Category: Uncategorized

New sensors track dopamine in the brain for more than a year

Anne Trafton |

Dopamine, a signaling molecule used throughout the brain, plays a major role in regulating our mood, as well as controlling movement. Many disorders, including Parkinson’s disease, depression, and schizophrenia, are linked to dopamine deficiencies.

MIT neuroscientists have now devised a way to measure dopamine in the brain for more than a year, which they believe will help them to learn much more about its role in both healthy and diseased brains.

“Despite all that is known about dopamine as a crucial signaling molecule in the brain, implicated in neurologic and neuropsychiatric conditions as well as our ability to learn, it has been impossible to monitor changes in the online release of dopamine over time periods long enough to relate these to clinical conditions,” says Ann Graybiel, an MIT Institute Professor, a member of MIT’s McGovern Institute for Brain Research, and one of the senior authors of the study.

Michael Cima, the David H. Koch Professor of Engineering in the Department of Materials Science and Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research, and Rober Langer, the David H. Koch Institute Professor and a member of the Koch Institute, are also senior authors of the study. MIT postdoc Helen Schwerdt is the lead author of the paper, which appears in the Sept. 12 issue of Communications Biology.

Long-term sensing

Dopamine is one of many neurotransmitters that neurons in the brain use to communicate with each other. Traditional systems for measuring dopamine — carbon electrodes with a shaft diameter of about 100 microns — can only be used reliably for about a day because they produce scar tissue that interferes with the electrodes’ ability to interact with dopamine.

In 2015, the MIT team demonstrated that tiny microfabricated sensors could be used to measure dopamine levels in a part of the brain called the striatum, which contains dopamine-producing cells that are critical for habit formation and reward-reinforced learning.

Because these probes are so small (about 10 microns in diameter), the researchers could implant up to 16 of them to measure dopamine levels in different parts of the striatum. In the new study, the researchers wanted to test whether they could use these sensors for long-term dopamine tracking.

“Our fundamental goal from the very beginning was to make the sensors work over a long period of time and produce accurate readings from day to day,” Schwerdt says. “This is necessary if you want to understand how these signals mediate specific diseases or conditions.”

To develop a sensor that can be accurate over long periods of time, the researchers had to make sure that it would not provoke an immune reaction, to avoid the scar tissue that interferes with the accuracy of the readings.

The MIT team found that their tiny sensors were nearly invisible to the immune system, even over extended periods of time. After the sensors were implanted, populations of microglia (immune cells that respond to short-term damage), and astrocytes, which respond over longer periods, were the same as those in brain tissue that did not have the probes inserted.

In this study, the researchers implanted three to five sensors per animal, about 5 millimeters deep, in the striatum. They took readings every few weeks, after stimulating dopamine release from the brainstem, which travels to the striatum. They found that the measurements remained consistent for up to 393 days.

“This is the first time that anyone’s shown that these sensors work for more than a few months. That gives us a lot of confidence that these kinds of sensors might be feasible for human use someday,” Schwerdt says.

Paul Glimcher, a professor of physiology and neuroscience at New York University, says the new sensors should enable more researchers to perform long-term studies of dopamine, which is essential for studying phenomena such as learning, which occurs over long time periods.

“This is a really solid engineering accomplishment that moves the field forward,” says Glimcher, who was not involved in the research. “This dramatically improves the technology in a way that makes it accessible to a lot of labs.”

Monitoring Parkinson’s

If developed for use in humans, these sensors could be useful for monitoring Parkinson’s patients who receive deep brain stimulation, the researchers say. This treatment involves implanting an electrode that delivers electrical impulses to a structure deep within the brain. Using a sensor to monitor dopamine levels could help doctors deliver the stimulation more selectively, only when it is needed.

The researchers are now looking into adapting the sensors to measure other neurotransmitters in the brain, and to measure electrical signals, which can also be disrupted in Parkinson’s and other diseases.

“Understanding those relationships between chemical and electrical activity will be really important to understanding all of the issues that you see in Parkinson’s,” Schwerdt says.

The research was funded by the National Institute of Biomedical Imaging and Bioengineering, the National Institute of Neurological Disorders and Stroke, the Army Research Office, the Saks Kavanaugh Foundation, the Nancy Lurie Marks Family Foundation, and Dr. Tenley Albright.

Can the brain recover after paralysis?

by The Brain |

Why is it that motor skills can be gained after paralysis but vision cannot recover in similar ways? – Ajay, Puppala

Thank you so much for this very important question, Ajay. To answer, I asked two local experts in the field, Pawan Sinha who runs the vision research lab at MIT, and Xavier Guell, a postdoc in John Gabrieli’s lab at the McGovern Institute who also works in the ataxia unit at Massachusetts General Hospital.

“Simply stated, the prospects of improvement, whether in movement or in vision, depend on the cause of the impairment,” explains Sinha. “Often, the cause of paralysis is stroke, a reduction in blood supply to a localized part of the brain, resulting in tissue damage. Fortunately, the brain has some ability to rewire itself, allowing regions near the damaged one to take on some of the lost functionality. This rewiring manifests itself as improvements in movement abilities after an initial period of paralysis. However, if the paralysis is due to spinal-cord transection (as was the case following Christopher Reeve’s tragic injury in 1995), then prospects for improvement are diminished.”

“Turning to the domain of sight,” continues Sinha, “stroke can indeed cause vision loss. As with movement control, these losses can dissipate over time as the cortex reorganizes via rewiring. However, if the blindness is due to optic nerve transection, then the condition is likely to be permanent. It is also worth noting that many cases of blindness are due to problems in the eye itself. These include corneal opacities, cataracts and retinal damage. Some of these conditions (corneal opacities and cataracts) are eminently treatable while others (typically those associated with the retina and optic nerve) still pose challenges to medical science.”

You might be wondering what makes lesions in the eye and spinal cord hard to overcome. Some systems (the blood, skin, and intestine are good examples) contain a continuously active stem cell population in adults. These cells can divide and replenish lost cells in damaged regions. While “adult-born” neurons can arise, elements of a degenerating or damaged retina, optic nerve, or spinal cord cannot be replaced as easily lost skin cells can. There is currently a very active effort in the stem cell community to understand how we might be able to replace neurons in all cases of neuronal degeneration and injury using stem cell technologies. To further explore lesions that specifically affect the brain, and how these might lead to a different outcome in the two systems, I turned to Xavier Guell.

“It might be true that visual deficits in the population are less likely to recover when compared to motor deficits in the population. However, the scientific literature seems to indicate that our body has a similar capacity to recover from both motor and visual injuries,” explains Guell. “The reason for this apparent contradiction is that visual lesions are usually not in the cerebral cortex (but instead in other places such as the retina or the lens), while motor lesions in the cerebral cortex are more common. In fact, a large proportion of people who suffer a stroke will have damage in the motor aspects of the cerebral cortex, but no damage in the visual aspects of the cerebral cortex. Crucially, recovery of neurological functions is usually seen when lesions are in the cerebral cortex or in other parts of the cerebrum or cerebellum. In this way, while our body has a similar capacity to recover from both motor and visual injuries, motor injuries are more frequently located in the parts of our body that have a better capacity to regain function (specifically, the cerebral cortex).”

In short, some cells cannot be replaced in either system, but stem cell research provides hope there. That said, there is remarkable plasticity in the brain, so when the lesion is located there, we can see recovery with training.

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

Why do I talk with my hands?

by The Brain |

This is a very interesting question sent to us by Gabriel Castellanos (thank you!) Many of us gesture with our hands when we speak (and even when we do not) as a form of non-verbal communication. How hand gestures are coordinated with speech remains unclear. In part, it is difficult to monitor natural hand gestures in fMRI-based brain imaging studies as you have to stay still.

“Performing hand movements when stuck in the bore of a scanner is really tough beyond simple signing and keypresses,” explains McGovern Principal Research Scientist Satrajit Ghosh. “Thus ecological experiments of co-speech with motor gestures have not been carried out in the context of a magnetic resonance scanner, and therefore little is known about language and motor integration within this context.”

There have been studies that use proxies such as co-verbal pushing of buttons, and also studies using other imaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), to monitor brain activity during gesturing, but it would be difficult to precisely spatially localize the regions involved in natural co-speech hand gesticulation using such approaches. Another possible avenue for addressing this question would be to look at patients with conditions that might implicate particular brain regions in coordinating hand gestures, but such approaches have not really pinpointed a pathway for coordinating speech and hand movements.

That said, co-speech hand gesturing plays an important role in communication. “More generally co-speech hand gestures are seen as a mechanism for emphasis and disambiguation of the semantics of a sentence, in addition to prosody and facial queues,” says Ghosh. “In fact, one may consider the act of speaking as one large orchestral score involving vocal tract movement, respiration, voicing, facial expression, hand gestures, and even whole body postures acting as different instruments coordinated dynamically by the brain. Based on our current understanding of language production, co-speech or gestural events would likely be planned at a higher level than articulation and therefore would likely activate inferior frontal gyrus, SMA, and others.”

How this orchestra is coordinated and conducted thus remains to be unraveled, but certainly the question is one that gets to the heart of human social interactions.

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

Constructing the striatum

by Sabbi Lall |

The striatum, the largest nucleus of the basal ganglia in the vertebrate brain, was historically thought to be a homogeneous group of cells. This view was overturned in a classic series of papers from MIT Institute Professor, Ann Graybiel. In previous work, Graybiel, who is also an investigator at MIT’s McGovern Institute, found that the striatum is highly organized, both structurally and functionally and in terms of connectivity. Graybiel has now collaborated with Z. Josh Huang’s lab at Cold Spring Harbor Laboratory to map the developmental lineage of cells that give rise to this complex architecture. The authors found that different functions of the striatum, such as execution of actions as opposed to evaluation of outcomes, are defined early on as part of the blueprint that constructs this brain region, rather than sculpted through a later mechanism.

Graybiel and colleagues tracked what is happening early in development by driving cell-specific fluorescent markers that allowed them to follow the progenitors that give rise to cells in the striatum. The striatum is known, thanks to Graybiel’s early work, to be organized into compartments called striosomes and the matrix. These have distinct connections to other brain regions. Broadly speaking, while striosomes are linked to value-based decision-making and reinforcement-based behaviors, the matrix has been linked to action execution. These regions are further subdivided into direct and indirect pathways. The direct pathway neurons are involved in releasing inhibition in other regions of the basal ganglia and thus actively promote action. Neurons projecting into the indirect pathway, instead inhibit “unwanted” actions that are not part of the current “cortical plan.” Based on their tracking, Graybiel and colleagues were indeed able to build a “fate map” that told them when the cells that build these different regions of the striatum commit to a functional path during development.

“It was already well known that individual neurons have lineages that can be traced back to early development, and many such lineages are now being traced,” says Graybiel. “What is so striking in what we have found with the Huang lab is that the earliest specification of lineages we find—at least with the markers that we have used—corresponds to what later become the two major neurochemically distinct compartments of the striatum, rather than many other divisions that might have been specified first. If this is so, then the fundamental developmental ground plan of the striatum is expressed later by these two distinct compartments of the striatum.”

Building the striatum turns out to be a symphony of organization embedded in lateral ganglion eminence cells, the source of cells during development that will end up in the striatum. Progenitors made early in development are somewhat committed: they can only generate spiny projection neurons (SPNs) that are striosomal. Following this in time, cells that will give rise to matrix SPNs appear. There is then a second mechanism laid over this initial ground plan that is switched on in both striosomal and matrisomal neurons and independently gives rise to neurons that will connect into direct as opposed to indirect pathways. This latter specification of direct-indirect pathway neurons is less rigid, but there is an overarching tendency for neurons expressing a certain neurotransmitter, dopamine, to appear earlier in developmental time. In short, progenitors move through an orchestrated process where they generate spiny projection neurons that can first sit in any area of the striatum, then where the ultimate fate of cells is more restricted at the level of striosome or matrix, and finally choices are made in both regions regarding indirect-direct pathway circuitry. Remarkably, these results suggest that even at the very earliest development of the striatum, its ultimate organization is already laid down in a way that distinguishes value-related circuit from movement-related circuits.

“What is thrilling,” says Graybiel, “is that there are lineage progressions— the step by step laying out of the brain’s organization— the turn out to match the striosome-matrix architecture of the striatum the were not even known to exist 40 years ago!”

The striatum is a hub regulating movement, emotion, motivation, evaluation, and learning, and linked to disorders such as Parkinson’s Disease and persistent negative valuations. This means that understanding its construction has important implications, perhaps even, one day, for rebuilding a striatum affected by neurodegeneration. That said, the findings have broader implications. Consider the worm, specifically, C. elegans. The complete lineage of cells that make up this organism is known, including where each neuron comes from, what it connects to, and its function and phenotype. There’s a clear relationship between lineage and function in this relatively simple organism with its highly stereotyped nervous system. Graybiel’s work suggests that in the big picture, early development in the forebrain is also providing a game plan. In this case, however, this groundwork underpins for circuits that underlie extremely complex behaviors, those that come to support the volitional and habitual behaviors that make up part of who we are as individuals.

 

A social side to face recognition by infants

Anne Trafton |

When interacting with an infant you have likely noticed that the human face holds a special draw from a very young age. But how does this relate to face recognition by adults, which is known to map to specific cortical regions? Rebecca Saxe, Associate Investigator at MIT’s McGovern Institute and John W. Jarve (1978) Professor in Brain and Cognitive Sciences, and her team have now considered two emerging theories regarding early face recognition, and come up with a third proposition, arguing that when a baby looks at a face, the response is also social, and that the resulting contingent interactions are key to subsequent development of organized face recognition areas in the brain.

By a certain age you are highly skilled at recognizing and responding to faces, and this correlates with activation of a number of face-selective regions of the cortex. This is incredibly important to reading the identities and intentions of other people, and selective categorical representation of faces in cortical areas is a feature shared by our primate cousins. While brain imaging tells us where face-responsive regions are in the adult cortex, how and when they emerge remains unclear.

In 2017, functional magnetic resonance imaging (fMRI) studies of human and macaque infants provided the first glimpse of how the youngest brains respond to faces. The scans showed that in 4-6 month human infants and equivalently aged macaques, regions known to be face-responsive in the adult brain are activated when shown movies of faces, but not in a selective fashion. Essentially fMRI argues that these specific, cortical regions are activated by faces, but a chair will do just as well. Upon further experience of faces over time, the specific cortical regions in macaques became face-selective, no longer responding to other objects.

There are two prevailing ideas in the field of how face preference, and eventually selectivity, arise through experience. These ideas are now considered in turn by Saxe and her team in an opinion piece in the September issue of Trends in Cognitive Sciences, and then a third, new theory proposed. The first idea centers on the way we dote over babies, centering our own faces right in their field of vision. The idea is that such frequent exposures to low level face features (curvilinear shape etc.) will eventually lead to co-activation of neurons that are responsive to all of the different aspects of facial features. If these neurons stimulated by different features are co-activated, and there’s a brain region where these neurons are also found together, this area with be stimulated eventually reinforcing emergence of a face category-specific area.

A second idea is that babies already have an innate “face template,” just as a duckling or chick already knows to follow its mother after hatching. So far there is little evidence for the second proposition, and the first fails to explain why babies seek out a face, rather than passively look upon and eventually “learn” the overlapping features that represent “face.”

Saxe, along with postdoc Lindsey Powell and graduate student Heather Kosakowski, instead now argue that the role a face plays in positive social interactions comes to drive organization of face-selective cortical regions. Taking the next step, the researchers propose that a prime suspect for linking social interactions to the development of face-selective areas is the medial prefrontal cortex (mPFC), a region linked to social cognition and behavior.

“I was asked to give a talk at a conference, and I wanted to talk about both the development of cortical face areas and the social role of the medial prefrontal cortex in young infants,” says Saxe. “I was puzzling over whether these two ideas were related, when I suddenly saw that they could be very fundamentally related.”

The authors argue that this relationship is supported by existing data that has shown that babies prefer dynamic faces and are more interested in faces that engage in a back and forth interaction. Regions of the mPFC are also known to activated during social interactions and known to be activated during exposure to dynamic faces in infants.

Powell is now using functional near infrared spectroscopy (fNIRS), a brain imaging technique that measures changes in blood flow to the brain, to test this hypothesis in infants. “This will allow us to see whether mPFC responses to social cues are linked to the development of face-responsive areas.”

In Daniel Deronda, the novel by George Eliot, the protagonist says “I think my life began with waking up and loving my mother’s face: it was so near to me, and her arms were round me, and she sang to me.” Perhaps this type of positively valenced social interaction, reinforced by the mPFC, is exactly what leads to the particular importance of faces and their selective categorical representation in the human brain. Further testing of the hypothesis proposed by Powell, Kosakowski, and Saxe will tell.

Neuroscientists get at the roots of pessimism

Anne Trafton |

Many patients with neuropsychiatric disorders such as anxiety or depression experience negative moods that lead them to focus on the possible downside of a given situation more than the potential benefit.

MIT neuroscientists have now pinpointed a brain region that can generate this type of pessimistic mood. In tests in animals, they showed that stimulating this region, known as the caudate nucleus, induced animals to make more negative decisions: They gave far more weight to the anticipated drawback of a situation than its benefit, compared to when the region was not stimulated. This pessimistic decision-making could continue through the day after the original stimulation.

The findings could help scientists better understand how some of the crippling effects of depression and anxiety arise, and guide them in developing new treatments.

“We feel we were seeing a proxy for anxiety, or depression, or some mix of the two,” says Ann Graybiel, an MIT Institute Professor, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study, which appears in the Aug. 9 issue of Neuron. “These psychiatric problems are still so very difficult to treat for many individuals suffering from them.”

The paper’s lead authors are McGovern Institute research affiliates Ken-ichi Amemori and Satoko Amemori, who perfected the tasks and have been studying emotion and how it is controlled by the brain. McGovern Institute researcher Daniel Gibson, an expert in data analysis, is also an author of the paper.

Emotional decisions

Graybiel’s laboratory has previously identified a neural circuit that underlies a specific kind of decision-making known as approach-avoidance conflict. These types of decisions, which require weighing options with both positive and negative elements, tend to provoke a great deal of anxiety. Her lab has also shown that chronic stress dramatically affects this kind of decision-making: More stress usually leads animals to choose high-risk, high-payoff options.

In the new study, the researchers wanted to see if they could reproduce an effect that is often seen in people with depression, anxiety, or obsessive-compulsive disorder. These patients tend to engage in ritualistic behaviors designed to combat negative thoughts, and to place more weight on the potential negative outcome of a given situation. This kind of negative thinking, the researchers suspected, could influence approach-avoidance decision-making.

To test this hypothesis, the researchers stimulated the caudate nucleus, a brain region linked to emotional decision-making, with a small electrical current as animals were offered a reward (juice) paired with an unpleasant stimulus (a puff of air to the face). In each trial, the ratio of reward to aversive stimuli was different, and the animals could choose whether to accept or not.

This kind of decision-making requires cost-benefit analysis. If the reward is high enough to balance out the puff of air, the animals will choose to accept it, but when that ratio is too low, they reject it. When the researchers stimulated the caudate nucleus, the cost-benefit calculation became skewed, and the animals began to avoid combinations that they previously would have accepted. This continued even after the stimulation ended, and could also be seen the following day, after which point it gradually disappeared.

This result suggests that the animals began to devalue the reward that they previously wanted, and focused more on the cost of the aversive stimulus. “This state we’ve mimicked has an overestimation of cost relative to benefit,” Graybiel says.

The study provides valuable insight into the role of the basal ganglia (a region that includes the caudate nucleus) in this type of decision-making, says Scott Grafton, a professor of neuroscience at the University of California at Santa Barbara, who was not involved in the research.

“We know that the frontal cortex and the basal ganglia are involved, but the relative contributions of the basal ganglia have not been well understood,” Grafton says. “This is a nice paper because it puts some of the decision-making process in the basal ganglia as well.”

A delicate balance

The researchers also found that brainwave activity in the caudate nucleus was altered when decision-making patterns changed. This change, discovered by Amemori, is in the beta frequency and might serve as a biomarker to monitor whether animals or patients respond to drug treatment, Graybiel says.

Graybiel is now working with psychiatrists at McLean Hospital to study patients who suffer from depression and anxiety, to see if their brains show abnormal activity in the neocortex and caudate nucleus during approach-avoidance decision-making. Magnetic resonance imaging (MRI) studies have shown abnormal activity in two regions of the medial prefrontal cortex that connect with the caudate nucleus.

The caudate nucleus has within it regions that are connected with the limbic system, which regulates mood, and it sends input to motor areas of the brain as well as dopamine-producing regions. Graybiel and Amemori believe that the abnormal activity seen in the caudate nucleus in this study could be somehow disrupting dopamine activity.

“There must be many circuits involved,” she says. “But apparently we are so delicately balanced that just throwing the system off a little bit can rapidly change behavior.”

The research was funded by the National Institutes of Health, the CHDI Foundation, the U.S. Office of Naval Research, the U.S. Army Research Office, MEXT KAKENHI, the Simons Center for the Social Brain, the Naito Foundation, the Uehara Memorial Foundation, Robert Buxton, Amy Sommer, and Judy Goldberg.

Testing the limits of artificial visual recognition systems

Anne Trafton |

While it can sometimes seem hard to see the forest from the trees, pat yourself on the back: as a human you are actually pretty good at object recognition. A major goal for artificial visual recognition systems is to be able to distinguish objects in the way that humans do. If you see a tree or a bush from almost any angle, in any degree of shading (or even rendered in pastels and pixels in a Monet), you would recognize it as a tree or a bush. However, such recognition has traditionally been a challenge for artificial visual recognition systems. Researchers at MIT’s McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences (BCS) have now directly examined and shown that artificial object recognition is quickly becoming more primate-like, but still lags behind when scrutinized at higher resolution.

In recent years, dramatic advances in “deep learning” have produced artificial neural network models that appear remarkably similar to aspects of primate brains. James DiCarlo, Peter de Florez Professor and Department Head of BCS, set out to determine and carefully quantify how well the current leading artificial visual recognition systems match humans and other higher primates when it comes to image categorization. In recent years, dramatic advances in “deep learning” have produced artificial neural network models that appear remarkably similar to aspects of primate brains, so DiCarlo and his team put these latest models through their paces.

Rishi Rajalingham, a graduate student in DiCarlo’s lab conducted the study as part of his thesis work at the McGovern Institute. As Rajalingham puts it “one might imagine that artificial vision systems should behave like humans in order to seamlessly be integrated into human society, so this tests to what extent that is true.”

The team focused on testing so-called “deep, convolutional neural networks” (DCNNs), and specifically those that had trained on ImageNet, a collection of large-scale category-labeled image sets that have recently been used as a library to train neural networks (called DCNNIC models). These specific models have thus essentially been trained in an intense image recognition bootcamp. The models were then pitted against monkeys and humans and asked to differentiate objects in synthetically constructed images. These synthetic images put the object being categorized in unusual backgrounds and orientations. The resulting images (such as the floating camel shown above) evened the playing field for the machine models (humans would ordinarily have a leg up on image categorization based on assessing context, so this was specifically removed as a confounder to allow a pure comparison of specific object categorization).

DiCarlo and his team found that humans, monkeys and DCNNIC models all appeared to perform similarly, when examined at a relatively coarse level. Essentially, each group was shown 100 images of 24 different objects. When you averaged how they did across 100 photos of a given object, they could distinguish, for example, camels pretty well overall. The researchers then zoomed in and examined the behavioral data at a much finer resolution (i.e. for each single photo of a camel), thus deriving more detailed “behavioral fingerprints” of primates and machines. These detailed analyses of how they did for each individual image revealed strong differences: monkeys still behaved very consistently like their human primate cousins, but the artificial neural networks could no longer keep up.

“I thought it was quite surprising that monkeys and humans are remarkably similar in their recognition behaviors, especially given that these objects (e.g. trucks, tanks, camels, etc.) don’t “mean” anything to monkeys” says Rajalingham. “It’s indicative of how closely related these two species are, at least in terms of these visual abilities.”

DiCarlo’s team gave the neural networks remedial homework to see if they could catch up upon extra-curricular training by now training the models on images that more closely resembled the synthetic images used in their study. Even with this extra training (which the humans and monkeys did not receive), they could not match a primate’s ability to discern what was in each individual image.

DiCarlo conveys that this is a glass half-empty and half-full story. Says DiCarlo, “The half full part is that, today’s deep artificial neural networks that have been developed based on just some aspects of brain function are far better and far more human-like in their object recognition behavior than artificial systems just a few years ago,” explains DiCarlo. “However, careful and systematic behavioral testing reveals that even for visual object recognition, the brain’s neural network still has some tricks up its sleeve that these artificial neural networks do not yet have.”

Dicarlo’s study begins to define more precisely when it is that the leading artificial neural networks start to “trip up”, and highlights a fundamental aspect of their architecture that struggles with categorization of single images. This flaw seems to be unaddressable through further brute force training. The work also provides an unprecedented and rich dataset of human (1476 anonymous humans to be exact) and primate behavior that will help act as a quantitative benchmark for improvement of artificial neural networks.

 

Image: Example of synthetic image used in the study. For category ‘camel’, 100 distinct, synthetic camel images were shown to DCNNIC models, humans and rhesus monkeys. 24 different categories were tested altogether.

Charting the cerebellum

Anne Trafton |

Small and tucked away under the cerebral hemispheres toward the back of the brain, the human cerebellum is still immediately obvious due to its distinct structure. From Galen’s second century anatomical description to Cajal’s systematic analysis of its projections, the cerebellum has long drawn the eyes of researchers studying the brain.  Two parallel studies from MIT’s McGovern institute have recently converged to support an unexpectedly complex level of non-motor cerebellar organization, that would not have been predicted from known motor representation regions.

Historically the cerebellum has primarily been considered to impact motor control and coordination. Think of this view as the cerebellum being the chain on a bicycle, registering what is happening up front in the cortex, and relaying the information so that the back wheel moves at a coordinated pace. This simple view has been questioned as cerebellar circuits have been traced to the basal ganglia and to neocortical regions via the thalamus. This new view suggests the cerebellum is a hub in a complex network, with potentially higher and non-motor functions including cognition and reward-based learning.

A collaboration between the labs of John Gabrieli, Investigator at the McGovern Institute for Brain Research and Jeremy Schmahmann, of the Ataxia Unit at Massachusetts General Hospital and Harvard Medical School, has now used functional brain imaging to give new insight into the cerebellar organization of non-motor roles, including working memory, language, and, social and emotional processing. In a complementary paper, a collaboration between Sheeba Anteraper of MIT’s Martinos Imaging Center and Gagan Joshi of the Alan and Lorraine Bressler Clinical and Research Program at Massachusetts General Hospital, has found changes in connectivity that occur in the cerebellum in autism spectrum disorder (ASD).

A more complex map of the cerebellum

Published in NeuroImage, and featured on the cover, the first study was led by author Xavier Guell, a postdoc in the Gabrieli and Schmahmann labs. The authors used fMRI data from the Human Connectome Project to examine activity in different regions of the cerebellum during specific tasks and at rest. The tasks used extended beyond motor activity to functions recently linked to the cerebellum, including working memory, language, and social and emotional processing. As expected, the authors saw that two regions assigned by other methods to motor activity were clearly modulated during motor tasks.

“Neuroscientists in the 1940s and 1950s described a double representation of motor function in the cerebellum, meaning that two regions in each hemisphere of the cerebellum are engaged in motor control,” explains Guell. “That there are two areas of motor representation in the cerebellum remains one of the most well-established facts of cerebellar macroscale physiology.”

When it came to assigning non-motor tasks, to their surprise, the authors identified three representations that localized to different regions of the cerebellum, pointing to an unexpectedly complex level of organization.

Guell explains the implications further. “Our study supports the intriguing idea that while two parts of the cerebellum are simultaneously engaged in motor tasks, three other parts of the cerebellum are simultaneously engaged in non-motor tasks. Our predecessors coined the term “double motor representation,” and we may now have to add “triple non-motor representation” to the dictionary of cerebellar neuroscience.”

A serendipitous discussion

What happened next, over a discussion of data between Xavier Guell and Sheeba Arnold Anteraper of the McGovern Institute for Brain Research that culminated in a paper led by Anteraper, illustrates how independent strands can meet and reinforce to give a fuller scientific picture.

The findings by Guell and colleagues made the cover of NeuroImage.
The findings by Guell and colleagues made the cover of NeuroImage.

Anteraper and colleagues examined brain images from high-functioning ASD patients, and looked for statistically-significant patterns, letting the data speak rather than focusing on specific ‘candidate’ regions of the brain. To her surprise, networks related to language were highlighted, as well as the cerebellum, regions that had not been linked to ASD, and that seemed at first sight not to be relevant. Scientists interested in language processing, immediately pointed her to Guell.

“When I went to meet him,” says Anteraper, “I saw immediately that he had the same research paper that I’d been reading on his desk. As soon as I showed him my results, the data fell into place and made sense.”

After talking with Guell, they realized that the same non-motor cerebellar representations he had seen, were independently being highlighted by the ASD study.

“When we study brain function in neurological or psychiatric diseases we sometimes have a very clear notion of what parts of the brain we should study” explained Guell, ”We instead asked which parts of the brain have the most abnormal patterns of functional connectivity to other brain areas? This analysis gave us a simple, powerful result. Only the cerebellum survived our strict statistical thresholds.”

The authors found decreased connectivity within the cerebellum in the ASD group, but also decreased strength in connectivity between the cerebellum and the social, emotional and language processing regions in the cerebral cortex.

“Our analysis showed that regions of disrupted functional connectivity mapped to each of the three areas of non-motor representation in the cerebellum. It thus seems that the notion of two motor and three non-motor areas of representation in the cerebellum is not only important for understanding how the cerebellum works, but also important for understanding how the cerebellum becomes dysfunctional in neurology and psychiatry.”

Guell says that many questions remain to be answered. Are these abnormalities in the cerebellum reproducible in other datasets of patients diagnosed with ASD? Why is cerebellar function (and dysfunction) organized in a pattern of multiple representations? What is different between each of these representations, and what is their distinct contribution to diseases such as ASD? Future work is now aimed at unraveling these questions.

The Learning Brain

by Shannon Fischer |

“There’s a slogan in education,” says McGovern Investigator John Gabrieli. “The first three years are learning to read, and after that you read to learn.”

For John Gabrieli, learning to read represents one of the most important milestones in a child’s life. Except, that is, when a child can’t. Children who cannot learn to read adequately by the first grade have a 90 percent chance of still reading poorly in the fourth grade, and 75 percent odds of struggling in high school. For the estimated 10 percent of schoolchildren with a reading disability, that struggle often comes with a host of other social and emotional challenges: anxiety, damaged self-esteem, increased risk for poverty and eventually, encounters with the criminal justice system.

Most reading interventions focus on classical dyslexia, which is essentially a coding problem—trouble moving letters into sound patterns in the brain. But other factors, such as inadequate vocabulary and lack of practice opportunities, hinder reading too. The diagnosis can be subjective, and for those who are diagnosed, the standard treatments help only some students. “Every teacher knows half to two-thirds have a good response, the other third don’t,” Gabrieli says. “It’s a mystery. And amazingly there’s been almost no progress on that.”

For the last two decades, Gabrieli has sought to unravel the neuroscience behind learning and reading disabilities and, ultimately, convert that understanding into new and better education
interventions—a sort of translational medicine for the classroom.

The Home Effect

In 2011, when Julia Leonard was a research assistant in Gabrieli’s lab, she planned to go into pediatrics. But she became drawn to the lab’s education projects and decided to join the lab as
a graduate student to learn more. By 2015, she helped coauthor a landmark study with postdoc Allyson Mackey, that sought neural markers for the academic “achievement gap,” which separates higher socioeconomic status (SES) children from their disadvantaged peers. It was the first study to make a connection between SES-linked differences in brain structure and educational markers. Specifically, they found children from wealthier backgrounds had thicker cortical brain regions, which correlated with better academic achievement.

“Being a doctor is a really awesome and powerful career,” she says. “But I was more curious about the research that could cause bigger changes in children’s lives.”

Leonard collaborated with Rachel Romeo, another graduate student in the Gabrieli lab who wanted to understand the powerful effect of SES on the developing brain. Romeo had a distinctive background in speech pathology and literacy, where she’d observed wealthier students progressing more quickly compared to their disadvantaged peers.

Their research is revealing a fascinating picture. In a 2017 study, Romeo compared how reading-disabled children from low and high SES backgrounds fared after an intensive summer reading intervention. Low SES children in the intervention improved most in their reading, and MRI scans revealed their brains also underwent greater structural changes in response to the intervention. Higher SES children did not appear to change much, either in skill or brain structure.

“In the few studies that have looked at SES effects on treatment outcomes,” Romeo says, “the research suggests that higher SES kids would show the most improvement. We were surprised to
find that this wasn’t true.” She suspects that the midsummer timing of the intervention may account for this. Lower SES kids’ performance often suffer most during a “summer slump,”
and would therefore have the greatest potential to improve from interventions at this time.

However, in another study this year, Leonard uncovered unique brain differences in lower-SES children. Only among lower-SES children was better reasoning ability associated with thicker
cortex in a key part of the brain. Same behavior, different neural signatures.

“So this becomes a really interesting basic science question,” Leonard says. “Does the brain support cognition the same way across everyone, or does it differ based on how you grow up?”

Not a One-Size-Fits-All

Critics of such “educational neuroscience” have highlighted the lack of useful interventions produced by this research. Gabrieli agrees that so far, little has emerged. “The painful thing is the slowness of this work. It’s mind-boggling,” Gabrieli admits. Every intervention requires all the usual human research requirements, plus coordinating with schools, parents, teachers, and so on. “It’s a huge process to do even the smallest intervention,” he explains. Partly because of that, the field is still relatively new.

But he disagrees with the idea that nothing will come from this research. Gabrieli’s lab previously identified neural markers in children who will go on to develop reading disabilities. These markers could even predict who would or would not respond to standard treatments that focus on phonetic letter-sound coding.

Romeo and Leonard’s work suggests that varied etiologies underlie reading disabilities, which may be the key. “For so long people have thought that reading disorders were just a unitary construct: kids are bad at reading, so let’s fix that with a one-size-fits-all treatment,” Romeo says.

Such findings may ultimately help resource-strapped schools target existing phonetic training rather than enrolling all struggling readers in the same program, to see some still fail.

Think Spaces

At the Oliver Hazard Perry School, a public K-8 school located on the South Boston waterfront, teachers like Colleen Labbe have begun to independently navigate similar problems as they try
to reach their own struggling students.

“A lot of times we look at assessments and put students in intervention groups like phonics,” Labbe says. “But it’s important to also ask what is happening for these students on their way to school and at home.”

For Labbe and Perry Principal Geoffrey Rose, brain science has proven transformative. They’ve embraced literature on neuroplasticity—the idea that brains can change if teachers find the right combination of intervention and circumstances, like the low-SES students who benefited in Romeo and Leonard’s study.

“A big myth is that the brain can’t grow and change, and if you can’t reach that student, you pass them off,” Labbe says.

The science has also been empowering to her students, validating their own powers of self-change. “I tell the kids, we’re going to build the goop!” she says, referring to the brain’s ability to make new connections.

“All kids can learn,” Rose agrees. “But the flip of that is, can all kids do school?” His job, he says, is to make sure they can.

The classrooms at Perry are a mix of students from different cultures and socioeconomic backgrounds, so he and Labbe have focused on helping teachers find ways to connect with these children and help them manage their stresses and thus be ready to learn. Teachers here are armed with “scaffolds”—digestible neuro- and cognitive science aids culled from Rose’s postdoctoral studies at Boston College’s Professional School Administrator Program for school leaders. These encourage teachers to be more aware of cultural differences and tendencies in themselves and their students, to better connect.

There are also “Think Spaces” tucked into classroom corners. “Take a deep breath and be calm,” read posters at these soothing stations, which are equipped with de-stressing tools, like squeezable balls, play-dough, and meditation-inspiring sparkle wands. It sounds trivial, yet studies have shown that poverty-linked stressors like food and home insecurity take a toll on emotion and memory-linked brain areas like the amygdala and hippocampus.

In fact, a new study by Clemens Bauer, a postdoc in Gabrieli’s lab, argues that mindfulness training can help calm amygdala hyperactivity, help lower self-perceived stress, and boost attention. His study was conducted with children enrolled in a Boston charter school.

Taking these combined approaches, Labbe says, she’s seen one of her students rise from struggling at the lowest levels of instruction, to thriving by year end. Labbe’s focus on understanding the girl’s stressors, her family environment, and what social and emotional support she really needed was key. “Now she knows she can do it,” Labbe says.

Rose and Labbe only wish they could better bridge the gap between educators like themselves and brain scientists like Gabrieli. To help forge these connections, Rose recently visited Gabrieli’s lab and looks forward to future collaborations. Brain research will provide critical insights into teaching strategy, he says, but the gap is still wide.

From Lab to Classroom

“I’m hugely impressed by principals and teachers who are passionately interested in understanding the brain,” Gabrieli says. Fortunately, new efforts are bridging educators and scientists.

This March, Gabrieli and the MIT Integrated Learning Initiative—MITili, which he also directs—announced a $30 million-dollar grant from the Chan Zuckerberg Initiative for a collaboration
between MIT, the Harvard Graduate School of Education, and Florida State University.

The grant aims to translate some of Gabrieli’s work into more classrooms. Specifically, he hopes to produce better diagnostics that can identify children at risk for dyslexia and other learning
disabilities before they even learn to read.

He hopes to also provide rudimentary diagnostics that identify the source of struggle, be it classic dyslexia, lack of home support, stress, or maybe a combination of factors. That in turn,
could guide treatment—standard phonetic care for some children, versus alternatives: social support akin to Labbe’s efforts, reading practice, or maybe just vocabulary-boosting conversation time with adults.

“We want to get every kid to be an adequate reader by the end of the third grade,” Gabrieli says. “That’s the ultimate goal for me: to help all children become learners.”

Michale Fee receives McKnight Technological Innovations in Neuroscience Award

by Julie Pryor |

McGovern Institute investigator Michale Fee has been selected to receive a 2018 McKnight Technological Innovations in Neuroscience Award for his research on “new technologies for imaging and analyzing neural state-space trajectories in freely-behaving small animals.”

“I am delighted to get support from the McKnight Foundation,” says Fee, who is also the Glen V. and Phyllis F. Dorflinger Professor in the Department of Brain and Cognitive Neurosciences at MIT. “We’re very excited about this project which aims to develop technology that will be a great help to the broader neuroscience community.”

Fee studies the neural mechanisms by which the brain, specifically that of juvenile songbirds, learns complex sequential behaviors. The way that songbirds learn a song through trial and error is analogous to humans learning complex behaviors, such as riding a bicycle. While it would be insightful to link such learning to neural activity, current methods for monitoring neurons can only monitor a limited field of neurons, a big issue since such learning and behavior involve complex interactions between larger circuits. While a wider field of view for recordings would help decipher neural changes linked to this learning paradigm, current microscopy equipment is large relative to a juvenile songbird, and microscopes that can record neural activity generally constrain the behavior of small animals. Ideally, technologies need to be lightweight (about 1 gram) and compact in size (the size of a dime), a far cry from current larger microscopes that weigh in at 3 grams. Fee hopes to be able to break these technical boundaries and miniaturize the recording equipment thus allowing recording of more neurons in naturally behaving small animals.

“We are thrilled that the McKnight Foundation has chosen to support this project. The technology that Michale’s developing will help to better visualize and understand the circuits underlying learning,” says Robert Desimone, director of MIT’s McGovern Institute for Brain Research.

In addition to development and miniaturization of the microscopy hardware itself, the award will support the development of technology that helps analyze the resulting images, so that the neuroscience community at large can more easily deploy and use the technology.