‘Role for left dorsomedial prefrontal cortex in self-generated, but not externally cued, language production’ with Deborah Levy

In this epidode, I talk with Deborah Levy, Lecturer at Princeton University, about her paper ‘Role for left dorsomedial prefrontal cortex in self-generated, but not externally cued, language production’, which just came out in Neurobiology of Language.

Levy D, Greicius Q, Wang C, Ko E, Xu D, Andrews J, Chang EF. Role for left dorsomedial prefrontal cortex in self-generated, but not externally cued, language production. Neurobiol Lang 2025; 6:nol_a_00166. [doi]

Levy website

Transcript

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Welcome to episode 34 of the Language Neuroscience Podcast.

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I'm Stephen Wilson and I'm a neuroscientist at the University of Queensland in Brisbane,

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Australia.

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My guest today is Deborah Levy.

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Deb is a language neuroscientist and lecturer in the Princeton Writing Program in Princeton,

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New Jersey.

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I'm making a few episodes about new papers that catch my eye in the journal Neurobiology

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of Language.

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This week we're going to talk about Deb's paper, "Role for Left, dorsomedial prefrontal

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cortex in self-generated, but not externally cued language production’, which just came out.

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It's a lovely paper, as you'll soon see.

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I should mention that I actually know Deb very well because she did her PhD in my lab

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at Vanderbilt, but we'll try to keep the inside jokes to a minimum.

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Okay, let's get to it.

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Hi, Deb, how are you?

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I'm doing great.

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How are you?

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I'm doing good and 

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as we've been talking about, you know, you're in your apartment in West Philly, yeah?

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That's correct, yep.

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I'm right by Clark Park and enjoying a nice four o'clock sunshine, I suppose, from

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my apartment.

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Yeah, it looks really, um, idyllic, actually. 

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It looks really peaceful and, you know, you've got this loft and like sunshine coming in

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through the windows, so very pleasant.

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And I'm just sitting here like in the early dawn hours, like nursing my first coffee that

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really should be a second coffee by this point.

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Yeah, well, it is pretty dreamy here.

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I agree.

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Thank you for complimenting it.

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But your background is very nice as well.

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I like the grey curtain.

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The grey curtain is because it's like, this is my office slash laundry.

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And if it wasn't for the grey curtain, you could see my laundry.

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In fact, you still actually can see it.

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Very peaceful piece.

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Yeah, but it's like a surprisingly good office for a laundry office.

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We know each other very well.

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You did study in my lab, which was awesome.

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Yes.

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But what we're going to talk about today is not that time, but this recent paper that you

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published in neurobiology of language.

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Before we get onto that, even though I kind of know this already, can you, for our listeners,

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talk about how you got interested in the field of language and the brain?

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Yeah.

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When I was very little, I think around the time I learned to read, I was already sort of

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really perplexed by the fact that my life had to be filtered through language after that.

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So, I remember sitting in the back seat of my parents' car and driving past a billboard

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and seeing it and being like, why don't I have the option not to read that?

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I would love I could just look at it.

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But now it's words.

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And that sort of mysterious filtering of the world through language continued to fascinate

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me for a very long time.

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So, when I was in high school, I got really into Charlie Kaufman movies, and I watched the

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Jill Bolte Taylor TED Talk and I was like, I want to spend my whole life thinking about

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how the brain and language interact.

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Just tell us a little bit about that TED Talk for people like them.

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Yeah.

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So that is a TED Talk by a neuroscientist who had a stroke in her left hemisphere and she

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kind of describes the experience of living her life through her right hemisphere only just

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for the period of time that she was experiencing symptoms.

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And it's really fascinating.

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You know, it kind of portrays it as this like meditative, holistic experience of life that

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is not kind of dictated by the constraints of language, which, you know, when I was 

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She's very pro-right hemisphere, isn't she?

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I found it like a little bit like scandalous actually.

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Yeah.

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I mean, I was too for a lot of my life.

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I think I, you know, longed for a life that wasn't constantly words in my head thinking

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about everything that was going on.

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Wow.

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Yeah.

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And then, you know, the more I learned about what the left hemisphere does, the more I was

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like, oh, this guy is pretty important.

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I think, I think I like that I have him around.

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Yeah, but anyway, so I think all of those sort of philosophical questions about what does

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it mean to live a life through language really kind of like lit my fire about this.

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And then, yeah, I went to NYU because they had this major called language in mind, which,

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you know, was very appealing given all of those interests.

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And it was kind of a philosophy psychology linguistics trifecta.

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And then when I got to school, I realized that the linguistics and the psych were really

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the things that I was most passionate about.

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So I switched to just a double major in those.

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And I worked in a couple of behavioral labs.

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And yeah, it was in a sociolinguistics lab with John Singler, a causal cognition lab with

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Bob Rehder and a visual perception lab with Denis Pelli.

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So, I was kind of working in all of those different environments and also doing teaching on

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the side.

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So, I did a class called teaching in psychology where I got to TA, the interest site class

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for the other undergrads coming in after I took the class, which I loved.

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So yeah, so that was all just quick note.

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Like so Denis Pelli is like very secretly famous as one of the coauthors of Psychtoolbox,

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which I think, which I think everybody, I mean, many, many of the experience and I feel

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to build on that, on that software, so yeah.

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Yeah, what was that like?

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Well, it was great.

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shoulders of Giants.

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Yeah, I had a lot of fun working in his lab.

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He was, I did my undergraduate thesis in his lab.

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So, it was about visual perception of letters at the sort of threshold of visibility and

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this experience of those letters kind of like springing into your awareness after just

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looking for a long time at something that seems like it isn't there, which we determined

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was sort of a categorical thing.

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Like either it's there, or it isn't.

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You kind of don't have any in between experience of them starting to appear.

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But he was also working on a project at the time called the Beauty Project that was about

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kind of aesthetic experiences of beauty.

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And through the years and I got to, I had a lot of hot takes actually as an undergrad, I

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came in really strong.

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And it didn't totally turn him off of me.

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So yeah, it was a lot of fun.

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And I didn't realize that he was so famous for psychtoolbox until I got to your lab and

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started using it and saw his name on all of the, you know, all of the documentation.

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Yeah.

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And we everyone citing Pellie, 1997.

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Yeah.

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And it's kind of really cool how early you got, you knew what your interest was.

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I don't know that I've met anybody that actually like underrated enrolled in a major that was

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essentially about language and brain.

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And that's like, you know, surprisingly, you were on your own target.

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Yeah.

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I think that the shape of my interest has morphed quite a bit.

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Yeah.

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But the underlying baseline has been really, really consistent since I was about four.

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So that's, that's really cool.

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Yeah.

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Okay.

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And so yeah, undergrad, yeah, you did a whole bunch of research as an undergrad.

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And did you go straight into your PhD after that?

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I did not.

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I worked in a computational memory lab at Penn for two years as a research assistant.

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So that is the Kahana lab, computational memory lab, studying memory.

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And basically, my job there was collecting intracranial data with patients undergoing

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monitoring for epilepsy.

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So, I would come in and have them do free recall tasks or, you know, spatial cognition kind

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of stuff, you know, set up a little laptop in front of them and encourage them to, you know,

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do this while they're hanging out, getting better, really.

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And yeah, that was a really different type of experience than I had in undergrad because,

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you know, I was going from behavioral psych and behavioral linguistics to this much

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more sort of neural computational perspective, which was really cool and really, really mind

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boggling for me just to see how many different ways you can look at the same types of questions.

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And I learned so much learned a lot.

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And yeah, and then after that is when I decided like all of this neural stuff is really cool,

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but I'm missing the language part.

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I really love to do some more language and then, you know, I applied to a very special lab.

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But actually, you applied to Vanderbilt before I was there.

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So I, so why, so like, why did you apply to Vanderbilt?

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Because I wasn't there. (Laughter)

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Well, I applied because I knew they had a hearing and speech program that was very good.

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And I knew that I wanted to do both work that was more kind of focused on language compared

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to what I had been doing as an RA.

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And also work that had some clinical applications.

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I think something I've thought about a lot as I've been in my, you know, sort of adult career

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is the balance between being interested in something scientifically and being, you know,

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interested in the people that are experiencing what you study.

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And I think I was very compelled to feel coupled with the people I was interested in.

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And I feel like I was working specifically in areas that would in some way benefit them,

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even if it was long term.

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Right.

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So that was a big pull.

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Okay.

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Um, so yeah, that's great.

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And then, yeah, so somehow, we got connected and, um, like, yeah, it was through Mike

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de Riesthal.

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I talked to Mike de Riesthal on the phone.

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I remember actually sitting in Jefferson hospital while I was on a case talking to Mike

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de Riesthal , like walking around the hallways of Jefferson.

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He seemed great.

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He said, you know, based on your interest, there's this guy coming in who you might really

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like working with, um, you know, do you want me to just set you guys up on a Skype call

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at the time?

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Probably.

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Yeah.

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Those were the days.

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Yeah.

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One thing that I remember from that is that you asked, like, what do I need?

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Like, what should I study before I start?

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And then I sent you like a 20 dot point, like syllabus for cognitive neuroscience of

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simple language, which I still have saved.

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And it was actually like, that, it's a good document, but it would probably take like about

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10 years to get through it all, but like, that makes me feel better about where I am.

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But, yeah, I did.

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I mean, I still look back at that too.

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I mean, when I try to think like, what are the things that I want to feel like I know

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or that I, you know, know are still ahead of me.

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I use that as sort of a benchmark.

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So great.

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Yeah.

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So two things from your PhD time.

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First, can you tell us about what you did with your involvement in the aphasia group at

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at Vanderbilt?

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Yeah.

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So the aphasia group at Vanderbilt is a very, very cool place.

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It's run by Dominique Harrington and she every Thursday has people come from really all

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of her Tennessee, but middle Tennessee is kind of the hub.

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And although some people commute like three hours to get there, it's very important to

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them.

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And it's basically a full day program where there's always kind of conversation and one-to-one

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speech therapy and a real community that's built around that.

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So, I came in again, like I mentioned, kind of trying to make sure I stayed connected with

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the people I was interested in, you know, the brains of.

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And I volunteered in that group for basically the duration of my PhD.

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I think it was maybe started the middle of my first year.

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Yeah.

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And so, I, you know, because I'm not a clinically trained speech pathologist, I was placed in

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sort of the, we called it the executive group.

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It was very both like relatively mild impairment.

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And I was, you know, just kind of hanging out with them and helping, you know, make sure

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everybody got birthday cards and, you know, do the, the planning for the group over the

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semester.

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But yeah, then Anna Kasdan joined me as well and she was also volunteering in the group and

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the two of us decided both that we wanted to sort of capture what was going on there because

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it seemed to be so important to the people in the group and also to do our own little spin

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off.

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So we had a music and arts group that we started at Vanderbilt, which was so fun.

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Anna's a pianist.

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So she ran a choir and I like to think of myself as an amateur artist.

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And so, I did, you know, little workshops and I tested them all out to make sure you could

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do them, you know, with one hand at home.

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And my husband had a lot of nights of me, you know, like my arm behind my, my back at the

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kitchen table, you know, for me.

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Yeah.

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And so that ended up being really, really fun as well.

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So, we have a couple of papers that are out about what that group is and its benefits.

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And yeah, I think that was a really important experience for me because it, it got me really

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thinking about like what, what the relationship between research and community participation

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is, I guess, like research and stakeholders and a lot of the work that I still think about

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a lot with a major friendly materials stemmed from that worlds and making sure that there's

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like a way to communicate that information.

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Yeah, that's really great that you got to know the people who we were working with in that

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way because I just think that it brought so much to the lab that you had that like really

232
00:14:07,400 --> 00:14:14,160
sort of those deep, you know, experiences with actually interacting with people in loads

233
00:14:14,160 --> 00:14:16,280 
of different contexts.

234
00:14:16,280 --> 00:14:20,000
And the other thing I wanted to ask you about is your dissertation, which is then published

235
00:14:20,000 --> 00:14:21,000 
in 2024.

236
00:14:21,000 --> 00:14:25,920 
Can you tell us just briefly about what you worked on for that paper?

237
00:14:25,920 --> 00:14:33,960
Yeah, so that was working with the database that you put together over the course of the

238
00:14:33,960 --> 00:14:38,920
five years, I believe, that you started a Vanderbilt until I wrote the paper.

239
00:14:38,920 --> 00:14:44,040
So that, with all of this speech language pathologists and all of the imaging that was

240
00:14:44,040 --> 00:14:50,680
a mass, we had a really big data set of people with left hemisphere stroke that were tracked

241
00:14:50,680 --> 00:14:53,200 
over the first year of their recovery.

242
00:14:53,200 --> 00:14:58,920
And my dissertation was about trying to predict from the clinical imaging, from what their

243
00:14:58,920 --> 00:15:02,040 
strokes look like on their MRIs.

244
00:15:02,040 --> 00:15:03,040 
Acutely.

245
00:15:03,040 --> 00:15:05,440 
Acutely, yes.

246
00:15:05,440 --> 00:15:09,280
What their language would look like at the one month time point, the three month time point

247
00:15:09,280 --> 00:15:10,920 
and the one year time point.

248
00:15:10,920 --> 00:15:17,760
And that was basically built with support vector regression model, like a machine learning

249
00:15:17,760 --> 00:15:24,840
type approach to predict from the brain images what the language recovery would look like.

250
00:15:24,840 --> 00:15:26,840 
And it did pretty good.

251
00:15:26,840 --> 00:15:28,320 
It did pretty good, didn't it?

252
00:15:28,320 --> 00:15:29,320 
Yeah.

253
00:15:29,320 --> 00:15:35,960
We can predict quite a lot just from the brain, which has a lot of implications, but I guess

254
00:15:35,960 --> 00:15:37,960 
we should get to that another time.

255
00:15:37,960 --> 00:15:38,960 
Yeah.

256
00:15:38,960 --> 00:15:46,840 
So, and then you went to your postdoc with the Chang Lab.

257
00:15:46,840 --> 00:15:49,200
And that is where the paper we're going to talk about today comes from.

258
00:15:49,200 --> 00:15:54,280
So can you tell me about like what it was like to move over to the Chang Lab and get started

259
00:15:54,280 --> 00:15:55,280 
there?

260
00:15:55,280 --> 00:15:56,280 
Yeah.

261
00:15:56,280 --> 00:15:58,640 
Well, it was a big geographical move.

262
00:15:58,640 --> 00:16:03,600
First of all, I moved from Tennessee back to Philadelphia for a month where I got married

263
00:16:03,600 --> 00:16:07,000 
and then immediately moved to San Francisco, like a week after.

264
00:16:07,000 --> 00:16:10,640 
So it was a big physical jump in space.

265
00:16:10,640 --> 00:16:15,120 
But then once I got there, I mean, it was, it was so cool.

266
00:16:15,120 --> 00:16:21,920
It's like an extremely inspiring group of people to work with and really fascinating populations

267
00:16:21,920 --> 00:16:27,320
of people that you can learn about through the neurosurgical resections, which is the

268
00:16:27,320 --> 00:16:29,480 
main data set that I worked with.

269
00:16:29,480 --> 00:16:39,120
So, Eddie is a neurosurgeon and whenever he has left hemisphere cases, he has us do preoperative

270
00:16:39,120 --> 00:16:40,120 
evaluations.

271
00:16:40,120 --> 00:16:45,920
So you see how their language is before surgery, two days after, two to fourish, you see how

272
00:16:45,920 --> 00:16:48,240
they're doing after their surgery.

273
00:16:48,240 --> 00:16:52,640
And then if there's impairment at that point, we follow up a month later and see how their

274
00:16:52,640 --> 00:16:54,160 
language is at that point as well.

275
00:16:54,160 --> 00:16:59,720
So, I got to follow up on very similar types of questions to the dissertation work, but

276
00:16:59,720 --> 00:17:05,840
in a totally different population of people where the, the lesions are, you know, sort of designed

277
00:17:05,840 --> 00:17:11,320 
by a neurosurgeon as opposed to just the, the result of stroke.

278
00:17:11,320 --> 00:17:13,320 
Designed by the MCA.

279
00:17:13,320 --> 00:17:17,640
By the MCA, yeah, although we're not the MCA is actually less relevant for this paper than

280
00:17:17,640 --> 00:17:18,640 
the ACA, but.

281
00:17:18,640 --> 00:17:20,840 
Oh, yeah, that's very true.

282
00:17:20,840 --> 00:17:21,840 
Yeah.

283
00:17:21,840 --> 00:17:22,840
Yeah.

284
00:17:22,840 --> 00:17:26,960
So, in Eddy's lab, would you, what kind of patient interaction this did you have?

285
00:17:26,960 --> 00:17:28,920 
Were you doing the testing and?

286
00:17:28,920 --> 00:17:29,920 
Yeah.

287
00:17:29,920 --> 00:17:33,840
So, I was doing evaluations there, which I was not doing in grad school.

288
00:17:33,840 --> 00:17:35,600 
So that was new for me.

289
00:17:35,600 --> 00:17:41,960
And that I think also added a whole new dimension to, to understanding what assessment is and

290
00:17:41,960 --> 00:17:44,640 
how that, how that bears out.

291
00:17:44,640 --> 00:17:48,800 
Interpersonally, it was fascinating and really.

292
00:17:48,800 --> 00:17:51,800 
Yeah, I learned a lot from, from that as well.

293
00:17:51,800 --> 00:17:57,360
And then for a good chunk of my time at the lab, I was also going to interoperative procedures.

294
00:17:57,360 --> 00:18:04,960
So, I would go into the surgeries and do language tasks during direct cortical stimulation.

295
00:18:04,960 --> 00:18:10,560
So that would be while the, the surgery is happening in order to make sure that it's safe

296
00:18:10,560 --> 00:18:12,320 
to remove certain areas.

297
00:18:12,320 --> 00:18:13,320 
Mm-hmm.

298
00:18:13,320 --> 00:18:17,560
The neurosurgeon stimulates those areas and then tests, you know, can the person still repeat

299
00:18:17,560 --> 00:18:21,800 
or can they still, you know, complete sentences, things like that?

300
00:18:21,800 --> 00:18:26,560
So, Eddie would have been doing the simulations while you were the one administering the language

301
00:18:26,560 --> 00:18:27,560 
stimuli.

302
00:18:27,560 --> 00:18:28,560 
Yeah, yeah, yeah.

303
00:18:28,560 --> 00:18:32,720 
Or documenting it, usually both.

304
00:18:32,720 --> 00:18:38,260
And how long, so I've done, I've been, had the great honor of being in the room one time

305
00:18:38,260 --> 00:18:45,360
throughout one of Eddie's surgeries, which was a seven hour day on a Friday, one day,

306
00:18:45,360 --> 00:18:49,360
or a very memorable experience.

307
00:18:49,360 --> 00:18:50,360 
Is that how it was for you?

308
00:18:50,360 --> 00:18:54,360
Like, you know, it was these very long days, like, um, we were usually not in there for

309
00:18:54,360 --> 00:18:56,120 
the full duration of the surgery.

310
00:18:56,120 --> 00:19:00,520
You would often we'd sort of like huddle in either the sub-sterile room or up in the, the

311
00:19:00,520 --> 00:19:05,240
lab and then kind of try to rush down at the exact moments when it was kind of most useful

312
00:19:05,240 --> 00:19:08,520
for us to be there because I'm not sure if this was your experience, but there's a lot

313
00:19:08,520 --> 00:19:11,520 
more people in the room during surgeries than I would have expected.

314
00:19:11,520 --> 00:19:13,840 
Oh, yeah, there was about 20 people in the room.

315
00:19:13,840 --> 00:19:14,840 
Exactly.

316
00:19:14,840 --> 00:19:19,520
Yeah, so especially when, you know, if you're coming with like a rig or speakers or a microphone

317
00:19:19,520 --> 00:19:22,320
stand, like, you don't want to be in there when you don't have to be because you don't

318
00:19:22,320 --> 00:19:24,560 
want to be in other people's way.

319
00:19:24,560 --> 00:19:29,240
So we, yeah, we would do, I'm, when I say we, I'm talking about me and other people who

320
00:19:29,240 --> 00:19:32,400 
were doing a drop at every search of different sorts.

321
00:19:32,400 --> 00:19:39,560
So, we would often come in around the time that the craniotomy was complete and, you know,

322
00:19:39,560 --> 00:19:49,240
the brain was exposed and then stay for the awake period of the surgery when they sort

323
00:19:49,240 --> 00:19:55,280
of like titrate the anesthetic to have the person be alert for the testing.

324
00:19:55,280 --> 00:19:58,120 
And that'd be about half an hour is right.

325
00:19:58,120 --> 00:20:02,600
Yeah, it would, it would range, but yeah, usually I think around half an hour.

326
00:20:02,600 --> 00:20:09,760
And then sometimes we would stick around for, sometimes the actual like resection procedure

327
00:20:09,760 --> 00:20:13,400 
if there was kind of ongoing monitoring during that.

328
00:20:13,400 --> 00:20:18,040
Or we would, you know, kind of pack up and leave as, you know, efficiently as we can to

329
00:20:18,040 --> 00:20:21,600 
make sure we're, you know, letting the clinician's due their job.

330
00:20:21,600 --> 00:20:26,240
But yeah, I would say we were, we were in and around the operating room for seven hours,

331
00:20:26,240 --> 00:20:30,120
but we were probably only in the operating room for, you know, half an hour to an hour.

332
00:20:30,120 --> 00:20:31,120 
Right.

333
00:20:31,120 --> 00:20:35,200
Yeah, waiting behind the things to come in and do our job and then get out of there.

334
00:20:35,200 --> 00:20:36,200 
Uh-huh.

335
00:20:36,200 --> 00:20:41,040
I was just remembering the one that I, that I was present for about like an hour into

336
00:20:41,040 --> 00:20:42,040 
it.

337
00:20:42,040 --> 00:20:46,680
This, this nurse, I think the senior nurse, like, you know, tapped me on the shoulder

338
00:20:46,680 --> 00:20:51,000
and, and pulled me outside and she was like, can you come outside and like, and then she's

339
00:20:51,000 --> 00:20:54,280 
like, who are you and why are you here? (Laughter)

340
00:20:54,280 --> 00:21:00,040 
And I was like, uh, Eddie invited me.

341
00:21:00,040 --> 00:21:08,360
And she was like, yeah, I also, I had done some operating room stuff at Penn when I was a research

342
00:21:08,360 --> 00:21:09,360 
assistant there.

343
00:21:09,360 --> 00:21:14,400
I will say the people in the operating rooms in San Francisco are very kind and very welcoming

344
00:21:14,400 --> 00:21:16,240 
of the, the vibe in San Francisco.

345
00:21:16,240 --> 00:21:17,240 
That was not my experience.

346
00:21:17,240 --> 00:21:23,080 
But I don't know only for that one, only for that one case, but yeah.

347
00:21:23,080 --> 00:21:28,840
Yeah, maybe I was just, uh, well, I would probably be holding a bunch of electronics so it seemed

348
00:21:28,840 --> 00:21:33,080 
like I had a reason to be there. (Laughter)

349
00:21:33,080 --> 00:21:34,080 
That's funny.

350
00:21:34,080 --> 00:21:35,080
Um, okay.

351
00:21:35,080 --> 00:21:40,080
So yeah, you're very much like embedded in this, in this project and, and doing the data

352
00:21:40,080 --> 00:21:42,080 
collection in many different ways.

353
00:21:42,080 --> 00:21:46,000 
Um, so, uh, let's talk about the paper, right?

354
00:21:46,000 --> 00:21:51,280
So it's called ‘Role for left dorsomedial prefrontal cortex in self-generated, but not externally

355
00:21:51,280 --> 00:21:52,280 
cued language production’.

356
00:21:52,280 --> 00:21:54,280 
Well, you really packed a lot into that title.

357
00:21:54,280 --> 00:21:55,280 
Yeah.

358
00:21:55,280 --> 00:21:56,280 
Yeah.

359
00:21:56,280 --> 00:21:58,480 
And this is in your biology of language 2025.

360
00:21:58,480 --> 00:22:04,840
Just came out and, um, like we talked about, like I'm trying to do some episodes about

361
00:22:04,840 --> 00:22:08,080
papers that are in the journal, because I'm on the editorial board at the journal and I want

362
00:22:08,080 --> 00:22:10,080
it to succeed.

363
00:22:10,080 --> 00:22:15,600
Um, and this paper really struck me as one of the super interesting ones, one of many,

364
00:22:15,600 --> 00:22:19,440 
um, but one that really caught my eye.

365
00:22:19,440 --> 00:22:28,760
Um, so it's about what you call the pre-SMA, um, which is not one of the most popular language

366
00:22:28,760 --> 00:22:29,760 
areas.

367
00:22:29,760 --> 00:22:32,680 
Um, so people might not be so familiar with it.

368
00:22:32,680 --> 00:22:37,440 
So can you start by, um, telling us about what is the pre-SMA?

369
00:22:37,440 --> 00:22:38,440 
Yeah.

370
00:22:38,440 --> 00:22:41,040 
So it's in the medial frontal cortex.

371
00:22:41,040 --> 00:22:46,920
So, I think most images that we see in the neurobiology of language tend to be lateral, you tend

372
00:22:46,920 --> 00:22:50,200
to see like the, the side of the brain with the sylvian fissure everything.

373
00:22:50,200 --> 00:22:56,120
So, if you instead kind of pride the brain open at the longitudinal fissure, I guess you would

374
00:22:56,120 --> 00:23:04,040
see, uh, the medial surface and, um, it's in the, the most kind of, and, well, not the most

375
00:23:04,040 --> 00:23:08,840
anterior part, but the pretty anterior, um, part of the medial prefrontal cortex.

376
00:23:08,840 --> 00:23:14,720
So, um, if you have the SMA that's kind of starting at the central sulcus, um, so that

377
00:23:14,720 --> 00:23:17,000 
stands for supplementary motor area.

378
00:23:17,000 --> 00:23:18,920 
Yes, supplementary motor area.

379
00:23:18,920 --> 00:23:25,480
Um, it's basically in front of that, um, there's a delineation called the, um, the VCA line

380
00:23:25,480 --> 00:23:31,960
that kind of distinguishes them that's aligned with the anterior commissure and that area in

381
00:23:31,960 --> 00:23:35,160 
front of the SMA is what's referred to as the pre-SMA.

382
00:23:35,160 --> 00:23:37,360 
So the pre-Supplementary motor area.

383
00:23:37,360 --> 00:23:38,360 
Okay.

384
00:23:38,360 --> 00:23:42,200
Um, and can you kind of, so you've kind of explained the, the relative, uh, some of the

385
00:23:42,200 --> 00:23:48,080
call location with respect to the SMA proper, um, what, and what, prior to your work, like,

386
00:23:48,080 --> 00:23:53,440
coming into it, like, what was generally known about the functional role of the SMA versus

387
00:23:53,440 --> 00:23:56,040 
the pre-SMA, just kind of big picture?

388
00:23:56,040 --> 00:23:57,040 
Yeah.

389
00:23:57,040 --> 00:24:04,200
So, generally, I think the SMA is more associated with, like, motor output, um, so some kind

390
00:24:04,200 --> 00:24:11,160
of, you know, connections with motor cortex and motor planning, um, things where if you

391
00:24:11,160 --> 00:24:16,520
were to lesion it, you would get, um, deficits in kind of movement or, or motor stuff.

392
00:24:16,520 --> 00:24:21,960
Um, whereas the pre-SMA seems to be more kind of abstract in what it works with.

393
00:24:21,960 --> 00:24:26,360 
So it's more associated with, um, prefrontal cortex.

394
00:24:26,360 --> 00:24:29,000 
It's less motor in terms of its connections.

395
00:24:29,000 --> 00:24:36,440
It, uh, seems to respond to less specific types of, um, tasks, uh, or, or different types

396
00:24:36,440 --> 00:24:37,920 
of tasks, I should say.

397
00:24:37,920 --> 00:24:42,600
Uh, it seems to be working at a level that's not quite as directly tied to motor output

398
00:24:42,600 --> 00:24:47,040 
and is more about either planning or decision making related to those.

399
00:24:47,040 --> 00:24:48,040 
Okay.

400
00:24:48,040 --> 00:24:49,040 
Great.

401
00:24:49,040 --> 00:24:54,760
Um, and, you know, I think the first person, I'm not sure if he was first, but like, the

402
00:24:54,760 --> 00:25:00,840
first prominent person to sort of relate these areas to language was Wilder Penfield.

403
00:25:00,840 --> 00:25:05,680
Um, do you think that he would, I mean, and was, do you think he was talking about SMA or

404
00:25:05,680 --> 00:25:08,080 
pre-SMA in his, um, work?

405
00:25:08,080 --> 00:25:09,840 
That's a good question.

406
00:25:09,840 --> 00:25:13,880
I'm trying, I, I remember an image that you're referring to, like, of the medial surface

407
00:25:13,880 --> 00:25:15,840 
with the hatching over that area.

408
00:25:15,840 --> 00:25:16,840 
Yeah.

409
00:25:16,840 --> 00:25:17,840 
Um, yeah.

410
00:25:17,840 --> 00:25:23,800
I, if I remember correctly, the, the, the pre-SMA wasn't like given its own name

411
00:25:23,800 --> 00:25:25,960 
until a little bit later than that.

412
00:25:25,960 --> 00:25:27,840 
Am I, do you agree?

413
00:25:27,840 --> 00:25:28,840 
I think I do.

414
00:25:28,840 --> 00:25:33,880
I mean, I, I'm not, um, I think he was talking about SMA, like, yeah, I think so.

415
00:25:33,880 --> 00:25:38,120
Um, and, and, you know, he noticed that when you, like, it was one of the three brain regions

416
00:25:38,120 --> 00:25:44,160
where if you stimulated it, you could cause, um, you know, kind of transient, um, aphasia

417
00:25:44,160 --> 00:25:45,160 
or speech arrest, really.

418
00:25:45,160 --> 00:25:49,560
I mean, it's not strictly, not necessarily aphasia, but, yeah, speech arrest.

419
00:25:49,560 --> 00:25:50,560 
So, yeah.

420
00:25:50,560 --> 00:25:55,040
You know, but I think, yeah, he's probably like really hitting on that motor, more posterior

421
00:25:55,040 --> 00:25:59,680 
area, whereas you guys are talking about the area in front of that.

422
00:25:59,680 --> 00:26:04,960
Um, and so that area that's in front of, like, so the pre-SMA, like, is there much prior

423
00:26:04,960 --> 00:26:10,120
to your papers, there much in the language literature about that region?

424
00:26:10,120 --> 00:26:15,800
I have a couple of thoughts on that, uh, trans-cortical motor aphasia is, you know, like 1885,

425
00:26:15,800 --> 00:26:18,040 
1886 is when it was first described.

426
00:26:18,040 --> 00:26:20,520 
Um, and, but not with an anatomical.

427
00:26:20,520 --> 00:26:21,520 
It's an anatomical basis.

428
00:26:21,520 --> 00:26:22,520 
No, it's an anatomical basis.

429
00:26:22,520 --> 00:26:23,520
And in the time.

430
00:26:23,520 --> 00:26:24,520 
Yeah.

431
00:26:24,520 --> 00:26:30,040
Um, I think the, the neuropsychology literature talks about this area a little bit more, um,

432
00:26:30,040 --> 00:26:35,760
when it comes to superior, um, medial front lesions that lead to what people might have

433
00:26:35,760 --> 00:26:41,480
called like a dis-executive syndrome or some kind of, uh, issue with, um, executive functioning

434
00:26:41,480 --> 00:26:46,840
that kind of ties into language, but isn't necessarily, um, specifically part of, like,

435
00:26:46,840 --> 00:26:47,840 
the language apparatus.

436
00:26:47,840 --> 00:26:50,840 
Mm-hmm.

437
00:26:50,840 --> 00:26:54,160 
So it grew up there, um, it also, I think, do you still--

438
00:26:54,160 --> 00:26:57,920
And you cited a lot of papers by my Queensland colleague, Gail Robinson?

439
00:26:57,920 --> 00:26:58,920 
Yes, I did.

440
00:26:58,920 --> 00:27:05,600
But, yeah, and she writes a lot about kind of distinctions between the, uh, medial frontal

441
00:27:05,600 --> 00:27:10,160
areas and lateral frontal areas, uh, and they're possible differential contributions to

442
00:27:10,160 --> 00:27:13,480 
different types of language, uh, dysfunction.

443
00:27:13,480 --> 00:27:18,840
And, yeah, so she's written lots and lots of case studies about, um, people either after

444
00:27:18,840 --> 00:27:25,240
stroke or after a tumor resection, um, sometimes there's neurodegenerative conditions like PSP or

445
00:27:25,240 --> 00:27:27,080 
or Parkinson's where this shows up.

446
00:27:27,080 --> 00:27:33,200
Um, and it's very tied to what she would call an energization, uh, deficit or what 

447
00:27:33,200 --> 00:27:38,640
Alexander would have called an energization deficit as well, um, which is kind of about

448
00:27:38,640 --> 00:27:42,640 
both initiating and sustaining a response over time.

449
00:27:42,640 --> 00:27:49,840
Um, that seems to be potentially domain general, not necessarily language specific, but certainly

450
00:27:49,840 --> 00:27:55,200
bears out in language and seems to be very tightly associated with those medial frontal

451
00:27:55,200 --> 00:27:56,200 
areas.

452
00:27:56,200 --> 00:28:02,520
Um, and then there's, Luria wrote about dynamic aphasia, which is what, you know, we kind

453
00:28:02,520 --> 00:28:10,920
of end up mapping this concept onto the, in terms of the profile, uh, in the 1940s, I believe,

454
00:28:10,920 --> 00:28:16,480 
and, Luria, my impression, I'd love to hear your thoughts on this.

455
00:28:16,480 --> 00:28:20,720
My impression is again that he's much more discussed among neuropsychologists than among,

456
00:28:20,720 --> 00:28:25,400 
like, modern day study years of the neurobiology of language.

457
00:28:25,400 --> 00:28:26,920 
Um, what are your thoughts?

458
00:28:26,920 --> 00:28:31,360 
Oh, yeah, I think he's, um, like, criminally neglected by our field.

459
00:28:31,360 --> 00:28:32,360 
Yeah.

460
00:28:32,360 --> 00:28:33,360 
Yeah.

461
00:28:33,360 --> 00:28:34,360 
Yeah.

462
00:28:34,360 --> 00:28:37,480
It's just like it's, it's very like, you know, just this whole stream of aphasiology

463
00:28:37,480 --> 00:28:41,240 
that we kind of ignore that had like a lot of insights.

464
00:28:41,240 --> 00:28:42,240 
Yeah.

465
00:28:42,240 --> 00:28:46,880
And it's always really interesting to kind of try and understand like how all of his concepts

466
00:28:46,880 --> 00:28:49,280 
mapped on to like Western concepts.

467
00:28:49,280 --> 00:28:50,280 
Right.

468
00:28:50,280 --> 00:28:51,280 
Yeah.

469
00:28:51,280 --> 00:28:54,400
And I think there's a reasonably good correspondence between dynamic aphasia and transcortical

470
00:28:54,400 --> 00:28:58,520
motor aphasia, but like there's subtle differences in the understanding.

471
00:28:58,520 --> 00:28:59,520 
Yeah.

472
00:28:59,520 --> 00:29:00,520 
Right.

473
00:29:00,520 --> 00:29:01,520 
Yeah.

474
00:29:01,520 --> 00:29:05,400
Um, so that is good for me to hear coming from you because I, when I started reading

475
00:29:05,400 --> 00:29:07,800 
Luria, I was like, oh my gosh, this is also relevant.

476
00:29:07,800 --> 00:29:09,240 
Why didn't I know this?

477
00:29:09,240 --> 00:29:13,000 
And I think it might just be because have to blame your teachers.

478
00:29:13,000 --> 00:29:15,000 
No, I wouldn't think like teachers.

479
00:29:15,000 --> 00:29:17,800
I would blame the fact that like Russia wasn't part of the United States.

480
00:29:17,800 --> 00:29:20,600 
And so there was like just a different research tradition.

481
00:29:20,600 --> 00:29:24,320
And, um, I think some of his stuff didn't even get translated until the 70s.

482
00:29:24,320 --> 00:29:30,160
And there was already, you know, some dominant, uh, you know, schools of thinking going

483
00:29:30,160 --> 00:29:32,880 
on with, you know, the Boston school and everything.

484
00:29:32,880 --> 00:29:39,840
So it kind of didn't match back up, but anyway, so he described dynamic aphasia back in the

485
00:29:39,840 --> 00:29:47,520
40s as, you know, pretty clearly this, uh, difference between language as kind of a function

486
00:29:47,520 --> 00:29:52,000
and language and practice when it comes to spontaneous speech or like this propositional

487
00:29:52,000 --> 00:29:53,240 
part of language.

488
00:29:53,240 --> 00:29:58,000
And, uh, that's pretty much exactly what we were observing in these patients that we're

489
00:29:58,000 --> 00:29:59,000 
going to talk about soon.

490
00:29:59,000 --> 00:30:00,000 
Yeah.

491
00:30:00,000 --> 00:30:01,000 
Okay.

492
00:30:01,000 --> 00:30:03,000 
I wasn't familiar with the term.

493
00:30:03,000 --> 00:30:08,600
Um, so it was a lot of kind of learning backwards after I had observed something, uh, that

494
00:30:08,600 --> 00:30:11,080 
it had actually existed for quite some time.

495
00:30:11,080 --> 00:30:12,080 
Yeah.

496
00:30:12,080 --> 00:30:17,640
Um, and then another person that, um, you cite that's talks about this area is Jeff

497
00:30:17,640 --> 00:30:24,160
Binder, um, with, um, in particular, his 2009, um, meta-analysis, which I find to be a

498
00:30:24,160 --> 00:30:25,600 
really useful paper.

499
00:30:25,600 --> 00:30:27,800 
Um, so what does he say about this area?

500
00:30:27,800 --> 00:30:31,880 
Yeah, I believe he says it's involved in sort of semantic selection.

501
00:30:31,880 --> 00:30:34,040 
Um, this is from the meta-analysis, right?

502
00:30:34,040 --> 00:30:37,760
So that's, you know, all these different studies about semantics and, you know, various

503
00:30:37,760 --> 00:30:42,960
different task forms, uh, and that mediocre, prefrontal area does show up.

504
00:30:42,960 --> 00:30:49,360
Um, and he also mentions that it's kind of quite overlooked in language literature overall.

505
00:30:49,360 --> 00:30:55,160
Um, but, yeah, I think it's about retrieving semantic concepts and kind of, um, deciding

506
00:30:55,160 --> 00:31:00,920
which ones are potentially relevant for the situation at hand to, to use.

507
00:31:00,920 --> 00:31:02,240
Okay, great.

508
00:31:02,240 --> 00:31:07,520
Um, yes, so there's like kind of definitely, um, a back drop in the literature, like even

509
00:31:07,520 --> 00:31:11,800
though this is not an area that's kind of, you know, usually talked about as being one

510
00:31:11,800 --> 00:31:15,720
of the major language areas, there's definitely like data out there that suggests that it

511
00:31:15,720 --> 00:31:17,960 
does play some kind of a role.

512
00:31:17,960 --> 00:31:24,760
Um, and then you, um, find yourself with, um, this very unique patient population.

513
00:31:24,760 --> 00:31:29,120
Um, and you notice some things with patients who have lesions to this area.

514
00:31:29,120 --> 00:31:33,240
So in, in your paper, you start the way that you presented it, I think is very effective,

515
00:31:33,240 --> 00:31:37,360
which is that you start with this case, vignette before you get into all the analyses.

516
00:31:37,360 --> 00:31:40,360
Um, so I thought that might also be a good way for us to talk about it.

517
00:31:40,360 --> 00:31:48,760
So can you tell me about the, the case of, um, EK, I guess, um, who's not anonymous, um,

518
00:31:48,760 --> 00:31:52,520 
and just tell me about what, what you, um, observed with, with him.

519
00:31:52,520 --> 00:31:53,520 
Yeah.

520
00:31:53,520 --> 00:31:58,000
Um, as a side note, I actually had initially written it as something like EK or Dr K,

521
00:31:58,000 --> 00:32:00,880
and he specifically wrote to me and asked like, can you just use my name?

522
00:32:00,880 --> 00:32:02,400 
This makes me feel really weird.

523
00:32:02,400 --> 00:32:06,320 
So, so if it's okay with you, I'll go with Edwin.

524
00:32:06,320 --> 00:32:08,960 
Yeah, yeah, you can go with it, however you like.

525
00:32:08,960 --> 00:32:09,960 
Yeah.

526
00:32:09,960 --> 00:32:16,480
Um, but yeah, so, uh, I met Edwin before his surgery in the pre-op appointment, um, and

527
00:32:16,480 --> 00:32:22,720
he was multi-lingual, no, he spoke Cantonese as well as English, possibly some other languages

528
00:32:22,720 --> 00:32:23,720 
as well.

529
00:32:23,720 --> 00:32:29,400
Um, so we were talking during the evaluation, we used the quick aphasia battery, and afterwards

530
00:32:29,400 --> 00:32:34,480
we did a little brief multi-lingualism screener and he had a lot of opinions about it, um,

531
00:32:34,480 --> 00:32:40,800
because his work, he's a, uh, at the time was a student studying linguistics at the PhD

532
00:32:40,800 --> 00:32:44,080 
level, um, specifically like language revitalization.

533
00:32:44,080 --> 00:32:48,240
So, he had a lot of, you know, opinions, and I remember just thinking it was really cool

534
00:32:48,240 --> 00:32:50,200 
and interesting to talk to him.

535
00:32:50,200 --> 00:32:58,200
And, you know, we were talking at a, a pretty high level about language and, um, yeah, and

536
00:32:58,200 --> 00:33:04,360
then two days later, I go to see him post-op and I was sometimes, I think, so his language

537
00:33:04,360 --> 00:33:06,000 
would have been normal pre-op?

538
00:33:06,000 --> 00:33:10,280
Yes, it, yeah, I, I believe just 10 out of 10 flying colors across the board.

539
00:33:10,280 --> 00:33:14,280
And, and, and so, well, yes, so why is he getting a chunk of his brain taken out?

540
00:33:14,280 --> 00:33:23,440
Yeah, so he had, uh, Astrocytoma in his medial frontal cortex, uh, which they, his life

541
00:33:23,440 --> 00:33:29,800
was going on pretty standardly until he was on a Zoom call with his PhD advisor actually,

542
00:33:29,800 --> 00:33:33,960
um, and had a seizure in the middle of the Zoom call, uh, which I believe was his first

543
00:33:33,960 --> 00:33:34,960 
ever seizure.

544
00:33:34,960 --> 00:33:38,200 
Um, first and I think, I think from your description.

545
00:33:38,200 --> 00:33:39,200 
I think that's true.

546
00:33:39,200 --> 00:33:40,200 
Yeah.

547
00:33:40,200 --> 00:33:48,840
Um, and, uh, yes, so then he went to, he was already in the San Francisco area, um, went to

548
00:33:48,840 --> 00:33:51,760 
UCSF and, you know, they found a mass on imaging.

549
00:33:51,760 --> 00:33:57,840
So he's going in for, um, resective surgery to have that area removed, essentially to have

550
00:33:57,840 --> 00:33:58,840 
the tumor taken out.

551
00:33:58,840 --> 00:33:59,840
Okay.

552
00:33:59,840 --> 00:34:05,200
So, yeah, so pre-op is where I met him the day before that appointment.

553
00:34:05,200 --> 00:34:10,960
Um, and we do language assessments just to make sure everything is, uh, we have a basis of

554
00:34:10,960 --> 00:34:14,400 
comparison really for what we see after the surgery.

555
00:34:14,400 --> 00:34:19,200
And yeah, I believe just tens across the board on the quick aphasia battery.

556
00:34:19,200 --> 00:34:24,760
I think he had described some word finding issues, but, uh, we were not picking them up with

557
00:34:24,760 --> 00:34:25,760 
the quick aphasia battery.

558
00:34:25,760 --> 00:34:28,920 
I think he was largely within normal limits.

559
00:34:28,920 --> 00:34:29,920 
Mm-hmm.

560
00:34:29,920 --> 00:34:31,160 
So that was pretty up.

561
00:34:31,160 --> 00:34:32,920 
Um, hmm.

562
00:34:32,920 --> 00:34:39,960
And then post-op I, I guess we should say is the, what was taken at, what was resected?

563
00:34:39,960 --> 00:34:43,960
And there's a very nice picture, there's a very nice picture of it in the, you know, one

564
00:34:43,960 --> 00:34:47,360 
that this, this is what you, this is an audio format.

565
00:34:47,360 --> 00:34:49,360 
So can you describe?

566
00:34:49,360 --> 00:34:54,480
Um, well, essentially the area I was describing before, just in front of the SMA, uh, the

567
00:34:54,480 --> 00:34:58,880 
pre-SMA is what is removed in, um, in this patient Edwin.

568
00:34:58,880 --> 00:35:03,560
So, um, it's pretty, I would say pretty specific to that area, it doesn't go too much into anterior

569
00:35:03,560 --> 00:35:06,080 
interior, it doesn't go too much inferior.

570
00:35:06,080 --> 00:35:11,160
It's pretty much just, I would say our region of interest, um, although it became interest

571
00:35:11,160 --> 00:35:12,640 
after the fact.

572
00:35:12,640 --> 00:35:13,640 
Yes, exactly.

573
00:35:13,640 --> 00:35:14,640 
Um, okay.

574
00:35:14,640 --> 00:35:20,480
And then how does he do, um, sorry, my dog's decided that she needs to enter this room,

575
00:35:20,480 --> 00:35:22,280 
but that's not going to happen.

576
00:35:22,280 --> 00:35:27,520
Um, what, so then what does he, uh, how does he look when you meet him again a few days

577
00:35:27,520 --> 00:35:28,520 
later?

578
00:35:28,520 --> 00:35:33,240
So when I first walked into the room, I was expecting maybe we'd have a pretty similar

579
00:35:33,240 --> 00:35:35,800 
interaction to the one we had beforehand.

580
00:35:35,800 --> 00:35:39,800
Um, sometimes I would sort of make predictions like, okay, based on the reception location, what

581
00:35:39,800 --> 00:35:40,800 
would I expect?

582
00:35:40,800 --> 00:35:45,840
Uh, and I didn't really have a ton of expectations about this particular area.

583
00:35:45,840 --> 00:35:51,320
So, um, and oftentimes we would see people after surgery and, you know, in about 30% of cases,

584
00:35:51,320 --> 00:35:52,760 
they're still within normal limits.

585
00:35:52,760 --> 00:35:55,560 
So I was thinking maybe that would be the case with him.

586
00:35:55,560 --> 00:35:58,080 
Uh, um, I'm only first started interacting.

587
00:35:58,080 --> 00:36:00,320 
I was still under that impression.

588
00:36:00,320 --> 00:36:01,640 
I said, like, hey, how are you?

589
00:36:01,640 --> 00:36:03,320 
You said, good, good to see you.

590
00:36:03,320 --> 00:36:05,440 
You know, that kind of, that kind of thing.

591
00:36:05,440 --> 00:36:10,200
Um, but then we start going into the quick aphasia battery, which one of the first tasks

592
00:36:10,200 --> 00:36:14,560
is this connected speech task, which is essentially just, you know, asking someone to speak

593
00:36:14,560 --> 00:36:18,040 
spontaneously about some experience in their life.

594
00:36:18,040 --> 00:36:21,880
In this case, it might be the surgery or it might be like a story about something from

595
00:36:21,880 --> 00:36:23,080 
their past.

596
00:36:23,080 --> 00:36:29,920
Um, and I noticed pretty quickly that he was almost not communicating at all.

597
00:36:29,920 --> 00:36:37,040
Um, he was trying to and he had, uh, you know, he would respond kind of with the first bit

598
00:36:37,040 --> 00:36:38,560 
of an answer to a question.

599
00:36:38,560 --> 00:36:40,480 
So I would say, like, what do you remember from surgery?

600
00:36:40,480 --> 00:36:47,400
And he'd say, well, I remember waking up and then it would be long pauses.

601
00:36:47,400 --> 00:36:52,880
And then I think I did a picture association task, you know, this very kind of like,

602
00:36:52,880 --> 00:36:56,920 
technical language is still, um, easily being retrieved.

603
00:36:56,920 --> 00:37:05,160
Um, but at a rate and, uh, you know, a sort of degree of unusual pausing that you would

604
00:37:05,160 --> 00:37:07,880 
not expect from somebody without a language impairment.

605
00:37:07,880 --> 00:37:12,440
So at that point, I thought, okay, so probably the rest of the evaluation is going to go much

606
00:37:12,440 --> 00:37:13,440 
more like this.

607
00:37:13,440 --> 00:37:18,000
Like I think he's probably quite anomic, um, I'm expecting there will be difficulties

608
00:37:18,000 --> 00:37:20,720 
naming pictures, difficulties describing things.

609
00:37:20,720 --> 00:37:22,720 
Um, yeah.

610
00:37:22,720 --> 00:37:28,280
So his conversational speech was like super sparse and just like he couldn't really generate anything

611
00:37:28,280 --> 00:37:29,940 
in answer to your questions.

612
00:37:29,940 --> 00:37:30,940 
Right.

613
00:37:30,940 --> 00:37:31,940 
Exactly.

614
00:37:31,940 --> 00:37:32,940 
All of them.

615
00:37:32,940 --> 00:37:40,600 
Um, um, um, um, um, um, um, um, uh, yeah, exactly.

616
00:37:40,600 --> 00:37:44,280 
Um, and it, it didn't seem like it was for any lack of trying.

617
00:37:44,280 --> 00:37:48,940
I mean, he, he was definitely working towards communicating with me, you know, making efforts

618
00:37:48,940 --> 00:37:54,900
to communicate with me, but just kind of nothing, nothing coming up to, you know, to be described

619
00:37:54,900 --> 00:37:55,900
in words.

620
00:37:55,900 --> 00:38:00,860
Um, but on the rest of the evaluation, he actually still did pretty much perfectly.

621
00:38:00,860 --> 00:38:03,220 
So he could name every picture.

622
00:38:03,220 --> 00:38:12,620
Uh, he could, um, describe the, the images of, you know, that illicit grammatical constructions.

623
00:38:12,620 --> 00:38:15,180 
He could do the motor speech tasks.

624
00:38:15,180 --> 00:38:19,300
He could say catastrophe, catastrophe, catastrophe, which is like a notoriously difficult word

625
00:38:19,300 --> 00:38:21,140 
if you have motor speech issues.

626
00:38:21,140 --> 00:38:27,340
Um, and, you know, if I had only seen the last two thirds of the evaluation, I'd probably

627
00:38:27,340 --> 00:38:32,780
say this person has normal language, uh, but it was really just that spontaneously generated

628
00:38:32,780 --> 00:38:36,260 
portion that was completely different.

629
00:38:36,260 --> 00:38:39,980 
And that was very striking to me.

630
00:38:39,980 --> 00:38:40,980
So yeah.

631
00:38:40,980 --> 00:38:46,940
And by the way, I just want to say like, um, listeners should check out the paper because

632
00:38:46,940 --> 00:38:52,420
your figure one A is like an extremely nice figure because it kind of encapsulates everything

633
00:38:52,420 --> 00:38:55,780 
about this paper in one neat little panel.

634
00:38:55,780 --> 00:39:00,340
It's got a picture of Edwin's brain with the missing region, like with the nice dotted

635
00:39:00,340 --> 00:39:01,340 
outline around it.

636
00:39:01,340 --> 00:39:03,980 
And it's got four sections from the quick aphasia battery.

637
00:39:03,980 --> 00:39:08,460
One is repetition and you get to see him repeating like a really complicated sentence.

638
00:39:08,460 --> 00:39:12,620
And these confrontation naming you get to see like these rather tricky low frequency

639
00:39:12,620 --> 00:39:17,780
lexical items that he retrieves speech motor, you get to see him saying catastrophe, catastrophe.

640
00:39:17,780 --> 00:39:21,420
And then the other corner is self-generated speech and it's like, what do you remember from

641
00:39:21,420 --> 00:39:22,500
the surgery?

642
00:39:22,500 --> 00:39:32,460
Um, so I work up, um, I, um, size, um, and it just really makes that like dissociation super

643
00:39:32,460 --> 00:39:35,420 
clear, um, and encapsulates.

644
00:39:35,420 --> 00:39:36,900 
So nice, nice figure.

645
00:39:36,900 --> 00:39:37,900 
It really should check it out.

646
00:39:37,900 --> 00:39:41,380 
Um, now what were you going to say next?

647
00:39:41,380 --> 00:39:51,500
Um, yeah, I guess I would say that well, one of the first things I thought is this is surprising,

648
00:39:51,500 --> 00:39:55,540
but I actually, it seems a little familiar to me and I remembered somebody I'd seen earlier

649
00:39:55,540 --> 00:40:02,500
that year who actually had sort of a similar presentation, um, after surgery, although slightly

650
00:40:02,500 --> 00:40:09,420 
less, um, kind of involved in the, the assessment.

651
00:40:09,420 --> 00:40:15,940
So, I remembered her evaluation and then I looked at her imaging and I was like, oh, that was

652
00:40:15,940 --> 00:40:16,940 
the same place.

653
00:40:16,940 --> 00:40:19,980 
Actually, I think that was almost exactly the same location.

654
00:40:19,980 --> 00:40:26,060
Um, so it definitely kind of like sparked a, an awareness in me that this might be a pattern

655
00:40:26,060 --> 00:40:28,780 
and not just an interesting case.

656
00:40:28,780 --> 00:40:35,500
Uh, uh, and then I did a follow up evaluation with, uh, Edwin a month later and he was essentially

657
00:40:35,500 --> 00:40:36,900 
back to his baseline.

658
00:40:36,900 --> 00:40:43,700
Um, so he could speak in great detail about the experience he had doing that evaluation

659
00:40:43,700 --> 00:40:48,300
and kind of realizing that he had difficulty communicating even though it didn't really feel

660
00:40:48,300 --> 00:40:50,140 
internally like he should.

661
00:40:50,140 --> 00:40:55,620
Um, so he gave a lot of really interesting insights, which he then brought up as a supplementary,

662
00:40:55,620 --> 00:40:57,740 
um, material for this paper.

663
00:40:57,740 --> 00:41:04,740
Um, that I thought were for a variety of reasons just shed so much light on what it's actually

664
00:41:04,740 --> 00:41:08,300 
like from the perspective of the person experiencing this.

665
00:41:08,300 --> 00:41:12,500
And, uh, yeah, I felt very strongly that he should be involved in, in writing this paper

666
00:41:12,500 --> 00:41:16,580 
up because it was so, you have to have active.

667
00:41:16,580 --> 00:41:19,540 
And so yeah, you've included him as a co-author and I love that.

668
00:41:19,540 --> 00:41:23,940
Like, you know, that, I mean, that's really like bringing the people that, whose brains we

669
00:41:23,940 --> 00:41:30,300
study into the process, um, even more richly than, you know, we would normally be like putting

670
00:41:30,300 --> 00:41:32,180 
them on advisory boards and stuff.

671
00:41:32,180 --> 00:41:36,660
I mean, I love the idea of like, you know, one of the participants being a co-author.

672
00:41:36,660 --> 00:41:42,060
I mean, and what would you say was the most like, what were the biggest insights that you

673
00:41:42,060 --> 00:41:47,580
got from his description of what it had been like to be in this, um, ultimately transient

674
00:41:47,580 --> 00:41:49,580 
aphasia?

675
00:41:49,580 --> 00:41:50,580 
Yeah.

676
00:41:50,580 --> 00:41:53,380 
I think two things stuck out to me.

677
00:41:53,380 --> 00:41:59,580
One was that metaphor about, uh, feeling, I should I direct readers to this metaphor in

678
00:41:59,580 --> 00:42:01,180 
the paper or should I read it out loud?

679
00:42:01,180 --> 00:42:02,980 
It says, uh, yeah, essentially.

680
00:42:02,980 --> 00:42:03,980 
Would it be a life?

681
00:42:03,980 --> 00:42:04,980 
Yeah.

682
00:42:04,980 --> 00:42:12,180
So, he has this metaphor about, um, trying to describe what it was like to retrieve the

683
00:42:12,180 --> 00:42:13,660 
words that he couldn't get.

684
00:42:13,660 --> 00:42:17,540
And he said it was as if I was a farmer and all the words were buried beneath the soil.

685
00:42:17,540 --> 00:42:21,500
I was constantly trying to find that one specific word in the field that contained all these different

686
00:42:21,500 --> 00:42:24,140 
words, except I didn't know where that word was.

687
00:42:24,140 --> 00:42:28,580
And so, I kept on digging and digging, just trying to locate that one word to no avail.

688
00:42:28,580 --> 00:42:37,100
So yeah, a, just like beautifully written, um, and b, I think sometimes people would ask

689
00:42:37,100 --> 00:42:41,300
me how do you know this wasn't just a straight up cognitive, like he just didn't, wasn't thinking

690
00:42:41,300 --> 00:42:42,860 
anything, didn't have anything to say.

691
00:42:42,860 --> 00:42:47,420
And it's just very apparent when you read the supplementary materials that there was lots

692
00:42:47,420 --> 00:42:51,100 
and lots and lots of very active and insightful thought happening.

693
00:42:51,100 --> 00:42:54,500
And it was just a matter of kind of bringing it to the level where it could be expressed

694
00:42:54,500 --> 00:42:56,780 
in words that was failing.

695
00:42:56,780 --> 00:43:00,260 
Um, so I think that really stuck out to me.

696
00:43:00,260 --> 00:43:05,100
Uh, I also think there's a moment where someone says he was struggling with lexical retrieval,

697
00:43:05,100 --> 00:43:09,260
but not with the word lexical retrieval, not with retrieving the brain retrieval.

698
00:43:09,260 --> 00:43:10,620 
Um, you know, he's lots of linguists.

699
00:43:10,620 --> 00:43:12,100 
That was one of his linguist friends, right?

700
00:43:12,100 --> 00:43:13,100 
Yeah, exactly.

701
00:43:13,100 --> 00:43:17,380 
Um, so I think that's, uh, a, just kind of key.

702
00:43:17,380 --> 00:43:24,300
And b, uh, gets at this, um, you know, interesting dissociation between like, what is it like

703
00:43:24,300 --> 00:43:28,940
to try to retrieve words in context versus to just have specialized language that for whatever

704
00:43:28,940 --> 00:43:31,940
reason is like at the tip of your tongue all the time, especially when you're around your

705
00:43:31,940 --> 00:43:33,620 
linguist friends, right?

706
00:43:33,620 --> 00:43:40,780
Um, and then another thing that stuck out to me actually was, um, he says something about

707
00:43:40,780 --> 00:43:47,500
appreciating when other people would fill in silences with stories or with, uh, attempts to kind

708
00:43:47,500 --> 00:43:53,100
of help them come up with the words he was thinking of, um, which I know at times, at least in

709
00:43:53,100 --> 00:43:57,900
sort of more therapeutic contexts, you're instructed to just give people time and like, not

710
00:43:57,900 --> 00:44:02,020
jump in and let them get to their own, you know, get to the words when they come.

711
00:44:02,020 --> 00:44:05,980
Um, and I think the fact that this was transient in his case might have a lot to do with this

712
00:44:05,980 --> 00:44:11,540
because he wasn't necessarily, you know, living for years and years with people jumping in all

713
00:44:11,540 --> 00:44:12,540 
the time.

714
00:44:12,540 --> 00:44:16,940
Um, but I did think that was very interesting from sort of an interpersonal perspective that,

715
00:44:16,940 --> 00:44:21,820 
uh, helping someone out is okay, um, if they're, yeah.

716
00:44:21,820 --> 00:44:27,020
I think, I think that there's, I think there's a really, so situationally dependent, like

717
00:44:27,020 --> 00:44:31,500
when to jump in and when to give space, um, when talking to people with aphasia.

718
00:44:31,500 --> 00:44:36,780
And I think that a, like a great, like a great, like a great communicator with people with

719
00:44:36,780 --> 00:44:41,980
aphasia gets this real sense of like exactly how to pull that, when to pull back and when

720
00:44:41,980 --> 00:44:47,020
to go forward with that, you know, I think that's, yeah, just something that comes with time.

721
00:44:47,020 --> 00:44:49,020 
Yeah, yeah.

722
00:44:49,020 --> 00:44:50,020 
Yeah.

723
00:44:50,020 --> 00:44:59,700
Um, okay, so you, you kind of saw the, this very unique, aphasia, you, you realized had

724
00:44:59,700 --> 00:45:04,460
that aha moment where you connected it to another individual that you'd seen and looked at her

725
00:45:04,460 --> 00:45:07,740 
scan and found that they had the same brain area.

726
00:45:07,740 --> 00:45:12,780
And so, you probably thought at that point, oh, I could, I could, I should write a paper.

727
00:45:12,780 --> 00:45:18,180
Um, and, you know, being a cognitive neuroscientist, you didn't just stick to anecdotes, um, and case

728
00:45:18,180 --> 00:45:20,540 
studies, um, you did an analysis.

729
00:45:20,540 --> 00:45:28,540
So can you tell us, um, how you decided to quantify, um, this disproportionate impairment

730
00:45:28,540 --> 00:45:31,940 
of spontaneous speech that was the hallmark of these cases?

731
00:45:31,940 --> 00:45:32,940 
Yeah.

732
00:45:32,940 --> 00:45:39,980
So we, we used, so we were using two different evaluations at different times in the history

733
00:45:39,980 --> 00:45:40,980 
of this lab.

734
00:45:40,980 --> 00:45:45,860
So like I said, these types of data were collected for, um, I believe it's like almost 15 years

735
00:45:45,860 --> 00:45:51,980
at this point that this, uh, pre-immediate post and, uh, one month post data set has been

736
00:45:51,980 --> 00:45:52,980 
acquired.

737
00:45:52,980 --> 00:46:01,580
Um, the self-generated speech deficit felt like it was most captured, in connected speech

738
00:46:01,580 --> 00:46:04,740 
because that's kind of the only time you can observe it.

739
00:46:04,740 --> 00:46:11,300
Um, so for the, the lab that scoring maps on to fluency, basically, there's a fluency

740
00:46:11,300 --> 00:46:19,340
rating that's kind of clinician, um, determined for when I asked spontaneous questions, um, that

741
00:46:19,340 --> 00:46:22,620 
require spontaneous speeches and answer.

742
00:46:22,620 --> 00:46:27,740
You sort of rate from, you know, zero to 10, like how, how fluent did that sound?

743
00:46:27,740 --> 00:46:32,100
And there are, you know, thoughts of thoughts about whether that is an ordinal scale or, you

744
00:46:32,100 --> 00:46:33,100 
know, um, yeah.

745
00:46:33,100 --> 00:46:34,100 
Okay.

746
00:46:34,100 --> 00:46:35,100 
So, yeah.

747
00:46:35,100 --> 00:46:36,100 
So, yeah.

748
00:46:36,100 --> 00:46:39,580
So the earlier patients were tested on the web or Western or phasor battery and for them,

749
00:46:39,580 --> 00:46:46,620
you use the fluency scale, which is a truly horrible scale that I really love like, I

750
00:46:46,620 --> 00:46:49,860 
love teaching it to students and tearing it apart and like help.

751
00:46:49,860 --> 00:46:54,140
I think that like, when I teach students about all the reasons why the web fluency scale is

752
00:46:54,140 --> 00:46:58,980
bad, it, it really gives them insights about assessment and like what an assessment should

753
00:46:58,980 --> 00:47:01,180 
be and shouldn't be, but all, but I'll not say it.

754
00:47:01,180 --> 00:47:03,220 
I understand why you chose it from your data.

755
00:47:03,220 --> 00:47:04,500 
That's what you had.

756
00:47:04,500 --> 00:47:06,900 
Um, and it doesn't make sense.

757
00:47:06,900 --> 00:47:07,900 
Yeah.

758
00:47:07,900 --> 00:47:08,900 
Right.

759
00:47:08,900 --> 00:47:09,900 
Yeah.

760
00:47:09,900 --> 00:47:11,900
And then at some point, I convinced Eddie to switch over to the quick phasor battery.

761
00:47:11,900 --> 00:47:14,020 
So the second half of your data set would be that.

762
00:47:14,020 --> 00:47:15,940 
So what did you do with those patients?

763
00:47:15,940 --> 00:47:19,940
Yeah.

764
00:47:19,940 --> 00:47:25,620
So we used the reduced, reduced rate, reduced length and overall communication impairment,

765
00:47:25,620 --> 00:47:30,820
ratings from the spontaneous speech, connected speech part of the quick phasor battery because

766
00:47:30,820 --> 00:47:38,260
it kind of captured the elements that we were most interested in studying from the perspective

767
00:47:38,260 --> 00:47:44,180
of like spontaneous speech that is, you know, sounds unnaturally slow and unnaturally kind

768
00:47:44,180 --> 00:47:49,540
of without content, basically without just there isn't much of it, the sparseness of the

769
00:47:49,540 --> 00:47:51,540 
spontaneous speech.

770
00:47:51,540 --> 00:47:58,100
So we took the average of those three ratings and used those as something to map this specific

771
00:47:58,100 --> 00:48:04,100
issue generating speech spontaneously and independently as captured through the connected

772
00:48:04,100 --> 00:48:05,100 
speech questions.

773
00:48:05,100 --> 00:48:06,100 
Cool.

774
00:48:06,100 --> 00:48:07,100 
Yeah.

775
00:48:07,100 --> 00:48:08,100 
So, yeah.

776
00:48:08,100 --> 00:48:12,140
So, you've kind of basically with the two different batteries you've in each case got a mechanism

777
00:48:12,140 --> 00:48:18,620
for deriving a scalar number that's that quantifies their, you know, difficulty generating

778
00:48:18,620 --> 00:48:21,940 
spontaneous speech.

779
00:48:21,940 --> 00:48:25,140
And yeah, like it makes, it all, it makes plenty of sense to me how you did it with the

780
00:48:25,140 --> 00:48:27,140 
data, the data set that you had.

781
00:48:27,140 --> 00:48:28,620 
I think that's always a challenge, right?

782
00:48:28,620 --> 00:48:31,860
It's like, you know, we've got this inside this thing that we want to describe and it's

783
00:48:31,860 --> 00:48:33,100 
like, well, how do we quantify it?

784
00:48:33,100 --> 00:48:35,700 
So that's how you quantified it.

785
00:48:35,700 --> 00:48:38,260 

And then you related it to their lesions. 786
00:48:38,260 --> 00:48:41,140 
So can you talk about how you did that?

787
00:48:41,140 --> 00:48:42,140 
Yeah.

788
00:48:42,140 --> 00:48:46,580
So this was with a voxel based lesion symptom mapping, which you are intimately familiar

789
00:48:46,580 --> 00:48:48,260 
with.

790
00:48:48,260 --> 00:48:56,820
And so, this essentially takes the integrity of a given voxel in the brain and checks if

791
00:48:56,820 --> 00:49:01,260
there is a difference between the behavior of people who do or don't have that voxel

792
00:49:01,260 --> 00:49:03,740 
as part of their lesion.

793
00:49:03,740 --> 00:49:06,900 
So it does that across all of the voxels in the brain.

794
00:49:06,900 --> 00:49:13,640
And then what results is a map of the voxels where those differences are pronounced, the

795
00:49:13,640 --> 00:49:17,100 
differences in a behavior given whether or not there's a lesion there.

796
00:49:17,100 --> 00:49:23,460
So, in our case, we use that spontaneous speech measure, the self-
generated speech measure

797
00:49:23,460 --> 00:49:28,180 
that comes from the WAB and the QAB as our behavior of interest.

798
00:49:28,180 --> 00:49:35,460 
And then we included overall QAB score or AQ as the, as a covariate.

799
00:49:35,460 --> 00:49:41,820
So, we can kind of account for the fact that this thing is going to be low while the overall

800
00:49:41,820 --> 00:49:44,180 
score is high.

801
00:49:44,180 --> 00:49:50,020
And we also co-variate out whether a proxy of speech was present or not because the presentation

802
00:49:50,020 --> 00:49:52,780
we were interested in was specifically not a motor speech presentation.

803
00:49:52,780 --> 00:49:57,460 
Like that was not the reason behind the diminished output.

804
00:49:57,460 --> 00:50:06,340
So yeah, we did VLSM and we found this really clean pre-SMA region of interest that's in

805
00:50:06,340 --> 00:50:08,780 
the figure, figure two of the paper.

806
00:50:08,780 --> 00:50:10,780 
Yeah, figure two A.

807
00:50:10,780 --> 00:50:11,780 
Yeah, figure two A.

808
00:50:11,780 --> 00:50:12,780
It's very clean.

809
00:50:12,780 --> 00:50:13,780 
It's very nice.

810
00:50:13,780 --> 00:50:16,940 
Yeah, it's super, super clean.

811
00:50:16,940 --> 00:50:21,420
And then we did the same thing with multivariate lesion symptom mapping, which is this kind of

812
00:50:21,420 --> 00:50:27,140
newer version of this type of analysis that instead of treating each voxel independently,

813
00:50:27,140 --> 00:50:32,380
it kind of uses them all as part of one big model and back projects onto each voxel like

814
00:50:32,380 --> 00:50:36,860 
the weight that it seems to have on the outcome of the behavior.

815
00:50:36,860 --> 00:50:39,740 
And they're very, very similar in their findings.

816
00:50:39,740 --> 00:50:40,740 
So that is.

817
00:50:40,740 --> 00:50:46,660
Yeah, as has been the case in I think every MLSM study that's been done.

818
00:50:46,660 --> 00:50:50,980
And I think it's kind of mysterious as to why it's so similar when like conceptually

819
00:50:50,980 --> 00:50:53,780 
it seems so much better like MLSM.

820
00:50:53,780 --> 00:50:55,820 
Yeah, but it just always gets the same result.

821
00:50:55,820 --> 00:51:02,140
And you obviously had used MLSM in your dissertation as well, so it made sense that you.

822
00:51:02,140 --> 00:51:04,380 
Yeah, although for a totally different purpose.

823
00:51:04,380 --> 00:51:08,540 
Yeah, it was about the outcome not about mapping the lesion base.

824
00:51:08,540 --> 00:51:10,740 
True, true.

825
00:51:10,740 --> 00:51:17,380
And maybe MLSM is actually really useful for outcome prediction, whereas for like leisure

826
00:51:17,380 --> 00:51:22,100
localize it, like behavior, like lesions into mapping, it actually kind of tends to just

827
00:51:22,100 --> 00:51:25,620 
give the same results you might get from VLSM.

828
00:51:25,620 --> 00:51:29,500
Yeah, I think there might be differences between whether there's like a particular brain

829
00:51:29,500 --> 00:51:32,860 
area that you suspect is involved versus a network of brain areas.

830
00:51:32,860 --> 00:51:37,260
Sometimes MLSM might be better for that, but in principle, but has anybody ever shown

831
00:51:37,260 --> 00:51:38,260 
it?

832
00:51:38,260 --> 00:51:43,700
Ivanova has a paper where she kind of talks about it, but I think she is of

833
00:51:43,700 --> 00:51:46,860 
the same opinion as us, like largely it's going to be the same.

834
00:51:46,860 --> 00:51:47,860 
Yeah.

835
00:51:47,860 --> 00:51:49,740 
Okay, so that's nice.

836
00:51:49,740 --> 00:51:53,100
So yeah, it replicates within MSLM, but and it's super clean.

837
00:51:53,100 --> 00:51:58,260
And then you have this one last analysis where you look at the relative risk kind of this

838
00:51:58,260 --> 00:52:03,980
sort of almost like a chi square type analysis of like having this pre-SMA damage and having

839
00:52:03,980 --> 00:52:06,940 
this behavioral manifestation, this unique kind of aphasia.

840
00:52:06,940 --> 00:52:09,580 
So what do you see there in your data set?

841
00:52:09,580 --> 00:52:16,700
Yeah, so basically, we looked at people who did have the resection and either did or didn't

842
00:52:16,700 --> 00:52:22,900
have the spontaneous speech deficit or people who did have the spontaneous speech deficit.

843
00:52:22,900 --> 00:52:24,500 
And either did or didn't have the resection.

844
00:52:24,500 --> 00:52:26,300 
Those were kind of the conditions of interest.

845
00:52:26,300 --> 00:52:35,020
So yeah, we find that you're basically if you have this deficit, you're 15 times more

846
00:52:35,020 --> 00:52:42,460
likely to have had this resection, that's a very kind of again, clear result.

847
00:52:42,460 --> 00:52:47,260
And I think what's kind of interesting too is if you look at figure three A versus figure

848
00:52:47,260 --> 00:52:51,940
three B, most of the people who had this resection and didn't have this deficit just kind

849
00:52:51,940 --> 00:52:53,220 
of didn't have a deficit.

850
00:52:53,220 --> 00:52:56,020 
Like either you're going to have nothing or you're going to have this.

851
00:52:56,020 --> 00:52:57,020 
Okay.

852
00:52:57,020 --> 00:52:58,020 
Okay.

853
00:52:58,020 --> 00:53:04,700
And so, about half the people with the pre-SMA resection to have the syndrome you describe,

854
00:53:04,700 --> 00:53:06,100 
are we calling it dynamic aphasia?

855
00:53:06,100 --> 00:53:09,980 
Like what's your preferred name for it when you think about it now?

856
00:53:09,980 --> 00:53:11,580 
Yeah, it's a great question.

857
00:53:11,580 --> 00:53:14,700 
I think I would be comfortable calling it dynamic aphasia.

858
00:53:14,700 --> 00:53:19,340
I was very careful in the paper to try not to be too tied to any particular tradition

859
00:53:19,340 --> 00:53:23,180
of thought around it, just because I didn't want to step on any toes where, you know, there's

860
00:53:23,180 --> 00:53:27,820
different theoretical assumptions, but I think dynamic aphasia is pretty clearly like the

861
00:53:27,820 --> 00:53:30,140 
clearest map onto what we observed.

862
00:53:30,140 --> 00:53:31,140 
Okay.

863
00:53:31,140 --> 00:53:36,500
So, half of them had dynamic aphasia and you're saying the other half had nothing more or less.

864
00:53:36,500 --> 00:53:39,940 
Or had sort of dynamic aphasia plus a motor speech deficit.

865
00:53:39,940 --> 00:53:40,940 
Oh, okay.

866
00:53:40,940 --> 00:53:41,940 
Yeah, okay.

867
00:53:41,940 --> 00:53:47,660
Yeah, because you required no, apraxia of speech to meet your core diagnostic criteria.

868
00:53:47,660 --> 00:53:52,020
And then you also saw that you occasionally saw dynamic aphasia in people with lesions

869
00:53:52,020 --> 00:53:53,420 
other than the SMA, right?

870
00:53:53,420 --> 00:53:55,980 
So what, who were those people?

871
00:53:55,980 --> 00:53:56,980 
Yeah.

872
00:53:56,980 --> 00:54:02,620
So, there's a sort of small trend towards it maybe being inferior frontal gyrus, but that's

873
00:54:02,620 --> 00:54:05,740 
a much smaller number of individuals who presented with this.

874
00:54:05,740 --> 00:54:11,820
So, you can see that on the color bar between A and C basically that in panel A of that figure,

875
00:54:11,820 --> 00:54:12,820 
there's three.

876
00:54:12,820 --> 00:54:13,820 
Yeah.

877
00:54:13,820 --> 00:54:14,820 
Figure three.

878
00:54:14,820 --> 00:54:15,820 
Okay.

879
00:54:15,820 --> 00:54:21,700
There's a very clear kind of hotspot in this pre-SMA area where, you know, online people

880
00:54:21,700 --> 00:54:25,420 
that have that perception and also have that deficit fall right there.

881
00:54:25,420 --> 00:54:31,220
Whereas in figure 3C, you have about three people who have this deficit where it kind

882
00:54:31,220 --> 00:54:35,620
of centers on the inferior frontal gyrus, which might be meaningful, but it's certainly

883
00:54:35,620 --> 00:54:38,900 
not everybody else with the deficit has an inferior frontal lesion.

884
00:54:38,900 --> 00:54:39,900 
Yeah.

885
00:54:39,900 --> 00:54:41,500 
It can be a little more widespread than that.

886
00:54:41,500 --> 00:54:42,500 
Okay.

887
00:54:42,500 --> 00:54:47,460
But it's really like pre-SMA is really the region that is much more strongly associated

888
00:54:47,460 --> 00:54:49,460 
with this than anything else.

889
00:54:49,460 --> 00:54:53,220 
At least based on the way we did, yeah.

890
00:54:53,220 --> 00:54:54,220 
Yeah.

891
00:54:54,220 --> 00:54:57,780 
And the surgical population is kind of good for this question, right?

892
00:54:57,780 --> 00:55:03,580
I mean, they offer you various advantages relative to other neurological conditions.

893
00:55:03,580 --> 00:55:04,580 
Yeah.

894
00:55:04,580 --> 00:55:05,580 
Yeah.

895
00:55:05,580 --> 00:55:08,580 
I mean, I think there's the precision of the lesions.

896
00:55:08,580 --> 00:55:13,620
I mean, it's not going to be just kind of a natural experiment based on, you know, where

897
00:55:13,620 --> 00:55:17,940 
the, an occlusion occurs in an artery or something like that.

898
00:55:17,940 --> 00:55:20,580 
And I think ACA occlusions are actually quite rare.

899
00:55:20,580 --> 00:55:21,580
Yeah.

900
00:55:21,580 --> 00:55:23,060 
They are quite rare.

901
00:55:23,060 --> 00:55:24,060 
Yeah.

902
00:55:24,060 --> 00:55:26,340 
So you don't have as many opportunities to study it in stroke.

903
00:55:26,340 --> 00:55:30,300
And when you do, it's rare that it's going to only damage this area because it's going

904
00:55:30,300 --> 00:55:33,060 
to be kind of depending on where the occlusion is.

905
00:55:33,060 --> 00:55:37,860
It can also affect the SMA, it can affect, you know, frontal areas in front of it, or,

906
00:55:37,860 --> 00:55:41,060 
you know, laterally, so, probably more so laterally.

907
00:55:41,060 --> 00:55:42,380 
But, yeah.

908
00:55:42,380 --> 00:55:48,980
So first of all, it gives you kind of opportunities to see precise lesions in that area.

909
00:55:48,980 --> 00:55:53,220
It also gives you an opportunity to do preoperative evaluation, which you generally are not going

910
00:55:53,220 --> 00:55:57,120
to have in stroke because you're not going to see people before they've had a stroke and

911
00:55:57,120 --> 00:55:58,120 
evaluate their language.

912
00:55:58,120 --> 00:56:00,300 
There's usually no reason for that to occur.

913
00:56:00,300 --> 00:56:07,740
So, it offers you that as well as the opportunity to interview people like right after they have had a surgery where there hasn’t been any reorganization 

914
00:56:12,460 --> 00:56:16,020 
And a month later, when often, it's their return to baseline.

915
00:56:16,020 --> 00:56:21,900 
So you get this kind of whole trajectory, which is really interesting.

916
00:56:21,900 --> 00:56:29,100
And neurodegenerative populations, neurodegenerative populations, you can also see some patterns

917
00:56:29,100 --> 00:56:30,300 
sort of like this.

918
00:56:30,300 --> 00:56:34,140 
But again, it's going to rarely target just that one area.

919
00:56:34,140 --> 00:56:39,620
And it's, you're not going to get these opportunities to hear about recovery as well, unfortunately,

920
00:56:39,620 --> 00:56:41,740 
because so, yeah.

921
00:56:41,740 --> 00:56:46,220
So, it is a really interesting and unique population to get to learn about this from.

922
00:56:46,220 --> 00:56:47,220
Okay.

923
00:56:47,220 --> 00:56:48,220 
Yeah.

924
00:56:48,220 --> 00:56:50,660 
Yeah, these transient aphasias are kind of really focal.

925
00:56:50,660 --> 00:56:52,420 
That's like their value.

926
00:56:52,420 --> 00:56:54,100 
But also a mystery.

927
00:56:54,100 --> 00:56:56,620 
But we'll talk about that in a second.

928
00:56:56,620 --> 00:57:03,580
Before that, like, you know, so, yeah, pre-SMA damage causes dynamic aphasia, to oversimplify

929
00:57:03,580 --> 00:57:04,580 
maybe.

930
00:57:04,580 --> 00:57:07,940 
Would you say that pre-SMA is therefore a language region?

931
00:57:07,940 --> 00:57:10,820 
Like, how do you end up coming down on that point?

932
00:57:10,820 --> 00:57:14,140 
Yeah, it's such a good question.

933
00:57:14,140 --> 00:57:19,380
I think that it's, and other people have said this before, I think it's really at that boundary

934
00:57:19,380 --> 00:57:20,900
between language and thought.

935
00:57:20,900 --> 00:57:28,700
I think there's been a lot of work suggesting that it's involved in either kind of like pushing

936
00:57:28,700 --> 00:57:30,780 
choices to the surface.

937
00:57:30,780 --> 00:57:34,660 
This has shown up in animal work.

938
00:57:34,660 --> 00:57:37,260 
It's shown up in the fluency tasks that are done by the Robinson lab.

939
00:57:37,260 --> 00:57:44,580
It seems to be possibly domain generally, just any time you have kind of like a wide space

940
00:57:44,580 --> 00:57:47,100 
of possibilities where you can do anything.

941
00:57:47,100 --> 00:57:53,140
It seems like it might be involved in sort of increasing the activation of any given arbitrary

942
00:57:53,140 --> 00:57:57,100 
choice within like a field of possible choices.

943
00:57:57,100 --> 00:58:01,860
And when that happens in sort of the language system, I think the idea is that you have this

944
00:58:01,860 --> 00:58:06,100
linguistic apparatus that's functioning fine, but if you don't have any specific cue

945
00:58:06,100 --> 00:58:11,900
or input about like what's worth discussing, like what rises above the threshold of, you

946
00:58:11,900 --> 00:58:17,540
know, like this is relevant to say right now, there's possibly just nothing that's pushing

947
00:58:17,540 --> 00:58:21,140 
anything to that level of like that is the thing that you should say.

948
00:58:21,140 --> 00:58:23,740 
That is the thing that is worth expressing right now.

949
00:58:23,740 --> 00:58:29,140 
Is that Gail Robinson's energization concept?

950
00:58:29,140 --> 00:58:35,180
I'm kind of using I think energization and also response selection or like task monitoring.

951
00:58:35,180 --> 00:58:38,180
And this is something at some point I would love to talk with her about like, you know,

952
00:58:38,180 --> 00:58:45,660
what is the kind of like really clear distinction between those things behaviorally because

953
00:58:45,660 --> 00:58:52,180
I think energization is like initiating the response and sustaining it over time, which

954
00:58:52,180 --> 00:58:54,100 
I think you can describe in a similar way, right?

955
00:58:54,100 --> 00:58:57,100
Like you can say first you have to decide something is worth saying and then you have to

956
00:58:57,100 --> 00:58:59,260
keep deciding to continue with it.

957
00:58:59,260 --> 00:59:06,340
Like there is this kind of continuous role of some process in deciding like, this is

958
00:59:06,340 --> 00:59:10,460
the thing to say now that you've said that this is the next thing to say you should keep

959
00:59:10,460 --> 00:59:14,980
going, you know, there's this sort of like decision making process around that process

960
00:59:14,980 --> 00:59:17,300 
moving forward.

961
00:59:17,300 --> 00:59:22,980
Whereas response selection or the task monitoring stuff is more about when there are higher

962
00:59:22,980 --> 00:59:30,220
low constraints around what you could say, like selecting between competing options, which,

963
00:59:30,220 --> 00:59:34,620
you know, I can see, I can see those kind of being based on a similar mechanism.

964
00:59:34,620 --> 00:59:37,780 
I can see them playing out in different ways.

965
00:59:37,780 --> 00:59:42,780
But I think the energization thing maybe crucially is more domain general according to this perspective.

966
00:59:42,780 --> 00:59:49,940
Like any kind of task whether it's verbal or gesture or drawing pictures like all of those

967
00:59:49,940 --> 00:59:55,260
would be affected by a Pre-SMA lesion or a medial frontal lesion in her theories.

968
00:59:55,260 --> 01:00:00,660
Whereas the sort of like high low constraint differences when there's, you know, maybe like

969
01:00:00,660 --> 01:00:06,100
higher closed probability for a given sense or something, those would be more associated

970
01:00:06,100 --> 01:00:10,340 
with lateral frontal regions and would be more of language specific.

971
01:00:10,340 --> 01:00:16,140
So, yeah, so the question of like, is this a language impairment or is this part of the

972
01:00:16,140 --> 01:00:17,140 
language system?

973
01:00:17,140 --> 01:00:23,220
I think when it comes to natural language and like producing discourse that is functional

974
01:00:23,220 --> 01:00:25,580 
in the world, you need this region.

975
01:00:25,580 --> 01:00:33,420 
I think that that I don't feel hesitant about saying it all.

976
01:00:33,420 --> 01:00:37,500
Whether this should be considered part of like the mechanics that support language, I

977
01:00:37,500 --> 01:00:39,580 
think this is something that sort of interfaces with that.

978
01:00:39,580 --> 01:00:46,140
I think this is something that pulls into that language system and pulls linguistic

979
01:00:46,140 --> 01:00:49,820
constructs to the surface to be expressed or, you know, kind of interfaces between the

980
01:00:49,820 --> 01:00:52,860 
thoughts themselves and that language system.

981
01:00:52,860 --> 01:00:53,860 
Do you have thoughts?

982
01:00:53,860 --> 01:00:55,820 
No, I think I agree with you.

983
01:00:55,820 --> 01:00:56,820 
Yeah, I think.

984
01:00:56,820 --> 01:00:59,020 
I guess so, yeah, I have thoughts.

985
01:00:59,020 --> 01:01:04,180
Like, I mean, one thing about the domain generality of a, like one thing that's really striking

986
01:01:04,180 --> 01:01:07,540 
is so you only have left hemisphere patients in your cohort.

987
01:01:07,540 --> 01:01:13,260
But in Binder's meta-analysis, it's super, super-lateralized, like the role of this region

988
01:01:13,260 --> 01:01:15,300 
in semantics.

989
01:01:15,300 --> 01:01:20,740
So, you know, to the extent that it's, you know, it probably is only the left that's relevant

990
01:01:20,740 --> 01:01:21,740 
for language.

991
01:01:21,740 --> 01:01:27,180 
So there probably is at least some specialization of its role.

992
01:01:27,180 --> 01:01:33,860
And I guess, well, you know, if you take seriously Edwin's description of the search for the

993
01:01:33,860 --> 01:01:39,540
items, like buried in like a farmer with buried in the field, like it's, it's not really a,

994
01:01:39,540 --> 01:01:43,540 
it's definitely not a sort of choosing among available choices, right?

995
01:01:43,540 --> 01:01:47,700
It's like, it's like finding anything that's, that's like, yeah, exactly.

996
01:01:47,700 --> 01:01:51,260
And you sort of specifically, I actually asked him at one point, we met with him about six

997
01:01:51,260 --> 01:01:55,340
months afterwards and asked him, like, do you remember having trouble selecting between options

998
01:01:55,340 --> 01:01:56,340 
on the menu?

999
01:01:56,340 --> 01:02:00,100 
Do you remember, you know, struggling to make the, any didn't?

1000
01:02:00,100 --> 01:02:03,500
And he actually specifically, I think I later sent him a draft of some lab meeting

1001
01:02:03,500 --> 01:02:09,220
slides I had where I was going to say, like, you know, maybe, maybe even if it doesn't feel

1002
01:02:09,220 --> 01:02:11,780 
like it's a selection deficit, it's still a selection deficit.

1003
01:02:11,780 --> 01:02:13,820 
And he was like, it's not a select deficit.

1004
01:02:13,820 --> 01:02:16,220 
It was really confident that it wasn't.

1005
01:02:16,220 --> 01:02:18,060 
That's so really cool.

1006
01:02:18,060 --> 01:02:19,060 
Yeah.

1007
01:02:19,060 --> 01:02:24,700
But again, I mean, there is, I think the, the personal experience versus the like theoretical

1008
01:02:24,700 --> 01:02:28,940
possibility, it's, you know, it's hard to disentangle, you know, what somebody has access to in their

1009
01:02:28,940 --> 01:02:31,700 
own lexicon and search.

1010
01:02:31,700 --> 01:02:34,980 
But, you know, I'm inclined to trust him on that.

1011
01:02:34,980 --> 01:02:41,420
Yeah, but I think with that searching in the soil analogy, like, there's, what I'm picturing

1012
01:02:41,420 --> 01:02:45,140 
is kind of like, this is a messy metaphor.

1013
01:02:45,140 --> 01:02:47,740 
You can decide whether to keep this in or not.

1014
01:02:47,740 --> 01:02:51,820
But if you imagine under the soil where he's trying to pick the, the plants or the crops,

1015
01:02:51,820 --> 01:02:55,660
that there's something that normally pushes some of those crops closer to the surface,

1016
01:02:55,660 --> 01:02:58,020 
where it's like, that's the one that's relevant here.

1017
01:02:58,020 --> 01:03:04,340
And it seems like that, that sort of elevator underneath the soil just wasn't there anymore.

1018
01:03:04,340 --> 01:03:10,780
So, yeah, it's not that there was a, it's not that there weren't words to be found is

1019
01:03:10,780 --> 01:03:14,780
that those words weren't being pushed to the surface to be selected for expression.

1020
01:03:14,780 --> 01:03:15,780 
Definitely keeping that in.

1021
01:03:15,780 --> 01:03:17,260 
I think that's a great metaphor.

1022
01:03:17,260 --> 01:03:20,380 
Yeah, hopefully they'll forgive the elevator under the soil.

1023
01:03:20,380 --> 01:03:26,100 
That's, you know, we, you know, it's a conversation.

1024
01:03:26,100 --> 01:03:27,700 
You came up with it on the fly.

1025
01:03:27,700 --> 01:03:34,940
So, the aphasias that we see in these post surgical patients are transient usually,

1026
01:03:34,940 --> 01:03:40,700
they're usually largely resolved by a month with just a few residual issues as you discuss

1027
01:03:40,700 --> 01:03:43,300 
and we, has been shown in other work.

1028
01:03:43,300 --> 01:03:48,300 
Isn't it interesting that something can cause such a profound deficit?

1029
01:03:48,300 --> 01:03:53,860 
And yet, the brain is able to find out another way round like this.

1030
01:03:53,860 --> 01:03:56,860 
And we don't really know where that other way round is, right?

1031
01:03:56,860 --> 01:04:04,420
Yeah, it'd be a really interesting fMRI study, I guess, to look at sort of within a month

1032
01:04:04,420 --> 01:04:09,660
or so, how, how is this area getting re reintegrated and, or, you know, this, this function

1033
01:04:09,660 --> 01:04:11,820 
getting reintegrated through other areas?

1034
01:04:11,820 --> 01:04:18,700
Yeah, when, and this, and in our, the task that we use that you've worked with a lot,

1035
01:04:18,700 --> 01:04:24,940
it does activate the, this area, it does activate the medial surface of the frontal lobe.

1036
01:04:24,940 --> 01:04:29,700 
So, yeah, adaptive language mapping, yeah.

1037
01:04:29,700 --> 01:04:35,340
So, you know, if we scan somebody like Edwin, when we would not see that, we would not see,

1038
01:04:35,340 --> 01:04:39,740 
we'd see at least a whole where they should have been in activation.

1039
01:04:39,740 --> 01:04:46,420
Whether we would see residual activation around his resection, we might, or would we just

1040
01:04:46,420 --> 01:04:50,740
see nothing, would we just see like the rest of the language network activating as normal

1041
01:04:50,740 --> 01:04:56,820
and the, you know, deficit has been overcome and we don't understand how that happened.

1042
01:04:56,820 --> 01:04:58,980 
That's probably, that's what I'm gonna...

1043
01:04:58,980 --> 01:05:01,380 
Yeah, we can get a surprise right hemisphere.

1044
01:05:01,380 --> 01:05:06,180
Yeah, but that would not be my expectation, but that would be the most exciting finding,

1045
01:05:06,180 --> 01:05:07,180 
yeah, definitely.

1046
01:05:07,180 --> 01:05:10,140
That would be, that would be by far the most exciting, but it's not something that we frequently

1047
01:05:10,140 --> 01:05:11,140 
seen.

1048
01:05:11,140 --> 01:05:12,140 
Yeah.

1049
01:05:12,140 --> 01:05:15,340 
Yeah, so it's kind of mysterious, right?

1050
01:05:15,340 --> 01:05:19,260 
Like, how do these individuals recover?

1051
01:05:19,260 --> 01:05:25,020 
Yeah, I think that's still an open question.

1052
01:05:25,020 --> 01:05:27,460 
Yeah, definitely.

1053
01:05:27,460 --> 01:05:29,580 
Last thing about the paper.

1054
01:05:29,580 --> 01:05:36,140
So, it starts with the line, "For an aphasia-friendly version of this article, please see," and then

1055
01:05:36,140 --> 01:05:41,980
you have a link to a, to a version of the paper, which is written in a way that's accessible

1056
01:05:41,980 --> 01:05:42,980 
to people with aphasia.

1057
01:05:42,980 --> 01:05:44,220 
It has simple language.

1058
01:05:44,220 --> 01:05:47,020 
It has iconography.

1059
01:05:47,020 --> 01:05:53,860
Can you tell us about why you make aphasia-friendly versions of all your papers and how you go about

1060
01:05:53,860 --> 01:05:55,860 
it and why you think it's important?

1061
01:05:55,860 --> 01:05:56,860 
Yeah, yeah.

1062
01:05:56,860 --> 01:06:00,380
So, I touched on this a little bit earlier with the aphasia group stuff.

1063
01:06:00,380 --> 01:06:06,700
I think that there's so much curiosity about what people are learning about aphasia, among

1064
01:06:06,700 --> 01:06:11,660
people with aphasia, and it's so hard for them to get that information, especially at sort

1065
01:06:11,660 --> 01:06:17,140
of the researcher-generated level as opposed to press releases or coverage.

1066
01:06:17,140 --> 01:06:23,060
So, that's something that Anna Kasdan and I got really passionate about in grad school,

1067
01:06:23,060 --> 01:06:26,660 
and I've tried to carry it through with me as I continue publishing.

1068
01:06:26,660 --> 01:06:31,940
So, yeah, I mean, especially for people where you're undergoing like an elective surgery,

1069
01:06:31,940 --> 01:06:35,740
or you have experienced something after surgery, or even if you've just had a stroke and it

1070
01:06:35,740 --> 01:06:40,380 
happens to, you know, align with something that has been studied.

1071
01:06:40,380 --> 01:06:43,660 
I think really great to be able to get from the researcher's mouth.

1072
01:06:43,660 --> 01:06:46,060 
Like, here's what I think you should understand about this.

1073
01:06:46,060 --> 01:06:49,300 
Here's what might be relevant for you and your family.

1074
01:06:49,300 --> 01:06:56,380
So, that's been a big part of what I try to and hope to continue trying to do as a researcher.

1075
01:06:56,380 --> 01:07:01,420
Like, make that research not just academic and actually get it out to the people that

1076
01:07:01,420 --> 01:07:03,420 
it's about.

1077
01:07:03,420 --> 01:07:09,060
So, in terms of making them at the time of this that I was drafting this one, I basically

1078
01:07:09,060 --> 01:07:12,900
just opened up a Google doc and I try to think like, okay, what are the key messages here?

1079
01:07:12,900 --> 01:07:17,220 
And, you know, where do I find free icons that demonstrate it?

1080
01:07:17,220 --> 01:07:18,980 
And that for me is kind of a fun process.

1081
01:07:18,980 --> 01:07:21,260 
I really like doing that.

1082
01:07:21,260 --> 01:07:27,340
But actually Anna and me and my husband Isaac, who I met at the computational memory lab,

1083
01:07:27,340 --> 01:07:34,940
he's a software developer, have now started working on an LLM-based version of this.

1084
01:07:34,940 --> 01:07:42,540
And, there was a lot of the upfront work for you with the extreme caveat that a researcher

1085
01:07:42,540 --> 01:07:46,460
who uses that absolutely has to check everything that comes out of it and the icons are going

1086
01:07:46,460 --> 01:07:49,300 
to require a lot of tweaking.

1087
01:07:49,300 --> 01:07:53,180
But it's sort of a way I'm hoping to motivate people to do this type of thing because it

1088
01:07:53,180 --> 01:07:57,460
might not feel like such a big lift if there's been a touch of the work done for you as

1089
01:07:57,460 --> 01:07:58,460 
a set.

1090
01:07:58,460 --> 01:08:01,260 
So, you get an LLM to write the first draft?

1091
01:08:01,260 --> 01:08:02,260 
Yeah.

1092
01:08:02,260 --> 01:08:04,260 
And then you, who get yourself?

1093
01:08:04,260 --> 01:08:07,100 
Yeah, lovely.

1094
01:08:07,100 --> 01:08:09,340 
So yeah, this is a great paper.

1095
01:08:09,340 --> 01:08:12,140 
I think everybody should read it.

1096
01:08:12,140 --> 01:08:16,940
And it's just like a beautiful description of an aphasia syndrome that doesn't get talked

1097
01:08:16,940 --> 01:08:23,580
about that much, but is very interesting and teaches us something about the language network.

1098
01:08:23,580 --> 01:08:30,460
So, the last thing I wanted to talk about beyond the paper is your current job.

1099
01:08:30,460 --> 01:08:35,620
So you're now a lecturer for the Princeton Writing Program, which I know is a job that you really

1100
01:08:35,620 --> 01:08:36,900 
love.

1101
01:08:36,900 --> 01:08:44,020
Can you tell us about how you came into that job and what led you in that direction?

1102
01:08:44,020 --> 01:08:45,020 
Yeah.

1103
01:08:45,020 --> 01:08:49,860 
So, I always loved teaching, always, always, always.

1104
01:08:49,860 --> 01:08:55,540
And when I was at Vanderbilt, I did the bold program, which was a center for teaching

1105
01:08:55,540 --> 01:09:00,100
based program where you got to design an online module for some existing class.

1106
01:09:00,100 --> 01:09:04,780 
I did it for the language psychology class at Vanderbilt.

1107
01:09:04,780 --> 01:09:08,340 
I did a bunch of teaching trainings at UCSF, their step-up program.

1108
01:09:08,340 --> 01:09:09,980 
I always loved teaching.

1109
01:09:09,980 --> 01:09:14,660
And as much as I loved grad school, I was actually thinking I would get more TA experience,

1110
01:09:14,660 --> 01:09:20,180
loved doing the TA for our very small class of already very qualified and brilliant master

1111
01:09:20,180 --> 01:09:21,180 
students.

1112
01:09:21,180 --> 01:09:26,940
But what I loved doing in undergrad was teaching, like, it was largely teaching freshman,

1113
01:09:26,940 --> 01:09:29,380 
actually, freshman and sophomores in the inter-site classes.

1114
01:09:29,380 --> 01:09:31,060 
So, I really liked teaching.

1115
01:09:31,060 --> 01:09:34,940 
I always knew I wanted to be involved in teaching in my career.

1116
01:09:34,940 --> 01:09:35,940 
And I always liked writing.

1117
01:09:35,940 --> 01:09:40,580
I don't think I touched on this in my sort of description of what brought me into the field.

1118
01:09:40,580 --> 01:09:46,060
But one of the ways I sort of thought about filtering the world through language was through

1119
01:09:46,060 --> 01:09:47,060 
writing.

1120
01:09:47,060 --> 01:09:49,020 
And I was the editor of a literary magazine at high school.

1121
01:09:49,020 --> 01:09:54,460
And I was very kind of involved in trying to make stories out of the world.

1122
01:09:54,460 --> 01:09:59,300
So yeah, the writing passion and the teaching passion have been there throughout my whole

1123
01:09:59,300 --> 01:10:01,260 
journey.

1124
01:10:01,260 --> 01:10:06,540
When I, it was 2022 that I went to Cold Spring Harbor for their Neurobiology of Language

1125
01:10:06,540 --> 01:10:09,540 
event, I guess.

1126
01:10:09,540 --> 01:10:14,660
It was like a week-long sort of seminar for students to work with people who are in the

1127
01:10:14,660 --> 01:10:17,300 
Neurobiology of Language, kind of like giants in the field.

1128
01:10:17,300 --> 01:10:22,700 
And I met a woman there, Srishti Nayak, who's at Vanderbilt now.

1129
01:10:22,700 --> 01:10:28,020
And she, on the first day, people kind of introduced themselves and said their career trajectories.

1130
01:10:28,020 --> 01:10:33,580
She mentioned that for two years she had taught a class at Princeton about graphic novels and

1131
01:10:33,580 --> 01:10:36,100 
the brain, graphic novels and psychology.

1132
01:10:36,100 --> 01:10:38,260 
And I was like, oh my god, that's my dream job.

1133
01:10:38,260 --> 01:10:39,260 
That sounds so cool.

1134
01:10:39,260 --> 01:10:43,020 
So I talked to her after, and I was like, what was that job?

1135
01:10:43,020 --> 01:10:45,900
What, where were you doing that?

1136
01:10:45,900 --> 01:10:47,420 
How can I do that?

1137
01:10:47,420 --> 01:10:51,660
And she told me about this Princeton writing program where essentially people from all

1138
01:10:51,660 --> 01:10:56,820
different disciplines get to design a class from scratch that teaches freshmen about

1139
01:10:56,820 --> 01:10:59,500 
scholarly writing and the process of scholarly writing.

1140
01:10:59,500 --> 01:11:03,020 
So yeah, basically, I was like, that is the job I want.

1141
01:11:03,020 --> 01:11:08,180
And the first year that it was available, I wasn't able to apply by the deadline, so I emailed

1142
01:11:08,180 --> 01:11:13,060
them and I was like, heads up, I'm applying next year, please don't lose my email.

1143
01:11:13,060 --> 01:11:20,700
And then the next year I applied and they brought me out for an interview.

1144
01:11:20,700 --> 01:11:22,700 
It was everything I dreamed it would be.

1145
01:11:22,700 --> 01:11:24,620 
It's so much fun.

1146
01:11:24,620 --> 01:11:28,460
And it gives you so much creativity around like what you get to think about, what you get

1147
01:11:28,460 --> 01:11:33,980 
to force 18 year olds to think about.

1148
01:11:33,980 --> 01:11:36,740 
Yeah, and I get to do all kinds of fun things.

1149
01:11:36,740 --> 01:11:42,340
I actually just recently bought a toy called Mind Flex from 2009, which I'm using in my class.

1150
01:11:42,340 --> 01:11:44,140 
What is that?

1151
01:11:44,140 --> 01:11:45,820 
It's, you wear a headset.

1152
01:11:45,820 --> 01:11:48,380 
It has like a single contact.

1153
01:11:48,380 --> 01:11:54,580
And theoretically, it is reading your brain waves to move a ball around a little, a little

1154
01:11:54,580 --> 01:11:57,420 
obstacle course, basically.

1155
01:11:57,420 --> 01:12:00,420 
Okay, just a question.

1156
01:12:00,420 --> 01:12:02,380 
You know, very unclear.

1157
01:12:02,380 --> 01:12:04,660 
But that makes it ripe for analysis in a writing class.

1158
01:12:04,660 --> 01:12:05,660
Does it not?

1159
01:12:05,660 --> 01:12:10,420
You have lots, you know, you can discuss about the nature of advertising and the nature of,

1160
01:12:10,420 --> 01:12:13,900 
you know, neuromania, especially in the early 2000s.

1161
01:12:13,900 --> 01:12:19,620
So, you know, there's a lot of kind of just picking interesting weird artifacts and, you know,

1162
01:12:19,620 --> 01:12:21,860 
forcing students to think about them at a high level.

1163
01:12:21,860 --> 01:12:23,940 
So, yeah, it's been a lot of fun.

1164
01:12:23,940 --> 01:12:25,620 
I could talk about it forever.

1165
01:12:25,620 --> 01:12:26,780 
That is so cool.

1166
01:12:26,780 --> 01:12:28,420 
Yeah, what are you neat?

1167
01:12:28,420 --> 01:12:31,620 
Like, you know, career, you're building for yourself.

1168
01:12:31,620 --> 01:12:34,180 
I can't wait to see what you do next.

1169
01:12:34,180 --> 01:12:35,180 
Yeah, yeah.

1170
01:12:35,180 --> 01:12:36,180
Yeah.

1171
01:12:36,180 --> 01:12:40,220 
I'm always surprising myself, so.

1172
01:12:40,220 --> 01:12:46,860
Well, I have to take my daughter to her flute workshop that is happening all this week,

1173
01:12:46,860 --> 01:12:50,780 
which is why we met super early in the morning, my time.

1174
01:12:50,780 --> 01:12:53,180 
So I will go and do that.

1175
01:12:53,180 --> 01:12:57,020 
And it was lovely talking with you and, you know, catching up.

1176
01:12:57,020 --> 01:13:01,140 
And, you know, thanks for walking us through this paper today.

1177
01:13:01,140 --> 01:13:02,140 
Yeah.

1178
01:13:02,140 --> 01:13:03,140 
Thank you so much.

1179
01:13:03,140 --> 01:13:05,780 
Thanks for thinking of me and, yeah, great to see you again.

1180
01:13:05,780 --> 01:13:06,780 
Yeah, you too.

1181
01:13:06,780 --> 01:13:07,780 
All right.

1182
01:13:07,780 --> 01:13:08,780
Take care.

1183
01:13:08,780 --> 01:13:09,780 
Bye.

1184
01:13:09,780 --> 01:13:10,780 
Okay.

1185
01:13:10,780 --> 01:13:11,780 
Well, that's it for episode 34.

1186
01:13:11,780 --> 01:13:13,300 
Thanks to Deb for joining me on the podcast.

1187
01:13:13,300 --> 01:13:20,020
And I've linked Deb's paper in the show notes and on the podcast website at langneurosci.org/podcast.

1188
01:13:20,020 --> 01:13:23,340 
Thanks also to Marcia Petyt for transcribing this episode.

1189
01:13:23,340 --> 01:13:26,340 
Please do consider submitting your papers to neurobiology of language.

1190
01:13:26,340 --> 01:13:30,540 
It's open access, society supported and has a great editorial team.

1191
01:13:30,540 --> 01:13:35,820
In my experience, I've had constructive reviews, fair decisions, and speedy publications.

1192
01:13:35,820 --> 01:13:39,420
If the article processing charge is barrier for your lab, that is always something you can

1193
01:13:39,420 --> 01:13:41,100 
talk to the editors about.

1194
01:13:41,100 --> 01:13:43,740
It's not going to stop your paper from getting published.

1195
01:13:43,740 --> 01:13:44,900 
Okay, bye for now.

1196
01:13:44,900 --> 01:13:45,540 
See you next time.