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Speaker 1: The analogy that I like to use for this is

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Speaker 1: imagine you are at a club and there's lots and

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Speaker 1: lots of noise going on, lots of things going on,

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Speaker 1: lots of people talking to each other, there's loud music

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Speaker 1: and so on, and you're trying to communicate something to

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Speaker 1: somebody who's dancing alongside gm, and it's very, very difficult

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Speaker 1: because of all of that noise. But now you get

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Speaker 1: real close to them and you kind of, you know,

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Speaker 1: hold your hand to their ears and you start to

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Speaker 1: talk directly into their ears. Now they're going to receive

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Speaker 1: that communication with much higher fidelity, be able to tune

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Speaker 1: out noise and selectively attend to that communication. You've enhanced

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Speaker 1: the communication between that person and the person they're talking to.

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Speaker 1: That's what happens at synapses. There's lots and lots of

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Speaker 1: synoptic firing, lots and lots of communication happening. But when

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Speaker 1: cells start to attach to each other, they communicate much

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Speaker 1: more preferentially. They can transmit signals that express that form

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Speaker 1: of learning. In other words, if there's an experience that

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Speaker 1: happens that is learned by the brain, the brain can

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Speaker 1: express a form of plasticity or a form of memory

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Speaker 1: in the strength of the connections. So if the connections

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Speaker 1: grow stronger, that's a signal that this memory has been learned.

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Speaker 1: And most of the information that we have about this

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Speaker 1: comes from animal models, comes from slice recordings where we

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Speaker 1: can see evidence for enhancements in the connectivity, enhancement of

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Speaker 1: the communication between cells as a result of a learning experience.

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Speaker 2: You've reminded me why I hate clubs. I'm sorry, no

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Speaker 2: one invites me to them anymore.

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Speaker 1: I share that with you. Hi, I'm Daniel.

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Speaker 3: I'm a particle physicist, and every day I rely more

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Speaker 3: on my computer's memory instead.

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Speaker 1: Of my own.

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Speaker 2: Yeah, I am Kelly Leader Smith. I'm a biologist, and

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Speaker 2: I'm having a lot more of those moments where you

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Speaker 2: walk into a room and think why am I in here?

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Speaker 3: I walk around campus here at you see Irvine and

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Speaker 3: a lot of folks go hey, Professor Whitson, and I

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Speaker 3: go hey, and I think I have no idea who you.

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Speaker 1: Are and why we know each other.

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Speaker 3: And sometimes it's just because they were in a class

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Speaker 3: I taught with four hundred people, and the relationship is

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Speaker 3: a little axometrical. And sometimes it's just because my memory

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Speaker 3: is terrible, and maybe we hit coffee and I've forgotten,

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Speaker 3: So I apologize to folks out there who I'm pretending

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Speaker 3: to recognize.

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Speaker 2: Yeah, yeah, no, I'm there also, And every once in

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Speaker 2: a while be standing in the grocery store and I'll

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Speaker 2: stand in front of the aisle for a little too

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Speaker 2: long and my daughter will go, you forgot, didn't you. Yeah,

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Speaker 2: I have no idea what I'm looking for right now.

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Speaker 2: And hopefully this doesn't give us too much anxiety thinking

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Speaker 2: about what the root cause of our memory problems are.

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Speaker 3: I have that as well. Is that a memory issue

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Speaker 3: or is that a distraction issue? Like where you're looking

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Speaker 3: for capers, but then you see a jar pickles and

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Speaker 3: it makes you think about that last time you had

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Speaker 3: a pickle, and then you're like, m I wonder if

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Speaker 3: you can pickle at home. And then five minutes later

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Speaker 3: you're dreaming about a whole pickling building in your backyard,

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Speaker 3: and you've forgotten that you were looking for capers.

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Speaker 2: So I don't like pickles. So no, that's not my scenario.

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Speaker 2: But sometimes it'll be like, you know, I see my

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Speaker 2: reflection in a bottle, and I'll be like, oh, is

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Speaker 2: that spot skin cancer? When am I gonna die.

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Speaker 1: There we go?

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Speaker 3: Yeah, exactly, but.

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Speaker 2: It is often distraction instead of forgetfulness. But yeah, sometimes

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Speaker 2: it's hard to disentangle those things.

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Speaker 3: Well, I just learned something deeply troubling about you that

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Speaker 3: you don't like pickles.

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Speaker 2: What you know, Daniel, You've made a really great point

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Speaker 2: about your memory, because we have definitely talked to about

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Speaker 2: and I think even on the show, we've talked about this.

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Speaker 3: That's embarrassing, but not as embarrassing as being closed minded

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Speaker 3: to the wonderful world of pickles. I'm with you, like

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Speaker 3: the big deal pickle, okay with the sandwich, but I'm

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Speaker 3: not munching on one in general. But have you ever

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Speaker 3: done home pickling, you know, like you can pickle cauliflower

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Speaker 3: or carrots.

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Speaker 1: It's really wonderful.

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Speaker 2: The only pickled item I've ever enjoyed is Cowboy candy,

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Speaker 2: which which is when you slice of really hot halopanos

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Speaker 2: and put them in like a sugar pickle. Oh so good.

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Speaker 2: That I love on sandwiches. Other than that, I have

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Speaker 2: not met a pickle that I like.

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Speaker 3: I'm sorry, I have to work on that, all right.

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Speaker 2: Okay, all right, well, I'll keep my mind open. But

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Speaker 2: today we got a wonderful question from our listener, Simon,

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Speaker 2: who is interested in memory, and so let's go ahead

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Speaker 2: and listen to Simon's question.

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Speaker 4: Now, Hi, Daniel and Kelly. This is Simon from New York.

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Speaker 4: I was a huge fan of Daniel and joege Explain

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Speaker 4: the Universe. However, I am loving the new show with

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Speaker 4: Kelly and never missed an episode. Here's my question, I

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Speaker 4: think mainly for Kelly. I am now into my seventies

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Speaker 4: and not surprisingly find myself reflecting a lot on my

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Speaker 4: life and the thousands, perhaps millions of memories that go

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Speaker 4: into a lifetime. I have often wondered exactly how the

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Speaker 4: human brain, using biological substances, chemicals, and electricity, actually doors memories.

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Speaker 4: I have tried to read about this, but have not

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Speaker 4: found anything to satisfy my curiosity or wonder Perhaps this

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Speaker 4: blurs too much into the question of what is consciousness

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Speaker 4: and sentience and it's not something you want to delve into.

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Speaker 4: But if you would like to tackle it, I would

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Speaker 4: love to hear about what insights biology and physics have

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Speaker 4: to offer. I have very recently been reading a lot

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Speaker 4: about computers, trying to understand how they really work and

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Speaker 4: that seems somewhat related, but only somewhat anyway, Thanks to

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Speaker 4: both of you for all you do. My weeks would

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Speaker 4: not be complete without listening to your podcast episodes.

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Speaker 2: All right, Simon, we love your accent.

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Speaker 1: We do.

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Speaker 2: I grew up in New Jersey and I miss hearing

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Speaker 2: that accent more often, so that was awesome, And thank

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Speaker 2: you for this fantastic question. And we got really lucky.

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Speaker 2: So folks, remember we talked in the past about whether

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Speaker 2: or not tripped a fan from Turn he actually makes

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Speaker 2: you sleepy on Thanksgiving and we interviewed Mark Mapstone for

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Speaker 2: that and he suggested that if we were interested in memory,

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Speaker 2: we should talk to his colleague, and his colleague was

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Speaker 2: willing to come on the show. So on today's show

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Speaker 2: we have doctor Michael Yassa. He's a professor at the

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Speaker 2: University of California, Irvine, where he's also the director of

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Speaker 2: the Center for the Neurobiology of Learning and Memory and

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Speaker 2: is the director of the UCI Brain Initiative. So like

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Speaker 2: clearly the perfect person for this topic.

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Speaker 3: Yes, amazing, and also it's one more notch on my

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Speaker 3: personal goal to have my entire neighborhood on the podcast.

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Speaker 3: For those of you who don't know people that you

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Speaker 3: see Irvine. Many of us live in this faculty neighborhood

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Speaker 3: right next to campus, so we're all friends and neighbors

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Speaker 3: and we know each other. And by now we've had

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Speaker 3: a significant fraction of that neighborhood on the podcast. Because

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Speaker 3: I want to know, like, hey, who's an expert flight

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Speaker 3: I'm like, oh, I know that guy who's on the

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Speaker 3: next street. And so it's a fantastic resource.

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Speaker 2: That is pretty great. So let's go ahead and bring

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Speaker 2: Mike on the show. But we should mention you were

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Speaker 2: off telling people about your amazing research ideas, so you

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Speaker 2: were not able to join us for this interview. So

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Speaker 2: I flew solo with Mike.

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Speaker 3: Thanks very much for handling this while I was goofing

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Speaker 3: around in the area my pleasure.

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Speaker 2: I had a blast. All right, Welcome to the show, Mike,

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Speaker 2: Thanks for being with us today.

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Speaker 1: Thanks for having me.

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Speaker 2: So what got you interested in studying memory? Let's start

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Speaker 2: by getting to know you of it?

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Speaker 1: Sure, So, I don't think that I really knew much

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Speaker 1: about memory when I was an undergraduate. I was fascinated

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Speaker 1: by the brain just by virtue of taking a couple

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Speaker 1: of classes that kind of inspired that passion, that love

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Speaker 1: for everything brain related. And one of the things that

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Speaker 1: was really fascinating about it is that I felt, even

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Speaker 1: at the time, this was in the late nineties, that

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Speaker 1: we knew next to nothing. Unlike other classes that I took,

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Speaker 1: where there was sort of a big body of knowledge,

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Speaker 1: it felt like with the brain, there's just so much

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Speaker 1: more that we really didn't understand. So that became really fascinating.

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Speaker 1: As I started to dive a little bit more deeply

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Speaker 1: into different aspects of how the brain functions, memory came

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Speaker 1: about as one that was front and the foremost, particularly

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Speaker 1: because we started to see or I started to see

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Speaker 1: that memory loss is just very devastating, unlike any other

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Speaker 1: cognitive domain that if you know, if you have attentional

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Speaker 1: deficit or if you have a deficit with executive function,

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Speaker 1: you know, that can be somewhat circumscribed. It's a contained

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Speaker 1: kind of deficit, not memory loss, which just utterly devastating,

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Speaker 1: you know, seeing patients with Alzheimer's disease, seeing patients with

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Speaker 1: various forms of memory loss, that was really compelling, and

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Speaker 1: I started to understand a bit more that memory is

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Speaker 1: what makes us who we are. It's so fundamental to

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Speaker 1: our core. It's the essence of our consciousness. Everything that

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Speaker 1: we do we do because of some experience that we've

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Speaker 1: had that we've been able to store, and it just

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Speaker 1: became not just fascinating, but like entirely all consuming. So

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Speaker 1: I focused on memory from all of its aspects. One

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Speaker 1: is trying to understand it's fundamental inner workings, and too

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Speaker 1: trying to understand how it breaks down in a variety

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Speaker 1: of differ and conditions. And if we can do that,

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Speaker 1: maybe we can help people. Yeah.

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Speaker 2: I've got a neighbor with Alzheimer's and it's been totally

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Speaker 2: devastating for the two of them. So yeah, So this

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Speaker 2: interview was inspired by a question that we got from

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Speaker 2: the listener and one of the things that they asked

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Speaker 2: about is does the concept of memory blur the line

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Speaker 2: with consciousness and sentience? How do you view these concepts?

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Speaker 1: Yeah, you know, it's interesting. There's a somewhat related question,

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Speaker 1: which is, you know you can have a computer have memory.

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Speaker 1: When we talked about memory and a computing platform in

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Speaker 1: a robot or a botic application, certainly when you think

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Speaker 1: about chat GPT, well that can hold on to memory

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Speaker 1: for some period of time and use that to guide

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Speaker 1: how it responds to the us there and so on.

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Speaker 1: So memory in and of itself may not be the

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Speaker 1: thing that I would say as associated with sentiens. I

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Speaker 1: think that it's the way that our memory works, not

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Speaker 1: just as humans, but as sort of you know, live organisms.

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Speaker 1: It's not like the way that you would do it

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Speaker 1: in a computer. So let me elaborate. Memory is stored

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Speaker 1: in a computer is very much one to one. Everything

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Speaker 1: that you see and learn, your storing with incredibly high fidelity.

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Speaker 1: You try to retrieve it twenty years from now, thirty

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Speaker 1: years from now. It's exactly to say there's no degradation.

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Speaker 1: But that creates a problem for a memory system, and

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Speaker 1: that it's more difficult to extract generalities. It's more difficult

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Speaker 1: to generate knowledge based on memory. But say as a

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Speaker 1: human and you're encoding memories all the time. These memories

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Speaker 1: are stored, but we know that there's blurriness of memories,

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Speaker 1: there's forgetting of memories. There's all sorts of things that

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Speaker 1: we tend to think of as memory problems, but in

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Speaker 1: fact one could argue they're not bugs, they're features of

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Speaker 1: the system. Because memory is not intended to be a

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Speaker 1: super high fidelity kind of system. It's intended to get

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Speaker 1: enough information in so that you can generalize knowledge, so

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Speaker 1: you can learn from experience and be able to guide

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Speaker 1: your future decision making. So, while a computer's memory is

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Speaker 1: really about storage, with high fidelity, you don't want to

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Speaker 1: write a filin word and then store and then later

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Speaker 1: on have an abstract version of it rather than what

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Speaker 1: you actually wrote. You want to have an accurate record

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Speaker 1: of what you actually wrote. But for the brain, what

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Speaker 1: you might want to get later on is that abstract version.

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Speaker 1: It's just enough knowledge to be able to guide your

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Speaker 1: future decision making. And that's the reality of how memory

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Speaker 1: evolved in live organisms. And maybe the thing that makes

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Speaker 1: it very different from nonsensient beings is that it never

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Speaker 1: really evolved to think too much about the past. It

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Speaker 1: evolved almost entirely to think about the future. So the

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Speaker 1: reason why you might store something is because you want

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Speaker 1: to use that knowledge to guide future decision making, to

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Speaker 1: make sure that you do things that are adaptive to

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Speaker 1: promote your survival. You're not going back to the same

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Speaker 1: poison as berry bush. You know, to run from a

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Speaker 1: bear out in the wild, as supposed to go up

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Speaker 1: and say Hello, those kinds of things are based on memory,

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Speaker 1: equipping us to make better predictions for the future.

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Speaker 2: So can we dig in a little bit more to

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Speaker 2: this trade off? So why can't you remember everything perfectly

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Speaker 2: and generalize when the time is right.

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Speaker 1: Yeah. So it turns out that you can mathematically and

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Speaker 1: computationally model this and it gives you a pretty straightforward answer.

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Speaker 1: And the way that it works is that if you

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Speaker 1: were to encode every single experience that you have in

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Speaker 1: a very high resolution and high fidelity kind of approach,

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Speaker 1: then it becomes very difficult to generalize that to new situations.

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Speaker 1: The representations in the brain become almost hyper specific. They're

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Speaker 1: very very specific to those instances in which they were encoded.

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Speaker 1: So being able to extract the generalities or the knowledge

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Speaker 1: that you can apply to new things requires that there's

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Speaker 1: enough blur, enough fuzziness across the different instances so you

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Speaker 1: can generalize that knowledge. I'll give you an example. If

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Speaker 1: I were to ask you what is the capital of

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Speaker 1: the United States, you'd have a very very quick answer

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Speaker 1: for me, which is Washington, d Z Exactly. Now, if

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Speaker 1: I ask you, when did you first learn that?

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Speaker 2: Now don't know.

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Speaker 1: So now if I had asked you the day right

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Speaker 1: after you first learned that, be in class or maybe

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Speaker 1: from parents, you would have a pretty good memory for it.

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Speaker 1: That's a pretty exciting thing that you just learned. But

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Speaker 1: the reality is you've learned it so many times over

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Speaker 1: so many different exposures. You've heard in a million times

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Speaker 1: in many different settings. And what's most important is not

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Speaker 1: so much the first time you heard it or the

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Speaker 1: last time you heard it, but the fact that you

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Speaker 1: extracted that piece of knowledge, that core piece of knowledge

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Speaker 1: out and now that's part of your body of knowledge.

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Speaker 1: So the specifics around how and when and where we

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Speaker 1: encoded specific things may not be all that important from

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Speaker 1: an evolutionary standpoint to hold on to as the memory

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Speaker 1: for the actual knowledge is important. That's what's going to

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Speaker 1: guide your future action. That's what's important to generalize the

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Speaker 1: new situations. So in some ways there's no evolutionary pressure

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Speaker 1: to hold on to the specifics over time. But it

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Speaker 1: is an interesting point like why can't we have both? Well,

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Speaker 1: you can think about it from an energetic standpoint. If

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Speaker 1: you have a limited resource system, why would you invest

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Speaker 1: your energy into storing the specifics when you know you're

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Speaker 1: not going to use them down the line.

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Speaker 2: Yeah, no, fair enough. So you mentioned the analogy of

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Speaker 2: the brain as a computer. Is there any analogy over

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Speaker 2: history comparing a brain to something else that you think

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Speaker 2: is a helpful analogy or the brain is just so

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Speaker 2: different it doesn't make sense to try to compare it

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Speaker 2: to anything we have experience with.

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Speaker 1: So that is a really interesting question. And I have

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Speaker 1: thought about this before, and I keep coming back to

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Speaker 1: the computer as the closest thing, and I always tell

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Speaker 1: people I think it is possible that we will get

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Speaker 1: to a point, perhaps with quantum computing and other types

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Speaker 1: of things, where we might be able to approximate a

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Speaker 1: human brain like functionality in a computer. To this day,

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Speaker 1: we don't have that, even with the incredible advent of

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Speaker 1: AI in large language models and all of those things.

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Speaker 1: Still there are certain things that human brains can do,

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Speaker 1: and mammalian brains in general can do that computers just

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Speaker 1: are not capable of. But there is not another device

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Speaker 1: out there that is capable of this level of processing

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Speaker 1: that I can think think of to associate with that analogy. Particularly.

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Speaker 1: I mean, I think one of the things that the

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Speaker 1: brain does, don't get me wrong, like the computational kinds

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Speaker 1: of things that have been built are incredible, and the

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Speaker 1: amount of the minuscule amount of time that it takes

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Speaker 1: for them to be able to process and provide an

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Speaker 1: answer is just wild beyond the imagination. But there are

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Speaker 1: still some things that brains can do that the computer scans,

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Speaker 1: and in particular it has to do with the ability

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Speaker 1: to extract knowledge, the ability to error correct, the ability

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Speaker 1: to do the kinds of decision making that we do

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Speaker 1: as humans that are very difficult to encapsuling in the computer.

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Speaker 1: The ability to have emotion emotional reactions. Those things are

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Speaker 1: still very difficult to model. While you can tell the

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Speaker 1: computer all you want about what we think the human

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Speaker 1: experience is, we still don't understand that well enough to

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Speaker 1: be able to model it computer.

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Speaker 2: Yeah, fair enough, all right, So let's pull back to

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Speaker 2: memory a little bit. So we've talked about how memory

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Speaker 2: differs from sentience. Are there different kinds of memory and

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Speaker 2: do our brains store different kinds of manator differently?

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Speaker 1: Yes, And oftentimes we just say memory as like one

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Speaker 1: big blanket umbrella kind of term, But it is important

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Speaker 1: to know that there are different memory types, different memory

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Speaker 1: systems in the brain, and they serve different functions. So

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Speaker 1: let me give you a couple of examples. One type

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Speaker 1: of memory, which I tend to like quite a bit,

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Speaker 1: we studied quite often in my research laboratory, is what

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Speaker 1: we call episodic memory, memory for episodes, memory for events

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Speaker 1: that happened to us, And we tend to kind of

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Speaker 1: operationally define it as remembering what happened, where it happened,

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Speaker 1: and when it happens, and whenever you have kind of

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Speaker 1: like the collection or the conjunction of those three, you

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Speaker 1: can label that as an episode and you have a

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Speaker 1: memory for a particular episode in your life. Typically you

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Speaker 1: think about that as also the root of our autobiographical memory,

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Speaker 1: our memory for autobiographical experiences, things that happen to us.

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Speaker 1: But that's very different from membering how to tie your

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Speaker 1: shoe races, or how to ride a bicycle, or how

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Speaker 1: to do something like your tennis swing or your golf swing.

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Speaker 1: You know, those kinds of things are trained in the

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Speaker 1: brain very differently. They involve very different systems. A lot

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Speaker 1: of times, they require more trial and error kind of learning,

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Speaker 1: and they tend to be a bit less accessible to consciousness.

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Speaker 1: So there will call sort of implicit kinds of learning

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Speaker 1: so if you look at how the brain is organized,

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Speaker 1: pretty much every patch of cortex is capable of some

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Speaker 1: form of memory. Another term that we typically use in

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Speaker 1: neuroscience is plasticity. The idea that the brain is plastic

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Speaker 1: means it's capable of change. So whenever you have experience,

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Speaker 1: cells that are responding to that experience are capable of change,

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Speaker 1: and that change typically is thought of as a change

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Speaker 1: in the connections and the way that cells communicate with

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Speaker 1: each other, but some change that reflects a record of

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Speaker 1: the experience that you had. Now, those changes happen throughout

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Speaker 1: They can happen in our visual cortex, our visual system,

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Speaker 1: our auditory system. They can happen in the episodic memory

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Speaker 1: systems in the brain, or they can happen in these

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Speaker 1: more implicit memory kinds of systems in the brain that

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Speaker 1: typically support more unconscious function like knowing how to ride

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Speaker 1: a bicycle, like knowing how to swing the golf club

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Speaker 1: and those kinds of things. Those are stored separately. And

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Speaker 1: we know this to be true because we see patients

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Speaker 1: that have deficits in one type of memory and not another,

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Speaker 1: because they have maybe a focal stroke or some damage

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Speaker 1: or some deficit that impacted one system and not the other.

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Speaker 1: So they struggle with that one type of memory that's

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Speaker 1: affected in that system, but everything else seems to be intact.

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Speaker 2: And by type of memory does that mean like category

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Speaker 2: of memories like the tennis swing and the other sort

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Speaker 2: of muscle memory things, or like I could forget high

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Speaker 2: school if I had a stroke in the right place right.

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Speaker 1: Although that's actually increasingly difficult. So typically, if there is

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Speaker 1: a stroke that is focal, it might affect motor memory.

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Speaker 1: It might affect memory that allows you to kind of

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Speaker 1: move your hands in the right way and be able

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Speaker 1: to support that kind of function. But episodic memory is

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Speaker 1: this really weird thing. Initially, it does depend on key

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Speaker 1: regions of the brain, one of them being the hippocampus.

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Speaker 1: That's a really important region for episodic memory. But over

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Speaker 1: time memories start to become somewhat independent of the hippi

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Speaker 1: campus they start to become stored elsewhere, and that's one

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Speaker 1: of the reasons why in Alzheimer's disease, where we know

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Speaker 1: the hippocampus is one of the earliest regions to degenerate.

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Speaker 1: As that starts to go away, you see a loss

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Speaker 1: of recent memories things that were recently acquired, maybe weeks, months,

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Speaker 1: or a couple of years before the decline started. But

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Speaker 1: things from long ago, like high school are preserved. And

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Speaker 1: the reason they're preserved is that they've now been consolidated

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Speaker 1: that sort of a technical term for made strength and

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Speaker 1: made resilience to loss. And that's because their stored sort

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Speaker 1: of in parallel throughout the brain. So just the focal

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Speaker 1: deficit there is likely not going to wipe out those

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Speaker 1: particular memories, but it's much more likely to wipe out say, yeah,

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Speaker 1: your ability to have the right swing or ride a

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Speaker 1: bicycle or anything like that. Those are the kinds of

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Speaker 1: things that are much more focally stored.

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Speaker 2: Okay, and can we dig a little bit more into

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Speaker 2: exactly how the brain stores memories. Is it like how

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Speaker 2: do neurons connect with each other? Yeah, let's dig in.

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Speaker 1: Yeah. So brain cells are very very unique compared to

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Speaker 1: other cells in the body. And provided this is again

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Speaker 1: I always tell my students, you know, this is true

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Speaker 1: ninety five percent of the time according to our knowledge today.

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Speaker 1: I sometimes things you know, several weeks from now, months, years,

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Speaker 1: things can get revised. So this is just according to

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Speaker 1: our current knowledge today, brain cells are able to communicate

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Speaker 1: with one another in a way that other cells in

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Speaker 1: the body are not able to. And the way that

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Speaker 1: they communicate with each other is using a combination of

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Speaker 1: electricity and chemistry. So the transmission of signals within a

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Speaker 1: brain cell is entirely electrical, and we can talk about

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Speaker 1: that in a second, but the transmission from one cell

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Speaker 1: to the next most of the time is chemical. It

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Speaker 1: involves the release of a neurochemical that goes from one cell,

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Speaker 1: binds to the other, and then initiates another electrical signal

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Speaker 1: from the next cell to the next cell. So it

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Speaker 1: goes really fast electrical, somewhat slower chemical, really fast electrical,

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Speaker 1: someone slower chemical, and so on, and you have this

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Speaker 1: sort of progression of communication between cells. And that's really

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Speaker 1: important because these cells need to bring in signals that

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Speaker 1: essentially encode the outside world and bring that knowledge into

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Speaker 1: the brain to create some sort of representation of it,

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Speaker 1: and then act on them. Allow us to move, allow

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Speaker 1: us to avoid a threat, allow us to seek reward,

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Speaker 1: all of those kinds of things. So that's how the

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Speaker 1: brain typically communicates. But your question is how does memory happen?

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Speaker 1: And there was sort of lots of answers over the years.

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Speaker 1: We used to think, well, maybe it's encoded in the

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Speaker 1: DNA and cells. Maybe it's encoded in the cell size,

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Speaker 1: maybe it's encoded in the whatever else is happening to

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Speaker 1: change cell shape. And the current answer is that it

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Speaker 1: is much more likely to be encoded in the connections

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Speaker 1: in the way that these cells communicate with one another.

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Speaker 1: So let's say sell A is firing and cell B

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Speaker 1: is receiving a signal from cell A. If I were

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Speaker 1: to modify some how the frequency by which sell A communicates,

437
00:22:02,760 --> 00:22:06,640
Speaker 1: would sell be by making it fire more, or increase

438
00:22:06,720 --> 00:22:11,040
Speaker 1: the neurotransmitter release, or increase the number of receptors on

439
00:22:11,119 --> 00:22:14,440
Speaker 1: the second cell that receives that neurotransmitter. I can make

440
00:22:14,480 --> 00:22:17,760
Speaker 1: it so that the communication between those cells is enhanced.

441
00:22:18,119 --> 00:22:20,320
Speaker 1: And the way that the analogy that I like to

442
00:22:20,400 --> 00:22:23,639
Speaker 1: use for this is imagine you are at a club

443
00:22:24,320 --> 00:22:26,640
Speaker 1: and there's lots and lots of noise going on, lots

444
00:22:26,680 --> 00:22:28,600
Speaker 1: of things going on, lots of people talking to each other,

445
00:22:28,600 --> 00:22:31,679
Speaker 1: there's loud music and so on, and you're trying to

446
00:22:31,680 --> 00:22:35,679
Speaker 1: communicate something to somebody who's dancing alongside you, and it's very,

447
00:22:35,760 --> 00:22:38,720
Speaker 1: very difficult because of all of that noise. But now

448
00:22:38,760 --> 00:22:40,280
Speaker 1: you get real close to them and you kind of,

449
00:22:40,280 --> 00:22:42,159
Speaker 1: you know, hold your hand to their ears and you

450
00:22:42,240 --> 00:22:45,160
Speaker 1: start to talk directly into their ears. Now they're going

451
00:22:45,200 --> 00:22:47,960
Speaker 1: to receive that communication with much higher fidelity, be able

452
00:22:47,960 --> 00:22:51,119
Speaker 1: to tune out noise and selectively attend to that communication.

453
00:22:51,440 --> 00:22:54,520
Speaker 1: You've enhanced the communication between that person and the person

454
00:22:54,520 --> 00:22:58,560
Speaker 1: they're talking to. That's what happens at synapses. There's lots

455
00:22:58,560 --> 00:23:01,600
Speaker 1: and lots of synaptic firing and lots of communication happening.

456
00:23:01,960 --> 00:23:04,240
Speaker 1: But when cells start to attach to each other, they

457
00:23:04,280 --> 00:23:08,800
Speaker 1: communicate much more preferentially. They can transmit signals that express

458
00:23:08,880 --> 00:23:11,800
Speaker 1: that form of learning. In other words, if there's an

459
00:23:11,800 --> 00:23:15,280
Speaker 1: experience that happens that is learned by the brain, the

460
00:23:15,320 --> 00:23:18,520
Speaker 1: brain can express a form of plasticity or a form

461
00:23:18,560 --> 00:23:22,320
Speaker 1: of memory in the strength of the connections. So if

462
00:23:22,359 --> 00:23:25,240
Speaker 1: the connections grow stronger, that's a signal that this memory

463
00:23:25,280 --> 00:23:28,080
Speaker 1: has been learned. And most of the information that we

464
00:23:28,119 --> 00:23:31,560
Speaker 1: have about this comes from animal models, comes from slice recordings,

465
00:23:31,600 --> 00:23:35,400
Speaker 1: where we can see evidence for enhancements in the connectivity,

466
00:23:35,760 --> 00:23:39,280
Speaker 1: enhancement in the communication between cells as a result of

467
00:23:39,320 --> 00:23:40,399
Speaker 1: a learning experience.

468
00:23:40,720 --> 00:23:43,920
Speaker 2: One. You've reminded me why I hate clubs. That's sorry,

469
00:23:43,920 --> 00:23:45,119
Speaker 2: no one invites me to them anymore.

470
00:23:45,200 --> 00:23:45,920
Speaker 1: I share that what you do?

471
00:23:46,680 --> 00:23:49,720
Speaker 2: Yeah yeah, Okay. So we've got these connections, they get strengthened,

472
00:23:49,760 --> 00:23:52,440
Speaker 2: but then it feels like there's another step between having

473
00:23:52,440 --> 00:23:54,919
Speaker 2: this connection and then having a like specific memory. So

474
00:23:54,960 --> 00:23:56,280
Speaker 2: you know, like we're not at the point where we

475
00:23:56,320 --> 00:23:58,880
Speaker 2: could even in mice and correct me if I'm wrong

476
00:23:58,880 --> 00:24:01,199
Speaker 2: about this, where we could like see which neurons are

477
00:24:01,240 --> 00:24:05,120
Speaker 2: firing together and know they're thinking about food. Yeah, yeah,

478
00:24:05,119 --> 00:24:06,160
Speaker 2: So where do we go from there?

479
00:24:06,280 --> 00:24:09,000
Speaker 1: So what you're talking about actually is a very very

480
00:24:09,040 --> 00:24:12,479
Speaker 1: big problem that we deal with in neuroscience and in

481
00:24:12,560 --> 00:24:17,040
Speaker 1: cognitive science, and it's the credit assignment problem. How do

482
00:24:17,119 --> 00:24:19,919
Speaker 1: you know that a particular cell is assigned to a

483
00:24:19,920 --> 00:24:22,600
Speaker 1: particular memory or a particular connection it's assigned to a

484
00:24:22,640 --> 00:24:25,359
Speaker 1: particular memory. And it's a very challenging question and we

485
00:24:25,400 --> 00:24:28,040
Speaker 1: don't know the answer yet, but we suspect There's been

486
00:24:28,240 --> 00:24:31,639
Speaker 1: a lot of research on what's called mechanisms of allocation.

487
00:24:32,400 --> 00:24:36,080
Speaker 1: In other words, how can you allocate particular synapses, brain

488
00:24:36,200 --> 00:24:40,560
Speaker 1: cell connections and cells to a particular memory and not

489
00:24:40,640 --> 00:24:43,719
Speaker 1: another right so how can we get the specificity that

490
00:24:43,800 --> 00:24:46,400
Speaker 1: we need in the system. And there's been a flurry

491
00:24:46,440 --> 00:24:49,840
Speaker 1: of work in recent years understanding there are certain proteins

492
00:24:49,840 --> 00:24:54,480
Speaker 1: that are used to quote label synapses, label particular cells,

493
00:24:54,960 --> 00:24:57,520
Speaker 1: and assign them to a one memory and not another.

494
00:24:58,119 --> 00:25:00,520
Speaker 1: It's just really brilliant work by some colleagues in the

495
00:25:00,520 --> 00:25:03,520
Speaker 1: field that is trying to really get at this specificity question.

496
00:25:03,560 --> 00:25:06,760
Speaker 1: Now we're still early days. We don't have final answers yet,

497
00:25:06,920 --> 00:25:10,119
Speaker 1: but I think we have some tentitive ideas that you

498
00:25:10,240 --> 00:25:13,240
Speaker 1: can with specific proteins that are expressed in the synaps

499
00:25:13,520 --> 00:25:16,320
Speaker 1: essentially label them or prime them to be the ones

500
00:25:16,359 --> 00:25:18,840
Speaker 1: that are modified by this experience and maybe not another.

501
00:25:19,119 --> 00:25:20,880
Speaker 2: Wow, and that's pretty cool, right.

502
00:25:20,840 --> 00:25:22,880
Speaker 1: Yeah, because from the looment of the time, we thought, well,

503
00:25:22,880 --> 00:25:25,160
Speaker 1: how can we ever have any specificity in our brains

504
00:25:25,280 --> 00:25:27,640
Speaker 1: if memory just activates cells, how do you know which

505
00:25:27,640 --> 00:25:29,880
Speaker 1: cells are the ones that are involved here? And this

506
00:25:29,960 --> 00:25:31,640
Speaker 1: might actually re find us with some answers.

507
00:25:31,720 --> 00:25:34,720
Speaker 2: Wow. I feel like I recently heard about the connectome project,

508
00:25:34,720 --> 00:25:36,720
Speaker 2: which I think is trying to figure out all of

509
00:25:36,760 --> 00:25:39,239
Speaker 2: the neurons. So does this suggest that once we have

510
00:25:39,280 --> 00:25:42,400
Speaker 2: a connectome next we need to work on the proteome

511
00:25:42,520 --> 00:25:45,080
Speaker 2: that connects to the connectome to really understand how all

512
00:25:45,119 --> 00:25:47,720
Speaker 2: of this works, or how helpful is this connect dome

513
00:25:47,720 --> 00:25:48,400
Speaker 2: project going to.

514
00:25:48,320 --> 00:25:51,160
Speaker 1: Be very helpful? I think that every time we try

515
00:25:51,200 --> 00:25:54,040
Speaker 1: it and map another home it is very helpful. At

516
00:25:54,080 --> 00:25:56,959
Speaker 1: some point we all have an everything owned and you know,

517
00:25:57,040 --> 00:25:59,919
Speaker 1: with that sort of large scale data effort, we're going

518
00:25:59,920 --> 00:26:02,760
Speaker 1: to need also the AI, the machine learning, all of

519
00:26:02,800 --> 00:26:05,200
Speaker 1: those tools people to pass through it and actually figure

520
00:26:05,240 --> 00:26:07,800
Speaker 1: out what's going on. But it's interesting, you know, Kelly,

521
00:26:08,000 --> 00:26:10,080
Speaker 1: When I was coming up as a student, there was

522
00:26:10,119 --> 00:26:13,280
Speaker 1: always this question that was asked by faculty in my

523
00:26:13,400 --> 00:26:16,720
Speaker 1: department and many other departments, And it's a theoretical question,

524
00:26:16,840 --> 00:26:19,480
Speaker 1: and I want to pose that question to you, which is,

525
00:26:19,680 --> 00:26:22,679
Speaker 1: if we were to map every single neuron in the

526
00:26:22,680 --> 00:26:26,520
Speaker 1: brain and every single connection in the brain, would we

527
00:26:26,560 --> 00:26:30,199
Speaker 1: have learned anything about how the brain actually functions? And

528
00:26:30,320 --> 00:26:33,199
Speaker 1: whenever they asked that question, you know, some of us

529
00:26:33,200 --> 00:26:34,919
Speaker 1: were attempted to say, well, yeah, of course you can

530
00:26:35,040 --> 00:26:37,080
Speaker 1: have all the data, right, And the answer they wanted

531
00:26:37,160 --> 00:26:38,960
Speaker 1: us to get to is no, you're no better off

532
00:26:39,000 --> 00:26:41,440
Speaker 1: because you have a ton of data but no way

533
00:26:41,520 --> 00:26:45,240
Speaker 1: to really test hypotheses and understand function. You have to

534
00:26:45,280 --> 00:26:47,040
Speaker 1: have the right model, You have to have the right

535
00:26:47,119 --> 00:26:50,000
Speaker 1: kind of strategy to go into that data and look

536
00:26:50,040 --> 00:26:53,280
Speaker 1: for what's necessary. But I would argue that over the

537
00:26:53,359 --> 00:26:57,200
Speaker 1: last twenty thirty years that thinking has evolved and now

538
00:26:57,760 --> 00:27:01,200
Speaker 1: we can go in in a completely unsupervised without having

539
00:27:01,240 --> 00:27:03,800
Speaker 1: a model, which means also we avoid some of the

540
00:27:03,840 --> 00:27:06,840
Speaker 1: biases that might come from a model and ask what

541
00:27:06,880 --> 00:27:09,520
Speaker 1: does the data tell us? Sure, there is an explosion

542
00:27:09,520 --> 00:27:12,040
Speaker 1: of data, but there are patterns that are hidden in there,

543
00:27:12,280 --> 00:27:14,640
Speaker 1: and if you train up AI enough, it can pick

544
00:27:14,680 --> 00:27:17,480
Speaker 1: up on those patterns and maybe tell us that there's

545
00:27:17,520 --> 00:27:19,720
Speaker 1: a new model. There's a different model. The way that

546
00:27:19,720 --> 00:27:22,520
Speaker 1: we were thinking about the brain informing hypotheses may not

547
00:27:22,560 --> 00:27:25,119
Speaker 1: have been right all along. So I go back to

548
00:27:25,200 --> 00:27:28,120
Speaker 1: things like the connect home projects and trying to resolve

549
00:27:28,160 --> 00:27:31,880
Speaker 1: the connect home, the proteome, the epigenome, all of those

550
00:27:32,000 --> 00:27:36,000
Speaker 1: kinds of things as different layers of knowledge about the

551
00:27:36,000 --> 00:27:38,960
Speaker 1: nervous system. And if we have all of that information,

552
00:27:39,000 --> 00:27:41,399
Speaker 1: it stands to reason that we should be able to

553
00:27:41,440 --> 00:27:45,119
Speaker 1: pick up on patterns, and patterns can maybe transform the

554
00:27:45,160 --> 00:27:47,720
Speaker 1: way that we think about the brain. Maybe we're all

555
00:27:47,720 --> 00:27:51,800
Speaker 1: along maybe the brain does quantum computing. Maybe all sorts

556
00:27:51,840 --> 00:27:54,560
Speaker 1: of things that we just would have never imagined that

557
00:27:54,600 --> 00:27:56,040
Speaker 1: are buried in that data.

558
00:27:56,119 --> 00:27:57,919
Speaker 2: It sounds like an exciting time to be in the field.

559
00:27:58,119 --> 00:27:59,719
Speaker 1: Oh. Absolutely, absolutely.

560
00:28:16,080 --> 00:28:17,280
Speaker 2: I'm going to pull us back a little bit. In

561
00:28:17,320 --> 00:28:19,720
Speaker 2: our conversation. You were talking about how you can strengthen

562
00:28:19,760 --> 00:28:23,600
Speaker 2: connections between neurons. Does that happen because you're thinking of

563
00:28:23,640 --> 00:28:25,960
Speaker 2: the same memory over and over again? And so how

564
00:28:25,960 --> 00:28:27,919
Speaker 2: do memories get strengthened and how do we lose some

565
00:28:28,000 --> 00:28:28,439
Speaker 2: of them?

566
00:28:28,520 --> 00:28:32,280
Speaker 1: Yeah, So the strengthening of memory, or the idea of consolidation,

567
00:28:32,400 --> 00:28:34,680
Speaker 1: which is exactly what it sounds like. It's making memories

568
00:28:34,720 --> 00:28:38,320
Speaker 1: more solidified or more strengthened. It can happen because we

569
00:28:38,440 --> 00:28:41,840
Speaker 1: are repeatedly rehearsing the memory, so we're thinking about it

570
00:28:41,880 --> 00:28:44,240
Speaker 1: over and over. But the good news, Kelly, is that

571
00:28:44,240 --> 00:28:48,400
Speaker 1: that happens completely incidentally, without you intentionally trying to do it.

572
00:28:48,640 --> 00:28:51,400
Speaker 1: Your brain constantly brings up old memories and thinks about them,

573
00:28:51,480 --> 00:28:53,520
Speaker 1: and even if you're not consciously aware of it, it happens

574
00:28:53,560 --> 00:28:56,440
Speaker 1: while you sleep at night. Okay, so the brain is

575
00:28:56,440 --> 00:28:59,760
Speaker 1: sort of on the back burner, constantly playing through these memories,

576
00:29:00,080 --> 00:29:02,320
Speaker 1: replaying through these memories, and even when you go to sleep,

577
00:29:02,480 --> 00:29:05,000
Speaker 1: it's replaying through these memories. So the strengthening act of

578
00:29:05,080 --> 00:29:08,880
Speaker 1: memories doesn't have to be this intentional thing, which I

579
00:29:08,920 --> 00:29:11,080
Speaker 1: think is a really powerful thing to tell students. Also,

580
00:29:11,240 --> 00:29:12,960
Speaker 1: you don't have to sit here and like regurgitate and

581
00:29:13,040 --> 00:29:15,480
Speaker 1: rehearse everything over and over and over. Just go through

582
00:29:15,520 --> 00:29:18,560
Speaker 1: and understand and then get a good night's sleep, right,

583
00:29:18,720 --> 00:29:20,520
Speaker 1: which is a lot of them really struggle to do.

584
00:29:20,840 --> 00:29:24,080
Speaker 1: But during that period you might think, well, that's just rest.

585
00:29:24,400 --> 00:29:26,960
Speaker 1: Actually the brain can be quite active during that period

586
00:29:27,000 --> 00:29:29,080
Speaker 1: of time and can be playing through these memories and

587
00:29:29,120 --> 00:29:32,760
Speaker 1: storing them and trying to make them morsistant to forgetting now.

588
00:29:33,160 --> 00:29:37,000
Speaker 1: One thing to note also is that when you replay memories,

589
00:29:37,040 --> 00:29:40,560
Speaker 1: when you bring back memories, you don't end up with

590
00:29:40,680 --> 00:29:44,720
Speaker 1: the same thing being stored again. So memories are reconstructive

591
00:29:44,720 --> 00:29:47,000
Speaker 1: in nature. If I were to bring back an experience,

592
00:29:47,000 --> 00:29:48,920
Speaker 1: say that I have from a few weeks ago and

593
00:29:49,040 --> 00:29:51,680
Speaker 1: talk about it with you, what I end up storing

594
00:29:51,760 --> 00:29:55,640
Speaker 1: now and reinforcing is a somewhat altered version of that memory.

595
00:29:55,880 --> 00:29:58,800
Speaker 1: It's not the same thing, because memory is reconstructed on

596
00:29:58,880 --> 00:30:01,880
Speaker 1: piecing together the pieces. Other pieces just get incorporated because

597
00:30:01,880 --> 00:30:04,239
Speaker 1: we're having a conversation about it. And then later on

598
00:30:04,280 --> 00:30:07,160
Speaker 1: what I remember is some amalgamation of all of those

599
00:30:07,200 --> 00:30:09,640
Speaker 1: experiences when I brought it back and changed it ever

600
00:30:09,760 --> 00:30:12,600
Speaker 1: so slightly. My colleague here at you see, Aravin Beth

601
00:30:12,640 --> 00:30:16,000
Speaker 1: Loftus built her career on studying false memories and how

602
00:30:16,080 --> 00:30:19,280
Speaker 1: they arise, and they are very, very frequent. They arise

603
00:30:19,360 --> 00:30:21,960
Speaker 1: all the time. We generate the more as we get older,

604
00:30:22,160 --> 00:30:24,640
Speaker 1: and they happen just by virtue of our memory being

605
00:30:24,960 --> 00:30:29,920
Speaker 1: a reconstructive system rather than a high fidelity video camera

606
00:30:30,240 --> 00:30:33,520
Speaker 1: or a picture of reality. It's just a sort of

607
00:30:33,520 --> 00:30:36,200
Speaker 1: a Hodgepodg construction of what that reality might have been.

608
00:30:36,360 --> 00:30:39,520
Speaker 1: And again we say, why is this happening? Why can't

609
00:30:39,560 --> 00:30:42,120
Speaker 1: we have a high fidelity version of things? And it's

610
00:30:42,160 --> 00:30:44,960
Speaker 1: possible that we just don't need to. So even with

611
00:30:45,040 --> 00:30:48,400
Speaker 1: these false memories arising, they're very artifactual, like you really

612
00:30:48,400 --> 00:30:50,760
Speaker 1: need to remember exactly what happened and where it happened,

613
00:30:50,800 --> 00:30:52,560
Speaker 1: that when it happened, Ergie, you just need to remember

614
00:30:52,600 --> 00:30:55,440
Speaker 1: the core knowledge. So if we focus on, hey, the

615
00:30:55,480 --> 00:30:59,040
Speaker 1: corenology is being remembered accurately. That's all that matters. Everything

616
00:30:59,080 --> 00:31:01,840
Speaker 1: else and go to crap, and nobody's going to be

617
00:31:01,920 --> 00:31:04,560
Speaker 1: less able to survive. So from a survival standpoint, it

618
00:31:04,600 --> 00:31:07,720
Speaker 1: certainly doesn't matter that you have this strengthening, be a

619
00:31:07,800 --> 00:31:10,440
Speaker 1: very high fidelity strengthening. It just matters that the core

620
00:31:10,480 --> 00:31:13,480
Speaker 1: component knowledge is the thing that strengthened, Like the capital

621
00:31:13,520 --> 00:31:16,480
Speaker 1: of the United States, Washington, everything else. Who cares.

622
00:31:16,880 --> 00:31:19,880
Speaker 2: But if the lawyer is you priding you for details

623
00:31:19,880 --> 00:31:22,640
Speaker 2: in a court case, that's when you're in some trouble.

624
00:31:22,880 --> 00:31:26,000
Speaker 1: Absolutely, and you know the good news is, well, the

625
00:31:26,160 --> 00:31:29,640
Speaker 1: somewhare good news is some lawyers, some federal judges, some

626
00:31:29,800 --> 00:31:35,040
Speaker 1: jury get the lecture about false memories and understand that

627
00:31:35,120 --> 00:31:37,000
Speaker 1: when you call witnesses to to stand and you're asking

628
00:31:37,080 --> 00:31:40,160
Speaker 1: very very specific questions, that their right collection is going

629
00:31:40,200 --> 00:31:42,880
Speaker 1: to be some combination of what actually happens, what the

630
00:31:42,960 --> 00:31:45,600
Speaker 1: brain sort of reconstructed it to be, what kinds of

631
00:31:45,680 --> 00:31:49,680
Speaker 1: questions are being asked, the pressure, any interrogation that happened earlier,

632
00:31:49,760 --> 00:31:52,800
Speaker 1: All of those things sort of weave their way into

633
00:31:52,840 --> 00:31:54,800
Speaker 1: that memory. You're never going to be able to get

634
00:31:54,800 --> 00:31:58,719
Speaker 1: this beautiful, accurate, one hundred percent depiction of what happens,

635
00:31:58,760 --> 00:32:01,400
Speaker 1: you're going to get some very of it that could

636
00:32:01,400 --> 00:32:03,000
Speaker 1: be quite a bit more corrupted.

637
00:32:03,240 --> 00:32:05,120
Speaker 2: Gosh, there could be a whole podcast on that topic.

638
00:32:05,760 --> 00:32:09,960
Speaker 2: So my co host wanted me to dig into how

639
00:32:10,000 --> 00:32:12,040
Speaker 2: we learn about this kind of stuff. So you mentioned

640
00:32:12,040 --> 00:32:15,200
Speaker 2: that there are animal models, but just focusing on people

641
00:32:15,280 --> 00:32:19,840
Speaker 2: right now, how good are our techniques for watching how

642
00:32:19,880 --> 00:32:22,240
Speaker 2: the brain works in living humans to sort of try

643
00:32:22,280 --> 00:32:23,560
Speaker 2: to get a handle on some of this stuff.

644
00:32:23,760 --> 00:32:27,840
Speaker 1: Yeah, So when we started out, the discipline of neuropsychology

645
00:32:28,000 --> 00:32:31,280
Speaker 1: had very, very poor tools available to it. So you

646
00:32:31,400 --> 00:32:34,400
Speaker 1: have to develop cognitive assessments, which have come a long way.

647
00:32:34,480 --> 00:32:36,480
Speaker 1: You can have the right kinds of cognitive assessments and

648
00:32:36,520 --> 00:32:38,720
Speaker 1: so on, but you really have to work with patients

649
00:32:38,720 --> 00:32:42,120
Speaker 1: and the clinic who come in presenting with memory problems,

650
00:32:42,200 --> 00:32:45,480
Speaker 1: with a variety of different conditions, and essentially it's akin

651
00:32:45,560 --> 00:32:49,239
Speaker 1: to leision studies. You're working with patients who might have

652
00:32:49,640 --> 00:32:52,000
Speaker 1: circumscribed focal deficit in the brain. You can see that

653
00:32:52,040 --> 00:32:54,760
Speaker 1: on structural MRI for example, and say this part of

654
00:32:54,760 --> 00:32:57,400
Speaker 1: the brain is damaged or missing. Therefore they have this

655
00:32:57,560 --> 00:32:59,760
Speaker 1: kind of function, So you might be able to say

656
00:32:59,800 --> 00:33:01,760
Speaker 1: something about the function of this part of the brain

657
00:33:01,800 --> 00:33:06,400
Speaker 1: in a healthy person. But our tools have evolved significantly since,

658
00:33:06,600 --> 00:33:09,640
Speaker 1: so two major advances that I can tell you are

659
00:33:09,680 --> 00:33:12,520
Speaker 1: still today. The chief ways by which we study this.

660
00:33:12,960 --> 00:33:14,880
Speaker 1: One is functional MRI, and we tend to do a

661
00:33:14,960 --> 00:33:16,680
Speaker 1: heck of a lot of that in my lab. So

662
00:33:16,840 --> 00:33:19,560
Speaker 1: functional MRI operates on the principle that you can put

663
00:33:19,600 --> 00:33:22,960
Speaker 1: somebody in the scanner totally intact brain and give them

664
00:33:23,040 --> 00:33:25,840
Speaker 1: a game to play, or a memory task, or any

665
00:33:25,840 --> 00:33:28,920
Speaker 1: sort of challenge that would engage the memory bits of

666
00:33:29,000 --> 00:33:32,200
Speaker 1: their brain, for example. And what you're imaging with functional

667
00:33:32,280 --> 00:33:35,920
Speaker 1: MRI is not neural activity directly. What you're imaging is

668
00:33:35,960 --> 00:33:39,840
Speaker 1: blood flow. The idea being that if there's a patch

669
00:33:39,920 --> 00:33:44,160
Speaker 1: of cortex patchup brain that is more active, that is

670
00:33:44,280 --> 00:33:47,840
Speaker 1: engaged in this challenge, it's going to require oxygen and

671
00:33:47,880 --> 00:33:51,360
Speaker 1: glucose and it's going to try to extract that out

672
00:33:51,360 --> 00:33:55,880
Speaker 1: of the blood flow. So by mapping how much oxygenated

673
00:33:55,960 --> 00:33:59,760
Speaker 1: blood and deoxygenated blood are going to different areas in

674
00:33:59,800 --> 00:34:03,040
Speaker 1: the brain, you can generate a contrast because it turns

675
00:34:03,040 --> 00:34:06,080
Speaker 1: out that the degree of oxygenation has a different magnetic signal.

676
00:34:06,320 --> 00:34:07,880
Speaker 1: So that's sort of a little hack that we pull

677
00:34:07,920 --> 00:34:11,600
Speaker 1: in MRI because we're changing ragnetic fields. So by measuring

678
00:34:11,640 --> 00:34:15,640
Speaker 1: that contrast, we can get an indirect proxy to where

679
00:34:15,719 --> 00:34:19,440
Speaker 1: neural activity might be by virtue of that blood flow change.

680
00:34:19,880 --> 00:34:22,400
Speaker 1: So that's been a really, really helpful technique since the

681
00:34:22,560 --> 00:34:25,480
Speaker 1: late nineties early two thousands. In the early days of

682
00:34:25,480 --> 00:34:28,319
Speaker 1: functional MRI, people did a bunch of like really just

683
00:34:28,440 --> 00:34:31,560
Speaker 1: awful studies because the technology was new and we didn't

684
00:34:31,560 --> 00:34:33,800
Speaker 1: know what to do with it, and folks didn't really

685
00:34:34,320 --> 00:34:36,839
Speaker 1: think beyond you know, the X marks the spot kind

686
00:34:36,840 --> 00:34:39,680
Speaker 1: of approach, Right, I want to know what the fill

687
00:34:39,719 --> 00:34:42,080
Speaker 1: in the blank part of the brain is. It even

688
00:34:42,120 --> 00:34:43,799
Speaker 1: got as absurd as I want to know what the

689
00:34:43,840 --> 00:34:46,200
Speaker 1: god part of the brain is. Right, So people start

690
00:34:46,239 --> 00:34:48,080
Speaker 1: to do those kinds of studies, try to go in

691
00:34:48,160 --> 00:34:50,800
Speaker 1: and say, X marks the spot, where's the stuff happening?

692
00:34:51,160 --> 00:34:54,919
Speaker 2: Is this the same system where they had that dead trout? Oh?

693
00:34:55,000 --> 00:34:58,080
Speaker 1: Yeah, you know that that trout study. Of course. So

694
00:34:58,239 --> 00:34:59,960
Speaker 1: at the end of the day, it is a statistic

695
00:35:00,160 --> 00:35:04,840
Speaker 1: approach to comparing activation, and yeah, that study is really

696
00:35:04,840 --> 00:35:07,480
Speaker 1: compelling because you could show that you see activation essentially

697
00:35:07,520 --> 00:35:10,920
Speaker 1: in something that is dead, and people every now and

698
00:35:10,960 --> 00:35:12,600
Speaker 1: then will kind of make fun of this, and remember

699
00:35:12,680 --> 00:35:15,080
Speaker 1: the old days of fMRI, when folks didn't really know

700
00:35:15,120 --> 00:35:16,920
Speaker 1: what they were doing, and you could get something like

701
00:35:16,960 --> 00:35:19,480
Speaker 1: this right, and in some cases you can even get

702
00:35:19,480 --> 00:35:21,520
Speaker 1: it to be published, which is crazy when you think

703
00:35:21,560 --> 00:35:25,400
Speaker 1: about it. But we've come a long way since. So

704
00:35:25,480 --> 00:35:28,160
Speaker 1: the beauty of functional MRI now is that one we

705
00:35:28,320 --> 00:35:30,359
Speaker 1: understand how to do the X marks the spot much

706
00:35:30,440 --> 00:35:32,239
Speaker 1: much better. We now have much better handle on this

707
00:35:32,440 --> 00:35:35,279
Speaker 1: historical challenges, the way to build the right contrasts, the

708
00:35:35,320 --> 00:35:37,800
Speaker 1: way to correct for multiple comparisons, all the things that

709
00:35:37,840 --> 00:35:40,880
Speaker 1: you tend to think of when you're doing large scale statistics.

710
00:35:41,600 --> 00:35:44,360
Speaker 1: That discipline had to kind of come to functional imaging

711
00:35:44,560 --> 00:35:46,920
Speaker 1: and inform it and that has happened, which is great.

712
00:35:47,640 --> 00:35:49,440
Speaker 1: But the second part is I told you before that

713
00:35:49,520 --> 00:35:52,719
Speaker 1: memory is all about the connections, and functional MRI initially

714
00:35:53,040 --> 00:35:56,400
Speaker 1: was all about blobbology, right, try to find little hotspots

715
00:35:56,400 --> 00:35:58,960
Speaker 1: in the brain. You pretty pictures the cover of science

716
00:35:58,960 --> 00:36:01,960
Speaker 1: and all that with little hotspots in the brain, and

717
00:36:02,040 --> 00:36:04,440
Speaker 1: that was the approach. But we know that memory is

718
00:36:04,440 --> 00:36:07,239
Speaker 1: not in the hotspots, memories and the connections, so we've

719
00:36:07,239 --> 00:36:11,360
Speaker 1: started to move much more towards connectivity analysis and asking

720
00:36:11,400 --> 00:36:14,759
Speaker 1: about how are the different parts of the brain communicating

721
00:36:14,840 --> 00:36:19,960
Speaker 1: dynamically and essentially coactivating with each other to support solving

722
00:36:19,960 --> 00:36:22,799
Speaker 1: this challenge or doing this memory test or memory game

723
00:36:22,880 --> 00:36:25,720
Speaker 1: while you're in the scanner. So that was the advent

724
00:36:25,719 --> 00:36:29,040
Speaker 1: of functional connectivity kinds of approaches which are I think

725
00:36:29,120 --> 00:36:32,319
Speaker 1: far more compelling, far more robust against some of the

726
00:36:32,360 --> 00:36:36,160
Speaker 1: initial critiques of a functional MRI, and they reflect the

727
00:36:36,200 --> 00:36:38,040
Speaker 1: true nature of how the brain works. The brain is

728
00:36:38,080 --> 00:36:41,640
Speaker 1: one big dynamical system. It's not just regions working in isolation.

729
00:36:41,960 --> 00:36:44,880
Speaker 1: Everything is connecting with each other, so we owe it

730
00:36:44,920 --> 00:36:47,239
Speaker 1: to ourselves to try to understand it from that much

731
00:36:47,239 --> 00:36:51,319
Speaker 1: more complex way. So functional MRI still remains a very

732
00:36:51,400 --> 00:36:54,000
Speaker 1: very powerful tool, but now the analysis that we can

733
00:36:54,040 --> 00:36:56,759
Speaker 1: do are just far more advanced. The other thing that

734
00:36:56,880 --> 00:37:00,640
Speaker 1: I think is just incredibly powerful, aside from anim models,

735
00:37:00,880 --> 00:37:05,040
Speaker 1: is the ability to directly record electrical activity from cells

736
00:37:05,640 --> 00:37:08,279
Speaker 1: or from what it's called local field potentials that the

737
00:37:08,360 --> 00:37:11,440
Speaker 1: areas around cells that have also electrical activity. It can

738
00:37:11,480 --> 00:37:16,120
Speaker 1: be measured directly in patients, and these are typically patients

739
00:37:16,160 --> 00:37:19,839
Speaker 1: that are going to undergo surgery for epileptic seizures. So

740
00:37:19,920 --> 00:37:22,759
Speaker 1: the surgery is done to remove the part of the

741
00:37:22,760 --> 00:37:26,160
Speaker 1: brain where the seizures are emanating from. And typically when

742
00:37:26,160 --> 00:37:28,120
Speaker 1: they come into a hospital, they're in a hospital for

743
00:37:28,120 --> 00:37:31,120
Speaker 1: about a week or so. They get implanted with electrodes

744
00:37:31,160 --> 00:37:33,719
Speaker 1: to record from the parts of the brain where the

745
00:37:33,760 --> 00:37:38,040
Speaker 1: clinician might suspect that epilepsy is happening, and they're taken

746
00:37:38,120 --> 00:37:41,400
Speaker 1: off of anti epileptic medication and essentially they're waiting to

747
00:37:41,480 --> 00:37:44,520
Speaker 1: induce a seizure, and once that seizure is induced, they

748
00:37:44,560 --> 00:37:47,160
Speaker 1: attract the location. That's how they decide on a way

749
00:37:47,200 --> 00:37:50,319
Speaker 1: to do the surgery. There's about a week or so

750
00:37:50,400 --> 00:37:53,879
Speaker 1: while they're in the hospital with electrodes penetrating deep into

751
00:37:53,920 --> 00:37:56,719
Speaker 1: their cortex and many of those electrodes directly into the

752
00:37:56,760 --> 00:37:59,759
Speaker 1: memory bits of the brain, like the hippocampus. And those

753
00:38:00,360 --> 00:38:04,640
Speaker 1: are just an incredible group of individuals because they also

754
00:38:04,880 --> 00:38:07,120
Speaker 1: most of them want to help science and they understand

755
00:38:07,120 --> 00:38:10,239
Speaker 1: the opportunity the scientists have while they're laying in a

756
00:38:10,239 --> 00:38:13,560
Speaker 1: hospital bed for about a week to understand something fundamental

757
00:38:13,600 --> 00:38:16,319
Speaker 1: about the brain. So every now and then they give

758
00:38:16,400 --> 00:38:18,680
Speaker 1: us the opportunity to give them a challenge, maybe on

759
00:38:18,719 --> 00:38:21,239
Speaker 1: an iPad or a computer while they're laying there and

760
00:38:21,440 --> 00:38:24,080
Speaker 1: they try to solve this challenge, try to play this

761
00:38:24,160 --> 00:38:27,000
Speaker 1: memory game or do this memory test while we're recording

762
00:38:27,120 --> 00:38:30,839
Speaker 1: direct electrical activity from their brain cells, which is incredible.

763
00:38:31,239 --> 00:38:34,560
Speaker 1: So it gives us almost the same degree of information

764
00:38:34,600 --> 00:38:36,480
Speaker 1: that you can get in an animal model. Now in

765
00:38:36,520 --> 00:38:38,040
Speaker 1: an animal model, in a road that you can stick

766
00:38:38,080 --> 00:38:41,400
Speaker 1: more electrones, you can get higher fidelity. And with patients

767
00:38:41,400 --> 00:38:43,720
Speaker 1: you have to do things that are only clinically warranted.

768
00:38:43,760 --> 00:38:46,160
Speaker 1: So there's an ethical obligation, of course to make sure

769
00:38:46,200 --> 00:38:48,920
Speaker 1: that nothing is being done that would ever put the

770
00:38:48,960 --> 00:38:53,120
Speaker 1: patient an increase risk. So that also poses some limitations

771
00:38:53,120 --> 00:38:55,920
Speaker 1: as to how you can record activity and get that data.

772
00:38:56,040 --> 00:38:58,640
Speaker 1: But it's just incredible access that we have to the

773
00:38:58,719 --> 00:39:02,520
Speaker 1: brain in partnership with these remarkable individuals, and we've learned

774
00:39:02,520 --> 00:39:04,200
Speaker 1: a heck of a lot about how the brain works

775
00:39:04,200 --> 00:39:07,120
Speaker 1: and how memory works from those direct electrical recordings.

776
00:39:07,719 --> 00:39:11,040
Speaker 2: Wow. I previously wrote a chapter on brain computer interfaces

777
00:39:11,080 --> 00:39:14,560
Speaker 2: and I was reading about like, utah arrays. Is this

778
00:39:14,719 --> 00:39:17,000
Speaker 2: the same thing or is this a different kind of electrode.

779
00:39:17,080 --> 00:39:20,239
Speaker 1: Yes, So, utah arrays are one way to do it.

780
00:39:20,680 --> 00:39:24,640
Speaker 1: Utah arrays are a little bit more invasive. They involve

781
00:39:24,800 --> 00:39:27,320
Speaker 1: several electrodes that are kind of going through the surface

782
00:39:27,400 --> 00:39:30,040
Speaker 1: of the cortex. At the same time, they're no longer

783
00:39:30,160 --> 00:39:33,160
Speaker 1: kind of the standard practice for most patients. They're still

784
00:39:33,280 --> 00:39:35,960
Speaker 1: used in some cases where they're clinically warranted, but in

785
00:39:36,000 --> 00:39:39,680
Speaker 1: many cases they're not because you suspect that what's happening

786
00:39:39,760 --> 00:39:42,800
Speaker 1: is deep into the brain, so you stick direct single

787
00:39:42,840 --> 00:39:45,600
Speaker 1: electrodes all the way down to where you suspect the

788
00:39:45,680 --> 00:39:48,440
Speaker 1: action might be, and you avoid some of the potential

789
00:39:48,520 --> 00:39:50,920
Speaker 1: damage that happens with UTAH rays. So they're used in

790
00:39:50,960 --> 00:39:54,760
Speaker 1: some clinical contexts, but in many others, we can stick

791
00:39:54,840 --> 00:39:58,719
Speaker 1: these much thinner, slimmer electrodes directly into the parts of

792
00:39:58,760 --> 00:40:01,440
Speaker 1: the brain that we suspect yet epilepsy is ennything from

793
00:40:01,719 --> 00:40:04,720
Speaker 1: so far less damage that way, and those patients typically

794
00:40:04,719 --> 00:40:08,120
Speaker 1: have better outcomes than patients implanted with guitar rays. Now,

795
00:40:08,200 --> 00:40:11,280
Speaker 1: your point about BCIs, and there's a number of companies

796
00:40:11,320 --> 00:40:14,160
Speaker 1: out there that are trying to develop brain computer interfaces

797
00:40:14,239 --> 00:40:16,960
Speaker 1: using these kinds of arrays. I think that's a particular

798
00:40:17,040 --> 00:40:20,560
Speaker 1: challenge for those enterprises is how do you create a

799
00:40:20,600 --> 00:40:23,759
Speaker 1: way to measure directly from the brain and to be

800
00:40:23,800 --> 00:40:26,880
Speaker 1: able to stimulate and influence the brain without causing too

801
00:40:26,960 --> 00:40:30,359
Speaker 1: much damage. Having electrones that are thin enough, that are

802
00:40:30,640 --> 00:40:33,080
Speaker 1: made from the right material so that you don't cause

803
00:40:33,120 --> 00:40:35,600
Speaker 1: a lot of tissue damage, because ideally, what you want

804
00:40:35,600 --> 00:40:37,400
Speaker 1: to do is create an interface that helps people, so

805
00:40:37,440 --> 00:40:40,200
Speaker 1: you don't want to inadvertently cause more damage.

806
00:40:40,400 --> 00:40:42,960
Speaker 2: Now, when we were thinking about UTAH rays and damage,

807
00:40:43,000 --> 00:40:45,359
Speaker 2: you know, we thought about like a cup with jello

808
00:40:45,480 --> 00:40:48,279
Speaker 2: in it, and you stick some needles in there, and

809
00:40:48,360 --> 00:40:50,400
Speaker 2: as you move the jello around, if the needles are

810
00:40:50,440 --> 00:40:52,120
Speaker 2: kind of staying in place, that would sort of mess

811
00:40:52,200 --> 00:40:53,600
Speaker 2: up the brain. Is that a good way to think

812
00:40:53,640 --> 00:40:55,080
Speaker 2: about it? Does it all move together?

813
00:40:55,520 --> 00:40:59,200
Speaker 1: Yeah? The brain certainly is as vulnerable maybe as a

814
00:40:59,239 --> 00:41:02,960
Speaker 1: cup of gello. But the key is also flexibility. So

815
00:41:02,960 --> 00:41:05,520
Speaker 1: you're right. When you have these electrodes, there's a bit

816
00:41:05,560 --> 00:41:08,759
Speaker 1: of a compromise. So want them to be flexible so

817
00:41:08,800 --> 00:41:10,719
Speaker 1: that they're moving with their brain. You're absolutely right, and

818
00:41:10,760 --> 00:41:13,439
Speaker 1: that'll cause less tissue damage. But at the same time,

819
00:41:13,520 --> 00:41:16,000
Speaker 1: flexibility you can come at a cost, which is what

820
00:41:16,120 --> 00:41:19,200
Speaker 1: they're targeting, might change. So you want to make them

821
00:41:19,200 --> 00:41:21,720
Speaker 1: flexible enough sort of they don't cause damage, but rigid

822
00:41:21,800 --> 00:41:23,960
Speaker 1: enough so that they can continue to target the same region,

823
00:41:24,800 --> 00:41:27,359
Speaker 1: so it is not an easy challenge at all. But

824
00:41:27,560 --> 00:41:30,520
Speaker 1: there's been some developments recently in doing these kinds of

825
00:41:30,600 --> 00:41:33,839
Speaker 1: arrays with animals, and we haven't yet pourted that over

826
00:41:33,920 --> 00:41:36,080
Speaker 1: to humans and done the FD approval and all of

827
00:41:36,080 --> 00:41:39,280
Speaker 1: those kinds of things. It's happening soon. There's already experiments

828
00:41:39,280 --> 00:41:41,840
Speaker 1: that try to test out one of the technologies like

829
00:41:41,920 --> 00:41:45,959
Speaker 1: neuropixel for example, or near epixels technologies. Those have been

830
00:41:46,400 --> 00:41:49,799
Speaker 1: incredibly powerful for animal models for reading from road insight

831
00:41:49,840 --> 00:41:53,400
Speaker 1: from non human primates and their small form factor. They're thinner,

832
00:41:53,400 --> 00:41:55,720
Speaker 1: but they have a ton of electrode contacts on there,

833
00:41:56,040 --> 00:41:58,200
Speaker 1: so it can really give you information from a lot

834
00:41:58,239 --> 00:42:02,480
Speaker 1: of different sounds simultaneou sleep. And pointing that over to

835
00:42:02,560 --> 00:42:04,840
Speaker 1: humans I think will be a really helpful thing to do.

836
00:42:05,160 --> 00:42:07,360
Speaker 1: But that's only been done in some limited experiments and

837
00:42:07,440 --> 00:42:10,200
Speaker 1: not widespread. You so hoping that some variants of those

838
00:42:10,280 --> 00:42:12,600
Speaker 1: kinds of technologies will make us way to primetime soon.

839
00:42:12,840 --> 00:42:16,160
Speaker 2: I've watched some videos of people with brain computer interfaces

840
00:42:16,200 --> 00:42:18,040
Speaker 2: that were able to do incredible things, But one of

841
00:42:18,080 --> 00:42:20,560
Speaker 2: the things that sounded totally devastating to me was if

842
00:42:20,600 --> 00:42:23,719
Speaker 2: I understand this correctly, it's over time, the brain has

843
00:42:23,719 --> 00:42:26,600
Speaker 2: a response to those electrodes and like kind of walls

844
00:42:26,600 --> 00:42:28,520
Speaker 2: them off and the connection gets less good. I don't

845
00:42:28,560 --> 00:42:31,120
Speaker 2: know exactly what's happening. Do we have any progress in

846
00:42:31,120 --> 00:42:31,560
Speaker 2: that area.

847
00:42:32,080 --> 00:42:34,360
Speaker 1: Well, so that's another thing that needs to be tackled. Also,

848
00:42:34,440 --> 00:42:37,840
Speaker 1: with some of these newer silicon probes, they're less likely

849
00:42:37,920 --> 00:42:41,800
Speaker 1: to have the inflammatory and the calcification kinds of responses

850
00:42:41,800 --> 00:42:44,080
Speaker 1: that happened around electrodes, because remember, this is a foreign

851
00:42:44,160 --> 00:42:47,840
Speaker 1: object entering the brain, and the brain's natural disposition towards

852
00:42:47,880 --> 00:42:50,840
Speaker 1: foreign objects is attack it, right. That's why we have

853
00:42:51,280 --> 00:42:54,319
Speaker 1: brains immune cells, we have microglia, we have a lot

854
00:42:54,320 --> 00:42:58,120
Speaker 1: of cells that are dedicated to detecting and eliminating foreign objects.

855
00:42:58,680 --> 00:43:02,000
Speaker 1: So you tend to see them of aggregate around electrodic

856
00:43:02,000 --> 00:43:05,760
Speaker 1: contact locations and things like that. But there are ways

857
00:43:05,800 --> 00:43:08,399
Speaker 1: with different substances to kind of maybe fool the brain

858
00:43:08,480 --> 00:43:10,880
Speaker 1: a little bit into thinking this is okay. You can

859
00:43:10,920 --> 00:43:13,040
Speaker 1: try to also reduce the brains immune response to some

860
00:43:13,120 --> 00:43:16,120
Speaker 1: extent when these things are coming in. So there's approaches

861
00:43:16,120 --> 00:43:18,319
Speaker 1: that are being developed to try to get better long

862
00:43:18,400 --> 00:43:21,120
Speaker 1: term outcomes, but yet we're still very early.

863
00:43:20,920 --> 00:43:22,799
Speaker 2: In this game, And for listeners who are maybe not

864
00:43:22,880 --> 00:43:25,640
Speaker 2: es familiar with brain computer interfaces, what are some reasons

865
00:43:25,680 --> 00:43:28,160
Speaker 2: that people might get a brain computer interface.

866
00:43:28,320 --> 00:43:31,360
Speaker 1: There's a variety of reasons. So, for example, for someone

867
00:43:31,400 --> 00:43:34,520
Speaker 1: who has lost the ability to control their limbs because

868
00:43:34,560 --> 00:43:37,640
Speaker 1: of a stroke or a focal deficit, being able to

869
00:43:37,680 --> 00:43:40,680
Speaker 1: have a brain computer interface shortcut signals so that they

870
00:43:40,680 --> 00:43:43,960
Speaker 1: can still control their limbs and their body is remarkable.

871
00:43:44,000 --> 00:43:46,840
Speaker 1: And patients who have had those kinds of approaches, it

872
00:43:46,920 --> 00:43:49,840
Speaker 1: is just life changing. They go from a paraplegic or

873
00:43:49,880 --> 00:43:52,160
Speaker 1: quadriplegic to being able to have use of their arms

874
00:43:52,239 --> 00:43:56,240
Speaker 1: or their legs again. So there's incredibly utility there. For

875
00:43:56,440 --> 00:44:00,880
Speaker 1: folks who might have epilepsy. For example, there is a

876
00:44:00,920 --> 00:44:05,279
Speaker 1: brain computer interface that is a stimulator that is implanted

877
00:44:05,280 --> 00:44:07,920
Speaker 1: in the brain that responds to the earliest science of

878
00:44:07,920 --> 00:44:11,480
Speaker 1: epileptic seizures and that is able to with electrical stimulation

879
00:44:11,600 --> 00:44:14,600
Speaker 1: essentially knock it out. So now instead of having to

880
00:44:14,680 --> 00:44:17,280
Speaker 1: have the person be going in for surgery and lomping

881
00:44:17,360 --> 00:44:19,440
Speaker 1: up parts of the brain or having them be devastated

882
00:44:19,480 --> 00:44:23,359
Speaker 1: by epileptic seizures, you can have an implanted BCI that

883
00:44:23,520 --> 00:44:26,400
Speaker 1: responds in a closed loop system, so it uses the

884
00:44:26,400 --> 00:44:29,279
Speaker 1: responses of the brain itself to tell the stimulator what

885
00:44:29,360 --> 00:44:32,000
Speaker 1: to do. And that's a completely closed loop, so it

886
00:44:32,040 --> 00:44:35,680
Speaker 1: doesn't require any user interventionally outside. That allows it to

887
00:44:35,719 --> 00:44:38,960
Speaker 1: do a much better job of helping the patients overcome

888
00:44:39,600 --> 00:44:43,080
Speaker 1: seizures or epilepsy. So there's a number of different uses.

889
00:44:43,160 --> 00:44:46,240
Speaker 1: I can imagine that for movement disorders, for a variety

890
00:44:46,280 --> 00:44:49,200
Speaker 1: of different conditions where you might want to implant something

891
00:44:49,280 --> 00:44:51,200
Speaker 1: that communicates with the brain and feeds at the right

892
00:44:51,239 --> 00:44:53,600
Speaker 1: signal at the right time, there's going to be a

893
00:44:53,719 --> 00:44:57,080
Speaker 1: huge use for a BCI. Then there's a whole other

894
00:44:57,160 --> 00:45:02,040
Speaker 1: class of uses that may not work vire implantation. Right,

895
00:45:02,120 --> 00:45:05,880
Speaker 1: So these external devices maybe devices that are communicating with

896
00:45:05,920 --> 00:45:10,600
Speaker 1: the brain and external fashion portable or multiple, and they

897
00:45:10,640 --> 00:45:14,960
Speaker 1: allow it to improve function for stroke rahabilitation, or improve

898
00:45:15,000 --> 00:45:17,480
Speaker 1: function in some other way. There's a lot of folks

899
00:45:17,520 --> 00:45:20,520
Speaker 1: also kind of you know, taking the transhumanist approach here

900
00:45:20,520 --> 00:45:22,760
Speaker 1: and trying to develop PCIs to just improve our function.

901
00:45:22,960 --> 00:45:25,319
Speaker 1: We just want to be better at something, right, So

902
00:45:25,360 --> 00:45:27,239
Speaker 1: what if I can control this robotic arm to do

903
00:45:27,320 --> 00:45:29,920
Speaker 1: some you know, whatever it is. So don't care too

904
00:45:30,000 --> 00:45:33,800
Speaker 1: much about those but certainly the utility for helping patients

905
00:45:33,880 --> 00:45:34,600
Speaker 1: is huge.

906
00:45:34,920 --> 00:45:37,200
Speaker 2: So getting a little more sci fi here, do you

907
00:45:37,239 --> 00:45:40,080
Speaker 2: think we'll ever be able to know what somebody is

908
00:45:40,120 --> 00:45:42,839
Speaker 2: thinking by having, you know, a cap on their head

909
00:45:42,920 --> 00:45:45,600
Speaker 2: or electrodes in their brain or is that just way

910
00:45:45,640 --> 00:45:46,279
Speaker 2: too far off?

911
00:45:46,520 --> 00:45:49,600
Speaker 1: I think we already do so. I think we are

912
00:45:49,719 --> 00:45:53,799
Speaker 1: to some extent with an electrocapsule. Egen is really the

913
00:45:53,840 --> 00:45:56,640
Speaker 1: technology that you're talking about, or there's other ways to

914
00:45:56,640 --> 00:46:00,480
Speaker 1: do it also, various ultrasound technologies and so on. You

915
00:46:00,520 --> 00:46:04,440
Speaker 1: can detect and stimulate pretty easily, but the problem is

916
00:46:04,560 --> 00:46:06,600
Speaker 1: the kinds of things that you can get the system

917
00:46:06,640 --> 00:46:09,360
Speaker 1: to do are still fairly rudimentary. So I can tell,

918
00:46:09,440 --> 00:46:12,600
Speaker 1: for example, based on eg signal whether the person is

919
00:46:12,640 --> 00:46:16,279
Speaker 1: going to move their hand or make some overt kind

920
00:46:16,320 --> 00:46:20,040
Speaker 1: of gesture, and motor control is a somewhat simpler system

921
00:46:20,080 --> 00:46:25,200
Speaker 1: than like memory, executive decision, or emotion or having very

922
00:46:25,200 --> 00:46:29,120
Speaker 1: complex feelings like guilt, right or things like that are

923
00:46:29,719 --> 00:46:33,160
Speaker 1: much more difficult to capture in the simple signals that

924
00:46:33,160 --> 00:46:36,640
Speaker 1: we're capturing. The BCIs right now. So do I anticipate

925
00:46:36,640 --> 00:46:38,960
Speaker 1: that at one point we'll be able to do one percent?

926
00:46:39,120 --> 00:46:42,000
Speaker 1: There's no doubt the technologies there. It's just a matter

927
00:46:42,080 --> 00:46:44,799
Speaker 1: of are we recording from enough are we able to

928
00:46:44,840 --> 00:46:48,399
Speaker 1: build the sophisticated models to model this function, and we're

929
00:46:48,400 --> 00:46:51,759
Speaker 1: making rapid progress in that arena. So from a sci

930
00:46:51,760 --> 00:46:55,400
Speaker 1: fi perspective, I'm sure you've heard this before. The moving vehicle,

931
00:46:55,480 --> 00:46:57,680
Speaker 1: the car was sci fi at one point until someone

932
00:46:57,680 --> 00:47:00,600
Speaker 1: invented it, right, So the things that we think about

933
00:47:00,600 --> 00:47:02,799
Speaker 1: a sci fi are just science that's not here yet.

934
00:47:02,880 --> 00:47:21,480
Speaker 2: All right, Yeah, the future is now. So let's talk

935
00:47:21,520 --> 00:47:24,279
Speaker 2: about losing memory a little bit as we wrap things up.

936
00:47:24,640 --> 00:47:27,440
Speaker 2: So we talked about how your brain is working overnight

937
00:47:27,480 --> 00:47:31,080
Speaker 2: to strengthen memories. How do we lose memories over time?

938
00:47:31,440 --> 00:47:33,160
Speaker 1: So there's a number of different ways to do this.

939
00:47:33,680 --> 00:47:37,040
Speaker 1: Memory can be lost because of decay, so just the

940
00:47:37,080 --> 00:47:40,200
Speaker 1: passage of time a lot of times can make memories

941
00:47:40,280 --> 00:47:43,480
Speaker 1: just harder to remember, harder to access. And we've known

942
00:47:43,520 --> 00:47:47,360
Speaker 1: this since eighteen eighties. Herman Ebbinghouse was the first to

943
00:47:47,440 --> 00:47:50,560
Speaker 1: kind of do this using experiments with himself. You would

944
00:47:50,680 --> 00:47:54,080
Speaker 1: learn lists of nonsense syllables and then try to map

945
00:47:54,120 --> 00:47:57,200
Speaker 1: his own forgetting curve. So how much forgetting happens over

946
00:47:57,200 --> 00:47:59,279
Speaker 1: the first twenty four hours, next twenty four hours, next

947
00:47:59,280 --> 00:48:02,000
Speaker 1: twenty four hours. Yeah, I know, experiments on yourself very

948
00:48:02,040 --> 00:48:04,480
Speaker 1: very boring times in eighteen eighty so it didn't have

949
00:48:04,520 --> 00:48:06,520
Speaker 1: too much else to do, so I did this with himself,

950
00:48:07,520 --> 00:48:09,920
Speaker 1: but maps what's called the forgetting curve, which we still

951
00:48:09,960 --> 00:48:11,880
Speaker 1: to this day. We can look at any sort of

952
00:48:11,880 --> 00:48:14,120
Speaker 1: memory function or any memory task that we do in

953
00:48:14,160 --> 00:48:16,320
Speaker 1: the lot and we see a very clear forgetting curve.

954
00:48:16,600 --> 00:48:18,600
Speaker 1: A lot of forget the first twenty four hours. Then

955
00:48:18,600 --> 00:48:20,839
Speaker 1: things to kind of taper off. So there's that, And

956
00:48:20,880 --> 00:48:24,440
Speaker 1: the suspicion is that mostly it's decay, but sometimes it

957
00:48:24,480 --> 00:48:28,200
Speaker 1: happens because of interference, because similar memories get in there

958
00:48:28,239 --> 00:48:30,760
Speaker 1: and kind of interfere with one another, compete with one another,

959
00:48:30,960 --> 00:48:32,799
Speaker 1: sort of the memory that you have for them get

960
00:48:32,800 --> 00:48:36,600
Speaker 1: a little bit fuzzy. And again these are maybe features,

961
00:48:36,640 --> 00:48:39,240
Speaker 1: not bugs, right where maybe the system is not intending

962
00:48:39,280 --> 00:48:42,160
Speaker 1: to hold onto memories with high fidelity for long. So

963
00:48:42,239 --> 00:48:46,000
Speaker 1: interference and decay help us extract what's most important and

964
00:48:46,120 --> 00:48:48,319
Speaker 1: keep that over time, and then other things can kind

965
00:48:48,320 --> 00:48:50,920
Speaker 1: of go away. So those are natural things that happen

966
00:48:50,920 --> 00:48:53,239
Speaker 1: in every brain all the time, and there's nothing to

967
00:48:53,280 --> 00:48:55,919
Speaker 1: be concerned about decay, interference, nothing to be concerned about.

968
00:48:56,080 --> 00:48:59,000
Speaker 1: But as we get older, and by older I hate

969
00:48:59,000 --> 00:49:01,800
Speaker 1: to say this, you know, talking about like forties.

970
00:49:01,400 --> 00:49:06,160
Speaker 2: And above that what I wanted to hear money, Well,

971
00:49:06,200 --> 00:49:06,600
Speaker 2: I'm going to.

972
00:49:06,640 --> 00:49:09,840
Speaker 1: Say graduate in our fourth decade and then maybe a

973
00:49:09,840 --> 00:49:14,160
Speaker 1: little bit more precipitously, over time, memory does get more difficult.

974
00:49:14,320 --> 00:49:18,160
Speaker 1: So things do degrade, and we start to maybe lose

975
00:49:18,200 --> 00:49:20,960
Speaker 1: to some extent, our ability to make new memories, encode

976
00:49:20,960 --> 00:49:23,520
Speaker 1: new memories, our memories. For the old stuff is still

977
00:49:23,600 --> 00:49:25,600
Speaker 1: there's still resilience, even though every now and then we

978
00:49:25,680 --> 00:49:28,520
Speaker 1: might have a problem with access. So we get distracted,

979
00:49:28,560 --> 00:49:30,919
Speaker 1: we lose retrieval cues, but you know it's there because

980
00:49:30,920 --> 00:49:33,840
Speaker 1: if you get the right reminder, boom, it comes back. Right.

981
00:49:34,000 --> 00:49:36,000
Speaker 1: It's just a matter of like tip of a tongue,

982
00:49:36,080 --> 00:49:38,440
Speaker 1: you know, being able to remember exactly that the right

983
00:49:38,560 --> 00:49:41,360
Speaker 1: queue for retrieval. That becomes more difficult when we're just

984
00:49:41,400 --> 00:49:44,760
Speaker 1: more distracted. But as we get older, making new memories

985
00:49:44,760 --> 00:49:47,640
Speaker 1: becomes harder. And then for some folks who might go

986
00:49:47,719 --> 00:49:52,080
Speaker 1: down the trajectory to Alzheimer's disease, right then that becomes

987
00:49:52,320 --> 00:49:54,880
Speaker 1: exceptionally more difficult. And that's one of the first things

988
00:49:54,920 --> 00:49:59,200
Speaker 1: to go. And there's a very difficult line between what's

989
00:49:59,440 --> 00:50:02,280
Speaker 1: normal I hate to say that word, maybe more typical

990
00:50:02,600 --> 00:50:05,960
Speaker 1: age associated memory impairment, which you can expect in every brain,

991
00:50:06,480 --> 00:50:08,640
Speaker 1: and that's something that may be a bit more costs

992
00:50:08,680 --> 00:50:10,879
Speaker 1: for concern because it may be going down to about

993
00:50:10,880 --> 00:50:14,400
Speaker 1: the Alzheimer's disease. Dissociating those two, say in the sixties

994
00:50:14,440 --> 00:50:17,120
Speaker 1: and seventies, is actually very difficult. It's not very easy

995
00:50:17,360 --> 00:50:20,400
Speaker 1: because both can start out as a form of forgetfulness.

996
00:50:20,920 --> 00:50:24,480
Speaker 1: But with Alzheimer's disease or with dementia, it's very progressive,

997
00:50:24,840 --> 00:50:27,280
Speaker 1: so it does get worse and worse and worse over time.

998
00:50:28,080 --> 00:50:30,680
Speaker 1: That change in a healthy aging brain or a typical

999
00:50:30,680 --> 00:50:34,439
Speaker 1: aging brain is far less deep, so you don't see

1000
00:50:34,480 --> 00:50:37,200
Speaker 1: too much changing over time. You don't see this degradation

1001
00:50:37,280 --> 00:50:41,000
Speaker 1: to the point where it becomes very noticeable by family, friends, neighbors,

1002
00:50:41,160 --> 00:50:44,160
Speaker 1: and so on. So those are the forms of memory

1003
00:50:44,320 --> 00:50:48,000
Speaker 1: loss or memory change that happened. Some totally innocuous, no

1004
00:50:48,120 --> 00:50:50,440
Speaker 1: cause for concern, they happen every day to everyone, to

1005
00:50:50,480 --> 00:50:53,239
Speaker 1: the best of us. And then some that are a

1006
00:50:53,280 --> 00:50:54,640
Speaker 1: bit more cause for concern.

1007
00:50:54,719 --> 00:50:57,479
Speaker 2: As we get older and mechanistically, is it just the

1008
00:50:57,560 --> 00:51:01,759
Speaker 2: messages are not getting sent between those anymore, or this is.

1009
00:51:01,760 --> 00:51:04,760
Speaker 1: Something we've done quite a bit of work on. Actually mechanistically,

1010
00:51:04,800 --> 00:51:07,759
Speaker 1: what seems to be the case is that first the

1011
00:51:07,840 --> 00:51:10,200
Speaker 1: part of the brain that's really important for encoding these

1012
00:51:10,200 --> 00:51:13,600
Speaker 1: episodic memories, the hippocampus, as I said, has a very

1013
00:51:13,600 --> 00:51:17,520
Speaker 1: interesting change and it's dynamic. So this is a massive

1014
00:51:17,560 --> 00:51:19,880
Speaker 1: information processing hub in the brain. Even though it's a

1015
00:51:19,880 --> 00:51:24,200
Speaker 1: small structure, it carries a big information processing load, and

1016
00:51:24,640 --> 00:51:27,560
Speaker 1: it kind of can shift its state from encoding new

1017
00:51:27,560 --> 00:51:31,479
Speaker 1: information to remembering old information. And there are a few

1018
00:51:31,600 --> 00:51:34,480
Speaker 1: changes that happen to the cells in that system as

1019
00:51:34,520 --> 00:51:37,640
Speaker 1: we get older that bias the system towards remembering old

1020
00:51:37,640 --> 00:51:41,040
Speaker 1: information and away from encoding new information. And there's lots

1021
00:51:41,040 --> 00:51:43,520
Speaker 1: of sort of reasons why that is, we tend to

1022
00:51:44,040 --> 00:51:49,160
Speaker 1: change the excitation inhibition balance, we tend to change neurotransmitter concentrations,

1023
00:51:49,200 --> 00:51:50,800
Speaker 1: all of those kinds of things as we get older

1024
00:51:51,000 --> 00:51:54,799
Speaker 1: that cause that change in the information processing balance. But

1025
00:51:54,920 --> 00:51:57,440
Speaker 1: the other thing that happens more in the context of

1026
00:51:57,640 --> 00:52:01,520
Speaker 1: Alzheimer's disease is you also start to deprive the hippocampus

1027
00:52:01,560 --> 00:52:03,880
Speaker 1: of its main input coming in from the rest of

1028
00:52:03,920 --> 00:52:07,239
Speaker 1: the brain. So there's a region that sits alongside the

1029
00:52:07,320 --> 00:52:11,400
Speaker 1: hippo campus called the antorhinal cortex, and that region shrivels

1030
00:52:11,480 --> 00:52:14,720
Speaker 1: up in Alzheimer's disease. It's one of the first regions

1031
00:52:14,760 --> 00:52:18,719
Speaker 1: to deposit what's called tangle pathology or tau tangles, and

1032
00:52:18,760 --> 00:52:22,319
Speaker 1: that's a marker of cell death. So there's massive cell

1033
00:52:22,400 --> 00:52:25,080
Speaker 1: loss that's happening in that's around cortex early on, which

1034
00:52:25,160 --> 00:52:29,840
Speaker 1: deprives the hippocampus of its principal inputs. So in computer

1035
00:52:29,920 --> 00:52:32,640
Speaker 1: science we have this old adage called garbage in garbage out,

1036
00:52:33,360 --> 00:52:36,560
Speaker 1: and that's essentially what's happening in the hippocampus. It's the

1037
00:52:36,640 --> 00:52:39,680
Speaker 1: quality of the information that's coming in starts to become

1038
00:52:39,840 --> 00:52:42,759
Speaker 1: much more degraded than the context of Alzheimer's c's, So

1039
00:52:42,760 --> 00:52:45,080
Speaker 1: we can't possibly expect it to do a good job

1040
00:52:45,239 --> 00:52:48,920
Speaker 1: encoding information and story with high fidelity if the information

1041
00:52:49,000 --> 00:52:52,359
Speaker 1: coming in is in some ways quote garbage. So that

1042
00:52:52,760 --> 00:52:54,680
Speaker 1: tends to be one of the things that is also

1043
00:52:54,719 --> 00:52:57,680
Speaker 1: mechanistically associated with memory loss in the aging brain.

1044
00:52:57,920 --> 00:53:00,360
Speaker 2: Do we know why that region starts to degree in

1045
00:53:00,400 --> 00:53:01,480
Speaker 2: some people and not others.

1046
00:53:01,719 --> 00:53:05,319
Speaker 1: You know, there's hypotheses and I have my pet hypothesies.

1047
00:53:05,320 --> 00:53:07,719
Speaker 1: In the field has its pet hypotheses and so on,

1048
00:53:07,800 --> 00:53:10,799
Speaker 1: But there's a variety of contributors. We suspect that in

1049
00:53:10,840 --> 00:53:15,680
Speaker 1: the context of Alzheimer's disease, it's a combination of inflammatory changes,

1050
00:53:15,760 --> 00:53:19,080
Speaker 1: so that the mirror immune system is disregulated in Alzheimer's disease,

1051
00:53:19,080 --> 00:53:22,800
Speaker 1: where the inflammatory response that normally is very healthy starts

1052
00:53:22,800 --> 00:53:24,959
Speaker 1: to kind of take a turn and become very pathological.

1053
00:53:25,400 --> 00:53:28,080
Speaker 1: There is vascular damage that happens as we get older,

1054
00:53:28,080 --> 00:53:31,120
Speaker 1: and we suspect that some of those early vascular insults,

1055
00:53:31,160 --> 00:53:33,680
Speaker 1: so related to blood flows, deifenning of the arteries and

1056
00:53:33,719 --> 00:53:38,040
Speaker 1: so on, can also contribute to that. There's metabolic regulation

1057
00:53:38,200 --> 00:53:41,800
Speaker 1: that changes, so the metabolic demands of different regions also

1058
00:53:42,120 --> 00:53:45,480
Speaker 1: becomes disregulated as we get older. And then one thing

1059
00:53:45,520 --> 00:53:48,120
Speaker 1: that we've worked quite a bit on is excitation inhibition

1060
00:53:48,239 --> 00:53:51,759
Speaker 1: balance and the notion that normally the brain keeps this

1061
00:53:51,920 --> 00:53:56,480
Speaker 1: dynamic beautifully between stop signals and ghost signals, and as

1062
00:53:56,520 --> 00:54:00,560
Speaker 1: we get older there actually is an overabunton of the

1063
00:54:00,600 --> 00:54:05,160
Speaker 1: ghost signals which can drive the brain pathologically more towards

1064
00:54:05,200 --> 00:54:07,640
Speaker 1: memory loss and not enough of the stop signals, so

1065
00:54:08,080 --> 00:54:10,920
Speaker 1: that imbalance can also be a contributor. So it's a

1066
00:54:11,000 --> 00:54:13,880
Speaker 1: multifactorial issue. There's lots of contributors. This is not just

1067
00:54:14,040 --> 00:54:16,920
Speaker 1: one single pathology kind of model, as much as some

1068
00:54:17,000 --> 00:54:19,160
Speaker 1: of the field likes to believe that. It's a bit

1069
00:54:19,200 --> 00:54:19,840
Speaker 1: more complex.

1070
00:54:20,080 --> 00:54:21,719
Speaker 2: So as someone who just learned that they're in the

1071
00:54:21,760 --> 00:54:24,440
Speaker 2: old category, I.

1072
00:54:24,360 --> 00:54:26,799
Speaker 1: Would necessarily call it that. I would just say, we're

1073
00:54:26,800 --> 00:54:30,160
Speaker 1: maybe done developing and we're now just kind of hitting

1074
00:54:30,160 --> 00:54:32,320
Speaker 1: that hump of we're going to start the aging process.

1075
00:54:32,600 --> 00:54:34,120
Speaker 2: Got it, got it, I'll think of it that way.

1076
00:54:34,120 --> 00:54:38,200
Speaker 2: But I like that better. What kinds of science based

1077
00:54:38,680 --> 00:54:40,920
Speaker 2: things can we do to maintain our memory? So I

1078
00:54:41,000 --> 00:54:43,120
Speaker 2: see all these apps, pitch me, is there any science

1079
00:54:43,160 --> 00:54:44,200
Speaker 2: behind any of those things?

1080
00:54:45,200 --> 00:54:47,960
Speaker 1: Well, the app stuff probably not. I will tell you

1081
00:54:48,040 --> 00:54:51,000
Speaker 1: that the four big things that are really really important,

1082
00:54:51,040 --> 00:54:53,279
Speaker 1: and when I say really important, I mean they are

1083
00:54:53,320 --> 00:54:57,680
Speaker 1: supported both by epideological data and by clinical trials. So

1084
00:54:57,760 --> 00:54:59,600
Speaker 1: these things are really really helpful. The first one is

1085
00:54:59,640 --> 00:55:03,600
Speaker 1: physical activity. We know that physical activity maintains brain health

1086
00:55:03,640 --> 00:55:06,600
Speaker 1: well into older adulthood. We know that it can delay

1087
00:55:06,640 --> 00:55:09,360
Speaker 1: the onset of Alzheimer's disease. It can make the outcomes

1088
00:55:09,400 --> 00:55:13,000
Speaker 1: better for patients and as protective it really is knocking out.

1089
00:55:13,040 --> 00:55:16,560
Speaker 1: So sedentary lifestyle is a risk factor. We've done work

1090
00:55:16,600 --> 00:55:19,719
Speaker 1: also to show that even activity as brief as ten

1091
00:55:19,719 --> 00:55:22,880
Speaker 1: minutes of walking can be helpful to memory. So it

1092
00:55:22,920 --> 00:55:25,160
Speaker 1: doesn't take a lot, you know, I would say thirty

1093
00:55:25,200 --> 00:55:27,960
Speaker 1: minutes of you know, mild to moderate activity like walking,

1094
00:55:28,320 --> 00:55:31,560
Speaker 1: risk walking, that's sufficient to be able to give people

1095
00:55:32,080 --> 00:55:34,440
Speaker 1: a way to handle that risk factory. The second thing

1096
00:55:34,520 --> 00:55:36,680
Speaker 1: that's really important, and this kind of goes back to

1097
00:55:36,680 --> 00:55:40,200
Speaker 1: your idea about apps and kind of cognitive engagement. It

1098
00:55:40,239 --> 00:55:42,319
Speaker 1: turns out that apps and brain games on all of

1099
00:55:42,360 --> 00:55:45,720
Speaker 1: that efficacy is a little bit tendless, so there's conflicting reports.

1100
00:55:46,080 --> 00:55:49,560
Speaker 1: But we know that social engagement is really important. So

1101
00:55:49,600 --> 00:55:52,439
Speaker 1: in other words, if you remove social engagement, if folks

1102
00:55:52,440 --> 00:55:55,680
Speaker 1: become isolated the same past retirement, that's a risk factory

1103
00:55:55,800 --> 00:55:58,719
Speaker 1: for sure. But if they're able to continue to be

1104
00:55:58,800 --> 00:56:02,840
Speaker 1: social build archer social networks in person, you know, volunteering,

1105
00:56:02,880 --> 00:56:06,280
Speaker 1: community centers, churches, synagogues, whatever it is, or being around

1106
00:56:06,320 --> 00:56:10,200
Speaker 1: people in general, and that can also combine with physical activity.

1107
00:56:10,280 --> 00:56:13,320
Speaker 1: So let's say it's dance class now, it's physical activity

1108
00:56:13,360 --> 00:56:16,440
Speaker 1: and social contact. Right, those kinds of things, even in

1109
00:56:16,440 --> 00:56:19,240
Speaker 1: interventional studies, have been shown to have very positive results.

1110
00:56:19,880 --> 00:56:23,120
Speaker 1: Then the third piece is diet. A heart healthy diet

1111
00:56:23,160 --> 00:56:25,520
Speaker 1: is a brain healthy diet. And the one that has

1112
00:56:25,560 --> 00:56:28,440
Speaker 1: been tried and true in clinical trials is the Mediterranean diet.

1113
00:56:28,960 --> 00:56:31,160
Speaker 1: So the Mediterranean which has lots and lots of variants,

1114
00:56:31,200 --> 00:56:34,640
Speaker 1: but I think very colorful, leafy, green vegetables and so on,

1115
00:56:35,160 --> 00:56:39,040
Speaker 1: healthy fats, and reducing you know, things like red meat

1116
00:56:39,080 --> 00:56:41,439
Speaker 1: and some on. So that one also has been tried

1117
00:56:41,480 --> 00:56:44,440
Speaker 1: in clinical trials compared to other diets and seems to

1118
00:56:44,600 --> 00:56:47,600
Speaker 1: be able to stave off risk. And then the last piece,

1119
00:56:47,680 --> 00:56:49,920
Speaker 1: which I think is one of the most important, to sleep.

1120
00:56:50,640 --> 00:56:54,000
Speaker 1: We all need good quality and quantity of sleep every night.

1121
00:56:54,080 --> 00:56:57,439
Speaker 1: It turns out that actually during sleep we go through

1122
00:56:57,440 --> 00:56:59,800
Speaker 1: a process of glymphatic clearance and we clear out on

1123
00:56:59,800 --> 00:57:01,960
Speaker 1: the life lot of the pathologies that can lead down

1124
00:57:02,000 --> 00:57:04,480
Speaker 1: the path to Alzheimer's. These or at least be contributors

1125
00:57:04,520 --> 00:57:08,160
Speaker 1: to it. And studies have shown that if you have

1126
00:57:08,280 --> 00:57:11,160
Speaker 1: sleep loss folks for example, who sleep less than six

1127
00:57:11,200 --> 00:57:13,239
Speaker 1: hours a night versus those who sleep more than seven

1128
00:57:13,320 --> 00:57:16,720
Speaker 1: or eight hours there's differences in their amyloid uptakes, so

1129
00:57:16,960 --> 00:57:19,520
Speaker 1: that emiloid pathology is one of the chief pathology in

1130
00:57:19,560 --> 00:57:21,880
Speaker 1: Alzheimer's disease. You see a lot more of it in

1131
00:57:21,920 --> 00:57:24,560
Speaker 1: those who sleep those fewer hours, and a lot less

1132
00:57:24,560 --> 00:57:27,640
Speaker 1: of it and those who sleep longer. So sleep disruption,

1133
00:57:27,800 --> 00:57:30,960
Speaker 1: sleep loss, that's a risk factor. The good news is

1134
00:57:31,120 --> 00:57:34,920
Speaker 1: most sleep problems are treatable, whether it's because of obstructive

1135
00:57:34,920 --> 00:57:38,280
Speaker 1: sleep apnea, or insomnia or any other reason, restless slag,

1136
00:57:38,520 --> 00:57:40,560
Speaker 1: all of those things. There's good treatments out there that

1137
00:57:40,600 --> 00:57:43,400
Speaker 1: will help people sleep better. So those are the four

1138
00:57:43,520 --> 00:57:45,600
Speaker 1: sort of chief things. There's many other smaller things, but

1139
00:57:45,880 --> 00:57:47,520
Speaker 1: those are the four ones that I like to lead

1140
00:57:47,600 --> 00:57:50,440
Speaker 1: with because they have just excellent data in their favor.

1141
00:57:50,680 --> 00:57:53,120
Speaker 2: Well, I'm excited about the sleep thing. I like sleeping.

1142
00:57:54,120 --> 00:57:56,200
Speaker 2: Maybe it's time to get out and exercise a little

1143
00:57:56,240 --> 00:57:59,320
Speaker 2: more so. My co host is a physicist. He always

1144
00:57:59,400 --> 00:58:03,160
Speaker 2: ends on a aliens. So here we go. If an

1145
00:58:03,160 --> 00:58:05,600
Speaker 2: alien were to land on Earth today, do you think

1146
00:58:05,640 --> 00:58:07,760
Speaker 2: they would store memories in a similar way.

1147
00:58:08,120 --> 00:58:09,840
Speaker 1: You know, if you had asked me that question ten

1148
00:58:09,920 --> 00:58:11,960
Speaker 1: years ago, I would have said, yes, I think our

1149
00:58:11,960 --> 00:58:15,600
Speaker 1: memory system is exceptional. It's wonderful, it's brilliant. Why not

1150
00:58:15,960 --> 00:58:18,240
Speaker 1: they should be like a model to strive to achieve.

1151
00:58:19,000 --> 00:58:20,960
Speaker 1: But I will take the opportunity since we've got a

1152
00:58:20,960 --> 00:58:23,360
Speaker 1: couple more minutes, Kelly, and tell you about something that

1153
00:58:23,840 --> 00:58:27,000
Speaker 1: changes my answer to this question. And that is the

1154
00:58:27,040 --> 00:58:30,160
Speaker 1: discovery that was made first by James Magaw who is

1155
00:58:30,240 --> 00:58:33,480
Speaker 1: the founding director of my center here at uc Aervine,

1156
00:58:33,600 --> 00:58:35,720
Speaker 1: and we continue to do work with this group of

1157
00:58:35,800 --> 00:58:40,480
Speaker 1: remarkable individuals who have what's called highly superior autobiographical memory.

1158
00:58:41,360 --> 00:58:43,439
Speaker 1: We spent a lot of time during this conversation talking

1159
00:58:43,480 --> 00:58:46,800
Speaker 1: about how memory is fallible. There's forgetting, there's interference. It's

1160
00:58:46,840 --> 00:58:49,919
Speaker 1: not meant to store everything with high fidelity because that's

1161
00:58:49,960 --> 00:58:52,919
Speaker 1: going to compromise your knowledge generation and so on. Well,

1162
00:58:52,960 --> 00:58:56,400
Speaker 1: these folks would beg to differ. And it's incredible because

1163
00:58:56,440 --> 00:58:59,000
Speaker 1: they do store things with high fidelity. They can remember

1164
00:58:59,200 --> 00:59:01,160
Speaker 1: everything that happened in our lives since that they were

1165
00:59:01,200 --> 00:59:04,240
Speaker 1: teenagers and tell you exactly what happened on what day

1166
00:59:04,240 --> 00:59:07,160
Speaker 1: of the week, what month, and someone. And they don't

1167
00:59:07,200 --> 00:59:11,360
Speaker 1: have a problem extracting generalities and knowledge. Also, So sometimes

1168
00:59:11,360 --> 00:59:12,960
Speaker 1: I'll jup with them and say I think you're like

1169
00:59:13,040 --> 00:59:17,000
Speaker 1: the X men of our generation, X people of our generation.

1170
00:59:17,400 --> 00:59:19,600
Speaker 1: It's remarkable and we still don't understand how they do

1171
00:59:19,680 --> 00:59:22,520
Speaker 1: it and how their brains are wired differently. So if

1172
00:59:22,520 --> 00:59:26,520
Speaker 1: aliens sufficiently advanced to aliens, I think, if they're reaching

1173
00:59:26,560 --> 00:59:29,120
Speaker 1: Earth before we reach them, they're probably far more advanced

1174
00:59:29,160 --> 00:59:31,600
Speaker 1: than us. They're more likely to have figure it out

1175
00:59:31,640 --> 00:59:34,360
Speaker 1: a way to do that which sort of combines the

1176
00:59:34,400 --> 00:59:37,480
Speaker 1: best of what we have built into our computers and

1177
00:59:37,520 --> 00:59:40,360
Speaker 1: what we have built into our brains. Just no compromise,

1178
00:59:40,440 --> 00:59:41,360
Speaker 1: no sacrifice.

1179
00:59:41,640 --> 00:59:44,120
Speaker 2: Awesome. Well, I wish Daniel we're here to hear that,

1180
00:59:44,280 --> 00:59:46,480
Speaker 2: but I'm sure he'll enjoy hearing that explanation when he

1181
00:59:46,480 --> 00:59:48,760
Speaker 2: gets back. Thank you so much for your time, Mike.

1182
00:59:48,840 --> 00:59:50,280
Speaker 2: This was absolutely fascinating.

1183
00:59:50,360 --> 00:59:52,640
Speaker 1: They're very welcome. I very much enjoyed to Kelly. Thank you.

1184
01:00:00,120 --> 01:00:03,880
Speaker 2: Daniel and Kelly's Extraordinary Universe is produced by iHeartRadio. We

1185
01:00:03,920 --> 01:00:06,280
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1186
01:00:06,520 --> 01:00:09,280
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1187
01:00:09,480 --> 01:00:11,120
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1188
01:00:11,240 --> 01:00:14,200
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1189
01:00:14,240 --> 01:00:17,240
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1190
01:00:17,280 --> 01:00:17,680
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1191
01:00:17,880 --> 01:00:18,880
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1192
01:00:19,000 --> 01:00:23,560
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1193
01:00:23,720 --> 01:00:24,520
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1194
01:00:24,520 --> 01:00:26,600
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1195
01:00:26,720 --> 01:00:30,680
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1196
01:00:30,680 --> 01:00:33,600
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1197
01:00:33,760 --> 01:00:35,280
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