Machine learning can predict bipolar disorder in children and teens

Bipolar disorder often begins in childhood or adolescence, triggering dramatic mood shifts and intense emotions that cause problems at home and school. But the condition is often overlooked or misdiagnosed until patients are older. New research suggests that machine learning, a type of artificial intelligence, could help by identifying children who are at risk of bipolar disorder so doctors are better prepared to recognize the condition if it develops.

On October 13, 2022, researchers led by McGovern Institute investigator John Gabrieli and collaborators at Massachusetts General Hospital reported in the Journal of Psychiatric Research that when presented with clinical data on nearly 500 children and teenagers, a machine learning model was able to identify about 75 percent of those who were later diagnosed with bipolar disorder. The approach performs better than any other method of predicting bipolar disorder, and could be used to develop a simple risk calculator for health care providers.

Gabrieli says such a tool would be particularly valuable because bipolar disorder is less common in children than conditions like major depression, with which it shares symptoms, and attention-deficit/ hyperactivity disorder (ADHD), with which it often co-occurs. “Humans are not well tuned to watch out for rare events,” he says. “If you have a decent measure, it’s so much easier for a machine to identify than humans. And in this particular case, [the machine learning prediction] was surprisingly robust.”

Detecting bipolar disorder

Mai Uchida, Director of Massachusetts General Hospital’s Child Depression Program, says that nearly two percent of youth worldwide are estimated to have bipolar disorder, but diagnosing pediatric bipolar disorder can be challenging. A certain amount of emotional turmoil is to be expected in children and teenagers, and even when moods become seriously disruptive, children with bipolar disorder are often initially diagnosed with major depression or ADHD. That’s a problem, because the medications used to treat those conditions often worsen the symptoms of bipolar disorder. Tailoring treatment to a diagnosis of bipolar disorder, in contrast, can lead to significant improvements for patients and their families. “When we can give them a little bit of ease and give them a little bit of control over themselves, it really goes a long way,” Uchida says.

In fact, a poor response to antidepressants or ADHD medications can help point a psychiatrist toward a diagnosis of bipolar disorder. So too can a child’s family history, in addition to their own behavior and psychiatric history. But, Uchida says, “it’s kind of up to the individual clinician to pick up on these things.”

Uchida and Gabrieli wondered whether machine learning, which can find patterns in large, complex datasets, could focus in on the most relevant features to identify individuals with bipolar disorder. To find out, they turned to data from a study that began in the 1990s. The study, headed by Joseph Biederman, Chief of the Clinical and Research Programs in Pediatric Psychopharmacology and Adult ADHD at Massachusetts General Hospital, had collected extensive psychiatric assessments of hundreds of children with and without ADHD, then followed those individuals for ten years.

To explore whether machine learning could find predictors of bipolar disorder within that data, Gabrieli, Uchida, and colleagues focused on 492 children and teenagers without ADHD, who were recruited to the study as controls. Over the ten years of the study, 45 of those individuals developed bipolar disorder.

Within the data collected at the study’s outset, the machine learning model was able to find patterns that associated with a later diagnosis of bipolar disorder. A few behavioral measures turned out to be particularly relevant to the model’s predictions: children and teens with combined problems with attention, aggression, and anxiety were most likely to later be diagnosed with bipolar disorder. These indicators were all picked up by a standard assessment tool called the Child Behavior Checklist.

Uchida and Gabrieli say the machine learning model could be integrated into the medical record system to help pediatricians and child psychiatrists catch early warning signs of bipolar disorder. “The information that’s collected could alert a clinician to the possibility of a bipolar disorder developing,” Uchida says. “Then at least they’re aware of the risk, and they may be able to maybe pick up on some of the deterioration when it’s happening and think about either referring them or treating it themselves.”

How touch dampens the brain’s response to painful stimuli

McGovern Investigator Fan Wang. Photo: Caitliin Cunningham

When we press our temples to soothe an aching head or rub an elbow after an unexpected blow, it often brings some relief. It is believed that pain-responsive cells in the brain quiet down when these neurons also receive touch inputs, say scientists at MIT’s McGovern Institute, who for the first time have watched this phenomenon play out in the brains of mice.

The team’s discovery, reported November 16, 2022, in the journal Science Advances, offers researchers a deeper understanding of the complicated relationship between pain and touch and could offer some insights into chronic pain in humans. “We’re interested in this because it’s a common human experience,” says McGovern Investigator Fan Wang. “When some part of your body hurts, you rub it, right? We know touch can alleviate pain in this way.” But, she says, the phenomenon has been very difficult for neuroscientists to study.

Modeling pain relief

Touch-mediated pain relief may begin in the spinal cord, where prior studies have found pain-responsive neurons whose signals are dampened in response to touch. But there have been hints that the brain was involved too. Wang says this aspect of the response has been largely unexplored, because it can be hard to monitor the brain’s response to painful stimuli amidst all the other neural activity happening there—particularly when an animal moves.

So while her team knew that mice respond to a potentially painful stimulus on the cheek by wiping their faces with their paws, they couldn’t follow the specific pain response in the animals’ brains to see if that rubbing helped settle it down. “If you look at the brain when an animal is rubbing the face, movement and touch signals completely overwhelm any possible pain signal,” Wang explains.

She and her colleagues have found a way around this obstacle. Instead of studying the effects of face-rubbing, they have focused their attention on a subtler form of touch: the gentle vibrations produced by the movement of the animals’ whiskers. Mice use their whiskers to explore, moving them back and forth in a rhythmic motion known as whisking to feel out their environment. This motion activates touch receptors in the face and sends information to the brain in the form of vibrotactile signals. The human brain receives the same kind of touch signals when a person shakes their hand as they pull it back from a painfully hot pan—another way we seek touch-mediate pain relief.

If you look at the brain when an animal is rubbing the face, movement and touch signals completely overwhelm any possible pain signal, says Wang.

Wang and her colleagues found that this whisker movement alters the way mice respond to bothersome heat or a poke on the face—both of which usually lead to face rubbing. “When the unpleasant stimuli were applied in the presence of their self-generated vibrotactile whisking…they respond much less,” she says. Sometimes, she says, whisking animals entirely ignore these painful stimuli.

In the brain’s somatosensory cortex, where touch and pain signals are processed, the team found signaling changes that seem to underlie this effect. “The cells that preferentially respond to heat and poking are less frequently activated when the mice are whisking,” Wang says. “They’re less likely to show responses to painful stimuli.” Even when whisking animals did rub their faces in response to painful stimuli, the team found that neurons in the brain took more time to adopt the firing patterns associated with that rubbing movement. “When there is a pain stimulation, usually the trajectory the population dynamics quickly moved to wiping. But if you already have whisking, that takes much longer,” Wang says.

Wang notes that even in the fraction of a second before provoked mice begin rubbing their faces, when the animals are relatively still, it can be difficult to sort out which brain signals are related to perceiving heat and poking and which are involved in whisker movement. Her team developed computational tools to disentangle these, and are hoping other neuroscientists will use the new algorithms to make sense of their own data.

Whisking’s effects on pain signaling seem to depend on dedicated touch-processing circuitry that sends tactile information to the somatosensory cortex from a brain region called the ventral posterior thalamus. When the researchers blocked that pathway, whisking no longer dampened the animals’ response to painful stimuli. Now, Wang says, she and her team are eager to learn how this circuitry works with other parts of the brain to modulate the perception and response to painful stimuli.

Wang says the new findings might shed light on a condition called thalamic pain syndrome, a chronic pain disorder that can develop in patients after a stroke that affects the brain’s thalamus. “Such strokes may impair the functions of thalamic circuits that normally relay pure touch signals and dampen painful signals to the cortex,” she says.

RNA-activated protein cutter protects bacteria from infection

Our growing understanding of the ways bacteria defend themselves against viruses continues to change the way scientists work and offer new opportunities to improve human health. Ancient immune systems known as CRISPR systems have already been widely adopted as powerful genome editing tools, and the CRISPR toolkit is continuing to expand. Now, scientists at MIT’s McGovern Institute have uncovered an unexpected and potentially useful tool that some bacteria use to respond to infection: an RNA-activated protein-cutting enzyme.

McGovern Fellows Jonathan Gootenberg and Omar Abudayyeh in their lab. Photo: Caitlin Cunningham

The enzyme is part of a CRISPR system discovered last year by McGovern Fellows Omar Abudayyeh and Jonathan Gootenberg. The system, found in bacteria from Tokyo Bay, originally caught their interest because of the precision with which its RNA-activated enzyme cuts RNA. That enzyme, Cas7-11, is considered a promising tool for editing RNA for both research and potential therapeutics. Now, the same researchers have taken a closer look at this bacterial immune system and found that once Cas7-11 has been activated by the right RNA, it also turns on an enzyme that snips apart a particular bacterial protein.

That makes the Cas7-11 system notably more complex than better-studied CRISPR systems, which protect bacteria simply by chopping up the genetic material of an invading virus. “This is a much more elegant and complex signaling mechanism to really defend the bacteria,” Abudayyeh says. A team led by Abudayyeh, Gootenberg, and collaborator Hiroshi Nishimasu at the University of Tokyo report these findings in the November 3, 2022, issue of the journal Science.

Protease programming

The team’s experiments reveal that in bacteria, activation of the protein-cutting enzyme, known as a protease, triggers a series of events that ultimately slow the organism’s growth. But the components of the CRISPR system can be engineered to achieve different outcomes. Gootenberg and Abudayyeh have already programmed the RNA-activated protease to report on the presence of specific RNAs in mammalian cells. With further adaptations, they say it might one day be used to diagnose or treat disease.

The discovery grew out of the researchers’ curiosity about how bacteria protect themselves from infection using Cas7-11. They knew that the enzyme was capable of cutting viral RNA, but there were hints that something more might be going on. They wondered whether a set of genes that clustered near the Cas7-11 gene might also be involved in the bacteria’s infection response, and when graduate students Cian Schmitt-Ulms and Kaiyi Jiang began experimenting with those proteins, they discovered that they worked with Cas7-11 to execute a surprisingly elaborate response to a target RNA.

One of those proteins was the protease Csx29. In the team’s test tube experiments, Csx29 and Cas7-11 couldn’t cut anything on their own—but in the presence of a target RNA, Cas7-11 switched it on. Even then, when the researchers mixed the protease with Cas7-11 and its RNA target and allowed them to mingle with other proteins, most of the proteins remained intact. But one, a protein called Csx30, was reliably snipped apart by the protein-cutting enzyme.

Their experiments had uncovered an enzyme that cut a specific protein, but only in the presence of its particular target RNA. It was unusual—and potentially useful. “That was when we knew we were onto something,” Abudayyeh says.

As the team continued to explore the system, they found that the Csx29’s RNA-activated cut frees a fragment of Csx30 that then works with other bacterial proteins to execute a key aspect of the bacteria’s response to infection—slowing down growth. “Our growth experiments suggest that the cleavage is modulating the bacteria’s stress response in some way,” Gootenberg says.

The scientists quickly recognized that this RNA-activated protease could have uses beyond its natural role in antiviral defense. They have shown that the system can be adapted so that when the protease cuts Csx30 in the presence of its target RNA, it generates an easy to detect fluorescent signal. Because Cas7-11 can be directed to recognize any target RNA, researchers can program the system to detect and report on any RNA of interest. And even though the original system evolved in bacteria, this RNA sensor works well in mammalian cells.

Gootenberg and Abudayyeh say understanding this surprisingly elaborate CRISPR system opens new possibilities by adding to scientists’ growing toolkit of RNA-guided enzymes. “We’re excited to see how people use these tools and how they innovate on them,” Gootenberg says. It’s easy to imagine both diagnostic and therapeutic applications, they say. For example, an RNA sensor could detect signatures of disease in patient samples or to limit delivery of a potential therapy to specific types of cells, enabling that drug to carry out its work without side effects.

In addition to Gootenberg, Abudayyeh, Schmitt-Ulms, and Jiang, Abudayyeh-Gootenberg lab postdoc Nathan Wenyuan Zhou contributed to the project. This work was supported by NIH grants 1R21-AI149694, R01-EB031957, and R56-HG011857, the McGovern Institute Neurotechnology (MINT) program, the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience, the G. Harold & Leila Y. Mathers Charitable Foundation, the MIT John W. Jarve (1978) Seed Fund for Science Innovation, the Cystic Fibrosis Foundation, Google Ventures, Impetus Grants, the NHGRI/TDCC Opportunity Fund, and the McGovern Institute.

RNA-sensing system controls protein expression in cells based on specific cell states

Researchers at the Broad Institute of MIT and Harvard and the McGovern Institute for Brain Research at MIT have developed a system that can detect a particular RNA sequence in live cells and produce a protein of interest in response. Using the technology, the team showed how they could identify specific cell types, detect and measure changes in the expression of individual genes, track transcriptional states, and control the production of proteins encoded by synthetic mRNA.

The platform, called Reprogrammable ADAR Sensors, or RADARS, even allowed the team to target and kill a specific cell type. The team said RADARS could one day help researchers detect and selectively kill tumor cells, or edit the genome in specific cells. The study appears today in Nature Biotechnology and was led by co-first authors Kaiyi Jiang (MIT), Jeremy Koob (Broad), Xi Chen (Broad), Rohan Krajeski (MIT), and Yifan Zhang (Broad).

“One of the revolutions in genomics has been the ability to sequence the transcriptomes of cells,” said Fei Chen, a core institute member at the Broad, Merkin Fellow, assistant professor at Harvard University, and co-corresponding author on the study. “That has really allowed us to learn about cell types and states. But, often, we haven’t been able to manipulate those cells specifically. RADARS is a big step in that direction.”

“Right now, the tools that we have to leverage cell markers are hard to develop and engineer,” added Omar Abudayyeh, a McGovern Institute Fellow and co-corresponding author on the study. “We really wanted to make a programmable way of sensing and responding to a cell state.”

Jonathan Gootenberg, who is also a McGovern Institute Fellow and co-corresponding author, says that their team was eager to build a tool to take advantage of all the data provided by single-cell RNA sequencing, which has revealed a vast array of cell types and cell states in the body.

“We wanted to ask how we could manipulate cellular identities in a way that was as easy as editing the genome with CRISPR,” he said. “And we’re excited to see what the field does with it.” 

Omar Abudayyeh, Jonathan Gootenberg and Fei Chen at the Broad Institute
Study authors (from left to right) Omar Abudayyeh, Jonathan Gootenberg, and Fei Chen. Photo: Namrita Sengupta

Repurposing RNA editing

The RADARS platform generates a desired protein when it detects a specific RNA by taking advantage of RNA editing that occurs naturally in cells.

The system consists of an RNA containing two components: a guide region, which binds to the target RNA sequence that scientists want to sense in cells, and a payload region, which encodes the protein of interest, such as a fluorescent signal or a cell-killing enzyme. When the guide RNA binds to the target RNA, this generates a short double-stranded RNA sequence containing a mismatch between two bases in the sequence — adenosine (A) and cytosine (C). This mismatch attracts a naturally occurring family of RNA-editing proteins called adenosine deaminases acting on RNA (ADARs).

In RADARS, the A-C mismatch appears within a “stop signal” in the guide RNA, which prevents the production of the desired payload protein. The ADARs edit and inactivate the stop signal, allowing for the translation of that protein. The order of these molecular events is key to RADARS’s function as a sensor; the protein of interest is produced only after the guide RNA binds to the target RNA and the ADARs disable the stop signal.

The team tested RADARS in different cell types and with different target sequences and protein products. They found that RADARS distinguished between kidney, uterine, and liver cells, and could produce different fluorescent signals as well as a caspase, an enzyme that kills cells. RADARS also measured gene expression over a large dynamic range, demonstrating their utility as sensors.

Most systems successfully detected target sequences using the cell’s native ADAR proteins, but the team found that supplementing the cells with additional ADAR proteins increased the strength of the signal. Abudayyeh says both of these cases are potentially useful; taking advantage of the cell’s native editing proteins would minimize the chance of off-target editing in therapeutic applications, but supplementing them could help produce stronger effects when RADARS are used as a research tool in the lab.

On the radar

Abudayyeh, Chen, and Gootenberg say that because both the guide RNA and payload RNA are modifiable, others can easily redesign RADARS to target different cell types and produce different signals or payloads. They also engineered more complex RADARS, in which cells produced a protein if they sensed two RNA sequences and another if they sensed either one RNA or another. The team adds that similar RADARS could help scientists detect more than one cell type at the same time, as well as complex cell states that can’t be defined by a single RNA transcript.

Ultimately, the researchers hope to develop a set of design rules so that others can more easily develop RADARS for their own experiments. They suggest other scientists could use RADARS to manipulate immune cell states, track neuronal activity in response to stimuli, or deliver therapeutic mRNA to specific tissues.

“We think this is a really interesting paradigm for controlling gene expression,” said Chen. “We can’t even anticipate what the best applications will be. That really comes from the combination of people with interesting biology and the tools you develop.”

This work was supported by the The McGovern Institute Neurotechnology (MINT) program, the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience, the G. Harold & Leila Y. Mathers Charitable Foundation, Massachusetts Institute of Technology, Impetus Grants, the Cystic Fibrosis Foundation, Google Ventures, FastGrants, the McGovern Institute, National Institutes of Health, the Burroughs Wellcome Fund, the Searle Scholars Foundation, the Harvard Stem Cell Institute, and the Merkin Institute.

Personal pursuits

This story originally appeared in the Fall 2022 issue of BrainScan.

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Many neuroscientists were drawn to their careers out of curiosity and wonder. Their deep desire to understand how the brain works drew them into the lab and keeps them coming back, digging deeper and exploring more each day. But for some, the work is more personal.

Several McGovern faculty say they entered their field because someone in their lives was dealing with a brain disorder that they wanted to better understand. They are committed to unraveling the basic biology of those conditions, knowing that knowledge is essential to guide the development of better treatments.

The distance from basic research to clinical progress is shortening, and many young neuroscientists hope not just to deepen scientific understanding of the brain, but to have direct impact on the lives of patients. Some want to know why people they love are suffering from neurological disorders or mental illness; others seek to understand the ways in which their own brains work differently than others. But above all, they want better treatments for people affected by such disorders.

Seeking answers

That’s true for Kian Caplan, a graduate student in MIT’s Department of Brain and Cognitive Sciences who was diagnosed with Tourette syndrome around age 13. At the time, learning that the repetitive, uncontrollable movements and vocal tics he had been making for most of his life were caused by a neurological disorder was something of a relief. But it didn’t take long for Caplan to realize his diagnosis came with few answers.

Graduate student Kian Caplan studies the brain circuits associated with Tourette syndrome and obsessive-compulsive disorder in Guoping Feng and Fan Wang’s labs at the McGovern Institute. Photo: Steph Stevens

Tourette syndrome has been estimated to occur in about six of every 1,000 children, but its neurobiology remains poorly understood.

“The doctors couldn’t really explain why I can’t control the movements and sounds I make,” he says. “They couldn’t really explain why my symptoms wax and wane, or why the tics I have aren’t always the same.”

That lack of understanding is not just frustrating for curious kids like Caplan. It means that researchers have been unable to develop treatments that target the root cause of Tourette syndrome. Drugs that dampen signaling in parts of the brain that control movement can help suppress tics, but not without significant side effects. Caplan has tried those drugs. For him, he says, “they’re not worth the suppression.”

Advised by Fan Wang and McGovern Associate Director Guoping Feng, Caplan is looking for answers. A mouse model of obsessive-compulsive disorder developed in Feng’s lab was recently found to exhibit repetitive movements similar to those of people with Tourette syndrome, and Caplan is working to characterize those tic-like movements. He will use the mouse model to examine the brain circuits underlying the two conditions, which often co-occur in people. Broadly, researchers think Tourette syndrome arises due to dysregulation of cortico-striatal-thalamo-cortical circuits, which connect distant parts of the brain to control movement. Caplan and Wang suspect that the brainstem — a structure found where the brain connects to the spinal cord, known for organizing motor movement into different modules — is probably involved, too.

Wang’s research group studies the brainstem’s role in movement, but she says that like most researchers, she hadn’t considered its role in Tourette syndrome until Caplan joined her lab. That’s one reason Caplan, who has long been a mentor and advocate for students with neurodevelopmental disorders, thinks neuroscience needs more neurodiversity.

“I think we need more representation in basic science research by the people who actually live with those conditions,” he says. Their experiences can lead to insights that may be inaccessible to others, he says, but significant barriers in academia often prevent this kind of representation. Caplan wants to see institutions make systemic changes to ensure that neurodiverse and otherwise minority individuals are able to thrive in academia. “I’m not an exception,” he says, “there should be more people like me here, but the present system makes that incredibly difficult.”

Overcoming adversity

Like Caplan, Lace Riggs faced significant challenges in her pursuit to study the brain. She grew up in Southern California’s Inland Empire, where issues of social disparity, chronic stress, drug addiction, and mental illness were a part of everyday life.

Postdoctoral fellow Lace Riggs studies the origins of neurodevelopmental conditions in Guoping Feng’s lab at the McGovern Institute. Photo: Lace Riggs

“Living in severe poverty and relying on government assistance without access to adequate education and resources led everyone I know and love to suffer tremendously, myself included,” says Riggs, a postdoctoral fellow in the Feng lab.

“There are not a lot of people like me who make it to this stage,” says Riggs, who has lost friends and family members to addiction, mental illness, and suicide. “There’s a reason for that,” she adds. “It’s really, really difficult to get through the educational system and to overcome socioeconomic barriers.”

Today, Riggs is investigating the origins of neurodevelopmental conditions, hoping to pave the way to better treatments for brain disorders by uncovering the molecular changes that alter the structure and function of neural circuits.

Riggs says that the adversities she faced early in life offered valuable insights in the pursuit of these goals. She first became interested in the brain because she wanted to understand how our experiences have a lasting impact on who we are — including in ways that leave people vulnerable to psychiatric problems.

“While the need for more effective treatments led me to become interested in psychiatry, my fascination with the brain’s unique ability to adapt is what led me to neuroscience,” says Riggs.

After finishing high school, Riggs attended California State University in San Bernardino and became the only member of her family to attend university or attempt a four-year degree. Today, she spends her days working with mice that carry mutations linked to autism or ADHD in humans, studying the animals’ behavior and monitoring their neural activity. She expects that aberrant neural circuit activity in these conditions may also contribute to mood disorders, whose origins are harder to tease apart because they often arise when genetic and environmental factors intersect. Ultimately, Riggs says, she wants to understand how our genes dictate whether an experience will alter neural signaling and impact mental health in a long-lasting way.

Riggs uses patch clamp electrophysiology to record the strength of inhibitory and excitatory synaptic input onto individual neurons (white arrow) in an animal model of autism. Image: Lace Riggs

“If we understand how these long-lasting synaptic changes come about, then we might be able to leverage these mechanisms to develop new and more effective treatments.”

While the turmoil of her childhood is in the past, Riggs says it is not forgotten — in part, because of its lasting effects on her own mental health.  She talks openly about her ongoing struggle with social anxiety and complex post-traumatic stress disorder because she is passionate about dismantling the stigma surrounding these conditions. “It’s something I have to deal with every day,” Riggs says. That means coping with symptoms like difficulty concentrating, hypervigilance, and heightened sensitivity to stress. “It’s like a constant hum in the background of my life, it never stops,” she says.

“I urge all of us to strive, not only to make scientific discoveries to move the field forward,” says Riggs, “but to improve the accessibility of this career to those whose lived experiences are required to truly accomplish that goal.”

Making and breaking habits

As part of our Ask the Brain series, science writer Shafaq Zia explores the question, “How are habits formed in the brain?”

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Have you ever wondered why it is so hard to break free of bad habits like nail biting or obsessive social networking?

When we repeat an action over and over again, the behavioral pattern becomes automated in our brain, according to Jill R. Crittenden, molecular biologist and scientific advisor at McGovern Institute for Brain Research at MIT. For over a decade, Crittenden worked as a research scientist in the lab of Ann Graybiel, where one of the key questions scientists are working to answer is, how are habits formed?

Making habits

To understand how certain actions get wired in our neural pathways, this team of McGovern researchers experimented with rats, who were trained to run down a maze to receive a reward. If they turned left, they would get rich chocolate milk and for turning right, only sugar water. With this, the scientists wanted to see whether these animals could “learn to associate a cue with which direction they should turn in the maze in order to get the chocolate milk reward.”

Over time, the rats grew extremely habitual in their behavior; “they always turned the the correct direction and the places where their paws touched, in a fairly long maze, were exactly the same every time,” said Crittenden.

This isn’t a coincidence. When we’re first learning to do something, the frontal lobe and basal ganglia of the brain are highly active and doing a lot of calculations. These brain regions work together to associate behaviors with thoughts, emotions, and, most importantly, motor movements. But when we repeat an action over and over again, like the rats running down the maze, our brains become more efficient and fewer neurons are required to achieve the goal. This means, the more you do something, the easier it gets to carry it out because the behavior becomes literally etched in our brain as our motor movements.

But habits are complicated and they come in many different flavors, according to Crittenden. “I think we don’t have a great handle on how the differences [in our many habits] are separable neurobiologically, and so people argue a lot about how do you know that something’s a habit.”

The easiest way for scientists to test this in rodents is to see if the animal engages in the behavior even in the absence of reward. In this particular experiment, the researchers take away the reward, chocolate milk, to see whether the rats continue to run down the maze correctly. And to take it even a step further, they mix the chocolate milk with lithium chloride, which would upset the rat’s stomach. Despite all this, the rats continue to run down the maze and turn left towards the chocolate milk, as they had learnt to do over and over again.

Breaking habits

So does that mean once a habit is formed, it is impossible to shake it? Not quite. But it is tough. Rewards are a key building block to forming habits because our dopamine levels surge when we learn that an action is unexpectedly rewarded. For example, when the rats first learn to run down the maze, they’re motivated to receive the chocolate milk.

But things get complicated once the habit is formed. Researchers have found that this dopamine surge in response to reward ceases after a behavior becomes a habit. Instead the brain begins to release dopamine at the first cue or action that was previously learned to lead to the reward, so we are motivated to engage in the full behavioral sequence anyway, even if the reward isn’t there anymore.

This means we don’t have as much self-control as we think we do, which may also be the reason why it’s so hard to break the cycle of addiction. “People will report that they know this is bad for them. They don’t want it. And nevertheless, they select that action,” said Crittenden.

One common method to break the behavior, in this case, is called extinction. This is where psychologists try to weaken the association between the cue and the reward that led to habit formation in the first place. For example, if the rat no longer associates the cue to run down the maze with a reward, it will stop engaging in that behavior.

So the next time you beat yourself up over being unable to stick to a diet or sleep at a certain time, give yourself some grace and know that with consistency, a new, healthier habit can be born.

MIT scientists discover new antiviral defense system in bacteria

Bacteria use a variety of defense strategies to fight off viral infection, and some of these systems have led to groundbreaking technologies, such as CRISPR-based gene-editing. Scientists predict there are many more antiviral weapons yet to be found in the microbial world.

A team led by researchers at the Broad Institute of MIT and Harvard and the McGovern Institute for Brain Research at MIT has discovered and characterized one of these unexplored microbial defense systems. They found that certain proteins in bacteria and archaea (together known as prokaryotes) detect viruses in surprisingly direct ways, recognizing key parts of the viruses and causing the single-celled organisms to commit suicide to quell the infection within a microbial community. The study is the first time this mechanism has been seen in prokaryotes and shows that organisms across all three domains of life — bacteria, archaea, and eukaryotes (which includes plants and animals) — use pattern recognition of conserved viral proteins to defend against pathogens.

The study appears in Science.

“This work demonstrates a remarkable unity in how pattern recognition occurs across very different organisms,” said senior author Feng Zhang, who is a core institute member at the Broad, the James and Patricia Poitras Professor of Neuroscience at MIT, a professor of brain and cognitive sciences and biological engineering at MIT, and an investigator at MIT’s McGovern Institute and the Howard Hughes Medical Institute. “It’s been very exciting to integrate genetics, bioinformatics, biochemistry, and structural biology approaches in one study to understand this fascinating molecular system.”

Microbial armory

In an earlier study, the researchers scanned data on the DNA sequences of hundreds of thousands of bacteria and archaea, which revealed several thousand genes harboring signatures of microbial defense. In the new study, they homed in on a handful of these genes encoding enzymes that are members of the STAND ATPase family of proteins, which in eukaryotes are involved in the innate immune response.

In humans and plants, the STAND ATPase proteins fight infection by recognizing patterns in a pathogen itself or in the cell’s response to infection. In the new study, the researchers wanted to know if the proteins work the same way in prokaryotes to defend against infection. The team chose a few STAND ATPase genes from the earlier study, delivered them to bacterial cells, and challenged those cells with bacteriophage viruses. The cells underwent a dramatic defensive response and survived.

The scientists next wondered which part of the bacteriophage triggers that response, so they delivered viral genes to the bacteria one at a time. Two viral proteins elicited an immune response: the portal, a part of the virus’s capsid shell, which contains viral DNA; and the terminase, the molecular motor that helps assemble the virus by pushing the viral DNA into the capsid. Each of these viral proteins activated a different STAND ATPase to protect the cell.

The finding was striking and unprecedented. Most known bacterial defense systems work by sensing viral DNA or RNA, or cellular stress due to the infection. These bacterial proteins were instead directly sensing key parts of the virus.

The team next showed that bacterial STAND ATPase proteins could recognize diverse portal and terminase proteins from different phages. “It’s surprising that bacteria have these highly versatile sensors that can recognize all sorts of different phage threats that they might encounter,” said co-first author Linyi Gao, a junior fellow in the Harvard Society of Fellows and a former graduate student in the Zhang lab.

Structural analysis

For a detailed look at how the microbial STAND ATPases detect the viral proteins, the researchers used cryo-electron microscopy to examine their molecular structure when bound to the viral proteins. “By analyzing the structure, we were able to precisely answer a lot of the questions about how these things actually work,” said co-first author Max Wilkinson, a postdoctoral researcher in the Zhang lab.

The team saw that the portal or terminase protein from the virus fits within a pocket in the STAND ATPase protein, with each STAND ATPase protein grasping one viral protein. The STAND ATPase proteins then group together in sets of four known as tetramers, which brings together key parts of the bacterial proteins called effector domains. This activates the proteins’ endonuclease function, shredding cellular DNA and killing the cell.

The tetramers bound viral proteins from other bacteriophages just as tightly, demonstrating that the STAND ATPases sense the viral proteins’ three-dimensional shape, rather than their sequence. This helps explain how one STAND ATPase can recognize dozens of different viral proteins. “Regardless of sequence, they all fit like a hand in a glove,” said Wilkinson.

STAND ATPases in humans and plants also work by forming multi-unit complexes that activate specific functions in the cell. “That’s the most exciting part of this work,” said Strecker. “To see this across the domains of life is unprecedented.”

The research was funded in part by the National Institutes of Health, the Howard Hughes Medical Institute, Open Philanthropy, the Edward Mallinckrodt, Jr. Foundation, the Poitras Center for Psychiatric Disorders Research, the Hock E. Tan and K. Lisa Yang Center for Autism Research, the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience, the Phillips family, J. and P. Poitras, and the BT Charitable Foundation.

Why do we dream?

As part of our Ask the Brain series, science writer Shafaq Zia answers the question, “Why do we dream?”

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One night, Albert Einstein dreamt that he was walking through a farm where he found a herd of cows against an electric fence. When the farmer switched on the fence, the cows suddenly jumped back, all at the same time. But to the farmer’s eyes, who was standing at the other end of the field, they seemed to have jumped one after another, in a wave formation. Einstein woke up and the Theory of Relativity was born.

Dreaming is one of the oldest biological phenomena; for as long as humans have slept, they’ve dreamt. But through most of our history, dreams have remained mystified, leaving scientists, philosophers, and artists alike searching for meaning.

In many aboriginal cultures, such as the Esa Eja community in Peruvian Amazon, dreaming is a sacred practice for gaining knowledge, or solving a problem, through the dream narrative. But in the last century or so, technological advancements have allowed neuroscientists to take up dreams as a matter of scientific inquiry in order to answer a much-pondered question — what is the purpose of dreaming?

Falling asleep

The human brain is a fascinating place. It is composed of approximately 80 billion neurons and it is their combined electrical chatter that generates oscillations known as brain waves. There are five types of brain waves —  alpha, beta, theta, delta, and gamma — that each indicate a different state between sleep and wakefulness.

Using EEG, a test that records electrical activity in the brain, scientists have identified that when we’re awake, our brain emits beta and gamma waves. These tend to have a stimulating effect and help us remain actively engaged in mental activities.

The differently named frequency bands of neural oscillations, or brainwaves: delta, theta, alpha, beta, and gamma.

But during the transition to sleep, the number of beta waves lowers significantly and the brain produces high levels of alpha waves. These waves regulate attention and help filter out distractions. A recent study led by McGovern Institute Director Robert Desimone, showed that people can actually enhance their attention by controlling their own alpha brain waves using neurofeedback training. It’s unknown how long these effects might last and whether this kind of control could be achieved with other types of brain waves, but the researchers are now planning additional studies to explore these questions.

Alpha waves are also produced when we daydream, meditate, or listen to the sound of rain. As our minds wander, many parts of the brain are engaged, including a specialized system called the “default mode network.” Disturbances in this network, explains Susan Whitfield-Gabrieli, a professor of psychology at Northeastern University and a McGovern Institute research affiliate, have been linked to various brain disorders including schizophrenia, depression and ADHD. By identifying the brain circuits associated with mind wandering, she says, we can begin to develop better treatment options for people suffering from these disorders.

Finally, as we enter a dreamlike state, the prefrontal cortex of the brain, responsible for keeping impulses in check, slowly grows less active. This is when there’s a spur in theta waves that leads to an unconstrained window of consciousness; there is little censorship from the mind, allowing for visceral dreams and creative thoughts.

The dreaming brain

“Every time you learn something, it happens so quickly,” said Dheeraj Roy, postdoctoral fellow in Guoping Feng’s lab at the McGovern Institute. “The brain is continuously recording information, but how do you take a break and then make sense of it all?”

This is where dreams come in, says Roy. During sleep, newly-formed memories are gradually stabilized into a more permanent form of long-term storage in the brain. Dreaming, he says, is influenced by the consolidation of these memories during sleep. Most dreams are made up of experiences, thoughts, emotion, places, and people we have already encountered in our lives. But, during dreaming, bits and pieces of these memories seem to be reorganized to create a particularly bizarre scenario: you’re talking to your sister when it suddenly begins to rain roses and you’re dancing at a New Year’s party.

This re-organization may not be so random; as the brain is processing memories, it pulls together the ones that are seemingly related to each other. Perhaps you dreamt of your sister because you were at a store recently where a candle smelt like her rose-scented perfume, which reminded you of the time you made a new year resolution to spend less money on flowers.

Some brain disorders, like Parkinson’s disease, have been associated with vivid, unpleasant dreams and erratic brain wave patterns. Researchers at the McGovern Institute hope that a better understanding of mechanics of the brain – including neural circuits and brain waves – will help people with Parkinson’s and other brain disorders.

So perhaps dreams aren’t instilled with meaning, symbolism, and wisdom in the way we’ve always imagined, and they simply reflect important biological processes taking place in our brain. But with all that science has uncovered about dreaming and the ways in which it links to creativity and memory, the magical essence of this universal human experience remains untainted.

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Do you have a question for The Brain? Ask it here.

McGovern Fellows recognized with life sciences innovation award

McGovern Institute Fellows Omar Abudayyeh and Jonathan Gootenberg have been named the inaugural recipients of the Termeer Scholars Awards, which recognize “emerging biomedical researchers that represent the future of the biotechnology industry.” The Termeer Foundation is a nonprofit organization focused on connecting life science innovators and catalyzing the creation of new medicines.

“The Termeer Foundation is committed to championing emerging biotechnology leaders and finding people who want to solve the biggest problems in human health,” said Belinda Termeer, president of the Termeer Foundation. “By supporting researchers like Omar and Jonathan, we plant the seeds for future success in individuals who are preparing to make significant contributions in academia and industry.”

The Abudayyeh-Gootenberg lab is developing a suite of new tools to enable next-generation cellular engineering, with uses in basic research, therapeutics and diagnostics. Building off the revolutionary biology of natural biological systems, including mobile genetic elements and CRISPR systems, the team develops new approaches for understanding and manipulating genomes, transcriptomes and cellular fate. The technologies have broad applications, including in oncology, aging and genetic disease.

These tools have been adopted by researchers over the world and formed the basis for four companies that Abudayyeh and Gootenberg have co-founded. They will receive a $50,000 grant to support professional development, knowledge advancement and/or stakeholder engagement and will become part of The Termeer Foundation’s signature Network of Termeer Fellows (first-time CEOs and entrepreneurs) and Mentors (experienced industry leaders).

“The Termeer Foundation is working to improve the long odds of biotechnology by identifying and supporting future biotech leaders; if we help them succeed as leaders, we can help their innovations reach patients,” said Alan Waltws, co-founder of the Termeer Foundation. “While our Termeer Fellows program has supported first time CEOs and entrepreneurs for the past five years, our new Termeer Scholars program will provide much needed support to the researchers whose innovative ideas represent the future of the biotechnology industry – researchers like Omar and Jonathan.”

Abudayyeh and Gootenberg were honored at the Termeer Foundation’s annual dinner in Boston on June 16, 2022.

Artificial neural networks model face processing in autism

Many of us easily recognize emotions expressed in others’ faces. A smile may mean happiness, while a frown may indicate anger. Autistic people often have a more difficult time with this task. It’s unclear why. But new research, published today in The Journal of Neuroscience, sheds light on the inner workings of the brain to suggest an answer. And it does so using a tool that opens new pathways to modeling the computation in our heads: artificial intelligence.

Researchers have primarily suggested two brain areas where the differences might lie. A region on the side of the primate (including human) brain called the inferior temporal (IT) cortex contributes to facial recognition. Meanwhile, a deeper region called the amygdala receives input from the IT cortex and other sources and helps process emotions.

Kohitij Kar, a research scientist in the lab of MIT Professor James DiCarlo, hoped to zero in on the answer. (DiCarlo, the Peter de Florez Professor in the Department of Brain and Cognitive Sciences, is a member of the McGovern Institute for Brain Research and director of MIT’s Quest for Intelligence.)

Kar began by looking at data provided by two other researchers: Shuo Wang, at Washington University in St. Louis, and Ralph Adolphs, at the California Institute of Technology. In one experiment, they showed images of faces to autistic adults and to neurotypical controls. The images had been generated by software to vary on a spectrum from fearful to happy, and the participants judged, quickly, whether the faces depicted happiness. Compared with controls, autistic adults required higher levels of happiness in the faces to report them as happy.

Modeling the brain

Kar, who is also a member of the Center for Brains, Minds and Machines, trained an artificial neural network, a complex mathematical function inspired by the brain’s architecture, to perform the same task. The network contained layers of units that roughly resemble biological neurons that process visual information. These layers process information as it passes from an input image to a final judgment indicating the probability that the face is happy. Kar found that the network’s behavior more closely matched the neurotypical controls than it did the autistic adults.

The network also served two more interesting functions. First, Kar could dissect it. He stripped off layers and retested its performance, measuring the difference between how well it matched controls and how well it matched autistic adults. This difference was greatest when the output was based on the last network layer. Previous work has shown that this layer in some ways mimics the IT cortex, which sits near the end of the primate brain’s ventral visual processing pipeline. Kar’s results implicate the IT cortex in differentiating neurotypical controls from autistic adults.

The other function is that the network can be used to select images that might be more efficient in autism diagnoses. If the difference between how closely the network matches neurotypical controls versus autistic adults is greater when judging one set of images versus another set of images, the first set could be used in the clinic to detect autistic behavioral traits. “These are promising results,” Kar says. Better models of the brain will come along, “but oftentimes in the clinic, we don’t need to wait for the absolute best product.”

Next, Kar evaluated the role of the amygdala. Again, he used data from Wang and colleagues. They had used electrodes to record the activity of neurons in the amygdala of people undergoing surgery for epilepsy as they performed the face task. The team found that they could predict a person’s judgment based on these neurons’ activity. Kar re-analyzed the data, this time controlling for the ability of the IT-cortex-like network layer to predict whether a face truly was happy. Now, the amygdala provided very little information of its own. Kar concludes that the IT cortex is the driving force behind the amygdala’s role in judging facial emotion.

Noisy networks

Finally, Kar trained separate neural networks to match the judgments of neurotypical controls and autistic adults. He looked at the strengths or “weights” of the connections between the final layers and the decision nodes. The weights in the network matching autistic adults, both the positive or “excitatory” and negative or “inhibitory” weights, were weaker than in the network matching neurotypical controls. This suggests that sensory neural connections in autistic adults might be noisy or inefficient.

To further test the noise hypothesis, which is popular in the field, Kar added various levels of fluctuation to the activity of the final layer in the network modeling autistic adults. Within a certain range, added noise greatly increased the similarity between its performance and that of the autistic adults. Adding noise to the control network did much less to improve its similarity to the control participants. This further suggest that sensory perception in autistic people may be the result of a so-called “noisy” brain.

Computational power

Looking forward, Kar sees several uses for computational models of visual processing. They can be further prodded, providing hypotheses that researchers might test in animal models. “I think facial emotion recognition is just the tip of the iceberg,” Kar says. They can also be used to select or even generate diagnostic content. Artificial intelligence could be used to generate content like movies and educational materials that optimally engages autistic children and adults. One might even tweak facial and other relevant pixels in what autistic people see in augmented reality goggles, work that Kar plans to pursue in the future.

Ultimately, Kar says, the work helps to validate the usefulness of computational models, especially image-processing neural networks. They formalize hypotheses and make them testable. Does one model or another better match behavioral data? “Even if these models are very far off from brains, they are falsifiable, rather than people just making up stories,” he says. “To me, that’s a more powerful version of science.”