Engineering brain dynamics with Addison Schwamb and BethAnna Jones

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On this episode of Engineering the Future, Addison Schwamb and BethAnna Jones, graduate students in the Preston M. Green Department of Electrical & Systems Engineering, describe their systems-based approaches to brain dynamics and dispel some science-fiction inspired misconceptions. With their adviser ShiNung Ching, associate professor of electrical & systems engineering in McKelvey Engineering, Schwamb and Jones use mathematics to understand how the brain works and apply that knowledge to address questions at the intersection of engineering, brain science and human health.

Addison Schwamb: The general public thinks that the scientists know way more about how the brain works than they actually do. The brain and our understanding of it at this point is a little bit like the wild west.

Shawn Ballard: Hello, and welcome to Engineering the Future, a show from the McKelvey School of Engineering at Washington University in St. Louis. I am Shawn Ballard, science writer, engineering enthusiast, and part-time podcast host. Today I am here with Addison Schwamb and BethAnna Jones, graduate students working with ShiNung Ching in the Preston M. Green Department of Electrical & Systems Engineering. Addison and BethAnna, welcome to the show.

Addison Schwamb: Thank you. 

BethAnna Jones: Thank you for having us.

SB: You're so welcome. Addison, you're a PhD candidate in electrical engineering. Can you describe what that field is and what got you into it?

AS: Sure. Yeah, so electrical engineering, a lot of people think of it as like circuits and power lines, which is totally that in part, but it's a lot broader than that. So, it sort of comes out from engineers trying to interact with electronics, and so then you get into control theory, which arose originally from people looking at how to control like robotics or automated systems. And then that sort of took on a life of its own and became control theory and system science, which is what Beth Anna does, and she can talk about that later. Or, signal processing, which is initially focused on processing like electrical signals or recording music and now can be used for images or signals from the brain.

So, it's a very wide field that encompasses sort of robotics and circuits and computers and power lines and how dynamic systems work. 

SB: So, lots of stuff that people would see around.

AS: Yeah, exactly. And I got into it because I really liked math and I really liked programming in high school, but I wanted something a little more hands-on with hardware and circuits and computer chips than you would get through a pure computer science, programming degree. So that's how I got into electrical engineering, and then wanted to do biomedical things. And so started exploring areas that had biomedical applications.

SB: Oh, cool. Okay. So, I want to hear a lot more about that. And I'm going to pause and get you, BethAnna, to chime in on that system science and mathematics part that Addison referred to. 

BAJ: Yeah. So, it kind of goes hand in hand, of course, with electrical engineering. We are in the same department. But system science and mathematics kind of focuses more on large systems and being able to break them down into coherent ways so that we can really understand, like, what is making these systems act the way they do, and like even are there individual components that we can study and analyze, and if we take away components, what happens to the system overall?

So, basically it's really just like looking at large systems and really just trying to understand what makes them tick. And it's interesting, I wasn't expecting to end up in system science, but I started my journey in mathematics and applied mathematics in particular. And especially I really have just always been fascinated by the idea of language and being able to describe things. And when I started studying math in high school and then eventually in college, I just really liked the idea of how you can just describe natural phenomena with math. I mean, that's the idea of math, you know, like x is equal to five tells you a lot about x, you know, in that example, x is five.

But just overall, as you get more complex, it's really perfectly describing a system. And then there's different ways you can analyze the mathematics that you've used to describe it. And it just tells us so much. So I really enjoyed math for that reason. 

And then I just followed that. And eventually I realized that I liked math as itself, but I really just wanted to apply it. So then that got me to applied mathematics. And then as I started to explore what I could do with that, I looked into like biology, I looked into chemistry, engineering, and I really liked the intersection of biology and engineering. And so then that got me here where I am today.

SB: So that's so perfect. I love how you talk about the intersections there, because that seems exactly to describe what you're working on with ShiNung Ching and his Brain Dynamics and Control Research Group here at WashU. So many interdisciplinary things going on there. Can you tell me more about that? Because I know you both mentioned them, but bring that all together for us and the work you're doing now. 

AS: Yeah, so our lab does a lot of work that is in several different themes, and it all has to do with how do we use math as a language, like BethAnna said, to describe what the brain is doing. And then what applications can we do with that? 

So, my work specifically is very clinically oriented. I get data from clinicians at BJC and St. Louis Children's. And then I analyze their brainwaves using engineering methods to both describe what it's doing and predict what might be happening. And then that's sort of with the goal of helping clinicians to better treat patients and identify better which patients need more aggressive treatment versus which ones are more stable. 

But we have people that do more basic science trying to understand and describe how the brain is working just as it functions normally. And BethAnna can talk about that. And then we have people that work at the intersection of machine learning and neuroscience. And so they build novel machine learning methods based on what we know about how the brain works.

SB: Oh, okay. So, sort of three parts there. I'm just going to restate – so you have the sort of patient care aspect, which you were describing; more of the sort of foundational, you know, how does the brain work? How can we describe that with math, right? And then designing new stuff based on that fundamental understanding.

AS: Exactly.

SB: That's cool. Tell me more about the maybe that more fundamental side then, BethAnna.

BAJ: Yeah, yeah. So, Addison working in like the clinical side and really looking at data. That's kind of what we tend to call like a bottom-up approach. And I tend to go more towards a top-down approach. So, I usually start by looking at the system – or the brain in our lab – as a whole, and then really thinking about, okay, what do we see the system doing? For the brain, and specifically memory, because that's what I look at most of the day, what does it look like for the brain to be doing memory?

SB: What does a sort of typical day in the lab look like for you or perhaps more broadly, like whether you're in the lab or you're working with, you know, clinicians? What's a typical day like?

AS: My lab work is basically just desk work at my computer and analyzing data or fitting models to data. So, it's a lot of computer programming. It's a lot of reading papers, a lot of math. When I am not in the lab, then I'm usually either meeting with Dr. Ching to discuss our current research in his office or sometimes I will go over to the medical campus and meet with the clinicians.

BAJ: My day looks very similar to Addison's. I also do a lot of coding. I do a lot of coding up of my models. A lot of my time is spent just on my iPad writing equations. Just kind of trying to figure out, like, okay, if I say, for example, the brain represents a stimulus like X, then what happens when I compare X to Y?

And then just looking, breaking that down mathematically and being like, okay, well, what does it mean if I tell the network to always minimize the difference between X and Y, for example? So it's a lot of just really breaking down math and really trying to understand, like, okay, if I tell the network to do this, what is it actually going to be doing?

And then, but also thinking about, we want to be exact in that way, but we also really want to leave room for the network to, quote, unquote, come up with its own ways to do things. 

So that's really where discovery happens is you start with these requirements and like, okay, well, I know we want the brain to remember information. We know we don't want it to overwrite other information. But then it's really how the brain does that is what we're interested in, not necessarily the fact that it does do that.

SB: What would you say is the best thing or perhaps your favorite thing about being part of Dr. Ching's lab? 

AS: Do you want to start this time?

BAJ: Yeah. I've always really appreciated the community in Dr. Ching's lab. We really like to just try to talk to each other. Sometimes it can be really challenging in such a dry lab where we're often just at our desks a lot of the time. But I think we do care about each other and care about the work that we're doing. So, I really appreciate that. 

And another thing I also just appreciate being part of a lab that has other women in it. That's really important. I didn't realize how important that would be when I first joined the lab, but it is. When I first joined, I had a mentor, Marin, who's part of the lab, and that really inspired me. And then Addison came along and like, yeah, just having each other's backs. 

Also, I really enjoy just the freedom to really kind of explore the ideas I have. Dr. Ching is really generous with encouraging us to research the things that we think about. And he really tries to push us, and not in a mean or cruel way of like, “you're never enough.” But always like, “I think you can do better, and you should do this. And I'll be here the whole time trying to help you do that.” So that's what I really appreciated. 

SB: Amazing. 

AS: I mean, all of the things that BethAnna said, I love about our lab. I think also I really like Dr. Ching's working style. It really is, he does a great job of walking the line between encouraging us to strive to be our best without being sort of a hard boss to work for. And I think that he understands how challenging doing a PhD is and that you need a lot of encouragement if you're going to do it well. But also giving us the confidence to do hard things.

SB: What are the biggest misconceptions people have about your fields? And how can you help us, people like me, to get that right?

AS: Oh, I think the biggest misconception that people have about people who work with the brain is, I think that the general public thinks that the scientists know way more about how the brain works than they actually do. The brain and our understanding of it at this point is a little bit like the wild west. There's a lot of people doing a lot of really great science in very disparate areas, and they all sort of just have to do with the brain. And there's not a unifying theory of how it all works together – the connectivity and the oscillation patterns and the neural signaling and neural transmitters, and how that not only functions to keep our bodies alive, but also to yield cognition and things like that. And so that's one reason why it's really exciting to study is because it's such a sort of young scientific field, I feel like.

But I think a lot of people that just read like pop psychology or things like that think that scientists sort of know what's going on to a much higher degree of certainty than any actual neuroscientist, I think, would claim themselves. 

SB: Okay, that is incredibly comforting because I did not think that I knew what was happening in the brain, so glad to hear that is shared. 

AS: Yes, no one knows what's happening in the brain.

BAJ: And I feel like it's something we have to remind ourselves on a daily basis as well. Just like, it's okay if I don't fully understand why it's doing that.

SB: Following up on that, Addison, this is one of those pervasive pop science things that surely it is not true that we only use 10% of our brain. But, if we don't use all of it, can we get superpowers?

AS: So, no, we cannot get superpowers. This is another one of those misconceptions. So, one of the ways that the brain works is there are excitatory neurons and inhibitory neurons, and so excitatory neurons turn the other neurons on, and inhibitory neurons turn the others off. And so, I'm not sure exactly what the number is, but probably that 10% number or the idea that we're only using part of our brains comes from the fact that not every neuron is excited all at the same time.

But actually, from a system science perspective, if you have every neuron sort of activating every other neuron, that's a chain reaction, which is how you get things like the atomic bomb. And so, I think if that were to happen in our brains, it would just sort of like the blue screen out and not these superpowers. You really need that turning off in order for those neurons to turn back on again.

SB: That's helpful. So, less Superman, more Chernobyl, if we turn everything on.

AS: Yes, yes.

SB: Gotcha. Okay, that doesn't sound great.

BAJ: And I mean, essentially as well, the brain only has a limited number of neurons, and with the wide variety of things that we can do with our brain – like thinking, long-term memory, just like planning ahead, things like that – you really need to, the brain really needs to use some of its neurons for some of those things, while other neurons work for other of those things. 

So, really, yeah, if you turn on all the neurons at once, it would just be a cacophony in your head, and yeah, it would be like Chernobyl, it would just blow up.

AS: Trying to remember things from your childhood while also thinking about what you're having for dinner tonight and a million other things.

BAJ: Probably lead to an anxiety attack, if I'm being honest.

AS: True.

SB: Just the two things you listed, I was like, well, I'm going to just go panic now, and see you guys later.

BAJ: Exactly.

SB: What about you, BethAnna? Any misconceptions you would like to clear up for the record?

BAJ: I feel like a popular one we get being in the Electrical & Systems Engineering department is everyone expects me to know how to fix a circuit. 

SB: You don’t?!

BAJ: I know the basics of electronics, but put a circuit in front of me, and I don't even know what I'm doing. I started off in math, and I really focus on the engineering part of it, less so the electrical. And the brain itself is an electrical thing, so I do need that understanding of just general electrics, but I think that's a big one, is everyone's like, “oh, my phone's not working, can you help me?” Or like, the power went off, you know? And I'm like, that's not what I do.

SB: Okay. Yeah, helpful. So, Electrical & Systems Engineering does not equal electrician, and definitely doesn't equal phone technician, so that one I'm just like, not cool that people would ask you that. Okay, excellent. Very helpful. 

What projects are you most excited about right now that you're working on?

BAJ: So I typically look at memory and just really spend a lot of my day thinking about how information is maintained through neural activation in the brain, especially over short time periods. That's useful for processing information, you know. When new information comes in, it's like, how do you react to that? And that reaction is a combination of you processing that information with what you already have in your brain, as well as like, telling your arms to do something, or your legs to run, or something if need be.

So, I really focus on that, like, processing, and specifically that's called working memory, where you're really incorporating new information that's coming in with information that's already there. And so, there's that incorporation aspect, but then there's also the aspect of how is information maintained over time, especially if neurons are having to activate, like, there's excitatory and inhibitory neurons as Addison said at one point, how are those neurons working together to really encode information, and what does encoding even look like for them? Like, if one neuron fires, does that mean one thing? Or if one group of neurons fires, does that mean triangle?

And so it's really just thinking about those questions of like, how does the brain's limited resources work to do all these things together, and especially in an online manner, like, in moment to moment, because there's so many things going on all the time that we need to react to, like, immediately, that the brain just needs to be able to.

But it's essentially a resource bottleneck question that I'm looking into, like, how can it, like, how can it still be encoding the fact that I'm in this room while I'm also processing the question I'm trying to answer? Like, how is that context and the current information all melded together with the use of neural firing?

SB: Fascinating. Complicated. What about you?

AS: Yeah, so I mentioned I do mostly clinical work, and I think my most exciting project, which is the one that I'm working on right now, has to do with children who had cardiac arrest in the hospital, and so essentially these children came into the hospital for one reason or another, some relating to an existing heart condition, some not related to that at all, and were put on EEG monitoring. It's a very common noninvasive monitoring technique.

And then these children that we've collected in the cohort had a cardiopulmonary arrest while their brains were being recorded on EEG, and so it's a really sort of exciting data set that we get to look at to see how exactly does the brain react to an event as extreme as a heart attack, and based on what the brain signaling looks like, can we then understand which children had more severe arrests? Is there a correlation with mortality or with age? 

We had both infants under one-year-old and then also children and adolescents from seven years old to, I think, 17 or 18, so there could be a difference there. And so that paper, just sort of looking at the difference in the brain signals, I have submitted, but now I'm working on doing modeling with that data to see if we can gain some more mechanistic insight into is it a change in the excitatory and inhibitory balance of the neurons that changes leading up to or during a cardiac arrest?

Is it something with the neurons sort of like self-activation, or just different connections between each other that is changing, and how can we take that knowledge and use it to inform clinicians that this child might be at risk for something, such as a cardiac arrest?

SB: Okay, I like that. So what's happening in the brain could then be sort of predictive. As you were talking, I was like, I hope that a lot happens in the brain when something that dramatic is happening in your body. Is that the case?

AS: So it's a lot in the sense that it's a very dramatic change, but actually in the most severe ones, if there's not enough blood getting to the brain, then it will start to shut down and have significantly less activity than normal, which doesn't necessarily mean that it can't, if CPR is performed, that you can't sort of come back online and recover. But yeah, if it's a very severe arrest, then it does actually do much less than normal.

SB: Interesting. Okay, I guess I was kind of thinking about it. Like, I hope the systems are like firing and like, “we're having an issue, we’ve got to fix this,” but perhaps the opposite of that, like, oh, systems are down.

AS: It's sort of a progression. So it's sort of the systems fire, and then if it can't get the oxygen necessary to keep firing, then there's no sort of physical way for it to continue to, for those neurons to be firing and triggering your senses and all of that.

SB: Wow. Okay, that is a lot of food for thought. I will look forward to reading that paper, is what I'll say there. Amazing. Thank you for sharing those projects, which both sound very complicated and illuminating. So again, thank you for doing your part to expand the knowledge of everyone.

I'm going to switch gears to just for fun, just for closing. I always like to ask folks this because I'm always looking for good recommendations. So what is your top media recommendation right now, whether it's books, TV, movies, podcast, like whatever, what are you into? What have you been enjoying lately that you could recommend to me and perhaps our listeners?

BAJ: I could start. So recently, my husband and I have been watching a lot of Stargate, which isn't really new. But I've never seen it, and my husband grew up on it. But it's really, I mean, there's a movie, there's a TV show, there's spinoffs. But the premise of it is that there's these explorers and they found this gate to other planets. And so it scratches my sci-fi itch that I have, but also just like I really love the writing and the character development.

And also there's this one character that like makes me really happy. Sam Carter, she's a scientist, and her job every day is to take just this crazy situation that it's in front of her and use the scientific knowledge she has to just come up with a solution. And like as a scientist myself, it's like wild the solutions she comes up with. And so I don't know, Stargate is simultaneously inspiring to me while also just being really entertaining.

AS: I think my media recommendation, this is my favorite podcast. It's called A History of Rock Music in 500 Songs. And it's in-depth sort of the history and the music of rock music as a genre. The creator of it starts in 1938 with the, I think the first recorded electric guitar on a song. It's a swing song and is going all the way to 1999. And currently he's in the year 1968. So yeah, it's very fun. 

SB: Wonderful. Well, thank you both so much for being on the podcast with me today. This was an absolute delight. And I will look forward to hearing from you both about tracking all of the mysterious things going on in the world of neuroscience. 

AS: Thank you so much for having us.

BAJ: Yeah, thank you.

AS: This was so fun. 

BAJ: It was great talking to you. 

[Music]

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