Author: Julie Pryor

Ila Fiete joins the McGovern Institute

by Sabbi Lall |

Ila Fiete, an associate professor in the Department of Brain and Cognitive Sciences at MIT recently joined the McGovern Institute as an associate investigator. Fiete is working to understand the circuits that underlie short-term memory, integration, and inference in the brain.

Think about the simple act of visiting a new town and getting to know its layout as you explore it. What places are reachable from others? Where are landmarks relative to each other? Where are you relative to these landmarks? How do you get from here to where you want to go next?

The process that occurs as your brain tries to transform the few routes you follow into a coherent map of the world is just one of myriad examples of hard computations that the brain is constantly performing. Fiete’s goal is to understand how the brain is able to carry out such computations, and she is developing and using multiple tools to this end. These approaches include pure theoretical approaches to examine neural codes, building numerical dynamical models of circuit operation, and techniques to extract information about the underlying circuit dynamics from neural data.

Spatial navigation is a particularly interesting nut to crack from a neural perspective: The mapping devices on your phone have access to global satellite data, previously constructed detailed maps of the town, various additional sensors, and excellent non-leaky memory. By contrast, the brain must build maps, plan routes, and determine goals all using noisy, local sensors, no externally provided maps, and with noisy, forgetful or leaky neurons. Fiete is particularly interested in elucidating how the brain deals with noisy and ambiguous cues from the world to arrive at robust estimates about the world that resolve ambiguity. She is also interested in how the networks that are important for memory and integration develop through plasticity, learning, and development in the brain.

Fiete earned a BS in mathematics and physics at the University of Michigan then obtained her PhD in 2004 at Harvard University in the Department of Physics. She held a postdoctoral appointment at the Kavli Institute for Theoretical Physics at the University of California, Santa Barbara from 2004 to 2006, while she was also a visiting member of the Center for Theoretical Biophysics at the University of California at San Diego. Fiete subsequently spent two years at Caltech as a Broad Fellow in brain circuitry, and in 2008 joined the faculty of the University of Texas at Austin. She is currently an HHMI faculty scholar.

Peering under the hood of fake-news detectors

by Rob Matheson |

New work from researchers at the McGovern Institute for Brain Research at MIT peers under the hood of an automated fake-news detection system, revealing how machine-learning models catch subtle but consistent differences in the language of factual and false stories. The research also underscores how fake-news detectors should undergo more rigorous testing to be effective for real-world applications.

Popularized as a concept in the United States during the 2016 presidential election, fake news is a form of propaganda created to mislead readers, in order to generate views on websites or steer public opinion.

Almost as quickly as the issue became mainstream, researchers began developing automated fake news detectors — so-called neural networks that “learn” from scores of data to recognize linguistic cues indicative of false articles. Given new articles to assess, these networks can, with fairly high accuracy, separate fact from fiction, in controlled settings.

One issue, however, is the “black box” problem — meaning there’s no telling what linguistic patterns the networks analyze during training. They’re also trained and tested on the same topics, which may limit their potential to generalize to new topics, a necessity for analyzing news across the internet.

In a paper presented at the Conference and Workshop on Neural Information Processing Systems, the researchers tackle both of those issues. They developed a deep-learning model that learns to detect language patterns of fake and real news. Part of their work “cracks open” the black box to find the words and phrases the model captures to make its predictions.

Additionally, they tested their model on a novel topic it didn’t see in training. This approach classifies individual articles based solely on language patterns, which more closely represents a real-world application for news readers. Traditional fake news detectors classify articles based on text combined with source information, such as a Wikipedia page or website.

“In our case, we wanted to understand what was the decision-process of the classifier based only on language, as this can provide insights on what is the language of fake news,” says co-author Xavier Boix, a postdoc in the lab of Eugene McDermott Professor Tomaso Poggio at the Center for Brains, Minds, and Machines (CBMM), a National Science Foundation-funded center housed within the McGovern Institute.

“A key issue with machine learning and artificial intelligence is that you get an answer and don’t know why you got that answer,” says graduate student and first author Nicole O’Brien ’17. “Showing these inner workings takes a first step toward understanding the reliability of deep-learning fake-news detectors.”

The model identifies sets of words that tend to appear more frequently in either real or fake news — some perhaps obvious, others much less so. The findings, the researchers say, points to subtle yet consistent differences in fake news — which favors exaggerations and superlatives — and real news, which leans more toward conservative word choices.

“Fake news is a threat for democracy,” Boix says. “In our lab, our objective isn’t just to push science forward, but also to use technologies to help society. … It would be powerful to have tools for users or companies that could provide an assessment of whether news is fake or not.”

The paper’s other co-authors are Sophia Latessa, an undergraduate student in CBMM; and Georgios Evangelopoulos, a researcher in CBMM, the McGovern Institute of Brain Research, and the Laboratory for Computational and Statistical Learning.

Limiting bias

The researchers’ model is a convolutional neural network that trains on a dataset of fake news and real news. For training and testing, the researchers used a popular fake news research dataset, called Kaggle, which contains around 12,000 fake news sample articles from 244 different websites. They also compiled a dataset of real news samples, using more than 2,000 from the New York Times and more than 9,000 from The Guardian.

In training, the model captures the language of an article as “word embeddings,” where words are represented as vectors — basically, arrays of numbers — with words of similar semantic meanings clustered closer together. In doing so, it captures triplets of words as patterns that provide some context — such as, say, a negative comment about a political party. Given a new article, the model scans the text for similar patterns and sends them over a series of layers. A final output layer determines the probability of each pattern: real or fake.

The researchers first trained and tested the model in the traditional way, using the same topics. But they thought this might create an inherent bias in the model, since certain topics are more often the subject of fake or real news. For example, fake news stories are generally more likely to include the words “Trump” and “Clinton.”

“But that’s not what we wanted,” O’Brien says. “That just shows topics that are strongly weighting in fake and real news. … We wanted to find the actual patterns in language that are indicative of those.”

Next, the researchers trained the model on all topics without any mention of the word “Trump,” and tested the model only on samples that had been set aside from the training data and that did contain the word “Trump.” While the traditional approach reached 93-percent accuracy, the second approach reached 87-percent accuracy. This accuracy gap, the researchers say, highlights the importance of using topics held out from the training process, to ensure the model can generalize what it has learned to new topics.

More research needed

To open the black box, the researchers then retraced their steps. Each time the model makes a prediction about a word triplet, a certain part of the model activates, depending on if the triplet is more likely from a real or fake news story. The researchers designed a method to retrace each prediction back to its designated part and then find the exact words that made it activate.

More research is needed to determine how useful this information is to readers, Boix says. In the future, the model could potentially be combined with, say, automated fact-checkers and other tools to give readers an edge in combating misinformation. After some refining, the model could also be the basis of a browser extension or app that alerts readers to potential fake news language.

“If I just give you an article, and highlight those patterns in the article as you’re reading, you could assess if the article is more or less fake,” he says. “It would be kind of like a warning to say, ‘Hey, maybe there is something strange here.’”

Joining the dots in large neural datasets

by Sabbi Lall |

You might have played ‘join the dots’, a puzzle where numbers guide you to draw until a complete picture emerges. But imagine a complex underlying image with no numbers to guide the sequence of joining. This is a problem that challenges scientists who work with large amounts of neural data. Sometimes they can align data to a stereotyped behavior, and thus define a sequence of neuronal activity underlying navigation of a maze or singing of a song learned and repeated across generations of birds. But most natural behavior is not stereotyped, and when it comes to sleeping, imagining, and other higher order activities, there is not even a physical behavioral readout for alignment. Michale Fee and colleagues have now developed an algorithm, seqNMF, that can recognize relevant sequences of neural activity, even when there is no guide to align to, such as an overt sequence of behaviors or notes.

“This method allows you to extract structure from the internal life of the brain without being forced to make reference to inputs or output,” says Michale Fee, a neuroscientist at the McGovern Institute at MIT, Associate Department Head and Glen V. and Phyllis F. Dorflinger Professor of Neuroscience in the Department of Brain and Cognitive Sciences, and investigator with the Simons Collaboration on the Global Brain. Fee conducted the study in collaboration with Mark S. Goldman of the University of California, Davis.

In order to achieve this task, the authors of the study, co-led by Emily L. Mackevicius and Andrew H. Bahle of the McGovern Institute,  took a process called convolutional non-negative matrix factorization (NMF), a tool that allows extraction of sparse, but important, features from complex and noisy data, and developed it so that it can be used to extract sequences over time that are related to a learned behavior or song. The new algorithm also relies on repetition, but tell-tale repetitions of neural activity rather than simplistic repetitions in the animal’s behavior. seqNMF can follow repeated sequences of firing over time that are not tied to a specific external reference time framework, and can extract relevant sequences of neural firing in an unsupervised fashion without the researcher supplying prior information.

In the current study, the authors initially applied and honed the system on synthetic datasets. These datasets started to show them that the algorithm could “join the dots” without additional informational input. When seqNMF performed well in these tests, they applied it to available open source data from rats, finding that they could extract sequences of neural firing in the hippocampus that are relevant to finding a water reward in a maze.

Having passed these initial tests, the authors upped the ante and challenged seqNMF to find relevant neural activity sequences in a non-stereotypical behavior: improvised singing by zebra finches that have not learned the signature songs of their species (untutored birds). The authors analyzed neural data from the HVC, a region of the bird brain previously linked to song learning. Since normal adult bird songs are stereotyped, the researchers could align neural activity with features in the song itself for well-tutored birds. Fee and colleagues then turned to untutored birds and found that they still had repeated neural sequences related to the “improvised” song, that are reminiscent of the tutored birds, but the patterns are messier. Indeed, the brain of the untutored bird will even initiate two distinct neural signatures at the same time, but seqNMF is able to see past the resulting neural cacophony, and decipher that multiple patterns are present but overlapping. Being able to find these levels of order in such neural datasets is near impossible using previous methods of analysis.

seqNMF can be applied, potentially, to any neural activity, and the researchers are now testing whether the algorithm can indeed be generalized to extract information from other types of neural data. In other words, now that it’s clear that seqNMF can find a relevant sequence of neural activity for a non-stereotypical behavior, scientists can examine whether the neural basis of behaviors in other organisms and even for activities such as sleep and imagination can be extracted. Indeed, seqNMF is available on GitHub for researchers to apply to their own questions of interest.

Welcoming the first McGovern Fellows

Anne Trafton |

We are delighted to kick off the new year by welcoming Omar Abuddayeh and Jonathan Gootenberg as the first members of our new McGovern Institute Fellows Program. The fellows program is a recently launched initiative that supports highly-talented and selected postdocs that are ready to initiate their own research program.

As McGovern Fellows, the pair will be given space, time, and support to help them follow scientific research directions of their own choosing. This provides an alternative to the traditional postdoctoral research route.

Abudayyeh and Gootenberg both defended their thesis in the fall of 2018, and graduated from the lab of Feng Zhang, who is the James and Patricia Poitras Professor of Neuroscience at MIT, a McGovern investigator and core member of the Broad Institute. During their time in the Zhang lab, Abudayyeh and Gootenberg worked on projects that sought and found new tools based on enzymes mined from bacterial CRISPR systems. Cas9 is the original programmable single-effector DNA-editing enzyme, and the new McGovern Fellows worked on teams that actively looked for CRISPR enzymes with properties distinct from and complementary to Cas9. In the course of their thesis work, they helped to identify RNA-guided RNA editing factors such as the Cas13 family. This work led to the development of the REPAIR system, which is capable of editing RNA, thus providing a CRISPR-based therapeutic avenue that is not based on permanent, heritable changes to the genome. In addition, they worked on a Cas13-based diagnostic system called SHERLOCK that can detect specific nucleic acid sequences. SHERLOCK is able to detect the presence of infectious agents such as Zika virus in an easily-deployable lateral flow format, similar to a pregnancy test.

We are excited to see the directions that the new McGovern Fellows take as they now arrive at the institute, and will keep you posted on scientific findings as they emerge from their labs.

 

Plugging into the brain

by Sabbi Lall |

Driven by curiosity and therapeutic goals, Anikeeva leaves no scientific stone unturned in her drive to invent neurotechnology.

The audience sits utterly riveted as Polina Anikeeva highlights the gaps she sees in the landscape of neural tools. With a background in optoelectronics, she has a decidedly unique take on the brain.

“In neuroscience,” says Anikeeva, “we are currently applying silicon-based neural probes with the elastic properties of a knife to a delicate material with the consistency of chocolate pudding—the brain.”

A key problem, summarized by Anikeeva, is that these sharp probes damage tissue, making such interfaces unreliable and thwarting long term brain studies of processes including development and aging. The state of the art is even grimmer in the clinic. An avid climber, Anikeeva recalls a friend sustaining a spinal cord injury. “She made a remarkable recovery,” explains Anikeeva, “but seeing the technology being used to help her was shocking. Not even the simplest electronic tools were used, it was basically lots of screws and physical therapy.” This crude approach, compared to the elegant optoelectronic tools familiar to Anikeeva, sparked a drive to bring advanced materials technology to biological systems.

Outside the box

As the group breaks up after the seminar, the chatter includes boxes, more precisely, thinking outside of them. An associate professor in material sciences and engineering at MIT, Anikeeva’s interest in neuroscience recently led to a McGovern Institute appointment. She sees her journey to neurobiology as serendipitous, having earned her doctorate designing light-emitting devices at MIT.

“I wanted to work on tools that don’t exist, and neuroscience seemed like an obvious choice. Neurons communicate in part through membrane voltage changes and as an electronics designer, I felt that I should be able to use voltage.”

Comfort at the intersection of sciences requires, according to Anikeeva, clarity and focus, also important in her chief athletic pursuits, running and climbing. Through long distant running, Anikeeva finds solitary time (“assuming that no one can chase me”) and the clarity to consider complicated technical questions. Climbing hones something different, absolute focus in the face of the often-tangled information that comes with working at scientific intersections.

“When climbing, you can only think about one thing, your next move. Only the most important thoughts float up.”

This became particularly important when, in Yosemite National Park, she made the decision to go up, instead of down, during an impending thunderstorm. Getting out depended on clear focus, despite imminent hypothermia and being exposed “on one of the tallest features in the area, holding large quantities of metal.” Polina and her climbing partner made it out, but her summary of events echoes her research philosophy: “What you learn and develop is a strong mindset where you don’t do the comfortable thing, the easy thing. Instead you always find, and execute, the most logical strategy.”

In this vein, Anikeeva’s research pursues two very novel, but exceptionally logical, paths to brain research and therapeutics: fiber development and magnetic nanomaterials.

Drawing new fibers

Walking into Anikeeva’s lab, the eye is immediately drawn to a robust metal frame containing, upon closer scrutiny, recognizable parts: a large drill bit, a motor, a heating element. This custom-built machine applies principles from telecommunications to draw multifunctional fibers using more “brain-friendly” materials.

“We start out with a macroscopic model, a preform, of the device that we ultimately want,” explains Anikeeva.

This “preform” is a transparent block of polymers, composites, and soft low-melting temperature metals with optical and electrical properties needed in the final fiber. “So, this could include
electrodes for recording, optical channels for optogenetics, microfluidics for drug delivery, and one day even components that allow chemical or mechanical sensing.” After sitting in a vacuum to remove gases and impurities, the two-inch by one-inch preform arrives at the fiber-drawing tower.

“Then we heat it and pull it, and the macroscopic model becomes a kilometer-long fiber with a lateral dimension of microns, even nanometers,” explains Anikeeva. “Take one of your hairs, and imagine that inside there are electrodes for recording, there are microfluidic channels to infuse drugs, optical channels for stimulation. All of this is combined in a single miniature form
factor, and it can be quite flexible and even stretchable.”

Construction crew

Anikeeva’s lab comprises an eclectic mix of 21 researchers from over 13 different countries, and a range of expertises, including materials science, chemistry, electrical and mechanical engineering, and neuroscience. In 2011, Andres Canales, a materials scientist from Mexico, was the second person to join Anikeeva’s lab.

“There was only an idea, a diagram,” explains Canales. “I didn’t want to work on biology when I arrived at MIT, but talking to Polina, seeing the pictures, thinking about what it would entail, I became very excited by the methods and the potential applications she was thinking of.”

Despite the lack of preliminary models, Anikeeva’s ideas were compelling. Elegant as the fibers are, the road involved painstaking, iterative refinement. From a materials perspective, drawing a fiber containing a continuous conductive element was challenging, as was validation of its properties. But the resulting fiber can deliver optogenetics vectors, monitor expression, and then stimulate neuronal activity in a single surgery, removing the spatial and temporal guesswork usually involved in such an experiment.

Seongjun Park, an electrical engineering graduate student in the lab, explains one biological challenge. “For long term recording in the spinal cord, there was even an additional challenge as the fiber needed to be stretchable to respond to the spine’s movement. For this we developed a drawing process compatible with an elastomer.”

The resulting fibers can be deployed chronically without the scar tissue accumulation that usually prevents long-term optical manipulation and drug delivery, making them good candidates for the treatment of brain disorders. The lab’s current papers find that these implanted fibers are useful for three months, and material innovations make them confident that longer time periods are possible.

Magnetic moments

Another wing of Anikeeva’s research aims to develop entirely non-invasive modalities, and use magnetic nanoparticles to stimulate the brain and deliver therapeutics.

“Magnetic fields are probably the best modality for getting any kind of stimulus to deep tissues,” explains Anikeeva, “because biological systems, except for very specialized systems, do not perceive magnetic fields. They go through us unattenuated, and they don’t couple to our physiology.”

In other words, magnetic fields can safely reach deep tissues, including the brain. Upon reaching their tissue targets these fields can be used to stimulate magnetic nanoparticles, which might one day, for example, be used to deliver dopamine to the brains of Parkinson’s disease patients. The alternating magnetic fields being used in these experiments are tiny, 100-1000 times smaller than fields clinically approved for MRI-based brain imaging.

Tiny fields, but they can be used to powerful effect. By manipulating magnetic moments in these nanoparticles, the magnetic field can cause heat dissipation by the particle that can stimulate thermal receptors in the nervous system. These receptors naturally detect heat, chili peppers and vanilla, but Anikeeva’s magnetic nanoparticles act as tiny heaters that activate these receptors, and, in turn, local neurons. This principle has already been used to activate the brain’s reward center in freely moving mice.

Siyuan Rao, a postdoc who works on the magnetic nanoparticles in collaboration with McGovern Investigator Guoping Feng, is unhesitating when asked what most inspires her.

“As a materials scientist, it is really rewarding to see my materials at work. We can remotely modulate mouse behavior, even turn hopeless behavior into motivation.”

Pushing the boundaries

Such collaborations are valued by Anikeeva. Early on she worked with McGovern Investigator Emilio Bizzi to use the above fiber technology in the spinal cord. “It is important to us to not just make these devices,” explains Anikeeva, “but to use them and show ourselves, and our colleagues, the types of experiments that they can enable.”

Far from an assembly line, the researchers in Anikeeva’s lab follow projects from ideation to deployment. “The student that designs a fiber, performs their own behavioral experiments, and data analysis,” says Anikeeva. “Biology is unforgiving. You can trivially design the most brilliant electrophysiological recording probe, but unless you are directly working in the system, it is easy to miss important design considerations.”

Inspired by this, Anikeeva’s students even started a project with Gloria Choi’s group on their own initiative. This collaborative, can-do ethos spreads beyond the walls of the lab, inspiring people around MIT.

“We often work with a teaching instructor, David Bono, who is an expert on electronics and magnetic instruments,” explains Alex Senko, a senior graduate student in the lab. “In his spare time, he helps those of us who work on electrical engineering flavored projects to hunt down components needed to build our devices.”

These components extend to whatever is needed. When a low frequency source was needed, the Anikeeva lab drafted a guitar amplifier.

Queried about difficulties that she faces having chosen to navigate such a broad swath of fields, Anikeeva is focused, as ever, on the unknown, the boundaries of knowledge.

“Honestly, I really, really enjoy it. It keeps me engaged and not bored. Even when thinking about complicated physics and chemistry, I always have eyes on the prize, that this will allow us to address really interesting neuroscience questions.”

With such thinking, and by relentlessly seeking the tools needed to accomplish scientific goals, Anikeeva and her lab continue to avoid the comfortable route, instead using logical routes toward new technologies.

What is CRISPR?

by The Brain |

CRISPR (which stands for Clustered Regularly Interspaced Short Palindromic Repeats) is not actually a single entity, but shorthand for a set of bacterial systems that are found with a hallmarked arrangement in the bacterial genome.

When CRISPR is mentioned, most people are likely thinking of CRISPR-Cas9, now widely known for its capacity to be re-deployed to target sequences of interest in eukaryotic cells, including human cells. Cas9 can be programmed to target specific stretches of DNA, but other enzymes have since been discovered that are able to edit DNA, including Cpf1 and Cas12b. Other CRISPR enzymes, Cas13 family members, can be programmed to target RNA and even edit and change its sequence.

The common theme that makes CRISPR enzymes so powerful, is that scientists can supply them with a guide RNA for a chosen sequence. Since the guide RNA can pair very specifically with DNA, or for Cas13 family members, RNA, researchers can basically provide a given CRISPR enzyme with a way of homing in on any sequence of interest. Once a CRISPR protein finds its target, it can be used to edit that sequence, perhaps removing a disease-associated mutation.

In addition, CRISPR proteins have been engineered to modulate gene expression and even signal the presence of particular sequences, as in the case of the Cas13-based diagnostic, SHERLOCK.

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

SHERLOCK: A CRISPR tool to detect disease

by Julie Pryor |

This animation depicts how Cas13 — a CRISPR-associated protein — may be adapted to detect human disease. This new diagnostic tool, called SHERLOCK, targets RNA (rather than DNA), and has the potential to transform research and global public health.

 

Is it worth the risk?

by Shannon Fischer |

During the Klondike Gold Rush, thousands of prospectors climbed Alaska’s dangerous Chilkoot Pass in search of riches. McGovern scientists are exploring how a once-overlooked part of the brain might be at the root of cost-benefit decisions like these. McGovern researchers are studying how the brain balances risk and reward to make decisions.

Is it worth speeding up on the highway to save a few minutes’ time? How about accepting a job that pays more, but requires longer hours in the office?

Scientists call these types of real-life situations cost-benefit conflicts. Choosing well is an essential survival ability—consider the animal that must decide when to expose itself to predation to gather more food.

Now, McGovern researchers are discovering that this fundamental capacity to make decisions may originate in the basal ganglia—a brain region once considered unimportant to the human
experience—and that circuits associated with this structure may play a critical role in determining our state of mind.

Anatomy of decision-making

A few years back, McGovern investigator Ann Graybiel noticed that in the brain imaging literature, a specific part of the cortex called the pregenual anterior cingulate cortex or pACC, was implicated in certain psychiatric disorders as well as tasks involving cost-benefit decisions. Thanks to her now classic neuroanatomical work defining the complex anatomy and function of the basal ganglia, Graybiel knew that the pACC projected back into the basal ganglia—including its largest cluster of neurons, the striatum.

The striatum sits beneath the cortex, with a mouse-like main body and curving tail. It seems to serve as a critical way-station, communicating with both the brain’s sensory and motor areas above, and the limbic system (linked to emotion and memory) below. Running through the striatum are striosomes, column-like neurochemical compartments. They wire down to a small, but important part of the brain called the substantia nigra, which houses the huge majority of the brain’s dopamine neurons—a key neurochemical heavily involved, much like the basal ganglia as a whole, in reward, learning, and movement. The pACC region related to mood control targeted these striosomes, setting up a communication line from the neocortex to the dopamine neurons.

Graybiel discovered these striosomes early in her career, and understood them to have distinct wiring from other compartments in the striatum, but picking out these small, hard-to-find striosomes posed a technological challenge—so it was exciting to have this intriguing link to the pACC and mood disorders.

Working with Ken-ichi Amemori, then a research scientist in her lab, she adapted a common human cost-benefit conflict test for macaque monkeys. The monkeys could elect to receive a food treat, but the treat would always be accompanied by an annoying puff of air to the eyes. Before they decided, a visual cue told them exactly how much treat they could get, and exactly how strong the air puff would be, so they could choose if the treat was worth it.

Normal monkeys varied their choices in a fairly rational manner, rejecting the treat whenever it seemed like the air puff was too strong, or the treat too small to be worth it—and this corresponded with activity in the pACC neurons. Interestingly, they found that some pACC neurons respond more when animals approach the combined offers, while other pACC neurons
fire more when the animals avoid the offers. “It is as though there are two opposing armies. And the one that wins, controls the state of the animal.” Moreover, when Graybiel’s team electrically stimulated these pACC neurons, the animals begin to avoid the offers, even offers that they normally would approach. “It is as though when the stimulation is on, they think the future is worse than it really is,” Graybiel says.

Intriguingly, this effect only worked in situations where the animal had to weigh the value of a cost against a benefit. It had no effect on a decision between two negatives or two positives, like two different sizes of treats. The anxiety drug diazepam also reversed the stimulatory effect, but again, only on cost-benefit choices. “This particular kind of mood-influenced cost-benefit
decision-making occurs not only under conflict conditions but in our regular day to day lives. For example: I know that if I eat too much chocolate, I might get fat, but I love it, I want it.”

Glass half empty

Over the next few years, Graybiel, with another research scientist in her lab, Alexander Friedman, unraveled the circuit behind the macaques’ choices. They adapted the test for rats and mice,
so that they could more easily combine the cellular and molecular technologies needed to study striosomes, such as optogenetics and mouse engineering.

They found that the cortex (specifically, the pre-limbic region of the prefrontal cortex in rodents) wires onto both striosomes and fast-acting interneurons that also target the striosomes. In a
healthy circuit, these interneurons keep the striosomes in check by firing off fast inhibitory signals, hitting the brakes before the striosome can get started. But if the researchers broke that corticalstriatal connection with optogenetics or chronic stress, the animals became reckless, going for the high-risk, high-reward arm of the maze like a gambler throwing caution to the wind. If they amplified this inhibitory interneuron activity, they saw the opposite effect. With these techniques, they could block the effects of prior chronic stress.

This summer, Graybiel and Amemori published another paper furthering the story and returning to macaques. It was still too difficult to hit striosomes, and the researchers could only stimulate the striatum more generally. However, they replicated the effects in past studies.

Many electrodes had no effect, a small number made the monkeys choose the reward more often. Nearly a quarter though made the monkeys more avoidant—and this effect correlated with a change in the macaques’ brainwaves in a manner reminiscent of patients with depression.

But the surprise came when the avoidant-producing stimulation was turned off, the effects lasted unexpectedly long, only returning to normal on the third day.

Graybiel was stunned. “This is very important, because changes in the brain can get set off and have a life of their own,” she says. “This is true for some individuals who have had a terrible experience, and then live with the aftermath, even to the point of suffering from post-traumatic stress disorder.”

She suspects that this persistent state may actually be a form of affect, or mood. “When we change this decision boundary, we’re changing the mood, such that the animal overestimates cost, relative to benefit,” she explains. “This might be like a proxy state for pessimistic decision-making experienced during anxiety and depression, but may also occur, in a milder form, in you and me.”

Graybiel theorizes that this may tie back into the dopamine neurons that the striosomes project to: if this avoidance behavior is akin to avoidance observed in rodents, then they are stimulating a circuit that ultimately projects to dopamine neurons of the substantia nigra. There, she believes, they could act to suppress these dopamine neurons, which in turn project to the rest of the brain, creating some sort of long-term change in their neural activity. Or, put more simply, stimulation of these circuits creates a depressive funk.

Bottom up

Three floors below the Graybiel lab, postdoc Will Menegas is in the early stages of his own work untangling the role of dopamine and the striatum in decision-making. He joined Guoping Feng’s lab this summer after exploring the understudied “tail of the striatum” at Harvard University.

While dopamine pathways influence many parts of the brain, examination of connections to the striatum have largely focused on the frontmost part of the striatum, associated with valuations.

But as Menegas showed while at Harvard, dopamine neurons that project to the rear of the striatum are different. Those neurons get their input from parts of the brain associated with general arousal and sensation—and instead of responding to rewards, they respond to novelty and intense stimuli, like air puffs and loud noises.

In a new study published in Nature Neuroscience, Menegas used a neurotoxin to disrupt the dopamine projection from the substantia nigra to the posterior striatum to see how this circuit influences behavior. Normal mice approach novel items cautiously and back away after sniffing at them, but the mice in Menegas’ study failed to back away. They stopped avoiding a port that gave an air puff to the face and they didn’t behave like normal mice when Menegas dropped a strange or new object—say, a lego—into their cage. Disrupting the nigral-posterior striatum
seemed to turn off their avoidance habit.

“These neurons reinforce avoidance the same way that canonical dopamine neurons reinforce approach,” Menegas explains. It’s a new role for dopamine, suggesting that there may be two different and distinct systems of reinforcement, led by the same neuromodulator in different parts of the striatum.

This research, and Graybiel’s discoveries on cost-benefit decision circuits, share clear parallels, though the precise links between the two phenomena are yet to be fully determined. Menegas plans to extend this line of research into social behavior and related disorders like autism in marmoset monkeys.

“Will wants to learn the methods that we use in our lab to work on marmosets,” Graybiel says. “I think that working together, this could become a wonderful story, because it would involve social interactions.”

“This a very new angle, and it could really change our views of how the reward system works,” Feng says. “And we have very little understanding of social circuits so far and especially in higher organisms, so I think this would be very exciting. Whatever we learn, it’s going to be new.”

Human choices

Based on their preexisting work, Graybiel’s and Menegas’ projects are well-developed—but they are far from the only McGovern-based explorations into ways this brain region taps into our behaviors. Maiya Geddes, a visiting scientist in John Gabrieli’s lab, has recently published a paper exploring the little-known ways that aging affects the dopamine-based nigral-striatum-hippocampus learning and memory systems.

In Rebecca Saxe’s lab, postdoc Livia Tomova just kicked off a new pilot project using brain imaging to uncover dopamine-striatal circuitry behind social craving in humans and the urge to rejoin peers. “Could there be a craving response similar to hunger?” Tomova wonders. “No one has looked yet at the neural mechanisms of this.”

Graybiel also hopes to translate her findings into humans, beginning with collaborations at the Pizzagalli lab at McLean Hospital in Belmont. They are using fMRI to study whether patients
with anxiety and depression show some of the same dysfunctions in the cortico-striatal circuitry that she discovered in her macaques.

If she’s right about tapping into mood states and affect, it would be an expanded role for the striatum—and one with significant potential therapeutic benefits. “Affect state” colors many psychological functions and disorders, from memory and perception, to depression, chronic stress, obsessive-compulsive disorder, and PTSD.

For a region of the brain once dismissed as inconsequential, McGovern researchers have shown the basal ganglia to influence not only our choices but our state of mind—suggesting that this “primitive” brain region may actually be at the heart of the human experience.

 

 

Tracking down changes in ADHD

Anne Trafton |

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

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

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

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

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

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

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

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

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

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

Mark Harnett’s “Holy Grail” experiment

by Julie Pryor |

Neurons in the human brain receive electrical signals from thousands of other cells, and long neural extensions called dendrites play a critical role in incorporating all of that information so the cells can respond appropriately.

Using hard-to-obtain samples of human brain tissue, McGovern neuroscientist Mark Harnett has now discovered that human dendrites have different electrical properties from those of other species. Their studies reveal that electrical signals weaken more as they flow along human dendrites, resulting in a higher degree of electrical compartmentalization, meaning that small sections of dendrites can behave independently from the rest of the neuron.

These differences may contribute to the enhanced computing power of the human brain, the researchers say.