Research Area: Brain Imaging

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

Back-and-forth exchanges boost children’s brain response to language

Anne Trafton |

A landmark 1995 study found that children from higher-income families hear about 30 million more words during their first three years of life than children from lower-income families. This “30-million-word gap” correlates with significant differences in tests of vocabulary, language development, and reading comprehension.

MIT cognitive scientists have now found that conversation between an adult and a child appears to change the child’s brain, and that this back-and-forth conversation is actually more critical to language development than the word gap. In a study of children between the ages of 4 and 6, they found that differences in the number of “conversational turns” accounted for a large portion of the differences in brain physiology and language skills that they found among the children. This finding applied to children regardless of parental income or education.

The findings suggest that parents can have considerable influence over their children’s language and brain development by simply engaging them in conversation, the researchers say.

“The important thing is not just to talk to your child, but to talk with your child. It’s not just about dumping language into your child’s brain, but to actually carry on a conversation with them,” says Rachel Romeo, a graduate student at Harvard and MIT and the lead author of the paper, which appears in the Feb. 14 online edition of Psychological Science.

Using functional magnetic resonance imaging (fMRI), the researchers identified differences in the brain’s response to language that correlated with the number of conversational turns. In children who experienced more conversation, Broca’s area, a part of the brain involved in speech production and language processing, was much more active while they listened to stories. This brain activation then predicted children’s scores on language assessments, fully explaining the income-related differences in children’s language skills.

“The really novel thing about our paper is that it provides the first evidence that family conversation at home is associated with brain development in children. It’s almost magical how parental conversation appears to influence the biological growth of the brain,” says John 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.

Beyond the word gap

Before this study, little was known about how the “word gap” might translate into differences in the brain. The MIT team set out to find these differences by comparing the brain scans of children from different socioeconomic backgrounds.

As part of the study, the researchers used a system called Language Environment Analysis (LENA) to record every word spoken or heard by each child. Parents who agreed to have their children participate in the study were told to have their children wear the recorder for two days, from the time they woke up until they went to bed.

The recordings were then analyzed by a computer program that yielded three measurements: the number of words spoken by the child, the number of words spoken to the child, and the number of times that the child and an adult took a “conversational turn” — a back-and-forth exchange initiated by either one.

The researchers found that the number of conversational turns correlated strongly with the children’s scores on standardized tests of language skill, including vocabulary, grammar, and verbal reasoning. The number of conversational turns also correlated with more activity in Broca’s area, when the children listened to stories while inside an fMRI scanner.

These correlations were much stronger than those between the number of words heard and language scores, and between the number of words heard and activity in Broca’s area.

This result aligns with other recent findings, Romeo says, “but there’s still a popular notion that there’s this 30-million-word gap, and we need to dump words into these kids — just talk to them all day long, or maybe sit them in front of a TV that will talk to them. However, the brain data show that it really seems to be this interactive dialogue that is more strongly related to neural processing.”

The researchers believe interactive conversation gives children more of an opportunity to practice their communication skills, including the ability to understand what another person is trying to say and to respond in an appropriate way.

While children from higher-income families were exposed to more language on average, children from lower-income families who experienced a high number of conversational turns had language skills and Broca’s area brain activity similar to those of children who came from higher-income families.

“In our analysis, the conversational turn-taking seems like the thing that makes a difference, regardless of socioeconomic status. Such turn-taking occurs more often in families from a higher socioeconomic status, but children coming from families with lesser income or parental education showed the same benefits from conversational turn-taking,” Gabrieli says.

Taking action

The researchers hope their findings will encourage parents to engage their young children in more conversation. Although this study was done in children age 4 to 6, this type of turn-taking can also be done with much younger children, by making sounds back and forth or making faces, the researchers say.

“One of the things we’re excited about is that it feels like a relatively actionable thing because it’s specific. That doesn’t mean it’s easy for less educated families, under greater economic stress, to have more conversation with their child. But at the same time, it’s a targeted, specific action, and there may be ways to promote or encourage that,” Gabrieli says.

Roberta Golinkoff, a professor of education at the University of Delaware School of Education, says the new study presents an important finding that adds to the evidence that it’s not just the number of words children hear that is significant for their language development.

“You can talk to a child until you’re blue in the face, but if you’re not engaging with the child and having a conversational duet about what the child is interested in, you’re not going to give the child the language processing skills that they need,” says Golinkoff, who was not involved in the study. “If you can get the child to participate, not just listen, that will allow the child to have a better language outcome.”

The MIT researchers now hope to study the effects of possible interventions that incorporate more conversation into young children’s lives. These could include technological assistance, such as computer programs that can converse or electronic reminders to parents to engage their children in conversation.

The research was funded by the Walton Family Foundation, the National Institute of Child Health and Human Development, a Harvard Mind Brain Behavior Grant, and a gift from David Pun Chan.

Microscopy technique could enable more informative biopsies

Anne Trafton |

MIT and Harvard Medical School researchers have devised a way to image biopsy samples with much higher resolution — an advance that could help doctors develop more accurate and inexpensive diagnostic tests.

For more than 100 years, conventional light microscopes have been vital tools for pathology. However, fine-scale details of cells cannot be seen with these scopes. The new technique relies on an approach known as expansion microscopy, developed originally in Edward Boyden’s lab at MIT, in which the researchers expand a tissue sample to 100 times its original volume before imaging it.

This expansion allows researchers to see features with a conventional light microscope that ordinarily could be seen only with an expensive, high-resolution electron microscope. It also reveals additional molecular information that the electron microscope cannot provide.

“It’s a technique that could have very broad application,” says Boyden, an associate professor of biological engineering and brain and cognitive sciences at MIT. He is also a member of MIT’s Media Lab and McGovern Institute for Brain Research, and an HHMI-Simons Faculty Scholar.

In a paper appearing in the 17 July issue of Nature Biotechnology, Boyden and his colleagues used this technique to distinguish early-stage breast lesions with high or low risk of progressing to cancer — a task that is challenging for human observers. This approach can also be applied to other diseases: In an analysis of kidney tissue, the researchers found that images of expanded samples revealed signs of kidney disease that can normally only be seen with an electron microscope.

“Using expansion microscopy, we are able to diagnose diseases that were previously impossible to diagnose with a conventional light microscope,” says Octavian Bucur, an instructor at Harvard Medical School, Beth Israel Deaconess Medical Center (BIDMC), and the Ludwig Center at Harvard, and one of the paper’s lead authors.

MIT postdoc Yongxin Zhao is the paper’s co-lead author. Boyden and Andrew Beck, a former associate professor at Harvard Medical School and BIDMC, are the paper’s senior authors.


“A few chemicals and a light microscope”

Boyden’s original expansion microscopy technique is based on embedding tissue samples in a dense, evenly generated polymer that swells when water is added. Before the swelling occurs, the researchers anchor to the polymer gel the molecules that they want to image, and they digest other proteins that normally hold tissue together.

This tissue enlargement allows researchers to obtain images with a resolution of around 70 nanometers, which was previously possible only with very specialized and expensive microscopes.

In the new study, the researchers set out to adapt the expansion process for biopsy tissue samples, which are usually embedded in paraffin wax, flash frozen, or stained with a chemical that makes cellular structures more visible.

The MIT/Harvard team devised a process to convert these samples into a state suitable for expansion. For example, they remove the chemical stain or paraffin by exposing the tissues to a chemical solvent called xylene. Then, they heat up the sample in another chemical called citrate. After that, the tissues go through an expansion process similar to the original version of the technique, but with stronger digestion steps to compensate for the strong chemical fixation of the samples.

During this procedure, the researchers can also add fluorescent labels for molecules of interest, including proteins that mark particular types of cells, or DNA or RNA with a specific sequence.

“The work of Zhao et al. describes a very clever way of extending the resolution of light microscopy to resolve detail beyond that seen with conventional methods,” says David Rimm, a professor of pathology at the Yale University School of Medicine, who was not involved in the research.

The researchers tested this approach on tissue samples from patients with early-stage breast lesions. One way to predict whether these lesions will become malignant is to evaluate the appearance of the cells’ nuclei. Benign lesions with atypical nuclei have about a fivefold higher probability of progressing to cancer than those with typical nuclei.

However, studies have revealed significant discrepancies between the assessments of nuclear atypia performed by different pathologists, which can potentially lead to an inaccurate diagnosis and unnecessary surgery. An improved system for differentiating benign lesions with atypical and typical nuclei could potentially prevent 400,000 misdiagnoses and hundreds of millions of dollars every year in the United States, according to the researchers.

After expanding the tissue samples, the MIT/Harvard team analyzed them with a machine learning algorithm that can rate the nuclei based on dozens of features, including orientation, diameter, and how much they deviate from true circularity. This algorithm was able to distinguish between lesions that were likely to become invasive and those that were not, with an accuracy of 93 percent on expanded samples compared to only 71 percent on the pre-expanded tissue.

“These two types of lesions look highly similar to the naked eye, but one has much less risk of cancer,” Zhao says.

The researchers also analyzed kidney tissue samples from patients with nephrotic syndrome, which impairs the kidneys’ ability to filter blood. In these patients, tiny finger-like projections that filter the blood are lost or damaged. These structures are spaced about 200 nanometers apart and therefore can usually be seen only with an electron microscope or expensive super resolution microscopes.

When the researchers showed the images of the expanded tissue samples to a group of scientists that included pathologists and nonpathologists, the group was able to identify the diseased tissue with 90 percent accuracy overall, compared to only 65 percent accuracy with unexpanded tissue samples.

“Now you can diagnose nephrotic kidney disease without needing an electron microscope, a very expensive machine,” Boyden says. “You can do it with a few chemicals and a light microscope.”

Uncovering patterns

Using this approach, the researchers anticipate that scientists could develop more precise diagnostics for many other diseases. To do that, scientists and doctors will need to analyze many more patient samples, allowing them to discover patterns that would be impossible to see otherwise.

“If you can expand a tissue by one-hundredfold in volume, all other things being equal, you’re getting 100 times the information,” Boyden says.

For example, researchers could distinguish cancer cells based on how many copies of a particular gene they have. Extra copies of genes such as HER2, which the researchers imaged in one part of this study, indicate a subtype of breast cancer that is eligible for specific treatments.

Scientists could also look at the architecture of the genome, or at how cell shapes change as they become cancerous and interact with other cells of the body. Another possible application is identifying proteins that are expressed specifically on the surface of cancer cells, allowing researchers to design immunotherapies that mark those cells for destruction by the patient’s immune system.

Boyden and his colleagues run training courses several times a month at MIT, where visitors can come and watch expansion microscopy techniques, and they have made their protocols available on their website. They hope that many more people will begin using this approach to study a variety of diseases.

“Cancer biopsies are just the beginning,” Boyden says. “We have a new pipeline for taking clinical samples and expanding them, and we are finding that we can apply expansion to many different diseases. Expansion will enable computational pathology to take advantage of more information in a specimen than previously possible.”

Humayun Irshad, a research fellow at Harvard/BIDMC and an author of the study, agrees: “Expanded images result in more informative features, which in turn result in higher-performing classification models.”

Other authors include Harvard pathologist Astrid Weins, who helped oversee the kidney study. Other authors from MIT (Fei Chen) and BIDMC/Harvard (Andreea Stancu, Eun-Young Oh, Marcello DiStasio, Vanda Torous, Benjamin Glass, Isaac E. Stillman, and Stuart J. Schnitt) also contributed to this study.

The research was funded, in part, by the New York Stem Cell Foundation Robertson Investigator Award, the National Institutes of Health Director’s Pioneer Award, the Department of Defense Multidisciplinary University Research Initiative, the Open Philanthropy Project, the Ludwig Center at Harvard, and Harvard Catalyst.

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.

Making brain implants smaller could prolong their lifespan

Anne Trafton |

Many diseases, including Parkinson’s disease, can be treated with electrical stimulation from an electrode implanted in the brain. However, the electrodes can produce scarring, which diminishes their effectiveness and can necessitate additional surgeries to replace them.

MIT researchers have now demonstrated that making these electrodes much smaller can essentially eliminate this scarring, potentially allowing the devices to remain in the brain for much longer.

“What we’re doing is changing the scale and making the procedure less invasive,” says Michael Cima, the David H. Koch Professor of Engineering in the Department of Materials Science and Engineering, a member of MIT’s Koch Institute for Integrative Cancer Research, and the senior author of the study, which appears in the May 16 issue of Scientific Reports.

Cima and his colleagues are now designing brain implants that can not only deliver electrical stimulation but also record brain activity or deliver drugs to very targeted locations.

The paper’s lead author is former MIT graduate student Kevin Spencer. Other authors are former postdoc Jay Sy, graduate student Khalil Ramadi, Institute Professor Ann Graybiel, and David H. Koch Institute Professor Robert Langer.

Effects of size

Many Parkinson’s patients have benefited from treatment with low-frequency electrical current delivered to a part of the brain involved in movement control. The electrodes used for this deep brain stimulation are a few millimeters in diameter. After being implanted, they gradually generate scar tissue through the constant rubbing of the electrode against the surrounding brain tissue. This process, known as gliosis, contributes to the high failure rate of such devices: About half stop working within the first six months.

Previous studies have suggested that making the implants smaller or softer could reduce the amount of scarring, so the MIT team set out to measure the effects of both reducing the size of the implants and coating them with a soft polyethylene glycol (PEG) hydrogel.

The hydrogel coating was designed to have an elasticity very similar to that of the brain. The researchers could also control the thickness of the coating. They found that when coated electrodes were pushed into the brain, the soft coating would fall off, so they devised a way to apply the hydrogel and then dry it, so that it becomes a hard, thin film. After the electrode is inserted, the film soaks up water and becomes soft again.

In mice, the researchers tested both coated and uncoated glass fibers with varying diameters and found that there is a tradeoff between size and softness. Coated fibers produced much less scarring than uncoated fibers of the same diameter. However, as the electrode fibers became smaller, down to about 30 microns (0.03 millimeters) in diameter, the uncoated versions produced less scarring, because the coatings increase the diameter.

This suggests that a 30-micron, uncoated fiber is the optimal design for implantable devices in the brain.

“Before this paper, no one really knew the effects of size,” Cima says. “Softer is better, but not if it makes the electrode larger.”

New devices

The question now is whether fibers that are only 30 microns in diameter can be adapted for electrical stimulation, drug delivery, and recording electrical activity in the brain. Cima and his colleagues have had some initial success developing such devices.

“It’s one of those things that at first glance seems impossible. If you have 30-micron glass fibers, that’s slightly thicker than a piece of hair. But it is possible to do,” Cima says.
Such devices could be potentially useful for treating Parkinson’s disease or other neurological disorders. They could also be used to remove fluid from the brain to monitor whether treatments are having the intended effect, or to measure brain activity that might indicate when an epileptic seizure is about to occur.

The research was funded by the National Institutes of Health and MIT’s Institute for Soldier Nanotechnologies.

High-resolution imaging with conventional microscopes

Anne Trafton |

MIT researchers have developed a way to make extremely high-resolution images of tissue samples, at a fraction of the cost of other techniques that offer similar resolution.

The new technique relies on expanding tissue before imaging it with a conventional light microscope. Two years ago, the MIT team showed that it was possible to expand tissue volumes 100-fold, resulting in an image resolution of about 60 nanometers. Now, the researchers have shown that expanding the tissue a second time before imaging can boost the resolution to about 25 nanometers.

This level of resolution allows scientists to see, for example, the proteins that cluster together in complex patterns at brain synapses, helping neurons to communicate with each other. It could also help researchers to map neural circuits, says Ed Boyden, an associate professor of biological engineering and brain and cognitive sciences at MIT.

“We want to be able to trace the wiring of complete brain circuits,” says Boyden, the study’s senior author. “If you could reconstruct a complete brain circuit, maybe you could make a computational model of how it generates complex phenomena like decisions and emotions. Since you can map out the biomolecules that generate electrical pulses within cells and that exchange chemicals between cells, you could potentially model the dynamics of the brain.”

This approach could also be used to image other phenomena such as the interactions between cancer cells and immune cells, to detect pathogens without expensive equipment, and to map the cell types of the body.

Former MIT postdoc Jae-Byum Chang is the first author of the paper, which appears in the April 17 issue of Nature Methods.

Double expansion

To expand tissue samples, the researchers embed them in a dense, evenly generated gel made of polyacrylate, a very absorbent material that’s also used in diapers. Before the gel is formed, the researchers label the cell proteins they want to image, using antibodies that bind to specific targets. These antibodies bear “barcodes” made of DNA, which in turn are attached to cross-linking molecules that bind to the polymers that make up the expandable gel. The researchers then break down the proteins that normally hold the tissue together, allowing the DNA barcodes to expand away from each other as the gel swells.

These enlarged samples can then be labeled with fluorescent probes that bind the DNA barcodes, and imaged with commercially available confocal microscopes, whose resolution is usually limited to hundreds of nanometers.

Using that approach, the researchers were previously able to achieve a resolution of about 60 nanometers. However, “individual biomolecules are much smaller than that, say 5 nanometers or even smaller,” Boyden says. “The original versions of expansion microscopy were useful for many scientific questions but couldn’t equal the performance of the highest-resolution imaging methods such as electron microscopy.”

In their original expansion microscopy study, the researchers found that they could expand the tissue more than 100-fold in volume by reducing the number of cross-linking molecules that hold the polymer in an orderly pattern. However, this made the tissue unstable.

“If you reduce the cross-linker density, the polymers no longer retain their organization during the expansion process,” says Boyden, who is a member of MIT’s Media Lab and McGovern Institute for Brain Research. “You lose the information.”

Instead, in their latest study, the researchers modified their technique so that after the first tissue expansion, they can create a new gel that swells the tissue a second time — an approach they call “iterative expansion.”

Mapping circuits

Using iterative expansion, the researchers were able to image tissues with a resolution of about 25 nanometers, which is similar to that achieved by high-resolution techniques such as stochastic optical reconstruction microscopy (STORM). However, expansion microscopy is much cheaper and simpler to perform because no specialized equipment or chemicals are required, Boyden says. The method is also much faster and thus compatible with large-scale, 3-D imaging.

The resolution of expansion microscopy does not yet match that of scanning electron microscopy (about 5 nanometers) or transmission electron microscopy (about 1 nanometer). However, electron microscopes are very expensive and not widely available, and with those microscopes, it is difficult for researchers to label specific proteins.

In the Nature Methods paper, the MIT team used iterative expansion to image synapses — the connections between neurons that allow them to communicate with each other. In their original expansion microscopy study, the researchers were able to image scaffolding proteins, which help to organize the hundreds of other proteins found in synapses. With the new, enhanced resolution, the researchers were also able to see finer-scale structures, such as the location of neurotransmitter receptors located on the surfaces of the “postsynaptic” cells on the receiving side of the synapse.

“My hope is that we can, in the coming years, really start to map out the organization of these scaffolding and signaling proteins at the synapse,” Boyden says.

Combining expansion microscopy with a new tool called temporal multiplexing should help to achieve that, he believes. Currently, only a limited number of colored probes can be used to image different molecules in a tissue sample. With temporal multiplexing, researchers can label one molecule with a fluorescent probe, take an image, and then wash the probe away. This can then be repeated many times, each time using the same colors to label different molecules.

“By combining iterative expansion with temporal multiplexing, we could in principle have essentially infinite-color, nanoscale-resolution imaging over large 3-D volumes,” Boyden says. “Things are getting really exciting now that these different technologies may soon connect with each other.”

The researchers also hope to achieve a third round of expansion, which they believe could, in principle, enable resolution of about 5 nanometers. However, right now the resolution is limited by the size of the antibodies used to label molecules in the cell. These antibodies are about 10 to 20 nanometers long, so to get resolution below that, researchers would need to create smaller tags or expand the proteins away from each other first and then deliver the antibodies after expansion.

This study was funded by the National Institutes of Health Director’s Pioneer Award, the New York Stem Cell Foundation Robertson Award, the HHMI-Simons Faculty Scholars Award, and the Open Philanthropy Project.

Precise technique tracks dopamine in the brain

Anne Trafton |

MIT researchers have devised a way to measure dopamine in the brain much more precisely than previously possible, which should allow scientists to gain insight into dopamine’s roles in learning, memory, and emotion.

Dopamine is one of the many neurotransmitters that neurons in the brain use to communicate with each other. Previous systems for measuring these neurotransmitters have been limited in how long they provide accurate readings and how much of the brain they can cover. The new MIT device, an array of tiny carbon electrodes, overcomes both of those obstacles.

“Nobody has really measured neurotransmitter behavior at this spatial scale and timescale. Having a tool like this will allow us to explore potentially any neurotransmitter-related disease,” says Michael Cima, the David H. Koch Professor of Engineering in the Department of Materials Science and Engineering, a member of MIT’s Koch Institute for Integrative Cancer Research, and the senior author of the study.

Furthermore, because the array is so tiny, it has the potential to eventually be adapted for use in humans, to monitor whether therapies aimed at boosting dopamine levels are succeeding. Many human brain disorders, most notably Parkinson’s disease, are linked to dysregulation of dopamine.

“Right now deep brain stimulation is being used to treat Parkinson’s disease, and we assume that that stimulation is somehow resupplying the brain with dopamine, but no one’s really measured that,” says Helen Schwerdt, a Koch Institute postdoc and the lead author of the paper, which appears in the journal Lab on a Chip.

Studying the striatum

For this project, Cima’s lab teamed up with David H. Koch Institute Professor Robert Langer, who has a long history of drug delivery research, and Institute Professor Ann Graybiel, who has been studying dopamine’s role in the brain for decades with a particular focus on a brain region called the striatum. Dopamine-producing cells within the striatum are critical for habit formation and reward-reinforced learning.

Until now, neuroscientists have used carbon electrodes with a shaft diameter of about 100 microns to measure dopamine in the brain. However, these can only be used reliably for about a day because they produce scar tissue that interferes with the electrodes’ ability to interact with dopamine, and other types of interfering films can also form on the electrode surface over time. Furthermore, there is only about a 50 percent chance that a single electrode will end up in a spot where there is any measurable dopamine, Schwerdt says.

The MIT team designed electrodes that are only 10 microns in diameter and combined them into arrays of eight electrodes. These delicate electrodes are then wrapped in a rigid polymer called PEG, which protects them and keeps them from deflecting as they enter the brain tissue. However, the PEG is dissolved during the insertion so it does not enter the brain.

These tiny electrodes measure dopamine in the same way that the larger versions do. The researchers apply an oscillating voltage through the electrodes, and when the voltage is at a certain point, any dopamine in the vicinity undergoes an electrochemical reaction that produces a measurable electric current. Using this technique, dopamine’s presence can be monitored at millisecond timescales.

Using these arrays, the researchers demonstrated that they could monitor dopamine levels in many parts of the striatum at once.

“What motivated us to pursue this high-density array was the fact that now we have a better chance to measure dopamine in the striatum, because now we have eight or 16 probes in the striatum, rather than just one,” Schwerdt says.

The researchers found that dopamine levels vary greatly across the striatum. This was not surprising, because they did not expect the entire region to be continuously bathed in dopamine, but this variation has been difficult to demonstrate because previous methods measured only one area at a time.

How learning happens

The researchers are now conducting tests to see how long these electrodes can continue giving a measurable signal, and so far the device has kept working for up to two months. With this kind of long-term sensing, scientists should be able to track dopamine changes over long periods of time, as habits are formed or new skills are learned.

“We and other people have struggled with getting good long-term readings,” says Graybiel, who is a member of MIT’s McGovern Institute for Brain Research. “We need to be able to find out what happens to dopamine in mouse models of brain disorders, for example, or what happens to dopamine when animals learn something.”

She also hopes to learn more about the roles of structures in the striatum known as striosomes. These clusters of cells, discovered by Graybiel many years ago, are distributed throughout the striatum. Recent work from her lab suggests that striosomes are involved in making decisions that induce anxiety.

This study is part of a larger collaboration between Cima’s and Graybiel’s labs that also includes efforts to develop injectable drug-delivery devices to treat brain disorders.

“What links all these studies together is we’re trying to find a way to chemically interface with the brain,” Schwerdt says. “If we can communicate chemically with the brain, it makes our treatment or our measurement a lot more focused and selective, and we can better understand what’s going on.”

Other authors of the paper are McGovern Institute research scientists Minjung Kim, Satoko Amemori, and Hideki Shimazu; McGovern Institute postdoc Daigo Homma; McGovern Institute technical associate Tomoko Yoshida; and undergraduates Harshita Yerramreddy and Ekin Karasan.

The research was funded by the National Institutes of Health, the National Institute of Biomedical Imaging and Bioengineering, and the National Institute of Neurological Disorders and Stroke.

Sensor traces dopamine released by single cells

Anne Trafton |

MIT chemical engineers have developed an extremely sensitive detector that can track single cells’ secretion of dopamine, a brain chemical responsible for carrying messages involved in reward-motivated behavior, learning, and memory.

Using arrays of up to 20,000 tiny sensors, the researchers can monitor dopamine secretion of single neurons, allowing them to explore critical questions about dopamine dynamics. Until now, that has been very difficult to do.

“Now, in real-time, and with good spatial resolution, we can see exactly where dopamine is being released,” says Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering and the senior author of a paper describing the research, which appears in the Proceedings of the National Academy of Sciences the week of Feb. 6.

Strano and his colleagues have already demonstrated that dopamine release occurs differently than scientists expected in a type of neural progenitor cell, helping to shed light on how dopamine may exert its effects in the brain.

The paper’s lead author is Sebastian Kruss, a former MIT postdoc who is now at Göttingen University, in Germany. Other authors are Daniel Salem and Barbara Lima, both MIT graduate students; Edward Boyden, an associate professor of biological engineering and brain and cognitive sciences, as well as a member of the MIT Media Lab and the McGovern Institute for Brain Research; Lela Vukovic, an assistant professor of chemistry at the University of Texas at El Paso; and Emma Vander Ende, a graduate student at Northwestern University.

“A global effect”

Dopamine is a neurotransmitter that plays important roles in learning, memory, and feelings of reward, which reinforce positive experiences.

Neurotransmitters allow neurons to relay messages to nearby neurons through connections known as synapses. However, unlike most other neurotransmitters, dopamine can exert its effects beyond the synapse: Not all dopamine released into a synapse is taken up by the target cell, allowing some of the chemical to diffuse away and affect other nearby cells.

“It has a local effect, which controls the signaling through the neurons, but also it has a global effect,” Strano says. “If dopamine is in the region, it influences all the neurons nearby.”

Tracking this dopamine diffusion in the brain has proven difficult. Neuroscientists have tried using electrodes that are specialized to detect dopamine, but even using the smallest electrodes available, they can place only about 20 near any given cell.

“We’re at the infancy of really understanding how these packets of chemicals move and their directionality,” says Strano, who decided to take a different approach.

Strano’s lab has previously developed sensors made from arrays of carbon nanotubes — hollow, nanometer-thick cylinders made of carbon, which naturally fluoresce when exposed to laser light. By wrapping these tubes in different proteins or DNA strands, scientists can customize them to bind to different types of molecules.

The carbon nanotube sensors used in this study are coated with a DNA sequence that makes the sensors interact with dopamine. When dopamine binds to the carbon nanotubes, they fluoresce more brightly, allowing the researchers to see exactly where the dopamine was released. The researchers deposited more than 20,000 of these nanotubes on a glass slide, creating an array that detects any dopamine secreted by a cell placed on the slide.

Dopamine diffusion

In the new PNAS study, the researchers used these dopamine sensors to explore a longstanding question about dopamine release in the brain: From which part of the cell is dopamine secreted?

To help answer that question, the researchers placed individual neural progenitor cells known as PC-12 cells onto the sensor arrays. PC-12 cells, which develop into neuron-like cells under the right conditions, have a starfish-like shape with several protrusions that resemble axons, which form synapses with other cells.

After stimulating the cells to release dopamine, the researchers found that certain dopamine sensors near the cells lit up immediately, while those farther away turned on later as the dopamine diffused away. Tracking those patterns over many seconds allowed the researchers to trace how dopamine spreads away from the cells.

Strano says one might expect to see that most of the dopamine would be released from the tips of the arms extending out from the cells. However, the researchers found that in fact more dopamine came from the sides of the arms.

“We have falsified the notion that dopamine should only be released at these regions that will eventually become the synapses,” Strano says. “This observation is counterintuitive, and it’s a new piece of information you can only obtain with a nanosensor array like this one.”

The team also showed that most of the dopamine traveled away from the cell, through protrusions extending in opposite directions. “Even though dopamine is not necessarily being released only at the tip of these protrusions, the direction of release is associated with them,” Salem says.

Other questions that could be explored using these sensors include how dopamine release is affected by the direction of input to the cell, and how the presence of nearby cells influences each cell’s dopamine release.

The research was funded by the National Science Foundation, the National Institutes of Health, a University of Illinois Center for the Physics of Living Cells Postdoctoral Fellowship, the German Research Foundation, and a Liebig Fellowship.

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.”

Neuroscientists get a glimpse into the workings of the baby brain

Anne Trafton |

In adults, certain regions of the brain’s visual cortex respond preferentially to specific types of input, such as faces or objects — but how and when those preferences arise has long puzzled neuroscientists.

One way to help answer that question is to study the brains of very young infants and compare them to adult brains. However, scanning the brains of awake babies in an MRI machine has proven difficult.

Now, neuroscientists at MIT have overcome that obstacle, adapting their MRI scanner to make it easier to scan infants’ brains as the babies watch movies featuring different types of visual input. Using these data, the team found that in some ways, the organization of infants’ brains is surprisingly similar to that of adults. Specifically, brain regions that respond to faces in adults do the same in babies, as do regions that respond to scenes.

“It suggests that there’s a stronger biological predisposition than I would have guessed for specific cortical regions to end up with specific functions,” says Rebecca Saxe, a professor of brain and cognitive sciences and member of MIT’s McGovern Institute for Brain Research.

Saxe is the senior author of the study, which appears in the Jan. 10 issue of Nature Communications. The paper’s lead author is former MIT graduate student Ben Deen, who is now a postdoc at Rockefeller University.

MRI adaptations

Functional MRI (magnetic resonance imaging) is the go-to technique for studying brain function in adults. However, very few researchers have taken on the challenge of trying to scan babies’ brains, especially while they are awake.

“Babies and MRI machines have very different needs,” Saxe points out. “Babies would like to do activities for two or three minutes and then move on. They would like to be sitting in a comfortable position, and in charge of what they’re looking at.”

On the other hand, “MRI machines would like to be loud and dark and have a person show up on schedule, stay still for the entire time, pay attention to one thing for two hours, and follow instructions closely,” she says.

To make the setup more comfortable for babies, the researchers made several modifications to the MRI machine and to their usual experimental protocols. First, they built a special coil (part of the MRI scanner that acts as a radio antenna) that allows the baby to recline in a seat similar to a car seat. A mirror in front of the baby’s face allows him or her to watch videos, and there is space in the machine for a parent or one of the researchers to sit with the baby.

The researchers also made the scanner much less noisy than a typical MRI machine. “It’s quieter than a loud restaurant,” Saxe says. “The baby can hear their parent talking over the sound of the scanner.”

Once the babies, who were 4 to 6 months old, were in the scanner, the researchers played the movies continuously while scanning the babies’ brains. However, they only used data from the time periods when the babies were actively watching the movies. From 26 hours of scanning 17 babies, the researchers obtained four hours of usable data from nine babies.

“The sheer tenacity of this work is truly amazing,” says Charles Nelson, a professor of pediatrics at Boston Children’s Hospital, who was not involved in the research. “The fact that they pulled this off is incredibly novel.”

Obtaining this data allowed the MIT team to study how infants’ brains respond to specific types of sensory input, and to compare their responses with those of adults.

“The big-picture question is, how does the adult brain come to have the structure and function that you see in adulthood? How does it get like that?” Saxe says. “A lot of the answer to that question will depend on having the tools to be able to see the baby brain in action. The more we can see, the more we can ask that kind of question.”

Distinct preferences

The researchers showed the babies videos of either smiling children or outdoor scenes such as a suburban street seen from a moving car. Distinguishing social scenes from the physical environment is one of the main high-level divisions that our brains make when interpreting the world.

“The questions we’re asking are about how you understand and organize your world, with vision as the main modality for getting you into these very different mindsets,” Saxe says. “In adults, there are brain regions that prefer to look at faces and socially relevant things, and brain regions that prefer to look at environments and objects.”

The scans revealed that many regions of the babies’ visual cortex showed the same preferences for scenes or faces seen in adult brains. This suggests that these preferences form within the first few months of life and refutes the hypothesis that it takes years of experience interpreting the world for the brain to develop the responses that it shows in adulthood.

The researchers also found some differences in the way that babies’ brains respond to visual stimuli. One is that they do not seem to have regions found in the adult brain that are “highly selective,” meaning these regions prefer features such as human faces over any other kind of input, including human bodies or the faces of other animals. The babies also showed some differences in their responses when shown examples from four different categories — not just faces and scenes but also bodies and objects.

“We believe that the adult-like organization of infant visual cortex provides a scaffolding that guides the subsequent refinement of responses via experience, ultimately leading to the strongly specialized regions observed in adults,” Deen says.

Saxe and colleagues now hope to try to scan more babies between the ages of 3 and 8 months so they can get a better idea of how these vision-processing regions change over the first several months of life. They also hope to study even younger babies to help them discover when these distinctive brain responses first appear.

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.