those who would have typical attentional behavior by age 11 versus those that went on to develop ADHD.

Whitfield-Gabrieli has replicated this finding in an independent sample of children and she is continuing to expand the analysis and check the results, as well as follow this cohort into the future. Should changes in resting state networks be a consistent biomarker, the next step is to initiate interventions prior to the point of crisis.

“We’ve recently been able to use mindfulness interventions, and show these reduce self-perceived stress and amygdala activation in response to fear, and we are also testing the effect of exercise interventions,” explained Whitfield-Gabrieli. “The hope is that by using predictive biomarkers we can augment children’s lifestyles with healthy interventions that can prevent risk converting to a psychiatric disorder.”

Can fMRI reveal insights into addiction and treatments?

by Sabbi Lall |

Many debilitating conditions like depression and addiction have biological signatures hidden in the brain well before symptoms appear.  What if brain scans could be used to detect these hidden signatures and determine the most optimal treatment for each individual? McGovern Investigator John Gabrieli is interested in this question and wrote about the use of imaging technologies as a predictive tool for brain disorders in a recent issue of Scientific American.

page from Scientific American article
McGovern Investigator John Gabrieli pens a story for Scientific American about the potential for brain imaging to predict the onset of mental illness.

“Brain scans show promise in predicting who will benefit from a given therapy,” says Gabrieli, who is also the Grover Hermann Professor in Brain and Cognitive Sciences at MIT. “Differences in neural activity may one day tell clinicians which depression treatment will be most effective for an individual or which abstinent alcoholics will relapse.”

Gabrieli cites research which has shown that half of patients treated for alcohol abuse go back to drinking within a year of treatment, and similar reversion rates occur for stimulants such as cocaine. Failed treatments may be a source of further anxiety and stress, Gabrieli notes, so any information we can glean from the brain to pinpoint treatments or doses that would help would be highly informative.

Current treatments rely on little scientific evidence to support the length of time needed in a rehabilitation facility, he says, but “a number suggest that brain measures might foresee who will succeed in abstaining after treatment has ended.”

Further data is needed to support this idea, but Gabrieli’s Scientific American piece makes the case that the use of such a technology may be promising for a range of addiction treatments including abuse of alcohol, nicotine, and illicit drugs.

Gabrieli also believes brain imaging has the potential to reshape education. For example, educational interventions targeting dyslexia might be more effective if personalized to specific differences in the brain that point to the source of the learning gap.

But for the prediction sciences to move forward in mental health and education, he concludes, the research community must design further rigorous studies to examine these important questions.

Controlling attention with brain waves

Anne Trafton |

Having trouble paying attention? MIT neuroscientists may have a solution for you: Turn down your alpha brain waves. In a new study, the researchers found that people can enhance their attention by controlling their own alpha brain waves based on neurofeedback they receive as they perform a particular task.

The study found that when subjects learned to suppress alpha waves in one hemisphere of their parietal cortex, they were able to pay better attention to objects that appeared on the opposite side of their visual field. This is the first time that this cause-and-effect relationship has been seen, and it suggests that it may be possible for people to learn to improve their attention through neurofeedback.

Desimone lab study shows that people can boost attention by manipulating their own alpha brain waves with neurofeedback training.

“There’s a lot of interest in using neurofeedback to try to help people with various brain disorders and behavioral problems,” says Robert Desimone, director of MIT’s McGovern Institute for Brain Research. “It’s a completely noninvasive way of controlling and testing the role of different types of brain activity.”

It’s unknown how long these effects might last and whether this kind of control could be achieved with other types of brain waves, such as beta waves, which are linked to Parkinson’s disease. The researchers are now planning additional studies of whether this type of neurofeedback training might help people suffering from attentional or other neurological disorders.

Desimone is the senior author of the paper, which appears in Neuron on Dec. 4. McGovern Institute postdoc Yasaman Bagherzadeh is the lead author of the study. Daniel Baldauf, a former McGovern Institute research scientist, and Dimitrios Pantazis, a McGovern Institute principal research scientist, are also authors of the paper.

Alpha and attention

There are billions of neurons in the brain, and their combined electrical signals generate oscillations known as brain waves. Alpha waves, which oscillate in the frequency of 8 to 12 hertz, are believed to play a role in filtering out distracting sensory information.

Previous studies have shown a strong correlation between attention and alpha brain waves, particularly in the parietal cortex. In humans and in animal studies, a decrease in alpha waves has been linked to enhanced attention. However, it was unclear if alpha waves control attention or are just a byproduct of some other process that governs attention, Desimone says.

To test whether alpha waves actually regulate attention, the researchers designed an experiment in which people were given real-time feedback on their alpha waves as they performed a task. Subjects were asked to look at a grating pattern in the center of a screen, and told to use mental effort to increase the contrast of the pattern as they looked at it, making it more visible.

During the task, subjects were scanned using magnetoencephalography (MEG), which reveals brain activity with millisecond precision. The researchers measured alpha levels in both the left and right hemispheres of the parietal cortex and calculated the degree of asymmetry between the two levels. As the asymmetry between the two hemispheres grew, the grating pattern became more visible, offering the participants real-time feedback.

McGovern postdoc Yasaman sits in a magnetoencephalography (MEG) scanner. Photo: Justin Knight

Although subjects were not told anything about what was happening, after about 20 trials (which took about 10 minutes), they were able to increase the contrast of the pattern. The MEG results indicated they had done so by controlling the asymmetry of their alpha waves.

“After the experiment, the subjects said they knew that they were controlling the contrast, but they didn’t know how they did it,” Bagherzadeh says. “We think the basis is conditional learning — whenever you do a behavior and you receive a reward, you’re reinforcing that behavior. People usually don’t have any feedback on their brain activity, but when we provide it to them and reward them, they learn by practicing.”

Although the subjects were not consciously aware of how they were manipulating their brain waves, they were able to do it, and this success translated into enhanced attention on the opposite side of the visual field. As the subjects looked at the pattern in the center of the screen, the researchers flashed dots of light on either side of the screen. The participants had been told to ignore these flashes, but the researchers measured how their visual cortex responded to them.

One group of participants was trained to suppress alpha waves in the left side of the brain, while the other was trained to suppress the right side. In those who had reduced alpha on the left side, their visual cortex showed a larger response to flashes of light on the right side of the screen, while those with reduced alpha on the right side responded more to flashes seen on the left side.

“Alpha manipulation really was controlling people’s attention, even though they didn’t have any clear understanding of how they were doing it,” Desimone says.

Persistent effect

After the neurofeedback training session ended, the researchers asked subjects to perform two additional tasks that involve attention, and found that the enhanced attention persisted. In one experiment, subjects were asked to watch for a grating pattern, similar to what they had seen during the neurofeedback task, to appear. In some of the trials, they were told in advance to pay attention to one side of the visual field, but in others, they were not given any direction.

When the subjects were told to pay attention to one side, that instruction was the dominant factor in where they looked. But if they were not given any cue in advance, they tended to pay more attention to the side that had been favored during their neurofeedback training.

In another task, participants were asked to look at an image such as a natural outdoor scene, urban scene, or computer-generated fractal shape. By tracking subjects’ eye movements, the researchers found that people spent more time looking at the side that their alpha waves had trained them to pay attention to.

“It is promising that the effects did seem to persist afterwards,” says Desimone, though more study is needed to determine how long these effects might last.

The research was funded by the McGovern Institute.

Word Play

by Sabbi Lall |

Ev Fedorenko uses the widely translated book “Alice in Wonderland” to test brain responses to different languages.

Language is a uniquely human ability that allows us to build vibrant pictures of non-existent places (think Wonderland or Westeros). How does the brain build mental worlds from words? Can machines do the same? Can we recover this ability after brain injury? These questions require an understanding of how the brain processes language, a fascination for Ev Fedorenko.

“I’ve always been interested in language. Early on, I wanted to found a company that teaches kids languages that share structure — Spanish, French, Italian — in one go,” says Fedorenko, an associate investigator at the McGovern Institute and an assistant professor in brain and cognitive sciences at MIT.

Her road to understanding how thoughts, ideas, emotions, and meaning can be delivered through sound and words became clear when she realized that language was accessible through cognitive neuroscience.

Early on, Fedorenko made a seminal finding that undermined dominant theories of the time. Scientists believed a single network was extracting meaning from all we experience: language, music, math, etc. Evolving separate networks for these functions seemed unlikely, as these capabilities arose recently in human evolution.

Language Regions
Ev Fedorenko has found that language regions of the brain (shown in teal) are sensitive to both word meaning and sentence structure. Image: Ev Fedorenko

But when Fedorenko examined brain activity in subjects while they read or heard sentences in the MRI, she found a network of brain regions that is indeed specialized for language.

“A lot of brain areas, like motor and social systems, were already in place when language emerged during human evolution,” explains Fedorenko. “In some sense, the brain seemed fully occupied. But rather than co-opt these existing systems, the evolution of language in humans involved language carving out specific brain regions.”

Different aspects of language recruit brain regions across the left hemisphere, including Broca’s area and portions of the temporal lobe. Many believe that certain regions are involved in processing word meaning while others unpack the rules of language. Fedorenko and colleagues have however shown that the entire language network is selectively engaged in linguistic tasks, processing both the rules (syntax) and meaning (semantics) of language in the same brain areas.

Semantic Argument

Fedorenko’s lab even challenges the prevailing view that syntax is core to language processing. By gradually degrading sentence structure through local word swaps (see figure), they found that language regions still respond strongly to these degraded sentences, deciphering meaning from them, even as syntax, or combinatorial rules, disappear.

The Fedorenko lab has shown that the brain finds meaning in a sentence, even when “local” words are swapped (2, 3). But when clusters of neighboring words are scrambled (4), the brain struggles to find its meaning.

“A lot of focus in language research has been on structure-building, or building a type of hierarchical graph of the words in a sentence. But actually the language system seems optimized and driven to find rich, representational meaning in a string of words processed together,” explains Fedorenko.

Computing Language

When asked about emerging areas of research, Fedorenko points to the data structures and algorithms underlying linguistic processing. Modern computational models can perform sophisticated tasks, including translation, ever more effectively. Consider Google translate. A decade ago, the system translated one word at a time with laughable results. Now, instead of treating words as providing context for each other, the latest artificial translation systems are performing more accurately. Understanding how they resolve meaning could be very revealing.

“Maybe we can link these models to human neural data to both get insights about linguistic computations in the human brain, and maybe help improve artificial systems by making them more human-like,” says Fedorenko.

She is also trying to understand how the system breaks down, how it over-performs, and even more philosophical questions. Can a person who loses language abilities (with aphasia, for example) recover — a very relevant question given the language-processing network occupies such specific brain regions. How are some unique people able to understand 10, 15 or even more languages? Do we need words to have thoughts?

Using a battery of approaches, Fedorenko seems poised to answer some of these questions.

Hearing through the clatter

by Sabbi Lall |

In a busy coffee shop, our eardrums are inundated with sound waves – people chatting, the clatter of cups, music playing – yet our brains somehow manage to untangle relevant sounds, like a barista announcing that our “coffee is ready,” from insignificant noise. A new McGovern Institute study sheds light on how the brain accomplishes the task of extracting meaningful sounds from background noise – findings that could one day help to build artificial hearing systems and aid development of targeted hearing prosthetics.

“These findings reveal a neural correlate of our ability to listen in noise, and at the same time demonstrate functional differentiation between different stages of auditory processing in the cortex,” explains Josh McDermott, an associate professor of brain and cognitive sciences at MIT, a member of the McGovern Institute and the Center for Brains, Minds and Machines, and the senior author of the study.

The auditory cortex, a part of the brain that responds to sound, has long been known to have distinct anatomical subregions, but the role these areas play in auditory processing has remained a mystery. In their study published today in Nature Communications, McDermott and former graduate student Alex Kell, discovered that these subregions respond differently to the presence of background noise, suggesting that auditory processing occurs in steps that progressively hone in on and isolate a sound of interest.

Background check

Previous studies have shown that the primary and non-primary subregions of the auditory cortex respond to sound with different dynamics, but these studies were largely based on brain activity in response to speech or simple synthetic sounds (such as tones and clicks). Little was known about how these regions might work to subserve everyday auditory behavior.

To test these subregions under more realistic conditions, McDermott and Kell, who is now a postdoctoral researcher at Columbia University, assessed changes in human brain activity while subjects listened to natural sounds with and without background noise.

While lying in an MRI scanner, subjects listened to 30 different natural sounds, ranging from meowing cats to ringing phones, that were presented alone or embedded in real-world background noise such as heavy rain.

“When I started studying audition,” explains Kell, “I started just sitting around in my day-to-day life, just listening, and was astonished at the constant background noise that seemed to usually be filtered out by default. Most of these noises tended to be pretty stable over time, suggesting we could experimentally separate them. The project flowed from there.”

To their surprise, Kell and McDermott found that the primary and non-primary regions of the auditory cortex responded differently to natural sound depending upon whether background noise was present.

brain regions responding to sound
Primary auditory cortex (outlined in white) responses change (blue) when background noise is present, whereas non-primary activity is robust to background noise (yellow). Image: Alex Kell

They found that activity of the primary auditory cortex was altered when background noise is present, suggesting that this region has not yet differentiated between meaningful sounds and background noise. Non-primary regions, however, respond similarly to natural sounds irrespective of whether noise is present, suggesting that cortical signals generated by sound are transformed or “cleaned up” to remove background noise by the time they reach the non-primary auditory cortex.

“We were surprised by how big the difference was between primary and non-primary areas,” explained Kell, “so we ran a bunch more subjects but kept seeing the same thing. We had a ton of questions about what might be responsible for this difference, and that’s why we ended up running all these follow-up experiments.”

A general principle

Kell and McDermott went on to test whether these responses were specific to particular sounds, and discovered that the above effect remained stable no matter the source or type of sound activity. Music, speech, or a squeaky toy, all activated the non-primary cortex region similarly, whether or not background noise was present.

The authors also tested whether attention is relevant. Even when the researchers sneakily distracted subjects with a visual task in the scanner, the cortical subregions responded to meaningful sound and background noise in the same way, showing that attention is not driving this aspect of sound processing. In other words, even when we are focused on reading a book, our brain is diligently sorting the sound of our meowing cat from the patter of heavy rain outside.

Future directions

The McDermott lab is now building computational models of the so-called “noise robustness” found in the Nature Communications study and Kell is pursuing a finer-grained understanding of sound processing in his postdoctoral work at Columbia, by exploring the neural circuit mechanisms underlying this phenomenon.

By gaining a deeper understanding of how the brain processes sound, the researchers hope their work will contribute to improve diagnoses and treatment of hearing dysfunction. Such research could help to reveal the origins of listening difficulties that accompany developmental disorders or age-related hearing loss. For instance, if hearing loss results from dysfunction in sensory processing, this could be caused by abnormal noise robustness in the auditory cortex. Normal noise robustness might instead suggest that there are impairments elsewhere in the brain, for example a break down in higher executive function.

“In the future,” McDermott says, “we hope these noninvasive measures of auditory function may become valuable tools for clinical assessment.”

Benefits of mindfulness for middle schoolers

by Anne Trafton |

Two new studies from investigators at the McGovern Institute at MIT suggest that mindfulness — the practice of focusing one’s awareness on the present moment — can enhance academic performance and mental health in middle schoolers. The researchers found that more mindfulness correlates with better academic performance, fewer suspensions from school, and less stress.

“By definition, mindfulness is the ability to focus attention on the present moment, as opposed to being distracted by external things or internal thoughts. If you’re focused on the teacher in front of you, or the homework in front of you, that should be good for learning,” says John Gabrieli, the Grover M. Hermann Professor in Health Sciences and Technology, a professor of brain and cognitive sciences, and a member of MIT’s McGovern Institute for Brain Research.

The researchers also showed, for the first time, that mindfulness training can alter brain activity in students. Sixth-graders who received mindfulness training not only reported feeling less stressed, but their brain scans revealed reduced activation of the amygdala, a brain region that processes fear and other emotions, when they viewed images of fearful faces.

“Mindfulness is like going to the gym. If you go for a month, that’s good, but if you stop going, the effects won’t last,” Gabrieli says. “It’s a form of mental exercise that needs to be sustained.”

Together, the findings suggest that offering mindfulness training in schools could benefit many students, says Gabrieli, who is the senior author of both studies.

“We think there is a reasonable possibility that mindfulness training would be beneficial for children as part of the daily curriculum in their classroom,” he says. “What’s also appealing about mindfulness is that there are pretty well-established ways of teaching it.”

In the moment

Both studies were performed at charter schools in Boston. In one of the papers, which appears today in the journal Behavioral Neuroscience, the MIT team studied about 100 sixth-graders. Half of the students received mindfulness training every day for eight weeks, while the other half took a coding class. The mindfulness exercises were designed to encourage students to pay attention to their breath, and to focus on the present moment rather than thoughts of the past or the future.

Students who received the mindfulness training reported that their stress levels went down after the training, while the students in the control group did not. Students in the mindfulness training group also reported fewer negative feelings, such as sadness or anger, after the training.

About 40 of the students also participated in brain imaging studies before and after the training. The researchers measured activity in the amygdala as the students looked at pictures of faces expressing different emotions.

At the beginning of the study, before any training, students who reported higher stress levels showed more amygdala activity when they saw fearful faces. This is consistent with previous research showing that the amygdala can be overactive in people who experience more stress, leading them to have stronger negative reactions to adverse events.

“There’s a lot of evidence that an overly strong amygdala response to negative things is associated with high stress in early childhood and risk for depression,” Gabrieli says.

After the mindfulness training, students showed a smaller amygdala response when they saw the fearful faces, consistent with their reports that they felt less stressed. This suggests that mindfulness training could potentially help prevent or mitigate mood disorders linked with higher stress levels, the researchers say.

Richard Davidson, a professor of psychology and psychiatry at the University of Wisconsin, says that the findings suggest there could be great benefit to implementing mindfulness training in middle schools.

“This is really one of the very first rigorous studies with children of that age to demonstrate behavioral and neural benefits of a simple mindfulness training,” says Davidson, who was not involved in the study.

Evaluating mindfulness

In the other paper, which appeared in the journal Mind, Brain, and Education in June, the researchers did not perform any mindfulness training but used a questionnaire to evaluate mindfulness in more than 2,000 students in grades 5-8. The questionnaire was based on the Mindfulness Attention Awareness Scale, which is often used in mindfulness studies on adults. Participants are asked to rate how strongly they agree with statements such as “I rush through activities without being really attentive to them.”

The researchers compared the questionnaire results with students’ grades, their scores on statewide standardized tests, their attendance rates, and the number of times they had been suspended from school. Students who showed more mindfulness tended to have better grades and test scores, as well as fewer absences and suspensions.

“People had not asked that question in any quantitative sense at all, as to whether a more mindful child is more likely to fare better in school,” Gabrieli says. “This is the first paper that says there is a relationship between the two.”

The researchers now plan to do a full school-year study, with a larger group of students across many schools, to examine the longer-term effects of mindfulness training. Shorter programs like the two-month training used in the Behavioral Neuroscience study would most likely not have a lasting impact, Gabrieli says.

“Mindfulness is like going to the gym. If you go for a month, that’s good, but if you stop going, the effects won’t last,” he says. “It’s a form of mental exercise that needs to be sustained.”

The research was funded by the Walton Family Foundation, the Poitras Center for Psychiatric Disorders Research at the McGovern Institute for Brain Research, and the National Council of Science and Technology of Mexico. Camila Caballero ’13, now a graduate student at Yale University, is the lead author of the Mind, Brain, and Education study. Caballero and MIT postdoc Clemens Bauer are lead authors of the Behavioral Neuroscience study. Additional collaborators were from the Harvard Graduate School of Education, Transforming Education, Boston Collegiate Charter School, and Calmer Choice.

Speaking many languages

by Sabbi Lall |

Ev Fedorenko studies the cognitive processes and brain regions underlying language, a signature cognitive skill that is uniquely and universally human. She investigates both people with linguistic impairments, and those that have exceptional language skills: hyperpolyglots, or people that are fluent in over a dozen languages. Indeed, she was recently interviewed for a BBC documentary about superlinguists as well as the New Yorker, for an article covering people with exceptional language skills.

When Fedorenko, an associate investigator at the McGovern Institute and assistant professor in the Department of Brain and Cognitive Sciences at MIT, came to the field, neuroscientists were still debating whether high-level cognitive skills such as language, are processed by multi-functional or dedicated brain regions. Using fMRI, Fedorenko and colleagues compared engagement of brain regions when individuals were engaged in linguistic vs. other high level cognitive tasks, such as arithmetic or music. Their data revealed a clear distinction between language and other cognitive processes, showing that our brains have dedicated language regions.

Here is my basic question. How do I get a thought from my mind into yours?

In the time since this key study, Fedorenko has continued to unpack language in the brain. How does the brain process the overarching rules and structure of language (syntax), as opposed to meanings of words? How do we construct complex meanings? What might underlie communicative difficulties in individuals diagnosed with autism? How does the aphasic brain recover language? Intriguingly, in contrast to individuals with linguistic difficulties, there are also individuals that stand out as being able to master many languages, so-called hyperpolyglots.

In 2013, she came across a young adult that had mastered over 30 languages, a prodigy in languages. To facilitate her analysis of processing of different languages Fedorenko has collected dozens of translations of Alice in Wonderland, for her ‘Alice in the language localizer Wonderland‘ project. She has already found that hyperpolyglots tend to show less activity in linguistic processing regions when reading in, or listening to, their native language, compared to carefully matched controls, perhaps indexing more efficient processing mechanisms. Fedorenko continues to study hyperpolyglots, along with other exciting new avenues of research. Stay tuned for upcoming advances in our understanding of the brain and language.

Evelina Fedorenko

Exploring Language

Evelina (Ev) Fedorenko aims to understand how the language system works in the brain. Her lab is unpacking the internal architecture of the brain’s language system and exploring the relationship between language and various cognitive, perceptual, and motor systems. To do this, her lab employs a range of approaches – from brain imaging to computational modeling – and works with a diverse populations, including polyglots and individuals with atypical brains. Language is a quintessential human ability, but the function that language serves has been debated for centuries. Fedorenko argues that language serves is primarily as a tool for communication, contrary to a prominent view that language is essential for thinking.

Ultimately, this cutting-edge work is uncovering the computations and representations that fuel language processing in the brain.