Brain Disorder: Schizophrenia

Brain activity pattern may be early sign of schizophrenia

Anne Trafton |

Schizophrenia, a brain disorder that produces hallucinations, delusions, and cognitive impairments, usually strikes during adolescence or young adulthood. While some signs can suggest that a person is at high risk for developing the disorder, there is no way to definitively diagnose it until the first psychotic episode occurs.

MIT neuroscientists working with researchers at Beth Israel Deaconess Medical Center, Brigham and Women’s Hospital, and the Shanghai Mental Health Center have now identified a pattern of brain activity correlated with development of schizophrenia, which they say could be used as a marker to diagnose the disease earlier.

“You can consider this pattern to be a risk factor. If we use these types of brain measurements, then maybe we can predict a little bit better who will end up developing psychosis, and that may also help tailor interventions,” says Guusje Collin, a visiting scientist at MIT’s McGovern Institute for Brain Research and the lead author of the paper.

The study, which appeared in the journal Molecular Psychiatry on Nov. 8, was performed at the Shanghai Mental Health Center. Susan Whitfield-Gabrieli, a visiting scientist at the McGovern Institute and a professor of psychology at Northeastern University, is one of the principal investigators for the study, along with Jijun Wang of the Shanghai Mental Health Center, William Stone of Beth Israel Deaconess Medical Center, the late Larry Seidman of Beth Israel Deaconess Medical Center, and Martha Shenton of Brigham and Women’s Hospital.

Abnormal connections

Before they experience a psychotic episode, characterized by sudden changes in behavior and a loss of touch with reality, patients can experience milder symptoms such as disordered thinking. This kind of thinking can lead to behaviors such as jumping from topic to topic at random, or giving answers unrelated to the original question. Previous studies have shown that about 25 percent of people who experience these early symptoms go on to develop schizophrenia.

The research team performed the study at the Shanghai Mental Health Center because the huge volume of patients who visit the hospital annually gave them a large enough sample of people at high risk of developing schizophrenia.

The researchers followed 158 people between the ages of 13 and 34 who were identified as high-risk because they had experienced early symptoms. The team also included 93 control subjects, who did not have any risk factors. At the beginning of the study, the researchers used functional magnetic resonance imaging (fMRI) to measure a type of brain activity involving “resting state networks.” Resting state networks consist of brain regions that preferentially connect with and communicate with each other when the brain is not performing any particular cognitive task.

“We were interested in looking at the intrinsic functional architecture of the brain to see if we could detect early aberrant brain connectivity or networks in individuals who are in the clinically high-risk phase of the disorder,” Whitfield-Gabrieli says.

One year after the initial scans, 23 of the high-risk patients had experienced a psychotic episode and were diagnosed with schizophrenia. In those patients’ scans, taken before their diagnosis, the researchers found a distinctive pattern of activity that was different from the healthy control subjects and the at-risk subjects who had not developed psychosis.

For example, in most people, a part of the brain known as the superior temporal gyrus, which is involved in auditory processing, is highly connected to brain regions involved in sensory perception and motor control. However, in patients who developed psychosis, the superior temporal gyrus became more connected to limbic regions, which are involved in processing emotions. This could help explain why patients with schizophrenia usually experience auditory hallucinations, the researchers say.

Meanwhile, the high-risk subjects who did not develop psychosis showed network connectivity nearly identical to that of the healthy subjects.

Early intervention

This type of distinctive brain activity could be useful as an early indicator of schizophrenia, especially since it is possible that it could be seen in even younger patients. The researchers are now performing similar studies with younger at-risk populations, including children with a family history of schizophrenia.

“That really gets at the heart of how we can translate this clinically, because we can get in earlier and earlier to identify aberrant networks in the hopes that we can do earlier interventions, and possibly even prevent psychiatric disorders,” Whitfield-Gabrieli says.

She and her colleagues are now testing early interventions that could help to combat the symptoms of schizophrenia, including cognitive behavioral therapy and neural feedback. The neural feedback approach involves training patients to use mindfulness meditation to reduce activity in the superior temporal gyrus, which tends to increase before and during auditory hallucinations.

The researchers also plan to continue following the patients in the current study, and they are now analyzing some additional data on the white matter connections in the brains of these patients, to see if those connections might yield additional differences that could also serve as early indicators of disease.

The research was funded by the National Institutes of Health, the Ministry of Science and Technology of China, and the Poitras Center for Psychiatric Disorders Research at MIT. Collin was supported by a Marie Curie Global Fellowship grant from the European Commission.

New sensors track dopamine in the brain for more than a year

Anne Trafton |

Dopamine, a signaling molecule used throughout the brain, plays a major role in regulating our mood, as well as controlling movement. Many disorders, including Parkinson’s disease, depression, and schizophrenia, are linked to dopamine deficiencies.

MIT neuroscientists have now devised a way to measure dopamine in the brain for more than a year, which they believe will help them to learn much more about its role in both healthy and diseased brains.

“Despite all that is known about dopamine as a crucial signaling molecule in the brain, implicated in neurologic and neuropsychiatric conditions as well as our ability to learn, it has been impossible to monitor changes in the online release of dopamine over time periods long enough to relate these to clinical conditions,” says Ann Graybiel, an MIT Institute Professor, a member of MIT’s McGovern Institute for Brain Research, and one of the senior authors of the study.

Michael Cima, the David H. Koch Professor of Engineering in the Department of Materials Science and Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research, and Rober Langer, the David H. Koch Institute Professor and a member of the Koch Institute, are also senior authors of the study. MIT postdoc Helen Schwerdt is the lead author of the paper, which appears in the Sept. 12 issue of Communications Biology.

Long-term sensing

Dopamine is one of many neurotransmitters that neurons in the brain use to communicate with each other. Traditional systems for measuring dopamine — carbon electrodes with a shaft diameter of about 100 microns — can only be used reliably for about a day because they produce scar tissue that interferes with the electrodes’ ability to interact with dopamine.

In 2015, the MIT team demonstrated that tiny microfabricated sensors could be used to measure dopamine levels in a part of the brain called the striatum, which contains dopamine-producing cells that are critical for habit formation and reward-reinforced learning.

Because these probes are so small (about 10 microns in diameter), the researchers could implant up to 16 of them to measure dopamine levels in different parts of the striatum. In the new study, the researchers wanted to test whether they could use these sensors for long-term dopamine tracking.

“Our fundamental goal from the very beginning was to make the sensors work over a long period of time and produce accurate readings from day to day,” Schwerdt says. “This is necessary if you want to understand how these signals mediate specific diseases or conditions.”

To develop a sensor that can be accurate over long periods of time, the researchers had to make sure that it would not provoke an immune reaction, to avoid the scar tissue that interferes with the accuracy of the readings.

The MIT team found that their tiny sensors were nearly invisible to the immune system, even over extended periods of time. After the sensors were implanted, populations of microglia (immune cells that respond to short-term damage), and astrocytes, which respond over longer periods, were the same as those in brain tissue that did not have the probes inserted.

In this study, the researchers implanted three to five sensors per animal, about 5 millimeters deep, in the striatum. They took readings every few weeks, after stimulating dopamine release from the brainstem, which travels to the striatum. They found that the measurements remained consistent for up to 393 days.

“This is the first time that anyone’s shown that these sensors work for more than a few months. That gives us a lot of confidence that these kinds of sensors might be feasible for human use someday,” Schwerdt says.

Paul Glimcher, a professor of physiology and neuroscience at New York University, says the new sensors should enable more researchers to perform long-term studies of dopamine, which is essential for studying phenomena such as learning, which occurs over long time periods.

“This is a really solid engineering accomplishment that moves the field forward,” says Glimcher, who was not involved in the research. “This dramatically improves the technology in a way that makes it accessible to a lot of labs.”

Monitoring Parkinson’s

If developed for use in humans, these sensors could be useful for monitoring Parkinson’s patients who receive deep brain stimulation, the researchers say. This treatment involves implanting an electrode that delivers electrical impulses to a structure deep within the brain. Using a sensor to monitor dopamine levels could help doctors deliver the stimulation more selectively, only when it is needed.

The researchers are now looking into adapting the sensors to measure other neurotransmitters in the brain, and to measure electrical signals, which can also be disrupted in Parkinson’s and other diseases.

“Understanding those relationships between chemical and electrical activity will be really important to understanding all of the issues that you see in Parkinson’s,” Schwerdt says.

The research was funded by the National Institute of Biomedical Imaging and Bioengineering, the National Institute of Neurological Disorders and Stroke, the Army Research Office, the Saks Kavanaugh Foundation, the Nancy Lurie Marks Family Foundation, and Dr. Tenley Albright.

Robotic system monitors specific neurons

Anne Trafton |

Recording electrical signals from inside a neuron in the living brain can reveal a great deal of information about that neuron’s function and how it coordinates with other cells in the brain. However, performing this kind of recording is extremely difficult, so only a handful of neuroscience labs around the world do it.

To make this technique more widely available, MIT engineers have now devised a way to automate the process, using a computer algorithm that analyzes microscope images and guides a robotic arm to the target cell.

This technology could allow more scientists to study single neurons and learn how they interact with other cells to enable cognition, sensory perception, and other brain functions. Researchers could also use it to learn more about how neural circuits are affected by brain disorders.

“Knowing how neurons communicate is fundamental to basic and clinical neuroscience. Our hope is this technology will allow you to look at what’s happening inside a cell, in terms of neural computation, or in a disease state,” says Ed Boyden, an associate professor of biological engineering and brain and cognitive sciences at MIT, and a member of MIT’s Media Lab and McGovern Institute for Brain Research.

Boyden is the senior author of the paper, which appears in the Aug. 30 issue of Neuron. The paper’s lead author is MIT graduate student Ho-Jun Suk.

Precision guidance

For more than 30 years, neuroscientists have been using a technique known as patch clamping to record the electrical activity of cells. This method, which involves bringing a tiny, hollow glass pipette in contact with the cell membrane of a neuron, then opening up a small pore in the membrane, usually takes a graduate student or postdoc several months to learn. Learning to perform this on neurons in the living mammalian brain is even more difficult.

There are two types of patch clamping: a “blind” (not image-guided) method, which is limited because researchers cannot see where the cells are and can only record from whatever cell the pipette encounters first, and an image-guided version that allows a specific cell to be targeted.

Five years ago, Boyden and colleagues at MIT and Georgia Tech, including co-author Craig Forest, devised a way to automate the blind version of patch clamping. They created a computer algorithm that could guide the pipette to a cell based on measurements of a property called electrical impedance — which reflects how difficult it is for electricity to flow out of the pipette. If there are no cells around, electricity flows and impedance is low. When the tip hits a cell, electricity can’t flow as well and impedance goes up.

Once the pipette detects a cell, it can stop moving instantly, preventing it from poking through the membrane. A vacuum pump then applies suction to form a seal with the cell’s membrane. Then, the electrode can break through the membrane to record the cell’s internal electrical activity.

The researchers achieved very high accuracy using this technique, but it still could not be used to target a specific cell. For most studies, neuroscientists have a particular cell type they would like to learn about, Boyden says.

“It might be a cell that is compromised in autism, or is altered in schizophrenia, or a cell that is active when a memory is stored. That’s the cell that you want to know about,” he says. “You don’t want to patch a thousand cells until you find the one that is interesting.”

To enable this kind of precise targeting, the researchers set out to automate image-guided patch clamping. This technique is difficult to perform manually because, although the scientist can see the target neuron and the pipette through a microscope, he or she must compensate for the fact that nearby cells will move as the pipette enters the brain.

“It’s almost like trying to hit a moving target inside the brain, which is a delicate tissue,” Suk says. “For machines it’s easier because they can keep track of where the cell is, they can automatically move the focus of the microscope, and they can automatically move the pipette.”

By combining several imaging processing techniques, the researchers came up with an algorithm that guides the pipette to within about 25 microns of the target cell. At that point, the system begins to rely on a combination of imagery and impedance, which is more accurate at detecting contact between the pipette and the target cell than either signal alone.

The researchers imaged the cells with two-photon microscopy, a commonly used technique that uses a pulsed laser to send infrared light into the brain, lighting up cells that have been engineered to express a fluorescent protein.

Using this automated approach, the researchers were able to successfully target and record from two types of cells — a class of interneurons, which relay messages between other neurons, and a set of excitatory neurons known as pyramidal cells. They achieved a success rate of about 20 percent, which is comparable to the performance of highly trained scientists performing the process manually.

Unraveling circuits

This technology paves the way for in-depth studies of the behavior of specific neurons, which could shed light on both their normal functions and how they go awry in diseases such as Alzheimer’s or schizophrenia. For example, the interneurons that the researchers studied in this paper have been previously linked with Alzheimer’s. In a recent study of mice, led by Li-Huei Tsai, director of MIT’s Picower Institute for Learning and Memory, and conducted in collaboration with Boyden, it was reported that inducing a specific frequency of brain wave oscillation in interneurons in the hippocampus could help to clear amyloid plaques similar to those found in Alzheimer’s patients.

“You really would love to know what’s happening in those cells,” Boyden says. “Are they signaling to specific downstream cells, which then contribute to the therapeutic result? The brain is a circuit, and to understand how a circuit works, you have to be able to monitor the components of the circuit while they are in action.”

This technique could also enable studies of fundamental questions in neuroscience, such as how individual neurons interact with each other as the brain makes a decision or recalls a memory.

Bernardo Sabatini, a professor of neurobiology at Harvard Medical School, says he is interested in adapting this technique to use in his lab, where students spend a great deal of time recording electrical activity from neurons growing in a lab dish.

“It’s silly to have amazingly intelligent students doing tedious tasks that could be done by robots,” says Sabatini, who was not involved in this study. “I would be happy to have robots do more of the experimentation so we can focus on the design and interpretation of the experiments.”

To help other labs adopt the new technology, the researchers plan to put the details of their approach on their web site, autopatcher.org.

Other co-authors include Ingrid van Welie, Suhasa Kodandaramaiah, and Brian Allen. The research was funded by Jeremy and Joyce Wertheimer, the National Institutes of Health (including the NIH Single Cell Initiative and the NIH Director’s Pioneer Award), the HHMI-Simons Faculty Scholars Program, and the New York Stem Cell Foundation-Robertson Award.

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.”

Finding a way in

by Elizabeth Dougherty |

Our perception of the world arises within the brain, based on sensory information that is sometimes ambiguous, allowing more than one interpretation. Familiar demonstrations of this point include the famous Necker cube and the “duck-rabbit” drawing (right) in which two different interpretations flip back and forth over time.

Another example is binocular rivalry, in which the two eyes are presented with different images that are perceived in alternation. Several years ago, this phenomenon caught the eye of Caroline Robertson, who is now a Harvard Fellow working in the lab of McGovern Investigator Nancy Kanwisher. Back when she was a graduate student at Cambridge University, Robertson realized that binocular rivalry might be used to probe the basis of autism, among the most mysterious of all brain disorders.

Robertson’s idea was based on the hypothesis that autism involves an imbalance between excitation and inhibition within the brain. Although widely supported by indirect evidence, this has been very difficult to test directly in human patients. Robertson realized that binocular rivalry might provide a way to perform such a test. The perceptual switches that occur during rivalry are thought to involve competition between different groups of neurons in the visual cortex, each group reinforcing its own interpretation via excitatory connections while suppressing the alternative interpretation through inhibitory connections. Thus, if the balance is altered in the brains of people with autism, the frequency of switching might also be different, providing a simple and easily measurable marker of the disease state.

To test this idea, Robertson recruited adults with and without autism, and presented them with two distinct and differently colored images in each eye. As expected, their perceptions switched back and forth between the two images, with short periods of mixed perception in between. This was true for both groups, but when she measured the timing of these switches, Robertson found that individuals with autism do indeed see the world in a measurably different way than people without the disorder. Individuals with autism cycle between the left and right images more slowly, with the intervening periods of mixed perception lasting longer than in people without autism. The more severe their autistic symptoms, as determined by a standard clinical behavioral evaluation, the greater the difference.

Robertson had found a marker for autism that is more objective than current methods that involve one person assessing the behavior of another. The measure is immediate and relies on brain activity that happens automatically, without people thinking about it. “Sensation is a very simple place to probe,” she says.

A top-down approach

When she arrived in Kanwisher’s lab, Robertson wanted to use brain imaging to probe the basis for the perceptual phenomenon that she had discovered. With Kanwisher’s encouragement, she began by repeating the behavioral experiment with a new group of subjects, to check that her previous results were not a fluke. Having confirmed that the finding was real, she then scanned the subjects using an imaging method called Magnetic Resonance Spectroscopy (MRS), in which an MRI scanner is reprogrammed to measure concentrations of neurotransmitters and other chemicals in the brain. Kanwisher had never used MRS before, but when Robertson proposed the experiment, she was happy to try it. “Nancy’s the kind of mentor who could support the idea of using a new technique and guide me to approach it rigorously,” says Robertson.

For each of her subjects, Robertson scanned their brains to measure the amounts of two key neurotransmitters, glutamate, which is the main excitatory transmitter in the brain, and GABA, which is the main source of inhibition. When she compared the brain chemistry to the behavioral results in the binocular rivalry task, she saw something intriguing and unexpected. In people without autism, the amount of GABA in the visual cortex was correlated with the strength of the suppression, consistent with the idea that GABA enables signals from one eye to inhibit those from the other eye. But surprisingly, there was no such correlation in the autistic individuals—suggesting that GABA was somehow unable to exert its normal suppressive effect. It isn’t yet clear exactly what is going wrong in the brains of these subjects, but it’s an early flag, says Robertson. “The next step is figuring out which part of the pathway is disrupted.”

A bottom-up approach

Robertson’s approach starts from the top-down, working backward from a measurable behavior to look for brain differences, but it isn’t the only way in. Another approach is to start with genes that are linked to autism in humans, and to understand how they affect neurons and brain circuits. This is the bottom-up approach of McGovern Investigator Guoping Feng, who studies a gene called Shank3 that codes for a protein that helps build synapses, the connections through which neurons send signals to each other. Several years ago Feng knocked out Shank3 in mice, and found that the mice exhibited behaviors reminiscent of human autism, including repetitive grooming, anxiety, and impaired social interaction and motor control.

These earlier studies involved a variety of different mutations that disabled the Shank3 gene. But when postdoc Yang Zhou joined Feng’s lab, he brought a new perspective. Zhou had come from a medical background and wanted to do an experiment more directly connected to human disease. So he suggested making a mouse version of a Shank3 mutation seen in human patients, and testing its effects.

Zhou’s experiment would require precise editing of the mouse Shank3 gene, previously a difficult and time-consuming task. But help was at hand, in the form of a collaboration with McGovern Investigator Feng Zhang, a pioneer in the development of genome-editing methods.

Using Zhang’s techniques, Zhou was able to generate mice with two different mutations: one that had been linked to human autism, and another that had been discovered in a few patients with schizophrenia.

The researchers found that mice with the autism-related mutation exhibited behavioral changes at a young age that paralleled behaviors seen in children with autism. They also found early changes in synapses within a brain region called the striatum. In contrast, mice with the schizophrenia-related gene appeared normal until adolescence, and then began to exhibit changes in behavior and also changes in the prefrontal cortex, a brain region that is implicated in human schizophrenia. “The consequences of the two different Shank3 mutations were quite different in certain aspects, which was very surprising to us,” says Zhou.

The fact that different mutations in just one gene can produce such different results illustrates exactly how complex these neuropsychiatric disorders can be. “Not only do we need to study different genes, but we also have to understand different mutations and which brain regions have what defects,” says Feng, who received funding from the Poitras Center for Affective Disorders research and the Simons Center for the Social Brain. Robertson and Kanwisher were also supported by the Simons Center.

Surprising plasticity

The brain alterations that lead to autism are thought to arise early in development, long before the condition is diagnosed, raising concerns that it may be difficult to reverse the effects once the damage is done. With the Shank3 knockout mice, Feng and his team were able to approach this question in a new way, asking what would happen if the missing gene were to be restored in adulthood.

To find the answer, lab members Yuan Mei and Patricia Monteiro, along with Zhou, studied another strain of mice, in which the Shank3 gene was switched off but could be reactivated at any time by adding a drug to their diet. When adult mice were tested six weeks after the gene was switched back on, they no longer showed repetitive grooming behaviors, and they also showed normal levels of social interaction with other mice, despite having grown up without a functioning Shank3 gene. Examination of their brains confirmed that many of the synaptic alterations were also rescued when the gene was restored.

Not every symptom was reversed by this treatment; even after six weeks or more of restored Shank3 expression, the mice continued to show heightened anxiety and impaired motor control. But even these deficits could be prevented if the Shank3 gene was restored earlier in life, soon after birth.

The results are encouraging because they indicate a surprising degree of brain plasticity, persisting into adulthood. If the results can be extrapolated to human patients, they suggest that even in adulthood, autism may be at least partially reversible if the right treatment can be found. “This shows us the possibility,” says Zhou. “If we could somehow put back the gene in patients who are missing it, it could help improve their life quality.”

Converging paths

Robertson and Feng are approaching the challenge of autism from different starting points, but already there are signs of convergence. Feng is finding early signs that his Shank3 mutant mice may have an altered balance of inhibitory and excitatory circuits, consistent with what Robertson and Kanwisher have found in humans.

Feng is continuing to study these mice, and he also hopes to study the effects of a similar mutation in non-human primates, whose brains and behaviors are more similar to those of humans than rodents. Robertson, meanwhile, is planning to establish a version of the binocular rivalry test in animal models, where it is possible to alter the balance between inhibition and excitation experimentally (for example, via a genetic mutation or a drug treatment). If this leads to changes in binocular rivalry, it would strongly support the link to the perceptual changes seen in humans.

One challenge, says Robertson, will be to develop new methods to measure the perceptions of mice and other animals. “The mice can’t tell us what they are seeing,” she says. “But it would also be useful in humans, because it would allow us to study young children and patients who are non-verbal.”

A multi-pronged approach

The imbalance hypothesis is a promising lead, but no single explanation is likely to encompass all of autism, according to McGovern director Bob Desimone. “Autism is a notoriously heterogeneous condition,” he explains. “We need to try multiple approaches in order to maximize the chance of success.”

McGovern researchers are doing exactly that, with projects underway that range from scanning children to developing new molecular and microscopic methods for examining brain changes in animal disease models. Although genetic studies provide some of the strongest clues, Desimone notes that there is also evidence for environmental contributions to autism and other brain disorders. “One that’s especially interesting to us is a maternal infection and inflammation, which in mice at least can affect brain development in ways we’re only beginning to understand.”

The ultimate goal, says Desimone, is to connect the dots and to understand how these diverse human risk factors affect brain function. “Ultimately, we want to know what these different pathways have in common,” he says. “Then we can come up with rational strategies for the development of new treatments.”

Study reveals a basis for attention deficits

Anne Trafton |

More than 3 million Americans suffer from attention deficit hyperactivity disorder (ADHD), a condition that usually emerges in childhood and can lead to difficulties at school or work.

A new study from MIT and New York University links ADHD and other attention difficulties to the brain’s thalamic reticular nucleus (TRN), which is responsible for blocking out distracting sensory input. In a study of mice, the researchers discovered that a gene mutation found in some patients with ADHD produces a defect in the TRN that leads to attention impairments.

The findings suggest that drugs boosting TRN activity could improve ADHD symptoms and possibly help treat other disorders that affect attention, including autism.

“Understanding these circuits may help explain the converging mechanisms across these disorders. For autism, schizophrenia, and other neurodevelopmental disorders, it seems like TRN dysfunction may be involved in some patients,” says Guoping Feng, the James W. and Patricia Poitras Professor of Neuroscience and a member of MIT’s McGovern Institute for Brain Research and the Stanley Center for Psychiatric Research at the Broad Institute.

Feng and Michael Halassa, an assistant professor of psychiatry, neuroscience, and physiology at New York University, are the senior authors of the study, which appears in the March 23 online edition of Nature. The paper’s lead authors are MIT graduate student Michael Wells and NYU postdoc Ralf Wimmer.

Paying attention

Feng, Halassa, and their colleagues set out to study a gene called Ptchd1, whose loss can produce attention deficits, hyperactivity, intellectual disability, aggression, and autism spectrum disorders. Because the gene is carried on the X chromosome, most individuals with these Ptchd1-related effects are male.

In mice, the researchers found that the part of the brain most affected by the loss of Ptchd1 is the TRN, which is a group of inhibitory nerve cells in the thalamus. It essentially acts as a gatekeeper, preventing unnecessary information from being relayed to the brain’s cortex, where higher cognitive functions such as thought and planning occur.

“We receive all kinds of information from different sensory regions, and it all goes into the thalamus,” Feng says. “All this information has to be filtered. Not everything we sense goes through.”

If this gatekeeper is not functioning properly, too much information gets through, allowing the person to become easily distracted or overwhelmed. This can lead to problems with attention and difficulty in learning.

The researchers found that when the Ptchd1 gene was knocked out in mice, the animals showed many of the same behavioral defects seen in human patients, including aggression, hyperactivity, attention deficit, and motor impairments. When the Ptchd1 gene was knocked out only in the TRN, the mice showed only hyperactivity and attention deficits.

Toward new treatments

At the cellular level, the researchers found that the Ptchd1 mutation disrupts channels that carry potassium ions, which prevents TRN neurons from being able to sufficiently inhibit thalamic output to the cortex. The researchers were also able restore the neurons’ normal function with a compound that boosts activity of the potassium channel. This intervention reversed the TRN-related symptoms but not any of the symptoms that appear to be caused by deficits of some other circuit.

“The authors convincingly demonstrate that specific behavioral consequences of the Ptchd1 mutation — attention and sleep — arise from an alteration of a specific protein in a specific brain region, the thalamic reticular nucleus. These findings provide a clear and straightforward pathway from gene to behavior and suggest a pathway toward novel treatments for neurodevelopmental disorders such as autism,” says Joshua Gordon, an associate professor of psychiatry at Columbia University, who was not involved in the research.

Most people with ADHD are now treated with psychostimulants such as Ritalin, which are effective in about 70 percent of patients. Feng and Halassa are now working on identifying genes that are specifically expressed in the TRN in hopes of developing drug targets that would modulate TRN activity. Such drugs may also help patients who don’t have the Ptchd1 mutation, because their symptoms are also likely caused by TRN impairments, Feng says.

The researchers are also investigating when Ptchd1-related problems in the TRN arise and at what point they can be reversed. And, they hope to discover how and where in the brain Ptchd1 mutations produce other abnormalities, such as aggression.

The research was funded by the Simons Foundation Autism Research Initiative, the National Institutes of Health, the Poitras Center for Affective Disorders Research, and the Stanley Center for Psychiatric Research at the Broad Institute.

Toward a better understanding of the brain

Anne Trafton |

In 2011, about a month after joining the MIT faculty, Feng Zhang attended a talk by Harvard Medical School Professor Michael Gilmore, who studies the pathogenic bacterium Enteroccocus. The scientist mentioned that these bacteria protect themselves from viruses with DNA-cutting enzymes known as nucleases, which are part of a defense system known as CRISPR.

“I had no idea what CRISPR was but I was interested in nucleases,” Zhang says. “I went to look up CRISPR, and that’s when I realized you might be able to engineer it for use for genome editing.”

Zhang devoted himself to adapting the system to edit genes in mammalian cells and recruited new members to his nascent lab at the Broad Institute of MIT and Harvard to work with him on this project. In January 2013, they reported their success in the journal Science.

Since then, scientists in fields from medicine to plant biology have begun using CRISPR to study gene function and investigate the possibility of correcting faulty genes that cause disease. Zhang now heads a lab of 19 scientists who continue to develop the system and pursue applications of genome editing, especially in neuroscience.

“The goal is to try to make our lives better by developing new technologies and using them to understand biological systems so that we can improve our treatment of disease and our quality of life,” says Zhang, who is also a member of MIT’s McGovern Institute for Brain Research and recently earned tenure in MIT’s Departments of Biological Engineering and Brain and Cognitive Sciences.

Understanding the brain

Growing up in Des Moines, Iowa, where his parents moved from China when he was 11, Zhang had plenty of opportunities to feed his interest in science. He participated in Science Bowl competitions and took special Saturday science classes, where he got his first introduction to molecular biology. Experiments such as extracting DNA from strawberries and transforming bacteria with genes for drug resistance whetted his appetite for genetic engineering, which was further stimulated by a showing of “Jurassic Park.”

“That really caught my attention,” he recalls. “It didn’t seem that far-fetched. I guess that’s what makes it good science fiction. It kind of tantalizes your imagination.”

As a sophomore in high school, Zhang began working with Dr. John Levy in a gene therapy lab at the Iowa Methodist Medical Center in Des Moines, where he studied green fluorescent protein (GFP). Scientists had recently figured out how to adapt this naturally occurring protein to tag and image proteins inside living cells. Zhang used it to track viral proteins within infected cells to determine how the proteins assemble to form new viruses. He also worked on a project to adapt GFP for a different purpose — protecting DNA from damage induced by ultraviolet light.

At Harvard University, where he earned his undergraduate degree, Zhang majored in chemistry and physics and did research under the mentorship of Xiaowei Zhuang, a professor of chemistry and chemical biology. “I was always interested in biology but I felt that it’s important to get a solid training in chemistry and physics,” he says.

While Zhang was at Harvard, a close friend was severely affected by a psychiatric disorder. That experience made Zhang think about whether such disorders could be approached just like cancer or heart disease, if only scientists knew more about their underlying causes.

“The difference is we’re at a much earlier stage of understanding psychiatric diseases. That got me really interested in trying to understand more about how the brain works,” he says.

At Stanford University, where Zhang earned his PhD in chemistry, he worked with Karl Deisseroth, who was just starting his lab with a focus on developing new technology for studying the brain. Zhang was the second student to join the lab, and he began working on a protein called channelrhodopsin, which he and Deisseroth believed held potential for engineering mammalian cells to respond to light.

The resulting technique, known as optogenetics, has transformed biological research. Collaborating with Edward Boyden, a member of the Deisseroth lab who is now a professor at MIT, Zhang adapted channelrhodopsin so that it could be inserted into neurons and make them light-sensitive. Using this approach, neuroscientists can now selectively activate and de-activate specific neurons in the brain, allowing them to map brain circuits and investigate how disruption of those circuits causes disease.

Better gene editing

After leaving Stanford, Zhang spent a year as a junior fellow at the Harvard Society of Fellows, studying brain development with Professor Paola Arlotta and collaborating with Professor George Church. That’s when he began to focus on gene editing — a type of genetic engineering that allows researchers to selectively delete a gene or replace it with a new one.

He began with zinc finger nucleases — enzymes that can be designed to target and cut specific DNA sequences. However, these proteins turned out to be challenging to work with, in part because it is so time-consuming to design a new protein for each possible DNA target.

That led Zhang to experiment with a different type of nucleases known as transcription activator-like effector nucleases (TALENs), but these also proved laborious to work with. “Learning how to use them is a project on its own,” Zhang says.

When he heard about CRISPR in early 2011, Zhang sensed that harnessing the natural bacterial process held the potential to solve many of the challenges associated with those earlier gene-editing techniques. CRISPR includes a nuclease called Cas9, which can be guided to the correct genetic target by RNA molecules known as guide strands. For each target, scientists need only design and synthesize a new RNA guide, which is much simpler than creating new TALEN and zinc finger proteins.

Since his first CRISPR paper in 2013, Zhang’s lab has devised many enhancements to the original system, such as making the targeting more precise and preventing unintended cuts in the wrong locations. They also recently reported another type of CRISPR system based on a different nuclease called Cpf1, which is simpler and has unique features that further expand the genome editing toolbox.

Zhang’s lab has become a hub for CRISPR research worldwide. It has shared CRISPR-Cas9 components in response to nearly 30,000 requests from academic laboratories around the world and has trained thousands of researchers in the use of CRISPR-Cas9 genome-editing technology through in-person events and online opportunities.

His team is now working on creating animal models of autism, Alzheimer’s, and other neurological disorders, and in the long term, they hope to develop CRISPR for use in humans to potentially cure diseases caused by defective genes.

“There are many genetic diseases that we don’t have any way of treating and this could be one way, but we still have to do a lot of work,” Zhang says.

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.

How we make emotional decisions

Anne Trafton |

Some decisions arouse far more anxiety than others. Among the most anxiety-provoking are those that involve options with both positive and negative elements, such choosing to take a higher-paying job in a city far from family and friends, versus choosing to stay put with less pay.

MIT researchers have now identified a neural circuit that appears to underlie decision-making in this type of situation, which is known as approach-avoidance conflict. The findings could help researchers to discover new ways to treat psychiatric disorders that feature impaired decision-making, such as depression, schizophrenia, and borderline personality disorder.

“In order to create a treatment for these types of disorders, we need to understand how the decision-making process is working,” says Alexander Friedman, a research scientist at MIT’s McGovern Institute for Brain Research and the lead author of a paper describing the findings in the May 28 issue of Cell.

Friedman and colleagues also demonstrated the first step toward developing possible therapies for these disorders: By manipulating this circuit in rodents, they were able to transform a preference for lower-risk, lower-payoff choices to a preference for bigger payoffs despite their bigger costs.

The paper’s senior author is Ann Graybiel, an MIT Institute Professor and member of the McGovern Institute. Other authors are postdoc Daigo Homma, research scientists Leif Gibb and Ken-ichi Amemori, undergraduates Samuel Rubin and Adam Hood, and technical assistant Michael Riad.

Making hard choices

The new study grew out of an effort to figure out the role of striosomes — clusters of cells distributed through the the striatum, a large brain region involved in coordinating movement and emotion and implicated in some human disorders. Graybiel discovered striosomes many years ago, but their function had remained mysterious, in part because they are so small and deep within the brain that it is difficult to image them with functional magnetic resonance imaging (fMRI).

Previous studies from Graybiel’s lab identified regions of the brain’s prefrontal cortex that project to striosomes. These regions have been implicated in processing emotions, so the researchers suspected that this circuit might also be related to emotion.

To test this idea, the researchers studied mice as they performed five different types of behavioral tasks, including an approach-avoidance scenario. In that situation, rats running a maze had to choose between one option that included strong chocolate, which they like, and bright light, which they don’t, and an option with dimmer light but weaker chocolate.

When humans are forced to make these kinds of cost-benefit decisions, they usually experience anxiety, which influences the choices they make. “This type of task is potentially very relevant to anxiety disorders,” Gibb says. “If we could learn more about this circuitry, maybe we could help people with those disorders.”

The researchers also tested rats in four other scenarios in which the choices were easier and less fraught with anxiety.

“By comparing performance in these five tasks, we could look at cost-benefit decision-making versus other types of decision-making, allowing us to reach the conclusion that cost-benefit decision-making is unique,” Friedman says.

Using optogenetics, which allowed them to turn cortical input to the striosomes on or off by shining light on the cortical cells, the researchers found that the circuit connecting the cortex to the striosomes plays a causal role in influencing decisions in the approach-avoidance task, but none at all in other types of decision-making.

When the researchers shut off input to the striosomes from the cortex, they found that the rats began choosing the high-risk, high-reward option as much as 20 percent more often than they had previously chosen it. If the researchers stimulated input to the striosomes, the rats began choosing the high-cost, high-reward option less often.

Paul Glimcher, a professor of physiology and neuroscience at New York University, describes the study as a “masterpiece” and says he is particularly impressed by the use of a new technology, optogenetics, to solve a longstanding mystery. The study also opens up the possibility of studying striosome function in other types of decision-making, he adds.

“This cracks the 20-year puzzle that [Graybiel] wrote — what do the striosomes do?” says Glimcher, who was not part of the research team. “In 10 years we will have a much more complete picture, of which this paper is the foundational stone. She has demonstrated that we can answer this question, and answered it in one area. A lot of labs will now take this up and resolve it in other areas.”

Emotional gatekeeper

The findings suggest that the striatum, and the striosomes in particular, may act as a gatekeeper that absorbs sensory and emotional information coming from the cortex and integrates it to produce a decision on how to react, the researchers say.

That gatekeeper circuit also appears to include a part of the midbrain called the substantia nigra, which has dopamine-containing cells that play an important role in motivation and movement. The researchers believe that when activated by input from the striosomes, these substantia nigra cells produce a long-term effect on an animal or human patient’s decision-making attitudes.

“We would so like to find a way to use these findings to relieve anxiety disorder, and other disorders in which mood and emotion are affected,” Graybiel says. “That kind of work has a real priority to it.”

In addition to pursuing possible treatments for anxiety disorders, the researchers are now trying to better understand the role of the dopamine-containing substantia nigra cells in this circuit, which plays a critical role in Parkinson’s disease and may also be involved in related disorders.

The research was funded by the National Institute of Mental Health, the CHDI Foundation, the Defense Advanced Research Projects Agency, the U.S. Army Research Office, the Bachmann-Strauss Dystonia and Parkinson Foundation, and the William N. and Bernice E. Bumpus Foundation.

From genes to brains

by Elizabeth Dougherty |

Many brain disorders are strongly influenced by genetics, and researchers have long hoped that the identification of genetic risk factors will provide clues to the causes and possible treatments of these mysterious conditions. In the early years, progress was slow. Many claims failed to replicate, and it became clear that in order to identify the important risk genes with confidence, researchers would need to examine the genomes of very large numbers of patients.

Until recently that would have been prohibitively expensive, but genome research has been accelerating fast. Just how fast was underlined by an announcement in January from a California-based company, Illumina, that it had achieved a long-awaited milestone: sequencing an entire human genome for under $1000. Seven years ago, this task would have cost $10M and taken weeks of work. The new system does the job in a few hours, and can sequence tens of thousands of genomes per year.

In parallel with these spectacular advances, another technological revolution has been unfolding over the past several years, with the development of a new method for editing the genome of living cells. This method, known as CRISPR, allows researchers to make precise changes to a DNA sequence—an advance that is expected to transform many areas of biomedical research and may ultimately form the basis of new treatments for human genetic disease.

The CRISPR technology, which is based on a natural bacterial defense system against viruses, uses a short strand of RNA as a “search string” to locate a corresponding DNA target sequence. This RNA string can be synthesized in the lab and can be designed to recognize any desired sequence of DNA. The RNA carries with it a protein called Cas9, which cuts the target DNA at the chosen location, allowing a new sequence to be inserted—providing researchers with a fast and flexible “search-and-replace” tool for editing the genome.

One of the pioneers in this field is McGovern Investigator Feng Zhang, who along with George Church of Harvard, was the first to show that CRISPR could be used to edit the human genome in living cells. Zhang is using the technology to study human brain disorders, building on the flood of new genetic discoveries that are emerging from advances in DNA sequencing. The Broad Institute, where Zhang holds a joint appointment, is a world leader in human psychiatric genetics, and will be among the first to acquire the new Illumina sequencing machines when they reach the market later this year.

By sequencing many thousands of individuals, geneticists are identifying the rare genetic variants that contribute to risk of diseases such as autism, schizophrenia and bipolar disorder. CRISPR will allow neuroscientists to study those gene variants in cells and in animal models. The goal, says McGovern Institute director Bob Desimone, is to understand the biological roots of brain disorders. “The biggest obstacle to new treatments has been our ignorance of fundamental mechanisms. But with these new technologies, we have a real opportunity to understand what’s wrong at the level of cells and circuits, and to identify the pressure points at which therapeutic intervention may be possible.”

Culture Club

In other fields, the influence of genetic variations on disease has turned out to be surprisingly difficult to unravel, and for neuropsychiatric disease, the challenge may be even greater. The brain is the most complex organ of the body, and the underlying pathologies that lead to disease are not yet well understood. Moreover, any given disorder may show a wide variation in symptoms from patient to patient, and it may also have many different genetic causes. “There are hundreds of genes that can contribute to autism or schizophrenia,” says McGovern Investigator Guoping Feng, who is also Poitras Professor of Neuroscience.

To study these genes, Feng and collaborators at the Broad Institute’s Stanley Center for Psychiatric Research are planning to screen thousands of cultures of neurons, grown in the tiny wells of cell culture plates. The neurons, which are grown from stem cells, can be engineered using CRISPR to contain the genetic variants that are linked to neuropsychiatric disease. Each culture will contain neurons with a different variant, and these will be examined for abnormalities that might be associated with disease.

Feng and colleagues hope this high-throughput platform will allow them to identify cellular traits, or phenotypes, that may be related to disease and which can then be studied in animal models to see if they cause defects in brain function or in behavior. In the longer term, this high-throughput platform can also be used to screen for new drugs that can reverse these defects.

Animal Kingdom

Cell cultures are necessary for large-scale screens, but ultimately the results must be translated into the context of brain circuits and behavior. “That means we must study animal models too,” says Feng.

Feng has created several mouse models of human brain disease by mutating genes that are linked to these disorders and examining the behavioral and cellular defects in the mutant animals. “We have models of obsessive-compulsive disorder and autism,” he explains. “By studying these mice we want to learn what’s wrong with their brains.”

So far, Feng has focused on single-gene models, but the majority of human psychiatric disorders are triggered by multiple genes acting in combination. One advantage of the new CRISPR method is that it allows researchers to introduce several mutations in parallel, and Zhang’s lab is now working to create autistic mice with more than one gene alteration.

Perhaps the most important advantage of CRISPR is that it can be applied to any species. Currently, almost all genetic modeling of human disease is restricted to mice. But while mouse models are convenient, they are limited, especially for diseases that affect higher brain functions and for which there are no clear parallels in rodents. “We also need to study species that are closer to humans,” says Feng.

Accordingly, he and Zhang are collaborating with colleagues in Oregon and China to use CRISPR to create primate models of neuropsychiatric disorders. Earlier this year, a team in China announced that they had used CRISPR to create transgenic monkeys that will be used to study defects in metabolism and immunity.

Feng and Zhang are planning to use a similar approach to study brain disorders, but in addition to macacques, they will also work with a smaller primate species, the marmoset. These animals, with their fast breeding cycles and complex behavioral repertoires, are ideal for genetic studies of behavior and brain function. And because they are very social with highly structured communication patterns, they represent a promising new model for understanding the neural basis of social cognition and its disruption in conditions such as autism.

Given their close evolutionary relationship to humans, marmoset models could also help accelerate the development of new therapies. Many experimental drugs for brain disorders have been tested successfully in mice, only to prove ineffective in subsequent human trials. These failures, which can be enormously expensive, have led many drug companies to cut back on their neuroscience R&D programs. Better animal models could reverse this trend by allowing companies to predict more accurately which drug candidates are most promising, before investing heavily in human clinical trials.

Feng’s mouse research provides an example of how this approach can work. He previously developed a mouse model of obsessive-compulsive disorder, in which the animals engage in obsessive self-grooming, and he has now shown that this effect can be reversed when the missing gene is reintroduced, even in adulthood. Other researchers have seemed similar results with other brain disorders such as Rett Syndrome, a condition that is often accompanied by autism. “The brain is amazingly plastic,” says Feng. “At least in a mouse, we have shown that the damage can often be repaired. If we can also show this in marmosets or other primate models, that would really give us hope that something similar is possible in humans.”

Human Race

Ultimately, to understand the genetic roots of human behavior, researchers must sequence the genomes of individual subjects in parallel with measurements of those same individuals’ behavior and brain function.

Such studies typically require very large sample sizes, but the plummeting cost of sequencing is now making this feasible. In China, for instance, a project is already underway to sequence the genomes of many thousands of individuals to uncover genetic influences on cognition and intelligence.

The next step will be to link the genetics to brain activity, says McGovern Investigator John Gabrieli, who also directs the Martinos Imaging Center at MIT. “It’s a big step to go from DNA to behavioral variation or clinical diagnosis. But we know those genes must affect brain function, so neuroimaging may help us to bridge that gap.”

But brain scans can be time-consuming, given that volunteers must perform behavioral tasks in the scanner. Studies are typically limited to a few dozen subjects, not enough to detect the often subtle effects of genomic variation.

One way to enlarge these studies, says Gabrieli, is to image the brain during rest rather than in a state of prompted activity. This procedure is fast and easy to replicate from lab to lab, and patterns of resting state activity have turned out to be surprisingly reproducible; moreover, Gabrieli is finding that differences in resting activity are associated with brain disorders such as autism, and he hopes that in the future it will be possible to relate these differences to the genetic factors that are emerging from genome studies at the Broad Institute and elsewhere.

“I’m optimistic that we’re going to see dramatic advances in our understanding of neuropsychiatric disease over the next few years.” — Bob Desimone

Confirming these associations will require a “big data” approach, in which results from multiple labs are consolidated into large repositories and analyzed for significant associations. Resting state imaging lends itself to this approach, says Gabrieli. “To find the links between brain function and genetics, big data is the direction we need to go to be successful.”

How soon might this happen? “It won’t happen overnight,” cautions Desimone. “There are a lot of dots that need to be connected. But we’ve seen in the case of genome research how fast things can move once the right technologies are in place. I’m optimistic that we’re going to see equally dramatic advances in our understanding of neuropsychiatric disease over the next few years.”