Researcher: John Gabrieli

Socioeconomic background linked to reading improvement

by Anne Trafton |

About 20 percent of children in the United States have difficulty learning to read, and educators have devised a variety of interventions to try to help them. Not every program helps every student, however, in part because the origins of their struggles are not identical.

MIT neuroscientist John Gabrieli is trying to identify factors that may help to predict individual children’s responses to different types of reading interventions. As part of that effort, he recently found that children from lower-income families responded much better to a summer reading program than children from a higher socioeconomic background.

Using magnetic resonance imaging (MRI), the research team also found anatomical changes in the brains of children whose reading abilities improved — in particular, a thickening of the cortex in parts of the brain known to be involved in reading.

“If you just left these children [with reading difficulties] alone on the developmental path they’re on, they would have terrible troubles reading in school. We’re taking them on a neuroanatomical detour that seems to go with real gains in reading ability,” says Gabrieli, the Grover M. Hermann Professor in Health Sciences and Technology, a professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Rachel Romeo, a graduate student in the Harvard-MIT Program in Health Sciences and Technology, and Joanna Christodoulou, an assistant professor of communication sciences and disorders at the Massachusetts General Hospital Institute of Health Professions, are the lead authors of the paper, which appears in the June 7 issue of the journal Cerebral Cortex.

Predicting improvement

In hopes of identifying factors that influence children’s responses to reading interventions, the MIT team set up two summer schools based on a program known as Lindamood-Bell. The researchers recruited students from a wide income range, although socioeconomic status was not the original focus of their study.

The Lindamood-Bell program focuses on helping students develop the sensory and cognitive processing necessary for reading, such as thinking about words as units of sound, and translating printed letters into word meanings.

Children participating in the study, who ranged from 6 to 9 years old, spent four hours a day, five days a week in the program, for six weeks. Before and after the program, their brains were scanned with MRI and they were given some commonly used tests of reading proficiency.

In tests taken before the program started, children from higher and lower socioeconomic (SES) backgrounds fared equally poorly in most areas, with one exception. Children from higher SES backgrounds had higher vocabulary scores, which has also been seen in studies comparing nondyslexic readers from different SES backgrounds.

“There’s a strong trend in these studies that higher SES families tend to talk more with their kids and also use more complex and diverse language. That tends to be where the vocabulary correlation comes from,” Romeo says.

The researchers also found differences in brain anatomy before the reading program started. Children from higher socioeconomic backgrounds had thicker cortex in a part of the brain known as Broca’s area, which is necessary for language production and comprehension. The researchers also found that these differences could account for the differences in vocabulary levels between the two groups.

Based on a limited number of previous studies, the researchers hypothesized that the reading program would have more of an impact on the students from higher socioeconomic backgrounds. But in fact, they found the opposite. About half of the students improved their scores, while the other half worsened or stayed the same. When analyzing the data for possible explanations, family income level was the one factor that proved significant.

“Socioeconomic status just showed up as the piece that was most predictive of treatment response,” Romeo says.

The same children whose reading scores improved also displayed changes in their brain anatomy. Specifically, the researchers found that they had a thickening of the cortex in a part of the brain known as the temporal occipital region, which comprises a large network of structures involved in reading.

“Mix of causes”

The researchers believe that their results may have been different than previous studies of reading intervention in low SES students because their program was run during the summer, rather than during the school year.

“Summer is when socioeconomic status takes its biggest toll. Low SES kids typically have less academic content in their summer activities compared to high SES, and that results in a slump in their skills,” Romeo says. “This may have been particularly beneficial for them because it may have been out of the realm of their typical summer.”

The researchers also hypothesize that reading difficulties may arise in slightly different ways among children of different SES backgrounds.

“There could be a different mix of causes,” Gabrieli says. “Reading is a complicated skill, so there could be a number of different factors that would make you do better or do worse. It could be that those factors are a little bit different in children with more enriched or less enriched environments.”

The researchers are hoping to identify more precisely the factors related to socioeconomic status, other environmental factors, or genetic components that could predict which types of reading interventions will be successful for individual students.

“In medicine, people call it personalized medicine: this idea that some people will really benefit from one intervention and not so much from another,” Gabrieli says. “We’re interested in understanding the match between the student and the kind of educational support that would be helpful for that particular student.”

The research was funded by the Ellison Medical Foundation, the Halis Family Foundation, Lindamood-Bell Learning Processes, and the National Institutes of Health.

Rethinking mental illness treatment

by Elizabeth Dougherty |

McGovern researchers are finding neural markers that could help improve treatment for psychiatric patients.

Ten years ago, Jim and Pat Poitras committed $20M to the McGovern Institute to establish the Poitras Center for Affective Disorders Research. The Poitras family had been longtime supporters of MIT, and because they had seen mental illness in their own family, they decided to support an ambitious new program at the McGovern Institute, with the goal of understanding the fundamental biological basis of depression, bipolar disorder, schizophrenia and other major psychiatric disorders.

The gift came at an opportune time, as the field was entering a new phase of discovery, with rapid advances in psychiatric genomics and brain imaging, and with the emergence of new technologies for genome editing and for the development of animal models. Over the past ten years, the Poitras Center has supported work in each of these areas, including Feng Zhang’s work on CRISPR-based genome editing, and Guoping Feng’s work on animal models for autism, schizophrenia and other psychiatric disorders.

This reflects a long-term strategy, says Robert Desimone, director of the McGovern Institute who oversees the Poitras Center. “But we must not lose sight of the overall goal, which is to benefit human patients. Insights from animal models and genomic medicine have the potential to transform the treatments of the future, but we are also interested in the nearer term, and in what we can do right now.”

One area where technology can have a near-term impact is human brain imaging, and in collaboration with clinical researchers at McLean Hospital, Massachusetts General Hospital and other institutions, the Poitras Center has supported an ambitious program to bring human neuroimaging closer to the clinic.

Discovering psychiatry’s crystal ball

A fundamental problem in psychiatry is that there are no biological markers for diagnosing mental illness or for indicating how best to treat it. Treatment decisions are based entirely on symptoms, and doctors and their patients will typically try one treatment, then if it does not work, try another, and perhaps another. The success rates for the first treatments are often less than 50%, and finding what works for an individual patient often means a long and painful process of trial and error.

“Someday, a person will be able to go to a hospital, get a brain scan, charge it to their insurance, and know that it helped the doctor select the best treatment,” says Satra Ghosh.

McGovern research scientist Susan Whitfield-Gabrieli and her colleagues are hoping to change this picture, with the help of brain imaging. Their findings suggest that brain scans can hold valuable information for psychiatrists and their patients. “We need a paradigm shift in how we use imaging. It can be used for more than research,” says Whitfield-Gabrieli, who is a member of McGovern Investigator John Gabrieli’s lab. “It would be a really big boost to be able use it to personalize psychiatric medicine.”

One of Whitfield-Gabrieli’s goals is to find markers that can predict which treatments will work for which patients. Another is to find markers that can predict the likely risk of disease in the future, allowing doctors to intervene before symptoms first develop. All of these markers need further validation before they are ready for the clinic, but they have the potential to meet a dire need to improve treatment for psychiatric disease.

A brain at rest

For Whitfield-Gabrieli, who both collaborates with and is married to Gabrieli, that paradigm shift began when she started to study the resting brain using functional magnetic resonance imaging (fMRI). Most brain imaging studies require the subject to perform a mental task in the scanner, but these are time-consuming and often hard to replicate in a clinical setting.In contrast, resting state imaging requires no task. The subject simply lies in the scanner and lets the mind wander. The patterns of activity can reveal functional connections within the brain, and are reliably consistent from study to study.

Whitfield-Gabrieli thought resting state scanning had the potential to help patients because it is simple and easy to perform.

“Even a 5-minute scan can contain useful information that could help people,” says Satrajit Ghosh, a principal research scientist in the Gabrieli lab who works closely with Whitfield-Gabrieli.

Whitfield-Gabrieli and her clinical collaborator Larry Seidman at Harvard Medical School decided to study resting state activity in patients with schizophrenia. They found a pattern of activity strikingly different from that of typical brains. The patients showed unusually strong activity in a set of interconnected brain regions known as the default mode network, which is typically activated during introspection. It is normally suppressed when a person attends to the outside world, but schizophrenia patients failed to show this suppression.

“The patient isn’t able to toggle between internal processing and external processing the way a typical individual can,” says Whitfield-Gabrieli, whose work is supported by the Poitras Center for Affective Disorders Research.

Since then, the team has observed similar disturbances in the default network in other disorders, including depression, anxiety, bipolar disorder, and ADHD. “We knew we were onto something interesting,” says Whitfield-Gabrieli. “But we kept coming back to the question: how can brain imaging help patients?”

fMRI on patients

Many imaging studies aim to understand the biological basis of disease and ultimately to guide the development of new drugs or other treatments. But this is a long-term goal, and Whitfield-Gabrieli wanted to find ways that brain imaging could have a more immediate impact. So she and Ghosh decided to use fMRI to look at differences among individual patients, and to focus on differences in how they responded to treatment.

“It gave us something objective to measure,” explains Ghosh. “Someone goes through a treatment, and they either get better or they don’t.” The project also had appeal for Ghosh because it was an opportunity for him to use his expertise in machine learning and other computational tools to build systems-level models of the brain.

For the first study, the team decided to focus on social anxiety disorder (SAD), which is typically treated with either prescription drugs or cognitive behavioral therapy (CBT). Both are moderately effective, but many patients do not respond to the first treatment they try.

The team began with a small study to test whether scans performed before the onset of treatment could predict who would respond best to the treatment. Working with Stefan Hofmann, a clinical psychologist at Boston University, they scanned 38 SAD patients before they began a 12-week course of CBT. At the end of their treatment, the patients were evaluated for clinical improvement, and the researchers examined the scans for patterns of activity that correlated with the improvement. The results were very encouraging; it turned out that predictions based on scan data were 5-fold better than the existing methods based on severity of symptoms at the time of diagnosis.

The researchers then turned to another condition, ADHD, which presents a similar clinical challenge, in that commonly used drugs—such as Adderall or Ritalin—work well, but not for everyone. So the McGovern team began a collaboration with psychiatrist Joseph Biederman, Chief of Clinical and Research Programs in Pediatric Psychopharmacology and Adult ADHD
at Massachusetts General Hospital, on a similar study, looking for markers of treatment response.

The study is still ongoing, and it will be some time before results emerge, but the researchers are optimistic. “If we could predict who would respond to which treatment and avoid months of trial and error, it would be totally transformative for ADHD,” says Biederman.

Another goal is to predict in advance who is likely to develop a given disease in the future. The researchers have scanned children who have close relatives with schizophrenia or depression, and who are therefore at increased risk of developing these disorders themselves. Surprisingly, the children show patterns of resting state connectivity similar to those of patients.

“I was really intrigued by this,” says Whitfield-Gabrieli. “Even though these children are not sick, they have the same profile as adults who are.”

Whitfield-Gabrieli and Seidman are now expanding their study through a collaboration with clinical researchers at the Shanghai Mental Institute in China, who plan to image and then follow 225 people who are showing early risk signs for schizophrenia. They hope to find markers that predict who will develop the disease and who will not.

“While there are no drugs available to prevent schizophrenia, it may be possible to reduce the risk or severity of the disorder through CBT, or through interventions that reduce stress and improve sleep and well-being,” says Whitfield-Gabrieli. “One likely key to success is early identification of those at highest risk. If we could diagnose early, we could do early interventions
and potentially prevent disorders.”

From association to prediction

The search for predictive markers represents a departure from traditional psychiatric imaging studies, in which a group of patients is compared with a control group of healthy subjects. Studies of this type can reveal average differences between the groups, which may provide clues to the underlying biology of the disease. But they don’t provide information about individual patients, and so they have not been incorporated into clinical practice.

The difference is critical for clinicians, says Biederman. “I treat individuals, not groups. To bring predictive scans to the clinic, we need to be sure the individual scan is informative for the person you are treating.”

To develop these predictions, Whitfield-Gabrieli and Ghosh must first use sophisticated computational methods such as ‘deep learning’ to identify patterns in their data and to build models that relate the patterns to the clinical outcomes. They must then show that these models can generalize beyond the original study population—for example, that predictions based on patients from Boston can be applied to patients from Shanghai. The eventual goal is a model that can analyze a previously unseen brain scan from any individual, and predict with high confidence whether that person will (for example) develop schizophrenia or respond successfully to a particular therapy.

Achieving this will be challenging, because it will require scanning and following large numbers of subjects from diverse demographic groups—thousands of people, not just tens or hundreds
as in most clinical studies. Collaborations with large hospitals, such as the one in Shanghai, can help. Whitfield-Gabrieli has also received funding to collect imaging, clinical, and behavioral
data from over 200 adolescents with depression and anxiety, as part of the National Institutes of Health’s Human Connectome effort. These data, collected in collaboration with clinicians at
McLean Hospital, MGH and Boston University, will be available not only for the Gabrieli team, but for researchers anywhere to analyze. This is important, because no one team or center can
do it alone, says Ghosh. “Data must be collected by many and shared by all.”

The ultimate goal is to study as many patients as possible now so that the tools can help many more later. “Someday, a person will be able to go to a hospital, get a brain scan, charge it to their insurance, and know that it helped the doctor select the best treatment,” says Ghosh. “We’re still far away from that. But that is what we want to work towards.”

Distinctive brain pattern may underlie dyslexia

Anne Trafton |

A distinctive neural signature found in the brains of people with dyslexia may explain why these individuals have difficulty learning to read, according to a new study from MIT neuroscientists.

The researchers discovered that in people with dyslexia, the brain has a diminished ability to acclimate to a repeated input — a trait known as neural adaptation. For example, when dyslexic students see the same word repeatedly, brain regions involved in reading do not show the same adaptation seen in typical readers.

This suggests that the brain’s plasticity, which underpins its ability to learn new things, is reduced, says John Gabrieli, the Grover M. Hermann Professor in Health Sciences and Technology, a professor of brain and cognitive sciences, and a member of MIT’s McGovern Institute for Brain Research.

“It’s a difference in the brain that’s not about reading per se, but it’s a difference in perceptual learning that’s pretty broad,” says Gabrieli, who is the study’s senior author. “This is a path by which a brain difference could influence learning to read, which involves so many demands on plasticity.”

Former MIT graduate student Tyler Perrachione, who is now an assistant professor at Boston University, is the lead author of the study, which appears in the Dec. 21 issue of Neuron.

Reduced plasticity

The MIT team used magnetic resonance imaging (MRI) to scan the brains of young adults with and without reading difficulties as they performed a variety of tasks. In the first experiment, the subjects listened to a series of words read by either four different speakers or a single speaker.

The MRI scans revealed distinctive patterns of activity in each group of subjects. In nondyslexic people, areas of the brain that are involved in language showed neural adaption after hearing words said by the same speaker, but not when different speakers said the words. However, the dyslexic subjects showed much less adaptation to hearing words said by a single speaker.

Neurons that respond to a particular sensory input usually react strongly at first, but their response becomes muted as the input continues. This neural adaptation reflects chemical changes in neurons that make it easier for them to respond to a familiar stimulus, Gabrieli says. This phenomenon, known as plasticity, is key to learning new skills.

“You learn something upon the initial presentation that makes you better able to do it the second time, and the ease is marked by reduced neural activity,” Gabrieli says. “Because you’ve done something before, it’s easier to do it again.”

The researchers then ran a series of experiments to test how broad this effect might be. They asked subjects to look at series of the same word or different words; pictures of the same object or different objects; and pictures of the same face or different faces. In each case, they found that in people with dyslexia, brain regions devoted to interpreting words, objects, and faces, respectively, did not show neural adaptation when the same stimuli were repeated multiple times.

“The brain location changed depending on the nature of the content that was being perceived, but the reduced adaptation was consistent across very different domains,” Gabrieli says.

He was surprised to see that this effect was so widespread, appearing even during tasks that have nothing to do with reading; people with dyslexia have no documented difficulties in recognizing objects or faces.

He hypothesizes that the impairment shows up primarily in reading because deciphering letters and mapping them to sounds is such a demanding cognitive task. “There are probably few tasks people undertake that require as much plasticity as reading,” Gabrieli says.

Early appearance

In their final experiment, the researchers tested first and second graders with and without reading difficulties, and they found the same disparity in neural adaptation.

“We got almost the identical reduction in plasticity, which suggests that this is occurring quite early in learning to read,” Gabrieli says. “It’s not a consequence of a different learning experience over the years in struggling to read.”

Gabrieli’s lab now plans to study younger children to see if these differences might be apparent even before children begin to learn to read. They also hope to use other types of brain measurements such as magnetoencephalography (MEG) to follow the time course of the neural adaptation more closely.

The research was funded by the Ellison Medical Foundation, the National Institutes of Health, and a National Science Foundation Graduate Research Fellowship.

Diagnosing depression before it starts

Anne Trafton |

A new brain imaging study from MIT and Harvard Medical School may lead to a screen that could identify children at high risk of developing depression later in life.

In the study, the researchers found distinctive brain differences in children known to be at high risk because of family history of depression. The finding suggests that this type of scan could be used to identify children whose risk was previously unknown, allowing them to undergo treatment before developing depression, says John Gabrieli, the Grover M. Hermann Professor in Health Sciences and Technology and a professor of brain and cognitive sciences at MIT.

“We’d like to develop the tools to be able to identify people at true risk, independent of why they got there, with the ultimate goal of maybe intervening early and not waiting for depression to strike the person,” says Gabrieli, an author of the study, which appears in the journal Biological Psychiatry.

Early intervention is important because once a person suffers from an episode of depression, they become more likely to have another. “If you can avoid that first bout, maybe it would put the person on a different trajectory,” says Gabrieli, who is a member of MIT’s McGovern Institute for Brain Research.

The paper’s lead author is McGovern Institute postdoc Xiaoqian Chai, and the senior author is Susan Whitfield-Gabrieli, a research scientist at the McGovern Institute.

Distinctive patterns

The study also helps to answer a key question about the brain structures of depressed patients. Previous imaging studies have revealed two brain regions that often show abnormal activity in these patients: the subgenual anterior cingulate cortex (sgACC) and the amygdala. However, it was unclear if those differences caused depression or if the brain changed as the result of a depressive episode.

To address that issue, the researchers decided to scan brains of children who were not depressed, according to their scores on a commonly used diagnostic questionnaire, but had a parent who had suffered from the disorder. Such children are three times more likely to become depressed later in life, usually between the ages of 15 and 30.

Gabrieli and colleagues studied 27 high-risk children, ranging in age from eight to 14, and compared them with a group of 16 children with no known family history of depression.

Using functional magnetic resonance imaging (fMRI), the researchers measured synchronization of activity between different brain regions. Synchronization patterns that emerge when a person is not performing any particular task allow scientists to determine which regions naturally communicate with each other.

The researchers identified several distinctive patterns in the at-risk children. The strongest of these links was between the sgACC and the default mode network — a set of brain regions that is most active when the mind is unfocused. This abnormally high synchronization has also been seen in the brains of depressed adults.

The researchers also found hyperactive connections between the amygdala, which is important for processing emotion, and the inferior frontal gyrus, which is involved in language processing. Within areas of the frontal and parietal cortex, which are important for thinking and decision-making, they found lower than normal connectivity.

Cause and effect

These patterns are strikingly similar to those found in depressed adults, suggesting that these differences arise before depression occurs and may contribute to the development of the disorder, says Ian Gotlib, a professor of psychology at Stanford University.

“The findings are consistent with an explanation that this is contributing to the onset of the disease,” says Gotlib, who was not involved in the research. “The patterns are there before the depressive episode and are not due to the disorder.”

The MIT team is continuing to track the at-risk children and plans to investigate whether early treatment might prevent episodes of depression. They also hope to study how some children who are at high risk manage to avoid the disorder without treatment.

Other authors of the paper are Dina Hirshfeld-Becker, an associate professor of psychiatry at Harvard Medical School; Joseph Biederman, director of pediatric psychopharmacology at Massachusetts General Hospital (MGH); Mai Uchida, an assistant professor of psychiatry at Harvard Medical School; former MIT postdoc Oliver Doehrmann; MIT graduate student Julia Leonard; John Salvatore, a former McGovern technical assistant; MGH research assistants Tara Kenworthy and Elana Kagan; Harvard Medical School postdoc Ariel Brown; and former MIT technical assistant Carlo de los Angeles.

Study links brain anatomy, academic achievement, and family income

Anne Trafton |

Many years of research have shown that for students from lower-income families, standardized test scores and other measures of academic success tend to lag behind those of wealthier students.

A new study led by researchers at MIT and Harvard University offers another dimension to this so-called “achievement gap”: After imaging the brains of high- and low-income students, they found that the higher-income students had thicker brain cortex in areas associated with visual perception and knowledge accumulation. Furthermore, these differences also correlated with one measure of academic achievement — performance on standardized tests.

“Just as you would expect, there’s a real cost to not living in a supportive environment. We can see it not only in test scores, in educational attainment, but within the brains of these children,” says MIT’s John Gabrieli, the Grover M. Hermann Professor in Health Sciences and Technology, professor of brain and cognitive sciences, and one of the study’s authors. “To me, it’s a call to action. You want to boost the opportunities for those for whom it doesn’t come easily in their environment.”

This study did not explore possible reasons for these differences in brain anatomy. However, previous studies have shown that lower-income students are more likely to suffer from stress in early childhood, have more limited access to educational resources, and receive less exposure to spoken language early in life. These factors have all been linked to lower academic achievement.

In recent years, the achievement gap in the United States between high- and low-income students has widened, even as gaps along lines of race and ethnicity have narrowed, says Martin West, an associate professor of education at the Harvard Graduate School of Education and an author of the new study.

“The gap in student achievement, as measured by test scores between low-income and high-income students, is a pervasive and longstanding phenomenon in American education, and indeed in education systems around the world,” he says. “There’s a lot of interest among educators and policymakers in trying to understand the sources of those achievement gaps, but even more interest in possible strategies to address them.”

Allyson Mackey, a postdoc at MIT’s McGovern Institute for Brain Research, is the lead author of the paper, which appears the journal Psychological Science. Other authors are postdoc Amy Finn; graduate student Julia Leonard; Drew Jacoby-Senghor, a postdoc at Columbia Business School; and Christopher Gabrieli, chair of the nonprofit Transforming Education.

Explaining the gap

The study included 58 students — 23 from lower-income families and 35 from higher-income families, all aged 12 or 13. Low-income students were defined as those who qualify for a free or reduced-price school lunch.

The researchers compared students’ scores on the Massachusetts Comprehensive Assessment System (MCAS) with brain scans of a region known as the cortex, which is key to functions such as thought, language, sensory perception, and motor command.

Using magnetic resonance imaging (MRI), they discovered differences in the thickness of parts of the cortex in the temporal and occipital lobes, whose primary roles are in vision and storing knowledge. Those differences correlated to differences in both test scores and family income. In fact, differences in cortical thickness in these brain regions could explain as much as 44 percent of the income achievement gap found in this study.

Previous studies have also shown brain anatomy differences associated with income, but did not link those differences to academic achievement.

“A number of labs have reported differences in children’s brain structures as a function of family income, but this is the first to relate that to variation in academic achievement,” says Kimberly Noble, an assistant professor of pediatrics at Columbia University who was not part of the research team.

In most other measures of brain anatomy, the researchers found no significant differences. The amount of white matter — the bundles of axons that connect different parts of the brain — did not differ, nor did the overall surface area of the brain cortex.

The researchers point out that the structural differences they did find are not necessarily permanent. “There’s so much strong evidence that brains are highly plastic,” says Gabrieli, who is also a member of the McGovern Institute. “Our findings don’t mean that further educational support, home support, all those things, couldn’t make big differences.”

In a follow-up study, the researchers hope to learn more about what types of educational programs might help to close the achievement gap, and if possible, investigate whether these interventions also influence brain anatomy.

“Over the past decade we’ve been able to identify a growing number of educational interventions that have managed to have notable impacts on students’ academic achievement as measured by standardized tests,” West says. “What we don’t know anything about is the extent to which those interventions — whether it be attending a very high-performing charter school, or being assigned to a particularly effective teacher, or being exposed to a high-quality curricular program — improves test scores by altering some of the differences in brain structure that we’ve documented, or whether they had those effects by other means.”

The research was funded by the Bill and Melinda Gates Foundation and the National Institutes of Health.

Try, try again? Study says no

Anne Trafton |

When it comes to learning languages, adults and children have different strengths. Adults excel at absorbing the vocabulary needed to navigate a grocery store or order food in a restaurant, but children have an uncanny ability to pick up on subtle nuances of language that often elude adults. Within months of living in a foreign country, a young child may speak a second language like a native speaker.

Brain structure plays an important role in this “sensitive period” for learning language, which is believed to end around adolescence. The young brain is equipped with neural circuits that can analyze sounds and build a coherent set of rules for constructing words and sentences out of those sounds. Once these language structures are established, it’s difficult to build another one for a new language.

In a new study, a team of neuroscientists and psychologists led by Amy Finn, a postdoc at MIT’s McGovern Institute for Brain Research, has found evidence for another factor that contributes to adults’ language difficulties: When learning certain elements of language, adults’ more highly developed cognitive skills actually get in the way. The researchers discovered that the harder adults tried to learn an artificial language, the worse they were at deciphering the language’s morphology — the structure and deployment of linguistic units such as root words, suffixes, and prefixes.

“We found that effort helps you in most situations, for things like figuring out what the units of language that you need to know are, and basic ordering of elements. But when trying to learn morphology, at least in this artificial language we created, it’s actually worse when you try,” Finn says.

Finn and colleagues from the University of California at Santa Barbara, Stanford University, and the University of British Columbia describe their findings in the July 21 issue of PLoS One. Carla Hudson Kam, an associate professor of linguistics at British Columbia, is the paper’s senior author.

Too much brainpower

Linguists have known for decades that children are skilled at absorbing certain tricky elements of language, such as irregular past participles (examples of which, in English, include “gone” and “been”) or complicated verb tenses like the subjunctive.

“Children will ultimately perform better than adults in terms of their command of the grammar and the structural components of language — some of the more idiosyncratic, difficult-to-articulate aspects of language that even most native speakers don’t have conscious awareness of,” Finn says.

In 1990, linguist Elissa Newport hypothesized that adults have trouble learning those nuances because they try to analyze too much information at once. Adults have a much more highly developed prefrontal cortex than children, and they tend to throw all of that brainpower at learning a second language. This high-powered processing may actually interfere with certain elements of learning language.

“It’s an idea that’s been around for a long time, but there hasn’t been any data that experimentally show that it’s true,” Finn says.

Finn and her colleagues designed an experiment to test whether exerting more effort would help or hinder success. First, they created nine nonsense words, each with two syllables. Each word fell into one of three categories (A, B, and C), defined by the order of consonant and vowel sounds.

Study subjects listened to the artificial language for about 10 minutes. One group of subjects was told not to overanalyze what they heard, but not to tune it out either. To help them not overthink the language, they were given the option of completing a puzzle or coloring while they listened. The other group was told to try to identify the words they were hearing.

Each group heard the same recording, which was a series of three-word sequences — first a word from category A, then one from category B, then category C — with no pauses between words. Previous studies have shown that adults, babies, and even monkeys can parse this kind of information into word units, a task known as word segmentation.

Subjects from both groups were successful at word segmentation, although the group that tried harder performed a little better. Both groups also performed well in a task called word ordering, which required subjects to choose between a correct word sequence (ABC) and an incorrect sequence (such as ACB) of words they had previously heard.

The final test measured skill in identifying the language’s morphology. The researchers played a three-word sequence that included a word the subjects had not heard before, but which fit into one of the three categories. When asked to judge whether this new word was in the correct location, the subjects who had been asked to pay closer attention to the original word stream performed much worse than those who had listened more passively.

Turning off effort

The findings support a theory of language acquisition that suggests that some parts of language are learned through procedural memory, while others are learned through declarative memory. Under this theory, declarative memory, which stores knowledge and facts, would be more useful for learning vocabulary and certain rules of grammar. Procedural memory, which guides tasks we perform without conscious awareness of how we learned them, would be more useful for learning subtle rules related to language morphology.

“It’s likely to be the procedural memory system that’s really important for learning these difficult morphological aspects of language. In fact, when you use the declarative memory system, it doesn’t help you, it harms you,” Finn says.

Still unresolved is the question of whether adults can overcome this language-learning obstacle. Finn says she does not have a good answer yet but she is now testing the effects of “turning off” the adult prefrontal cortex using a technique called transcranial magnetic stimulation. Other interventions she plans to study include distracting the prefrontal cortex by forcing it to perform other tasks while language is heard, and treating subjects with drugs that impair activity in that brain region.

The research was funded by the National Institute of Child Health and Human Development and the National Science Foundation.

Inside the adult ADHD brain

Anne Trafton |

About 11 percent of school-age children in the United States have been diagnosed with attention deficit hyperactivity disorder (ADHD). While many of these children eventually “outgrow” the disorder, some carry their difficulties into adulthood: About 10 million American adults are currently diagnosed with ADHD.

In the first study to compare patterns of brain activity in adults who recovered from childhood ADHD and those who did not, MIT neuroscientists have discovered key differences in a brain communication network that is active when the brain is at wakeful rest and not focused on a particular task. The findings offer evidence of a biological basis for adult ADHD and should help to validate the criteria used to diagnose the disorder, according to the researchers.

Diagnoses of adult ADHD have risen dramatically in the past several years, with symptoms similar to those of childhood ADHD: a general inability to focus, reflected in difficulty completing tasks, listening to instructions, or remembering details.

“The psychiatric guidelines for whether a person’s ADHD is persistent or remitted are based on lots of clinical studies and impressions. This new study suggests that there is a real biological boundary between those two sets of patients,” says MIT’s John Gabrieli, the Grover M. Hermann Professor of Health Sciences and Technology, professor of brain and cognitive sciences, and an author of the study, which appears in the June 10 issue of the journal Brain.

Shifting brain patterns

This study focused on 35 adults who were diagnosed with ADHD as children; 13 of them still have the disorder, while the rest have recovered. “This sample really gave us a unique opportunity to ask questions about whether or not the brain basis of ADHD is similar in the remitted-ADHD and persistent-ADHD cohorts,” says Aaron Mattfeld, a postdoc at MIT’s McGovern Institute for Brain Research and the paper’s lead author.

The researchers used a technique called resting-state functional magnetic resonance imaging (fMRI) to study what the brain is doing when a person is not engaged in any particular activity. These patterns reveal which parts of the brain communicate with each other during this type of wakeful rest.

“It’s a different way of using functional brain imaging to investigate brain networks,” says Susan Whitfield-Gabrieli, a research scientist at the McGovern Institute and the senior author of the paper. “Here we have subjects just lying in the scanner. This method reveals the intrinsic functional architecture of the human brain without invoking any specific task.”

In people without ADHD, when the mind is unfocused, there is a distinctive synchrony of activity in brain regions known as the default mode network. Previous studies have shown that in children and adults with ADHD, two major hubs of this network — the posterior cingulate cortex and the medial prefrontal cortex — no longer synchronize.

In the new study, the MIT team showed for the first time that in adults who had been diagnosed with ADHD as children but no longer have it, this normal synchrony pattern is restored. “Their brains now look like those of people who never had ADHD,” Mattfeld says.

“This finding is quite intriguing,” says Francisco Xavier Castellanos, a professor of child and adolescent psychiatry at New York University who was not involved in the research. “If it can be confirmed, this pattern could become a target for potential modification to help patients learn to compensate for the disorder without changing their genetic makeup.”

Lingering problems

However, in another measure of brain synchrony, the researchers found much more similarity between both groups of ADHD patients.

In people without ADHD, when the default mode network is active, another network, called the task positive network, is suppressed. When the brain is performing tasks that require focus, the task positive network takes over and suppresses the default mode network. If this reciprocal relationship degrades, the ability to focus declines.

Both groups of adult ADHD patients, including those who had recovered, showed patterns of simultaneous activation of both networks. This is thought to be a sign of impairment in executive function — the management of cognitive tasks — that is separate from ADHD, but occurs in about half of ADHD patients. All of the ADHD patients in this study performed poorly on tests of executive function. “Once you have executive function problems, they seem to hang in there,” says Gabrieli, who is a member of the McGovern Institute.

The researchers now plan to investigate how ADHD medications influence the brain’s default mode network, in hopes that this might allow them to predict which drugs will work best for individual patients. Currently, about 60 percent of patients respond well to the first drug they receive.

“It’s unknown what’s different about the other 40 percent or so who don’t respond very much,” Gabrieli says. “We’re pretty excited about the possibility that some brain measurement would tell us which child or adult is most likely to benefit from a treatment.”

The research was funded by the Poitras Center for Affective Disorders Research at the McGovern Institute.

Even when test scores go up, some cognitive abilities don’t

Anne Trafton |

To evaluate school quality, states require students to take standardized tests; in many cases, passing those tests is necessary to receive a high-school diploma. These high-stakes tests have also been shown to predict students’ future educational attainment and adult employment and income.

Such tests are designed to measure the knowledge and skills that students have acquired in school — what psychologists call “crystallized intelligence.” However, schools whose students have the highest gains on test scores do not produce similar gains in “fluid intelligence” — the ability to analyze abstract problems and think logically — according to a new study from MIT neuroscientists working with education researchers at Harvard University and Brown University.

In a study of nearly 1,400 eighth-graders in the Boston public school system, the researchers found that some schools have successfully raised their students’ scores on the Massachusetts Comprehensive Assessment System (MCAS). However, those schools had almost no effect on students’ performance on tests of fluid intelligence skills, such as working memory capacity, speed of information processing, and ability to solve abstract problems.

“Our original question was this: If you have a school that’s effectively helping kids from lower socioeconomic environments by moving up their scores and improving their chances to go to college, then are those changes accompanied by gains in additional cognitive skills?” says John Gabrieli, the Grover M. Hermann Professor of Health Sciences and Technology, professor of brain and cognitive sciences, and senior author of a forthcoming Psychological Science paper describing the findings.

Instead, the researchers found that educational practices designed to raise knowledge and boost test scores do not improve fluid intelligence. “It doesn’t seem like you get these skills for free in the way that you might hope, despite learning a lot by being a good student,” says Gabrieli, who is also a member of MIT’s McGovern Institute for Brain Research.

Measuring cognition

This study grew out of a larger effort to find measures beyond standardized tests that can predict long-term success for students. “As we started that study, it struck us that there’s been surprisingly little evaluation of different kinds of cognitive abilities and how they relate to educational outcomes,” Gabrieli says.

The data for the Psychological Science study came from students attending traditional, charter, and exam schools in Boston. Some of those schools have had great success improving their students’ MCAS scores — a boost that studies have found also translates to better performance on the SAT and Advanced Placement tests.

The researchers calculated how much of the variation in MCAS scores was due to the school that students attended. For MCAS scores in English, schools accounted for 24 percent of the variation, and they accounted for 34 percent of the math MCAS variation. However, the schools accounted for very little of the variation in fluid cognitive skills — less than 3 percent for all three skills combined.

In one example of a test of fluid reasoning, students were asked to choose which of six pictures completed the missing pieces of a puzzle — a task requiring integration of information such as shape, pattern, and orientation.

“It’s not always clear what dimensions you have to pay attention to get the problem correct. That’s why we call it fluid, because it’s the application of reasoning skills in novel contexts,” says Amy Finn, an MIT postdoc and lead author of the paper.

Even stronger evidence came from a comparison of about 200 students who had entered a lottery for admittance to a handful of Boston’s oversubscribed charter schools, many of which achieve strong improvement in MCAS scores. The researchers found that students who were randomly selected to attend high-performing charter schools did significantly better on the math MCAS than those who were not chosen, but there was no corresponding increase in fluid intelligence scores.

However, the researchers say their study is not about comparing charter schools and district schools. Rather, the study showed that while schools of both types varied in their impact on test scores, they did not vary in their impact on fluid cognitive skills.

“What’s nice about this study is it seems to narrow down the possibilities of what educational interventions are achieving,” says Daniel Willingham, a professor of psychology at the University of Virginia who was not part of the research team. “We’re usually primarily concerned with outcomes in schools, but the underlying mechanisms are also important.”

The researchers plan to continue tracking these students, who are now in 10th grade, to see how their academic performance and other life outcomes evolve. They have also begun to participate in a new study of high school seniors to track how their standardized test scores and cognitive abilities influence their rates of college attendance and graduation.

Implications for education

Gabrieli notes that the study should not be interpreted as critical of schools that are improving their students’ MCAS scores. “It’s valuable to push up the crystallized abilities, because if you can do more math, if you can read a paragraph and answer comprehension questions, all those things are positive,” he says.

He hopes that the findings will encourage educational policymakers to consider adding practices that enhance cognitive skills. Although many studies have shown that students’ fluid cognitive skills predict their academic performance, such skills are seldom explicitly taught.

“Schools can improve crystallized abilities, and now it might be a priority to see if there are some methods for enhancing the fluid ones as well,” Gabrieli says.

Some studies have found that educational programs that focus on improving memory, attention, executive function, and inductive reasoning can boost fluid intelligence, but there is still much disagreement over what programs are consistently effective.

The research was a collaboration with the Center for Education Policy Research at Harvard University, Transforming Education, and Brown University, and was funded by the Bill and Melinda Gates Foundation and the National Institutes of Health.

Brain scans may help diagnose dyslexia

Anne Trafton |

About 10 percent of the U.S. population suffers from dyslexia, a condition that makes learning to read difficult. Dyslexia is usually diagnosed around second grade, but the results of a new study from MIT could help identify those children before they even begin reading, so they can be given extra help earlier.

The study, done with researchers at Boston Children’s Hospital, found a correlation between poor pre-reading skills in kindergartners and the size of a brain structure that connects two language-processing areas.

Previous studies have shown that in adults with poor reading skills, this structure, known as the arcuate fasciculus, is smaller and less organized than in adults who read normally. However, it was unknown if these differences cause reading difficulties or result from lack of reading experience.

“We were very interested in looking at children prior to reading instruction and whether you would see these kinds of differences,” says John Gabrieli, the Grover M. Hermann Professor of Health Sciences and Technology, professor of brain and cognitive sciences and a member of MIT’s McGovern Institute for Brain Research.

Gabrieli and Nadine Gaab, an assistant professor of pediatrics at Boston Children’s Hospital, are the senior authors of a paper describing the results in the Aug. 14 issue of the Journal of Neuroscience. Lead authors of the paper are MIT postdocs Zeynep Saygin and Elizabeth Norton.

The path to reading

The new study is part of a larger effort involving approximately 1,000 children at schools throughout Massachusetts and Rhode Island. At the beginning of kindergarten, children whose parents give permission to participate are assessed for pre-reading skills, such as being able to put words together from sounds.

“From that, we’re able to provide — at the beginning of kindergarten — a snapshot of how that child’s pre-reading abilities look relative to others in their classroom or other peers, which is a real benefit to the child’s parents and teachers,” Norton says.

The researchers then invite a subset of the children to come to MIT for brain imaging. The Journal of Neuroscience study included 40 children who had their brains scanned using a technique known as diffusion-weighted imaging, which is based on magnetic resonance imaging (MRI).

This type of imaging reveals the size and organization of the brain’s white matter — bundles of nerves that carry information between brain regions. The researchers focused on three white-matter tracts associated with reading skill, all located on the left side of the brain: the arcuate fasciculus, the inferior longitudinal fasciculus (ILF) and the superior longitudinal fasciculus (SLF).

When comparing the brain scans and the results of several different types of pre-reading tests, the researchers found a correlation between the size and organization of the arcuate fasciculus and performance on tests of phonological awareness — the ability to identify and manipulate the sounds of language.

Phonological awareness can be measured by testing how well children can segment sounds, identify them in isolation, and rearrange them to make new words. Strong phonological skills have previously been linked with ease of learning to read. “The first step in reading is to match the printed letters with the sounds of letters that you know exist in the world,” Norton says.

The researchers also tested the children on two other skills that have been shown to predict reading ability — rapid naming, which is the ability to name a series of familiar objects as quickly as you can, and the ability to name letters. They did not find any correlation between these skills and the size or organization of the white-matter structures scanned in this study.

Early intervention

The left arcuate fasciculus connects Broca’s area, which is involved in speech production, and Wernicke’s area, which is involved in understanding written and spoken language. A larger and more organized arcuate fasciculus could aid in communication between those two regions, the researchers say.

Gabrieli points out that the structural differences found in the study don’t necessarily reflect genetic differences; environmental influences could also be involved. “At the moment when the children arrive at kindergarten, which is approximately when we scan them, we don’t know what factors lead to these brain differences,” he says.

The researchers plan to follow three waves of children as they progress to second grade and evaluate whether the brain measures they have identified predict poor reading skills.

“We don’t know yet how it plays out over time, and that’s the big question: Can we, through a combination of behavioral and brain measures, get a lot more accurate at seeing who will become a dyslexic child, with the hope that that would motivate aggressive interventions that would help these children right from the start, instead of waiting for them to fail?” Gabrieli says.

For at least some dyslexic children, offering extra training in phonological skills can help them improve their reading skills later on, studies have shown.

The research was funded by the National Institutes of Health, the Poitras Center for Affective Disorders Research, the Ellison Medical Foundation and the Halis Family Foundation.

Predicting how patients respond to therapy

Anne Trafton |

Social anxiety is usually treated with either cognitive behavioral therapy or medications. However, it is currently impossible to predict which treatment will work best for a particular patient. The team of researchers from MIT, Boston University (BU) and Massachusetts General Hospital (MGH) found that the effectiveness of therapy could be predicted by measuring patients’ brain activity as they looked at photos of faces, before the therapy sessions began.

The findings, published this week in the Archives of General Psychiatry, may help doctors more accurately choose treatments for social anxiety disorder, which is estimated to affect around 15 million people in the United States.

“Our vision is that some of these measures might direct individuals to treatments that are more likely to work for them,” says John Gabrieli, the Grover M. Hermann Professor of Brain and Cognitive Sciences at MIT, a member of the McGovern Institute for Brain Research and senior author of the paper.

Lead authors of the paper are MIT postdoc Oliver Doehrmann and Satrajit Ghosh, a research scientist in the McGovern Institute.

Choosing treatments

Sufferers of social anxiety disorder experience intense fear in social situations, interfering with their ability to function in daily life. Cognitive behavioral therapy aims to change the thought and behavior patterns that lead to anxiety. For social anxiety disorder patients, that might include learning to reverse the belief that others are watching or judging them.

The new paper is part of a larger study that MGH and BU recently ran on cognitive behavioral therapy for social anxiety, led by Mark Pollack, director of the Center for Anxiety and Traumatic Stress Disorders at MGH, and Stefan Hofmann, director of the Social Anxiety Program at BU.

“This was a chance to ask if these brain measures, taken before treatment, would be informative in ways above and beyond what physicians can measure now, and determine who would be responsive to this treatment,” Gabrieli says.

Currently doctors might choose a treatment based on factors such as ease of taking pills versus going to therapy, the possibility of drug side effects, or what the patient’s insurance will cover. “From a science perspective there’s very little evidence about which treatment is optimal for a person,” Gabrieli says.

The researchers used functional magnetic resonance imaging (fMRI) to image the brains of patients before and after treatment. There have been many imaging studies showing brain differences between healthy people and patients with neuropsychiatric disorders, but so far imaging has not been established as a way to predict patient response to particular treatments.

Measuring brain activity

In the new study, the researchers measured differences in brain activity as patients looked at images of angry or neutral faces. After 12 weeks of cognitive behavioral therapy, patients’ social anxiety levels were tested. The researchers found that patients who had shown a greater difference in activity in high-level visual processing areas during the face-response task showed the most improvement after therapy.

The findings are an important step towards improving doctors’ ability to choose the right treatment for psychiatric disorders, says Greg Siegle, associate professor of psychiatry at the University of Pittsburgh. “It’s really critical that somebody do this work, and they did it very well,” says Siegle, who was not part of the research team. “It moves the field forward, and brings psychology into more of a rigorous science, using neuroscience to distinguish between clinical cases that at first appear homogeneous.”

Gabrieli says it’s unclear why activity in brain regions involved with visual processing would be a good predictor of treatment outcome. One possibility is that patients who benefited more were those whose brains were already adept at segregating different types of experiences, Gabrieli says.

The researchers are now planning a follow-up study to investigate whether brain scans can predict differences in response between cognitive behavioral therapy and drug treatment.

“Right now, all by itself, we’re just giving somebody encouraging or discouraging news about the likely outcome” of therapy, Gabrieli says. “The really valuable thing would be if it turns out to be differentially sensitive to different treatment choices.”

The research was funded by the Poitras Center for Affective Disorders Research and the National Institute of Mental Health.