Satrajit Ghosh

Personalized Medicine

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. Satrajit Ghosh hopes to change this picture, and his research suggests that individual brain scans and speaking patterns can hold valuable information for guiding psychiatrists and patients. His research group develops novel analytic platforms that use such information to create robust, predictive models around human health. Current areas include depression, suicide, anxiety disorders, autism, Parkinson’s disease, and brain tumors.

Ed Boyden

Engineering Matter and Mind

Ed Boyden develops new tools for probing, analyzing, and engineering brain circuits. He uses a range of approaches, including synthetic biology, nanotechnology, chemistry, electrical engineering, and optics to develop tools capable of revealing fundamental mechanisms underlying complex brain processes.

Boyden may be best known for pioneering the development of optogenetics, a powerful method that enables neuronal activity to be controlled with light. He also led the team that invented expansion microscopy, in which a specimen is embedded in a gel that swells as it absorbs water, thereby expanding nanoscale features to a size where they can be seen using conventional microscopes. He is now seeking to systematically integrate these technologies to create detailed maps and models of brain circuitry.

Virtual Tour of Boyden Lab

How the brain switches between different sets of rules

Cognitive flexibility — the brain’s ability to switch between different rules or action plans depending on the context — is key to many of our everyday activities. For example, imagine you’re driving on a highway at 65 miles per hour. When you exit onto a local street, you realize that the situation has changed and you need to slow down.

When we move between different contexts like this, our brain holds multiple sets of rules in mind so that it can switch to the appropriate one when necessary. These neural representations of task rules are maintained in the prefrontal cortex, the part of the brain responsible for planning action.

A new study from MIT has found that a region of the thalamus is key to the process of switching between the rules required for different contexts. This region, called the mediodorsal thalamus, suppresses representations that are not currently needed. That suppression also protects the representations as a short-term memory that can be reactivated when needed.

“It seems like a way to toggle between irrelevant and relevant contexts, and one advantage is that it protects the currently irrelevant representations from being overwritten,” says Michael Halassa, an assistant professor of brain and cognitive sciences and a member of MIT’s McGovern Institute for Brain Research.

Halassa is the senior author of the paper, which appears in the Nov. 19 issue of Nature Neuroscience. The paper’s first author is former MIT graduate student Rajeev Rikhye, who is now a postdoc in Halassa’s lab. Aditya Gilra, a postdoc at the University of Bonn, is also an author.

Changing the rules

Previous studies have found that the prefrontal cortex is essential for cognitive flexibility, and that a part of the thalamus called the mediodorsal thalamus also contributes to this ability. In a 2017 study published in Nature, Halassa and his colleagues showed that the mediodorsal thalamus helps the prefrontal cortex to keep a thought in mind by temporarily strengthening the neuronal connections in the prefrontal cortex that encode that particular thought.

In the new study, Halassa wanted to further investigate the relationship between the mediodorsal thalamus and the prefrontal cortex. To do that, he created a task in which mice learn to switch back and forth between two different contexts — one in which they must follow visual instructions and one in which they must follow auditory instructions.

In each trial, the mice are given both a visual target (flash of light to the right or left) and an auditory target (a tone that sweeps from high to low pitch, or vice versa). These targets offer conflicting instructions. One tells the mouse to go to the right to get a reward; the other tells it to go left. Before each trial begins, the mice are given a cue that tells them whether to follow the visual or auditory target.

“The only way for the animal to solve the task is to keep the cue in mind over the entire delay, until the targets are given,” Halassa says.

The researchers found that thalamic input is necessary for the mice to successfully switch from one context to another. When they suppressed the mediodorsal thalamus during the cuing period of a series of trials in which the context did not change, there was no effect on performance. However, if they suppressed the mediodorsal thalamus during the switch to a different context, it took the mice much longer to switch.

By recording from neurons of the prefrontal cortex, the researchers found that when the mediodorsal thalamus was suppressed, the representation of the old context in the prefrontal cortex could not be turned off, making it much harder to switch to the new context.

In addition to helping the brain switch between contexts, this process also appears to help maintain the neural representation of the context that is not currently being used, so that it doesn’t get overwritten, Halassa says. This allows it to be activated again when needed. The mice could maintain these representations over hundreds of trials, but the next day, they had to relearn the rules associated with each context.

Sabine Kastner, a professor of psychology at the Princeton Neuroscience Institute, described the study as a major leap forward in the field of cognitive neuroscience.

“This is a tour-de-force from beginning to end, starting with a sophisticated behavioral design, state-of-the-art methods including causal manipulations, exciting empirical results that point to cell-type specific differences and interactions in functionality between thalamus and cortex, and a computational approach that links the neuroscience results to the field of artificial intelligence,” says Kastner, who was not involved in the research.

Multitasking AI

The findings could help guide the development of better artificial intelligence algorithms, Halassa says. The human brain is very good at learning many different kinds of tasks — singing, walking, talking, etc. However, neural networks (a type of artificial intelligence based on interconnected nodes similar to neurons) usually are good at learning only one thing. These networks are subject to a phenomenon called “catastrophic forgetting” — when they try to learn a new task, previous tasks become overwritten.

Halassa and his colleagues now hope to apply their findings to improve neural networks’ ability to store previously learned tasks while learning to perform new ones.

The research was funded by the National Institutes of Health, the Brain and Behavior Foundation, the Klingenstein Foundation, the Pew Foundation, the Simons Foundation, the Human Frontiers Science Program, and the German Ministry of Education.

Brain activity pattern may be early sign of schizophrenia

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

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

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

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

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

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

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.