Brain Disorder: ADHD

Nancy Kanwisher

Elements of Perception

Nancy Kanwisher’s group studies the functional organization of the human brain as a window into the architecture of the mind. Over the last 20 years her lab has played a central role in the identification of several dozen regions of the cortex in humans that are engaged in particular components of perception and cognition. Many of these regions are very specifically engaged in a single mental function, such as perceiving faces, places, bodies, or words, or understanding the meanings of sentences or the mental states of others. Other regions bring together unexpected combinations of functions that may ultimately provide the strongest constraints on the computations conducted in those regions. Each of these regions is present in approximately the same location in virtually every normal person. 

Virtual Tour of Kanwisher Lab

Ann Graybiel

Probing the Deep Brain

Ann Graybiel studies the basal ganglia, forebrain structures that are profoundly important for normal brain function. Dysfunction in these regions is implicated in neurologic and neuropsychiatric disorders ranging from Parkinson’s disease and Huntington’s disease to obsessive-compulsive disorder, anxiety and depression, and addiction. Graybiel’s laboratory is uncovering circuits underlying both the neural deficits related to these disorders, as well as the role that the basal ganglia play in guiding normal learning, motivation, and behavior.

John Gabrieli

Images of Mind

John Gabrieli’s goal is to understand the organization of memory, thought, and emotion in the human brain, and to use that understanding to help people live happier, more productive lives. By combining brain imaging with behavioral tests, he studies the neural basis of these abilities in human subjects. One important research theme is to understand the neural basis of learning in children and to identify ways that neuroscience could help to improve learning in the classroom. In collaboration with clinical colleagues, Gabrieli also seeks to use brain imaging to better understand, diagnose, and select treatments for neurological and psychiatric diseases.

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.

Robert Desimone

Paying Attention

Our brains are constantly bombarded with sensory information. The ability to distinguish relevant information from irrelevant distractions is a critical skill, one that is impaired in many brain disorders. By studying the visual system of humans and animals, Robert Desimone has shown that when we attend to something specific, neurons in certain brain regions fire in unison – like a chorus rising above the noise – allowing the relevant information to be “heard” more efficiently by other regions of the brain.

How the brain switches between different sets of rules

Anne Trafton |

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.

Tracking down changes in ADHD

Anne Trafton |

Attention deficit hyperactivity disorder (ADHD) is marked by difficulty maintaining focus on tasks, and increased activity and impulsivity. These symptoms ultimately interfere with the ability to learn and function in daily tasks, but the source of the problem could lie at different levels of brain function, and it is hard to parse out exactly what is going wrong.

A new study co-authored by McGovern Institute Associate Investigator Michael Halassa has managed to develop tasks that dissociate lower from higher level brain functions so that disruption to these processes can be more specifically checked in ADHD. The results of this study, carried out in collaboration with co-corresponding authors Wei Ji Ma, Andra Mihali and researchers from New York University, illuminate how brain function is disrupted in ADHD, and highlights a role for perceptual deficits in this condition.

The underlying deficit in ADHD has largely been attributed to executive function — higher order processing and the ability of the brain to integrate information and focus attention. But there have been some hints, largely through reports from those with ADHD, that the very ability to accurately receive sensory information, might be altered. Some people with ADHD, for example, have reported impaired visual function and even changes in color processing. Cleanly separating these perceptual brain functions from the impact of higher order cognitive processes has proven difficult, however. It is not clear whether people with and without ADHD encode visual signals received by the eye in the same way.

“We realized that psychiatric diagnoses in general are based on clinical criteria and patient self-reporting,” says Halassa, who is also a board certified psychiatrist and an assistant professor in MIT’s Department of Brain and Cognitive Sciences. “Psychiatric diagnoses are imprecise, but neurobiology is progressing to the point where we can use well-controlled parameters to standardize criteria, and relate disorders to circuits,” he explains. “If there are problems with attention, is it the spotlight of attention itself that’s affected in ADHD, or the ability of a person to control where this spotlight is focused?”

To test how people with and without ADHD encode visual signals in the brain, Halassa, Ma, Mihali, and collaborators devised a perceptual encoding task in which subjects were asked to provide answers to simple questions about the orientation and color of lines and shapes on a screen. The simplicity of this test aimed to remove high-level cognitive input and provide a measure of accurate perceptual coding.

To measure higher-level executive function, the researchers provided subjects with rules about which features and screen areas were relevant to the task, and they switched relevance throughout the test. They monitored whether subjects cognitively adapted to the switch in rules – an indication of higher-order brain function. The authors also analyzed psychometric curve parameters, common in psychophysics, but not yet applied to ADHD.

“These psychometric parameters give us specific information about the parts of sensory processing that are being affected,” explains Halassa. “So, if you were to put on sunglasses, that would shift threshold, indicating that input is being affected, but this wouldn’t necessarily affect the slope of the psychometric function. If the slope is affected, this starts to reflect difficulty in seeing a line or color. In other words, these tests give us a finer readout of behavior, and how to map this onto particular circuits.”

The authors found that changes in visual perception were robustly associated with ADHD, and these changes were also correlated with cognitive function. Individuals with more clinically severe ADHD scored lower on executive function, and basic perception also tracked with these clinical records of disease severity. The authors could even sort ADHD from control subjects, based on their perceptual variability alone. All of this goes to say that changes in perception itself are clearly present in this ADHD cohort, and that they decline alongside changes in executive function.

“This was unexpected,” points out Halassa. “We didn’t expect so much to be explained by lower sensitivity to stimuli, and to see that these tasks become harder as cognitive pressure increases. It wasn’t clear that cognitive circuits might influence processing of stimuli.”

Understanding the true basis of changes in behavior in disorders such as ADHD can be hard to tease apart, but the study gives more insight into changes in the ADHD brain, and supports the idea that quantitative follow up on self-reporting by patients can drive a stronger understanding — and possible targeted treatment — of such disorders. Testing a larger number of ADHD patients and validating these measures on a larger scale is now the next research priority.

Ann Graybiel wins 2018 Gruber Neuroscience Prize

Anne Trafton |

Institute Professor Ann Graybiel, a professor in the Department of Brain and Cognitive Sciences and member of MIT’s McGovern Institute for Brain Research, is being recognized by the Gruber Foundation for her work on the structure, organization, and function of the once-mysterious basal ganglia. She was awarded the prize alongside Okihide Hikosaka of the National Institute of Health’s National Eye Institute and Wolfram Schultz of the University of Cambridge in the U.K.

The basal ganglia have long been known to play a role in movement, and the work of Graybiel and others helped to extend their roles to cognition and emotion. Dysfunction in the basal ganglia has been linked to a host of disorders including Parkinson’s disease, Huntington’s disease, obsessive-compulsive disorder and attention-deficit hyperactivity disorder, and to depression and anxiety disorders. Graybiel’s research focuses on the circuits thought to underlie these disorders, and on how these circuits act to help us form habits in everyday life.

“We are delighted that Ann has been honored with the Gruber Neuroscience Prize,” says Robert Desimone, director of the McGovern Institute. “Ann’s work has truly elucidated the complexity and functional importance of these forebrain structures. Her work has driven the field forward in a fundamental fashion, and continues to do so.’

Graybiel’s research focuses broadly on the striatum, a hub in basal ganglia-based circuits that is linked to goal-directed actions and habits. Prior to her work, the striatum was considered to be a primitive forebrain region. Graybiel found that the striatum instead has a complex architecture consisting of specialized zones: striosomes and the surrounding matrix. Her group went on to relate these zones to function, finding that striosomes and matrix differentially influence behavior. Among other important findings, Graybiel has shown that striosomes are focal points in circuits that link mood-related cortical regions with the dopamine-containing neurons of the midbrain, which are implicated in learning and motivation and which undergo degeneration in Parkinson’s disorder and other clinical conditions. She and her group have shown that these regions are activated by drugs of abuse, and that they influence decision-making, including decisions that require weighing of costs and benefits.

Graybiel continues to drive the field forward, finding that striatal neurons spike in an accentuated fashion and ‘bookend’ the beginning and end of behavioral sequences in rodents and primates. This activity pattern suggests that the striatum demarcates useful behavioral sequences such, in the case of rodents, pressing levers or running down mazes to receive a reward. Additionally, she and her group worked on miniaturized tools for chemical sensing and delivery as part of a continued drive toward therapeutic intervention in collaboration with the laboratories of Robert Langer in the Department of Chemical Engineering and Michael Cima, in the Department of Materials Science and Engineering.

“My first thought was of our lab, and how fortunate I am to work with such talented and wonderful people,” says Graybiel.  “I am deeply honored to be recognized by this prestigious award on behalf of our lab.”

The Gruber Foundation’s international prize program recognizes researchers in the areas of cosmology, neuroscience and genetics, and includes a cash award of $500,000 in each field. The medal given to award recipients also outlines the general mission of the foundation, “for the fundamental expansion of human knowledge,” and the prizes specifically honor those whose groundbreaking work fits into this paradigm.

Graybiel, a member of the MIT Class of 1971, has also previously been honored with the National Medal of Science, the Kavli Award, the James R. Killian Faculty Achievement Award at MIT, Woman Leader of Parkinson’s Science award from the Parkinson’s Disease Foundation, and has been recognized by the National Parkinson Foundation for her contributions to the understanding and treatment of Parkinson’s disease. Graybiel is a member of the National Academy of Sciences, the National Academy of Medicine, and the American Academy of Arts and Sciences.

The Gruber Neuroscience Prize will be presented in a ceremony at the annual meeting of the Society for Neuroscience in San Diego this coming November.

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.”

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.