Thursday, October 18th, 2018

Time	Event
12:00a	Tang family gift supports MIT.nano, MIT Quest for Intelligence The Tang family of Hong Kong has made a $20 million gift to MIT to name the Tang Family Imaging Suite in the new MIT.nano facility and establish the Tang Family Catalyst Fund to support the MIT Quest for Intelligence. The Imaging Suite in MIT.nano is part of a highly specialized facility for viewing, measuring, and understanding at the nanoscale. With design features that include a 5-million-pound slab of concrete for stabilization, isolated construction of individual spaces, and technology to minimize mechanical and electromagnetic interference, MIT.nano’s imaging suites provide the “quiet” environment needed for this sensitive work. “We are grateful for the Tang family’s generosity and visionary investment in nanoscale research at MIT,” says Vladimir Bulović, inaugural director of MIT.nano and the Fariborz Maseeh Professor in Emerging Technology. “The imaging suite will allow scientists and engineers to decipher the structure and function of matter with precision that has not been possible before and, armed with this new knowledge, identify promising opportunities for innovation in health, energy, communications and computing, and a host of other fields.” The Tang Family Catalyst Fund will provide $5 million for artificial intelligence (AI) research activities and operations, with a special focus on projects at the intersection of AI and financial technology. “AI tools and technologies are going to revolutionize many industries and disciplines,” says Anantha P. Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “We are delighted to have the support of the Tang family as we lead the development and discovery of these new tools and technologies.” “The Quest is fueled by cross-disciplinary collaboration and the support of enterprising people such as the Tang family who see great value in exploration and discovery,” says Antonio Torralba, inaugural director of The Quest for Intelligence. “From seed grants for early-stage faculty and student research, to undergraduate and graduate student activities, the Tang Family Catalyst Fund will kindle new ideas that advance machine learning.” (Comment on this)
11:00a	Electrical properties of dendrites help explain our brain’s unique computing power 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, MIT neuroscientists have 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. “It’s not just that humans are smart because we have more neurons and a larger cortex. From the bottom up, neurons behave differently,” says Mark Harnett, the Fred and Carole Middleton Career Development Assistant Professor of Brain and Cognitive Sciences. “In human neurons, there is more electrical compartmentalization, and that allows these units to be a little bit more independent, potentially leading to increased computational capabilities of single neurons.” Harnett, who is also a member of MIT’s McGovern Institute for Brain Research, and Sydney Cash, an assistant professor of neurology at Harvard Medical School and Massachusetts General Hospital, are the senior authors of the study, which appears in the Oct. 18 issue of Cell. The paper’s lead author is Lou Beaulieu-Laroche, a graduate student in MIT’s Department of Brain and Cognitive Sciences. Neural computation Dendrites can be thought of as analogous to transistors in a computer, performing simple operations using electrical signals. Dendrites receive input from many other neurons and carry those signals to the cell body. If stimulated enough, a neuron fires an action potential — an electrical impulse that then stimulates other neurons. Large networks of these neurons communicate with each other to generate thoughts and behavior. The structure of a single neuron often resembles a tree, with many branches bringing in information that arrives far from the cell body. Previous research has found that the strength of electrical signals arriving at the cell body depends, in part, on how far they travel along the dendrite to get there. As the signals propagate, they become weaker, so a signal that arrives far from the cell body has less of an impact than one that arrives near the cell body. Dendrites in the cortex of the human brain are much longer than those in rats and most other species, because the human cortex has evolved to be much thicker than that of other species. In humans, the cortex makes up about 75 percent of the total brain volume, compared to about 30 percent in the rat brain. Although the human cortex is two to three times thicker than that of rats, it maintains the same overall organization, consisting of six distinctive layers of neurons. Neurons from layer 5 have dendrites long enough to reach all the way to layer 1, meaning that human dendrites have had to elongate as the human brain has evolved, and electrical signals have to travel that much farther. In the new study, the MIT team wanted to investigate how these length differences might affect dendrites’ electrical properties. They were able to compare electrical activity in rat and human dendrites, using small pieces of brain tissue removed from epilepsy patients undergoing surgical removal of part of the temporal lobe. In order to reach the diseased part of the brain, surgeons also have to take out a small chunk of the anterior temporal lobe. With the help of MGH collaborators Cash, Matthew Frosch, Ziv Williams, and Emad Eskandar, Harnett’s lab was able to obtain samples of the anterior temporal lobe, each about the size of a fingernail. Evidence suggests that the anterior temporal lobe is not affected by epilepsy, and the tissue appears normal when examined with neuropathological techniques, Harnett says. This part of the brain appears to be involved in a variety of functions, including language and visual processing, but is not critical to any one function; patients are able to function normally after it is removed. Once the tissue was removed, the researchers placed it in a solution very similar to cerebrospinal fluid, with oxygen flowing through it. This allowed them to keep the tissue alive for up to 48 hours. During that time, they used a technique known as patch-clamp electrophysiology to measure how electrical signals travel along dendrites of pyramidal neurons, which are the most common type of excitatory neurons in the cortex. These experiments were performed primarily by Beaulieu-Laroche. Harnett’s lab (and others) have previously done this kind of experiment in rodent dendrites, but his team is the first to analyze electrical properties of human dendrites. Unique features The researchers found that because human dendrites cover longer distances, a signal flowing along a human dendrite from layer 1 to the cell body in layer 5 is much weaker when it arrives than a signal flowing along a rat dendrite from layer 1 to layer 5. They also showed that human and rat dendrites have the same number of ion channels, which regulate the current flow, but these channels occur at a lower density in human dendrites as a result of the dendrite elongation. They also developed a detailed biophysical model that shows that this density change can account for some of the differences in electrical activity seen between human and rat dendrites, Harnett says. Nelson Spruston, senior director of scientific programs at the Howard Hughes Medical Institute Janelia Research Campus, described the researchers’ analysis of human dendrites as “a remarkable accomplishment.” “These are the most carefully detailed measurements to date of the physiological properties of human neurons,” says Spruston, who was not involved in the research. “These kinds of experiments are very technically demanding, even in mice and rats, so from a technical perspective, it’s pretty amazing that they’ve done this in humans.” The question remains, how do these differences affect human brainpower? Harnett’s hypothesis is that because of these differences, which allow more regions of a dendrite to influence the strength of an incoming signal, individual neurons can perform more complex computations on the information. “If you have a cortical column that has a chunk of human or rodent cortex, you’re going to be able to accomplish more computations faster with the human architecture versus the rodent architecture,” he says. There are many other differences between human neurons and those of other species, Harnett adds, making it difficult to tease out the effects of dendritic electrical properties. In future studies, he hopes to explore further the precise impact of these electrical properties, and how they interact with other unique features of human neurons to produce more computing power. The research was funded by the National Sciences and Engineering Research Council of Canada, the Dana Foundation David Mahoney Neuroimaging Grant Program, and the National Institutes of Health. (Comment on this)
2:00p	Cryptographic protocol enables greater collaboration in drug discovery MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery. Datasets of drug-target interactions (DTI), which show whether candidate compounds act on target proteins, are critical in helping researchers develop new medications. Models can be trained to crunch datasets of known DTIs and then, using that information, find novel drug candidates. In recent years, pharmaceutical firms, universities, and other entities have become open to pooling pharmacological data into larger databases that can greatly improve training of these models. Due to intellectual property matters and other privacy concerns, however, these datasets remain limited in scope. Cryptography methods to secure the data are so computationally intensive they don’t scale well to datasets beyond, say, tens of thousands of DTIs, which is relatively small. In a paper published today in Science, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) describe a neural network securely trained and tested on a dataset of more than a million DTIs. The network leverages modern cryptographic tools and optimization techniques to keep the input data private, while running quickly and efficiently at scale. The team’s experiments show the network performs faster and more accurately than existing approaches; it can process massive datasets in days, whereas other cryptographic frameworks would take months. Moreover, the network identified several novel interactions, including one between the leukemia drug imatinib and an enzyme ErbB4 — mutations of which have been associated with cancer — which could have clinical significance. “People realize they need to pool their data to greatly accelerate the drug discovery process and enable us, together, to make scientific advances in solving important human diseases, such as cancer or diabetes. But they don’t have good ways of doing it,” says corresponding author Bonnie Berger, the Simons Professor of Mathematics and a principal investigator at CSAIL. “With this work, we provide a way for these entities to efficiently pool and analyze their data at a very large scale.” Joining Berger on the paper are co-first authors Brian Hie and Hyunghoon Cho, both graduate students in electrical engineering and computer science and researchers in CSAIL’s Computation and Biology group. “Secret sharing” data The new paper builds on previous work by the researchers in protecting patient confidentiality in genomic studies, which find links between particular genetic variants and incidence of disease. That genomic data could potentially reveal personal information, so patients can be reluctant to enroll in the studies. In that work, Berger, Cho, and a former Stanford University PhD student developed a protocol based on a cryptography framework called “secret sharing,” which securely and efficiently analyzes datasets of a million genomes. In contrast, existing proposals could handle only a few thousand genomes. Secret sharing is used in multiparty computation, where sensitive data is divided into separate “shares” among multiple servers. Throughout computation, each party will always have only its share of the data, which appears fully random. Collectively, however, the servers can still communicate and perform useful operations on the underlying private data. At the end of the computation, when a result is needed, the parties combine their shares to reveal the result. “We used our previous work as a basis to apply secret sharing to the problem of pharmacological collaboration, but it didn’t work right off the shelf,” Berger says. A key innovation was reducing the computation needed in training and testing. Existing predictive drug-discovery models represent the chemical and protein structures of DTIs as graphs or matrices. These approaches, however, scale quadratically, or squared, with the number of DTIs in the dataset. Basically, processing these representations becomes extremely computationally intensive as the size of the dataset grows. “While that may be fine for working with the raw data, if you try that in secure computation, it’s infeasible,” Hie says. The researchers instead trained a neural network that relies on linear calculations, which scale far more efficiently with the data. “We absolutely needed scalability, because we’re trying to provide a way to pool data together [into] much larger datasets,” Cho says. The researchers trained a neural network on the STITCH dataset, which has 1.5 million DTIs, making it the largest publicly available dataset of its kind. In training, the network encodes each drug compound and protein structure as a simple vector representation. This essentially condenses the complicated structures as 1s and 0s that a computer can easily process. From those vectors, the network then learns the patterns of interactions and noninteractions. Fed new pairs of compounds and protein structures, the network then predicts if they’ll interact. The network also has an architecture optimized for efficiency and security. Each layer of a neural network requires some activation function that determines how to send the information to the next layer. In their network, the researchers used an efficient activation function called a rectified linear unit (ReLU). This function requires only a single, secure numerical comparison of an interaction to determine whether to send (1) or not send (0) the data to the next layer, while also never revealing anything about the actual data. This operation can be more efficient in secure computation compared to more complex functions, so it reduces computational burden while ensuring data privacy. “The reason that’s important is we want to do this within the secret sharing framework … and we don’t want to ramp up the computational overhead,” Berger says. In the end, “no parameters of the model are revealed and all input data — the drugs, targets, and interactions — are kept private.” Finding interactions The researchers pitted their network against several state-of-the-art, plaintext (unencrypted) models on a portion of known DTIs from DrugBank, a popular dataset containing about 2,000 DTIs. In addition to keeping the data private, the researchers’ network outperformed all of the models in prediction accuracy. Only two baseline models could reasonably scale to the STITCH dataset, and the researchers’ model achieved nearly double the accuracy of those models. The researchers also tested drug-target pairs with no listed interactions in STITCH, and found several clinically established drug interactions that weren’t listed in the database but should be. In the paper, the researchers list the top strongest predictions, including: droloxifene and an estrogen receptor, which reached phase III clinical trials as a treatment for breast cancer; and seocalcitol and a vitamin D receptor to treat other cancers. Cho and Hie independently validated the highest-scoring novel interactions via contract research organizations. Next, the researchers are working with partners to establish their collaborative pipeline in a real-world setting. “We are interested in putting together an environment for secure computation, so we can run our secure protocol with real data,” Cho says. (Comment on this)

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