Research Area: Cognitive Neuroscience

​perception, memory, language, social cognition, attention, empathy, brain disorders, aging, cognitive flexibility, developing brain

Three from MIT awarded 2020 Guggenheim Fellowships

by Julie Pryor |

MIT faculty members Sabine Iatridou, Jonathan Gruber, and Rebecca Saxe are among 175 scientists, artists, and scholars awarded 2020 fellowships from the John Simon Guggenheim Foundation. Appointed on the basis of prior achievement and exceptional promise, the 2020 Guggenheim Fellows were selected from almost 3,000 applicants.

“It’s exceptionally encouraging to be able to share such positive news at this terribly challenging time” says Edward Hirsch, president of the foundation. “A Guggenheim Fellowship has always offered practical assistance, helping fellows do their work, but for many of the new fellows, it may be a lifeline at a time of hardship, a survival tool as well as a creative one.”

Since 1925, the foundation has granted more the $375 million in fellowships to over 18,000 individuals, including Nobel laureates, Fields medalists, poets laureate, and winners of the Pulitzer Prize, among other internationally recognized honors. This year’s MIT recipients include a linguist, an economist, and a cognitive neuroscientist.

Rebecca Saxe is an associate investigator of the McGovern Institute and the John W. Jarve (1978) Professor in Brain and Cognitive Sciences. She studies human social cognition, using a combination of behavioral testing and brain imaging technologies. She is best known for her work on brain regions specialized for abstract concepts such as “theory of mind” tasks that involve understanding the mental states of other people. She also studies the development of the human brain during early infancy. She obtained her PhD from MIT and was a Harvard University junior fellow before joining the MIT faculty in 2006. Saxe was chosen in 2012 as a Young Global Leader by the World Economic Forum, and she received the 2014 Troland Award from the National Academy of Sciences. Her TED Talk, “How we read each other’s minds” has been viewed over 3 million times.

Jonathan Gruber is the Ford Professor of Economics at MIT, the director of the Health Care Program at the National Bureau of Economic Research, and the former president of the American Society of Health Economists. He has published more than 175 research articles, has edited six research volumes, and is the author of “Public Finance and Public Policy,” a leading undergraduate text; “Health Care Reform,” a graphic novel; and “Jump-Starting America: How Breakthrough Science Can Revive Economic Growth and the American Dream.” In 2006 he received the American Society of Health Economists Inaugural Medal for the best health economist in the nation aged 40 and under. He served as deputy sssistant secretary for economic policy at the U.S. Department of the Treasury. He was a key architect of Massachusetts’ ambitious health reform effort, and became an inaugural member of the Health Connector Board, the main implementing body for that effort. He served as a technical consultant to the Obama administration and worked with both the administration and Congress to help craft the Affordable Care Act. In 2011, he was named “One of the Top 25 Most Innovative and Practical Thinkers of Our Time” by Slate magazine.

Sabine Iatridou is professor of linguistics in MIT’s Department of Linguistics and Philosophy. Her work focuses on syntax and the syntax-semantics interface, as well as comparative linguistics. She is the author and coauthor of a series of innovative papers about tense and modality that opened up whole new domains of research for the field. Since those publications, she has made foundational contributions to many branches of linguistics that connect form with meaning. She is the recipient of the National Young Investigator Award (USA), of an honorary doctorate from the University of Crete in Greece, and of an award from the Royal Dutch Academy of Sciences. She was elected fellow of the Linguistic Society of America. She is co-founder and co-director of the CreteLing Summer School of Linguistics.

“As we grapple with the difficulties of the moment, it is also important to look to the future,” says Hirsch. “The artists, writers, scholars, and scientific researchers supported by the fellowship will help us understand and learn from what we are enduring individually and collectively, and it is an honor for the foundation to help them do their essential work.”

Uncovering the functional architecture of a historic brain area

by Sabbi Lall |

In 1840 a patient named Leborgne was admitted to a hospital near Paris: he was only able repeat the word “Tan.” This loss of speech drew the attention of Paul Broca who, after Leborgne’s death, identified lesions in his frontal lobe in the left hemisphere. These results echoed earlier findings from French neurologist Marc Dax. Now known as “Broca’s area,” the roles of this brain region have been extended to mental functions far beyond speech articulation. So much so, that the underlying functional organization of Broca’s area has become a source of discussion and some confusion.

McGovern Investigator Ev Fedorenko is now calling, in a paper at Trends in Cognitive Sciences, for recognition that Broca’s area consists of functionally distinct, specialized regions, with one sub-region very much dedicated to language processing.

“Broca’s area is one of the first regions you learn about in introductory psychology and neuroscience classes, and arguably laid the foundation for human cognitive neuroscience,” explains Ev Fedorenko, who is also an assistant professor in MIT’s Department of Brain and Cognitive Sciences. “This patch of cortex and its connections with other brain areas and networks provides a microcosm for probing some core questions about the human brain.”

Broca’s area, shown in red. Image: Wikimedia

Language is a uniquely human capability, and thus the discovery of Broca’s area immediately captured the attention of researchers.

“Because language is universal across cultures, but unique to the human species, studying Broca’s area and constraining theories of language accordingly promises to provide a window into one of the central abilities that make humans so special,” explains co-author Idan Blank, a former postdoc at the McGovern Institute who is now an assistant professor of psychology at UCLA.

Function over form

Broca’s area is found in the posterior portion of the left inferior frontal gyrus (LIFG). Arguments and theories abound as to its function. Some consider the region as dedicated to language or syntactic processing, others argue that it processes multiple types of inputs, and still others argue it is working at a high level, implementing working memory and cognitive control. Is Broca’s area a highly specialized circuit, dedicated to the human-specific capacity for language and largely independent from the rest high-level cognition, or is it a CPU-like region, overseeing diverse aspects of the mind and orchestrating their operations?

“Patient investigations and neuroimaging studies have now associated Broca’s region with many processes,” explains Blank. “On the one hand, its language-related functions have expanded far beyond articulation, on the other, non-linguistic functions within Broca’s area—fluid intelligence and problem solving, working memory, goal-directed behavior, inhibition, etc.—are fundamental to ‘all of cognition.’”

While brain anatomy is a common path to defining subregions in Broca’s area, Fedorenko and Blank argue that instead this approach can muddy the water. In fact, the anatomy of the brain, in terms of cortical folds and visible landmarks that originally stuck out to anatomists, vary from individual to individual in terms of their alignment with the underlying functions of brain regions. While these variations might seem small, they potentially have a huge impact on conclusions about functional regions based on traditional analysis methods. This means that the same bit of anatomy (like, say, the posterior portion of a gyrus) could be doing different things in different brains.

“In both investigations of patients with brain damage and much of brain imaging work, a lot of confusion has stemmed from the use of macroanatomical areas (like the inferior frontal gyrus (IFG)) as ‘units of analysis’,” explains Fedorenko. “When some researchers found IFG activation for a syntactic manipulation, and others for a working memory manipulation, the field jumped to the conclusion that syntactic processing relies on working memory. But these effects might actually be arising in totally distinct parts of the IFG.”

The only way to circumvent this problem is to turn to functional data and aggregate information from functionally defined areas across individuals. Using this approach, across four lines of evidence from the last decade, Fedorenko and Blank came to a clear conclusion: Broca’s area is not a monolithic region with a single function, but contains distinct areas, one dedicated to language processing, and another that supports domain-general functions like working memory.

“We just have to stop referring to macroanatomical brain regions (like gyri and sulci, or their parts) when talking about the functional architecture of the brain,” explains Fedorenko. “I am delighted to see that more and more labs across the world are recognizing the inter-individual variability that characterizes the human brain– this shift is putting us on the right path to making fundamental discoveries about how our brain works.”

Indeed, accounting for distinct functional regions, within Broca’s area and elsewhere, seems essential going forward if we are to truly understand the complexity of the human brain.

Study explores brain basis of special interests

by Julie Pryor |

Did you know that 88% of children on the autism spectrum have an affinity — or special interest that they are particularly passionate about?

We are curious about this.

The Gabrieli lab is exploring the brain basis of these special interests in kids with and without autism. The PAL (Project on Affinities and Language) study uses noninvasive and child-friendly fMRI methods to study whether affinities can activate language regions of the brain. The lab is currently looking for 7–12-year-old children with and without autism who have a special interest or passion.

Interested in participating?

Sign up here

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

McGovern lab manager creates art inspired by science

by Julie Pryor |

Michal De-Medonsa, technical associate and manager of the Jazayeri lab, created a large wood mosaic for her lab. We asked Michal to tell us a bit about the mosaic, her inspiration, and how in the world she found the time to create such an exquisitely detailed piece of art.

______

Jazayeri lab manager Michal De-Medonsa holds her wood mosaic entitled “JazLab.” Photo: Caitlin Cunningham

Describe this piece of art for us.

To make a piece this big (63″ x 15″), I needed several boards of padauk wood. I could have just etched each board as a whole unit and glued the 13 or so boards to each other, but I didn’t like the aesthetic. The grain and color within each board would look beautiful, but the line between each board would become obvious, segmented, and jarring when contrasted with the uniformity within each board. Instead, I cut out about 18 separate squares out of each board, shuffled all 217 pieces around, and glued them to one another in a mosaic style with a larger pattern (inspired by my grandfather’s work in granite mosaics).

What does this mosaic mean to you?

Once every piece was shuffled, the lines between single squares were certainly visible, but as a feature, were far less salient than had the full boards been glued to one another. As I was working on the piece, I was thinking about how the same concept holds true in society. Even if there is diversity within a larger piece (an institution, for example), there is a tendency for groups to form within the larger piece (like a full board), diversity becomes separated. This isn’t a criticism of any institution, it is human nature to form in-groups. It’s subconscious (so perhaps the criticism is that we, as a society, don’t give that behavior enough thought and try to ameliorate our reflex to group with those who are “like us”). The grain of the wood is uniform, oriented in the same direction, the two different cutting patterns create a larger pattern within the piece, and there are smaller patterns between and within single pieces. I love creating and finding patterns in my art (and life). Alfred North Whitehead wrote that “understanding is the apperception of pattern as such.” True, I believe, in science, art, and the humanities. What a great goal – to understand.​

Tell us about the name of this piece.

Every large piece I make is inspired by the people I make it for, and is therefore named after them. This piece is called JazLab. Having lived around the world, and being a descendant of a nomadic people, I don’t consider any one place home, but am inspired by every place I’ve lived. In all of my work, you can see elements of my Jewish heritage, antiquity, the Middle East, Africa, and now MIT.

How has MIT influenced your art?

MIT has influenced me in the most obvious way MIT could influence anyone – technology. Before this series, I made very small versions of this type of work, designing everything on a piece of paper with a pencil and a ruler, and making every cut by hand. Each of those small squares would take ~2 hours (depending on the design), and I was limited to softer woods.

Since coming to MIT, I learned that I had access to the Hobby Shop with a huge array of power tools and software. I began designing my patterns on the computer and used power tools to make the cuts. I actually struggled a lot with using the tech – not because it was hard (which, it really is when you just start out), but rather because it felt like I was somehow “cheating.” How is this still art? And although this is something I still think about often, I’ve tried to look at it in this way: every generation, in their time, used the most advanced technology. The beauty and value of the piece doesn’t come from how many bruises, cuts, and blisters your machinery gave you, or whether you scraped the wood out with your nails, but rather, once you were given a tool, what did you decide to do with it? My pieces still have a huge hand-on-material work, but I am working on accepting that using technology in no way devalues the work.

Given your busy schedule with the Jazayeri lab, how did you find the time to create this piece of art?

I took advantage of any free hour I could. Two days out of the week, the hobby shop is open until 9pm, and I would additionally go every Saturday. For the parts that didn’t require the shop (adjusting each piece individually with a carving knife, assembling them, even most of the glueing) I would just work  at home – often very late into the night.

______

JazLab is on display in the Jazayeri lab in MIT Bldg 46.

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.

Drug combination reverses hypersensitivity to noise

Anne Trafton |

People with autism often experience hypersensitivity to noise and other sensory input. MIT neuroscientists have now identified two brain circuits that help tune out distracting sensory information, and they have found a way to reverse noise hypersensitivity in mice by boosting the activity of those circuits.

One of the circuits the researchers identified is involved in filtering noise, while the other exerts top-down control by allowing the brain to switch its attention between different sensory inputs.

The researchers showed that restoring the function of both circuits worked much better than treating either circuit alone. This demonstrates the benefits of mapping and targeting multiple circuits involved in neurological disorders, says Michael Halassa, an assistant professor of brain and cognitive sciences and a member of MIT’s McGovern Institute for Brain Research.

“We think this work has the potential to transform how we think about neurological and psychiatric disorders, [so that we see them] as a combination of circuit deficits,” says Halassa, the senior author of the study. “The way we should approach these brain disorders is to map, to the best of our ability, what combination of deficits are there, and then go after that combination.”

MIT postdoc Miho Nakajima and research scientist L. Ian Schmitt are the lead authors of the paper, which appears in Neuron on Oct. 21. Guoping Feng, the James W. and Patricia Poitras Professor of Neuroscience and a member of the McGovern Institute, is also an author of the paper.

Hypersensitivity

Many gene variants have been linked with autism, but most patients have very few, if any, of those variants. One of those genes is ptchd1, which is mutated in about 1 percent of people with autism. In a 2016 study, Halassa and Feng found that during development this gene is primarily expressed in a part of the thalamus called the thalamic reticular nucleus (TRN).

That study revealed that neurons of the TRN help the brain to adjust to changes in sensory input, such as noise level or brightness. In mice with ptchd1 missing, TRN neurons fire too fast, and they can’t adjust when noise levels change. This prevents the TRN from performing its usual sensory filtering function, Halassa says.

“Neurons that are there to filter out noise, or adjust the overall level of activity, are not adapting. Without the ability to fine-tune the overall level of activity, you can get overwhelmed very easily,” he says.

In the 2016 study, the researchers also found that they could restore some of the mice’s noise filtering ability by treating them with a drug called EBIO that activates neurons’ potassium channels. EBIO has harmful cardiac side effects so likely could not be used in human patients, but other drugs that boost TRN activity may have a similar beneficial effect on hypersensitivity, Halassa says.

In the new Neuron paper, the researchers delved more deeply into the effects of ptchd1, which is also expressed in the prefrontal cortex. To explore whether the prefrontal cortex might play a role in the animals’ hypersensitivity, the researchers used a task in which mice have to distinguish between three different tones, presented with varying amounts of background noise.

Normal mice can learn to use a cue that alerts them whenever the noise level is going to be higher, improving their overall performance on the task. A similar phenomenon is seen in humans, who can adjust better to noisier environments when they have some advance warning, Halassa says. However, mice with the ptchd1 mutation were unable to use these cues to improve their performance, even when their TRN deficit was treated with EBIO.

This suggested that another brain circuit must be playing a role in the animals’ ability to filter out distracting noise. To test the possibility that this circuit is located in the prefrontal cortex, the researchers recorded from neurons in that region while mice lacking ptch1 performed the task. They found that neuronal activity died out much faster in these mice than in the prefrontal cortex of normal mice. That led the researchers to test another drug, known as modafinil, which is FDA-approved to treat narcolepsy and is sometimes prescribed to improve memory and attention.

The researchers found that when they treated mice missing ptchd1 with both modafinil and EBIO, their hypersensitivity disappeared, and their performance on the task was the same as that of normal mice.

Targeting circuits

This successful reversal of symptoms suggests that the mice missing ptchd1 experience a combination of circuit deficits that each contribute differently to noise hypersensitivity. One circuit filters noise, while the other helps to control noise filtering based on external cues. Ptch1 mutations affect both circuits, in different ways that can be treated with different drugs.

Both of those circuits could also be affected by other genetic mutations that have been linked to autism and other neurological disorders, Halassa says. Targeting those circuits, rather than specific genetic mutations, may offer a more effective way to treat such disorders, he says.

“These circuits are important for moving things around the brain — sensory information, cognitive information, working memory,” he says. “We’re trying to reverse-engineer circuit operations in the service of figuring out what to do about a real human disease.”

He now plans to study circuit-level disturbances that arise in schizophrenia. That disorder affects circuits involving cognitive processes such as inference — the ability to draw conclusions from available information.

The research was funded by the Simons Center for the Social Brain at MIT, the Stanley Center for Psychiatric Research at the Broad Institute, the McGovern Institute for Brain Research at MIT, the Pew Foundation, the Human Frontiers Science Program, the National Institutes of Health, the James and Patricia Poitras Center for Psychiatric Disorders Research at MIT, a Japan Society for the Promotion of Science Fellowship, and a National Alliance for the Research of Schizophrenia and Depression Young Investigator Award.

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.