From summer internships as an undergraduate studying neuroscience at the University of Notre Dame, Sadie Zacharek developed interests in areas ranging from neuroimaging to developmental psychopathologies, from basic-science research to clinical translation. When she interviewed with John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology and Cognitive Neuroscience, for a position in his lab as a graduate fellow, everything came together.
“The brain provides a window not only into dysfunction but also into response to treatment,” she says. “John and I both wanted to explore how we might use neuroimaging as a step toward personalized medicine.”
Zacharek joined the Gabrieli lab in 2020 and currently holds the Sheldon and Janet Razin’59 Fellowship for 2023-2024. In the Gabrieli lab, she has been designing and helping launch studies focusing on the neural mechanisms driving childhood depression and social anxiety disorder with the aim of developing strategies to predict which treatments will be most effective for individual patients.
Helping children and adults
“Depression in children is hugely understudied,” says Zacharek. “Most of the research has focused on adult and adolescent depression.” But the clinical presentation differs in the two groups, she says. “In children, irritability can be the primary presenting symptom rather than melancholy.” To get to the root of childhood depression, she is exploring both the brain basis of the disorder and how the parent-child relationship might influence symptoms. “Parents help children develop their emotion-regulation skills,” she says. “Knowing the underlying mechanisms could, in family-focused therapy, help them turn a ‘downward spiral’ into irritability, into an ‘upward spiral,’ away from it.”
The studies she is conducting include functional magnetic resonance imaging (fMRI) of children to explore their brain responses to positive and negative stimuli, fMRI of both the child and parent to compare maps of their brains’ functional connectivity, and magnetic resonance spectroscopy to explore the neurochemical environment of both, including quantities of neurometabolites that indicate inflammation (higher levels have been found to correlate with depressive pathology).
“If we could find a normative range for neurochemicals and then see how far someone has deviated in depression, or a neural signature of elevated activity in a brain region, that could serve as a biomarker for future interventions,” she says. “Such a biomarker would be especially relevant for children given that they are less able to articulately convey their symptoms or internal experience.”
“The brain provides a window not only into dysfunction but also into response to treatment.” – Sadie Zacharek
Social anxiety disorder is a chronic and disabling condition that affects about 7.1 percent of U.S. adults. Treatment usually involves cognitive behavior therapy (CBT), and then, if there is limited response, the addition of a selective serotonin reuptake inhibitor (SSRI), as an anxiolytic.
But what if research could reveal the key neurocircuitry of social anxiety disorder as well as changes associated with treatment? That could open the door to predicting treatment outcome.
Zacharek is collecting neuroimaging data, as well as clinical assessments, from participants. The participants diagnosed with social anxiety disorder will then undergo 12 weeks of group CBT, followed by more data collection, and then individual CBT for 12 weeks plus an SSRI for those who do not benefit from the group CBT. The results from those two time points will help determine the best treatment for each person.
“We hope to build a predictive model that could enable clinicians to scan a new patient and select the optimal treatment,” says Zacharek. “John’s many long-standing relationships with clinicians in this area make all of these translational studies possible.”
Maedbh King came to MIT to make a difference in mental health. As a postdoctoral fellow in the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center, she is building computer models aimed at helping clinicians improve diagnosis and treatment, especially for young people with neurodevelopmental and psychiatric disorders.
Tapping two large patient-data sources, King is working to analyze critical biological and behavioral information to better categorize patients’ mental health conditions, including autism spectrum disorder, attention-deficit hyperactivity disorder (ADHD), anxiety, and suicidal thoughts — and to provide more predictive approaches to addressing them. Her strategy reflects the center’s commitment to a holistic understanding of human brain function using theoretical and computa-tional neuroscience.
“Today, treatment decisions for psychiatric disorders are derived entirely from symptoms, which leaves clinicians and patients trying one treatment and, if it doesn’t work, trying another,” says King. “I hope to help change that.”
King grew up in Dublin, Ireland, and studied psychology in college; gained neuroimaging and programming skills while earning a master’s degree from Western University in Canada; and received her doctorate from the University of California, Berkeley, where she built maps and models of the human brain. In fall 2022, King joined the lab of Satrajit Ghosh, a McGovern Institute principal research scientist whose team uses neuroimaging, speech communication, and machine learning to improve assessments and treatments for mental health and neurological disorders.
King is pursuing several projects using the Healthy Brain Network, a landmark mental health study of children and adolescents in New York City. She and lab colleagues are extracting data from cognitive and other assessments — such as language patterns, favorite school subjects, and family mental illness history — from roughly 4,000 participants to provide more-nuanced understanding of their neurodevelopmental disorders, such as autism or ADHD.
“Computational models are powerful. They can identify patterns that can’t be obtained with the human eye through electronic records,” says King.
With this database, one can develop “very rich clinical profiles of these young people,” including their challenges and adaptive strengths, King explains. “We’re interested in placing these participants within a spectrum of symptoms, rather than just providing a binary label of, ‘has this disorder’ or ‘doesn’t have it.’ It’s an effort to subtype based on these phenotypic assessments.”
In other research, King is developing tools to detect risk factors for suicide among adolescents. Working with psychiatrists at Children’s Hospital of Philadelphia, she is using detailed questionnaires from some 20,000 youths who visited the hospital’s emergency department over several years; about one-tenth had tried to take their own lives. The questionnaires collect information about demographics, lifestyle, relationships, and other aspects of patients’ lives.
“One of the big questions the physicians want to answer is, Are there any risk predictors we can identify that can ultimately prevent, or at least mitigate, future suicide attempts?” King says. “Computational models are powerful. They can identify patterns that can’t be obtained with the human eye through electronic records.”
King is passionate about producing findings to help practitioners, whether they’re clinicians, teachers, parents, or policy makers, and the populations they’re studying. “This applied work,” she says, “should be communicated in a way that can be useful.
Real-time feedback about brain activity can help adolescents with depression or anxiety quiet their minds, according to a new study from MIT scientists. The researchers, led by McGovern research affiliate Susan Whitfield-Gabrieli, have used functional magnetic resonance imaging (fMRI) to show patients what’s happening in their brain as they practice mindfulness inside the scanner and to encourage them to focus on the present. They report in the journal Molecular Psychiatry that doing so settles down neural networks that are associated with symptoms of depression.
“We know this mindfulness meditation is really good for kids and teens, and we think this real-time fMRI neurofeedback is really a way to engage them and provide a visual representation of how they’re doing,” says Whitfield-Gabrieli. “And once we train people how to do mindfulness meditation, they can do it on their own at any time, wherever they are.”
The approach could be a valuable tool to alleviate or prevent depression in young people, which has been on the rise in recent years and escalated alarmingly during the Covid-19 pandemic. “This has gone from bad to catastrophic, in my perspective,” Whitfield-Gabrieli says. “We have to think out of the box and come up some really innovative ways to help.”
Default mode network
Mindfulness meditation, in which practitioners focus their awareness on the present moment, can modulate activity within the brain’s default mode network, which is so named because it is most active when a person is not focused on any particular task. Two hubs within the default mode network, the medial prefrontal cortex and the posterior cingulate cortex, are of particular interest to Whitfield-Gabrieli and her colleagues, due to a potential role in the symptoms of depression and anxiety.
“These two core hubs are very engaged when we’re thinking about the past or the future and we’re not really engaged in the present moment,” she explains. “If we’re in a healthy state of mind, we may be reminiscing about the past or planning for the future. But if we’re depressed, that reminiscing may turn into rumination or obsessively rehashing the past. If we’re particularly anxious, we may be obsessively worrying about the future.”
Whitfield-Gabrieli explains that these key hubs are often hyperconnected in people with anxiety and depression. The more tightly correlated the activity of the two regions are, the worse a person’s symptoms are likely to be. Mindfulness, she says, can help interrupt that hyperconnectivity.
“Mindfulness really helps to focus on the now, which just precludes all of this mind wandering and repetitive negative thinking,” she explains. In fact, she and her colleagues have found that mindfulness practice can reduce stress and improve attention in children. But she acknowledges that it can be difficult to engage young people and help them focus on the practice.
Tuning the mind
To help people visualize the benefits of their mindfulness practice, the researchers developed a game that can be played while an MRI scanner tracks a person’s brain activity. On a screen inside the scanner, the participant sees a ball and two circles. The circle at the top of the screen represents a desirable state in which the activity of the brain’s default mode network has been reduced, and the activity of a network the brain uses to focus on attention-demanding tasks—the frontal parietal network—has increased. An initial fMRI scan identifies these networks in each individual’s brain, creating a customized mental map on which the game is based.
“They’re training their brain to tune their mind. And they love it.” – Susan Whitfield-Gabrieli
As the person practices mindfulness meditation, which they learn prior to entering the scanner, the default mode network in the brain quiets while the frontal parietal mode activates. When the scanner detects this change, the ball moves and eventually enters its target. With an initial success, the target shrinks, encouraging even more focus. When the participant’s mind wanders from their task, the default mode network activation increases (relative to the frontal parietal network) and the ball moves down towards the second circle, which represents an undesirable state. “Basically, they’re just moving this ball with their brain,” Whitfield-Gabrieli says. “They’re training their brain to tune their mind. And they love it.”
Nine individuals between the ages of 17 and 19 with a history of major depression or anxiety disorders tried this new approach to mindfulness training, and for each of them, Whitfield-Gabrieli’s team saw a reduction in connectivity within the default mode network. Now they are working to determine whether an electroencephalogram, in which brain activity is measured with noninvasive electrodes, can be used to provide similar neurofeedback during mindfulness training—an approach that could be more accessible for broad clinical use.
Whitfield-Gabrieli notes that hyperconnectivity in the default mode network is also associated with psychosis, and she and her team have found that mindfulness meditation with real-time fMRI feedback can help reduce symptoms in adults with schizophrenia. Future studies are planned to investigate how the method impacts teens’ ability to establish a mindfulness practice and its potential effects on depression symptoms.
When we make complex decisions, we have to take many factors into account. Some choices have a high payoff but carry potential risks; others are lower risk but may have a lower reward associated with them.
A new study from MIT sheds light on the part of the brain that helps us make these types of decisions. The research team found a group of neurons in the brain’s striatum that encodes information about the potential outcomes of different decisions. These cells become particularly active when a behavior leads a different outcome than what was expected, which the researchers believe helps the brain adapt to changing circumstances.
“A lot of this brain activity deals with surprising outcomes, because if an outcome is expected, there’s really nothing to be learned. What we see is that there’s a strong encoding of both unexpected rewards and unexpected negative outcomes,” says Bernard Bloem, a former MIT postdoc and one of the lead authors of the new study.
Impairments in this kind of decision-making are a hallmark of many neuropsychiatric disorders, especially anxiety and depression. The new findings suggest that slight disturbances in the activity of these striatal neurons could swing the brain into making impulsive decisions or becoming paralyzed with indecision, the researchers say.
Rafiq Huda, a former MIT postdoc, is also a lead author of the paper, which appears in Nature Communications. Ann Graybiel, an MIT Institute Professor and member of MIT’s McGovern Institute for Brain Research, is the senior author of the study.
Learning from experience
The striatum, located deep within the brain, is known to play a key role in making decisions that require evaluating outcomes of a particular action. In this study, the researchers wanted to learn more about the neural basis of how the brain makes cost-benefit decisions, in which a behavior can have a mixture of positive and negative outcomes.
To study this kind of decision-making, the researchers trained mice to spin a wheel to the left or the right. With each turn, they would receive a combination of reward (sugary water) and negative outcome (a small puff of air). As the mice performed the task, they learned to maximize the delivery of rewards and to minimize the delivery of air puffs. However, over hundreds of trials, the researchers frequently changed the probabilities of getting the reward or the puff of air, so the mice would need to adjust their behavior.
As the mice learned to make these adjustments, the researchers recorded the activity of neurons in the striatum. They had expected to find neuronal activity that reflects which actions are good and need to be repeated, or bad and that need to be avoided. While some neurons did this, the researchers also found, to their surprise, that many neurons encoded details about the relationship between the actions and both types of outcomes.
The researchers found that these neurons responded more strongly when a behavior resulted in an unexpected outcome, that is, when turning the wheel in one direction produced the opposite outcome as it had in previous trials. These “error signals” for reward and penalty seem to help the brain figure out that it’s time to change tactics.
Most of the neurons that encode these error signals are found in the striosomes — clusters of neurons located in the striatum. Previous work has shown that striosomes send information to many other parts of the brain, including dopamine-producing regions and regions involved in planning movement.
“The striosomes seem to mostly keep track of what the actual outcomes are,” Bloem says. “The decision whether to do an action or not, which essentially requires integrating multiple outcomes, probably happens somewhere downstream in the brain.”
The findings could be relevant not only to mice learning a task, but also to many decisions that people have to make every day as they weigh the risks and benefits of each choice. Eating a big bowl of ice cream after dinner leads to immediate gratification, but it might contribute to weight gain or poor health. Deciding to have carrots instead will make you feel healthier, but you’ll miss out on the enjoyment of the sweet treat.
“From a value perspective, these can be considered equally good,” Bloem says. “What we find is that the striatum also knows why these are good, and it knows what are the benefits and the cost of each. In a way, the activity there reflects much more about the potential outcome than just how likely you are to choose it.”
This type of complex decision-making is often impaired in people with a variety of neuropsychiatric disorders, including anxiety, depression, schizophrenia, obsessive-compulsive disorder, and posttraumatic stress disorder. Drug abuse can also lead to impaired judgment and impulsivity.
“You can imagine that if things are set up this way, it wouldn’t be all that difficult to get mixed up about what is good and what is bad, because there are some neurons that fire when an outcome is good and they also fire when the outcome is bad,” Graybiel says. “Our ability to make our movements or our thoughts in what we call a normal way depends on those distinctions, and if they get blurred, it’s real trouble.”
The new findings suggest that behavioral therapy targeting the stage at which information about potential outcomes is encoded in the brain may help people who suffer from those disorders, the researchers say.
The research was funded by the National Institutes of Health/National Institute of Mental Health, the Saks Kavanaugh Foundation, the William N. and Bernice E. Bumpus Foundation, the Simons Foundation, the Nancy Lurie Marks Family Foundation, the National Eye Institute, the National Institute of Neurological Disease and Stroke, the National Science Foundation, the Simons Foundation Autism Research Initiative, and JSPS KAKENHI.
We all do it. One second you’re fully focused on the task in front of you, a conversation with a friend, or a professor’s lecture, and the next second your mind is wandering to your dinner plans.
But how does that happen?
“We spend so much of our daily lives engaged in things that are completely unrelated to what’s in front of us,” says Aaron Kucyi, neuroscientist and principal research scientist in the department of psychology at Northeastern. “And we know very little about how it works in the brain.”
So Kucyi and colleagues at Massachusetts General Hospital, Boston University, and the McGovern Institute at MIT started scanning people’s brains using functional magnetic resonance imaging (fMRI) to get an inside look. Their results, published Friday in the journal Nature Communications, add complexity to our understanding of the wandering mind.
It turns out that spacing out might not deserve the bad reputation that it receives. Many more parts of the brain seem to be engaged in mind-wandering than previously thought, supporting the idea that it’s actually a quite dynamic and fundamental function of our psychology.
“Many of those things that we do when we’re spacing out are very adaptive and important to our lives,” says Kucyi, the paper’s first author. We might be drafting an email in our heads while in the shower, or trying to remember the host’s spouse’s name while getting dressed for a party. Moments when our minds wander can allow space for creativity and planning for the future, he says, so it makes sense that many parts of the brain would be engaged in that kind of thinking.
But mind wandering may also be detrimental, especially for those suffering from mental illness, explains the study’s senior author, Susan Whitfield-Gabrieli. “For many of us, mind wandering may be a healthy, positive and constructive experience, like reminiscing about the past, planning for the future, or engaging in creative thinking,” says Whitfield-Gabrieli, a professor of psychology at Northeastern University and a McGovern Institute research affiliate. “But for those suffering from mental illness such as depression, anxiety or psychosis, reminiscing about the past may transform into ruminating about the past, planning for the future may become obsessively worrying about the future and creative thinking may evolve into delusional thinking.”
Identifying the brain circuits associated with mind wandering, she says, may reveal new targets and better treatment options for people suffering from these disorders.
Inside the wandering mind
To study wandering minds, the researchers first had to set up a situation in which people were likely to lose focus. They recruited test subjects at the McGovern Institute’s Martinos Imaging Center to complete a simple, repetitive, and rather boring task. With an fMRI scanner mapping their brain activity, participants were instructed to press a button whenever an image of a city scene appeared on a screen in front of them and withhold a response when a mountain image appeared.
Throughout the experiment, the subjects were asked whether they were focused on the task at hand. If a subject said their mind was wandering, the researchers took a close look at their brain scans from right before they reported loss of focus. The data was then fed into a machine-learning algorithm to identify patterns in the neurological connections involved in mind-wandering (called “stimulus-independent, task-unrelated thought” by the scientists).
Scientists previously identified a specialized system in the brain considered to be responsible for mind-wandering. Called the “default mode network,” these parts of the brain activated when someone’s thoughts were drifting away from their immediate surroundings and deactivated when they were focused. The other parts of the brain, that theory went, were quiet when the mind was wandering, says Kucyi.
The “default mode network” did light up in Kucyi’s data. But parts of the brain associated with other functions also appeared to activate when his subjects reported that their minds had wandered.
For example, the “default mode network” and networks in the brain related to controlling or maintaining a train of thought also seemed to be communicating with one another, perhaps helping explain the ability to go down a rabbit hole in your mind when you’re distracted from a task. There was also a noticeable lack of communication between the “default mode network” and the systems associated with sensory input, which makes sense, as the mind is wandering away from the person’s immediate environment.
“It makes sense that virtually the whole brain is involved,” Kucyi says. “Mind-wandering is a very complex operation in the brain and involves drawing from our memory, making predictions about the future, dynamically switching between topics that we’re thinking about, fluctuations in our mood, and engaging in vivid visual imagery while ignoring immediate visual input,” just to name a few functions.
The “default mode network” still seems to be key, Kucyi says. Virtual computer analysis suggests that if you took the regions of the brain in that network out of the equation, the other brain regions would not be able to pick up the slack in mind-wandering.
Kucyi, however, didn’t just want to identify regions of the brain that lit up when someone said their mind was wandering. He also wanted to be able to use that generalized pattern of brain activity to be able to predict whether or not a subject would say that their focus had drifted away from the task in front of them.
That’s where the machine-learning analysis of the data came in. The idea, Kucyi says, is that “you could bring a new person into the scanner and not even ask them whether they were mind-wandering or not, and have a good estimate from their brain data whether they were.”
The ADHD brain
To test the patterns identified through machine learning, the researchers brought in a new set of test subjects – people diagnosed with ADHD. When the fMRI scans lit up the parts of the brain Kucyi and his colleagues had identified as engaged in mind-wandering in the first part of the study, the new test subjects reported that their thoughts had drifted from the images of cities and mountains in front of them. It worked.
Kucyi doesn’t expect fMRI scans to become a new way to diagnose ADHD, however. That wasn’t the goal. Perhaps down the road it could be used to help develop treatments, he suggests. But this study was focused on “informing the biological mechanisms behind it.”
John Gabrieli, a co-author on the study and director of the imaging center at MIT’s McGovern Institute, adds that “there is recent evidence that ADHD patients with more mind-wandering have many more everyday practical and clinical difficulties than ADHD patients with less mind-wandering. This is the first evidence about the brain basis for that important difference, and points to what neural systems ought to be the targets of intervention to help ADHD patients who struggle the most.”
For Kucyi, the study of “mind-wandering” goes beyond ADHD. And the contents of those straying thoughts may be telling, he says.
“We just asked people whether they were focused on the task or away from the task, but we have no idea what they were thinking about,” he says. “What are people thinking about? For example, are those more positive thoughts or negative thoughts?” Such answers, which he hopes to explore in future research, could help scientists better understand other pathologies such as depression and anxiety, which often involve rumination on upsetting or worrisome thoughts.
Whitfield-Gabrieli and her team are already exploring whether behavioral interventions, such as mindfulness based real-time fMRI neurofeedback, can be used to help train people suffering from mental illness to modulate their own brain networks and reduce hallucinations, ruminations, and other troubling symptoms.
“We hope that our research will have clinical implications that extend far beyond the potential for identifying treatment targets for ADHD,” she says.
Dealing with a global pandemic has taken a toll on the mental health of millions of people. A team of MIT and Harvard University researchers has shown that they can measure those effects by analyzing the language that people use to express their anxiety online.
Using machine learning to analyze the text of more than 800,000 Reddit posts, the researchers were able to identify changes in the tone and content of language that people used as the first wave of the Covid-19 pandemic progressed, from January to April of 2020. Their analysis revealed several key changes in conversations about mental health, including an overall increase in discussion about anxiety and suicide.
“We found that there were these natural clusters that emerged related to suicidality and loneliness, and the amount of posts in these clusters more than doubled during the pandemic as compared to the same months of the preceding year, which is a grave concern,” says Daniel Low, a graduate student in the Program in Speech and Hearing Bioscience and Technology at Harvard and MIT and the lead author of the study.
The analysis also revealed varying impacts on people who already suffer from different types of mental illness. The findings could help psychiatrists, or potentially moderators of the Reddit forums that were studied, to better identify and help people whose mental health is suffering, the researchers say.
“When the mental health needs of so many in our society are inadequately met, even at baseline, we wanted to bring attention to the ways that many people are suffering during this time, in order to amplify and inform the allocation of resources to support them,” says Laurie Rumker, a graduate student in the Bioinformatics and Integrative Genomics PhD Program at Harvard and one of the authors of the study.
Satrajit Ghosh, a principal research scientist at MIT’s McGovern Institute for Brain Research, is the senior author of the study, which appears in the Journal of Internet Medical Research. Other authors of the paper include Tanya Talkar, a graduate student in the Program in Speech and Hearing Bioscience and Technology at Harvard and MIT; John Torous, director of the digital psychiatry division at Beth Israel Deaconess Medical Center; and Guillermo Cecchi, a principal research staff member at the IBM Thomas J. Watson Research Center.
A wave of anxiety
The new study grew out of the MIT class 6.897/HST.956 (Machine Learning for Healthcare), in MIT’s Department of Electrical Engineering and Computer Science. Low, Rumker, and Talkar, who were all taking the course last spring, had done some previous research on using machine learning to detect mental health disorders based on how people speak and what they say. After the Covid-19 pandemic began, they decided to focus their class project on analyzing Reddit forums devoted to different types of mental illness.
“When Covid hit, we were all curious whether it was affecting certain communities more than others,” Low says. “Reddit gives us the opportunity to look at all these subreddits that are specialized support groups. It’s a really unique opportunity to see how these different communities were affected differently as the wave was happening, in real-time.”
The researchers analyzed posts from 15 subreddit groups devoted to a variety of mental illnesses, including schizophrenia, depression, and bipolar disorder. They also included a handful of groups devoted to topics not specifically related to mental health, such as personal finance, fitness, and parenting.
Using several types of natural language processing algorithms, the researchers measured the frequency of words associated with topics such as anxiety, death, isolation, and substance abuse, and grouped posts together based on similarities in the language used. These approaches allowed the researchers to identify similarities between each group’s posts after the onset of the pandemic, as well as distinctive differences between groups.
The researchers found that while people in most of the support groups began posting about Covid-19 in March, the group devoted to health anxiety started much earlier, in January. However, as the pandemic progressed, the other mental health groups began to closely resemble the health anxiety group, in terms of the language that was most often used. At the same time, the group devoted to personal finance showed the most negative semantic change from January to April 2020, and significantly increased the use of words related to economic stress and negative sentiment.
They also discovered that the mental health groups affected the most negatively early in the pandemic were those related to ADHD and eating disorders. The researchers hypothesize that without their usual social support systems in place, due to lockdowns, people suffering from those disorders found it much more difficult to manage their conditions. In those groups, the researchers found posts about hyperfocusing on the news and relapsing back into anorexia-type behaviors since meals were not being monitored by others due to quarantine.
Using another algorithm, the researchers grouped posts into clusters such as loneliness or substance use, and then tracked how those groups changed as the pandemic progressed. Posts related to suicide more than doubled from pre-pandemic levels, and the groups that became significantly associated with the suicidality cluster during the pandemic were the support groups for borderline personality disorder and post-traumatic stress disorder.
The researchers also found the introduction of new topics specifically seeking mental health help or social interaction. “The topics within these subreddit support groups were shifting a bit, as people were trying to adapt to a new life and focus on how they can go about getting more help if needed,” Talkar says.
While the authors emphasize that they cannot implicate the pandemic as the sole cause of the observed linguistic changes, they note that there was much more significant change during the period from January to April in 2020 than in the same months in 2019 and 2018, indicating the changes cannot be explained by normal annual trends.
Mental health resources
This type of analysis could help mental health care providers identify segments of the population that are most vulnerable to declines in mental health caused by not only the Covid-19 pandemic but other mental health stressors such as controversial elections or natural disasters, the researchers say.
Additionally, if applied to Reddit or other social media posts in real-time, this analysis could be used to offer users additional resources, such as guidance to a different support group, information on how to find mental health treatment, or the number for a suicide hotline.
“Reddit is a very valuable source of support for a lot of people who are suffering from mental health challenges, many of whom may not have formal access to other kinds of mental health support, so there are implications of this work for ways that support within Reddit could be provided,” Rumker says.
The researchers now plan to apply this approach to study whether posts on Reddit and other social media sites can be used to detect mental health disorders. One current project involves screening posts in a social media site for veterans for suicide risk and post-traumatic stress disorder.
The research was funded by the National Institutes of Health and the McGovern Institute.
The Wang lab studies the neural circuit basis of sensory perception. Wang is specifically interested in uncovering the neural circuits underlying: (1) Active touch sensation including the tactile processing stream and motor control of touch sensors on the face, (2) pain sensation including both sensory-discriminative and affective aspects of pain and (3) general anesthesia including the process of active pain-suppression. Wang uses a range of techniques to gain traction on these questions, including genetic, viral, electrophysiology, and in vivo imaging.
Mice use their whiskers to sense and explore the physical environment. Sensory information is first detected by trigeminal sensory neurons that innervate these whiskers and then processed by circuits in the brainstem, thalamus, and cortex that process information (such as the distance from or texture of an object). Whisker movement is driven by facial motor neurons, which also receive complex inputs from the brain. The Wang lab is mapping the detailed neural connectivity in this sensorimotor system using combinations of genetic and viral tools, as well as in vivo recording and functional manipulations of defined populations of neurons in this system. Through these approaches, her team is determining the roles that specific neural populations play in touch perception and touch-guided behaviors.
Pain vs No Pain
Pain perception involves two main aspects: the type of pain being felt, and the suffering and negative emotions evoked by this pain. Painful stimuli activate numerous brain regions, sometimes called the pain matrix. However, the identities of neurons and their exact roles in processing pain in each of these regions remain opaque. The Wang lab is mapping detailed connectivity of neurons linked to pain perception, recording in vivo activity using electrophysiology and imaging approaches, as well as manipulating pain-activated neurons (using activity-dependent tools) in multiple regions of the pain matrix to understand both sensory and affective pain perception, and how changes in this system contribute to the suffering associated with chronic pain.
It is well known that in humans, belief/placebo, focused attention (such as in emergency situations or in battlefield), as well as other conditions can actually block pain perception. The Wang lab is interested in dissecting the central circuits that mediate such pain-suppression. Specifically, they are studying neural mechanisms underlying anesthetics-, placebo-, and stress-induced analgesia.
Circuits of Addiction
A new direction the Wang lab is pursuing is pinpointing circuits that play a role in opioid addiction. Wang and her team have identified neurons that are either activated or suppressed by morphine. They are currently testing the hypothesis that specific groups of morphine-inhibited neurons, when re-activated, can cause animals to crave less morphine and undertake non-drug-seeking activities. Once critical brain circuits and clusters of neurons involved in morphine-addiction are identified, the Wang lab will examine their connectivity, plasticity, and changes in function when influenced by morphine. Gaining a deeper understanding of how drugs of abuse affect these circuits will help pave the way for future treatments
Wang will join the McGovern Institute as an investigator in January 2021, also arriving at MIT to join the Department of Brain and Cognitive Sciences. Wang obtained her PhD at Columbia University with Richard Axel in 1998. She conducted her postdoctoral work at Stanford University with Mark Tessier-Lavigne. Wang subsequently joined Duke University as a Professor in the Department of Neurobiology in 2003, and was later appointed the Morris N. Broad Distinguished Professor of Neurobiology at Duke University School of Medicine.
Honors and Awards
Member, American Academy of Arts and Sciences
Society for Neuroscience, Special Lecture, 2019
Keck Foundation Award, 2016
Brain Research Foundation Scientific Innovation Award, 2015
Elected AAAS Fellow, 2014
NIH Pioneer Award, 2013
After being forced to relocate from their MIT dorms during the COVID19 crisis, two members of the Saxe lab are now applying their psychology skills to study the impact of mandatory relocation and social isolation on mental health.
“When ‘social distancing’ measures hit MIT, we tried to process how the implementation of these policies would impact the landscape of our social lives,” explains graduate student Heather Kosakowski, who conceived of the study late one evening with undergraduate Michelle Hung. This landscape is broad, examining the effects of being uprooted and physically relocated from a place, but also changes in social connections, including friendships and even dating life.
“I started speculating about how my life and the lives of other MIT students would change,” says Hung. “I was overwhelmed, sad, and scared. But then we realized that we were actually equipped to find the answers to our questions by conducting a study.”
Together, Kosakowski and Hung developed a survey to measure how the social behavior of MIT students, postdocs, and staff is changing over the course of the pandemic. Survey questions were designed to measure loneliness and other aspects of mental health. The survey was sent to members of the MIT community and shared on social media in mid-March, when the pandemic hit the US, and MIT made the unprecedented decision to send students home, shift to online instruction, and dramatically ramp down operations on campus.
More than 500 people responded to the initial survey, ranging in age from 18 to 60, living in cities and countries around the world. Many but not all of those who responded were affiliated with MIT. Kosakowski and Hung are sending follow-up surveys to participants every two weeks and the team plans to collect data for the duration of the pandemic.
“Throwing myself into creating the survey was a way to cope with feeling sad about leaving a community I love,” explains Hung, who flew home to California in March and admits that she struggles with feelings of loneliness now that she’s off campus.
Although it is too soon to form any conclusions about their research, Hung predicts that feelings of loneliness may actually diminish over the course of the pandemic.
“Humans have an impressive ability to adapt to change,” she says. “And I think in this virtual world, people will find novel ways to stay connected that we couldn’t have predicted.”
Whether we find ourselves feeling more or less lonely as this COVID-19 crisis comes to an end, both Kosakowski and Hung agree that it will fundamentally change life as we know it.
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Abnormal levels of stress hormones such as adrenaline and cortisol are linked to a variety of mental health disorders, including depression and posttraumatic stress disorder (PTSD). MIT researchers have now devised a way to remotely control the release of these hormones from the adrenal gland, using magnetic nanoparticles.
This approach could help scientists to learn more about how hormone release influences mental health, and could eventually offer a new way to treat hormone-linked disorders, the researchers say.
“We’re looking how can we study and eventually treat stress disorders by modulating peripheral organ function, rather than doing something highly invasive in the central nervous system,” says Polina Anikeeva, an MIT professor of materials science and engineering and of brain and cognitive sciences.
To achieve control over hormone release, Dekel Rosenfeld, an MIT-Technion postdoc in Anikeeva’s group, has developed specialized magnetic nanoparticles that can be injected into the adrenal gland. When exposed to a weak magnetic field, the particles heat up slightly, activating heat-responsive channels that trigger hormone release. This technique can be used to stimulate an organ deep in the body with minimal invasiveness.
Anikeeva and Alik Widge, an assistant professor of psychiatry at the University of Minnesota and a former research fellow at MIT’s Picower Institute for Learning and Memory, are the senior authors of the study. Rosenfeld is the lead author of the paper, which appears today in Science Advances.
Anikeeva’s lab has previously devised several novel magnetic nanomaterials, including particles that can release drugs at precise times in specific locations in the body.
In the new study, the research team wanted to explore the idea of treating disorders of the brain by manipulating organs that are outside the central nervous system but influence it through hormone release. One well-known example is the hypothalamic-pituitary-adrenal (HPA) axis, which regulates stress response in mammals. Hormones secreted by the adrenal gland, including cortisol and adrenaline, play important roles in depression, stress, and anxiety.
“Some disorders that we consider neurological may be treatable from the periphery, if we can learn to modulate those local circuits rather than going back to the global circuits in the central nervous system,” says Anikeeva, who is a member of MIT’s Research Laboratory of Electronics and McGovern Institute for Brain Research.
As a target to stimulate hormone release, the researchers decided on ion channels that control the flow of calcium into adrenal cells. Those ion channels can be activated by a variety of stimuli, including heat. When calcium flows through the open channels into adrenal cells, the cells begin pumping out hormones. “If we want to modulate the release of those hormones, we need to be able to essentially modulate the influx of calcium into adrenal cells,” Rosenfeld says.
Unlike previous research in Anikeeva’s group, in this study magnetothermal stimulation was applied to modulate the function of cells without artificially introducing any genes.
To stimulate these heat-sensitive channels, which naturally occur in adrenal cells, the researchers designed nanoparticles made of magnetite, a type of iron oxide that forms tiny magnetic crystals about 1/5000 the thickness of a human hair. In rats, they found these particles could be injected directly into the adrenal glands and remain there for at least six months. When the rats were exposed to a weak magnetic field — about 50 millitesla, 100 times weaker than the fields used for magnetic resonance imaging (MRI) — the particles heated up by about 6 degrees Celsius, enough to trigger the calcium channels to open without damaging any surrounding tissue.
The heat-sensitive channel that they targeted, known as TRPV1, is found in many sensory neurons throughout the body, including pain receptors. TRPV1 channels can be activated by capsaicin, the organic compound that gives chili peppers their heat, as well as by temperature. They are found across mammalian species, and belong to a family of many other channels that are also sensitive to heat.
This stimulation triggered a hormone rush — doubling cortisol production and boosting noradrenaline by about 25 percent. That led to a measurable increase in the animals’ heart rates.
Treating stress and pain
The researchers now plan to use this approach to study how hormone release affects PTSD and other disorders, and they say that eventually it could be adapted for treating such disorders. This method would offer a much less invasive alternative to potential treatments that involve implanting a medical device to electrically stimulate hormone release, which is not feasible in organs such as the adrenal glands that are soft and highly vascularized, the researchers say.
Another area where this strategy could hold promise is in the treatment of pain, because heat-sensitive ion channels are often found in pain receptors.
“Being able to modulate pain receptors with this technique potentially will allow us to study pain, control pain, and have some clinical applications in the future, which hopefully may offer an alternative to medications or implants for chronic pain,” Anikeeva says. With further investigation of the existence of TRPV1 in other organs, the technique can potentially be extended to other peripheral organs such as the digestive system and the pancreas.
The research was funded by the U.S. Defense Advance Research Projects Agency ElectRx Program, a Bose Research Grant, the National Institutes of Health BRAIN Initiative, and a MIT-Technion fellowship.
Mood and attentional disorders amongst teens are an increasing concern, for parents, society, and for peers. A recent Pew research center survey found conditions such as depression and anxiety to be the number one concern that young students had about their friends, ranking above drugs or bullying.
“We’re seeing an epidemic in teen anxiety and depression,” explains McGovern Research Affiliate Susan Whitfield-Gabrieli.
“Scientists are finding a huge increase in suicide ideation and attempts, something that hit home for me as a mother of teens. Emergency rooms in hospitals now have guards posted outside doors of these teenagers that attempted suicide—this is a pressing issue,” explains Whitfield-Gabrieli who is also director of the Northeastern University Biomedical Imaging Center and a member of the Poitras Center for Psychiatric Disorders Research.
Finding new methods for discovering early biomarkers for risk of psychiatric disorders would allow early interventions and avoid reaching points of crisis such as suicide ideation or attempts. In research published recently in JAMA Psychiatry, Whitfield-Gabrieli and colleagues found that signatures predicting future development of depression and attentional symptoms can be detected in children as young as seven years old.
While previous work had suggested that there may be biomarkers that predict development of mood and attentional disorders, identifying early biomarkers prior to an onset of illness requires following a cohort of pre-teens from a young age, and monitoring them across years. This effort to have a proactive, rather than reactive, approach to the development of symptoms associated with mental disorders is exactly the route Whitfield-Gabrieli and colleagues took.
“One of the exciting aspects of this study is that the cohort is not pre-selected for already having symptoms of psychiatric disorders themselves or even in their family,” explained Whitfield-Gabrieli. “It’s an unbiased cohort that we followed over time.”
In some past studies, children were pre-selected, for example a major depressive disorder diagnosis in the parents, but Whitfield-Gabrieli and colleagues, Silvia Bunge from Berkeley and Laurie Cutting from Vanderbilt, recruited a range of children without preconditions, and examined them at age 7, then again 4 years later. The researchers examined resting state functional connectivity, and compared this to scores on the child behavioral checklist (CBCL), allowing them to relate differences in the brain to a standardized analysis of behavior that can be linked to psychiatric disorders. The CBCL is used both in research and in the clinic and his highly predictive of disorders including ADHD, so that changes in the brain could be related to changes in a widely used clinical scoring system.
“Over the four years, some people got worse, some got better, and some stayed the same according the CBCL. We could relate this directly to differences in brain networks, and could identify at age 7 who would get worse,” explained Whitfield-Gabrieli.
Brain network changes
The authors analyzed differences in resting state network connectivity, regions across the brain that rise and fall in activity level together, as visualized using fMRI. Reduced connectivity between these regions may allow us to get a handle on reduced “top-down” control of neural circuits. The dorsolateral prefrontal region is linked to executive function, external attention, and emotional control. Increased connection with the medial prefrontal cortex is known to be present in attention deficit hyperactivity disorder (ADHD), while a reduced connection to a different brain region, the sgACC, is seen in major depressive disorder. The question remained as to whether these changes can be seen prior to the onset of diagnosable attentional or mood disorders.
Whitfield-Gabrieli and colleagues found that these resting state networks varied in the brains of children that would later develop anxiety/depression and ADHD symptoms. Weaker scores in connectivity between the dorsolateral and medial prefrontal cortical regions tended to be seen in children whose attention scores went on to improve. Analysis of the resting state networks above could differentiate those who would have typical attentional behavior by age 11 versus those that went on to develop ADHD.
Whitfield-Gabrieli has replicated this finding in an independent sample of children and she is continuing to expand the analysis and check the results, as well as follow this cohort into the future. Should changes in resting state networks be a consistent biomarker, the next step is to initiate interventions prior to the point of crisis.
“We’ve recently been able to use mindfulness interventions, and show these reduce self-perceived stress and amygdala activation in response to fear, and we are also testing the effect of exercise interventions,” explained Whitfield-Gabrieli. “The hope is that by using predictive biomarkers we can augment children’s lifestyles with healthy interventions that can prevent risk converting to a psychiatric disorder.”