by Laura Carter | School of Science + Julie Pryor | McGovern Institute |
As an Iraq war veteran, Omar Rutledge is deeply familiar with post-traumatic stress – recurring thoughts and memories that persist long after a danger has passed – and he knows that a brain altered by trauma is not easily fixed. But as a graduate student in the Department of Brain and Cognitive Sciences, Rutledge is determined to change that. He wants to understand exactly how trauma alters the brain – and whether the tools of neuroscience can be used to help fellow veterans with post-traumatic stress disorder (PTSD) heal from their experiences.
“In the world of PTSD research, I look to my left and to my right, and I don’t see other veterans, certainly not former infantrymen,” says Rutledge, who served in the US Army and was deployed to Iraq from March 2003 to July 2004. “If there are so few of us in this space, I feel like I have an obligation to make a difference for all who suffer from the traumatic experiences of war.”
Rutledge is uniquely positioned to make such a difference in the lab of McGovern Investigator John Gabrieli, where researchers use technologies like magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG) to peer into the human brain and explore how it powers our thoughts, memories, and emotions. Rutledge is studying how PTSD weakens the connection between the amygdala, which is responsible for emotions like fear, and the prefrontal cortex, which regulates or controls these emotional responses. He hopes these studies will eventually lead to the development of wearable technologies that can retrain the brain to be less responsive to triggering events.
“I feel like it has been a mission of mine to do this kind of work.”
Though Covid-19 has unexpectedly paused some aspects of his research, Rutledge is pursuing another line of research inspired both by the mandatory social distancing protocols imposed during the lockdown and his own experiences with social isolation. Does chronic social isolation cause physical or chemical changes in the brain similar to those seen in PTSD? And does loneliness exacerbate symptoms of PTSD?
“There’s this hypervigilance that occurs in loneliness, and there’s also something very similar that occurs in PTSD — a heightened awareness of potential threats,” says Rutledge, who is the recipient of Michael Ferrara Graduate Fellowship provided by the Poitras Center, a fellowship made possible by the many friends and family of Michael Ferrara. “The combination of the two may lead to more adverse reactions in people with PTSD.”
In the future, Rutledge hopes to explore whether chronic loneliness impairs reasoning and logic skills and has a deeper impact on veterans who have PTSD.
Although his research tends to resurface painful memories of his own combat experiences, Rutledge says if it can help other veterans heal, it’s worth it. “In the process, it makes me a little bit stronger as well,” he adds.
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.
The brain circuits that influence our decisions, cognitive functions, and ultimately, our actions are intimately connected with the circuits that give rise to our motivations. By exploring these relationships, scientists at McGovern are seeking knowledge that might suggest new strategies for changing our habits or treating motivation-disrupting conditions such as depression and addiction.
In Ann Graybiel’s lab, researchers have been examining how the brain makes choices that carry both positive and negative consequences — deciding to take on a higher-paying but more demanding job, for example. Psychologists call these dilemmas approach-avoidance conflicts, and resolving them not only requires weighing the good versus the bad, but also motivation to engage with the decision.
Emily Hueske, a research scientist in the Graybiel lab, explains that everyone has their own risk tolerance when it comes to such decisions, and certain psychiatric conditions, including depression and anxiety disorders, can shift the tipping point at which a person chooses to “approach” or “avoid.”
Studies have shown that neurons in the striatum (see image below), a region deep in the brain involved in both motivation and movement, activate as we grapple with these decisions. Graybiel traced this activity even further, to tiny compartments within the striatum called striosomes.
(She discovered striosomes many years ago and has been studying their function for decades.)
A motivational switch
In 2015, Graybiel’s team manipulated striosome signaling within genetically engineered mice and changed the way animals behave in approach-avoidance conflict situations. Taking cues from an assessment used to evaluate approach-avoidance behavior in patients, they presented mice with opportunities to obtain chocolate while experiencing unwelcome exposure in a brightly lit area.
Experimentally activating neurons in striosomes had a dramatic effect, causing mice to venture into brightly lit areas that they would normally avoid. With striosomal circuits switched on, “this animal all of a sudden is like a different creature,” Graybiel says.
Two years later, they found that chronic stress and other factors can also disrupt this signaling and change the choices animals make.
Age of ennui
This November, Alexander Friedman, who worked as a research scientist in the Graybiel lab, and Hueske reported in Cell that they found an age-related decline in motivation-modulated learning in mice and rats. Neurons within striosomes became more active than the cells that surround them as animals learned to assign positive and negative values to potential choices. And older mice were less engaged than their younger counterparts in the type of learning required to make these cost-benefit analyses. A similar lack of motivation was observed in a mouse model of Huntington’s disease, a neurodegenerative disorder that is often associated with mood
disturbances in patients.
“This coincides with our previous findings that striosomes are critically important for decisions that involve a conflict.”
“This coincides with our previous findings that striosomes are critically important for decisions that involve a conflict,” says Friedman, who is now an assistant professor at the University of Texas at El Paso.
Graybiel’s team is continuing to investigate these uniquely positioned compartments in the brain, expecting to shed light on the mechanisms that underlie both learning and motivation.
“There’s no learning without motivation, and in fact, motivation can be influenced by learning,” Hueske says. “The more you learn, the more excited you might be to engage in the task. So the two are intertwined.”
The aging brain
Researchers in John Gabrieli’s lab are also seeking to understand the circuits that link motivation to learning, and recently, his team reported that they, too, had found an age-related decline in motivation-modulated learning.
Studies in young adults have shown that memory improves when the brain circuits that process motivation and memory interact. Gabrieli and neurologist Maiya Geddes, who worked in Gabrieli’s lab as a postdoctoral fellow, wondered whether this holds true in older adults, particularly as memory declines.
To find out, the team recruited 40 people to participate in a brain imaging study. About half of the participants were between the ages of 18 and 30, while the others were between the ages of 49 and 84. While inside an fMRI scanner, each participant was asked to commit certain words to memory and told their success would determine how much money they received for participating in the experiment.
Not surprisingly, when participants were asked 24 hours later to recall the words, the younger group performed better overall than the older group. In young people, incentivized memory tasks triggered activity in parts of the brain involved in both memory and motivation. But in older adults, while these two parts of the brain could be activated independently, they did not seem to be communicating with one another.
“It seemed that the older adults, at least in terms of their brain response, did care about the kind of incentives that we were offering,” says Geddes, who is now an assistant professor at McGill University. “But for whatever reason, that wasn’t allowing them to benefit in terms of improved memory performance.”
Since the study indicates the brain still can anticipate potential rewards, Geddes is now exploring whether other sources of motivation, such as social rewards, might more effectively increase healthful decisions and behaviors in older adults.
Understanding how the brain generates and responds to motivation is not only important for improving learning strategies. Lifestyle choices such as exercise and social engagement can help people preserve cognitive function and improve their quality of life as they age, and Gabrieli says activating the right motivational circuits could help encourage people to implement healthy changes.
By pinpointing these motivational circuits in mice, Graybiel hopes that her research will lead to better treatment strategies for people struggling with motivational challenges, including Parkinson’s disease. Her team is now exploring whether striosomes serve as part of a value-sensitive switch, linking our intentions to dopamine-containing neurons in the midbrain that can modulate our actions.
“Perhaps this motivation is critical for the conflict resolution, and striosomes combine two worlds, dopaminergic motivation and cortical knowledge, resulting in motivation to learn,” Friedman says.
“Now we know that these challenges have a biological basis, and that there are neural circuits that can promote or reduce our feeling of motivational energy,” explains Graybiel. “This realization in itself is a major step toward learning how we can control these circuits both behaviorally and by highly selective therapeutic targeting.”
When the coronavirus pandemic shut down offices, labs, and classrooms across the MIT campus last spring, many members of the MIT community found it challenging to remain connected to one another in meaningful ways. Motivated by a desire to bring the neuroscience community back together, the McGovern Institute hosted a virtual storytelling competition featuring a selection of postdocs, grad students, and staff from across the institute.
“This has been an unprecedented year for us all,” says McGovern Institute Director Robert Desimone. “It has been twenty years since Pat and Lore McGovern founded the McGovern Institute, and despite the challenges this anniversary year has brought to our community, I have been inspired by the strength and perseverance demonstrated by our faculty, postdocs, students and staff. The resilience of this neuroscience community – and MIT as a whole – is indeed something to celebrate.”
The McGovern Institute had initially planned to hold a large 20th anniversary celebration in the atrium of Building 46 in the fall of 2020, but the pandemic made a gathering of this size impossible. The institute instead held a series of virtual events, including the November 12 story slam on the theme of resilience.
“It’s been really heartening to see the compassion that’s emerged during this situation. People are looking out for each other, and thinking about each other, and checking in with each other.
Usually our social interactions are just built into the day, and now we need to be more deliberate.
The need for human connection has become so apparent these last few weeks as we’ve all been physically distancing. Usually our social interactions are just built into the day, and now we need to be more deliberate.
I’ve started writing a letter to a different person every day – something that I never took the time to do before! Especially as scientists, communication and collaboration are central to what we do. I’ve been amazed at how quickly we’re adapting to this situation and finding ways to keep connecting with each other – whether it’s virtual conferences or Zoom lab meetings or Slack channels. Plus seeing other people’s pets has been a bonus!
Overall I’ve just been really grateful and awed to see people come together, and support each other, and keep things moving forward during a tough time.”
Halie Olson, a graduate student in the labs of John Gabrieli and Rebecca Saxe, studies how early life experiences and environments impact brain development.
Researchers often approach autism spectrum disorder (ASD) through the lens of what might “break down.” While this approach has value, autism is an extremely heterogeneous condition, and diagnosed individuals have a broad range of abilities.
The Gabrieli lab is embracing this diversity and leveraging the strengths of diagnosed individuals by researching their specific “affinities.”
Affinities involve a strong passion for specific topics, ranging from insects to video game characters, and can include impressive feats of knowledge and focus.
The biological basis of these affinities and associated abilities remains unclear, which is intriguing to John Gabrieli and his lab.
“A striking aspect of autism is the great variation from individual to individual,” explains McGovern Investigator John Gabrieli. “Understanding what motivates an individual child may inform how to best help that child reach his or her communicative potential.”
Affinities have traditionally been seen as a distraction “interfering” with conventional teaching and learning. This mindset was upended by the 2014 book Life Animated by Ron Suskind, whose autistic son Owen seemingly lost his ability to speak around age three. Despite this setback, Owen maintained a deep affinity for Disney movies and characters. Rather than extinguishing this passion, the Suskinds embraced it as a path to connection.
Reframing such affinities as a strength not a frustration, and a path to communication rather than a roadblock, caught the attention of Kristy Johnson, a PhD student at the MIT Media Lab, who also has a non-verbal child with autism.
“My interest is in empowering and understanding populations that have traditionally been hard to study, including those with non-verbal and minimally verbal autism,” explains Johnson. “One way to do that is through affinities.”
But even identifying affinities is difficult. An interest in “trains” might mean 18th-century smokestacks to one child, and the purple line of the MBTA commuter rail to another. Serendipitously, she mentioned her interest to Gabrieli one day. He slammed his hands on the table, jumped up, and ran to find lab members Anila D’Mello and Halie Olson, who were gearing up to pursue the neural basis of affinities in autism. A collaboration was born.
What followed was six months of intense discussion. How can an affinity be accurately defined? How can individually tailored experiments be adequately controlled? What makes a robust comparison group? How can task-related performance differences between individuals with autism be accounted for?
The handful of studies that had used fMRI neuroimaging to examine affinities in autism had focused on the brain’s reward circuitry. D’Mello and Olson wanted to examine the language network of the brain — a well-defined network of brain regions whose activation can be measured by fMRI. Affinities trigger communication in some individuals with autism (Suskind’s family were using Disney characters to engage and communicate, not simply as a reward). Was the language network being engaged by affinities? Could these results point to a way of tailoring learning for all types of development?
“The language network involves lots of regions across the brain, including temporal, parietal, frontal, and subcortical areas, which play specific roles in different aspects of language processing” explains Olson. “We were interested in a task that used affinities to tap the language network.”
By studying this network, the team is testing whether affinities can elicit “typical” activation in regions of the brain that are sometimes assumed to not be engaged in autism. The approach may help develop better paradigms for studying other tasks with individuals with autism. Regardless of whether there are differences between the group diagnosed with autism and typically developing children, insight will likely be gained into how personalized special interests influence engagement of the language network.
The resulting study is task-free, removing the variable of differing motor or cognitive skill sets. Kids watch videos of their individual affinity in the fMRI scanner, and then listen to stories based on that affinity. They also watch and listen to “neutral” videos and stories about nature that are consistent across all children. Identifying affinities robustly so that the right stimulus can be presented is critical. Rather than an interest in bugs, affinities are often very specific (bugs that eat other bugs). But identifying and cross-checking affinities is something the group is becoming adept at. The results are emerging, but the effects that the team are seeing are significant, and preliminary data suggest that affinities engage networks beyond reward circuits.
“We have a small sample right now, but across the sample, there seems to be a difference in activation in the brain’s language network when listening to affinity stories compared to neutral stories,” explains D’Mello. “The biggest surprise is that the differences are evident in single subjects.”
The work is already raising exciting new questions. Are there other brain regions engaged by affinities? How would such information inform education and intervention paradigms? In addition, the team is showing it’s possible to derive information from individualized, naturalistic experimental paradigms, a message for brain imaging and behavioral studies in general. The researchers also hope the results inspire parents, teachers, and psychologists to perceive and engage with an individual’s affinities in new ways.
“This could really help teach us to communicate with and motivate very young and non-verbal kids on the spectrum in a way that is interesting and meaningful to them,” D’Mello explains.
By studying the strengths of individuals with autism, these researchers are showing that, through embracing neurodiversity, we can enhance science, our understanding of the brain, and perhaps even our understanding of ourselves.
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.”
Many debilitating conditions like depression and addiction have biological signatures hidden in the brain well before symptoms appear. What if brain scans could be used to detect these hidden signatures and determine the most optimal treatment for each individual? McGovern Investigator John Gabrieli is interested in this question and wrote about the use of imaging technologies as a predictive tool for brain disorders in a recent issue of Scientific American.
“Brain scans show promise in predicting who will benefit from a given therapy,” says Gabrieli, who is also the Grover Hermann Professor in Brain and Cognitive Sciences at MIT. “Differences in neural activity may one day tell clinicians which depression treatment will be most effective for an individual or which abstinent alcoholics will relapse.”
Gabrieli cites research which has shown that half of patients treated for alcohol abuse go back to drinking within a year of treatment, and similar reversion rates occur for stimulants such as cocaine. Failed treatments may be a source of further anxiety and stress, Gabrieli notes, so any information we can glean from the brain to pinpoint treatments or doses that would help would be highly informative.
Current treatments rely on little scientific evidence to support the length of time needed in a rehabilitation facility, he says, but “a number suggest that brain measures might foresee who will succeed in abstaining after treatment has ended.”
Further data is needed to support this idea, but Gabrieli’s Scientific American piece makes the case that the use of such a technology may be promising for a range of addiction treatments including abuse of alcohol, nicotine, and illicit drugs.
Gabrieli also believes brain imaging has the potential to reshape education. For example, educational interventions targeting dyslexia might be more effective if personalized to specific differences in the brain that point to the source of the learning gap.
But for the prediction sciences to move forward in mental health and education, he concludes, the research community must design further rigorous studies to examine these important questions.
As part of our Ask the Brain series, Anila D’Mello, a postdoctoral fellow in John Gabrieli’s lab answers the question,”What is the social brain?”
“Knock Knock.” “Who’s there?” “The Social Brain.” “The Social Brain, who?”
Call and response jokes, like the “Knock Knock” joke above, leverage our common understanding of how a social interaction typically proceeds. Joke telling allows us to interact socially with others based on our shared experiences and understanding of the world. But where do these abilities “live” in the brain and how does the social brain develop?
Neuroimaging and lesion studies have identified a network of brain regions that support social interaction, including the ability to understand and partake in jokes – we refer to this as the “social brain.” This social brain network is made up of multiple regions throughout the brain that together support complex social interactions. Within this network, each region likely contributes to a specific type of social processing. The right temporo-parietal junction, for instance, is important for thinking about another person’s mental state, whereas the amygdala is important for the interpretation of emotional facial expressions and fear processing. Damage to these brain regions can have striking effects on social behaviors. One recent study even found that individuals with bigger amygdala volumes had larger and more complex social networks!
Though social interaction is such a fundamental human trait, we aren’t born with a prewired social brain.
Much of our social ability is grown and honed over time through repeated social interactions. Brain networks that support social interaction continue to specialize into adulthood. Neuroimaging work suggests that though newborn infants may have all the right brain parts to support social interaction, these regions may not yet be specialized or connected in the right way. This means that early experiences and environments can have large influences on the social brain. For instance, social neglect, especially very early in development, can have negative impacts on social behaviors and on how the social brain is wired. One prominent example is that of children raised in orphanages or institutions, who are sometimes faced with limited adult interaction or access to language. Children raised in these conditions are more likely to have social challenges including difficulties forming attachments. Prolonged lack of social stimulation also alters the social brain in these children resulting in changes in amygdala size and connections between social brain regions.
The social brain is not just a result of our environment. Genetics and biology also contribute to the social brain in ways we don’t yet fully understand. For example, individuals with autism / autistic individuals may experience difficulties with social interaction and communication. This may include challenges with things like understanding the punchline of a joke. These challenges in autism have led to the hypothesis that there may be differences in the social brain network in autism. However, despite documented behavioral differences in social tasks, there is conflicting brain imaging evidence for whether differences exist between people with and without autism in the social brain network.
Examples such as that of autism imply that the reality of the social brain is probably much more complex than the story painted here. It is likely that social interaction calls upon many different parts of the brain, even beyond those that we have termed the “social brain,” that must work in concert to support this highly complex set of behaviors. These include regions of the brain important for listening, seeing, speaking, and moving. In addition, it’s important to remember that the social brain and regions that make it up do not stand alone. Regions of the social brain also play an intimate role in language, humor, and other cognitive processes.
“Knock Knock” “Who’s there?” “The Social Brain” “The Social Brain, who?” “I just told you…didn’t you read what I wrote?”
Anila D’Mello earned her bachelor’s degree in psychology from Georgetown University in 2012, and went on to receive her PhD in Behavior, Cognition, and Neuroscience from American University in 2017. She joined the Gabrieli lab as a postdoc in 2017 and studies the neural correlates of social communication in autism.
Do you have a question for The Brain? Ask it here.
Two MIT professors have found a strong relationship between students’ grades and how much sleep they’re getting. What time students go to bed and the consistency of their sleep habits also make a big difference. And no, getting a good night’s sleep just before a big test is not good enough — it takes several nights in a row of good sleep to make a difference.
Those are among the conclusions from an experiment in which 100 students in an MIT engineering class were given Fitbits, the popular wrist-worn devices that track a person’s activity 24/7, in exchange for the researchers’ access to a semester’s worth of their activity data. The findings — some unsurprising, but some quite unexpected — are reported today in the journal Science of Learning in a paper by former MIT postdoc Kana Okano, professors Jeffrey Grossman and John Gabrieli, and two others.
One of the surprises was that individuals who went to bed after some particular threshold time — for these students, that tended to be 2 a.m., but it varied from one person to another — tended to perform less well on their tests no matter how much total sleep they ended up getting.
The study didn’t start out as research on sleep at all. Instead, Grossman was trying to find a correlation between physical exercise and the academic performance of students in his class 3.091 (Introduction to Solid-State Chemistry). In addition to having 100 of the students wear Fitbits for the semester, he also enrolled about one-fourth of them in an intense fitness class in MIT’s Department of Athletics, Physical Education, and Recreation, with the help of assistant professors Carrie Moore and Matthew Breen, who created the class specifically for this study. The thinking was that there might be measurable differences in test performance between the two groups.
There wasn’t. Those without the fitness classes performed just as well as those who did take them. “What we found at the end of the day was zero correlation with fitness, which I must say was disappointing since I believed, and still believe, there is a tremendous positive impact of exercise on cognitive performance,” Grossman says.
He speculates that the intervals between the fitness program and the classes may have been too long to show an effect. But meanwhile, in the vast amount of data collected during the semester, some other correlations did become obvious. While the devices weren’t explicitly monitoring sleep, the Fitbit program’s proprietary algorithms did detect periods of sleep and changes in sleep quality, primarily based on lack of activity.
These correlations were not at all subtle, Grossman says. There was essentially a straight-line relationship between the average amount of sleep a student got and their grades on the 11 quizzes, three midterms, and final exam, with the grades ranging from A’s to C’s. “There’s lots of scatter, it’s a noisy plot, but it’s a straight line,” he says. The fact that there was a correlation between sleep and performance wasn’t surprising, but the extent of it was, he says. Of course, this correlation can’t absolutely prove that sleep was the determining factor in the students’ performance, as opposed to some other influence that might have affected both sleep and grades. But the results are a strong indication, Grossman says, that sleep “really, really matters.”
“Of course, we knew already that more sleep would be beneficial to classroom performance, from a number of previous studies that relied on subjective measures like self-report surveys,” Grossman says. “But in this study the benefits of sleep are correlated to performance in the context of a real-life college course, and driven by large amounts of objective data collection.”
The study also revealed no improvement in scores for those who made sure to get a good night’s sleep right before a big test. According to the data, “the night before doesn’t matter,” Grossman says. “We’ve heard the phrase ‘Get a good night’s sleep, you’ve got a big day tomorrow.’ It turns out this does not correlate at all with test performance. Instead, it’s the sleep you get during the days when learning is happening that matter most.”
Another surprising finding is that there appears to be a certain cutoff for bedtimes, such that going to bed later results in poorer performance, even if the total amount of sleep is the same. “When you go to bed matters,” Grossman says. “If you get a certain amount of sleep — let’s say seven hours — no matter when you get that sleep, as long as it’s before certain times, say you go to bed at 10, or at 12, or at 1, your performance is the same. But if you go to bed after 2, your performance starts to go down even if you get the same seven hours. So, quantity isn’t everything.”
Quality of sleep also mattered, not just quantity. For example, those who got relatively consistent amounts of sleep each night did better than those who had greater variations from one night to the next, even if they ended up with the same average amount.
This research also helped to provide an explanation for something that Grossman says he had noticed and wondered about for years, which is that on average, the women in his class have consistently gotten better grades than the men. Now, he has a possible answer: The data show that the differences in quantity and quality of sleep can fully account for the differences in grades. “If we correct for sleep, men and women do the same in class. So sleep could be the explanation for the gender difference in our class,” he says.
More research will be needed to understand the reasons why women tend to have better sleep habits than men. “There are so many factors out there that it could be,” Grossman says. “I can envision a lot of exciting follow-on studies to try to understand this result more deeply.”
“The results of this study are very gratifying to me as a sleep researcher, but are terrifying to me as a parent,” says Robert Stickgold, a professor of psychiatry and director of the Center for Sleep and Cognition at Harvard Medical School, who was not connected with this study. He adds, “The overall course grades for students averaging six and a half hours of sleep were down 50 percent from other students who averaged just one hour more sleep. Similarly, those who had just a half-hour more night-to-night variation in their total sleep time had grades that dropped 45 percent below others with less variation. This is huge!”
Stickgold says “a full quarter of the variation in grades was explained by these sleep parameters (including bedtime). All students need to not only be aware of these results, but to understand their implication for success in college. I can’t help but believe the same is true for high school students.” But he adds one caution: “That said, correlation is not the same as causation. While I have no doubt that less and more variable sleep will hurt a student’s grades, it’s also possible that doing poorly in classes leads to less and more variable sleep, not the other way around, or that some third factor, such as ADHD, could independently lead to poorer grades and poorer sleep.”
The team also included technical assistant Jakub Kaezmarzyk and Harvard Business School researcher Neha Dave. The study was supported by MIT’s Department of Materials Science and Engineering, the Lubin Fund, and the MIT Integrated Learning Initiative.