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Welcome to episode 30 of the Language Neuroscience Podcast.

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

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Australia.

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I can't believe that SNL will be here next month.

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I've been serving as an amateur travel agent lately, helping people make their holiday

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plans, lining up trips to the Great Barrier Reef, surfing lessons, hiking adventures in

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the pristine rainforest wilderness around here.

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If you're on the fence, it's not too late to make a plan to come to the conference and

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spend some time in this beautiful part of the world.

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Okay, my guest today is Maaike Vandermosten.

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Maaike is an Associate Professor in the Department of Neurosciences and head of Speech and Language

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research at KU Leuven, in Belgium.

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She has two fascinating lines of research, one on the neural basis of developmental dyslexia

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and a more recent but rapidly growing focus on neuroplasticity in recovery from aphasia,

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a topic that is obviously of special interest to me.

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In honor of her extremely impressive research achievements, Maaike was a winner last year

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of the 2023 Early Career Award from the Society for the Neurobiology of Language.

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Today we're going to talk about both of her lines of work.

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Okay, let's get to it.

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Hi Maaike, how are you today?

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Hi, yes, Stephen.

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I'm fine, thanks for asking.

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I'm also thanks for inviting me to this podcast.

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Oh yeah, I'm really looking forward to it.

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So can you tell me where you are today and what time is it, what's it like where you are?

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Yeah, so at the moment I'm in Leuven in Belgium and it's 9 o'clock in the morning, so it's

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not that early anymore, but I have already a school rush for the kids and so on, so I'm

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now starting working day here in the 11th.

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Oh, okay, you've got the kid to school retain as well, yeah, me too.

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Yeah, and in the beginning of September it's a bit more hectic than we still have to get

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used to it again, so it's a bit more hectic than normally.

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All right, because the school year would have just started for you guys, right?

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Yeah, this week.

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Okay.

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And my oldest daughter, she's now going to secondary school, so there was also a big change,

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so all these things have to find their place now.

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Oh, okay, what grade does secondary school start for you guys?

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Seventh grade, and she's twelve.

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Okay, same here, same here, so my daughter will go there in two years, like she's just finishing

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up fifth grade now.

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Okay.

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Yeah, so we'll be there soon too.

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And so Leuven, is that, are you from Leuven or did you move there for work or?

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No, I'm originally, so Belgium is quite small, so I lived in between Leuven and

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Brussels, so I came to Leuven for the studies, but it's only like 25 km, so it is

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very small, so it's very close by a big in terms of business.

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All right, yeah, now it is a small country.

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I mean, I remember one time that I sort of went there when I was in the Netherlands, I

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sort of went to Belgium by bike, and I didn't even realize I was about to enter Belgium,

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it was the first time I'd ever crossed an international border on a bike path, that was

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kind of cool.

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Yeah, so Leuven looks really beautiful when I was

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googling it when I was looking you up.

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It looks like it's got like a really ancient university, and that's where you work?

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Yeah, so it's a very old university, and so it has a lot of long traditions.

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And the city itself is very nice, it's quite small, so it's a lot of students living here,

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but it's, I like the combination of, it's still a city, but it's also very calm, so you

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can do everything by bike and there's not so many cars and so on, so it's nice.

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Yeah, it seems quite idyllic, I can see why you've kind of spent your whole life there

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apparently.

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Okay, so like, I always like to find out like how people got interested in our field, language

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and the brain, and I noticed that you actually, you have a degree in speech pathology, that

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was interesting to see, but how did you get to this field?

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Like, did you, were you interested in languages as a kid or the brain or anything like that?

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Like, what was your path into the field?

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Yeah, I did study Speech and Language Pathology and Audiology, and it's not

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that, when I was a kid, it's not that I only like languages, I was specifically interested

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in languages.

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I remember, for example, in secondary school, I chose like classic languages like Latin

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and Greek, because I like to study language, but I also how much like, totally different

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type of course, like physics, history, so it was, I had a bit of a more like a broad interest

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and not specifically in language, and I remember when I was 18, I had to make the choice of

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what I would study and then I found it very difficult because I had these broad interests,

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and at the time when I was 18, I was also very fascinated by politics and the recent history

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of our country and Europe and so on.

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So I first decided to do a Bachelor in Political Science, so it was something to a different

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way.

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Okay.

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And I'm still happy that I did because it was a good basis also to understand the politics

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here in Belgium, for example, because it's quite complicated here.

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So, so I first did that, but then I also missed a bit of, yeah, more biologically oriented

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courses, and then I went in the summer on a kind of volunteering camp, which was in

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Bulgaria where we worked with in an orphanage, and there I realized that what gives me most

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satisfaction or what makes me most happy is to really provide care for people.

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So then I realized studying the wrong topic, to the moment with political science.

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Oh, you didn't think politics was going to provide care for people? (Laughter)

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It should be in the long term, but I think if you look at politics here in Belgium, it goes

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very slowly, so, it's very difficult to have an impact.

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And I, yeah, I always had it also when I had to make the choice for university studies,

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yeah, I think I was hesitating between something like Speech and Language Pathology or Political

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Sciences, and because of the volunteering camp that I did, I realized that I would like

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to do something more with helping people in a more direct way.

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And then I think the Speech and Language pathology and Audiology was together here in Leuven,

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so it was, and it was quite a, also a very diverse programme of courses, as so we had, because

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of the Audiology and of the Physics as well, but also of course Linguistics, Psychology courses,

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and also more medical oriented courses like Neuroanatomy so on.

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So for me that was a good choice, because I didn't have to choose for one topic, so I had

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A bit of everything.

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And then, the education of Speech and Language and Audiology, I realized I

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liked the courses on the brain most.

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So I was always very fascinated by how the, what's a neural basis of language, and especially

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what it goes wrong like in persons with aphasia.

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I had my first internship with persons with aphasia and it was for me something, I

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remember it very vividly because it was, yeah, it had a big impact on me, seeing what

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impact was for the persons with aphasia and how also how different it can be depending

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on the person.

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It always has a very different expression of the language problems.

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Yeah, every patient is different, aren't they?

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Like, there's no two that are identical.

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Yeah, I also shared that fascination when I first met people with aphasia, and every time

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I would meet somebody, I feel like I would learn something new about language, just by

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seeing all the different ways it could break down.

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And with the internship, you try to help them, you try something like certain kind of

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intervention, but in the clinical practice, there was often not enough time to really try

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to understand why something was working for this person and not for the other.

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So it was also for me the realization that I would like to continue in research to really

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understand why certain things are working and why others are not.

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Okay, so even while you were doing the Master's degree, you thought, "Oh, okay, I actually

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want to become a researcher?"

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Yeah, I think the idea emerged throughout the Master's degree, because then you get more

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topics where you have more papers to read, and I felt there was still a lot to, and it still

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is the case, a lot to discover on how the brain is processing language, so it was a very interesting

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field for me.

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Right.

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So did you go straight into your PhD after that, or did you work as a clinician at all?

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No, I went straight to the PhDs, so after I graduated.

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So yeah, I first wanted to, ideally when I graduated, I wanted to have a combination of research

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with clinical practice, but then I was offered the PhD, and then it was still possible to combine

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it to a lot of clinical work.

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You could still do some, on a voluntary basis, some, you can still work a bit in clinical

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practice but not that extensively.

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Yeah.

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I think there will be such a problem.

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Okay.

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And is that when you started working on kids in dyslexia, like how did that transition

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happen, like into that topic area?

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Yeah.

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Yeah, I must admit, I wanted first to work a bit more on aphasia, for example, and look more in their

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general processes, but then I came across a PhD position, which was on developmental dyslexia,

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but it was also looking at the neural correlates of dyslexia, and it also had a very interdisciplinary

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team, so we had promoted from educational science, from the scale, and rather from, it was

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a physicist, and then, so for Radeology, so I felt for me it was a good combination of

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inputs from different directions, so therefore I decided to go for the PhD, although it was

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more on developmental dyslexia, and it was in adults, so that I did my study at my PhD study,

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so it was adults with dyslexia, but I still, yeah, it was a very good combination of input

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I received, so I could learn a lot from that topic.

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Okay.

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I didn't realize you were working with adults back then.

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But now you've kind of got this big kid project, I think it's called Dysco, or how do you say

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it?

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How do you say it?

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Yeah, I call it Dysco, which is for dyslexia collaboration, so this from dyslexia and

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Co from collaboration, and yeah, so as I, as I said during the PhD, I worked with adults,

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we looked at brain processes in adults, really structural MRIs, so we looked with diffusion MRI

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of right mental connectivity in adults with dyslexia, but it always, I think it was a very

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valid question often after a presentation, or when I was discussing my work with others,

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there was this question, like, yeah, would you find differences in adults with dyslexia in

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these white matter connections?

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But this might be the result of years of reading failure because it's, yeah, white matter 

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is plastic.

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So, what we see in the adults is maybe just the consequence of the fact that they have

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reading difficulties and not the cause of it, not the origin of it.

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And there was also, back then, also studies like Dehaene, who had his theory on neuronal recycling,

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so that the brain is effect not predestined for learning to read, but something that through

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our development, through our reading development, you have to adjust your brain to do this new

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skill to reading, so by relying on the more existing language network and the existing visual

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network.

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So, there was this whole idea of reorganization, right?

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For learning to read.

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So, it also means that what we saw in the adults is really like the product of all these

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three organizations that has been going on.

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So, therefore, it was for me when I wrote a proposal for my postdoc, I wanted to go

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earlier to really look at the brain even before the children with dyslexia start to 

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read and write, 

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to see a bit more and to disentangle a bit more the causes and the consequences.

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Okay.

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So, did you, so can you tell me about how you went about setting up that longitudinal study?

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Because I mean, that's like, that's a huge amount of work, especially you know, you're

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starting to think about that just as you're finishing a PhD, huh?

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Yeah, yeah, yeah.

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Now, we have a little experience already in it with more than three other kids, pre-reading kids

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that have been scanned and followed up, but indeed, back then when I started my postdoc,

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there was within this course, so there was already a tradition to do longitudinal studies,

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starting pre-reading was fully believable.

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So, but the design was already just at once, so the idea is then that you started kindergarten,

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last your kindergarten, so before they started read and write

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You search for kids who have a risk for dyslexia or for the family

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risks or parents or a sibling who has dyslexia.

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And then you followed them up so that you can based on the reading data in grade one, grade

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two and three, you can make a diagnosis of dyslexia, so you can classify who of these pre-readers

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has developed dyslexia and who has developed typical reading skills.

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So that approach was already set within the dysco collaboration that we had hearing and

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having K-reven.

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And then I decided to my postdoc to add the neuroimaging part, so to have also in these

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kids not only behavioral data, but also neuroimaging data.

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We did focus more on the structural MRI, so the one way that the diffusion and the main

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reason was, yeah, of course I had so much difficulties to have that done in adults, but also in kids

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it's very difficult to do functional MRI, especially if you want to tap on specific language

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processes that have to do a task and in five years old it's really a challenge.

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So I think I was happy at least that when I started this first scan in the young children

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that I, that was a structural MRI, so the kids could watch a movie which is as you know

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when kids are very helpful to names, lines, telling in a scanner.

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So we had an advantage in the structural MRI, so the kids were watching a movie.

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They did some functional MRI, but it's the quality of the data was much worse than the

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structural MRI.

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And also something I realized after doing the MRI in the young adults which were very

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cooperative and they were very easy to scan, going to the five year olds, it just takes a

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lot of time to prepare them, you have to motivate them, there's all kinds of games that

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they also have to like  because of the problem.

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So there's a lot more effort going into the scanning of young children.

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Oh, absolutely.

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Yeah, so, okay, so Dysco kind of a collaboration that you joined and you added on the whole

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neuro component to it.

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So there was, you were coming into this with the idea that like you have to look at kids

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before they start learning language so that you can kind of like not just have that confound

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of wondering whether what you're seeing is the consequence of having been a struggling

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reader for X many years, but you want to see them from the get go before they even start

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to learn to read and then you're going to track them longitudinally and then you added

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on this whole neuro component.

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And as you said, probably a better idea to focus on structural, rather than functional

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given the cooperation, cooperation abilities of kids.

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So in your paper is that like a kind of a good summary? Yeah, exactly. Yeah, yeah.

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So, in your paper, papers, you talk about like the submarine protocol and the night and damsel

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protocol and I think these must be like ways of getting the kids to cooperate.

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So can you kind of just share what that's like, like working with these like really uncooperative

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research participants?

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Yeah, yeah.

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I think for us the most important thing is that they feel at ease when they before they go

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in the scanner so that they trusts us and they feel a bit at ease, it's a situation.

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So what we do is they come one hour beforehand.

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So first we sent them a video they can watch at home about the scanner.

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We make it like a very playful movie.

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So they are prepared already before so they know a bit what will come.

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But then the most important part is the one hour before the scanning is that we play all

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kinds of games with them.

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So for example, that they have to become aware of how it's like that they can't move.

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Because if you say to a child like, "Oh, move, they think, okay, moving my head like 10 centimeters

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is still perfectly fine."

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So for example, do some games that they have in candy that you put on their nose and it has

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to stay there and if it falls off, then the parents can eat it otherwise they can eat

241
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it so it's very small things.

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So yeah, so they realize, "Okay, it has to be like really still that we have to be in the scanner."

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So this kind of game that really helps and then for each game they play, they get a key

244
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and at the end they have all the keys that can open the castle which is in the MRI scanner

245
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or they can open the ice, the glow and this kind of stuff.

246
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Oh wow, so the prize is that they get to go into the scanner and be scanned? (Laughter)

247
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Yeah, but I think at the end it's more depends.

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The parents were worried, I think, the kids, especially at the young age, they go with the

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flow, they like the games and they see it as a kind of game.

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So I think we have very little, yeah, very few children who are not willing to be in the

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scanner.

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So most of them, like I think it's like 95% of the kids went into the scanner and they also

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came back to the protocol world.

254
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I say that last thing again?

255
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So I think that the protocol was working because they were coming back.

256
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Oh right, oh, for longitudinal now, yeah.

257
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Oh, absolutely.

258
00:18:53,840 --> 00:18:57,320
Yeah, I mean that's kind of something that I've learned in my research too because we do

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longitudinal aphasor stuff and like, you know, you learn that like you have to treat people

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very kindly because otherwise you won't be seeing them again.

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00:19:04,800 --> 00:19:09,040
Like you're going to be getting a lot of sort of calls that go straight to voicemail if

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they have an unpleasant experience in the scanner.

263
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They might not tell you that they hated it, but you just that you weren't here from them

264
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again.

265
00:19:17,320 --> 00:19:18,320
Yeah, indeed.

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In a more recent project, we also had a longitudinal data in persons with aphasia using MRI and

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then I always told the postdoc with experience that scanning young children is very

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challenging and it is.

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But any persons with aphasia is also very challenging because they are often a bit more afraid

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because sometimes we're difficult to communicate and so that they understand what will be going

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on.

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So I think definitely with the process for aphasia you're also to put a lot of effort in

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the preparation and in getting them through the process.

274
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Oh, totally.

275
00:19:53,080 --> 00:19:57,600
Yeah, it's a different set of challenges, but it's again, like, you know, it's not like when

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you're scanning sort of healthy controls and it's like, if something goes wrong,

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you just get another one.

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Like each person, each participant is like kind of a labour of love, I think, when you

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work with these populations.

280
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So can you kind of share like, what did you learn?

281
00:20:14,760 --> 00:20:19,440
So when you did start to do this research, what did you learn about?

282
00:20:19,440 --> 00:20:24,480
So you're going to end up dividing the kids into those who become dyslexic and those who

283
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become typically need developing readers and kind of look back at what their brains look

284
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like before that happened.

285
00:20:30,360 --> 00:20:34,160
So can you kind of tell me what changes, what differences you've seen in the brains of

286
00:20:34,160 --> 00:20:39,880
kids that are going to go on to become dyslexic versus those that will not?

287
00:20:39,880 --> 00:20:44,320
Yeah, so concerning the white matter, so we as we did the adults, we looked at the white

288
00:20:44,320 --> 00:20:49,040
matter connections, looked at the more dorsal connection of the reading which is in the left

289
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Arcuate fasiculus, but we also looked at the more

290
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Ventral connection between frontal and occipital regions by the IFOF (Inferior Front-Occipital Fasciculus).

291
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And so in the adults, we found that there was, there was, yeah, the fractional anisotropy,

292
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which is an index of white matter organization, that that one was lowering in the adults

293
00:21:06,480 --> 00:21:07,480
With dyslexia, yeah.

294
00:21:07,480 --> 00:21:08,480
And so when we looked at the...

295
00:21:08,480 --> 00:21:09,480
In what track,

296
00:21:09,480 --> 00:21:10,480
Sorry?

297
00:21:10,480 --> 00:21:12,480
In what track was it lower in the adults?

298
00:21:12,480 --> 00:21:15,480
Yeah, so in the left arcuate fasciculus

299
00:21:15,480 --> 00:21:16,480
Arcuate, okay.

300
00:21:16,480 --> 00:21:18,480
Left arcuate, OK.

301
00:21:18,480 --> 00:21:24,920
Yeah, so there was an over fractional anisotropy and then when we looked at the pre-reading

302
00:21:24,920 --> 00:21:30,000
brain, so we could indeed compare the pre-readers who developed dyslexia versus the one

303
00:21:30,000 --> 00:21:31,880
who developed it with our reading skills.

304
00:21:31,880 --> 00:21:41,960
We also found a lower FA in the left arcuate fasciculus, so we found this difference, again also a

305
00:21:41,960 --> 00:21:47,200
pre-reading, so it seemed to suggest that it was not just a consequence of a different reading

306
00:21:47,200 --> 00:21:53,560
experience, the person with dyslexia had, but it's really there from the very start.

307
00:21:53,560 --> 00:21:59,080
So even before they started to read and write, what we also saw is that...

308
00:21:59,080 --> 00:22:05,400
And that was a bit unexpected, we also saw in the right arcuate fasciculus, also a lower FA

309
00:22:05,400 --> 00:22:08,480
for the pre-reading children who developed dyslexia.

310
00:22:08,480 --> 00:22:13,800
So that was a bit unexpected because I think in dyslexia, in the research fields, the

311
00:22:13,800 --> 00:22:21,880
right is often considered also to help compensate for the reading difficulties.

312
00:22:21,880 --> 00:22:22,880
Yeah.

313
00:22:22,880 --> 00:22:30,320
So I think that what we thought maybe that we would find more like an increased FA

314
00:22:30,320 --> 00:22:34,840
may mean the right in these children, but that was not a case.

315
00:22:34,840 --> 00:22:39,160
Also, if you look at other pre-reading MRI studies that were happening more or less at

316
00:22:39,160 --> 00:22:45,360
the same time, they often also find bilateral differences, so not only restricted to the

317
00:22:45,360 --> 00:22:52,840
left, but they also have some evidence showing that the other right can be like a compensation 

318
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for helping, compensating for the reading difficulties.

319
00:22:58,440 --> 00:23:03,320
So therefore, it was a bit surprised to find also in the right lower FA in the dyslexic

320
00:23:03,320 --> 00:23:04,320
readers.

321
00:23:04,320 --> 00:23:05,320
Okay.

322
00:23:05,320 --> 00:23:11,680
Yeah, it is a bit surprising, but if other labs are seeing that too, that's reassuring.

323
00:23:11,680 --> 00:23:15,320
And you were seeing these differences in the Dorsal Tracks, right?

324
00:23:15,320 --> 00:23:20,560
Were other labs also replicating that finding or did people see those in ventral too?

325
00:23:20,560 --> 00:23:21,560
Yeah.

326
00:23:21,560 --> 00:23:26,440
I think most studies that used this longitudinal approach and looked at the brain of pre-reader

327
00:23:26,440 --> 00:23:32,280
who later developed, for reading skills, like they did a very similar study in the lab

328
00:23:32,280 --> 00:23:35,400
of Nadine Gaab from MIT.

329
00:23:35,400 --> 00:23:41,400
And also from Miguel Steida, they also had a very similar study, and both of them they also

330
00:23:41,400 --> 00:23:48,760
found that these left arcuates were differently developed in the pre-readers with poor reading

331
00:23:48,760 --> 00:23:49,760
skills.

332
00:23:49,760 --> 00:23:56,600
And that was replicated even in the lab of Nadine Gabb, they even have a study in infants who

333
00:23:56,600 --> 00:23:59,360
have a family risk for dyslexia.

334
00:23:59,360 --> 00:24:03,960
And also, they found this in the left arcuate fasciculus difference.

335
00:24:03,960 --> 00:24:12,560
So I think the left arcuate fasciculus is confirmed also in other independent samples.

336
00:24:12,560 --> 00:24:19,560
However, there's now also more recent large skills studies using thousands of kids from

337
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the lab.

338
00:24:20,560 --> 00:24:25,320
But then with a very wide age range, like often from 8 to 18, and there they don't see this

339
00:24:25,320 --> 00:24:30,120
link with reading skills and have in the left arcuate fasciculus.

340
00:24:30,120 --> 00:24:32,640
They don't see it very clearly.

341
00:24:32,640 --> 00:24:38,240
So I think I know, I think it has something to do probably also that in these large skills

342
00:24:38,240 --> 00:24:42,480
studies that a lot of ages are combined into one big sample.

343
00:24:42,480 --> 00:24:43,480
Yeah.

344
00:24:43,480 --> 00:24:45,240
Well, didn't you?

345
00:24:45,240 --> 00:24:51,040
In your paper where you first reported this, which is the 2017 paper, I think, and I don't

346
00:24:51,040 --> 00:24:52,680
want to butcher your colleague's name.

347
00:24:52,680 --> 00:24:55,960
So maybe you can say the name of the first one.

348
00:24:55,960 --> 00:24:56,960
Yeah.

349
00:24:56,960 --> 00:24:58,200
Jolijan Vandermosten.

350
00:24:58,200 --> 00:24:59,200
Thank you.

351
00:24:59,200 --> 00:25:03,560
So in that 2017 paper, there's also a longitudinal component to that too.

352
00:25:03,560 --> 00:25:09,060
And I was struck by the fact that you saw this effect on the arcuate in the pre-reading

353
00:25:09,060 --> 00:25:11,520
time point, the left arcuate.

354
00:25:11,520 --> 00:25:15,440
And then by the sort of post-reading time point, like a year or two later, when they've begun

355
00:25:15,440 --> 00:25:19,480
to read, you actually saw that that difference had normalized between these groups.

356
00:25:19,480 --> 00:25:24,280
So that, if that's the case, that would seem to, that would kind of maybe explain why it's

357
00:25:24,280 --> 00:25:27,760
not being observed in this sort of diverse age later sample, right?

358
00:25:27,760 --> 00:25:29,600
But it's also very mysterious finding.

359
00:25:29,600 --> 00:25:31,960
Like, what do you, what do you make of that?

360
00:25:31,960 --> 00:25:32,960
Yeah.

361
00:25:32,960 --> 00:25:36,880
So I think in the, you know, sample at least that the pre-reading difference is for a

362
00:25:36,880 --> 00:25:41,840
bit clearer because then the more in primary school, the more they had to learn to read, then

363
00:25:41,840 --> 00:25:47,960
we don't see the, the clear difference anymore between the children with dyslexia and without.

364
00:25:47,960 --> 00:25:51,640
I think it has something to do with the fact that when you learn to read, of course, it's

365
00:25:51,640 --> 00:25:55,920
A new experience, and probably that also has an impact on these five matters.

366
00:25:55,920 --> 00:26:02,080
So the right matter is not is a result of age-related maturation, but also of experience

367
00:26:02,080 --> 00:26:03,640
induced changes.

368
00:26:03,640 --> 00:26:08,600
And I can imagine that maybe once you learn to read and write, you get more this experience,

369
00:26:08,600 --> 00:26:11,800
induced changes that also have an impact.

370
00:26:11,800 --> 00:26:15,400
And maybe therefore the relation is a bit as clear.

371
00:26:15,400 --> 00:26:21,200
We did two an additional study where we have, where we looked in this left arcuate fasciculus

372
00:26:21,200 --> 00:26:26,600
more specifically at the cluster where we found the group difference pre-reading between

373
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the dyslexics and the long dyslexic pre-readers.

374
00:26:30,480 --> 00:26:33,480
And if we look within that cluster, we do see it is also, we can see that the difference

375
00:26:33,480 --> 00:26:37,440
is also still present in grade two and also in grade five.

376
00:26:37,440 --> 00:26:45,240
So if we look a bit more specific, the difference seems to be there across primary school.

377
00:26:45,240 --> 00:26:54,640
But I think on the average tract, so if you look at the FA across the whole tract, the difference

378
00:26:54,640 --> 00:26:57,400
was stronger pre-reading than post-reading.

379
00:26:57,400 --> 00:27:02,880
And I think it has something to do with the fact that the longer, the further throughout development

380
00:27:02,880 --> 00:27:08,360
the more you have also this experience induced changes in the white matter.

381
00:27:08,360 --> 00:27:09,360
Okay.

382
00:27:09,360 --> 00:27:15,720
And what part of the accurate is that real key part where you're still seeing the differences?

383
00:27:15,720 --> 00:27:16,720
even later?

384
00:27:16,720 --> 00:27:20,720
It was more in the temporal prietal part.

385
00:27:20,720 --> 00:27:28,280
And it was, so I think it's something that also needs to be further investigateded in to see

386
00:27:28,280 --> 00:27:36,320
how, to what extent do these differences between the group today remain present across primary

387
00:27:36,320 --> 00:27:37,320
school?

388
00:27:37,320 --> 00:27:42,480
Because I think, for example, in our sample, what we saw, if we look at FA across the

389
00:27:42,480 --> 00:27:47,240
whole track in the arcuate fasciculus, indeed the group difference disappeared.

390
00:27:47,240 --> 00:27:53,600
But it was especially the Children with dyslexia who had already some early interventions.

391
00:27:53,600 --> 00:28:01,440
They seem to have a larger increase, so it might be something that is, because of the intervention,

392
00:28:01,440 --> 00:28:06,320
they followed them, followed an intervention quite early, and that seemed to have helped them

393
00:28:06,320 --> 00:28:09,600
in maybe catching up in the left arcuate fasciculus.

394
00:28:09,600 --> 00:28:10,600
Yeah.

395
00:28:10,600 --> 00:28:17,320
You had this sort of exploratory correlation where the kids who got more intervention were

396
00:28:17,320 --> 00:28:20,120
having greater changes over time, right?

397
00:28:20,120 --> 00:28:22,960
And you've been following up in recent work.

398
00:28:22,960 --> 00:28:28,400
I haven't really looked at those papers in depth as you know, but I think you've been doing

399
00:28:28,400 --> 00:28:32,840
some more purposeful interventions that is recently, right?

400
00:28:32,840 --> 00:28:38,360
Yeah, so the idea was indeed because we, with a longitudinal study, it's very difficult

401
00:28:38,360 --> 00:28:46,520
to disentangle age related or age maturation versus more changes that are induced by the environment

402
00:28:46,520 --> 00:28:48,440
or by the experiences you have.

403
00:28:48,440 --> 00:28:52,960
So we decided to do an intervention study because then we could control a bit better these

404
00:28:52,960 --> 00:28:58,200
two processes and to disentangle them a bit better.

405
00:28:58,200 --> 00:29:04,440
So the idea was to do reading intervention, but we also decided to do it very early already

406
00:29:04,440 --> 00:29:10,280
in kindergarten, so more like a preventive reading intervention, because there's quite some

407
00:29:10,280 --> 00:29:16,720
behavioral evidence that shows that if you do interventions later, so starting in

408
00:29:16,720 --> 00:29:21,680
grade three, for example, then they are less effective than if you do it early, so in grade

409
00:29:21,680 --> 00:29:26,560
one or even in kindergarten, they often call it the dyslexia paradox.

410
00:29:26,560 --> 00:29:33,400
It's also put forward by the lack of nothing yet because in clinical practice often the kids

411
00:29:33,400 --> 00:29:39,080
with dyslexia, they only receive their intervention after the diagnosis is given, but to get a

412
00:29:39,080 --> 00:29:43,080
diagnosis you have to show severe deficit in reading, but also persistent.

413
00:29:43,080 --> 00:29:48,440
So often it takes some time to be able to make diagnosis and so they also advise that

414
00:29:48,440 --> 00:29:53,560
the intensive intervention for reading often only takes place in grade three a later, but

415
00:29:53,560 --> 00:29:59,480
then we know from behavioral intervention studies that then the impact is smaller than

416
00:29:59,480 --> 00:30:01,640
if you would do it earlier.

417
00:30:01,640 --> 00:30:09,960
So the idea was with a new study to go to kindergarten to select children who are at risk for dyslexia,

418
00:30:09,960 --> 00:30:12,880
because they can't give a diagnosis yet.

419
00:30:12,880 --> 00:30:20,640
So they are at risk for dyslexia and then we split the group, we randomly assigned half

420
00:30:20,640 --> 00:30:27,640
of the pre-readers at risk to a reading intervention and the other half did also a kind of intervention

421
00:30:27,640 --> 00:30:33,840
very similar, but instead of reading games on a tablet, they played Lego and Lego build

422
00:30:33,840 --> 00:30:35,520
games on tablets.

423
00:30:35,520 --> 00:30:41,800
So we checked beforehand, there was a similar motivation for both games and also in terms

424
00:30:41,800 --> 00:30:46,600
of expectancies from the parents, it was also very similar.

425
00:30:46,600 --> 00:30:52,520
And both groups played an equal amount of time on the tablets, the one group, training

426
00:30:52,520 --> 00:30:59,440
on reading and the other group, training on other skills, more spatial visual skills.

427
00:30:59,440 --> 00:31:02,640
And did it help them?

428
00:31:02,640 --> 00:31:09,600
So behaviorally we decided that at risk group who played the reading intervention, they improved

429
00:31:09,600 --> 00:31:16,680
for lateral knowledge, also basic reading skills were better than the at risk group who played

430
00:31:16,680 --> 00:31:18,760
the control intervention.

431
00:31:18,760 --> 00:31:21,080
So they got better.

432
00:31:21,080 --> 00:31:26,120
And if we looked at the white matter tracts, we didn't see an impact on the fractional

433
00:31:26,120 --> 00:31:27,120
anisotropy.

434
00:31:27,120 --> 00:31:31,720
So we first saw, like, we would have expected that this different reading experience would

435
00:31:31,720 --> 00:31:34,760
have an impact on the white matter tracts.

436
00:31:34,760 --> 00:31:39,960
But in the FA values of the fractional anisotropy, we didn't see an effect.

437
00:31:39,960 --> 00:31:44,520
But if we had also an additional MRI scan, where we looked more specifically at myelination 

438
00:31:44,520 --> 00:31:47,120
and there we did see an effect.

439
00:31:47,120 --> 00:31:52,640
So there we saw an increase in myelination in the children who played the reading intervention

440
00:31:52,640 --> 00:31:58,040
and the increase was not present in the children who played the control intervention.

441
00:31:58,040 --> 00:31:59,040
OK.

442
00:31:59,040 --> 00:32:01,040
And what tract was that seen in?

443
00:32:01,040 --> 00:32:02,040
Yeah.

444
00:32:02,040 --> 00:32:03,520
So the myelination was quite widespread.

445
00:32:03,520 --> 00:32:06,440
We didn't see it in the left arcuate fasciculous.

446
00:32:06,440 --> 00:32:14,200
So there was the group of intervention of the reading intervention, they increased more

447
00:32:14,200 --> 00:32:16,800
in myelination than the control group.

448
00:32:16,800 --> 00:32:18,800
But we also saw it in the ventral tracks.

449
00:32:18,800 --> 00:32:22,800
So it was not very specific.

450
00:32:22,800 --> 00:32:26,760
So it was a bit more widespread than we saw originally.

451
00:32:26,760 --> 00:32:30,320
We also looked at the grey matter.

452
00:32:30,320 --> 00:32:34,800
And there we also saw the thickness of the left Supramarginal gyrus.

453
00:32:34,800 --> 00:32:39,280
We saw an increase in the thickness in the group with the reading intervention and

454
00:32:39,280 --> 00:32:41,840
not in the control group.

455
00:32:41,840 --> 00:32:47,000
So there it was more localized, but for the myelination it seemed to be a bit more widespread.

456
00:32:47,000 --> 00:32:53,160
And that's a great brain region for a phonological disorder, certainly, if you believe that dyslexia

457
00:32:53,160 --> 00:32:55,720
is a phonological disorder.

458
00:32:55,720 --> 00:32:59,600
But yeah, before we, I mean, so just stepping back a bit on grey matter, right?

459
00:32:59,600 --> 00:33:07,160
So like, what are the grey matter predictors of becoming dyslexic?

460
00:33:07,160 --> 00:33:09,680
Like you mentioned before, the white matter are predictors.

461
00:33:09,680 --> 00:33:10,920
Are the grey matter predictors?

462
00:33:10,920 --> 00:33:11,920
Yeah.

463
00:33:11,920 --> 00:33:14,160
So for the white matter, we found for dyslexia.

464
00:33:14,160 --> 00:33:19,760
The left arcuate fasciculus is a good predictor for later reading abilities and for the grey matter.

465
00:33:19,760 --> 00:33:22,200
It was specifically the left fusiform gyrus.

466
00:33:22,200 --> 00:33:26,440
So very close to the region of word form area or sometimes it's also called the  

467
00:33:26,440 --> 00:33:27,440
Letterbox.

468
00:33:27,440 --> 00:33:34,160
So the region where you are able to identify letters, regardless in which font they are written.

469
00:33:34,160 --> 00:33:41,760
And also it's also a region involved in recognizes, recognizing larger parts of words or combination

470
00:33:41,760 --> 00:33:42,760
of letters.

471
00:33:42,760 --> 00:33:45,280
So it's more for this processes.

472
00:33:45,280 --> 00:33:46,280
And they're also pre-reading.

473
00:33:46,280 --> 00:33:51,360
We already see that the volume is smaller in the children who later develop dyslexia,

474
00:33:51,360 --> 00:33:55,720
relative to the ones who will develop typical reading skills.

475
00:33:55,720 --> 00:33:56,720
Okay.

476
00:33:56,720 --> 00:34:01,520
So what's the, can you just tell me the citation for that finding from your lab?

477
00:34:01,520 --> 00:34:02,520
Yeah.

478
00:34:02,520 --> 00:34:07,080
So it's a study, first author is Caroline Beelen.

479
00:34:07,080 --> 00:34:12,560
So she, I can send you the papers.

480
00:34:12,560 --> 00:34:13,560
I know I have it.

481
00:34:13,560 --> 00:34:14,560
I do have that one.

482
00:34:14,560 --> 00:34:19,000
I'm just trying to match up what you're talking about to what I have reviewed.

483
00:34:19,000 --> 00:34:23,840
But it's also there because, there was also an unexpected little bit like with the white matter predictors.

484
00:34:23,840 --> 00:34:28,640
And we found a lower FA in both the left and the right arcuates.

485
00:34:28,640 --> 00:34:31,760
Also for the grey and for the fusiform gyrus.

486
00:34:31,760 --> 00:34:35,600
We also found it left and right.

487
00:34:35,600 --> 00:34:37,680
That there was a lower volume.

488
00:34:37,680 --> 00:34:44,960
Again, the left was a bit more pronounced in that it was relating more to individual differences

489
00:34:44,960 --> 00:34:48,120
like phonological abilities in these kids.

490
00:34:48,120 --> 00:34:52,040
And also we did a whole brain analysis.

491
00:34:52,040 --> 00:34:57,880
So not looking for region, but just looking more at the voxel level.

492
00:34:57,880 --> 00:35:01,280
Then we only replicated in the left fusiform.

493
00:35:01,280 --> 00:35:04,280
So it was a bit strong in the left than the right.

494
00:35:04,280 --> 00:35:10,880
So it's interesting that you're seeing this pre-reading difference in the fusiform

495
00:35:10,880 --> 00:35:16,800
gyrus, but then the change that you elicit through your training program is in the supramarginal

496
00:35:16,800 --> 00:35:18,040
gyrus.

497
00:35:18,040 --> 00:35:23,360
So do you think that's kind of like are you training a compensatory mechanism rather than like

498
00:35:23,360 --> 00:35:24,360
making them normal?

499
00:35:24,360 --> 00:35:27,280
Like, what do you think is going on with that?

500
00:35:27,280 --> 00:35:33,360
Yeah, so indeed we might have expected to see some changes also more in this around

501
00:35:33,360 --> 00:35:35,880
this visual word from area.

502
00:35:35,880 --> 00:35:38,480
That we saw it only in the supramarginal gyrus.

503
00:35:38,480 --> 00:35:46,000
I think the training was really focused on connecting sounds, phonemes to letters to

504
00:35:46,000 --> 00:35:47,000
graphemes.

505
00:35:47,000 --> 00:35:49,880
So really about this grapheme-phoneme coupling.

506
00:35:49,880 --> 00:35:54,000
So I think that's probably the reason why we see the bit more in supramarginal gyrus because

507
00:35:54,000 --> 00:36:00,200
there you have to do this coupling more between this phonological and this, between the graphemes

508
00:36:00,200 --> 00:36:01,440
and the phonemes.

509
00:36:01,440 --> 00:36:05,160
So I think it's very because of the focus of the training in the range of those really

510
00:36:05,160 --> 00:36:07,920
on that aspect of reading.

511
00:36:07,920 --> 00:36:14,880
Maybe if you would do, I can maybe imagine if you do a longer training also including

512
00:36:14,880 --> 00:36:19,760
more advanced reading skills where it also becomes important to directly recognize the visual

513
00:36:19,760 --> 00:36:23,200
words based on the orthographic representation.

514
00:36:23,200 --> 00:36:29,200
Then maybe we might see, might train more this visual words from area than we did now.

515
00:36:29,200 --> 00:36:31,760
But it's a little bit of a question.

516
00:36:31,760 --> 00:36:38,880
And I think with regard to the pre-reading differences, I indeed also expected because

517
00:36:38,880 --> 00:36:43,720
of the phonological problems versus with dyslexia have to find pre-reading deficit for this

518
00:36:43,720 --> 00:36:44,720
temporoparietal regions.

519
00:36:44,720 --> 00:36:49,800
But for the memory, we only found it in the fusiform.

520
00:36:49,800 --> 00:36:56,640
I thought it was first because we looked at the orthotical regions and SDG for example is

521
00:36:56,640 --> 00:36:58,520
a very broad region.

522
00:36:58,520 --> 00:37:06,000
But even if we did it more like on a voxel-based search, we didn't find what we expected.

523
00:37:06,000 --> 00:37:10,360
We didn't find this pre-reading differences in temporoparietal region.

524
00:37:10,360 --> 00:37:13,000
Well, you know, the data is the data, right? (Laughter)

525
00:37:13,000 --> 00:37:17,760
You can't change the facts.

526
00:37:17,760 --> 00:37:19,760
Okay, well that's very cool.

527
00:37:19,760 --> 00:37:25,840
So I know that most of your career has been focused on developmental dyslexia.

528
00:37:25,840 --> 00:37:34,240
But you also know like, when I wrote to you a week or two ago, it was about your very interesting

529
00:37:34,240 --> 00:37:41,360
aphasia paper that you just published as a pre-print and then I also saw at SNL in Marseille.

530
00:37:41,360 --> 00:37:45,520
So can we shift gears and talk about that new direction in your research?

531
00:37:45,520 --> 00:37:46,520
Yeah, okay.

532
00:37:46,520 --> 00:37:50,120
So it's a little bit of a shift.

533
00:37:50,120 --> 00:37:55,560
I know from my two research lines, one more on developmental dyslexia and one more on

534
00:37:55,560 --> 00:38:01,960
aphasia because it was when I started the tenor track here at KU Leuven.

535
00:38:01,960 --> 00:38:07,120
Then it was always my interest to work on aphasia.

536
00:38:07,120 --> 00:38:13,520
So I think I saw a lot of links, a lot of things in common between these two.

537
00:38:13,520 --> 00:38:17,960
So I think a lot of the methodology, like the longitudinal and the predictive modeling that

538
00:38:17,960 --> 00:38:21,760
we use in dyslexia, I could also apply it to aphasia.

539
00:38:21,760 --> 00:38:27,120
So the first project I did on aphasia was more about longitudinal, follow-up and neuroplasticity.

540
00:38:27,120 --> 00:38:32,000
So very much linked to the development of dyslexia.

541
00:38:32,000 --> 00:38:43,400
So I think now both research lines are as big or equally, equal number of researchers working

542
00:38:43,400 --> 00:38:44,400
on it.

543
00:38:44,400 --> 00:38:51,040
And I'm very happy that I can now combine these two research fields because I feel I get

544
00:38:51,040 --> 00:38:55,640
a lot of insight from the development of dyslexia fields that can help me understanding

545
00:38:55,640 --> 00:38:57,640
something in aphasia.

546
00:38:57,640 --> 00:39:03,440
So I was really happy to be able to sort out the research line in aphasia.

547
00:39:03,440 --> 00:39:04,440
Yeah, that's cool.

548
00:39:04,440 --> 00:39:09,400
So you're able to really bring all those sort of CogNeuro skills that you've developed

549
00:39:09,400 --> 00:39:14,040
and imaging skills and apply them in this different population.

550
00:39:14,040 --> 00:39:20,440
Yeah, a lot of the same challenges, like, you know, needing to do longitudinal, needing

551
00:39:20,440 --> 00:39:24,280
to deal with brains that are changing in shape and size.

552
00:39:24,280 --> 00:39:25,800
Yeah, that's cool.

553
00:39:25,800 --> 00:39:29,640
And it's very interesting to hear that, like, you know, aphasia was kind of like your initial

554
00:39:29,640 --> 00:39:30,960
interest in the field, right?

555
00:39:30,960 --> 00:39:33,720
And now you're getting back to it.

556
00:39:33,720 --> 00:39:38,400
I think for me, that sort of happened a little bit too with my postdoc, where I worked

557
00:39:38,400 --> 00:39:39,400
on PPA.

558
00:39:39,400 --> 00:39:46,680
I worked on PPA with Mary L Gorno-Tempini and like, I really wanted to do like acute stroke.
 
559
00:39:46,680 --> 00:39:51,000
And I was, when I started that postdoc, I was like, can I like do some stroke stuff

560
00:39:51,000 --> 00:39:52,000
on the side?

561
00:39:52,000 --> 00:39:53,000
She's like, why would you want to?

562
00:39:53,000 --> 00:39:54,000
But sure.

563
00:39:54,000 --> 00:39:58,320
And then of course, like, I didn't have time, like, I just worked on PPA for like five years.

564
00:39:58,320 --> 00:40:04,560
And then I found my way back to acute stroke eventually, which is, you know, took a while.

565
00:40:04,560 --> 00:40:07,360
And all the stuff that I learned along the way was invaluable.

566
00:40:07,360 --> 00:40:10,000
And then you come to the field with a new perspective that you bring from somewhere else

567
00:40:10,000 --> 00:40:11,000
that's cool.

568
00:40:11,000 --> 00:40:12,000
Yeah.

569
00:40:12,000 --> 00:40:14,000
And I feel a bit the same.

570
00:40:14,000 --> 00:40:19,160
So I still have this dyslexia research brand, and then I slowly throughout the past years,

571
00:40:19,160 --> 00:40:25,360
I try to have some easier research and, yeah, I think there's a lot of things that come

572
00:40:25,360 --> 00:40:28,160
on in the box and it can be learned from the two fields.

573
00:40:28,160 --> 00:40:29,160
Yeah.

574
00:40:29,160 --> 00:40:30,160
So, yeah, definitely.

575
00:40:30,160 --> 00:40:33,800
So this paper, it's very nice.

576
00:40:33,800 --> 00:40:38,720
It's very like, it has a clear question and a clear answer.

577
00:40:38,720 --> 00:40:46,920
So can you kind of tell me, tell our listeners, like, what's the question that you set out

578
00:40:46,920 --> 00:40:48,960
to address with this paper?

579
00:40:48,960 --> 00:40:49,960
Yeah.

580
00:40:49,960 --> 00:40:52,480
So the, we should name the author too.

581
00:40:52,480 --> 00:40:53,480
We should name the citation.

582
00:40:53,480 --> 00:40:54,480
Yeah.

583
00:40:54,480 --> 00:40:57,720
I think it's the first author is Pieter De Clercq.

584
00:40:57,720 --> 00:41:00,200
And so he's like a very brilliant researcher.

585
00:41:00,200 --> 00:41:06,360
We did a lot of efforts in this paper and in a lot of the, all the nice, all the nice

586
00:41:06,360 --> 00:41:08,080
analyses and so on.

587
00:41:08,080 --> 00:41:11,320
And in fact, this week he's his last week.

588
00:41:11,320 --> 00:41:13,120
He's here at KU Leuven.

589
00:41:13,120 --> 00:41:17,160
He's moving now to industry, so to a company.

590
00:41:17,160 --> 00:41:21,760
He's not his leaving academia, but he's like a very talented researcher.

591
00:41:21,760 --> 00:41:26,960
He has a background in psychology, but also a Master's AI.

592
00:41:26,960 --> 00:41:29,400
So he was, I think, on the job market.

593
00:41:29,400 --> 00:41:32,960
He was, yeah, many people wanted to have him.

594
00:41:32,960 --> 00:41:35,360
Yeah, well, that'll be great for him.

595
00:41:35,360 --> 00:41:38,400
And it's a bit of a loss to our field.

596
00:41:38,400 --> 00:41:39,400
Yeah.

597
00:41:39,400 --> 00:41:40,400
Yeah.

598
00:41:40,400 --> 00:41:41,400
Yeah.

599
00:41:41,400 --> 00:41:45,000
And so the studies in MRI study, so in dyslexia we 

600
00:41:45,000 --> 00:41:49,560
often focused on the structural MRI, but it, of course, has a lot of limitations,

601
00:41:49,560 --> 00:41:53,200
because you never know if you investigate a certain region, what is in the function of

602
00:41:53,200 --> 00:41:58,040
that region, you have to, yeah, assume a certain function based on the correlation you find 

603
00:41:58,040 --> 00:42:0 
behavioral or other studies.

604
00:42:01,840 --> 00:42:08,560
And so the idea was now more to really have, yeah, functional MRIs, so that we could really

605
00:42:08,560 --> 00:42:14,920
find the language network or define it better in persons with aphasia.

606
00:42:14,920 --> 00:42:20,520
And there was, yeah, of course, we knew about the work from Ev Fedorenko, who has put a lot

607
00:42:20,520 --> 00:42:26,320
of efforts in, in reliably defining this language network.

608
00:42:26,320 --> 00:42:31,160
And, but we had a lot of questions, all, okay, it's, what about versus with aphasia?

609
00:42:31,160 --> 00:42:37,080
Because maybe in young adults, you see a clear distinction between a language network and

610
00:42:37,080 --> 00:42:41,680
a network, which is more involved in higher order, cognitive functioning, so the multiple

611
00:42:41,680 --> 00:42:45,600
demand network.

612
00:42:45,600 --> 00:42:48,000
The group of Ev Fedorenko has shown in multiple studies that there is wide dissociation,

613
00:42:48,000 --> 00:42:54,480
so there's also much overlap if you look at individual, individually defined language

614
00:42:54,480 --> 00:43:00,920
network, then in these voxels, you will not see a lot of activity when they do a cognitive

615
00:43:00,920 --> 00:43:01,920
demand and tasks.

616
00:43:01,920 --> 00:43:05,320
So that's what they found, but that's often in young adults.

617
00:43:05,320 --> 00:43:10,680
So in our study, yeah, I just want to, like, kind of sum that up just to keep make sure

618
00:43:10,680 --> 00:43:11,680
everybody is on the same page.

619
00:43:11,680 --> 00:43:18,240
So, so yeah, like Ev has shown with her collaborators, like Idan Blank and Cory

620
00:43:18,240 --> 00:43:26,520
Shain and Yavda Yatchek, that there is this, you know, language network is left lateralized,

621
00:43:26,520 --> 00:43:30,360
frontal temporal, mostly, we all know where that is.

622
00:43:30,360 --> 00:43:34,960
And then it contrasts with this multiple demand network that's bilateral and it has nodes

623
00:43:34,960 --> 00:43:41,960
in the Insula, sort of superior, more sort of dorsal lateral prefrontal superior-ish

624
00:43:41,960 --> 00:43:43,200
parietal.

625
00:43:43,200 --> 00:43:46,080
So it's kind of got quite a different anatomy to it.

626
00:43:46,080 --> 00:43:51,000
And it does seem to overlap in parts if you don't look too closely, like, especially in

627
00:43:51,000 --> 00:43:56,120
the, like, kind of in the frontal operculum, but Ev and her colleagues have basically found

628
00:43:56,120 --> 00:43:59,840
that, you know, if you look at an individual basis, there isn't much overlap.

629
00:43:59,840 --> 00:44:03,280
So what, you know, might look in a group analysis, like, overlapping networks and not really

630
00:44:03,280 --> 00:44:04,840
overlapping networks.

631
00:44:04,840 --> 00:44:11,320
And then, you know, she's also found that, you know, like you just said, like the language

632
00:44:11,320 --> 00:44:16,840
network doesn't respond to cognitively demanding tasks and similarly cognitively demanding tasks.

633
00:44:16,840 --> 00:44:20,520
So, and the multiple demand network doesn't respond to language.

634
00:44:20,520 --> 00:44:23,680
But like you just said, that's all been done in normals.

635
00:44:23,680 --> 00:44:28,240
And a lot of people have speculated that, like, in aphasia, like, maybe the MD network is

636
00:44:28,240 --> 00:44:29,240
compensatory, right?

637
00:44:29,240 --> 00:44:34,480
So, like, when the language network is damaged, maybe you are going to rely on the MD network

638
00:44:34,480 --> 00:44:37,760
as a compensatory mechanism.

639
00:44:37,760 --> 00:44:41,320
And this is an idea of much interest.

640
00:44:41,320 --> 00:44:47,200
It's not, and it's got some evidence in favor of it, but you tested it very directly here.

641
00:44:47,200 --> 00:44:48,200
Yeah.

642
00:44:48,200 --> 00:44:54,120
So I did the main aim of that study was to see maybe persons with aphasia, maybe a compensate

643
00:44:54,120 --> 00:44:58,760
for their language deficit by relying more of this multiple demand network.

644
00:44:58,760 --> 00:45:02,560
And I think it's, it's very important to get insight in that, because I think also in

645
00:45:02,560 --> 00:45:08,600
terms of intervention, if there is, if indeed doing language or more relying also on this

646
00:45:08,600 --> 00:45:12,520
multiple demand network, maybe then it's good to train more of these cognitive skills and

647
00:45:12,520 --> 00:45:16,120
maybe the natural transfer to your language skills or have an impact on language, but if

648
00:45:16,120 --> 00:45:21,880
it's really like separate networks, also in process for aphasia, then maybe if there's

649
00:45:21,880 --> 00:45:26,520
of intervention, you also maybe we should target them more really language processing and

650
00:45:26,520 --> 00:45:30,840
try to improve that, other than focusing on cognitive skills, for example.

651
00:45:30,840 --> 00:45:37,920
So I think for me, it's like, yeah, very important to know how it's working persons with aphasia,

652
00:45:37,920 --> 00:45:43,600
because we assume they have a large lesion in the left language network.

653
00:45:43,600 --> 00:45:50,160
So in order to come to language, maybe then as a backup, they start to use more, or the

654
00:45:50,160 --> 00:45:52,760
right, or more, these multiple demand features.

655
00:45:52,760 --> 00:45:56,960
So, and in this paper, we're really focused on these, these multiple demand features.

656
00:45:56,960 --> 00:46:04,440
So, we asked, we had a group of 15 persons with aphasia, a stroke, a necrotic stage, and

657
00:46:04,440 --> 00:46:12,040
then we had the group of controls, so they were each matched, so it's also older healthy controls.

658
00:46:12,040 --> 00:46:16,600
So we first also need to know, what is, yeah, how is it in the healthy controls?

659
00:46:16,600 --> 00:46:18,000
Oh, hang on a sec.

660
00:46:18,000 --> 00:46:21,840
Can I ask you something before you go into it?

661
00:46:21,840 --> 00:46:23,240
What did you think you were going to find?

662
00:46:23,240 --> 00:46:28,840
Like, did you or got say that the people with aphasia are going to rely on the MD network?

663
00:46:28,840 --> 00:46:30,800
Or did you think you were going to get null result?

664
00:46:30,800 --> 00:46:37,880
I have to say back about, I think based on the literature, there is some evidence that

665
00:46:37,880 --> 00:46:43,560
the multiple demand that's where it would have been, that can be recruited also in persons

666
00:46:43,560 --> 00:46:47,520
who have faced any language processing.

667
00:46:47,520 --> 00:46:52,240
But on the other hand, there's, yeah, for example, if you look at, if I look more at

668
00:46:52,240 --> 00:46:57,200
the literature on interventions, there is very little evidence that if you train cognitive

669
00:46:57,200 --> 00:47:01,000
skills, then it transfers to language skills.

670
00:47:01,000 --> 00:47:07,000
So in that perspective, I thought maybe it is slightly more to separate networks.

671
00:47:07,000 --> 00:47:13,800
So I think it was a bit, yeah, two lines of evidence, which made it a bit difficult to

672
00:47:13,800 --> 00:47:16,960
know what we would expect.

673
00:47:16,960 --> 00:47:22,000
And especially also, I think it was good that we had an age control, like age matched

674
00:47:22,000 --> 00:47:28,840
control group, because also in adults, you could think they need a bit more resources to communicate,

675
00:47:28,840 --> 00:47:33,280
maybe they need more cognitive resources to do that.

676
00:47:33,280 --> 00:47:36,320
So it's good that we have that group as well, because otherwise, if you wouldn't have

677
00:47:36,320 --> 00:47:40,120
age matched controls, then we would see some, the views of the multiple demand that's

678
00:47:40,120 --> 00:47:45,560
where they're language processing in persons with aphasia, who'd also be just be an age

679
00:47:45,560 --> 00:47:50,520
effect, but it's just, well, they're older than do that.

680
00:47:50,520 --> 00:47:51,520
Yes.

681
00:47:51,520 --> 00:47:53,160
Okay, so you weren't, so yeah, you're right.

682
00:47:53,160 --> 00:47:56,040
Yeah, you definitely wouldn't need all age matched controls.

683
00:47:56,040 --> 00:47:58,680
So you were kind of like of two minds as to what you were going to find.

684
00:47:58,680 --> 00:48:01,840
You were genuinely, could thought it could have gone either way.

685
00:48:01,840 --> 00:48:02,840
Yeah.

686
00:48:02,840 --> 00:48:03,840
In the use of that.

687
00:48:03,840 --> 00:48:06,800
Okay, so now can you tell us what you did exactly?

688
00:48:06,800 --> 00:48:13,760
Yeah, I think the most important analysis is when we, so the participants, it's language

689
00:48:13,760 --> 00:48:19,080
tasks, like reading tasks, where there was a contrast between reading sentences versus

690
00:48:19,080 --> 00:48:23,960
pseudo-wrench reading, so that at the end, when you have this contrast that you can expect

691
00:48:23,960 --> 00:48:28,240
really the language processing, but then it's more the semantic and syntactic processing

692
00:48:28,240 --> 00:48:29,520
that you would extract.

693
00:48:29,520 --> 00:48:34,960
We also had another language task, a listening task, very similar.

694
00:48:34,960 --> 00:48:41,880
So here it is, and to index sentences, and then your contrast is then with the graded speech.

695
00:48:41,880 --> 00:48:48,280
We used a contrast which really has no information on the phonemes, no semantic, no syntax.

696
00:48:48,280 --> 00:48:59,000
So here in the listening task, yeah, we also could look at what is maintained, both the

697
00:48:59,000 --> 00:49:01,920
phonological, semantic, and syntactic information.

698
00:49:01,920 --> 00:49:08,480
So we had these two language localizers, but then we also had multiple demand localizers,

699
00:49:08,480 --> 00:49:14,080
so they had to do a visual spatial task, and we already saw a grid with a square that

700
00:49:14,080 --> 00:49:18,200
was colored, and then we say another grid, and then they have to combine these two grids

701
00:49:18,200 --> 00:49:21,520
to say where the squares were colored.

702
00:49:21,520 --> 00:49:26,520
So it's like a visual working memory task.

703
00:49:26,520 --> 00:49:34,960
And so the main analysis is that we looked per individual, what are the most active voxels

704
00:49:34,960 --> 00:49:37,040
during this multiple demand task.

705
00:49:37,040 --> 00:49:42,720
So then we could really individual define what is, for this subject, the multiple demand

706
00:49:42,720 --> 00:49:43,720
network.

707
00:49:43,720 --> 00:49:48,120
So we had a set of voxels that were selected for each subject, so for each subject, it's

708
00:49:48,120 --> 00:49:53,440
a bit of a different selection, so it's really based on this localizer task.

709
00:49:53,440 --> 00:50:00,360
And then within these set of voxels that were selected, we looked at are these voxels active

710
00:50:00,360 --> 00:50:01,840
during language processing.

711
00:50:01,840 --> 00:50:08,720
So we looked at the devalues when they were doing this language localizer task, within

712
00:50:08,720 --> 00:50:13,400
this subject specific multiple demands network.

713
00:50:13,400 --> 00:50:14,400
Yeah.

714
00:50:14,400 --> 00:50:16,160
That's a bit more about it.

715
00:50:16,160 --> 00:50:17,160
Say again?

716
00:50:17,160 --> 00:50:19,160
Yeah, that's the approach.

717
00:50:19,160 --> 00:50:20,160
Yeah, okay.

718
00:50:20,160 --> 00:50:27,280
So there's a written language task and control, a spoken language task and control, then

719
00:50:27,280 --> 00:50:33,120
there's this difficult versus easy working memory contrast for the MD network.

720
00:50:33,120 --> 00:50:40,960
And then you kind of use this approach of finding the individual voxels that are the most

721
00:50:40,960 --> 00:50:42,560
responsive to each of these things.

722
00:50:42,560 --> 00:50:45,880
But you do it quite differently to her actually, because like she does it in these little

723
00:50:45,880 --> 00:50:49,760
parcels that she's come up with back in 2010 and been using ever since.

724
00:50:49,760 --> 00:50:53,160
But as far as I can understand, you guys did it like in the whole network.

725
00:50:53,160 --> 00:50:57,640
You just kind of took the whole language network and said where are the most responsive

726
00:50:57,640 --> 00:50:59,480
voxels and the same for the MD, right?

727
00:50:59,480 --> 00:51:00,480
Is that correct?

728
00:51:00,480 --> 00:51:01,480
Yeah, indeed.

729
00:51:01,480 --> 00:51:08,080
We also provide in the supplementary information, the approach that Fedorenko is using with

730
00:51:08,080 --> 00:51:11,960
the individual parcels, so really individual regions.

731
00:51:11,960 --> 00:51:16,080
But we felt something that, yeah, you have them, it's sometimes very small regions.

732
00:51:16,080 --> 00:51:20,920
So if you then look at the 10% most active voxels often, it's a lot of noise that you're measuring.

733
00:51:20,920 --> 00:51:27,160
So we felt if we take a 10% most active voxels across the whole language network or across

734
00:51:27,160 --> 00:51:34,560
the whole multiple the month network, we have maybe a bit less biased and also maybe a

735
00:51:34,560 --> 00:51:38,440
more less noisy activation veteran that we can experience.

736
00:51:38,440 --> 00:51:39,440
Yeah.

737
00:51:39,440 --> 00:51:41,760
And it would make it easier for people with aphasia too.

738
00:51:41,760 --> 00:51:45,520
We have some of the parcels might be completely destroyed.

739
00:51:45,520 --> 00:51:49,800
And from Ev's point of view, it wouldn't matter anyway because she claims that all the parcels

740
00:51:49,800 --> 00:51:54,480
are basically identical in their function, so it doesn't really matter.

741
00:51:54,480 --> 00:51:58,160
Anyway, I mean, this is like kind of a technical detail, but I couldn't help but notice that you

742
00:51:58,160 --> 00:52:01,440
were doing it in a unique way.

743
00:52:01,440 --> 00:52:06,440
So, but I thought it seems reasonable.

744
00:52:06,440 --> 00:52:07,440
Okay.

745
00:52:07,440 --> 00:52:14,480
So, what did you find when you looked at how these MD voxels respond, how did they respond

746
00:52:14,480 --> 00:52:17,680
when the participants were doing the language contrasts?

747
00:52:17,680 --> 00:52:18,680
Yeah.

748
00:52:18,680 --> 00:52:25,600
So, we saw that in this subject specific in the network, there was no activation during

749
00:52:25,600 --> 00:52:26,600
language processing.

750
00:52:26,600 --> 00:52:33,840
So, when they did the language localizing task, we could not find any significant activation

751
00:52:33,840 --> 00:52:37,720
in this MD network.

752
00:52:37,720 --> 00:52:41,080
This was a case for the controls for the healthy controls, but it was also the case for the

753
00:52:41,080 --> 00:52:42,080
person's with aphasia.

754
00:52:42,080 --> 00:52:43,080
Yeah.

755
00:52:43,080 --> 00:52:47,600
So, it was not that the person's phoenix that they are using is multiple amount features

756
00:52:47,600 --> 00:52:49,640
while they're processing language.

757
00:52:49,640 --> 00:52:50,640
Yeah.

758
00:52:50,640 --> 00:52:55,440
So, the control finding is essentially a replication in older adults of Dietrich at

759
00:52:55,440 --> 00:53:03,160
old 2020 and some of the other studies while the aphasia finding is very novel and

760
00:53:03,160 --> 00:53:07,640
really directly addresses that question of like, you know, are people with aphasia going

761
00:53:07,640 --> 00:53:11,960
to differentiate their own MD network to make up for their loss of language regions and

762
00:53:11,960 --> 00:53:14,400
basically you saw no evidence for that at all, huh?

763
00:53:14,400 --> 00:53:15,400
Yeah.

764
00:53:15,400 --> 00:53:20,480
So, I think doing, because that's maybe an important remark, doing passive language listening

765
00:53:20,480 --> 00:53:26,720
or just a basic reading task, then indeed we don't see even the person with aphasia,

766
00:53:26,720 --> 00:53:31,720
we don't see any activation in this multiple demand regions.

767
00:53:31,720 --> 00:53:37,120
I do, I'm still not convinced, for example, if we would do more complex language tasks like

768
00:53:37,120 --> 00:53:44,320
when we were talking now, thinking about what I would say and there's a lot of more task

769
00:53:44,320 --> 00:53:48,440
going on than the task we provided in the scanner.

770
00:53:48,440 --> 00:53:51,320
So, I think that might still be different.

771
00:53:51,320 --> 00:53:55,000
So, in daily communication where you have an interaction with another person, you have

772
00:53:55,000 --> 00:53:58,600
to listen and you have to think about what you will say already.

773
00:53:58,600 --> 00:54:04,320
I assume or I think there there might be more involvement of this MD network, but in the

774
00:54:04,320 --> 00:54:09,480
conditions that we test that, where you have more like a passive listening task, for example,

775
00:54:09,480 --> 00:54:13,560
it's natural speech, but it's more like passive listening than we don't see involvement

776
00:54:13,560 --> 00:54:15,040
of the MD network.

777
00:54:15,040 --> 00:54:16,040
Yeah.

778
00:54:16,040 --> 00:54:20,000
Well, you might think that for people with aphasia like, you know, even everyday language

779
00:54:20,000 --> 00:54:23,200
processing could be expected to be more cognitively demanding.

780
00:54:23,200 --> 00:54:26,440
I mean, certainly they report it to be such.

781
00:54:26,440 --> 00:54:34,760
So, maybe it's a pretty strong argument that that's not the kind of the way that compensation

782
00:54:34,760 --> 00:54:35,760
logs.

783
00:54:35,760 --> 00:54:38,800
Yeah, yeah, yeah, true, yeah.

784
00:54:38,800 --> 00:54:44,520
So how do you think they do, if they're not using the MD network to process language,

785
00:54:44,520 --> 00:54:48,280
how are they making up for the damaged language areas?

786
00:54:48,280 --> 00:54:56,040
Yeah, I think in our city, now we looked at, we looked at left and right, so maybe the

787
00:54:56,040 --> 00:55:01,200
right, homologue regions, or maybe take over, we couldn't, we did some, unless we didn't

788
00:55:01,200 --> 00:55:07,000
find any evidence at the right, it's maybe helping a bit more, but I'm very fascinated

789
00:55:07,000 --> 00:55:08,000
about that.

790
00:55:08,000 --> 00:55:13,080
So I think it would be nice to invest a bit more in that, it's maybe, because you always

791
00:55:13,080 --> 00:55:16,960
see this right activation also where you process language.

792
00:55:16,960 --> 00:55:19,760
It's probably a bit less crucial, but it is also there.

793
00:55:19,760 --> 00:55:24,360
So I think that is something I would like to invest a bit more, so maybe there are

794
00:55:24,360 --> 00:55:33,040
these right homologue regions, maybe they are maybe better in compensating for the deficits

795
00:55:33,040 --> 00:55:35,800
in the language network in the left.

796
00:55:35,800 --> 00:55:41,840
So we're planning to do those who study, so we have patients with a left hemisphere lesions

797
00:55:41,840 --> 00:55:47,760
in the MCA regions, but also with the right hemisphere lesions.

798
00:55:47,760 --> 00:55:53,520
So I think that would be maybe nice to have a little bit of sample of stroke patients

799
00:55:53,520 --> 00:55:58,200
who have both lesions, but one sample has been left and the other has been the right,

800
00:55:58,200 --> 00:56:01,200
and then the ability to affect the language network.

801
00:56:01,200 --> 00:56:06,760
Well, that'll be interesting, because I mean, we really understudy right hemisphere strokes

802
00:56:06,760 --> 00:56:08,080
for their language.

803
00:56:08,080 --> 00:56:12,960
I mean, I don't think they don't have frank aphasia by and large, but I still wish that we

804
00:56:12,960 --> 00:56:15,720
could know more about them.

805
00:56:15,720 --> 00:56:21,720
Yeah, and so I think that's a new project for this starting now, so for the future.

806
00:56:21,720 --> 00:56:27,040
Okay, so this paper is probably, I know it's a pre-print, it's probably under review, how

807
00:56:27,040 --> 00:56:33,080
are you going to get it through over the line with your first author heading off into industry

808
00:56:33,080 --> 00:56:34,080
job?

809
00:56:34,080 --> 00:56:43,320
Yeah, he is very helpful, he will continue working on the papers that were submitted now,

810
00:56:43,320 --> 00:56:45,920
or that are submitted.

811
00:56:45,920 --> 00:56:53,920
Yeah, I think at the moment, the limitation of the paper is currently that it's a smaller

812
00:56:53,920 --> 00:56:59,920
sample, but at the other hand, we use a very sensitive approach where you really have

813
00:56:59,920 --> 00:57:06,840
individual activation patterns, so I think that composes a bit, and I think the fact that

814
00:57:06,840 --> 00:57:11,400
we can now extend the findings of a red-pore-in-boulder adults and also to a person's with

815
00:57:11,400 --> 00:57:12,400
Aphasia.

816
00:57:12,400 --> 00:57:19,240
I think it's a nice finding or something important to share with the search for.

817
00:57:19,240 --> 00:57:21,600
Personally, I don't think the sample size is too small.

818
00:57:21,600 --> 00:57:24,400
I think it was well-powered.

819
00:57:24,400 --> 00:57:28,640
If there was going to be an effect, you should have been able to see it.

820
00:57:28,640 --> 00:57:33,960
If there was going to be any effect worth getting excited about, I don't feel that that's

821
00:57:33,960 --> 00:57:35,960
the major limitation.

822
00:57:35,960 --> 00:57:39,560
Do you have any other follow-ups apart from your writer looking at right-hemisphere

823
00:57:39,560 --> 00:57:40,560
stroke?

824
00:57:40,560 --> 00:57:43,000
We do a lot.

825
00:57:43,000 --> 00:57:48,320
Yeah, I think most of my research now, the new research project, what we aim to do there

826
00:57:48,320 --> 00:57:54,560
is to look with more functional neuroimaging, but more naturalistic paradigms, because

827
00:57:54,560 --> 00:57:58,520
I have the feeling with the kids, but also with persons with aphasia, often you're a bit

828
00:57:58,520 --> 00:58:03,160
restricted in what you can test, so often that it's structural MRI because then they

829
00:58:03,160 --> 00:58:05,440
don't need to do the task.

830
00:58:05,440 --> 00:58:10,120
So because it's difficult to do very complex tasks in these populations like young children

831
00:58:10,120 --> 00:58:13,120
or persons with aphasia.

832
00:58:13,120 --> 00:58:17,240
And I think now with this new trend to naturalistic paradigms, I feel that then the shift is not

833
00:58:17,240 --> 00:58:22,120
to, it's not a complex paradigm, so also young children and person's with aphasia can do

834
00:58:22,120 --> 00:58:27,000
these paradigms, but it's of course more complex to analyze the data, but I feel with the

835
00:58:27,000 --> 00:58:33,640
new analyzed techniques that are available, we can then also look at specific language

836
00:58:33,640 --> 00:58:38,440
processes, for example, more phonological aspects, some more semantics, and it's already in

837
00:58:38,440 --> 00:58:43,200
one paradigm, so I think there's both in the young kids and in person with aphasia, I

838
00:58:43,200 --> 00:58:50,160
do know a lot of both with EEG and MRI on the more naturalistic paradigms, and then with

839
00:58:50,160 --> 00:58:55,680
the coding analysis, you can link the neural responses to features that are within the story,

840
00:58:55,680 --> 00:59:00,520
they have listened to, so I think that is a bit of a new direction for me because I feel

841
00:59:00,520 --> 00:59:05,760
it's feasible to acquire these data in these difficult populations, and I get a bit

842
00:59:05,760 --> 00:59:12,320
more specific information on the function of language, so otherwise with the structural

843
00:59:12,320 --> 00:59:15,080
measures, it's always very indirect.

844
00:59:15,080 --> 00:59:24,440
Yeah, yeah, no, I mean functional is, yeah, functional is where it's at, right, for this

845
00:59:24,440 --> 00:59:32,080
population, knowing what's going on with the surviving brain areas is maybe more important

846
00:59:32,080 --> 00:59:37,840
than being exactly cataloging what areas were damaged.

847
00:59:37,840 --> 00:59:42,720
That's really interesting that you're wanting to do that naturalistic and decoding and stuff,

848
00:59:42,720 --> 00:59:48,480
I'm also very interested in that as a new direction, so I think a lot of us are probably

849
00:59:48,480 --> 00:59:54,400
seeing all the developments in natural language processing, and then just seeing the success

850
00:59:54,400 --> 01:00:00,040
of some of our colleagues who've had with these techniques in healthy controls like last

851
01:00:00,040 --> 01:00:06,960
year, I think I talked with Alex Huth and Jean-Remi King on the podcast about both of

852
01:00:06,960 --> 01:00:13,760
them using similar approaches, and we've got a lot of interest in like, you know, porting

853
01:00:13,760 --> 01:00:19,000
those approaches over into the aphasia world, so it's going to be great to see what kinds

854
01:00:19,000 --> 01:00:20,000
of that.

855
01:00:20,000 --> 01:00:25,640
It feels like a very exciting direction to go with this, it's rapidly changing, and I think

856
01:00:25,640 --> 01:00:32,080
a lot of things become possible, and I think it will help us to be more exciting, the

857
01:00:32,080 --> 01:00:34,320
more difficult populations to do that.

858
01:00:34,320 --> 01:00:39,520
Yeah, let's go to that advantage of being kind of like more approachable for the participants,

859
01:00:39,520 --> 01:00:40,520
right?

860
01:00:40,520 --> 01:00:44,480
Like if you're not asking into the complex tasks, they can just get in the scanner and listen

861
01:00:44,480 --> 01:00:49,960
to a podcast or watch a movie or whatever, and as you said, you can analyze the data

862
01:00:49,960 --> 01:00:54,600
at multiple levels at once from the same data set, you can be looking at phonology or semantics

863
01:00:54,600 --> 01:00:57,400
or syntax or however you code it.

864
01:00:57,400 --> 01:01:01,520
And so I think for the participants, it's easier for the researcher, it's more complex

865
01:01:01,520 --> 01:01:07,280
for the analyzer, more complex, but I think it's definitely a good approach for, in difficult

866
01:01:07,280 --> 01:01:09,000
to test populations.

867
01:01:09,000 --> 01:01:12,640
Yeah, cool, well that's a great new direction.

868
01:01:12,640 --> 01:01:18,080
Okay, well, I guess I should let you get to your day.

869
01:01:18,080 --> 01:01:24,920
For me, it's dinner time, but for you, it's probably time to get to work and you know.

870
01:01:24,920 --> 01:01:32,160
Well, I have a good balance with Pieter, so it's not really, so I'll put you nice to look

871
01:01:32,160 --> 01:01:33,160
forward to it.

872
01:01:33,160 --> 01:01:40,280
Okay, well, tell him, congratulations from me on a beautiful paper that's, I think really

873
01:01:40,280 --> 01:01:44,400
it really provides a very clear evidence on a question that a lot of people are interested

874
01:01:44,400 --> 01:01:47,360
in, so yeah, it's a great paper.

875
01:01:47,360 --> 01:01:48,360
I agree.

876
01:01:48,360 --> 01:01:53,440
Yeah, okay, well, it was very nice to talk to you.

877
01:01:53,440 --> 01:01:54,920
Thanks for taking the time.

878
01:01:54,920 --> 01:01:56,920
Yeah, many thanks for having me.

879
01:01:56,920 --> 01:01:59,840
It's a nice experience, it's my set.

880
01:01:59,840 --> 01:02:00,840
Yeah, good.

881
01:02:00,840 --> 01:02:02,920
I think that's a bit of a use too.

882
01:02:02,920 --> 01:02:08,920
Yeah, yeah, there's not a lot of podcasts about the neuroscience of language. (Laughter)

883
01:02:08,920 --> 01:02:15,640
All right, well, I hope to catch up with you at a future conference.

884
01:02:15,640 --> 01:02:19,880
Okay, thank you and look forward to see you on the next conference.

885
01:02:19,880 --> 01:02:21,480
Okay, take care, bye.

886
01:02:21,480 --> 01:02:22,480
Bye-bye.

887
01:02:22,480 --> 01:02:29,720
All right, well, that's it for episode 30.

888
01:02:29,720 --> 01:02:33,360
Thank you, Maaike, for joining me on the podcast, and thank you all for listening.

889
01:02:33,360 --> 01:02:37,080
I'd like to acknowledge the support of the journal, Neurobiology of Language, who have

890
01:02:37,080 --> 01:02:39,800
kindly covered part of the cost of transcription.

891
01:02:39,800 --> 01:02:43,040
We just got a nice revised and resume on the fifth paper my lab has submitted to this

892
01:02:43,040 --> 01:02:44,040
journal.

893
01:02:44,040 --> 01:02:47,600
Just like on all of our previous submissions, we got thoughtful, constructive reviews

894
01:02:47,600 --> 01:02:52,000
from well-chosen reviewers who clearly have deep relevant expertise and actually care about

895
01:02:52,000 --> 01:02:53,320
making our paper better.

896
01:02:53,320 --> 01:02:55,920
I'd encourage everyone to consider submitting your work there.

897
01:02:55,920 --> 01:02:57,280
It's a great journal.

898
01:02:57,280 --> 01:03:01,280
Thanks also to Marcia Petyt for editing the transcript of this episode.

899
01:03:01,280 --> 01:03:02,280
Bye for now.

900
01:03:02,280 --> 01:03:02,760
See you next time.

901
01:03:02,760 --> 01:03:13,160
[Music]