OHBM 2019 Keynote interviews: Catie Chang

By Nils Muhlert

Resting-state fMRI has seen increasing attention over the last decade. The majority of these studies have focussed on static resting state networks, often considering the spatial topography or extent of components. A number of researchers are however considering how these networks change over time - dynamic changes - and what these temporal shifts in networks tell us about cognition and behaviour. Catie Chang, an assistant professor of computer science and electrical engineering at Vanderbilt University, has focussed on this question since her PhD - with her work exploiting signal analysis techniques to understand what drives and affects these dynamic changes in fMRI signals and networks.

As our first keynote interview for OHBM 2019, we found out about how Catie honed her craft, what we can gain from investigating these signals, and her experiences of life as a new PI.

(Catie Chang, center)

Nils Muhlert (NM): I'm here today with Catie Chang, one of our keynote speakers at OHBM2019. Thanks Catie for joining us.

Catie Chang (CC): Thank you so much.

NM: First, can you tell us a bit about your background? What turned your work towards functional connectivity?

CC: My first experience in a human neuroimaging lab was at Stanford, working with Vinod Menon and Michael Greicius. They were pioneering ideas about brain networks, dynamics, resting state connectivity, and applications to neurological and neuropsychiatric disorders back around 2005, 2006 and earlier. I was very influenced by their perspectives and found this an interesting and exciting research field. That got me considering many ideas about brain connectivity, about resting state.

Then, I went to work with Gary Glover for my PhD in the Radiological Sciences lab at Stanford, and the emphasis in that lab and environment was on the physiology and the physics of imaging signals. This led me to questions like, what is the physiological basis of the signal changes that we're measuring? Can we better acquire these signals, improve our analysis and post-processing? And can we combine signals from different modalities to improve our interpretations? So I really found my home at the intersection of these different worlds.

NM: Did you spend a lot of time looking at noise? Trying to work out where the signal was actually coming from?

CC: I started out looking at, I guess what we kind of consider noise, which is the influence of systemic physiology on fMRI signals. So when you take a deep breath, for example, this induces a very large BOLD signal change.

But we were interested in it not only from the perspective of how this introduces noise into our signals, but also how it can introduce new information into the data. The first research question I started working on with Gary was, how can we use the fact that there's this large, systemic influence on the BOLD signal to calibrate for hemodynamic timing differences between different regions that may not be related to underlying neural activity? Can we use a breath holding task, and if we find timing delays across the brain in the breath-holding BOLD response, can this help us pinpoint fMRI timing differences between brain regions that are not neural in origin, but may be more vascular, hemodynamic related.

Throughout, my work has been looking at two sides of the same coin - noise on the one side and trying to clean up the data, but on the other side, looking at the discarded component, which is often very valuable for a different purpose. And if we can disentangle these influences on the signal, then we have the power to use those components in different ways.

NM: You also mentioned a few people there. So Mike Greicius, who we've interviewed before for the blog - he came across as thoughtful. Do you think that's influenced how you supervise your own students, now that you're building your own lab in Vanderbilt?

CC: Yeah, Mike was a really influential mentor to me. He was always giving me great advice about for instance, not getting too caught up in certain details, instead seeing the big picture and the more interesting questions.

To be honest, I am very detail oriented, so I keep this advice in mind when I mentor students. I try to be very involved in the details, but on the other hand, I also try to step back and say, are we providing an important message? Is this research going in the right direction? And so having many complementary mentoring styles throughout my work from Mike Greicius, Gary Glover, and Jeff Duyn and David Leopold, who I worked with as a postdoc -- they've all shown me very different but very valuable perspectives.

NM: How have you found that process - moving to becoming a PI, having your own lab?

CC: I really love it. There's so many interesting things that come with starting your own lab: working with my students, and the collaborators here at Vanderbilt, that's one of the best parts of being here. They're just brilliant, great collaborators, great colleagues. But it's also been very busy. So I'm not even one year in to this new position, and the past year has been a blur. Many new things to get used to in this environment. For example I've taken on some teaching. And I've discovered I really like teaching.

NM: Don't let anyone know - they'll pull you in for loads of it!

One thing that's come up in your work is the idea that you could use the dynamics of functional connectivity as a biomarker for cognitive and clinical studies, and clinical trials. Do you think this is feasible over the next 5-10 years? Are there steps being made towards that? How's the validation process going?

CC: I think that looking at dynamics is very promising for studying cognitive and clinical questions. The idea here is: can we get more information from the signal if we open up the dimension of time, and aspects of the signal that may change over time? This notion opens the possibility that we can look at features of the data that reflect state changes and cognitive processes that may be really relevant markers of different disorders.

But there are still many challenges that we have to address at the same time as we do this exploratory research. It's hard to go from having a hypothesis about brain dynamics to knowing exactly what metrics and features of the signal we should isolate to test for these questions. We (as a field) are also working out how we carry out the statistical testing, for example, to see if “dynamics” is really the core element that's disrupted in a given disorder, or if, perhaps, some of those apparent signal dynamics are just an offshoot of some other, simpler phenomena. We're at least starting to dig into that. There's a lot of exciting progress being made by many research groups and it's interesting to see where that will go.

We also face a lot of challenges in the dynamics world, because fMRI has a low signal-to-noise ratio, with many different things that can cause fluctuations within a voxel other than neural activity. And so trying to interpret and clearly link the phenomena that we observe to a conclusion about brain function is challenging.

NM: So what would you say you're most proud of in your career? What kind of work, would you say, stands out?

CC: Whatever I can do that's helpful to researchers, I feel proud of. And so when people ask for code to isolate physiological signals, for example, then I'm really happy I can share that. My deep interest is trying to understand signals and mine them for information. So I'm really excited about the work that goes toward resolving particular influences on the fMRI signal. For instance, a subject’s level of alertness is one factor that can change fMRI signals, but on the other hand, it's also something very interesting we can study in itself. Our recent work examines how we can detect natural changes in alertness from fMRI spatiotemporal dynamics, which I also find to be a fascinating direction.

NM: So this is maybe going back a little bit over what you've already said. But one issue that some people have been struggling with is about the underlying physiological basis of resting state functional connectivity networks. And people are starting to look at whether there are particularly high densities of neurotransmitter receptors within the hubs of these networks that might aid coordination of this activity. Do you think we're moving closer towards having that understanding of how these networks emerge?

CC: I think we're moving closer. I mean, it's hard for me to say how close we are -- but for example, many researchers are combining fMRI with other techniques to perturb neural activity in specific ways and understand how that impacts resting state networks, which I believe is an important direction. I think that a bridge between non-invasive human imaging and more invasive animal or patient studies is really helping to provide that link. In animals, of course, there are many more flexible manipulations that we can do to try to understand the precise impact of activating or inhibiting certain brain regions on large scale connectivity. And so that'll be really important to bridge these types of research.

NM:What can we expect from your lab over the coming years, then?

CC: One direction is that in Vanderbilt, there's close collaboration between engineering and the medical center. So I'm really excited about the collaborations that we're forming with the medical school as well as the imaging center here. And so I've started to work together with Vicky Morgan and Dario Englot here, for example, forming ideas for how we can use fMRI methods to understand epilepsy. They've been carrying out this type of research for a long time, and I'm really excited to be collaborating with them.

Another area that we're trying to push is carrying out multimodal studies to understand changes in alertness, and how that relates to changes that we see in fMRI signals. We're developing ways of performing more detailed characterization of the effects of these kinds of state changes on fMRI data.

We're collecting, I guess, “mega scans”, where we have fMRI together with EEG, eye tracking, cardiac and respiratory signals, and behavioral measures. So my subjects may not like me very much [laughs], and now we have so much data, all these different data types, how do we combine them? But the more information that we have, the more that we can start to piece together the puzzle of what moment-to-moment fMRI signal changes reflect, and the signatures of specific ongoing neural and physiological processes. We're asking whether we can better capture and understand that. If we can figure out ways to integrate these external measurements, which are all complementary measures of humans and what state they're in, then it'll be really exciting.


NM: And so last, can you give us some insight into what you'll be discussing in your keynote lecture?

CC: The main theme is along the lines of what we've been talking about - the more that we can understand and disentangle the sources of signal or network fluctuations, the more rich and clear information we can extract from our data. That can lead us to more sensitive biomarkers, and sensitive measures and inferences of neural activity from fMRI. And when we combine fMRI with other modalities, such as EEG, then it can help us draw that information out of the data.

NM: That sounds very comprehensive!

​CC: I'm going to make it more specific, and I have this terrible habit of changing my talks the night before, so who knows [laughs].

NM: Thanks for joining us here and we look forward to your talk!