 So first I'd like to thank the organizers of the Congress for allowing me to come and speak to you today in Dr. Insel's absence. He was scheduled to be here, but had to attend a meeting on the Brain Initiative in Minneapolis. So because I oversee a technology portfolio at NIMH and I am involved at some level in the Brain Initiative, I was selected to come in his absence. As I'm sure most of you know, on April 2nd, President Obama made a big announcement in the White House about the Brain Initiative. He did this in front of 200 scientists and with Dr. Collins at his side and we at NIH were very excited to hear about his dedication and commitment to the work that we do at the NIH and we were all very interested to know how this was going to be executed and unlike Clay's talk where he very clearly defined what the goals of Minescope is and how the Institute is going to approach this initiative, we're in quite a bit of a different situation at NIH and I will be happy to answer as many questions as I can. My talk is very short. Tom usually leaves a lot of time for questions. Hopefully I'll be able to address those for you. But the bottom line is we all know that brain disorders affect all of us. There are many brain-based disorders that the work of NIH is dedicated to understanding and the Brain Initiative is an attempt to understand better how the brain works and what its role is in these disorders. So we've listed a number of different diseases here that are represented by many of the institutes at NIH. In general we have something called the NIH Blueprint for Neuroscience Research and that represents 16 institutes at NIH that have an interest and a focus on neuroscience research. So the need is great to understand how the brain works. Brain disorders affect over a hundred million Americans alone and rates are increasing not only in America, the United States, but also across the world in autism, Alzheimer's disease and in our soldiers with post-traumatic stress disorder and TBI. And with those increases in these disorders, obviously there's an increase in the cost to society. $200 billion alone are spent on treatment and care of folks with dementia. As you see on the graph here, brain disorders account for the largest amount of dysfunction and cost in society. Okay, so the president's goal of understanding the dynamic nature of the brain is really quite lofty and quite unreasonable to expect that of this year commitment of 40 million dollars is actually going to be able to address that. But what we like to think of is he's he's dedicated to the research and hopefully with some dedicated funding towards developing techniques and tools that will study the brain in a focused way. We may be able to continue this program and find some some great advances. So at the NIH we support research at a number of different scales. Neuroscientists are dedicated to understanding in a number of model systems as well as research focused on the humans. And what we what we know is that we were reasonably good at measuring activity through imaging technologies that we correlate with certain behaviors or activity. And we know a lot about the molecular basis of how neurons communicate with each other. But what we don't know is sort of what comes in between. And we're hoping that this brain initiative in the first instance anyway will focus on developing technologies that will help bridge that gap. So one of the questions is what scale should we focus our efforts at? What model system should we dedicate our resources to? And asking a group as large as the folks at NIH to come up with that decision is really quite difficult. So Dr. Collins engaged the advice of an advisory committee. Not updated slides. So what what we have represented here are a number of projects that we funded at the NIH where folks are looking at different different scales and different models. This is just an example of work of Thomas Sudhoff at Stanford. We've shown some work by Carol Svoboda at Janelia Farms looking at imaging of synaptic activity. And work by Jeff Lickman and Josh Sains at Harvard created this technology called BrainBow. BrainBow allows you to label hundreds of different cells with varying intensities of fluorescence. And I think there's a couple of slides shown here how you can distinguish these different cells in the hippocampus here shown in layer 5 cortex. And this is just you know one example of technology development that has advanced neuro anatomical tracing and studies of brain function in animal models. This was developed for the mouse, but it's also used in Drosophila and C. Elegant. So one of the technologies that I'm particularly excited about is this recent development of clarity. Probably most of you have heard about it, but if not I can briefly describe it. Carl Dysaroth applied to one of our special programs at NIH called the Transformative R01. And this is an unusual program that allows people to submit proposals for really big projects, large amounts of money that they're not constrained by the usual budget caps. And Carl came to me a couple years ago at the Neuroscience meeting and said, you know, I have this new thing I'm really excited about. What do you think? And I said, go for it. Apply for it. Ask for what you need. And he did. And he came in with a lot of preliminary data. It was reviewed in an unusual review process with these large scale initiatives. Don't go through the standard study section process. And after much discussion, we were able to get support for this project. And very quickly, Carl turned around and published paper in, I believe, April of this year in nature. And this technology allows a different type of neuroanatomy. He can clear an entire brain. The lipids are basically removed from the brain and replaced by a hydrogel. And this allows you to stain the tissue multiple times. He can stain for a variety of different antibodies, take them out, do another series. And this is shown in the mouse brain, but it's also applicable to any other organism. In fact, the technique could be applied to any different organ in the body as well. We're particularly interested in the brain, of course. And we have a video just showing a three dimensional view through a mouse brain that's stained for thigh one. And this is going to take you through at a low resolution and then zoom in higher where you can see full projections throughout the entire brain. So this is the type of technology that had it come, you know, a year after the brain initiative was announced, we'd say success. This is really fantastic. So the bar is high. We need we need more technologies like this. And some other technology that's been supported at the Institute also from Stanford. Believe me, we also fund people all over. These examples have just coincidentally come from there. Mark Schnitzer has developed a miniature microscope that he can put on a behaving mouse and monitor neuronal activity using calcium imaging. And I can show you another video. So you can see that the cells on the right here are lighting up as the animal traverses this maze. And this technology is something that's been supported with regular research funding as well as small business funding. The NIH has a small business innovation program and Dr. Schnitzer has received funding in both ways. I'm going to skip the slide, have a couple more videos. The work on the right is work from Van Bedeen and his colleagues. And this is part of the Human Connectome Project, which was also funded by the Blueprint for Neuroscience. This is another large scale activity that Blueprint sponsored. And they work separately in Boston, but the main component of the Human Connectome Project has been funded at Wash U and University of Minnesota. And those data are made available, the first release was early this spring. The Human Connectome Project has imaging data from twins in the St. Louis area, and they'll be looking at these subjects over time. And as the data come in, they make them available. So it's a great resource. So again, just a few examples, but we think the science is ready. There's lots of progress made in tools and technologies, but we need to speed up the process. We need to facilitate the development of the tools for the wider neuroscience community to use. And that's what we think the first phase of the brain initiative is going to focus on. So despite these advances, we're still limited in our ability to understand how the brain works, how it encodes, stores and retrieves information, and new levels of understanding will be achieved through sophisticated tools and technologies capable of assessing how the parts of the brain work together to generate patterns of activity and how these patterns of activity are translated into behaviors and how dysfunction of these brain circuits are involved in disorders. So the goals of the brain initiative are to again accelerate the development of these technologies, construct a dynamic picture of brain function and how it integrates neuronal and circuit activity over time and space, and to build on a growing scientific foundation, including neuroscience, genetics, physics, engineering, et cetera, to catalyze interdisciplinary effort of unprecedented scope. How are we going to achieve the goal or the vision of the president? He said at his announcement, he said, as humans, we can identify galaxies light years away. We can study particles smaller than an atom, but we still haven't unlocked the mystery of the three pounds of matter that sits between our ears. But today scientists possess the capability to study individual neurons and figure out the main functions of certain areas of the brain. So there's this enormous mystery waiting to be unlocked and the brain initiative will change that by giving scientists the tools they need to get a dynamic picture of the brain in action and better understand how we think and how we learn and how we remember. And that knowledge could be, will be transformative. So how will it work? That's the big question. As I mentioned, they put together, they put together an advisory committee that's going to help summarize the state of the art, help make suggestions for where the dollar should be focused in the coming years. And the way that committee is working is they've held a series of workshops where they invite specialists in a number of different fields. These are two-day workshops in the, so first let me just call out the folks that are on the advisory group. The co-chairs are Cory Bargman and Bill Newsom and then the regular members are David Anderson, Emery Brown, Carl Diceroth, John Donahue, Peter McLeish, Eve Marder, Richard Norman, Josh Sains, Marc Schnitzer, Terry Sinowski, David Tank, Roger Chen, and Camille Ugrabel. So in these workshops, they invite about 10 to 15 specialists that come in and give 15-minute talks. They're asked to give their view of what's most needed in their area of research and how these limited dollars could be best spent to achieve the goals of the Brain Initiative. After they each present, there's an afternoon of discussion where they go back and forth with those invited speakers. And then the following day, the committee meets alone and discusses in greater depth and detail what was presented the day before. At the end of the four workshops, they're supposed to come back to the director with a set of recommendations and a summary of these meetings. And at that point, the folks, myself and several others at the institutes, will be tasked with putting together RFAs to get these initiatives out on the street. This slide shows a list of the dollars that are potentially going to be invested in the Brain Initiative. I can only speak for NIH. We can't really speak for the other agencies how much money they're going to be put in or whether this money is additional money or the same dollars that they're directing towards neuroscience research. Anyway, for FY14, NIH is dedicated to spending $40 million on this initiative. And we're often meeting in collaboration with all of these other agencies and trying to figure out how we can make the best progress by combining our efforts. So I do apologize. This is an older set of slides and I've already said those things. So the four workshops that I mentioned started in May. The first was held in San Francisco and focused on molecular approaches. The one in June was on large-scale recording technologies. The July one was computation theory and big data. And the one that's being held today and tomorrow is at the University of Minnesota on human brain measures and measurements and analysis. And so the folks who are invited to give their input to these meetings are listed in the next series of slides. The first one, genetic access to neurons. We heard from Nat Hines, Jerry Rubin, Hong Kui, and Feng Zhang. Monitoring circuits. Lauren Luger from Janelia at Sushi Miyake and Alice Ting. Tracing circuits. We heard from Clay. We heard from Ed Calloway and Lee Kuan Lu and Carl Diceroth. And I don't know how to say his first name. It's a cough. Sorry. In June, the large-scale recording technologies included, I won't read all the names. You can see them all up there. But these folks all have great advice and great goals for how we should spend the money. And the computation theory and big data group is listed here. And then I don't have today's because they just posted the agenda last week. And I have the old slides. So there's also a presidential commission for the study of bioethical issues surrounding the Brain Initiative, making sure that the work that we go ahead and fund is done in a thoughtful and ethical way. I was able to sit in on one of these discussions, well, through a webinar last week. And it was really a very interesting and thoughtful group of people contributing. And this is a quote that Tom likes to end with. The new directions in science are launched by new tools much more often than by new concepts. The effect of a concept-driven revolution is to explain old things in new ways. The effect of a tool-driven revolution is to discover new things that have yet to be explained. So I'm happy to take any questions and try to clarify any misconceptions about the Brain Initiative if anyone has them. Could you elaborate on discussions that may have been had on the data sharing model? So I can tell you in terms of the data sharing that we, my office, where I work and Dr. Insel and the Institute is very, very committed to data sharing. We've been involved in a number of discussions across the Institute's how we can better facilitate that. So one way we've been putting in more terms and conditions and awards that people are expected to share their data. There's been always a $500,000 any grant over 500 you have to. But we're now, especially with technologies and tool development, making that a standard exception. So the initiatives will be hopefully out shortly after neuroscience. As you see, it was FY14 dollars. So they're going to be happening really quickly. So people should be paying attention. So Michelle, what if you're someone who's not, let's say an engineer, you're more of a basic neuroscientist, but applying some of these new tools to, let's say, new model systems or addressing new questions? Is that something that? So from my perspective in general and work that we support, we're constantly trying to encourage these collaborations from neuroscientists with engineers, biostatisticians, neuroinformaticists, getting people to look at a question from different angles. We think that it's more valuable if you have that input. Okay. Thank you. So there was a recent meeting in Washington concerning the completion of the National Centers for Biomedical Computing at the National Centers of Biomedical Computing, the NCBCs. Obviously that is very relevant to the neuroinformatics community as a whole. And that program is now kind of coming to an end. Now, given that there are all these new exciting developments going on at NIH, could you comment on what we can expect to see in terms of the programs that will replace the NCBCs? And specifically, are they going to be kind of smaller scale so that they can affect researchers at a slightly lower level than big centers, which are pretty big? So it's hard to say because we have not received the recommendation from the advisory group yet. I can see that some of those programs that have been very valuable, this might be kind of a way to keep them going. But until we get the recommendations, and frankly, the director of NIH is going to have a lot of say in which direction we go. There is, as I'm sure you know, a large program now from the Common Fund, the Big Data to Knowledge Program, where I think a lot of the issues related to the Brain Initiative can be addressed. And in fact, I had another slide that there's an RFI, a request for information. I think the deadline is September 5th. So if you have some input to that, it would be great to go and put that in. Hi, Gordon Shepherd. I think, as you know, there are many in the field who are concerned about the possible effect on normal R01 funding. Absolutely. So what I can say is only relevant to fiscal year 14, these 40 million dollars are aside from our regular fiscal year 14 budget, which we still don't have. So this part of it will be sort of organized and managed through the blueprint. And these are new dollars. What happens in the coming fiscal years, we don't know. But you know, what I say is I think that our focus should continue to fund innovative research that's going to help us understand the brain. So I think the key question is, will this lower the funding line? No. Now, the new budget may lower the funding line. Oh, yes. But this will not. One last quick question. As neuroscientists, what can we do to help you give us Brain Initiative money? I didn't hear it. Could you repeat it? Yes. My question is, as neuroscientists, how can we help you better provide to us Brain Initiative money? Absolutely. So there is a NIH brain webpage. Type in NIH brain. There is a place to give your feedback and comments. And really, we're listening. We're trying to spend the money in the most useful, efficient, helpful way. So nothing's been written yet. The initiatives are in a lot of people's heads. We're listening, and we really do want your input.