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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!
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By Shruti Vij & Nils Muhlert Peter Bandettini has been a key figure in neuroimaging for over 25 years. His career started with earnest, in a PhD working with James S. Hyde and R. Scott Hinks in Wisconsin, where he pioneered the development of functional MRI. Now at the NIH, Peter’s work has examined the sources of functional contrast and noise in BOLD, the temporal variability of resting-state fMRI and, more recently, layer-dependent activity in fMRI. We found out about his history working alongside other founding members of OHBM, his advice for early career researchers and the unique challenges of working at the National Institutes of Health. Shruti Vij (SV): I would like to start by asking you about your background and why and how you became interested in neuroimaging. Peter Bandettini (PB): I started out as an undergraduate in physics at Marquette University. I was interested in the brain the entire time there. Even from high school, I remember reading a famous Scientific American article showing the first functional CT and PET scans. That was inspiring. I was always interested in the brain. At the same time, I wanted to study a “hard” science like physics. I thought that the integration between the two would be useful. Although I wasn't quite sure what I wanted to do. I could have been an engineer, I was potentially interested in medicine, but then I decided to go into grad school in a biophysics department, and luckily, it led me to brain function research. SV: Great! What do you see happening with neuroimaging in your country? What kinds of research and what kinds of advances? PB: I think that the Brain Initiative is the latest big thing in the United States. In Europe, there’s the Human Brain Project and I think together, they are really trying to step back and understand the brain at a more fundamental, a more mechanistic level. Right now, the exciting thing is that both programs are focusing on methods. There's methods development that is leading into more brain modeling, and then there’s the development of a more integrated structure for sharing data. In fMRI there's a lot of work on things like data pooling, machine learning, things like extracting information about individual subjects, as opposed to group studies. Actually trying to get more clinical traction from the data. SV: Awesome. What research or other contributions are you most proud of in your career? PB: I'm really proud of a lot of things, many of them from really good collaborations. I was lucky enough to be in the right place at the right time at the biophysics department at the Medical College of Wisconsin, to be working with Eric Wong who was developing the hardware for echo planar imaging. That was the right place at the right time to get going quickly as a graduate student in helping to start functional MRI. I was a grad school student, and I submitted my first first-authored paper ever - and on fMRI - which happened by luck to be the first paper ever published on fMRI (by a week) - to MRM as a communication. They published it quickly.And so I'm very proud of that. That said, our group was, by most accounts, the third group that successfully performed fMRI - behind MGH and Minnesota. We might have been the first group to perform fMRI of motor cortex activation - as shown in this paper. I was also part of pushing the initial use of correlation analysis for fMRI data. That was our second paper. I’ve been told that, in that paper was the very first use of the term “FMRI”.It’s important to emphasize that neither of those would have been possible without the incredibly rich environment of colleagues and resources. In particular, Eric Wong, a fellow grad student, was most fundamentally important. I'm very proud of pushing the concept of embedded contrast in fMRI data like multi-echo or simultaneous spin-echo, gradient-echo, pushing the temporal and spatial resolution. But right now, I'm proud of being able to lead and direct a group. They do all the work, I now feel more like an enabler of everything from multivariate assessment, to pattern effect assessment to resting state. I'm very proud of helping my grad students achieve great things as well. Recently I've been going to very high resolution and looking at layer-dependent fMRI, so you can actually start to untangle input and output connections from layer fMRI activation. So I'm proud of being able to integrate everything from the acquisition side with physics, to the basic neuroscience and also the data analysis as well. So to try to bring it all together. SV: Great! You played a part in the creation of OHBM. What was that like? PB: So that was really exciting. It's interesting that it first started out even before OHBM. Peter Fox had a regular meeting. And I think a number of people came together and thought, this can be bigger. It was exciting to be part of that process at the very beginning. I remember trying to get everything organized and trying to figure out: “okay, so we're going to have a council and we're going to have a program committee, and this is what we’re going to do.” Another memory is back in 2001, in Brighton, we hired a meeting management company called L&L, which we still use today (under a different name). It was a big decision then. And we were like, yeah, I think we should go with them. And it's amazing what impact it had. Also, back when the meeting was in San Antonio, one of the early OHBMs, around 2000, I remember sitting in council, and we had the idea of having a separate day for education courses, and having education courses in the morning as parallel sessions. That was very exciting. I was the chair of the education committee at the time, and back then I had to organize every single parallel morning session for two years - that was really challenging! Ed Bullmore took over after that, so there are a lot of good memories of that. I knew OHBM would grow. At the time, it seemed to be just another meeting - as I was young and had limited perspective on these things. At the same time, we all knew there was always something special about it. OHBM seemed united around the methods, and as opposed to just a cognitive neuroscience meeting or neuroscience or ISMRM, It’s about the brain imaging methods. And that was effective for bringing the community together. And there are many people who've attended every single year, and after about five years, everyone ended up knowing everyone else. And so it's become this really large, nice, extended sort of family in some regard where we all kind of know each other and know what everybody does. And that’s a good feeling. SV: And what did you imagine it would be like? PB: I thought it would likely go more towards the neuroscience direction. I didn't think it would go in the direction of the methods, continually improving. I always thought the methods would get better but now they're getting qualitatively much better, and they're becoming integrated. And I didn't imagine it would maintain the same cohesion. I mean, before it was small and cohesive. And it's somehow both grown and scaled but that cohesiveness has scaled with the times as well, which I think is really unique. So I didn't imagine that. I also didn't quite imagine that it would be as respected. It was always a grassroots movement of a meeting, but now it's a really respected meeting. And people look at it as their main meeting that they go to. I think that respect and that reputation is still growing. So that's been a nice thing that I didn't really completely expect. SV: What have you found the most rewarding in your involvement with OHBM? PB: The most rewarding thing has been that we really did get to invent it from the ground up. That was rewarding, to be able to figure things out as you're going along. But it's also rewarding, that it’s been a catalyst for so many people: to make science more than just doing the science asking the questions, presenting your paper. Instead it was about having a real community, knowing the people involved in the field and looking forward to going to the meeting, not just to give a talk and exchange information, but to get a better appreciation of what's going on in other people's groups and actually catch up with old friends. That I find really satisfying. SV: What advice would you give to young investigators? PB: I think people need to be a little more bold, because even with the established literature, there's a lot of room for complementary information. And I wouldn't be afraid to have data that contradicts results, because everything is relatively new. And we're still trying to figure out what's going on and how to interpret things. Another thing is to try to always think in an integrated way. Never be afraid of not being an expert. I'm coming from a physics background. Many of the physicists who I work with, sometimes aren't just physicists. I think that the people who really become successful are those who are not afraid to think outside their domain and gain confidence. Right now, I feel more like a neuroscientist than a physicist, even though my background, and training and PhD, were in physics departments. It's interesting how the tendency is to lock in and say, I'm a physicist, but for the last 20 years, I've been doing more neuroscience, processing and physiology. So I try to think of myself more in those domains as I expand out. So never limit yourself. That would be another main piece of advice. SV: Until recently you were the editor in chief of Neuroimage. How was that experience? And what would you tell trainees who are looking into going into editorial jobs? PB: When I started this work, I wasn't thinking of getting into an editorial job. I said yes to everything. I think that we're all really lucky to be in a situation like this, where there's so many opportunities. So I said, “Yes!” And I never took that for granted. I still try not to take it for granted. So I said yes to always reviewing papers and I would always do the best I can reviewing them. Then my number of reviews caught editor's attention. They said, “Oh, he says yes to all our papers, and he really likes to review papers, let's make him an editor.” I enjoyed that. And as you get older, it's probably good to say no, to just manage your time. But still, I still haven't figured that out to be honest. So what I would tell people is, when I agree to review papers, I look at it less like, "Oh, it's a duty i'm doing". Instead I feel like I would read the paper anyway so I might as well review it. So my advice to people is that getting into editorial work takes a certain mindset, it takes time, it takes a certain amount of confidence in making decisions about papers, but you learn about what's a good paper, what's a bad paper. You learn all the processes of sending back feedback and doing reviews and what a good review actually means. So that's helpful and it helps you write your own paper. The whole editing process has helped me so much in terms of my own writing. And it broadened my horizons a lot, especially being editor in chief. I got 10 to 20 papers in a day that I had to assess quickly and then send out to the senior editors. And so that gives you a very broad perspective and a very up to date perspective on the field because you're given the latest things. It's a good four to six months before they're published. So that's what you gain. Maybe some advice to people, I think I would just say “yes” to review as many papers as possible. I think it's also important to not just find flaws in papers. Finding flaws is good, but it's also too easy to dismiss papers as bad just because it has this, this and this flaw. I would recommend that people be more accepting. The goal is to help the author get it published, it's not to stop them. Usually a reviewer should, as much as possible, look at themselves as trying to help the process if it's above a threshold, as opposed to trying to stop the paper from being published. SV: Over all these years, has it been hard to maintain a work-life balance? PB: Yeah! [laughs] There's certain things though, like I have to go running every day or do 30 minutes of aerobic exercise, but that's like an addiction anyway. So that's easy to maintain that balance. And I'm really lucky to have a great job at the NIH, where I can turn it off when I need to. But to have a good work-life balance, you do need to develop a certain amount of discipline. It's so easy just to have your work spill over. And when I was a graduate student, I had no balance. I lost track of what day of the week it was. I'd be working all night, working whenever. But now I have a family, three boys and a wife, and sometimes there's a need to compromise on both ends. You know, even going to a meeting like this, it's a week and a half away and all kinds of craziness is happening at home. But as long as you have a good schedule, and compartmentalize enough, then it's good. I think that's the key, to be disciplined and to do certain things no matter what. I know I'm going to go running, to try to get a certain amount of sleep. And I know that I'm going to try to spend this much time with my family. I don't meet these goals all the time but I really try to work when I’m at work and focus on my family when I’m at home. The more you can compartmentalise in a disciplined way, the easier it is to achieve this balance. But that said, I fail all the time with this. SV: So you've been at the NIH for a while now. Are there specific challenges at the NIH that other people would not be aware of? PB: NIH is a unique place. It's so good in the sense that all the researchers there have a certain budget and don't have to write grants - they have incredible resources. At the same time you're working for the government, which is different, in the sense that there's certain rules that apply to government employees that I'm still learning - even after 20 years. That limits you in certain things. If you're a PI in the extramural world, I always think that you can write grants and be an entrepreneur, because you can build an empire, depending on how successful you are at getting grants. You can collaborate with industry, you could have your startup company, whatever. At the NIH, you're more confined. It frees you to do other things, but there are conflicts of interest - like for the flight here, I had to pick a flight on a government contract carrier that I didn't have a frequent flyer program on or whatever. You have to because you're working for the government. And also you have your budget. And in the way you get approval, you're assessed scientifically every four years. But it's not like you have a grant review. So there's less uncertainty. And it's potentially easy to coast if you want to, but luckily there's a good enough environment, really motivated professors and people. I've been so well supported at the NIH; all the good stuff outweighs the quirky, government stuff. SV: Thank you so much, Peter, for this oral history. I'm sure everybody will really enjoy hearing about it. Thank you. PB: Well, thank you. |
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