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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
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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
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and at the end they have all the keys that can open the castle which is in the MRI scanner
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or they can open the ice, the glow and this kind of stuff.
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Oh wow, so the prize is that they get to go into the scanner and be scanned? (Laughter)
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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.
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I say that last thing again?
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So I think that the protocol was working because they were coming back.
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Oh right, oh, for longitudinal now, yeah.
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Oh, absolutely.
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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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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.
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They might not tell you that they hated it, but you just that you weren't here from them
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again.
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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.
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Oh, totally.
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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.
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So can you kind of share like, what did you learn?
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So when you did start to do this research, what did you learn about?
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So you're going to end up dividing the kids into those who become dyslexic and those who
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become typically need developing readers and kind of look back at what their brains look
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like before that happened.
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So can you kind of tell me what changes, what differences you've seen in the brains of
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kids that are going to go on to become dyslexic versus those that will not?
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Yeah, so concerning the white matter, so we as we did the adults, we looked at the white
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matter connections, looked at the more dorsal connection of the reading which is in the left
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Arcuate fasiculus, but we also looked at the more
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Ventral connection between frontal and occipital regions by the IFOF (Inferior Front-Occipital Fasciculus).
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And so in the adults, we found that there was, there was, yeah, the fractional anisotropy,
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which is an index of white matter organization, that that one was lowering in the adults
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With dyslexia, yeah.
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And so when we looked at the...
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In what track,
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Sorry?
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In what track was it lower in the adults?
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Yeah, so in the left arcuate fasciculus
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Arcuate, okay.
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Left arcuate, OK.
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Yeah, so there was an over fractional anisotropy and then when we looked at the pre-reading
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brain, so we could indeed compare the pre-readers who developed dyslexia versus the one
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who developed it with our reading skills.
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We also found a lower FA in the left arcuate fasciculus, so we found this difference, again also a
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pre-reading, so it seemed to suggest that it was not just a consequence of a different reading
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experience, the person with dyslexia had, but it's really there from the very start.
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So even before they started to read and write, what we also saw is that...
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And that was a bit unexpected, we also saw in the right arcuate fasciculus, also a lower FA
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for the pre-reading children who developed dyslexia.
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So that was a bit unexpected because I think in dyslexia, in the research fields, the
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right is often considered also to help compensate for the reading difficulties.
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Yeah.
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So I think that what we thought maybe that we would find more like an increased FA
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may mean the right in these children, but that was not a case.
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Also, if you look at other pre-reading MRI studies that were happening more or less at
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the same time, they often also find bilateral differences, so not only restricted to the
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left, but they also have some evidence showing that the other right can be like a compensation
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for helping, compensating for the reading difficulties.
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So therefore, it was a bit surprised to find also in the right lower FA in the dyslexic
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readers.
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Okay.
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Yeah, it is a bit surprising, but if other labs are seeing that too, that's reassuring.
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And you were seeing these differences in the Dorsal Tracks, right?
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Were other labs also replicating that finding or did people see those in ventral too?
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Yeah.
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I think most studies that used this longitudinal approach and looked at the brain of pre-reader
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who later developed, for reading skills, like they did a very similar study in the lab
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of Nadine Gaab from MIT.
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And also from Miguel Steida, they also had a very similar study, and both of them they also
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found that these left arcuates were differently developed in the pre-readers with poor reading
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skills.
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And that was replicated even in the lab of Nadine Gabb, they even have a study in infants who
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have a family risk for dyslexia.
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And also, they found this in the left arcuate fasciculus difference.
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So I think the left arcuate fasciculus is confirmed also in other independent samples.
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However, there's now also more recent large skills studies using thousands of kids from
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the lab.
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But then with a very wide age range, like often from 8 to 18, and there they don't see this
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link with reading skills and have in the left arcuate fasciculus.
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They don't see it very clearly.
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So I think I know, I think it has something to do probably also that in these large skills
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studies that a lot of ages are combined into one big sample.
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Yeah.
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Well, didn't you?
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In your paper where you first reported this, which is the 2017 paper, I think, and I don't
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want to butcher your colleague's name.
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So maybe you can say the name of the first one.
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Yeah.
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Jolijan Vandermosten.
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Thank you.
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So in that 2017 paper, there's also a longitudinal component to that too.
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And I was struck by the fact that you saw this effect on the arcuate in the pre-reading
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time point, the left arcuate.
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And then by the sort of post-reading time point, like a year or two later, when they've begun
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to read, you actually saw that that difference had normalized between these groups.
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So that, if that's the case, that would seem to, that would kind of maybe explain why it's
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not being observed in this sort of diverse age later sample, right?
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But it's also very mysterious finding.
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Like, what do you, what do you make of that?
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Yeah.
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So I think in the, you know, sample at least that the pre-reading difference is for a
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bit clearer because then the more in primary school, the more they had to learn to read, then
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we don't see the, the clear difference anymore between the children with dyslexia and without.
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I think it has something to do with the fact that when you learn to read, of course, it's
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A new experience, and probably that also has an impact on these five matters.
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So the right matter is not is a result of age-related maturation, but also of experience
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induced changes.
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And I can imagine that maybe once you learn to read and write, you get more this experience,
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induced changes that also have an impact.
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And maybe therefore the relation is a bit as clear.
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We did two an additional study where we have, where we looked in this left arcuate fasciculus
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more specifically at the cluster where we found the group difference pre-reading between
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the dyslexics and the long dyslexic pre-readers.
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And if we look within that cluster, we do see it is also, we can see that the difference
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is also still present in grade two and also in grade five.
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So if we look a bit more specific, the difference seems to be there across primary school.
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But I think on the average tract, so if you look at the FA across the whole tract, the difference
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was stronger pre-reading than post-reading.
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And I think it has something to do with the fact that the longer, the further throughout development
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the more you have also this experience induced changes in the white matter.
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Okay.
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And what part of the accurate is that real key part where you're still seeing the differences?
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even later?
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It was more in the temporal prietal part.
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And it was, so I think it's something that also needs to be further investigateded in to see
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how, to what extent do these differences between the group today remain present across primary
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school?
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Because I think, for example, in our sample, what we saw, if we look at FA across the
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whole track in the arcuate fasciculus, indeed the group difference disappeared.
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But it was especially the Children with dyslexia who had already some early interventions.
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They seem to have a larger increase, so it might be something that is, because of the intervention,
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they followed them, followed an intervention quite early, and that seemed to have helped them
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in maybe catching up in the left arcuate fasciculus.
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Yeah.
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You had this sort of exploratory correlation where the kids who got more intervention were
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having greater changes over time, right?
397
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And you've been following up in recent work.
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I haven't really looked at those papers in depth as you know, but I think you've been doing
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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
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to disentangle age related or age maturation versus more changes that are induced by the environment
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00:28:46,520 --> 00:28:48,440
or by the experiences you have.
403
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So we decided to do an intervention study because then we could control a bit better these
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two processes and to disentangle them a bit better.
405
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So the idea was to do reading intervention, but we also decided to do it very early already
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in kindergarten, so more like a preventive reading intervention, because there's quite some
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behavioral evidence that shows that if you do interventions later, so starting in
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grade three, for example, then they are less effective than if you do it early, so in grade
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one or even in kindergarten, they often call it the dyslexia paradox.
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It's also put forward by the lack of nothing yet because in clinical practice often the kids
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with dyslexia, they only receive their intervention after the diagnosis is given, but to get a
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diagnosis you have to show severe deficit in reading, but also persistent.
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So often it takes some time to be able to make diagnosis and so they also advise that
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the intensive intervention for reading often only takes place in grade three a later, but
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then we know from behavioral intervention studies that then the impact is smaller than
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if you would do it earlier.
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So the idea was with a new study to go to kindergarten to select children who are at risk for dyslexia,
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because they can't give a diagnosis yet.
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So they are at risk for dyslexia and then we split the group, we randomly assigned half
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of the pre-readers at risk to a reading intervention and the other half did also a kind of intervention
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very similar, but instead of reading games on a tablet, they played Lego and Lego build
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games on tablets.
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So we checked beforehand, there was a similar motivation for both games and also in terms
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of expectancies from the parents, it was also very similar.
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And both groups played an equal amount of time on the tablets, the one group, training
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on reading and the other group, training on other skills, more spatial visual skills.
427
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And did it help them?
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So behaviorally we decided that at risk group who played the reading intervention, they improved
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for lateral knowledge, also basic reading skills were better than the at risk group who played
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the control intervention.
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So they got better.
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And if we looked at the white matter tracts, we didn't see an impact on the fractional
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anisotropy.
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So we first saw, like, we would have expected that this different reading experience would
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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
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But if we had also an additional MRI scan, where we looked more specifically at myelination
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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
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And what tract was that seen in?
443
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Yeah.
444
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So the myelination was quite widespread.
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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.
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Developmental dyslexia and neuroplasticity in aphasia with Maaike Vandermosten
Sep 16, 2024•1 hr 3 min•Season 4Ep. 30
Episode description
In this episode, I talk with Maaike Vandermosten, Associate Professor in the Department of Neurosciences at KU Leuven, about the neural basis of developmental dyslexia, and neuroplasticity in recovery from aphasia.
Vanderauwera J, Wouters J, Vandermosten M, Ghesquière P. Early dynamics of white matter deficits in children developing dyslexia. Dev Cogn Neurosci 2017; 27: 69-77. [doi]
Beelen C, Vanderauwera J, Wouters J, Vandermosten M, Ghesquière P. Atypical gray matter in children with dyslexia before the onset of reading instruction. Cortex 2019; 121: 399–413. [doi]
Phan TV, Sima D, Smeets D, Ghesquière P, Wouters J, Vandermosten M. Structural brain dynamics across reading development: A longitudinal MRI study from kindergarten to grade 5. Hum Brain Mapp 2021; 42: 4497-509. [doi]
Clercq PD, Gonsalves AR, Gerrits R, Vandermosten M. Individualized functional localization of the language and multiple demand network in chronic post-stroke aphasia. bioRxiv 2024; 2024.01.12.575350. [doi]
Transcript
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