Flythrough of image data collected from mouse hippocampus, with neurons expressing Yellow Fluorescent Protein, showing both the large volume accessible with Expansion Microscopy (ExM) and the sub-diffraction limited resolution needed to reveal synaptic structure. Animation by Sputnik Animation based on data from Ed Boyden Lab at MIT.
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Translational Neuroscience Seminar Series
Seminar: Expansion Microscopy
In one aspect of vision, computers catch up to primate brain
For decades, neuroscientists have been trying to design computer networks that can mimic visual skills such as recognizing objects, which the human brain does very accurately and quickly.
Until now, no computer model has been able to match the primate brain at visual object recognition during a brief glance. However, a new study from MIT neuroscientists has found that one of the latest generation of these so-called “deep neural networks” matches the primate brain.
Because these networks are based on neuroscientists’ current understanding of how the brain performs object recognition, the success of the latest networks suggest that neuroscientists have a fairly accurate grasp of how object recognition works, says James DiCarlo, a professor of neuroscience and head of MIT’s Department of Brain and Cognitive Sciences and the senior author of a paper describing the study in the Dec. 11 issue of the journal PLoS Computational Biology.
“The fact that the models predict the neural responses and the distances of objects in neural population space shows that these models encapsulate our current best understanding as to what is going on in this previously mysterious portion of the brain,” says DiCarlo, who is also a member of MIT’s McGovern Institute for Brain Research.
This improved understanding of how the primate brain works could lead to better artificial intelligence and, someday, new ways to repair visual dysfunction, adds Charles Cadieu, a postdoc at the McGovern Institute and the paper’s lead author.
Other authors are graduate students Ha Hong and Diego Ardila, research scientist Daniel Yamins, former MIT graduate student Nicolas Pinto, former MIT undergraduate Ethan Solomon, and research affiliate Najib Majaj.
Inspired by the brain
Scientists began building neural networks in the 1970s in hopes of mimicking the brain’s ability to process visual information, recognize speech, and understand language.
For vision-based neural networks, scientists were inspired by the hierarchical representation of visual information in the brain. As visual input flows from the retina into primary visual cortex and then inferotemporal (IT) cortex, it is processed at each level and becomes more specific until objects can be identified.
To mimic this, neural network designers create several layers of computation in their models. Each level performs a mathematical operation, such as a linear dot product. At each level, the representations of the visual object become more and more complex, and unneeded information, such as an object’s location or movement, is cast aside.
“Each individual element is typically a very simple mathematical expression,” Cadieu says. “But when you combine thousands and millions of these things together, you get very complicated transformations from the raw signals into representations that are very good for object recognition.”
For this study, the researchers first measured the brain’s object recognition ability. Led by Hong and Majaj, they implanted arrays of electrodes in the IT cortex as well as in area V4, a part of the visual system that feeds into the IT cortex. This allowed them to see the neural representation — the population of neurons that respond — for every object that the animals looked at.
The researchers could then compare this with representations created by the deep neural networks, which consist of a matrix of numbers produced by each computational element in the system. Each image produces a different array of numbers. The accuracy of the model is determined by whether it groups similar objects into similar clusters within the representation.
“Through each of these computational transformations, through each of these layers of networks, certain objects or images get closer together, while others get further apart,” Cadieu says.
The best network was one that was developed by researchers at New York University, which classified objects as well as the macaque brain.
More processing power
Two major factors account for the recent success of this type of neural network, Cadieu says. One is a significant leap in the availability of computational processing power. Researchers have been taking advantage of graphical processing units (GPUs), which are small chips designed for high performance in processing the huge amount of visual content needed for video games. “That is allowing people to push the envelope in terms of computation by buying these relatively inexpensive graphics cards,” Cadieu says.
The second factor is that researchers now have access to large datasets to feed the algorithms to “train” them. These datasets contain millions of images, and each one is annotated by humans with different levels of identification. For example, a photo of a dog would be labeled as animal, canine, domesticated dog, and the breed of dog.
At first, neural networks are not good at identifying these images, but as they see more and more images, and find out when they were wrong, they refine their calculations until they become much more accurate at identifying objects.
Cadieu says that researchers don’t know much about what exactly allows these networks to distinguish different objects.
“That’s a pro and a con,” he says. “It’s very good in that we don’t have to really know what the things are that distinguish those objects. But the big con is that it’s very hard to inspect those networks, to look inside and see what they really did. Now that people can see that these things are working well, they’ll work more to understand what’s happening inside of them.”
DiCarlo’s lab now plans to try to generate models that can mimic other aspects of visual processing, including tracking motion and recognizing three-dimensional forms. They also hope to create models that include the feedback projections seen in the human visual system. Current networks only model the “feedforward” projections from the retina to the IT cortex, but there are 10 times as many connections that go from IT cortex back to the rest of the system.
This work was supported by the National Eye Institute, the National Science Foundation, and the Defense Advanced Research Projects Agency.
New way to turn genes on
Using a gene-editing system originally developed to delete specific genes, MIT researchers have now shown that they can reliably turn on any gene of their choosing in living cells.
This new application for the CRISPR/Cas9 gene-editing system should allow scientists to more easily determine the function of individual genes, according to Feng Zhang, the W.M. Keck Career Development Professor in Biomedical Engineering in MIT’s Departments of Brain and Cognitive Sciences and Biological Engineering, and a member of the Broad Institute and MIT’s McGovern Institute for Brain Research.
This approach also enables rapid functional screens of the entire genome, allowing scientists to identify genes involved in particular diseases. In a study published in the Dec. 10 online edition of Nature, Zhang and colleagues identified several genes that help melanoma cells become resistant to a cancer drug.
Silvana Konermann, a graduate student in Zhang’s lab, and Mark Brigham, a McGovern Institute postdoc, are the paper’s lead authors.
A new function for CRISPR
The CRISPR system relies on cellular machinery that bacteria use to defend themselves from viral infection. Researchers have previously harnessed this cellular system to create gene-editing complexes that include a DNA-cutting enzyme called Cas9 bound to a short RNA guide strand that is programmed to bind to a specific genome sequence, telling Cas9 where to make its cut.
In the past two years, scientists have developed Cas9 as a tool for turning genes off or replacing them with a different version. In the new study, Zhang and colleagues engineered the Cas9 system to turn genes on, rather than knock them out. Scientists have tried to do this before using proteins that are individually engineered to target DNA at specific sites. However, these proteins are difficult to work with. “If you use the older generation of tools, getting the technology to do what you actually want is a project on its own,” Konermann says. “It takes a lot of time and is also quite expensive.”
There have also been attempts to use CRISPR to turn on genes by inactivating the part of the Cas9 enzyme that cuts DNA and linking Cas9 to pieces of proteins called activation domains. These domains recruit the cellular machinery necessary to begin reading copying RNA from DNA, a process known as transcription.
However, these efforts have been unable to consistently turn on gene transcription. Zhang and his colleagues, Osamu Nureki and Hiroshi Nishimasu at the University of Tokyo, decided to overhaul the CRISPR-Cas9 system based on an analysis they published earlier this year of the structure formed when Cas9 binds to the guide RNA and its target DNA. “Based on knowing its 3-D shape, we can think about how to rationally improve the system,” Zhang says.
In previous efforts, scientists had tried to attach the activation domains to either end of the Cas9 protein, with limited success. From their structural studies, the MIT team realized that two small loops of the RNA guide poke out from the Cas9 complex and could be better points of attachment because they allow the activation domains to have more flexibility in recruiting transcription machinery.
Using their revamped system, the researchers activated about a dozen genes that had proven difficult or impossible to turn on using the previous generation of Cas9 activators. Each gene showed at least a twofold boost in transcription, and for many genes, the researchers found multiple orders of magnitude increase in activation.
Genome-scale activation screening
Once the researchers had shown that the system was effective at activating genes, they created a library of 70,290 guide RNAs targeting all of the more than 20,000 genes in the human genome.
They screened this library to identify genes that confer resistance to a melanoma drug called PLX-4720. Drugs of this type work well in patients whose melanoma cells have a mutation in the BRAF gene, but cancer cells that survive the treatment can grow into new tumors, allowing the cancer to recur.
To discover the genes that help cells become resistant, the researchers delivered CRISPR components to a large population of melanoma cells grown in the lab, with each cell receiving a different guide RNA targeting a different gene. After treating the cells with PLX-4720, they identified several genes that helped the cells to survive — some previously known to be involved in drug resistance, as well as several novel targets.
Studies like this could help researchers discover new cancer drugs that prevent tumors from becoming resistant.
“You could start with a drug that targets the mutated BRAF along with combination therapy that targets genes that allow the cell to survive. If you target both of them at the same time, you could likely prevent the cells from developing resistance mechanisms that enable further growth despite drug treatment,” Konermann says.
Scientists have tried to do large-scale screens like this by delivering single genes carried by viruses, but that does not work with all genes.
“This new technique could allow you to sample a larger spectrum of genes that might be playing a role,” says Levi Garraway, an associate professor of medicine at Dana-Farber Cancer Institute who was not involved in the research. “This is really a technology development paper, but the tantalizing results from the drug resistance screen speak to the rich biological possibilities of this approach.”
Zhang’s lab also plans to use this technique to screen for genes that, when activated, could correct the effects of autism or neurodegenerative diseases such as Alzheimer’s. He also plans to make the necessary reagents available to academic labs that want to use them, through the Addgene repository.
The research was funded by the National Institute of Mental Health; the National Institute of Neurological Disorders and Stroke; the Keck, Searle Scholars, Klingenstein, Vallee, and Simons foundations; and Bob Metcalfe.
Live Long and Prosper in the New Year!
Season’s Greetings from your friends at the McGovern Institute for Brain Research at MIT.
From genes to brains
Many brain disorders are strongly influenced by genetics, and researchers have long hoped that the identification of genetic risk factors will provide clues to the causes and possible treatments of these mysterious conditions. In the early years, progress was slow. Many claims failed to replicate, and it became clear that in order to identify the important risk genes with confidence, researchers would need to examine the genomes of very large numbers of patients.
Until recently that would have been prohibitively expensive, but genome research has been accelerating fast. Just how fast was underlined by an announcement in January from a California-based company, Illumina, that it had achieved a long-awaited milestone: sequencing an entire human genome for under $1000. Seven years ago, this task would have cost $10M and taken weeks of work. The new system does the job in a few hours, and can sequence tens of thousands of genomes per year.
In parallel with these spectacular advances, another technological revolution has been unfolding over the past several years, with the development of a new method for editing the genome of living cells. This method, known as CRISPR, allows researchers to make precise changes to a DNA sequence—an advance that is expected to transform many areas of biomedical research and may ultimately form the basis of new treatments for human genetic disease.
The CRISPR technology, which is based on a natural bacterial defense system against viruses, uses a short strand of RNA as a “search string” to locate a corresponding DNA target sequence. This RNA string can be synthesized in the lab and can be designed to recognize any desired sequence of DNA. The RNA carries with it a protein called Cas9, which cuts the target DNA at the chosen location, allowing a new sequence to be inserted—providing researchers with a fast and flexible “search-and-replace” tool for editing the genome.
One of the pioneers in this field is McGovern Investigator Feng Zhang, who along with George Church of Harvard, was the first to show that CRISPR could be used to edit the human genome in living cells. Zhang is using the technology to study human brain disorders, building on the flood of new genetic discoveries that are emerging from advances in DNA sequencing. The Broad Institute, where Zhang holds a joint appointment, is a world leader in human psychiatric genetics, and will be among the first to acquire the new Illumina sequencing machines when they reach the market later this year.
By sequencing many thousands of individuals, geneticists are identifying the rare genetic variants that contribute to risk of diseases such as autism, schizophrenia and bipolar disorder. CRISPR will allow neuroscientists to study those gene variants in cells and in animal models. The goal, says McGovern Institute director Bob Desimone, is to understand the biological roots of brain disorders. “The biggest obstacle to new treatments has been our ignorance of fundamental mechanisms. But with these new technologies, we have a real opportunity to understand what’s wrong at the level of cells and circuits, and to identify the pressure points at which therapeutic intervention may be possible.”
Culture Club
In other fields, the influence of genetic variations on disease has turned out to be surprisingly difficult to unravel, and for neuropsychiatric disease, the challenge may be even greater. The brain is the most complex organ of the body, and the underlying pathologies that lead to disease are not yet well understood. Moreover, any given disorder may show a wide variation in symptoms from patient to patient, and it may also have many different genetic causes. “There are hundreds of genes that can contribute to autism or schizophrenia,” says McGovern Investigator Guoping Feng, who is also Poitras Professor of Neuroscience.
To study these genes, Feng and collaborators at the Broad Institute’s Stanley Center for Psychiatric Research are planning to screen thousands of cultures of neurons, grown in the tiny wells of cell culture plates. The neurons, which are grown from stem cells, can be engineered using CRISPR to contain the genetic variants that are linked to neuropsychiatric disease. Each culture will contain neurons with a different variant, and these will be examined for abnormalities that might be associated with disease.
Feng and colleagues hope this high-throughput platform will allow them to identify cellular traits, or phenotypes, that may be related to disease and which can then be studied in animal models to see if they cause defects in brain function or in behavior. In the longer term, this high-throughput platform can also be used to screen for new drugs that can reverse these defects.
Animal Kingdom
Cell cultures are necessary for large-scale screens, but ultimately the results must be translated into the context of brain circuits and behavior. “That means we must study animal models too,” says Feng.
Feng has created several mouse models of human brain disease by mutating genes that are linked to these disorders and examining the behavioral and cellular defects in the mutant animals. “We have models of obsessive-compulsive disorder and autism,” he explains. “By studying these mice we want to learn what’s wrong with their brains.”
So far, Feng has focused on single-gene models, but the majority of human psychiatric disorders are triggered by multiple genes acting in combination. One advantage of the new CRISPR method is that it allows researchers to introduce several mutations in parallel, and Zhang’s lab is now working to create autistic mice with more than one gene alteration.
Perhaps the most important advantage of CRISPR is that it can be applied to any species. Currently, almost all genetic modeling of human disease is restricted to mice. But while mouse models are convenient, they are limited, especially for diseases that affect higher brain functions and for which there are no clear parallels in rodents. “We also need to study species that are closer to humans,” says Feng.
Accordingly, he and Zhang are collaborating with colleagues in Oregon and China to use CRISPR to create primate models of neuropsychiatric disorders. Earlier this year, a team in China announced that they had used CRISPR to create transgenic monkeys that will be used to study defects in metabolism and immunity.
Feng and Zhang are planning to use a similar approach to study brain disorders, but in addition to macacques, they will also work with a smaller primate species, the marmoset. These animals, with their fast breeding cycles and complex behavioral repertoires, are ideal for genetic studies of behavior and brain function. And because they are very social with highly structured communication patterns, they represent a promising new model for understanding the neural basis of social cognition and its disruption in conditions such as autism.
Given their close evolutionary relationship to humans, marmoset models could also help accelerate the development of new therapies. Many experimental drugs for brain disorders have been tested successfully in mice, only to prove ineffective in subsequent human trials. These failures, which can be enormously expensive, have led many drug companies to cut back on their neuroscience R&D programs. Better animal models could reverse this trend by allowing companies to predict more accurately which drug candidates are most promising, before investing heavily in human clinical trials.
Feng’s mouse research provides an example of how this approach can work. He previously developed a mouse model of obsessive-compulsive disorder, in which the animals engage in obsessive self-grooming, and he has now shown that this effect can be reversed when the missing gene is reintroduced, even in adulthood. Other researchers have seemed similar results with other brain disorders such as Rett Syndrome, a condition that is often accompanied by autism. “The brain is amazingly plastic,” says Feng. “At least in a mouse, we have shown that the damage can often be repaired. If we can also show this in marmosets or other primate models, that would really give us hope that something similar is possible in humans.”
Human Race
Ultimately, to understand the genetic roots of human behavior, researchers must sequence the genomes of individual subjects in parallel with measurements of those same individuals’ behavior and brain function.
Such studies typically require very large sample sizes, but the plummeting cost of sequencing is now making this feasible. In China, for instance, a project is already underway to sequence the genomes of many thousands of individuals to uncover genetic influences on cognition and intelligence.
The next step will be to link the genetics to brain activity, says McGovern Investigator John Gabrieli, who also directs the Martinos Imaging Center at MIT. “It’s a big step to go from DNA to behavioral variation or clinical diagnosis. But we know those genes must affect brain function, so neuroimaging may help us to bridge that gap.”
But brain scans can be time-consuming, given that volunteers must perform behavioral tasks in the scanner. Studies are typically limited to a few dozen subjects, not enough to detect the often subtle effects of genomic variation.
One way to enlarge these studies, says Gabrieli, is to image the brain during rest rather than in a state of prompted activity. This procedure is fast and easy to replicate from lab to lab, and patterns of resting state activity have turned out to be surprisingly reproducible; moreover, Gabrieli is finding that differences in resting activity are associated with brain disorders such as autism, and he hopes that in the future it will be possible to relate these differences to the genetic factors that are emerging from genome studies at the Broad Institute and elsewhere.
“I’m optimistic that we’re going to see dramatic advances in our understanding of neuropsychiatric disease over the next few years.” — Bob Desimone
Confirming these associations will require a “big data” approach, in which results from multiple labs are consolidated into large repositories and analyzed for significant associations. Resting state imaging lends itself to this approach, says Gabrieli. “To find the links between brain function and genetics, big data is the direction we need to go to be successful.”
How soon might this happen? “It won’t happen overnight,” cautions Desimone. “There are a lot of dots that need to be connected. But we’ve seen in the case of genome research how fast things can move once the right technologies are in place. I’m optimistic that we’re going to see equally dramatic advances in our understanding of neuropsychiatric disease over the next few years.”
Secret Santa for Seniors
MIT researchers to win awards from the Society for Neuroscience
Three neuroscientists at MIT have been selected to receive awards from the Society for Neuroscience (SfN).
Tomaso Poggio, a founding member of the McGovern Institute for Brain Research at MIT, will receive the Swartz Prize for Theoretical and Computational Neuroscience; Feng Zhang, a member of the McGovern Institute and an assistant professor in the Department of Brain and Cognitive Sciences, will receive the Young Investigator Award; and Sung-Yon Kim, a Simons postdoctoral fellow of the Life Sciences Research Foundation at MIT, will receive the Donald B. Lindsley Prize in Behavioral Neuroscience. The awards will be presented during Neuroscience 2014, the SfN’s annual meeting in Washington, D.C.
Swartz Prize for Theoretical and Computational Neuroscience
The $25,000 Swartz Prize for Theoretical and Computational Neuroscience, supported by the Swartz Foundation, recognizes an individual who has produced a significant cumulative contribution to theoretical models or computational methods in neuroscience.
“Dr. Poggio’s contributions to the development of computational and theoretical models of the human visual system have served to advance our understanding of how human systems learn from experience,” said Carol Mason, president of SfN. “It is an honor to recognize him as a founder and driving force in the field of computational neuroscience.”
Poggio, the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences and the director of the Center for Brains, Minds and Machines, develops computational models of the brain to understand human intelligence. Specifically, he has developed models that mimic the ways that humans learn to recognize objects, such as faces, and actions, such as motion — applications now present in digital cameras and some cars. Poggio is currently working to develop more complex models that mimic the forward as well as feedback signals that the human brain uses during visual recognition. The ultimate goal of this research is to better understand how the brain works and to apply this technology to build intelligent machines.
Young Investigator Award
The SfN has also named two winners of this year’s Young Investigator Award: Feng Zhang of MIT and Diana Bautista of the University of California at Berkeley.
The $15,000 award recognizes the outstanding achievements and contributions by a young neuroscientist who has recently received his or her advanced professional degree.
“Drs. Zhang and Bautista are two young neuroscientists who have demonstrated remarkable dedication to their work,” Mason said. “Their creative research is advancing their respective fields, and their commitment to helping other scientists succeed is an inspiration to us all.”
Zhang, who is also a core member of the Broad Institute of MIT and Harvard and the W. M. Keck Career Development Professor in Biomedical Engineering, uses synthetic biology methods to study brain disease. As a graduate student at Stanford University, Zhang was instrumental in advancing the development of optogenetic technology, which allows researchers to manipulate genetically modified neurons with light. More recently, Zhang was a leader in the development of the CRISPR-Cas9 method for genome editing – a powerful new technology with many applications in biomedical research, including the potential to treat human genetic disease.
Donald B. Lindsley Prize in Behavioral Neuroscience
The SfN will award the Donald B. Lindsley Prize to Sung-Yon Kim, a postdoc in Kwanghun Chung’s lab at the Picower Institute for Learning and Memory.
Supported by The Grass Foundation, the prize recognizes an outstanding PhD thesis in the area of general behavioral neuroscience. Kim, who earned his PhD at Stanford University, used optogenetics to study the brain circuits underlying anxiety.
“The Society is pleased to honor Dr. Kim’s groundbreaking research in the neuroanatomical basis of anxiety behavior,” said Mason. “His approach to behavioral neuroscience will likely have a broad and lasting impact on biology and medicine.”
2014 Winter Clothing Drive
As winter and cold weather approaches, many people begin looking for opportunities to give back to their communities. The Thanksgiving season is a favorite time for charitable giving, and we encourage those looking to have a positive impact in someone’s life to lend us a hand in supporting CASPAR, a charitable neighbor of MIT right here in Cambridge.
CASPAR is a nonprofit organization that provides services to those affected by substance abuse disorders in Cambridge and Somerville. They have worked closely with MIT since 1994, when the Institute helped to build CASPAR’s Emergency Services Center (ESC) and Shelter on MIT property. Located just across the street from Ashdown, MIT’s oldest graduate residence, the ESC welcomes dozens of individuals struggling with drug use, alcohol abuse, and homelessness in from the cold. At the Center, CASPAR provides medical and mental health care, nutritional food, personal hygiene supplies, clean clothes, counseling, and employment housing and treatment referrals in an environment that is welcoming and safe.
Please join the McGovern Institute’s Winter Supply Collection Drive to benefit CASPAR. Our office has partnered with the MIT Office of Government and Community Relations and the Department of Facilities for this annual drive; we know that, together, we can continue MIT’s support of the work that CASPAR does for our community.
CASPAR is in need of gently used items such as: jeans, sweatshirts/fleeces, hats, gloves, winter boots, coats/vests, men’s belts, linens, twin-size blankets, towels, new socks, new undergarments, and new toiletries such as feminine products and packaged disposable razors. Larger sizes of all items are especially welcome. Drop off boxes will be located in McGovern Headquarters through Nov. 17.