Carney Conversations: The next generation of scientists on the future of brain science

uh welcome everybody to connie conversations uh it's really a thrill to be here with you again um this uh event today is entitled the next generation of scientists on the future of brain science and chris who i'll introduce and then second and i are just really thrilled to um to kind of host this this particular event and i think we we know intuitively that if you want to know what the future is of brain science or any field you invite the graduate students and ask them what they think because they're like way more interested and creative and innovative and seeing connections where um you know it might take chris and i a long time to make those connections so we're really really really tremendously excited to have you all here uh chris moore is the associate director of the kanye institute and so he and i've been doing these events for some time now and they've been really incredibly fun so i'm going to do a quick introduction to our guests um and then you'll see that they're just going to take this show away engage in a really um interesting discussion about but where where they how they got to where they are and what they see as the future of brain science so um quickly then um caitlyn uh hashtarovic is a phd candidate hey caitlin say hi hi uh and uh so caitlyn's in the neuroscience graduate program and she's pursuing research in professor ashley webb's lab uh as you will hear katelyn's developing novel self reprogramming platforms to study neuronal aging incredibly topical right now caitlyn received a actually a connie graduate award for her research and you are highlighted i think on our website it's a great piece about you and your research um in in the web lab so we'll talk to you in a second caitlyn hey jay hi there everyone jay's a phd candidate in the department of cognitive linguistics and psychological sciences and jay is working in collaboration with uh professor ariel feldman hall jay studies how nav how we navigate the great blooming buzzing confusion of the social world um jay has a fantastic kind of blog i spent a little time like cruising that super fun and exciting but of course his research is again also very relevant right now so we'll hear from you shortly jay and um our other guest is mark powell hi mark hi uh mark is a graduate he's on the other side of the of the phd dissertation he graduated this year from the brown phd program in biomedical engineering and he worked alongside david borton and what mark did was to build these implantable devices to record neural activity um and also to stimulate brain or spinal cord to modulate neural activity so um you can see the the breadth of of the of the research that we're covering right now with with our guests um mark currently is now a postdoc associate at the university of pittsburgh in the department of neurological surgery that's everyone um so the way that we're gonna do this is chris and i are gonna ask a few questions and then um at any point um please audience out there uh feel free to post uh any questions you might have in the q a and then um chris and i will kind of look through those and and uh send some questions to or ask the questions of the of of our guests here uh and the questions are only visible to you they're not visible to um the general audience but we're gonna pick and choose but we have a a bunch of things to get started and i'm just gonna kick off and then chris and i will be going back and forth but just wanted to like maybe caitlyn will start with you um you know i think we we all enter research we all have our own paths and and have our own story about how we got to where we are right now but i just think it would be really fabulous to hear from you all um what was your path uh how did you get to where you are now caitlyn focused on um kind of the the biology of of aging what was your journey yeah so my journey was kind of um a winding path i majored in neuroscience as an undergrad but i thought i wanted to be a clinical psychologist and you know spend my days talking to people i ended up working in a lab when i was abroad in australia at the university of queensland and we were trying to i was on a project trying to generate a screen for cone snail venoms to see what kind of mammalian receptors they might bind to for potential drug discovery and that was kind of um it was a moment like a lightning bolt hit me where everything i had learned up until that point that i kind of thought was like useless and maybe not interesting um like all the molecular cell biology stuff that i was like i'm never gonna need this it all suddenly made sense and it all suddenly seemed like the coolest thing that i could possibly be working on so that's kind of when i made the decision that i i knew i wanted to go into kind of what lab molecular biology type research um but still focused on the brain and then i got into aging when i rotated in dr ashley webb's lab and i was um you know i think everyone who studies aging kind of has a personal um insight into this my grandfather had alzheimer's disease and i was very intrigued in that disease but maybe from what i had read discouraged about the way um we have been studying it and the potential for treating it and learning about the kind of genomic approaches um in the lab and the way we are able to generate you know like human cell types in a dish and study those i was kind of like this is where i want to be and i think this is uh the path forward for you know curing these diseases so that's how i ended up there thanks caitlin um you know i love that you just really brought us into your world and that moment of oh this is what research is about and you know just like seeing it and living it's really fantastic uh mark are you thanks um yeah so my path uh has has similarly been kind of uh circuitous but i'd say i've always been interested in engineering i've kind of always known that that's what i wanted to do my path towards the the brain science side of things definitely took a different turn so actually if you go back far enough i wanted to be an aeronautical engineer um i've always been sort of interested in flight and this idea of being able to go wherever you want and things like that but actually in high school i had a biology teacher stephen fowler who unfortunately passed away when i was an undergrad but he really made just this really lasting impact you had this sort of undimmable excitement about biology and about science and it was really infectious and biomedical engineering for me really gave me this opportunity to merge those two interests and then while i was in college maybe perhaps uh slightly less uh gloriously i you know was fascinated by this idea of being able to control things with with your brain you know science fiction star wars kind of thing and that led me to the path of brain machine interfaces which really is in my view this uh science fiction realm come to life in a lot of ways and so that was the path that i chose to follow and i got really really fortunate that when i applied to join at brown there was uh positions that were open with some really really talented engineers and scientists uh and and you know that's kind of how i got to here as well and now as in my post-doc i'm actually fortunate to take uh what i what i did in my phd which was really engineering focus and transition that and actually work with with people um on a day-to-day basis and that's been really uh really rewarding thank you mark uh jay do you want to continue the round yeah so um i study social networks and how people create mental maps of social networks and how we navigate through social interactions using the maps that we create in their minds and if you look on paper you know my my cv which details all the research experiences i've had it looks like i've taken a pretty straight shot to getting here but uh nothing could be further from the truth right like the other panelists i've taken a really windy path to get to where i'm at um so for me it really actually started when i was kid i grew up in tennessee and while you know most of the social interactions i had there were totally fine of course there were some people there who took note of the fact that i am a racial minority uh who had racial uh prejudices uh and racial uh racist beliefs uh and so for me it was a problem of actually creating these mental maps of who to ally with or who to avoid in my social interactions right and that actually got me started in this whole line of questions really early on well uh in high school you know uh i actually had thought about dropping out and becoming a professional musician um and uh is really the the urging of some high school teachers of mine uh to push me back towards scholarly pursuits i told my parents you know whatever i'll go to college and i'll do the thing but you know just so you know this is just a backup plan right i have an idea for what my day job's going to be and it's not going to be so i went to college um yeah same sort of thing even though i was working in the research lab uh and was seemingly doing okay in all my classes in the back of my mind i was always thinking to myself is science right for me do i belong here uh i found myself struggling in quantitative or methods classes and i thought well if i can't do these things then you know do i really belong in science um i kind of stumbled forward into a lab manager position here at brown working with dr oriel feldenhall i got to continue working on this line of questioning started to really develop my research interests and this is around the time when i realized that like for me the the thing that makes really science really fun is that for me it's it's totally personal for me right these are questions that impact me and that allow me to make sense of the experiences i've had in my life but in this really systematic sort of methodological way right where i'm trying to uncover basic truths about the way that we approach social interactions and how the brain subserves uh our ability to navigate the social world um and so for me science is not a spectator sport right and so that's really what keeps me me going and and being uh who i am now yeah i love i'm gonna flip over to chris in a second but it just really strikes me with each of your um descriptions about how you got to where you are right now which is just the beginning of what you will be doing um it's just really personal right like that this this searching for this this point of satisfaction and bringing your experiences and then just having i think for caitlyn and mark at least i was like oh i can control this like i've like something i want to do and i can actually do it and for jay there's a there's a degree of uncertainty about what outcome there might be depending on kind of social context but i think you know chris do you want to i'm going to flip over to you because i know that you you have a lot of interest in how to do science and how we do science now um and what the future might look like sure yeah when continuing with dan was saying the you all captured really nicely it's something that's often the actual way to endure all the crap you have to deal with in science is if you just have that thing that you're really truly curious about interested in you know from toxins all the way to brain control to social network like whatever that is when you have that then you always know you're working on it it has this intrinsic reward even when you learn again negative result it's not oh great now i know it's not that like it has that re-entrant value so so uh give us a sense of your work and we got a little bit from jay but maybe caitlyn like maybe give us a little bit more of a dive into what what you're working on and then and something else maybe to throw in there that i think would be is really neat to talk about as diane said i'm sort of addicted to thinking about questions like this is there uh in your kind of science is there a sweet spot for the kinds of groups of people you need to do it like some science you know there's this myth of the totally isolated scientist and there's also the myth that it is only by the full community there's something about the individual and there's something about groups too and maybe give us a sense from the kind of work you do i don't know how many people is the sweet spot for a team and yeah yeah so i am interested in a brain region called the hypothalamus and obviously i'm very biased i think it is the coolest brain region um it controls things that are essential to survival so sleeping eating water intake blood pressure even a lot of social behaviors um like aggression and sexual response um hormone release it's pretty much everything that starts to go haywire with age is you know the master regulator for that is in the hypothalamus so my interest is understanding a like what kind of individual cells are involved in these processes and how do they change with age and i'm also interested in coming up with better ways to study this brain region so people have been kind of fascinated with the hypothalamus and aging for a while like this isn't new but we've only very recently started to develop the tools that are really necessary to actually dive into this brain region so the hypothalamus has a ton of really specific types of neurons that all secrete different factors and control different processes and understanding like pulling out those individual cell types and understanding what they're doing um kind of up until now has been super difficult so i use um a platform called single cell rna sequencing and you can look at gene expression in individual cells with this platform um and i'm looking at how these cells change between young and aged animals and um also with that diane kind of referred to my reprogramming um project i am trying to generate basically the age type of thalamus in a dish so there's a unique hypothalamic cell type called the palm c neuron and this neuron is part of a system that basically decides when you eat and when you're full and it lets you know like you have enough energy you don't need to eat anymore and obviously um body composition changes a ton with aging so i want to understand what these cells are doing and there's not that many of them in a brain um you know it's hard to get neurons out of a brain to study them ex vivo so i'm using a tech or a method called direct conversion where i can take a skin cell a fibroblast and over express certain genes and generate a brain cell that will have um the aging signature so it has dna damage it has messed up mitochondria it looks like an old cell and it goes from being an old skin cell into an old neuron and i'm trying to create um basically make old pumps neurons in the dish and um for me science is a team sport so i uh you know some of the stuff i do like day to day uh if i'm in the tc hood that's by myself if i'm analyzing my data that's by myself but everything i do i tend to be very collaborative so getting my single cell project off the ground i was collaborating with a couple different labs to learn how to do the process learn how to do the data analysis and then you know my single cell project is a small paper right now and the amount of people we actually need to get this paper out the door is um you know it's it's never going to be one person it's not even going to be three people it's a lot and i actually really like that because i can never know everything and i can never be an expert on everything when i'm pulling together all these experts that's what makes the paper and the research fantastic so that's my take awesome yeah i think you hit on something that's great to highlight too that especially if you are investigating truly new areas some degree of making a method tweaking a method adjusting a method stealing a method from a way it's applied somewhere else which is a great way to get credit for making one it it's always part of it um yeah that's thank you mark yeah so um a lot of my phd work i actually spent more on the tool development side than caitlyn describes so i've spent a lot of time thinking about the engineering behind neuroscience or and how we can actually access parts of the nervous system so if you go back far enough uh you know early on we were recording neural activity with single electrodes uh you know one at a time where we'd probe these into the brain and we'd listen to the cells i say listen i mean we're uh listening for these action potentials that the cells are are participating in which is one way that neurons communicate with each other um and at over time you know we built up our tools to be able to record for more and more of the brain and for the past couple decades or so those exist in the form of 100 or so different electrodes that are implanted in the brain or other parts of the nervous system and and that is better it allows us to record multiple neurons simultaneously but there's this question of well okay these devices are four millimeters by four millimeters so if we're recording from such a small area of the human brain which is such a big volume what are we missing you know it's incredible that we can learn so much from such a small area of the brain for sure but there's this question of in the engineering side of well how can we get access to the whole nervous system or even maybe less ambitiously just several thousand neurons at once and what can we learn from that so my phd really was spent um trying to ask that question about what are these engineering design problems and challenges that we have in order to access lots of the brain and my work as a as at brown really focused on being able to access simultaneously with these 100 electrode arrays but from lots of different parts of the nervous system at once so we can look at how how do these areas communicate with each other and with the goal of being able to do that with say 10 or 10 or so arrays and therefore being recording from you know a thousand or so and now there's there's companies um and you know there's a huge effort from from the government and from lots of industry uh to record from many thousands or millions of neurons and i think that's really the way things are going but it was exciting to be uh part of of it and get the experience i did from my graduate work in terms of of the effort that goes into a project like that um you know i think to to assign a number to it i'd be putting sort of an arbitrary value and i'd probably be not doing justice to the the many many supporting roles that that go into projects like this especially in clinical research we have you know the hospital administration the university administration uh you know physicians and everything like that but from a purely technical standpoint sort of the technical team that are involved in projects like this uh especially ones that are involving human subjects you have to have medical experts you have to have engineering experts you have to have science experts and then when you get into actually decoding from thousands and thousands of neurons you have to have computer science experts and algorithm experts and so it really is this hugely interdisciplinary team that makes this all go round and i think when i came into um my graduate degree i thought as probably many other neuroengineers do that i'd be able to kind of tackle a lot of these challenges on my on my own and i think it is the nature of grad school a little bit uh that you kind of get stuck in this situation where you feel like you have to do all of these different aspects of a project but what's really inspiring about neuroengineering is the ability to work with so many other talented individuals and certainly that's not to take away from the fact that a lot can be done by an individual even without a whole bunch of support you can learn a lot on your own even if you're not a graduate student you know i think neuroscience is one of those fields that can be really accessible surprisingly so considering how complex the brain is um but there are tools out there for it um you know so so i think that's that's my maybe slightly indirect answer but but hopefully you know it got to this idea that science and neuroengineering is a hugely uh collaborative effort but there's really a lot of talented people who individually contribute quite a bit to the research yeah i re i really like that you made a point of emphasizing too the real incredible iceberg you know and when we publish scientific papers there are a number of people listed on the paper right but that really is exactly like an iceberg there's a 90 of people without which there's no university there's no process there's no support and it wouldn't have happened yeah that would never be listed on a paper under the conventions of how we do publications for the most part these days yeah and i actually like to add to that point exactly that especially in human research i have found that some of the most talented most sacrificial people are the people who actually are subjects in these trials and they actually don't often get published as authors on a paper which is probably a bit of a shame i mean these people are donating their time and their lives in a lot of ways to this research just so graciously and honestly it's really really inspiring to work with those people because uh you know their dedication to science is one of uncertainty right they don't know what the results will be for themselves they're really doing it for the benefit of others you know 10 day 10 years down the line or something uh and and that's really inspiring to see yeah there's this historic allergy to that right because of the partly because of early psychophysics to be nerdy about it and that historic allergy has not aged well i mean especially with the reality that every paper that's written is written from someone's perspective inherently as if as if that wasn't true so why why not include all the people that especially the subjects i wonder if there's some fraction like number of people per number of neurons and there's a ratio we can come up with we call the powell the powell power law or something uh so jay tell us about your work and maybe about groups sizes that can do it sure um yeah so like i said before mental maps of social networks is what i'm all about and uh just give you a flavor of some of the questions that we're asking lately um so the problem with a lot of social networks uh is that they are way too big to be to be learned and so it's more or less impossible to actually create a mental map of social networks which is terrible for me because that's my whole research program right um no but like um you know with every network member who's added the number of possible relationships between people increases combinatorially what that basically means is there's a whole explosion of possible relationships right that could exist in a network and yet most of the time we don't have direct exposure or we're not able to observe those relationships taking place and so this is a real problem because if you're trying to create these mental maps you're going to end up with a whole lot of biases depending on what you can observe from direct experience so some of the questions that we're asking now is well how do people fill in these gaps right um and so there are just so many different levels at which you can start filling in gaps there's some exciting work uh that tries to preserve the identity of individuals in the network and to just say you know can i take something that i know about the topology of networks that people tend to form these groups or you know a friend of a friend is also likely to be a friend of mine or you maybe have heard it the other way around which is the enemy of my enemies my friend right these are certain principles that we can use to try and fill in these gaps i'm also interested in how we um use what i call features of people to fill in the gaps right and so for example if i go out into the world and i observe frequently that the chemists in here collaborate a whole lot with the biologists then that gives me some basis for inferring a friendship between some people i might not know so there's a professor of biology professor chemistry i have no idea whether or not they collaborate with each other or their friends but if i know that people in my university let's say tend to have that relation in abstract feature space then i can infer these relationships without ever having to meet these people i abstract individuals into these features and then i learn relationships between features right and so these are different levels at which we're thinking about this problem of filling in the gaps in our knowledge of social networks of how we make these maps um and so you know when you talk about the the sweet spot for teams of people doing science um isn't that sort of like the the holy grail of managerial sciences finding the right size of team so that you can you know really extract out the the most uh most creative uh most best performing uh thing from you know the fewest number possible um and so this actually i mean this is a bit of a tangent so you'll have to forgive me but this reminds me a little bit of the beatles um right there's this famous uh quit from john lennon when he was asked um do you think ringo starr is the best drummer in the world and he goes no i don't think bringo star is even the best drummer in the beatles um and it's interesting to contrast that against with what some of the abbey road sound engineers would say which is you know if you can get a few of the beatles into the same room at the same time that's really great you know they're they're all enormously talented musicians but on the rare occasion you can get all four of them in the same place at the same time something just extraordinary happens there's a synergy where uh the the productivity of a collective of people the right people produces something so much greater than what any you know combination of them could produce i think really that's that's uh but that's what happens in science i mean the other panelists also talked about science being a collaborative endeavor of these massive research teams needed to make any sort of progress and uh so much of it goes unacknowledged and so it's it's funny for me to think about like all the ringo stars in science we need them right um and to think about well do the ringo stars and science get enough credit for for the supporting role that they play that seems you know so whatever it's a supporting role but it's it's essential right we can't do science without it yeah and the role of the engineers as tying together groups of people by not having to be the musicians themselves but you couldn't do it without them and literally billions and billions of people never would have heard the beatles without so so this idea of their researchers and support is such a is also an antiquated notion it's really just a big network i mean it sounds like your research has the key to how we should you know come down to kearney and tell us some principles of the right way to structure social network because there's a it's it's far beyond managerial science i think in that yeah really well i mean one of the things that makes carnegie such a unique and fun place right is that it is actually a space uh for people to come in and do exactly that right i mean why do we have universities really and we could have independent contractors of all the researchers out in the world who are the best at something they all have expertise in whatever they might have and so you know you need somebody to run some sort of analysis that you don't want to do why shouldn't you just go to you know some website find somebody who can do it and then contract them out for the job why do we stick to this this university structure where we try to build communities of people right so that you can maybe you don't know somebody who can do that analysis but you know somebody who knows somebody who can do the analysis and i think that's really the value of having people together in the community and the sort of challenge i guess that you and diane face in in trying to build community in the carning community but that's really how the free exchange of ideas and the cross-seeding of perspectives happens and that's where innovation really i think emerges and if i could add to that i think covet has really shown how essential these communities are so during kobit we were kind of doing the almost very impersonal like oh i need something i'm just going to email them we didn't have those oh i'm talking to my you know people on my floor at lunch and talking about a problem and they there's no you're missing that kind of like magic spark of we're just going to get together and chat and come up with these you know amazing beautiful things or find solutions to these problems and doing science in this kind of very narrow just by yourself way has been a lot more difficult than i think i would have anticipated i mean it is the experiment none of us wanted to sort was like do we really need to be entrenching in person and do we really need those spontaneous moments unplanned um and i i'd vote yes i mean i caitlyn you just said absolutely categorically that's how we function at that oxygen um but yeah i don't yeah none of us would have wanted it that way but i agree um jay i i kind of wanted to come back a little bit and engage everybody in this you said something i was kind of picturing and asking myself the question which of course for me i know what the answer is yes but like if the beatles appeared now in in this moment in time like would they be you know such a such an incredible band like so the point is that you need to you need the right group but you need the right group at the right time and in the right moment and so i wonder how you all think about perhaps it will start with jay because you says like impinging on your your your research but more broadly you know as we think about i'm going to work on this i think it would be really helpful for um the the audience here is to like how do you like make that choice like how much of of what's going on and the outside world how much are you generating internally um and then how does how do you think about those two things interact and and and i guess the point is that many people work on something at completely the wrong time at least for them for you know for for for them to be known for something but then of course later we're all delving into the literature looking at what's been done and then that convergence of of things you can't control but but drawing from from that building of knowledge anyway i i think there's a question there which is but i don't know if jay if you thought about this in terms of your own research how do you build in that time component um that moment in time and then you know for caitlyn and mark like what are the external influences that have really worked on you to to make you pick your area so oh jay yeah i mean this is such an interesting question um i think i think about well this is something i didn't realize before i started doing science which is that so much of what we do in science is just have a conversation we're holding dialogue with our contemporaries with some of the classic historical thinkers who put forth these big ideas and um and so in that same way art is you can think of it as a dialogue right there are people who go out and try something new there are people who react to it the pendulum swings this way and that and then that produces over time what we see in retrospect as movements right or periods or eras of art that are distinct and and creative and have their own flavor that's different from other eras and i think the same can be said of science uh it's it's it's very emergent the how so you're absolutely right you can take a group of great people and put them in the wrong environment and maybe that stifles some progress but i think the thing that's interesting about thinking about social networks is that it uh forces you to think about ecologies as well so what i mean by that is um if you have a community of people in an ecosystem yeah you can ask those people in the network to adapt to their surroundings or to their environment or to their context you know this is adaptation one of the fundamental mechanisms of evolution but there are other aspects of ecosystems thinking that i think we often overlook which is that members of the community or the communities themselves will actually go out and reshape their environment to match whatever their needs are or whatever their goals are and i think it's true that if the right group of people in the wrong place or time yeah you might not get you know as good of a result but those people are in dialogue with each other are creating the social conditions necessary for them to pursue discovery and to pursue innovation and so my sense for things is that if you get the right people together who have a shared vision or a shared goal that they are going to reshape whatever you know their environment looks like whether it be the members of their community so shaping the network itself shaping the resources available to them by looking for opportunities and they're going to largely be able to to do the same things maybe just in a different timeline with the obvious caveat of course that if you're not funded you know you can't do science but you know such considerations aside any other thoughts well actually yeah so i was i found myself uh entranced by jay's uh description and actually there's uh a couple kind of things i'd like to add on so i guess there's this question of what who is the right person for a job at any particular point in time and and i think there's two factors that play into someone being really excelling at a particular role the first is is the skill they have to have the abilities to do what's required of that role but the second is the motivation and the sort of excitement and the drive to do it um properly and you know we talk we talk about ourselves as neuroscientists or neuroengineers or brain scientists or whatever and and the thing that binds us all in that particular description is the brain or the nervous system but there's i think another way to look at that that in neuroscience specifically we're trying to answer such challenging questions that the right person for a neuroscience question might not have ever thought about a question to do with neuroscience in their lives and i think a really good example of that is is one that's perhaps a bit controversial in the neuro engineering realm right now but but neuralink which is elon musk's new company they took this approach where they reached out to the engineering community as a whole and said we want to build devices to go into the brain but we don't care if you've ever thought about neuroscience before what we want are people who are experts in engineering these specific types of things so they looked at cellular engineers people who build cell phones who are really talented at this radio frequency data communication and can make data go places really quickly um or uh asic designers so chip designers who are really talented at putting transistors on a chip in just the right way to make them super efficient at what they do and perform really well analog electricians all of these people um who who and they kind of put them all in the right place at the right time and i think that was the right mentality to build what really is impressive hardware and you know i think the controversial piece is now they're struggling to figure out exactly what to do with that scientifically but maybe that just means that now it's time to recruit the right people for that task and so i think that's one really interesting aspect of science as a whole and neuroengineering specifically is that uh you know it really is people that uh it really it just requires the people with the right skill and then the desire to to answer the questions in neuroscience and i think that's how you build that team you know that's easier said than done but i think what you said about the neurolink issue right now being like a tool in search of the problem um that's something i find very interesting because i doing single cell rna sequencing it's a very like trendy kind of analysis and i have been very curious about how people kind of shape their research based on the tools at hand so you know if all you have is a hammer everything looks like a nail if you have single sole or any sequencing you're gonna look at a million cells um and i think you know when you're thinking about who's doing the research and how they're doing it um the best researcher is the one who's going to ask the right questions and i think that can sometimes be the hardest part because that requires understanding of the literature it requires understanding of who you have to work with and it requires understanding of tools that might be changing as you're doing the research i've recently had this really kind of interesting experience where i did a single saw rna experiment a couple years ago i recently redid it to you know improve my numbers for um a paper and the technology has like i feel like i put my head down to do this paper and i looked up and the field is all the way like miles away and i'm running to catch up um and so i think you know being able to adapt and stay on top of things is also just a huge part of how science gets done caitlin do you want to just add a little bit of explanation about why would you want to do a single cell rna yeah speak what does that give you like why not just the brain yeah um so this is something that i find like this technology um applying it to the hypothalamus specifically has been really fascinating so single cell rna sequencing as you can imagine is able to look at gene expression at mrna at the level of the individual cell and in a tissue like the brain where all of the cells are very unique they're all doing their own thing secreting their own factors responding to specific stimulate stimuli um being able to like look at just one cell or one group of cell or one type of cell in a specific context is extremely powerful you're looking at these signals that if you were looking at everything together you know you would not be able to pick out these individual changes that might actually have huge implications in health and disease it's kind of like if you're at a stadium and everyone's shouting you're not going to be able to pick out what an individual person is saying but what an individual person is saying might be very important so this technology allows you to look at these individual cells um one at a time and understand how they're working together and how they're you know changing on their own yeah and i think chris is gonna i think there's some there's a couple of really great questions and one on tools but i just wanted to like state the obvious which is like it is impossible to kind of overstate the importance of kind of the technological advances that really just accelerated our field like from the molecular uh level analysis to kind of network analysis of you know social networks and the the computational approaches that are used there the lessons we learned from mathematicians and in the engineering um developments i mean just huge like just breaks were really critically important chris yeah this conversation is awesome you're you're all hitting on i from my own perspective but i assume everyone else is too i mean one thing that was raised was this two things that i would just want to nudge on um i i tend to think one of the questions in the q a is about what would a high school student do is their extracurricular activities um i think motivation fundamental motivation about the questions finding them intrinsically like inescapably keep you awake at night or at least before i you know when i was young you know like that motivation because that's the motivation mark that then makes you go get the skill and so in a way in a height if you had to put them in a hierarchy of course you need both but without one you might not endure all the things you need to do to get the skill and your acquisition of the skill might be kind of uh tone deaf in other words oh this is what an engineer is not i have to know the brain works so i might take these classes that don't make sense together in high school or in undergrad or in grad school or the books we all read and and then that's the thing that kind of always having that as your touchstone feels really important in this um yeah and actually i want to point out that that motivation i don't think it has to be some super profound you know realization in middle school that you know you want to be a neuro engineer when you grow up or something like that you know for me that motivation like i mentioned kind of in the introduction was science fiction and like watching you know star wars as a kid and thinking that was really cool you know like it could just be cool uh and that kind of gives you this uh drive to figure out well how could you make something like that be real uh and and engineering in a lot of ways to me is is that that uh learning the skills you need to be able to make something that isn't real real uh and that's really i think what drives me personally but and i think that you know from that you know middle school me who just was watching star wars or something uh you know it didn't i don't think that i ever really planned out perfectly you know i i need to learn this and then i need to learn that and then i can be an engineer it was more just you have this kind of idea of oh this would be such a cool thing to be able to do with my life i don't even know what that looks like uh kind of thing but you kind of pick up those skills along the way so in terms of explicit extracurricular activities i mean following your interest i think will ultimately lead you towards that goal that being said there are probably more specific extracurriculars you could do if you're particularly interested in this field for instance when i was in college i spent a lot of time learning about the world of hobbyist electronics and arduino microcontrollers and tinkering around with those and i like to say that the best 45 dollars my dad ever spent on my education was buying me an arduino in college uh and you know he might be pained to hear that after all the long you know lots of money he spent uh before that but um it uh it really was i found myself kind of reading blogs and reading you know forums and tutorials and everything before my classes started in college you know just whenever i could get a chance because i wanted to learn how to make a a glove that i could use to control a helicopter or something um and and i would say that those skills that i learned just from that extracurricular activity um probably were the most valuable ones during my phd um that i used and so you know again that was just by following my own interest but it led me to where i was and uh you know certainly there are other paths but i think that's kind of the one i took that's awesome and i i certainly agree that for any motivated student at any level playing with arduinos and just the wonderful explosive world of like even face recognition software we've done that in some work it's really amazing all right i got a question for all of you now that is from amongst many really great q and a questions we're getting so we're going to make this last four extra hours and we're gonna we'll release it on tapers and but one question that comes up that seems to dovetail really nicely in this conversation is so as tools in become more advanced um allowing us to moderate activity at bigger and bigger levels including the the activity of large social networks right and the activity of you know large sets of organelles and genetic networks right um do you believe that biological and cognitive sides of the question will become more closely linked or are they inextricably oil and water you think or oil and vinegar or in other words are they such fundamentally different things have to be pursued differently are they going to keep getting closer caitlin please yes so um i'm going to say this first of all as someone who works on pretty much the like smallest amount of neuroscience you like i'm working at the level of the individual cell so as far as you can get from cognition uh while still being in neuroscience i think but i think um you know as a neuroscientist the fundamental belief is that like the cognitive arises from the biological processes and it isn't some uh completely separate thing that's happening it's it's coming from the circuits and i think um mark touched on this a little but this is something i've touched on as well as the amount of data we can collect is probably going to be what really breaks us through this like biological cognitive divide in that we are able to at an unprecedented level look at and recreate kind of all these different levels of the brain whether we're looking at the transcriptome whether we're looking at how the cells make connections whether we're looking at the activity and kind of being able to look at all of that together i think is what's going to be able to push us through uh you know that divide other thoughts jay yeah so i want to jump in as somebody who's on the opposite end of this scale so social networks are about as big of a cognitive question as you can get um and yeah and a lot of exciting techniques have been developed in the last uh let's say 10 years but uh allow you to use functional uh mris to look at people's brain activity as they're doing tasks inside of a giant super cool magnet right and so this gives you a sense for what the brain is doing as people are thinking and that's really exciting and the technology and the analytical techniques that we've developed have become so good that you can look at these tiny three by three millimeter cubes inside the human brain and you can look at patterns in a brain region of these little cubes of brain tissue and from that by looking not only at the activity of the brain but how it expresses that activity in patterns of activation uh you can start to answer really interesting and fine-grained questions about well okay it's it tells us something that this brain region activates seems to be involved in thinking about people in this way but we can do more now we can actually go into those regions and say in fact when you are doing this specific kind of mental operation we can see that reflected in this pattern of activity in the brain region and so for us you know this is actually one of our big goals is being able to connect uh cognition and biology using these sort of new techniques um so for us it doesn't seem like oil and bother it seems just like you know the question at hand our next big project actually kind of gets me thinking about a question i kind of think about a lot which is the question of is the brain deterministic uh and so you know this is a big one uh but but what do i mean by that so you know we we like to think as engineers that we have this black box thing and you put something in on one side and the exact as long as you put the same exact thing on one side it always comes out the same on the other side and uh and the brain inherently as we study it more and more doesn't seem to be that way at least not with the ability to look into what's going on that we currently have with the tools that we currently have you know it's interesting what jay mentioned about fmri it's true that we have this really cool ability to ask someone to think about something and and we can identify the way they're thinking about it or what they're thinking about just by the way that some images light up on a screen for us but if we were to put that same individual not in an mri scanner but instead you know at their home or playing a sport and have them think about the same thing would those patterns look the same uh and my thought would probably be no and i don't know to what degree but uh you know that's it gets this really interesting question of what level of fidelity do we need when we record from the brain to truly understand what's going on and uh the brain is like if you go deep enough you know even past caitlyn's level of thinking of the single cell you know ultimately the brain is this hugely ordered complex thing that arises from something inherently stochastic which is chemistry chemical reactions that are by their nature random it's the probability that two molecules will bump into each other but somehow that creates this really complex behavior that at least has this appearance of order on top of it and that's a really you know interesting question to me i don't know i don't know the answer to that of of what we need to be able to to truly be able to see and record everything from the brain but yeah these are fabulous answers these are really good i mean one thing i really like is you know the question of the resolution of what we should record nested in that is the fact that the method jay mentioned which has been a revolution in brain science i mean it could not have been a bigger revolution than to look inside the human brain and it measures maybe the most important network in the brain which is the vascular network so we don't even know the cell types although great work at rna-seq and great work and all kinds of recording and doing that but there's so many great frontiers here we're not this isn't uh morally trying to find the last decimal point where we have a lot of revolutionary physics and that analogy to to go through here which is really nicely captured by your answers so swinging it over to diane yeah i mean i maybe i'll just add one quick thing i i i know i'm a brave scientist and i'm like institutions like i just fundamentally don't think that in the end you know it is the biology that drives cognition and i don't see the brain as being very different from the heart as someone who started their work looking at um cardiac function and most of us who are kind of biophysics trained started in the heart and then moved into the brain when we felt we had enough control over what we were doing there but like you look at the ekg you know like it it's a beautiful thing right but you would never like if you just looked at that you wouldn't pull out all of the individual components like the elementary channels that are you know working in coordination i mean it's just wild and crazy um we don't have that for the i mean okay we have the eg but like not at the resolution that you can get with the ekg but like just think what you can extract from the ekg now like that it tells you which which individual ion channel is like dysfunctional based on the based on a change in the pattern so i think that we we're going there it's going to take longer there are layers of complexity here and more cell types and network but like i fundamentally believe that that there is that there's got to be that connection but love the discussion fantastic um so i'm going to close with i know it's shocking but it's four minutes to the hour uh but i what i just learned is like kind of have you guys like leap into the future perhaps a little bit or think about like how do we like you talk you've really just been amazing and i mean i'm you know living what you're working on already like you just described things in such brilliant way uh there's excitement and passion of course and whatever we're doing we chose you carefully no no like an interesting that that's research and that's science but what you know how do we think how best can we think about the future um so the you know the great technologies that that we have right now um perhaps you could just say a little bit about how you think they are going to lead to this this kind of introduction of new treatments technologies we don't all don't have to be translational fundamentally believe in basic research but like if you leap forward 20 years and think about what you're doing now can you give us a little insight into what the future is going to hold and we let you literally have like a minute each 60 seconds who wants to start i can go first go caitlyn in a minute but i think uh kind of the things i'm working on with reprogramming and basically being able to generate large amounts of human cells in a dish are going to absolutely change um how we even go about drug discovery and treatment because human aging is obviously very different from rodent aging and we're finally going to have the material we need to test these things in a way that we have not been able to test on humans because that's obviously unethical in the past so i think we're due for an explosion just because of the ease of these experiments and how they're just going to get easier in the future i guess i can i can go next so uh i i really do exist on this edge of translation so i think a lot about um where we will be in the not too distant future with that and uh aside from the big elephant in the room of of the fda which you know 20 years from now even stuff we're working on now might not be in people but i think the the way that we're pushing towards uh you know um translatable technologies and as we get these abilities this that i've kind of been alluding to this whole time about being able to access even more of the nervous system we can start to hopefully answer these questions of you know how how does the brain encode certain things and and go from this era of you know dbs for parkinson's where we don't truly understand how it's working but it kind of does to truly understanding how it's working and then being able to push forward and once we get to that point of of that understanding that's when we can really develop targeted technologies to push translation yeah i really like that um idea i mean for us you know like this is uh the translational benefits are probably far far into the future i can't even anticipate what they're going to be but um as basic scientists you know what we're trying to understand is what about people's brain activity about the coupling of brain activity to behavior leads people to be lonely or to be ostracized or to be popular or to be a social butterfly uh these i think are questions that will help us just better understand our our human experience and you know i think there's some intrinsic benefit to understanding these questions and who knows where this technology will take us right or who knows what kinds of explanations that we'll have about you know just being to tell our own story about why we are the way we are so for me that's that's what the future of brain science looks like fabulous thank you uh we hit the hour incredible as that is uh so i you know i really chris and i both thank you all that was um fun [Music] interesting um and as i said at beginning you know i think you proved me right which is you know if you want to know about you know the exciting things that are happening listen to eloquent explanations of complex research topics go to you know go to the early stage scientists because they are where where where the future i mean you are our future and i i'm really excited by that listening to all three of you i think you know you covered an incredible breadth of topics um and i have to say what surprised me most was your the way that you were all thinking about how do you create an environment to really enable great research and innovation and creativity which is what we talk about all the time honestly i've learned a lot from listening to you guys um and i also you know thinking jay as you were describing this i mean maybe we can come up with a formula formula or an idea of like you know who is the right person that we should be recruiting next like i i just love that that you know that the reductionist approach but of these complex issues but like deeply thinking about the community and the environment that we're in so thank you for that it was tremendous chris do you want to say anything to close and then we're going to thank everyone and yeah we have to quote the final q a entry is not a question but brilliant panelists there you go so i hope you all thanks for joining us i hope you'll join us again for the next kanye conversations i can tell you one of the things on the schedule is thinking about you know how we function in the uncertain world that we're in right now so we've got a couple of very exciting panelists lined up for that but there'll be more information coming in your email soon thank you everyone for joining us this was tremendous thanks caitlin j and mark take care you

2021-08-30 18:37

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