A stereoscopic system for viewing the temporal evolution of brain activity clusters in response to linguistic stimuli
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Forbes, Angus
Villegas, Javier
Almryde, Kyle R.
Plante, Elena
In this paper, we present a novel application, 3D+Time Brain View, for the stereoscopic visualization of functional Magnetic Resonance Imaging (fMRI) data gathered from participants exposed to unfamiliar spoken languages. An analysis technique based on Independent Component Analysis (ICA) is used to identify statistically significant clusters of brain activity and their changes over time during different testing sessions. That is, our system illustrates the temporal evolution of participants' brain activity as they are introduced to a foreign language through displaying these clusters as they change over time. The raw fMRI data is presented as a stereoscopic pair in an immersive environment utilizing passive stereo rendering. The clusters are presented using a ray casting technique for volume rendering. Our system incorporates the temporal information and the results of the ICA into the stereoscopic 3D rendering, making it easier for domain experts to explore and analyze the data. 
 So, as John said, my paper today is titled, A Stereoscopic System for Viewing the Temporal Evolution of Brain Activity Clusters in Response to Linguistic Stimuli. And it's really a collaboration between me and a postdoc in the Creative Coding Lab within the School of Information and also with a researcher in Dr. Elena Plants' lab in the Department of Speech, Language, and Hearing Science, both at University of Arizona. So today, I'm going to present a prototype of our stereoscopic neuroimaging tool. It's ongoing work titled, The Tool is Called the 3D Brain plus Time View. I'll discuss what motivated us to create the tool in the first place, describe some of our design decisions, give a brief overview of the mostly positive responses to the application by the domain experts in the speech, language, and hearing department, and discuss some future directions and hopefully get some feedback from you. So the 3D Brain plus Time View is a specialized volume visualization application that displays clusters of statistical data gathered from fMRI machines. And the data is related to language acquisition tasks, which we'll talk about in a second. It utilizes stereoscopic visualization to enable domain experts to more effectively explore data from various vantage points. And it makes it easy for domain experts to view the temporal evolution of these brain clusters, of the brain activity rather. So just some quick screenshots of the application. This is obviously in 2D. You can see some brain activity on the outside of the brain. And then here's a prototype using our stereoscopic video wall. It's a passive projection system using polarized lenses. And the user can rotate the brain, zoom into the brain, turn certain clusters on or off in order to get some insight into the activity of the brain. So just a little background about the speech, language, and hearing lab. Dr. Plants, current research is focused on understanding what happens to the brain during the process of learning. And in particular, she explores, as I said, language acquisition with the hopes that by understanding language acquisition better, potentially she or the researchers can help people with language disorders. And she's previously known for doing studies that show that there are both structural and activation differences between normal language learners and those with neurogenic communication disorders. Her research uses magnetic resonance imaging or MRI machines. And their lab, they have one and they force lots of undergraduates to go into it so they can study it. And I actually wasn't aware of this. And I was, I'm not sure how aware you are of neuroimaging. So there's a difference between MRI and fMRI. The same machine allows you to do both. But MRI studies brain anatomy and fMRI or functional MRI studies brain function. And this is a slide they used to explain it to me. So here's the anatomy of Homer Simpson using an MRI scan. And here's an fMRI which studies the brain function of Homer Simpson. And somewhere in there, allegedly, is language acquisition or language activity. So MRI machines or rather the MRI scan is much higher resolution, but it operates much more slowly. So you normally get a single volume after a long span of time. Whereas fMRI takes lots of scans with lower resolution, but allows you to see where blood is flowing through the brain in response to particular tasks. So the goal of Alena Plant's experiments was to use fMRI to find significant clusters of brain activity related to language. And to measure how clusters appear and change over time during language learning tasks in order to generate insight as to what a normal brain does when it encounters new language and hopefully shed. The study is pretty interesting and I thought I'd just briefly describe it to you. So previous studies have shown that both adults and children can segment a sentence into individual words while listening to speech. And so the idea there is that as I'm speaking, there's actually not a pause between every word, if you looked at an audio signal, you wouldn't see any pauses between words. And yet as English speakers, it's very easy for us, even when someone's talking quickly, to distinguish where a word ends and a word begins. And to not get confused by syllables that are part of the same word. So the studies have shown that the way we do this is our brains automatically recognize high transitional probability coinciding with sequential syllables that make up individual words and a low transitional probability marks boundaries between words. And here's an example of the difference between the two. If you have a sentence, it's not the robber stole money, it's the robber stole money from the bank. And yet we're able to realize that robber, those two syllables, are likely to go together and form a single word where our e from, even though they run together, doesn't occur in very many words or if in any words, and therefore we interpret that as a word break. And there's another example below. So the transitional probability of syllables is used during the initial learning process to segment speech streams using statistical learning. And the interesting thing about this study is that most previous studies have done neuroimaging on the brain after subjects were exposed to stimuli to study the outcome of a learning activity. And the difference here is that she's interested in looking at the learning process during the learning activity. And so I thought this was very interesting is that humans begin learning language, both adults and children, even when they're not consciously aware of it, simply by being exposed to hearing grammatically sensible sentences. And our brains are able to quickly make sense out of sentences with high predictability stimuli. And so the specific experiment took a bunch of subjects and played them Norwegian sentences. None of them knew Norwegian ahead of time. And here's an example of sentences played while the subject was in the MRI machine. I don't speak Norwegian, so I'm probably pronouncing it wrong, but it's Nils, Vil, Re-ice, Tilkeena. So that's a high predictable stimuli that the syllable Re and ice appear, I guess, in various sentences over and over again throughout the testing. So the human brain is able to understand that that's a word and not a boundary between words. So what they did is they scrambled up the speech signals. And they did it in such a way that if you asked the subject if the sentence sounded wrong, they wouldn't know because they didn't know Norwegian. So here's an example where we take that same syllable cluster, Re-ice, in the one word, and they just move it to what's normally a single syllable. So Nils, Vil, Se, Tilre, Keena. But the hypothesis is that by measuring the brain activity, you would notice different activation patterns on grammatical sentences or sentences that are normal Norwegian and sentences that are abnormal Norwegian, even if you don't know Norwegian. So they took 32 subjects with normal language learning abilities. 16 were exposed to the learnable sentences and 16 were exposed to these ungrammatical unlearnable sentences. And for each subject, there were three scan sessions that lasted eight minutes each with a five minute break in between. And within each session, they heard interleave stimulus, so lots of different Norwegian sentences dispersed with a short break of something, I don't know, I can't remember what it was, but it wasn't Norwegian. It was maybe English or maybe some music. And again, the interesting thing is that humans exhibit these language activation patterns even when they don't understand the language. But subjects who are exposed to the ungrammatical Norwegian, on the other hand, did not exhibit any interesting brain activation patterns. And these are not computationally generated. These are drawn by hand by one of the researchers in her lab. So this is the unlearnable sentences. They applied some statistical analyses to see particular brain activity. And I guess they expected that in certain regions associated, they know or associated with language, they thought those would light up. But in fact, they didn't light up. And in fact, the brain activity got less and less as the learning tasks went on, which maybe the way they interpret that is that the brains didn't know what to do with these ungrammatical sentences, wasn't able to learn from it, and so basically gave up trying. On the other hand, if they play their grammatical Norwegian sentences, the brain, there's a clear difference. There's five different easily identifiable clusters, and some of them do indeed take place where they know brain activity that's related to language occurs. And more interestingly to them is that these clusters don't stay still over the eight-minute scans. Again, they did three eight-minute scans. The clusters, the same clusters with the same activation patterns move around the brain, which is, they don't quite know how to interpret it. I don't think, but at least they're developing a map of how, what occurs during language learning. And an interesting thing about this is that the clusters aren't necessarily spatially contiguous. They're instead defined by these temporal patterns of activation. And I'm going to show just an example of what this means. So here we see, so the analysis that they do looks at all the voxels, the fMRI voxels. So it's doing a volume scan. It's pretty low resolution. It's 100 by 100 by 100, but that still creates a million voxels. They look at each voxel independently and try and trace its activation pattern. And then, so here's an example, they're tracing just three voxels over time that have a similar activation pattern. And then what they do is they do an ICA, independent component analysis, in order to map different activation patterns to the different voxels of the brain. And so here's a simplified example. On the top, you see these observed signals. Those voxels are lighting up in different ways with different intensities over different times. And then the ICA tries to separate out different activation patterns and can find, for example, the first one. Those voxels are coming online and offline or pulsing over a certain rate of time. The second one's more like a step function. Nothing happens at all of a sudden that goes online. And here's kind of a more of a linear function. Interesting thing to note is that sometimes these clusters, it'll find clusters that are rather that do overlap, which is interesting to them because there may be a particular voxel of a brain might actually have multiple functionalities. And again, the resolution's fairly low, so it might be that having a higher resolution would find some separation between the clusters. So where the stereoscopic parts comes in and where the visualization tool comes in came from a discussion with the researchers in this lab where they realized, or rather I realized, they told me that there were no existing tools that were useful to them, especially ones that visualized ICA clusters, in a panic analysis clusters, as they changed over time. So I see them running out of time so relatively quickly. Their workflow was rather complicated, strenuous, involved a lot of manual labor. Even after the pre-processing of doing the statistical analyses, just to load the data and to look at it took a long time. And the main problem was hard for them to see the most important aspects of the data, the data they cared about, the spatial layout, and the temporal evolution of the clusters. So I'll skip through this, but basically it's painful for every cluster, for every file, for every subject, they have to click a bunch of buttons. And there's some screenshots of their interface, and they can view the clusters, but again, they're viewing it in 2D. They have to move through it at orthogonal angles. They usually have three windows open for each of the axes, and they basically find it a little bit difficult to create, to intuit what's going on with these clusters. So they're a little frustrated, and it was even more complicated. It was exacerbated by the fact they wanted to see these things change over time. So they tried some 3D Viz software, but found it difficult to customize for their specific desires, and they hadn't actually tried, they've tried 3D stuff, but they hadn't tried stereoscopic visualization because they wrote it off as eye candy, and they thought maybe it would be good for public outreach, but they didn't see how it would actually be good for them as domain experts. So we, part of our 3D Brain Plus Time view tool was to make the workflow easier for the neuroscientists in the speech, language, and hearing lab. The interface is pretty straightforward. You can turn clusters on and off. You can change the opacity of the main brain, and you can move a slider to watch the clusters change over time. There's a problem in 2D is that once you have lots of clusters on it once, even if they are changing over time, it becomes difficult to distinguish them. So by displaying them stereoscopically, it's much easier to view the multiple clusters, especially when they overlap. So I'll just show you a few images quickly. So this is a researcher in that lab talking about interacting with the brain. Right now there's actually an operator off screen who's doing what he's asking him to do, so there's no real interaction in this case, in this version, besides keyboards. And here's a slightly nicer version with a Fong shading, where we see three clusters. This is a time one, and I don't know if you can see what's going on. I should have brought a video, but you can kind of see the clusters changing over time. So even though this is relatively rumentary, this was a quantum leap over what they had before in one view, just by moving a slider, they can see these clusters appear and disappear and evolve. And then of course, here's a version with a opacity turned on all the way, where you can see the clusters coming on and off. And you can change the opacity, so it's somewhere in between, you can see a little deeper into the brain and then rotate the brain. So in conclusion, I see a lot of time, it's important to work closely with the main experts in order to figure out what's actually useful to them. So just doing stereoscopic, they wrote off, just doing the temporal sliders they didn't care about, but putting them together, they found was useful. So one wasn't really useful without the other, but once they were together, they were super excited and wanted us to continue working on it. So lots to do. The main one is to make it not just a visualization tool, but more of a visual analytics tool, where we can actually include some of the pre-processing choices with the tool to be able to show the statistics and the visualization, not just the final clusters to change the thresholds to annotate the clusters and to merge the whole thing into one tool. And finally, right now, as you saw, we have this in the video wall in our lab. It would be nice to have to buy them at low cost 3D projector or monitor, or maybe to use some HMDs so that they can use it in their lab. And I'll take some questions. Thanks a lot.
 So, as John said, my paper today is titled, A Stereoscopic System for Viewing the Temporal  Evolution of Brain Activity Clusters in Response to Linguistic Stimuli.  And it's really a collaboration between me and a postdoc in the Creative Coding Lab  within the School of Information and also with a researcher in Dr. Elena Plants' lab  in the Department of Speech, Language, and Hearing Science, both at University of Arizona.  So today, I'm going to present a prototype of our stereoscopic neuroimaging tool.  It's ongoing work titled, The Tool is Called the 3D Brain plus Time View.  I'll discuss what motivated us to create the tool in the first place, describe some of  our design decisions, give a brief overview of the mostly positive responses to the application  by the domain experts in the speech, language, and hearing department, and discuss some future  directions and hopefully get some feedback from you.  So the 3D Brain plus Time View is a specialized volume visualization application that displays  clusters of statistical data gathered from fMRI machines.  And the data is related to language acquisition tasks, which we'll talk about in a second.  It utilizes stereoscopic visualization to enable domain experts to more effectively explore  data from various vantage points.  And it makes it easy for domain experts to view the temporal evolution of these brain  clusters, of the brain activity rather.  So just some quick screenshots of the application.  This is obviously in 2D.  You can see some brain activity on the outside of the brain.  And then here's a prototype using our stereoscopic video wall.  It's a passive projection system using polarized lenses.  And the user can rotate the brain, zoom into the brain, turn certain clusters on or off  in order to get some insight into the activity of the brain.  So just a little background about the speech, language, and hearing lab.  Dr. Plants, current research is focused on understanding what happens to the brain during  the process of learning.  And in particular, she explores, as I said, language acquisition with the hopes that by  understanding language acquisition better, potentially she or the researchers can help  people with language disorders.  And she's previously known for doing studies that show that there are both structural and  activation differences between normal language learners and those with neurogenic communication  disorders.  Her research uses magnetic resonance imaging or MRI machines.  And their lab, they have one and they force lots of undergraduates to go into it so they  can study it.  And I actually wasn't aware of this.  And I was, I'm not sure how aware you are of neuroimaging.  So there's a difference between MRI and fMRI.  The same machine allows you to do both.  But MRI studies brain anatomy and fMRI or functional MRI studies brain function.  And this is a slide they used to explain it to me.  So here's the anatomy of Homer Simpson using an MRI scan.  And here's an fMRI which studies the brain function of Homer Simpson.  And somewhere in there, allegedly, is language acquisition or language activity.  So MRI machines or rather the MRI scan is much higher resolution, but it operates much  more slowly.  So you normally get a single volume after a long span of time.  Whereas fMRI takes lots of scans with lower resolution, but allows you to see where blood  is flowing through the brain in response to particular tasks.  So the goal of Alena Plant's experiments was to use fMRI to find significant clusters  of brain activity related to language.  And to measure how clusters appear and change over time during language learning tasks in  order to generate insight as to what a normal brain does when it encounters new language  and hopefully shed.  The study is pretty interesting and I thought I'd just briefly describe it to you.  So previous studies have shown that both adults and children can segment a sentence into individual  words while listening to speech.  And so the idea there is that as I'm speaking, there's actually not a pause between every  word, if you looked at an audio signal, you wouldn't see any pauses between words.  And yet as English speakers, it's very easy for us, even when someone's talking quickly,  to distinguish where a word ends and a word begins.  And to not get confused by syllables that are part of the same word.  So the studies have shown that the way we do this is our brains automatically recognize  high transitional probability coinciding with sequential syllables that make up individual  words and a low transitional probability marks boundaries between words.  And here's an example of the difference between the two.  If you have a sentence, it's not the robber stole money, it's the robber stole money from  the bank.  And yet we're able to realize that robber, those two syllables, are likely to go together  and form a single word where our e from, even though they run together, doesn't occur in  very many words or if in any words, and therefore we interpret that as a word break.  And there's another example below.  So the transitional probability of syllables is used during the initial learning process  to segment speech streams using statistical learning.  And the interesting thing about this study is that most previous studies have done neuroimaging  on the brain after subjects were exposed to stimuli to study the outcome of a learning  activity.  And the difference here is that she's interested in looking at the learning process during the  learning activity.  And so I thought this was very interesting is that humans begin learning language, both  adults and children, even when they're not consciously aware of it, simply by being exposed  to hearing grammatically sensible sentences.  And our brains are able to quickly make sense out of sentences with high predictability  stimuli.  And so the specific experiment took a bunch of subjects and played them Norwegian sentences.  None of them knew Norwegian ahead of time.  And here's an example of sentences played while the subject was in the MRI machine.  I don't speak Norwegian, so I'm probably pronouncing it wrong, but it's Nils, Vil,  Re-ice, Tilkeena.  So that's a high predictable stimuli that the syllable Re and ice appear, I guess, in  various sentences over and over again throughout the testing.  So the human brain is able to understand that that's a word and not a boundary between words.  So what they did is they scrambled up the speech signals.  And they did it in such a way that if you asked the subject if the sentence sounded  wrong, they wouldn't know because they didn't know Norwegian.  So here's an example where we take that same syllable cluster, Re-ice, in the one word,  and they just move it to what's normally a single syllable.  So Nils, Vil, Se, Tilre, Keena.  But the hypothesis is that by measuring the brain activity, you would notice different  activation patterns on grammatical sentences or sentences that are normal Norwegian and  sentences that are abnormal Norwegian, even if you don't know Norwegian.  So they took 32 subjects with normal language learning abilities.  16 were exposed to the learnable sentences and 16 were exposed to these ungrammatical  unlearnable sentences.  And for each subject, there were three scan sessions that lasted eight minutes each with  a five minute break in between.  And within each session, they heard interleave stimulus, so lots of different Norwegian sentences  dispersed with a short break of something, I don't know, I can't remember what it was,  but it wasn't Norwegian.  It was maybe English or maybe some music.  And again, the interesting thing is that humans exhibit these language activation patterns  even when they don't understand the language.  But subjects who are exposed to the ungrammatical Norwegian, on the other hand, did not exhibit  any interesting brain activation patterns.  And these are not computationally generated.  These are drawn by hand by one of the researchers in her lab.  So this is the unlearnable sentences.  They applied some statistical analyses to see particular brain activity.  And I guess they expected that in certain regions associated, they know or associated  with language, they thought those would light up.  But in fact, they didn't light up.  And in fact, the brain activity got less and less as the learning tasks went on, which  maybe the way they interpret that is that the brains didn't know what to do with these  ungrammatical sentences, wasn't able to learn from it, and so basically gave up trying.  On the other hand, if they play their grammatical Norwegian sentences, the brain, there's a  clear difference.  There's five different easily identifiable clusters, and some of them do indeed take  place where they know brain activity that's related to language occurs.  And more interestingly to them is that these clusters don't stay still over the eight-minute  scans.  Again, they did three eight-minute scans.  The clusters, the same clusters with the same activation patterns move around the brain,  which is, they don't quite know how to interpret it.  I don't think, but at least they're developing a map of how, what occurs during language  learning.  And an interesting thing about this is that the clusters aren't necessarily spatially  contiguous.  They're instead defined by these temporal patterns of activation.  And I'm going to show just an example of what this means.  So here we see, so the analysis that they do looks at all the voxels, the fMRI voxels.  So it's doing a volume scan.  It's pretty low resolution.  It's 100 by 100 by 100, but that still creates a million voxels.  They look at each voxel independently and try and trace its activation pattern.  And then, so here's an example, they're tracing just three voxels over time that have a similar  activation pattern.  And then what they do is they do an ICA, independent component analysis, in order to map different  activation patterns to the different voxels of the brain.  And so here's a simplified example.  On the top, you see these observed signals.  Those voxels are lighting up in different ways with different intensities over different  times.  And then the ICA tries to separate out different activation patterns and can find, for example,  the first one.  Those voxels are coming online and offline or pulsing over a certain rate of time.  The second one's more like a step function.  Nothing happens at all of a sudden that goes online.  And here's kind of a more of a linear function.  Interesting thing to note is that sometimes these clusters, it'll find clusters that are  rather that do overlap, which is interesting to them because there may be a particular  voxel of a brain might actually have multiple functionalities.  And again, the resolution's fairly low, so it might be that having a higher resolution  would find some separation between the clusters.  So where the stereoscopic parts comes in and where the visualization tool comes in came  from a discussion with the researchers in this lab where they realized, or rather I  realized, they told me that there were no existing tools that were useful to them, especially  ones that visualized ICA clusters, in a panic analysis clusters, as they changed over time.  So I see them running out of time so relatively quickly.  Their workflow was rather complicated, strenuous, involved a lot of manual labor.  Even after the pre-processing of doing the statistical analyses, just to load the data  and to look at it took a long time.  And the main problem was hard for them to see the most important aspects of the data,  the data they cared about, the spatial layout, and the temporal evolution of the clusters.  So I'll skip through this, but basically it's painful for every cluster, for every file,  for every subject, they have to click a bunch of buttons.  And there's some screenshots of their interface, and they can view the clusters, but again,  they're viewing it in 2D.  They have to move through it at orthogonal angles.  They usually have three windows open for each of the axes, and they basically find it a  little bit difficult to create, to intuit what's going on with these clusters.  So they're a little frustrated, and it was even more complicated.  It was exacerbated by the fact they wanted to see these things change over time.  So they tried some 3D Viz software, but found it difficult to customize for their specific  desires, and they hadn't actually tried, they've tried 3D stuff, but they hadn't tried stereoscopic  visualization because they wrote it off as eye candy, and they thought maybe it would  be good for public outreach, but they didn't see how it would actually be good for them  as domain experts.  So we, part of our 3D Brain Plus Time view tool was to make the workflow easier for the  neuroscientists in the speech, language, and hearing lab.  The interface is pretty straightforward.  You can turn clusters on and off.  You can change the opacity of the main brain, and you can move a slider to watch the clusters  change over time.  There's a problem in 2D is that once you have lots of clusters on it once, even if they  are changing over time, it becomes difficult to distinguish them.  So by displaying them stereoscopically, it's much easier to view the multiple clusters,  especially when they overlap.  So I'll just show you a few images quickly.  So this is a researcher in that lab talking about interacting with the brain.  Right now there's actually an operator off screen who's doing what he's asking him to  do, so there's no real interaction in this case, in this version, besides keyboards.  And here's a slightly nicer version with a Fong shading, where we see three clusters.  This is a time one, and I don't know if you can see what's going on.  I should have brought a video, but you can kind of see the clusters changing over time.  So even though this is relatively rumentary, this was a quantum leap over what they had  before in one view, just by moving a slider, they can see these clusters appear and disappear  and evolve.  And then of course, here's a version with a opacity turned on all the way, where you  can see the clusters coming on and off.  And you can change the opacity, so it's somewhere in between, you can see a little deeper into  the brain and then rotate the brain.  So in conclusion, I see a lot of time, it's important to work closely with the main experts  in order to figure out what's actually useful to them.  So just doing stereoscopic, they wrote off, just doing the temporal sliders they didn't  care about, but putting them together, they found was useful.  So one wasn't really useful without the other, but once they were together, they were super  excited and wanted us to continue working on it.  So lots to do.  The main one is to make it not just a visualization tool, but more of a visual analytics tool,  where we can actually include some of the pre-processing choices with the tool to be  able to show the statistics and the visualization, not just the final clusters to change the  thresholds to annotate the clusters and to merge the whole thing into one tool.  And finally, right now, as you saw, we have this in the video wall in our lab.  It would be nice to have to buy them at low cost 3D projector or monitor, or maybe to  use some HMDs so that they can use it in their lab.  And I'll take some questions.  Thanks a lot.
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