Brain Disorder: Bipolar Disorder

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.”

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 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.

Genome Editing with CRISPR – Cas9

Anne Trafton |

This animation depicts the CRISPR-Cas9 method for genome editing – a powerful new technology with many applications in biomedical research, including the potential to treat human genetic disease. Feng Zhang, a leader in the development of this technology, is a faculty member at MIT, an investigator at the McGovern Institute for Brain Research, and a core member of the Broad Institute.

 

Calcium reveals connections between neurons

Anne Trafton |

A team led by MIT neuroscientists has developed a way to monitor how brain cells coordinate with each other to control specific behaviors, such as initiating movement or detecting an odor.

The researchers’ new imaging technique, based on the detection of calcium ions in neurons, could help them map the brain circuits that perform such functions. It could also provide new insights into the origins of autism, obsessive-compulsive disorder and other psychiatric diseases, says Guoping Feng, senior author of a paper appearing in the Oct. 18 issue of the journal Neuron.

“To understand psychiatric disorders we need to study animal models, and to find out what’s happening in the brain when the animal is behaving abnormally,” says Feng, the James W. and Patricia Poitras Professor of Neuroscience and a member of the McGovern Institute for Brain Research at MIT. “This is a very powerful tool that will really help us understand animal models of these diseases and study how the brain functions normally and in a diseased state.”

The lead author of the Neuron paper is McGovern Institute postdoc Qian Chen.

Performing any kind of brain function requires many neurons in different parts of the brain to communicate with each other. They achieve this communication by sending electrical signals, triggering an influx of calcium ions into active cells. Using dyes that bind to calcium, researchers have imaged neural activity in neurons. However, the brain contains thousands of cell types, each with distinct functions, and the dye is taken up nonselectively by all cells, making it impossible to pinpoint calcium in specific cell types with this approach.

To overcome this, the MIT-led team created a calcium-imaging system that can be targeted to specific cell types, using a type of green fluorescent protein (GFP). Junichi Nakai of Saitama University in Japan first developed a GFP that is activated when it binds to calcium, and one of the Neuron paper authors, Loren Looger of the Howard Hughes Medical Institute, modified the protein so its signal is strong enough to use in living animals.

The MIT researchers then genetically engineered mice to express this protein in a type of neuron known as pyramidal cells, by pairing the gene with a regulatory DNA sequence that is only active in those cells. Using two-photon microscopy to image the cells at high speed and high resolution, the researchers can identify pyramidal cells that are active when the brain is performing a specific task or responding to a certain stimulus.

In this study, the team was able to pinpoint cells in the somatosensory cortex that are activated when a mouse’s whiskers are touched, and olfactory cells that respond to certain aromas.

This system could be used to study brain activity during many types of behavior, including long-term phenomena such as learning, says Matt Wachowiak, an associate professor of physiology at the University of Utah. “These mouse lines should be really useful to many different research groups who want to measure activity in different parts of the brain,” says Wachowiak, who was not involved in this research.

The researchers are now developing mice that express the calcium-sensitive proteins and also exhibit symptoms of autistic behavior and obsessive-compulsive disorder. Using these mice, the researchers plan to look for neuron firing patterns that differ from those of normal mice. This could help identify exactly what goes wrong at the cellular level, offering mechanistic insights into those diseases.

“Right now, we only know that defects in neuron-neuron communications play a key role in psychiatric disorders. We do not know the exact nature of the defects and the specific cell types involved,” Feng says. “If we knew what cell types are abnormal, we could find ways to correct abnormal firing patterns.”

The researchers also plan to combine their imaging technology with optogenetics, which enables them to use light to turn specific classes of neurons on or off. By activating specific cells and then observing the response in target cells, they will be able to precisely map brain circuits.

The research was funded by the Poitras Center for Affective Disorders Research, the National Institutes of Health and the McNair Foundation