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Edward Boyden wins BBVA Foundation Frontiers of Knowledge Award

Anne Trafton |

Edward S. Boyden, a professor of media arts and sciences, biological engineering, and brain and cognitive sciences at MIT, has won the BBVA Foundation Frontiers of Knowledge Award in Biomedicine for his role in the development of optogenetics, a technique for controlling brain activity with light. Gero Miesenböck of the University of Oxford and Karl Deisseroth of Stanford University were also honored with the prize for their role in developing and refining the technique.

The BBVA Foundation Frontiers of Knowledge Awards are given annually for “outstanding contributions and radical advances in a broad range of scientific, technological and artistic areas.” The €400.000 prize in the category of biomedicine will be shared among the three neuroscientists.

“If we imagine the brain as a computer, optogenetics is a keyboard that allows us to send extremely precise commands,” says Boyden, a a faculty member at the MIT Media Lab with a joint appointment at MIT’s McGovern Institute for Brain Research. “It is a tool whereby we can control the brain with exquisite precision.”

Boyden joins an illustrious list of prize laureates including physicist Stephen Hawking and artificial intelligence pioneer Marvin Minsky of MIT, who died on January 24.

The BBVA Foundation will host the winners at an awards ceremony on June 21, 2016 at the foundation’s headquarters in Madrid, Spain.

About the BBVA Foundation Frontiers of Knowledge Awards

The BBVA Foundation promotes, funds and disseminates world-class scientific research and artistic creation, in the conviction that science, culture and knowledge hold the key to better opportunities for all world citizens. The Foundation designs and implements its programs in partnership with some of the leading scientific and cultural organizations in Spain and abroad, striving to identify and prioritize those projects with the power to significantly advance the frontiers of the known world.

The juries in each of eight categories are made up of leading international experts in their respective fields, who arrive at their decisions in a wholly independent manner, applying internationally recognized metrics of excellence. The BBVA Foundation is aided in the organization of the awards by the Spanish National Research Council (CSIC).

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.

Stanley Center & Poitras Center Translational Neuroscience Joint Seminar: Deanna M. Barch

Anne Trafton |

“Connectomics and Psychopathology: A Tale of Many Regions”

Abstract: A growing body of research clearly indicates that both functional and structural connectivity within and between core brain systems is a critical determinant of cognitive and affective function in both health and disease. This talk will first briefly overview the state of the art methods for assessing human brain connectivity. Next this talk will illustrate the ways in which variation in brain connectivity relates to variation in cognitive and affective functions in healthy individuals, as well as how impairments in functional brain connectivity relate to impaired cognitive and affective function associated with risk for psychopathology and manifest illness.

Special Seminar: Gordon Fishell, PhD

Anne Trafton |

Gordon Fishell, PhD
Julius Raynes Professor of Neuroscience, Associate Director of the NYU Neuroscience Institute

My laboratory is interested in the developmental steps that allow the startling repertoire of interneurons to develop and integrate into the developing nervous system. This process begins with their specification during which genetic programs initiated within progenitors relegate interneurons into specific cardinal classes. Subsequent to this neuronal activity is fundamental for both the laminar positioning, as well as the dendritic and axonal arborization of at least some interneuron subtypes. These results suggest that sensory information complements earlier established genetic programs to shape the way interneuronal subtypes integrate into nascent cortical circuits. The challenge moving forward is to understand how genetically similar interneuron subtypes are able to incorporate into diverse circuitries as different as the inhibitory circuitry that makes up the basal ganglia versus the excitatory circuits that make up the cerebral cortex.

Study finds altered brain chemistry in people with autism

Anne Trafton |

MIT and Harvard University neuroscientists have found a link between a behavioral symptom of autism and reduced activity of a neurotransmitter whose job is to dampen neuron excitation. The findings suggest that drugs that boost the action of this neurotransmitter, known as GABA, may improve some of the symptoms of autism, the researchers say.

Brain activity is controlled by a constant interplay of inhibition and excitation, which is mediated by different neurotransmitters. GABA is one of the most important inhibitory neurotransmitters, and studies of animals with autism-like symptoms have found reduced GABA activity in the brain. However, until now, there has been no direct evidence for such a link in humans.

“This is the first connection in humans between a neurotransmitter in the brain and an autistic behavioral symptom,” says Caroline Robertson, a postdoc at MIT’s McGovern Institute for Brain Research and a junior fellow of the Harvard Society of Fellows. “It’s possible that increasing GABA would help to ameliorate some of the symptoms of autism, but more work needs to be done.”

Robertson is the lead author of the study, which appears in the Dec. 17 online edition of Current Biology. The paper’s senior author is Nancy Kanwisher, the Walter A. Rosenblith Professor of Brain and Cognitive Sciences and a member of the McGovern Institute. Eva-Maria Ratai, an assistant professor of radiology at Massachusetts General Hospital, also contributed to the research.

Too little inhibition

Many symptoms of autism arise from hypersensitivity to sensory input. For example, children with autism are often very sensitive to things that wouldn’t bother other children as much, such as someone talking elsewhere in the room, or a scratchy sweater. Scientists have speculated that reduced brain inhibition might underlie this hypersensitivity by making it harder to tune out distracting sensations.

In this study, the researchers explored a visual task known as binocular rivalry, which requires brain inhibition and has been shown to be more difficult for people with autism. During the task, researchers show each participant two different images, one to each eye. To see the images, the brain must switch back and forth between input from the right and left eyes.

For the participant, it looks as though the two images are fading in and out, as input from each eye takes its turn inhibiting the input coming in from the other eye.

“Everybody has a different rate at which the brain naturally oscillates between these two images, and that rate is thought to map onto the strength of the inhibitory circuitry between these two populations of cells,” Robertson says.

She found that nonautistic adults switched back and forth between the images nine times per minute, on average, and one of the images fully suppressed the other about 70 percent of the time. However, autistic adults switched back and forth only half as often as nonautistic subjects, and one of the images fully suppressed the other only about 50 percent of the time.

Performance on this task was also linked to patients’ scores on a clinical evaluation of communication and social interaction used to diagnose autism: Worse symptoms correlated with weaker inhibition during the visual task.

The researchers then measured GABA activity using a technique known as magnetic resonance spectroscopy, as autistic and typical subjects performed the binocular rivalry task. In nonautistic participants, higher levels of GABA correlated with a better ability to suppress the nondominant image. But in autistic subjects, there was no relationship between performance and GABA levels. This suggests that GABA is present in the brain but is not performing its usual function in autistic individuals, Robertson says.

“GABA is not reduced in the autistic brain, but the action of this inhibitory pathway is reduced,” she says. “The next step is figuring out which part of the pathway is disrupted.”

“This is a really great piece of work,” says Richard Edden, an associate professor of radiology at the Johns Hopkins University School of Medicine. “The role of inhibitory dysfunction in autism is strongly debated, with different camps arguing for elevated and reduced inhibition. This kind of study, which seeks to relate measures of inhibition directly to quantitative measures of function, is what we really to need to tease things out.”

Early diagnosis

In addition to offering a possible new drug target, the new finding may also help researchers develop better diagnostic tools for autism, which is now diagnosed by evaluating children’s social interactions. To that end, Robertson is investigating the possibility of using EEG scans to measure brain responses during the binocular rivalry task.

“If autism does trace back on some level to circuitry differences that affect the visual cortex, you can measure those things in a kid who’s even nonverbal, as long as he can see,” she says. “We’d like it to move toward being useful for early diagnostic screenings.”

Music in the brain

Anne Trafton |

Scientists have long wondered if the human brain contains neural mechanisms specific to music perception. Now, for the first time, MIT neuroscientists have identified a neural population in the human auditory cortex that responds selectively to sounds that people typically categorize as music, but not to speech or other environmental sounds.

Music in the brain

Anne Trafton |

Scientists have long wondered if the human brain contains neural mechanisms specific to music perception. Now, for the first time, MIT neuroscientists have identified a neural population in the human auditory cortex that responds selectively to sounds that people typically categorize as music, but not to speech or other environmental sounds.

“It has been the subject of widespread speculation,” says Josh McDermott, the Frederick A. and Carole J. Middleton Assistant Professor of Neuroscience in the Department of Brain and Cognitive Sciences at MIT. “One of the core debates surrounding music is to what extent it has dedicated mechanisms in the brain and to what extent it piggybacks off of mechanisms that primarily serve other functions.”

The finding was enabled by a new method designed to identify neural populations from functional magnetic resonance imaging (fMRI) data. Using this method, the researchers identified six neural populations with different functions, including the music-selective population and another set of neurons that responds selectively to speech.

“The music result is notable because people had not been able to clearly see highly selective responses to music before,” says Sam Norman-Haignere, a postdoc at MIT’s McGovern Institute for Brain Research.

“Our findings are hard to reconcile with the idea that music piggybacks entirely on neural machinery that is optimized for other functions, because the neural responses we see are highly specific to music,” says Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience at MIT and a member of MIT’s McGovern Institute for Brain Research.

Norman-Haignere is the lead author of a paper describing the findings in the Dec. 16 online edition of Neuron. McDermott and Kanwisher are the paper’s senior authors.

Mapping responses to sound

For this study, the researchers scanned the brains of 10 human subjects listening to 165 natural sounds, including different types of speech and music, as well as everyday sounds such as footsteps, a car engine starting, and a telephone ringing.

The brain’s auditory system has proven difficult to map, in part because of the coarse spatial resolution of fMRI, which measures blood flow as an index of neural activity. In fMRI, “voxels” — the smallest unit of measurement — reflect the response of hundreds of thousands or millions of neurons.

“As a result, when you measure raw voxel responses you’re measuring something that reflects a mixture of underlying neural responses,” Norman-Haignere says.

To tease apart these responses, the researchers used a technique that models each voxel as a mixture of multiple underlying neural responses. Using this method, they identified six neural populations, each with a unique response pattern to the sounds in the experiment, that best explained the data.

“What we found is we could explain a lot of the response variation across tens of thousands of voxels with just six response patterns,” Norman-Haignere says.

One population responded most to music, another to speech, and the other four to different acoustic properties such as pitch and frequency.

The key to this advance is the researchers’ new approach to analyzing fMRI data, says Josef Rauschecker, a professor of physiology and biophysics at Georgetown University.

“The whole field is interested in finding specialized areas like those that have been found in the visual cortex, but the problem is the voxel is just not small enough. You have hundreds of thousands of neurons in a voxel, and how do you separate the information they’re encoding? This is a study of the highest caliber of data analysis,” says Rauschecker, who was not part of the research team.

Layers of sound processing

The four acoustically responsive neural populations overlap with regions of “primary” auditory cortex, which performs the first stage of cortical processing of sound. Speech and music-selective neural populations lie beyond this primary region.

“We think this provides evidence that there’s a hierarchy of processing where there are responses to relatively simple acoustic dimensions in this primary auditory area. That’s followed by a second stage of processing that represents more abstract properties of sound related to speech and music,” Norman-Haignere says.

The researchers believe there may be other brain regions involved in processing music, including its emotional components. “It’s inappropriate at this point to conclude that this is the seat of music in the brain,” McDermott says. “This is where you see most of the responses within the auditory cortex, but there’s a lot of the brain that we didn’t even look at.”

Kanwisher also notes that “the existence of music-selective responses in the brain does not imply that the responses reflect an innate brain system. An important question for the future will be how this system arises in development: How early it is found in infancy or childhood, and how dependent it is on experience?”

The researchers are now investigating whether the music-selective population identified in this study contains subpopulations of neurons that respond to different aspects of music, including rhythm, melody, and beat. They also hope to study how musical experience and training might affect this neural population.

 

Special Seminar: Matthew State, PhD

Anne Trafton |

Recent advances in high throughput genomic technologies, coupled with large patient cohorts and and an evolving culture of rapid data sharing have led to remarkable advances in the understanding of the genetics of autism spectrum disorders. To date, the lion’s share of this progress has been with regard to the contribution of rare and de novo mutations, both in DNA sequence and chromosomal structure. The ability now to reliably and systematically identify ASD risk genes and loci provides important initial insights into both the opportunities as well as the challenges the field now faces in moving from gene discovery to an actionable understanding of pathophysiological mechanisms underlying these complex common neurodevelopmental syndromes. The lecture will provide an overview of what is now known about the genomic architecture and specific risk mutations associated with ASD, address the particular challenges posed by the discovery of mutations that have large biological effect but low population allele frequency, and consider the role that whole genome sequencing will play in the near future in enhancing the understanding of the developmental aspects of ASD risk.

How a single gene contributes to autism and schizophrenia

Anne Trafton |

Although it is known that psychiatric disorders have a strong genetic component, untangling the web of genes contributing to each disease is a daunting task. Scientists have found hundreds of genes that are mutated in patients with disorders such as autism, but each patient usually has only a handful of these variations.

To further complicate matters, some of these genes contribute to more than one disorder. One such gene, known as Shank3, has been linked to both autism and schizophrenia.

MIT neuroscientists have now shed some light on how a single gene can play a role in more than one disease. In a study appearing in the Dec. 10 online edition of Neuron, they revealed that two different mutations of the Shank3 gene produce some distinct molecular and behavioral effects in mice.

“This study gives a glimpse into the mechanism by which different mutations within the same gene can cause distinct defects in the brain, and may help to explain how they may contribute to different disorders,” says Guoping Feng, the James W. and Patricia Poitras Professor of Neuroscience at MIT, a member of MIT’s McGovern Institute for Brain Research, a member of the Stanley Center for Psychiatric Research at the Broad Institute, and the senior author of the study.

The findings also suggest that identifying the brain circuits affected by mutated genes linked to psychiatric disease could help scientists develop more personalized treatments for patients in the future, Feng says.

The paper’s lead authors are McGovern Institute research scientist Yang Zhou, graduate students Tobias Kaiser and Xiangyu Zhang, and research affiliate Patricia Monteiro.

Disrupted communication

The protein encoded by Shank3 is found in synapses — the junctions between neurons that allow them to communicate with each other. Shank3 is a scaffold protein, meaning it helps to organize hundreds of other proteins clustered on the postsynaptic cell membrane, which are required to coordinate the cell’s response to signals from the presynaptic cell.

In 2011, Feng and colleagues showed that by deleting Shank3 in mice they could induce two of the most common traits of autism — avoidance of social interaction, and compulsive, repetitive behavior. A year earlier, researchers at the University of Montreal identified a Shank3 mutation in patients suffering from schizophrenia, which is characterized by hallucinations, cognitive impairment, and abnormal social behavior.

Feng wanted to find out how these two different mutations in the Shank3 gene could play a role in such different disorders. To do that, he and his colleagues engineered mice with each of the two mutations: The schizophrenia-related mutation results in a truncated version of the Shank3 protein, while the autism-linked mutation leads to a total loss of the Shank3 protein.

Behaviorally, the mice shared many defects, including strong anxiety. However, the mice with the autism mutation had very strong compulsive behavior, manifested by excessive grooming, which was rarely seen in mice with the schizophrenia mutation.

In the mice with the schizophrenia mutation, the researchers saw a type of behavior known as social dominance. These mice trimmed the whiskers and facial hair of the genetically normal mice sharing their cages, to an extreme extent. This is a typical way for mice to display their social dominance, Feng says.

By activating the mutations in different parts of the brain and at different stages of development, the researchers found that the two mutations affected brain circuits in different ways. The autism mutation exerted its effects early in development, primarily in a part of the brain known as the striatum, which is involved in coordinating motor planning, motivation, and habitual behavior. Feng believes that disruption of synapses in the striatum contributes to the compulsive behavior seen in those mice.

In mice carrying the schizophrenia-associated mutation, early development was normal, suggesting that truncated Shank3 can adequately fill in for the normal version during this stage. However, later in life, the truncated version of Shank3 interfered with synaptic functions and connections in the brain’s cortex, where executive functions such as thought and planning occur. This suggests that different segments of the protein — including the stretch that is missing in the schizophrenia-linked mutation — may be crucial for different roles, Feng says.

The new paper represents an important first step in understanding how different mutations in the same gene can lead to different diseases, says Joshua Gordon, an associate professor of psychiatry at Columbia University.

“The key is to identify how the different mutations alter brain function in different ways, as done here,” says Gordon, who was not involved in the research. “Autism strikes early in childhood, while schizophrenia typically arises in adolescence or early adulthood. The finding that the autism-associated mutation has effects at a younger age than the schizophrenia-associated mutation is particularly intriguing in this context.”

Modeling disease

Although only a small percentage of autism patients have mutations in Shank3, many other variant synaptic proteins have been associated with the disorder. Future studies should help to reveal more about the role of the many genes and mutations that contribute to autism and other disorders, Feng says. Shank3 alone has at least 40 identified mutations, he says.

“We cannot consider them all to be the same,” he says. “To really model these diseases, precisely mimicking each human mutation is critical.”

Understanding exactly how these mutations influence brain circuits should help researchers develop drugs that target those circuits and match them with the patients who would benefit most, Feng says, adding that a tremendous amount of work needs to be done to get to that point.

His lab is now investigating what happens in the earliest stages of the development of mice with the autism-related Shank3 mutation, and whether any of those effects can be reversed either during development or later in life.

The research was funded by the Simons Center for the Social Brain at MIT, the Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard, the Poitras Center for Affective Disorders Research at MIT, and National Institute of Mental Health.