Research Area: Brain Imaging

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

Why do I talk with my hands?

by The Brain |

This is a very interesting question sent to us by Gabriel Castellanos (thank you!) Many of us gesture with our hands when we speak (and even when we do not) as a form of non-verbal communication. How hand gestures are coordinated with speech remains unclear. In part, it is difficult to monitor natural hand gestures in fMRI-based brain imaging studies as you have to stay still.

“Performing hand movements when stuck in the bore of a scanner is really tough beyond simple signing and keypresses,” explains McGovern Principal Research Scientist Satrajit Ghosh. “Thus ecological experiments of co-speech with motor gestures have not been carried out in the context of a magnetic resonance scanner, and therefore little is known about language and motor integration within this context.”

There have been studies that use proxies such as co-verbal pushing of buttons, and also studies using other imaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), to monitor brain activity during gesturing, but it would be difficult to precisely spatially localize the regions involved in natural co-speech hand gesticulation using such approaches. Another possible avenue for addressing this question would be to look at patients with conditions that might implicate particular brain regions in coordinating hand gestures, but such approaches have not really pinpointed a pathway for coordinating speech and hand movements.

That said, co-speech hand gesturing plays an important role in communication. “More generally co-speech hand gestures are seen as a mechanism for emphasis and disambiguation of the semantics of a sentence, in addition to prosody and facial queues,” says Ghosh. “In fact, one may consider the act of speaking as one large orchestral score involving vocal tract movement, respiration, voicing, facial expression, hand gestures, and even whole body postures acting as different instruments coordinated dynamically by the brain. Based on our current understanding of language production, co-speech or gestural events would likely be planned at a higher level than articulation and therefore would likely activate inferior frontal gyrus, SMA, and others.”

How this orchestra is coordinated and conducted thus remains to be unraveled, but certainly the question is one that gets to the heart of human social interactions.

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A social side to face recognition by infants

Anne Trafton |

When interacting with an infant you have likely noticed that the human face holds a special draw from a very young age. But how does this relate to face recognition by adults, which is known to map to specific cortical regions? Rebecca Saxe, Associate Investigator at MIT’s McGovern Institute and John W. Jarve (1978) Professor in Brain and Cognitive Sciences, and her team have now considered two emerging theories regarding early face recognition, and come up with a third proposition, arguing that when a baby looks at a face, the response is also social, and that the resulting contingent interactions are key to subsequent development of organized face recognition areas in the brain.

By a certain age you are highly skilled at recognizing and responding to faces, and this correlates with activation of a number of face-selective regions of the cortex. This is incredibly important to reading the identities and intentions of other people, and selective categorical representation of faces in cortical areas is a feature shared by our primate cousins. While brain imaging tells us where face-responsive regions are in the adult cortex, how and when they emerge remains unclear.

In 2017, functional magnetic resonance imaging (fMRI) studies of human and macaque infants provided the first glimpse of how the youngest brains respond to faces. The scans showed that in 4-6 month human infants and equivalently aged macaques, regions known to be face-responsive in the adult brain are activated when shown movies of faces, but not in a selective fashion. Essentially fMRI argues that these specific, cortical regions are activated by faces, but a chair will do just as well. Upon further experience of faces over time, the specific cortical regions in macaques became face-selective, no longer responding to other objects.

There are two prevailing ideas in the field of how face preference, and eventually selectivity, arise through experience. These ideas are now considered in turn by Saxe and her team in an opinion piece in the September issue of Trends in Cognitive Sciences, and then a third, new theory proposed. The first idea centers on the way we dote over babies, centering our own faces right in their field of vision. The idea is that such frequent exposures to low level face features (curvilinear shape etc.) will eventually lead to co-activation of neurons that are responsive to all of the different aspects of facial features. If these neurons stimulated by different features are co-activated, and there’s a brain region where these neurons are also found together, this area with be stimulated eventually reinforcing emergence of a face category-specific area.

A second idea is that babies already have an innate “face template,” just as a duckling or chick already knows to follow its mother after hatching. So far there is little evidence for the second proposition, and the first fails to explain why babies seek out a face, rather than passively look upon and eventually “learn” the overlapping features that represent “face.”

Saxe, along with postdoc Lindsey Powell and graduate student Heather Kosakowski, instead now argue that the role a face plays in positive social interactions comes to drive organization of face-selective cortical regions. Taking the next step, the researchers propose that a prime suspect for linking social interactions to the development of face-selective areas is the medial prefrontal cortex (mPFC), a region linked to social cognition and behavior.

“I was asked to give a talk at a conference, and I wanted to talk about both the development of cortical face areas and the social role of the medial prefrontal cortex in young infants,” says Saxe. “I was puzzling over whether these two ideas were related, when I suddenly saw that they could be very fundamentally related.”

The authors argue that this relationship is supported by existing data that has shown that babies prefer dynamic faces and are more interested in faces that engage in a back and forth interaction. Regions of the mPFC are also known to activated during social interactions and known to be activated during exposure to dynamic faces in infants.

Powell is now using functional near infrared spectroscopy (fNIRS), a brain imaging technique that measures changes in blood flow to the brain, to test this hypothesis in infants. “This will allow us to see whether mPFC responses to social cues are linked to the development of face-responsive areas.”

In Daniel Deronda, the novel by George Eliot, the protagonist says “I think my life began with waking up and loving my mother’s face: it was so near to me, and her arms were round me, and she sang to me.” Perhaps this type of positively valenced social interaction, reinforced by the mPFC, is exactly what leads to the particular importance of faces and their selective categorical representation in the human brain. Further testing of the hypothesis proposed by Powell, Kosakowski, and Saxe will tell.

Neuroscientists get at the roots of pessimism

Anne Trafton |

Many patients with neuropsychiatric disorders such as anxiety or depression experience negative moods that lead them to focus on the possible downside of a given situation more than the potential benefit.

MIT neuroscientists have now pinpointed a brain region that can generate this type of pessimistic mood. In tests in animals, they showed that stimulating this region, known as the caudate nucleus, induced animals to make more negative decisions: They gave far more weight to the anticipated drawback of a situation than its benefit, compared to when the region was not stimulated. This pessimistic decision-making could continue through the day after the original stimulation.

The findings could help scientists better understand how some of the crippling effects of depression and anxiety arise, and guide them in developing new treatments.

“We feel we were seeing a proxy for anxiety, or depression, or some mix of the two,” says Ann Graybiel, an MIT Institute Professor, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study, which appears in the Aug. 9 issue of Neuron. “These psychiatric problems are still so very difficult to treat for many individuals suffering from them.”

The paper’s lead authors are McGovern Institute research affiliates Ken-ichi Amemori and Satoko Amemori, who perfected the tasks and have been studying emotion and how it is controlled by the brain. McGovern Institute researcher Daniel Gibson, an expert in data analysis, is also an author of the paper.

Emotional decisions

Graybiel’s laboratory has previously identified a neural circuit that underlies a specific kind of decision-making known as approach-avoidance conflict. These types of decisions, which require weighing options with both positive and negative elements, tend to provoke a great deal of anxiety. Her lab has also shown that chronic stress dramatically affects this kind of decision-making: More stress usually leads animals to choose high-risk, high-payoff options.

In the new study, the researchers wanted to see if they could reproduce an effect that is often seen in people with depression, anxiety, or obsessive-compulsive disorder. These patients tend to engage in ritualistic behaviors designed to combat negative thoughts, and to place more weight on the potential negative outcome of a given situation. This kind of negative thinking, the researchers suspected, could influence approach-avoidance decision-making.

To test this hypothesis, the researchers stimulated the caudate nucleus, a brain region linked to emotional decision-making, with a small electrical current as animals were offered a reward (juice) paired with an unpleasant stimulus (a puff of air to the face). In each trial, the ratio of reward to aversive stimuli was different, and the animals could choose whether to accept or not.

This kind of decision-making requires cost-benefit analysis. If the reward is high enough to balance out the puff of air, the animals will choose to accept it, but when that ratio is too low, they reject it. When the researchers stimulated the caudate nucleus, the cost-benefit calculation became skewed, and the animals began to avoid combinations that they previously would have accepted. This continued even after the stimulation ended, and could also be seen the following day, after which point it gradually disappeared.

This result suggests that the animals began to devalue the reward that they previously wanted, and focused more on the cost of the aversive stimulus. “This state we’ve mimicked has an overestimation of cost relative to benefit,” Graybiel says.

The study provides valuable insight into the role of the basal ganglia (a region that includes the caudate nucleus) in this type of decision-making, says Scott Grafton, a professor of neuroscience at the University of California at Santa Barbara, who was not involved in the research.

“We know that the frontal cortex and the basal ganglia are involved, but the relative contributions of the basal ganglia have not been well understood,” Grafton says. “This is a nice paper because it puts some of the decision-making process in the basal ganglia as well.”

A delicate balance

The researchers also found that brainwave activity in the caudate nucleus was altered when decision-making patterns changed. This change, discovered by Amemori, is in the beta frequency and might serve as a biomarker to monitor whether animals or patients respond to drug treatment, Graybiel says.

Graybiel is now working with psychiatrists at McLean Hospital to study patients who suffer from depression and anxiety, to see if their brains show abnormal activity in the neocortex and caudate nucleus during approach-avoidance decision-making. Magnetic resonance imaging (MRI) studies have shown abnormal activity in two regions of the medial prefrontal cortex that connect with the caudate nucleus.

The caudate nucleus has within it regions that are connected with the limbic system, which regulates mood, and it sends input to motor areas of the brain as well as dopamine-producing regions. Graybiel and Amemori believe that the abnormal activity seen in the caudate nucleus in this study could be somehow disrupting dopamine activity.

“There must be many circuits involved,” she says. “But apparently we are so delicately balanced that just throwing the system off a little bit can rapidly change behavior.”

The research was funded by the National Institutes of Health, the CHDI Foundation, the U.S. Office of Naval Research, the U.S. Army Research Office, MEXT KAKENHI, the Simons Center for the Social Brain, the Naito Foundation, the Uehara Memorial Foundation, Robert Buxton, Amy Sommer, and Judy Goldberg.

Charting the cerebellum

Anne Trafton |

Small and tucked away under the cerebral hemispheres toward the back of the brain, the human cerebellum is still immediately obvious due to its distinct structure. From Galen’s second century anatomical description to Cajal’s systematic analysis of its projections, the cerebellum has long drawn the eyes of researchers studying the brain.  Two parallel studies from MIT’s McGovern institute have recently converged to support an unexpectedly complex level of non-motor cerebellar organization, that would not have been predicted from known motor representation regions.

Historically the cerebellum has primarily been considered to impact motor control and coordination. Think of this view as the cerebellum being the chain on a bicycle, registering what is happening up front in the cortex, and relaying the information so that the back wheel moves at a coordinated pace. This simple view has been questioned as cerebellar circuits have been traced to the basal ganglia and to neocortical regions via the thalamus. This new view suggests the cerebellum is a hub in a complex network, with potentially higher and non-motor functions including cognition and reward-based learning.

A collaboration between the labs of John Gabrieli, Investigator at the McGovern Institute for Brain Research and Jeremy Schmahmann, of the Ataxia Unit at Massachusetts General Hospital and Harvard Medical School, has now used functional brain imaging to give new insight into the cerebellar organization of non-motor roles, including working memory, language, and, social and emotional processing. In a complementary paper, a collaboration between Sheeba Anteraper of MIT’s Martinos Imaging Center and Gagan Joshi of the Alan and Lorraine Bressler Clinical and Research Program at Massachusetts General Hospital, has found changes in connectivity that occur in the cerebellum in autism spectrum disorder (ASD).

A more complex map of the cerebellum

Published in NeuroImage, and featured on the cover, the first study was led by author Xavier Guell, a postdoc in the Gabrieli and Schmahmann labs. The authors used fMRI data from the Human Connectome Project to examine activity in different regions of the cerebellum during specific tasks and at rest. The tasks used extended beyond motor activity to functions recently linked to the cerebellum, including working memory, language, and social and emotional processing. As expected, the authors saw that two regions assigned by other methods to motor activity were clearly modulated during motor tasks.

“Neuroscientists in the 1940s and 1950s described a double representation of motor function in the cerebellum, meaning that two regions in each hemisphere of the cerebellum are engaged in motor control,” explains Guell. “That there are two areas of motor representation in the cerebellum remains one of the most well-established facts of cerebellar macroscale physiology.”

When it came to assigning non-motor tasks, to their surprise, the authors identified three representations that localized to different regions of the cerebellum, pointing to an unexpectedly complex level of organization.

Guell explains the implications further. “Our study supports the intriguing idea that while two parts of the cerebellum are simultaneously engaged in motor tasks, three other parts of the cerebellum are simultaneously engaged in non-motor tasks. Our predecessors coined the term “double motor representation,” and we may now have to add “triple non-motor representation” to the dictionary of cerebellar neuroscience.”

A serendipitous discussion

What happened next, over a discussion of data between Xavier Guell and Sheeba Arnold Anteraper of the McGovern Institute for Brain Research that culminated in a paper led by Anteraper, illustrates how independent strands can meet and reinforce to give a fuller scientific picture.

The findings by Guell and colleagues made the cover of NeuroImage.
The findings by Guell and colleagues made the cover of NeuroImage.

Anteraper and colleagues examined brain images from high-functioning ASD patients, and looked for statistically-significant patterns, letting the data speak rather than focusing on specific ‘candidate’ regions of the brain. To her surprise, networks related to language were highlighted, as well as the cerebellum, regions that had not been linked to ASD, and that seemed at first sight not to be relevant. Scientists interested in language processing, immediately pointed her to Guell.

“When I went to meet him,” says Anteraper, “I saw immediately that he had the same research paper that I’d been reading on his desk. As soon as I showed him my results, the data fell into place and made sense.”

After talking with Guell, they realized that the same non-motor cerebellar representations he had seen, were independently being highlighted by the ASD study.

“When we study brain function in neurological or psychiatric diseases we sometimes have a very clear notion of what parts of the brain we should study” explained Guell, ”We instead asked which parts of the brain have the most abnormal patterns of functional connectivity to other brain areas? This analysis gave us a simple, powerful result. Only the cerebellum survived our strict statistical thresholds.”

The authors found decreased connectivity within the cerebellum in the ASD group, but also decreased strength in connectivity between the cerebellum and the social, emotional and language processing regions in the cerebral cortex.

“Our analysis showed that regions of disrupted functional connectivity mapped to each of the three areas of non-motor representation in the cerebellum. It thus seems that the notion of two motor and three non-motor areas of representation in the cerebellum is not only important for understanding how the cerebellum works, but also important for understanding how the cerebellum becomes dysfunctional in neurology and psychiatry.”

Guell says that many questions remain to be answered. Are these abnormalities in the cerebellum reproducible in other datasets of patients diagnosed with ASD? Why is cerebellar function (and dysfunction) organized in a pattern of multiple representations? What is different between each of these representations, and what is their distinct contribution to diseases such as ASD? Future work is now aimed at unraveling these questions.

The Learning Brain

by Shannon Fischer |

“There’s a slogan in education,” says McGovern Investigator John Gabrieli. “The first three years are learning to read, and after that you read to learn.”

For John Gabrieli, learning to read represents one of the most important milestones in a child’s life. Except, that is, when a child can’t. Children who cannot learn to read adequately by the first grade have a 90 percent chance of still reading poorly in the fourth grade, and 75 percent odds of struggling in high school. For the estimated 10 percent of schoolchildren with a reading disability, that struggle often comes with a host of other social and emotional challenges: anxiety, damaged self-esteem, increased risk for poverty and eventually, encounters with the criminal justice system.

Most reading interventions focus on classical dyslexia, which is essentially a coding problem—trouble moving letters into sound patterns in the brain. But other factors, such as inadequate vocabulary and lack of practice opportunities, hinder reading too. The diagnosis can be subjective, and for those who are diagnosed, the standard treatments help only some students. “Every teacher knows half to two-thirds have a good response, the other third don’t,” Gabrieli says. “It’s a mystery. And amazingly there’s been almost no progress on that.”

For the last two decades, Gabrieli has sought to unravel the neuroscience behind learning and reading disabilities and, ultimately, convert that understanding into new and better education
interventions—a sort of translational medicine for the classroom.

The Home Effect

In 2011, when Julia Leonard was a research assistant in Gabrieli’s lab, she planned to go into pediatrics. But she became drawn to the lab’s education projects and decided to join the lab as
a graduate student to learn more. By 2015, she helped coauthor a landmark study with postdoc Allyson Mackey, that sought neural markers for the academic “achievement gap,” which separates higher socioeconomic status (SES) children from their disadvantaged peers. It was the first study to make a connection between SES-linked differences in brain structure and educational markers. Specifically, they found children from wealthier backgrounds had thicker cortical brain regions, which correlated with better academic achievement.

“Being a doctor is a really awesome and powerful career,” she says. “But I was more curious about the research that could cause bigger changes in children’s lives.”

Leonard collaborated with Rachel Romeo, another graduate student in the Gabrieli lab who wanted to understand the powerful effect of SES on the developing brain. Romeo had a distinctive background in speech pathology and literacy, where she’d observed wealthier students progressing more quickly compared to their disadvantaged peers.

Their research is revealing a fascinating picture. In a 2017 study, Romeo compared how reading-disabled children from low and high SES backgrounds fared after an intensive summer reading intervention. Low SES children in the intervention improved most in their reading, and MRI scans revealed their brains also underwent greater structural changes in response to the intervention. Higher SES children did not appear to change much, either in skill or brain structure.

“In the few studies that have looked at SES effects on treatment outcomes,” Romeo says, “the research suggests that higher SES kids would show the most improvement. We were surprised to
find that this wasn’t true.” She suspects that the midsummer timing of the intervention may account for this. Lower SES kids’ performance often suffer most during a “summer slump,”
and would therefore have the greatest potential to improve from interventions at this time.

However, in another study this year, Leonard uncovered unique brain differences in lower-SES children. Only among lower-SES children was better reasoning ability associated with thicker
cortex in a key part of the brain. Same behavior, different neural signatures.

“So this becomes a really interesting basic science question,” Leonard says. “Does the brain support cognition the same way across everyone, or does it differ based on how you grow up?”

Not a One-Size-Fits-All

Critics of such “educational neuroscience” have highlighted the lack of useful interventions produced by this research. Gabrieli agrees that so far, little has emerged. “The painful thing is the slowness of this work. It’s mind-boggling,” Gabrieli admits. Every intervention requires all the usual human research requirements, plus coordinating with schools, parents, teachers, and so on. “It’s a huge process to do even the smallest intervention,” he explains. Partly because of that, the field is still relatively new.

But he disagrees with the idea that nothing will come from this research. Gabrieli’s lab previously identified neural markers in children who will go on to develop reading disabilities. These markers could even predict who would or would not respond to standard treatments that focus on phonetic letter-sound coding.

Romeo and Leonard’s work suggests that varied etiologies underlie reading disabilities, which may be the key. “For so long people have thought that reading disorders were just a unitary construct: kids are bad at reading, so let’s fix that with a one-size-fits-all treatment,” Romeo says.

Such findings may ultimately help resource-strapped schools target existing phonetic training rather than enrolling all struggling readers in the same program, to see some still fail.

Think Spaces

At the Oliver Hazard Perry School, a public K-8 school located on the South Boston waterfront, teachers like Colleen Labbe have begun to independently navigate similar problems as they try
to reach their own struggling students.

“A lot of times we look at assessments and put students in intervention groups like phonics,” Labbe says. “But it’s important to also ask what is happening for these students on their way to school and at home.”

For Labbe and Perry Principal Geoffrey Rose, brain science has proven transformative. They’ve embraced literature on neuroplasticity—the idea that brains can change if teachers find the right combination of intervention and circumstances, like the low-SES students who benefited in Romeo and Leonard’s study.

“A big myth is that the brain can’t grow and change, and if you can’t reach that student, you pass them off,” Labbe says.

The science has also been empowering to her students, validating their own powers of self-change. “I tell the kids, we’re going to build the goop!” she says, referring to the brain’s ability to make new connections.

“All kids can learn,” Rose agrees. “But the flip of that is, can all kids do school?” His job, he says, is to make sure they can.

The classrooms at Perry are a mix of students from different cultures and socioeconomic backgrounds, so he and Labbe have focused on helping teachers find ways to connect with these children and help them manage their stresses and thus be ready to learn. Teachers here are armed with “scaffolds”—digestible neuro- and cognitive science aids culled from Rose’s postdoctoral studies at Boston College’s Professional School Administrator Program for school leaders. These encourage teachers to be more aware of cultural differences and tendencies in themselves and their students, to better connect.

There are also “Think Spaces” tucked into classroom corners. “Take a deep breath and be calm,” read posters at these soothing stations, which are equipped with de-stressing tools, like squeezable balls, play-dough, and meditation-inspiring sparkle wands. It sounds trivial, yet studies have shown that poverty-linked stressors like food and home insecurity take a toll on emotion and memory-linked brain areas like the amygdala and hippocampus.

In fact, a new study by Clemens Bauer, a postdoc in Gabrieli’s lab, argues that mindfulness training can help calm amygdala hyperactivity, help lower self-perceived stress, and boost attention. His study was conducted with children enrolled in a Boston charter school.

Taking these combined approaches, Labbe says, she’s seen one of her students rise from struggling at the lowest levels of instruction, to thriving by year end. Labbe’s focus on understanding the girl’s stressors, her family environment, and what social and emotional support she really needed was key. “Now she knows she can do it,” Labbe says.

Rose and Labbe only wish they could better bridge the gap between educators like themselves and brain scientists like Gabrieli. To help forge these connections, Rose recently visited Gabrieli’s lab and looks forward to future collaborations. Brain research will provide critical insights into teaching strategy, he says, but the gap is still wide.

From Lab to Classroom

“I’m hugely impressed by principals and teachers who are passionately interested in understanding the brain,” Gabrieli says. Fortunately, new efforts are bridging educators and scientists.

This March, Gabrieli and the MIT Integrated Learning Initiative—MITili, which he also directs—announced a $30 million-dollar grant from the Chan Zuckerberg Initiative for a collaboration
between MIT, the Harvard Graduate School of Education, and Florida State University.

The grant aims to translate some of Gabrieli’s work into more classrooms. Specifically, he hopes to produce better diagnostics that can identify children at risk for dyslexia and other learning
disabilities before they even learn to read.

He hopes to also provide rudimentary diagnostics that identify the source of struggle, be it classic dyslexia, lack of home support, stress, or maybe a combination of factors. That in turn,
could guide treatment—standard phonetic care for some children, versus alternatives: social support akin to Labbe’s efforts, reading practice, or maybe just vocabulary-boosting conversation time with adults.

“We want to get every kid to be an adequate reader by the end of the third grade,” Gabrieli says. “That’s the ultimate goal for me: to help all children become learners.”

How music lessons can improve language skills

Anne Trafton |

Many studies have shown that musical training can enhance language skills. However, it was unknown whether music lessons improve general cognitive ability, leading to better language proficiency, or if the effect of music is more specific to language processing.

A new study from MIT has found that piano lessons have a very specific effect on kindergartners’ ability to distinguish different pitches, which translates into an improvement in discriminating between spoken words. However, the piano lessons did not appear to confer any benefit for overall cognitive ability, as measured by IQ, attention span, and working memory.

“The children didn’t differ in the more broad cognitive measures, but they did show some improvements in word discrimination, particularly for consonants. The piano group showed the best improvement there,” says Robert Desimone, director of MIT’s McGovern Institute for Brain Research and the senior author of the paper.

The study, performed in Beijing, suggests that musical training is at least as beneficial in improving language skills, and possibly more beneficial, than offering children extra reading lessons. The school where the study was performed has continued to offer piano lessons to students, and the researchers hope their findings could encourage other schools to keep or enhance their music offerings.

Yun Nan, an associate professor at Beijing Normal University, is the lead author of the study, which appears in the Proceedings of the National Academy of Sciences the week of June 25.

Other authors include Li Liu, Hua Shu, and Qi Dong, all of Beijing Normal University; Eveline Geiser, a former MIT research scientist; Chen-Chen Gong, an MIT research associate; and 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.

Benefits of music

Previous studies have shown that on average, musicians perform better than nonmusicians on tasks such as reading comprehension, distinguishing speech from background noise, and rapid auditory processing. However, most of these studies have been done by asking people about their past musical training. The MIT researchers wanted to perform a more controlled study in which they could randomly assign children to receive music lessons or not, and then measure the effects.

They decided to perform the study at a school in Beijing, along with researchers from the IDG/McGovern Institute at Beijing Normal University, in part because education officials there were interested in studying the value of music education versus additional reading instruction.

“If children who received music training did as well or better than children who received additional academic instruction, that could a justification for why schools might want to continue to fund music,” Desimone says.

The 74 children participating in the study were divided into three groups: one that received 45-minute piano lessons three times a week; one that received extra reading instruction for the same period of time; and one that received neither intervention. All children were 4 or 5 years old and spoke Mandarin as their native language.

After six months, the researchers tested the children on their ability to discriminate words based on differences in vowels, consonants, or tone (many Mandarin words differ only in tone). Better word discrimination usually corresponds with better phonological awareness — the awareness of the sound structure of words, which is a key component of learning to read.

Children who had piano lessons showed a significant advantage over children in the extra reading group in discriminating between words that differ by one consonant. Children in both the piano group and extra reading group performed better than children who received neither intervention when it came to discriminating words based on vowel differences.

The researchers also used electroencephalography (EEG) to measure brain activity and found that children in the piano group had stronger responses than the other children when they listened to a series of tones of different pitch. This suggest that a greater sensitivity to pitch differences is what helped the children who took piano lessons to better distinguish different words, Desimone says.

“That’s a big thing for kids in learning language: being able to hear the differences between words,” he says. “They really did benefit from that.”

In tests of IQ, attention, and working memory, the researchers did not find any significant differences among the three groups of children, suggesting that the piano lessons did not confer any improvement on overall cognitive function.

Aniruddh Patel, a professor of psychology at Tufts University, says the findings also address the important question of whether purely instrumental musical training can enhance speech processing.

“This study answers the question in the affirmative, with an elegant design that directly compares the effect of music and language instruction on young children. The work specifically relates behavioral improvements in speech perception to the neural impact of musical training, which has both theoretical and real-world significance,” says Patel, who was not involved in the research.

Educational payoff

Desimone says he hopes the findings will help to convince education officials who are considering abandoning music classes in schools not to do so.

“There are positive benefits to piano education in young kids, and it looks like for recognizing differences between sounds including speech sounds, it’s better than extra reading. That means schools could invest in music and there will be generalization to speech sounds,” Desimone says. “It’s not worse than giving extra reading to the kids, which is probably what many schools are tempted to do — get rid of the arts education and just have more reading.”

Desimone now hopes to delve further into the neurological changes caused by music training. One way to do that is to perform EEG tests before and after a single intense music lesson to see how the brain’s activity has been altered.

The research was funded by the National Natural Science Foundation of China, the Beijing Municipal Science and Technology Commission, the Interdiscipline Research Funds of Beijing Normal University, and the Fundamental Research Funds for the Central Universities.

Calcium-based MRI sensor enables more sensitive brain imaging

Anne Trafton |

MIT neuroscientists have developed a new magnetic resonance imaging (MRI) sensor that allows them to monitor neural activity deep within the brain by tracking calcium ions.

Because calcium ions are directly linked to neuronal firing — unlike the changes in blood flow detected by other types of MRI, which provide an indirect signal — this new type of sensing could allow researchers to link specific brain functions to their pattern of neuron activity, and to determine how distant brain regions communicate with each other during particular tasks.

“Concentrations of calcium ions are closely correlated with signaling events in the nervous system,” says Alan Jasanoff, an MIT professor of biological engineering, brain and cognitive sciences, and nuclear science and engineering, an associate member of MIT’s McGovern Institute for Brain Research, and the senior author of the study. “We designed a probe with a molecular architecture that can sense relatively subtle changes in extracellular calcium that are correlated with neural activity.”

In tests in rats, the researchers showed that their calcium sensor can accurately detect changes in neural activity induced by chemical or electrical stimulation, deep within a part of the brain called the striatum.

MIT research associates Satoshi Okada and Benjamin Bartelle are the lead authors of the study, which appears in the April 30 issue of Nature Nanotechnology. Other authors include professor of brain and cognitive sciences and Picower Institute for Learning and Memory member Mriganka Sur, Research Associate Nan Li, postdoc Vincent Breton-Provencher, former postdoc Elisenda Rodriguez, Wellesley College undergraduate Jiyoung Lee, and high school student James Melican.

Tracking calcium

A mainstay of neuroscience research, MRI allows scientists to identify parts of the brain that are active during particular tasks. The most commonly used type, known as functional MRI, measures blood flow in the brain as an indirect marker of neural activity. Jasanoff and his colleagues wanted to devise a way to map patterns of neural activity with specificity and resolution that blood-flow-based MRI techniques can’t achieve.

“Methods that are able to map brain activity in deep tissue rely on changes in blood flow, and those are coupled to neural activity through many different physiological pathways,” Jasanoff says. “As a result, the signal you see in the end is often difficult to attribute to any particular underlying cause.”

Calcium ion flow, on the other hand, can be directly linked with neuron activity. When a neuron fires an electrical impulse, calcium ions rush into the cell. For about a decade, neuroscientists have been using fluorescent molecules to label calcium in the brain and image it with traditional microscopy. This technique allows them to precisely track neuron activity, but its use is limited to small areas of the brain.

The MIT team set out to find a way to image calcium using MRI, which enables much larger tissue volumes to be analyzed. To do that, they designed a new sensor that can detect subtle changes in calcium concentrations outside of cells and respond in a way that can be detected with MRI.

The new sensor consists of two types of particles that cluster together in the presence of calcium. One is a naturally occurring calcium-binding protein called synaptotagmin, and the other is a magnetic iron oxide nanoparticle coated in a lipid that can also bind to synaptotagmin, but only when calcium is present.

Calcium binding induces these particles to clump together, making them appear darker in an MRI image. High levels of calcium outside the neurons correlate with low neuron activity; when calcium concentrations drop, it means neurons in that area are firing electrical impulses.

Detecting brain activity

To test the sensors, the researchers injected them into the striatum of rats, a region that is involved in planning movement and learning new behaviors. They then gave the rats a chemical stimulus that induces short bouts of neural activity, and found that the calcium sensor reflected this activity.

They also found that the sensor picked up activity induced by electrical stimulation in a part of the brain involved in reward.

This approach provides a novel way to examine brain function, says Xin Yu, a research group leader at the Max Planck Institute for Biological Cybernetics in Tuebingen, Germany, who was not involved in the research.

“Although we have accumulated sufficient knowledge on intracellular calcium signaling in the past half-century, it has seldom been studied exactly how the dynamic changes in extracellular calcium contribute to brain function, or serve as an indicator of brain function,” Yu says. “When we are deciphering such a complicated and self-adapted system like the brain, every piece of information matters.”

The current version of the sensor responds within a few seconds of the initial brain stimulation, but the researchers are working on speeding that up. They are also trying to modify the sensor so that it can spread throughout a larger region of the brain and pass through the blood-brain barrier, which would make it possible to deliver the particles without injecting them directly to the test site.

With this kind of sensor, Jasanoff hopes to map patterns of neural activity with greater precision than is now possible. “You could imagine measuring calcium activity in different parts of the brain and trying to determine, for instance, how different types of sensory stimuli are encoded in different ways by the spatial pattern of neural activity that they induce,” he says.

The research was funded by the National Institutes of Health and the MIT Simons Center for the Social Brain.

The quest to understand intelligence

by Elizabeth Doherty |

McGovern investigators study intelligence to answer a practical question for both educators and computer scientists. Can intelligence be improved?

A nine-year-old girl, a contestant on a game show, is standing on stage. On a screen in front of her, there appears a twelve-digit number followed by a six-digit number. Her challenge is to divide the two numbers as fast as possible.

The timer begins. She is racing against three other contestants, two from China and one, like her, from Japan. Whoever answers first wins, but only if the answer is correct.

The show, called “The Brain,” is wildly popular in China, and attracts players who display their memory and concentration skills much the way American athletes demonstrate their physical skills in shows like “American Ninja Warrior.” After a few seconds, the girl slams the timer and gives the correct answer, faster than most people could have entered the numbers on a calculator.

The camera pans to a team of expert judges, including McGovern Director Robert Desimone, who had arrived in Nanjing just a few hours earlier. Desimone shakes his head in disbelief. The task appears to make extraordinary demands on working memory and rapid processing, but the girl explains that she solves it by visualizing an abacus in her mind—something she has practiced intensively.

The show raises an age-old question: What is intelligence, exactly?

The study of intelligence has a long and sometimes contentious history, but recently, neuroscientists have begun to dissect intelligence to understand the neural roots of the distinct cognitive skills that contribute to it. One key question is whether these skills can be improved individually with training and, if so, whether those improvements translate into overall intelligence gains. This research has practical implications for multiple domains, from brain science to education to artificial intelligence.

“The problem of intelligence is one of the great problems in science,” says Tomaso Poggio, a McGovern investigator and an expert on machine learning. “If we make progress in understanding intelligence, and if that helps us make progress in making ourselves smarter or in making machines that help us think better, we can solve all other problems more easily.”

Brain training 101

Many studies have reported positive results from brain training, and there is now a thriving industry devoted to selling tools and games such as Lumosity and BrainHQ. Yet the science behind brain training to improve intelligence remains controversial.

A case in point is the “n-back” working memory task, in which subjects are presented with a rapid sequence of letters or visual patterns, and must report whether the current item matches the last, last-but-one, last-but-two, and so on. The field of brain training received a boost in 2008 when a widely discussed study claimed that a few weeks of training on a challenging version of this task could boost fluid intelligence, the ability to solve novel problems. The report generated excitement and optimism when it first appeared, but several subsequent attempts to reproduce the findings have been unsuccessful.

Among those unable to confirm the result was McGovern Investigator John Gabrieli, who recruited 60 young adults and trained them forty minutes a day for four weeks on an n-back task similar to that of the original study.

Six months later, Gabrieli re-evaluated the participants. “They got amazingly better at the difficult task they practiced. We have great imaging data showing changes in brain activation as they performed the task from before to after,” says Gabrieli. “And yet, that didn’t help them do better on any other cognitive abilities we could measure, and we measured a lot of things.”

The results don’t completely rule out the value of n-back training, says Gabrieli. It may be more effective in children, or in populations with a lower average intelligence than the individuals (mostly college students) who were recruited for Gabrieli’s study. The prospect that training might help disadvantaged individuals holds strong appeal. “If you could raise the cognitive abilities of a child with autism, or a child who is struggling in school, the data tells us that their life would be a step better,” says Gabrieli. “It’s something you would wish for people, especially for those where something is holding them back from the expression of their other abilities.”

Music for the brain

The concept of early intervention is now being tested by Desimone, who has teamed with Chinese colleagues at the recently-established IDG/McGovern Institute at Beijing Normal University to explore the effect of music training on the cognitive abilities of young children.

The researchers recruited 100 children at a neighborhood kindergarten in Beijing, and provided them with a semester-long intervention, randomly assigning children either to music training or (as a control) to additional reading instruction. Unlike the so-called “Mozart Effect,” a scientifically unsubstantiated claim that passive listening to music increases intelligence, the new study requires active learning through daily practice. Several smaller studies have reported cognitive benefits from music training, and Desimone finds the idea plausible given that musical cognition involves several mental functions that are also implicated in intelligence. The study is nearly complete, and results are expected to emerge within a few months. “We’re also collecting data on brain activity, so if we see improvements in the kids who had music training, we’ll also be able to ask about its neural basis,” says Desimone. The results may also have immediate practical implications, since the study design reflects decisions that schools must make in determining how children spend their time. “Many schools are deciding to cut their arts and music programs to make room for more instruction in academic core subjects, so our study is relevant to real questions schools are facing.”

Intelligent classrooms

In another school-based study, Gabrieli’s group recently raised questions about the benefits of “teaching to the test.” In this study, postdoc Amy Finn evaluated over 1300 eighth-graders in the Boston public schools, some enrolled at traditional schools and others at charter schools that emphasize standardized test score improvements. The researchers wanted to find out whether raised test scores were accompanied by improvement of cognitive skills that are linked to intelligence. (Charter school students are selected by lottery, meaning that any results are unlikely to reflect preexisting differences between the two groups of students.) As expected, charter school students showed larger improvements in test scores (relative to their scores from 4 years earlier). But when Finn and her colleagues measured key aspects of intelligence, such as working memory, processing speed, and reasoning, they found no difference between the students who enrolled in charter schools and those who did not. “You can look at these skills as the building blocks of cognition. They are useful for reasoning in a novel situation, an ability that is really important for learning,” says Finn. “It’s surprising that school practices that increase achievement don’t also increase these building blocks.”

Gabrieli remains optimistic that it will eventually be possible to design scientifically based interventions that can raise children’s abilities. Allyson Mackey, a postdoc in his lab, is studying the use of games to exercise the cognitive skills in a classroom setting. As a graduate student at University of California, Berkeley, Mackey had studied the effects of games such as “Chocolate Fix,” in which players match shapes and flavors, represented by color, to positions in a grid based on hints, such as, “the upper left position is strawberry.”

These games gave children practice at thinking through and solving novel problems, and at the end of Mackey’s study, the students—from second through fourth grades—showed improved measures of skills associated with intelligence. “Our results suggest that these cognitive skills are specifically malleable, although we don’t yet know what the active ingredients were in this program,” says Mackey, who speaks of the interventions as if they were drugs, with dosages, efficacies and potentially synergistic combinations to be explored. Mackey is now working to identify the most promising interventions—those that boost cognitive abilities, work well in the classroom, and are engaging for kids—to try in Boston charter schools. “It’s just the beginning of a three-year process to methodically test interventions to see if they work,” she says.

Brain training…for machines

While Desimone, Gabrieli and their colleagues look for ways to raise human intelligence, Poggio, who directs the MIT-based Center for Brains, Minds and Machines, is trying to endow computers with more human-like intelligence. Computers can already match human performance on some specific tasks such as chess. Programs such as Apple’s “Siri” can mimic human speech interpretation, not perfectly but well enough to be useful. Computer vision programs are approaching human performance at rapid object recognitions, and one such system, developed by one of Poggio’s former postdocs, is now being used to assist car drivers. “The last decade has been pretty magical for intelligent computer systems,” says Poggio.

Like children, these intelligent systems learn from past experience. But compared to humans or other animals, machines tend to be very slow learners. For example, the visual system for automobiles was trained by presenting it with millions of images—traffic light, pedestrian, and so on—that had already been labeled by humans. “You would never present so many examples to a child,” says Poggio. “One of our big challenges is to understand how to make algorithms in computers learn with many fewer examples, to make them learn more like children do.”

To accomplish this and other goals of machine intelligence, Poggio suspects that the work being done by Desimone, Gabrieli and others to understand the neural basis of intelligence will be critical. But he is not expecting any single breakthrough that will make everything fall into place. “A century ago,” he says, “scientists pondered the problem of life, as if ‘life’—what we now call biology—were just one problem. The science of intelligence is like biology. It’s a lot of problems, and a lot of breakthroughs will have to come before a machine appears that is as intelligent as we are.”

Study finds early signatures of the social brain

Anne Trafton |

Humans use an ability known as theory of mind every time they make inferences about someone else’s mental state — what the other person believes, what they want, or why they are feeling happy, angry, or scared.

Behavioral studies have suggested that children begin succeeding at a key measure of this ability, known as the false-belief task, around age 4. However, a new study from MIT has found that the brain network that controls theory of mind has already formed in children as young as 3.

The MIT study is the first to use functional magnetic resonance imaging (fMRI) to scan the brains of children as young as age 3 as they perform a task requiring theory of mind — in this case, watching a short animated movie involving social interactions between two characters.

“The brain regions involved in theory-of-mind reasoning are behaving like a cohesive network, with similar responses to the movie, by age 3, which is before kids tend to pass explicit false-belief tasks,” says Hilary Richardson, an MIT graduate student and the lead author of the study.

Rebecca Saxe, an MIT professor of brain and cognitive sciences and an associate member of MIT’s McGovern Institute for Brain Research, is the senior author of the paper, which appears in the March 12 issue of Nature Communications. Other authors are Indiana University graduate student Grace Lisandrelli and Wellesley College undergraduate Alexa Riobueno-Naylor.

Thinking about others

In 2003, Saxe first showed that theory of mind is seated in a brain region known as the right temporo-parietal junction (TPJ). The TPJ coordinates with other regions, including several parts of the prefrontal cortex, to form a network that is active when people think about the mental states of others.

The most commonly used test of theory of mind is the false-belief test, which probes whether the subject understands that other people may have beliefs that are not true. A classic example is the Sally-Anne test, in which a child is asked where Sally will look for a marble that she believes is in her own basket, but that Anne has moved to a different spot while Sally wasn’t looking. To pass, the subject must reply that Sally will look where she thinks the marble is (in her basket), not where it actually is.

Until now, neuroscientists had assumed that theory-of-mind studies involving fMRI brain scans could only be done with children at least 5 years of age, because the children need to be able to lie still in a scanner for about 20 minutes, listen to a series of stories, and answer questions about them.

Richardson wanted to study children younger than that, so that she could delve into what happens in the brain’s theory-of-mind network before the age of 5. To do that, she and Saxe came up with a new experimental protocol, which calls for scanning children while they watch a short movie that includes simple social interactions between two characters.

The animated movie they chose, called “Partly Cloudy,” has a plot that lends itself well to the experiment. It features Gus, a cloud who produces baby animals, and Peck, a stork whose job is to deliver the babies. Gus and Peck have some tense moments in their friendship because Gus produces baby alligators and porcupines, which are difficult to deliver, while other clouds create kittens and puppies. Peck is attacked by some of the fierce baby animals, and he isn’t sure if he wants to keep working for Gus.

“It has events that make you think about the characters’ mental states and events that make you think about their bodily states,” Richardson says.

The researchers spent about four years gathering data from 122 children ranging in age from 3 to 12 years. They scanned the entire brain, focusing on two distinct networks that have been well-characterized in adults: the theory-of-mind network and another network known as the pain matrix, which is active when thinking about another person’s physical state.

They also scanned 33 adults as they watched the movie so that they could identify scenes that provoke responses in either of those two networks. These scenes were dubbed theory-of-mind events and pain events. Scans of children revealed that even in 3-year-olds, the theory-of-mind and pain networks responded preferentially to the same events that the adult brains did.

“We see early signatures of this theory-of-mind network being wired up, so the theory-of-mind brain regions which we studied in adults are already really highly correlated with one another in 3-year-olds,” Richardson says.

The researchers also found that the responses in 3-year-olds were not as strong as in adults but gradually became stronger in the older children they scanned.

Patterns of development

The findings offer support for an existing hypothesis that says children develop theory of mind even before they can pass explicit false-belief tests, and that it continues to develop as they get older. Theory of mind encompasses many abilities, including more difficult skills such as understanding irony and assigning blame, which tend to develop later.

Another hypothesis is that children undergo a fairly sudden development of theory of mind around the age of 4 or 5, reflected by their success in the false-belief test. The MIT data, which do not show any dramatic changes in brain activity when children begin to succeed at the false-belief test, do not support that theory.

“Scientists have focused really intensely on the changes in children’s theory of mind that happen around age 4, when children get a better understanding of how people can have wrong or biased or misinformed beliefs,” Saxe says. “But really important changes in how we think about other minds happen long before, and long after, this famous landmark. Even 2-year-olds try to figure out why different people like different things — this might be why they get so interested in talking about everybody’s favorite colors. And even 9-year-olds are still learning about irony and negligence. Theory of mind seems to undergo a very long continuous developmental process, both in kids’ behaviors and in their brains.”

Now that the researchers have data on the typical trajectory of theory of mind development, they hope to scan the brains of autistic children to see whether there are any differences in how their theory-of-mind networks develop. Saxe’s lab is also studying children whose first exposure to language was delayed, to test the effects of early language on the development of theory of mind.

The research was funded by the National Science Foundation, the National Institutes of Health, and the David and Lucile Packard Foundation.

Seeing the brain’s electrical activity

Anne Trafton |

Neurons in the brain communicate via rapid electrical impulses that allow the brain to coordinate behavior, sensation, thoughts, and emotion. Scientists who want to study this electrical activity usually measure these signals with electrodes inserted into the brain, a task that is notoriously difficult and time-consuming.

MIT researchers have now come up with a completely different approach to measuring electrical activity in the brain, which they believe will prove much easier and more informative. They have developed a light-sensitive protein that can be embedded into neuron membranes, where it emits a fluorescent signal that indicates how much voltage a particular cell is experiencing. This could allow scientists to study how neurons behave, millisecond by millisecond, as the brain performs a particular function.

“If you put an electrode in the brain, it’s like trying to understand a phone conversation by hearing only one person talk,” says Edward Boyden, an associate professor of biological engineering and brain and cognitive sciences at MIT. “Now we can record the neural activity of many cells in a neural circuit and hear them as they talk to each other.”

Boyden, who is also a member of MIT’s Media Lab, McGovern Institute for Brain Research, and Koch Institute for Integrative Cancer Research, and an HHMI-Simons Faculty Scholar, is the senior author of the study, which appears in the Feb. 26 issue of Nature Chemical Biology. The paper’s lead authors are MIT postdocs Kiryl Piatkevich and Erica Jung.

Imaging voltage

For the past two decades, scientists have sought a way to monitor electrical activity in the brain through imaging instead of recording with electrodes. Finding fluorescent molecules that can be used for this kind of imaging has been difficult; not only do the proteins have to be very sensitive to changes in voltage, they must also respond quickly and be resistant to photobleaching (fading that can be caused by exposure to light).

Boyden and his colleagues came up with a new strategy for finding a molecule that would fulfill everything on this wish list: They built a robot that could screen millions of proteins, generated through a process called directed protein evolution, for the traits they wanted.

“You take a gene, then you make millions and millions of mutant genes, and finally you pick the ones that work the best,” Boyden says. “That’s the way that evolution works in nature, but now we’re doing it in the lab with robots so we can pick out the genes with the properties we want.”

The researchers made 1.5 million mutated versions of a light-sensitive protein called QuasAr2, which was previously engineered by Adam Cohen’s lab at Harvard University. (That work, in turn, was based on the molecule Arch, which the Boyden lab reported in 2010.) The researchers put each of those genes into mammalian cells (one mutant per cell), then grew the cells in lab dishes and used an automated microscope to take pictures of the cells. The robot was able to identify cells with proteins that met the criteria the researchers were looking for, the most important being the protein’s location within the cell and its brightness.

The research team then selected five of the best candidates and did another round of mutation, generating 8 million new candidates. The robot picked out the seven best of these, which the researchers then narrowed down to one top performer, which they called Archon1.

Mapping the brain

A key feature of Archon1 is that once the gene is delivered into a cell, the Archon1 protein embeds itself into the cell membrane, which is the best place to obtain an accurate measurement of a cell’s voltage.

Using this protein, the researchers were able to measure electrical activity in mouse brain tissue, as well as in brain cells of zebrafish larvae and the worm Caenorhabditis elegans. The latter two organisms are transparent, so it is easy to expose them to light and image the resulting fluorescence. When the cells are exposed to a certain wavelength of reddish-orange light, the protein sensor emits a longer wavelength of red light, and the brightness of the light corresponds to the voltage of that cell at a given moment in time.

The researchers also showed that Archon1 can be used in conjunction with light-sensitive proteins that are commonly used to silence or stimulate neuron activity — these are known as optogenetic proteins — as long as those proteins respond to colors other than red. In experiments with C. elegans, the researchers demonstrated that they could stimulate one neuron using blue light and then use Archon1 to measure the resulting effect in neurons that receive input from that cell.

Cohen, the Harvard professor who developed the predecessor to Archon1, says the new MIT protein brings scientists closer to the goal of imaging millisecond-timescale electrical activity in live brains.

“Traditionally, it has been excruciatingly labor-intensive to engineer fluorescent voltage indicators, because each mutant had to be cloned individually and then tested through a slow, manual patch-clamp electrophysiology measurement. The Boyden lab developed a very clever high-throughput screening approach to this problem,” says Cohen, who was not involved in this study. “Their new reporter looks really great in fish and worms and in brain slices. I’m eager to try it in my lab.”

The researchers are now working on using this technology to measure brain activity in mice as they perform various tasks, which Boyden believes should allow them to map neural circuits and discover how they produce specific behaviors.

“We will be able to watch a neural computation happen,” he says. “Over the next five years or so we’re going to try to solve some small brain circuits completely. Such results might take a step toward understanding what a thought or a feeling actually is.”

The research was funded by the HHMI-Simons Faculty Scholars Program, the IET Harvey Prize, the MIT Media Lab, the New York Stem Cell Foundation Robertson Award, the Open Philanthropy Project, John Doerr, the Human Frontier Science Program, the Department of Defense, the National Science Foundation, and the National Institutes of Health, including an NIH Director’s Pioneer Award.