Many fledgling neuroscientists who are eager to dive deep into the statistical analysis of functional MRI data know of Martin Lindquist. Martin is a professor in the department of biostatistics at the Johns Hopkins University. His popular “Principles of fMRI” Massive Open Online Course (MOOC) and associated book have reached an audience of more than 80,000 students worldwide.
Our interview with Martin follows his path through various academic disciplines, eventually leading him towards educating the current generation of neuroimagers and winning the 2018 OHBM Education in Neuroimaging Award.
Claude Bajada (CB): Prof. Lindquist, you're a statistician by training but according to your CV your first academic foray was in the “History of Ideas” at Stockholm University. Can you tell us a bit about that?
Martin Lindquist (ML): When I was a highschool student, I didn't really know what I wanted to do with my life and what direction to take. At that time Sweden had compulsory military service and since I was a bit younger than my peers, I had to wait. So I decided to have a go at some history and philosophy. I’ve always enjoyed it, and still do, but I realised that it wasn’t the career for me.
CB: How did you link that to statistics? Was that a smooth progression?
ML: Well, after my military service, I applied to the Royal Institute of Technology’s Statistics program - it was really more like an engineering program, a STEM Science and technology type thing. So at the time I still didn’t know that I was going to work in statistics, but I was now focused on science and technology.
CB: Was this like a masters degree?
ML: Yes, it was a masters.
CB: How did you get into neuroscience?
ML: I actually did my master’s thesis on neural spiking. I then went to do a PhD at Rutgers University in the US. My PhD was in statistics doing fMRI-type analysis.
CB: Neuroimaging is a multidisciplinary field, there are psychologists, physicians, statisticians. Would you consider yourself a neuroscientist or a statistician if someone had to ask? Or a historian?
ML: Not a historian (laughs). I like to consider myself a little bit of everything. I think if I was pressed, I’d say I was a statistician because that’s what my degree is in. I have a PhD in statistics, I’m in a biostatistics department and the colleagues that I interact with on a daily basis are statisticians, so it feels like my home.But at the same time I’m excited about applying statistics to neuroscience. It just doesn’t feel as honest to say that I’m a neuroscientist because I don’t have that training and background. So I’d probably say I’m a statistician, but I like the idea of being a little bit of everything
CB: We are conducting this interview because you won the 2018 OHBM Education in Neuroimaging Award. Last year you interviewed the previous winner Mark Cohen. I’m curious, how does it feel to be on the other side this year?
ML: Awesome. It was great to help interview Mark last year, he really deserved the award. And it is very humbling to have been chosen to win the award myself.
CB: You have taught a lot of statistics to budding neuroscientists and have also changed fields from history, which is not a STEM subject, to engineering. How difficult do you think is it to understand the needs of students who don’t necessarily have a STEM background but come into neuroscience?
ML: I think, as any educator, you have to remember what it felt like to not know anything. What it felt like to enter a discipline or to learn about a new subject. It can be hard. I like trying to break down information into the smallest possible components that I can. Then I try to re-build things from these components and that seems to work with people. Sometimes people get lost but at least they have something to hold on to. My goal is that everyone gets something out of it. My strategy is to make content as small and manageable as possible, then sort of grow it, and you know, if you understand 80% of it, that’s ok! Everyone will take a different amount home.
CB: You’re talking about modular structures, these little chunks that people can build on, and I suppose in that sense MOOCS are really great because they do come in little bite sized pieces.
ML: This is true.
CB: I remember I took your Coursera course while doing my PhD and I found MOOCS amazing to fill little gaps in my knowledge. How did you you get into teaching MOOCS and do you think that they will be the future of education?
ML: That’s actually a pretty interesting question. My department, which is the department of biostatistics at Johns Hopkins University, was one of the pioneers in MOOCS. A few of my colleagues created a data science programme. It’s a set of 10 courses and they started about 3 or 4 years ago and they’ve had 5 million students, it’s pretty amazing! Those were the first real MOOC blockbusters and people saw what was happening and how exciting it was, so a lot of people in the department started playing around with MOOCS. We had some expertise and we kept saying “let’s try this.”
At one point, our department was running more MOOCs than most universities - in fact, had it been a university, our department would have been ranked fourth! At this point we probably had 50 MOOC classes. Finally we hired a videographer from a local arts school and she helped us with a lot of the editing and we streamlined the process. It became the culture of the department. So I guess I was at the right place at the right time! I mean had I been somewhere else, it might not have been considered possible, because it is a lot of work.
CB: How do you gauge what your students are finding hard to grapple with and more than that, what are the things that they find hard?
ML: Remember your earlier question “how do you teach people who may not be so STEMie”? If you are teaching a workshop, you see it on their face. If you are looking and interacting with your audience you can see when people are puzzled, you can see when they are nodding their heads and you can feed off that. That really helped me figure out what worked and what didn’t work. With the MOOCS this was really not clear, so I tried to use some of the knowledge from previous workshops. In Coursera you had student feedback, but it just didn’t have that same personal touch, so it was unclear exactly what needed to be tweaked. That was a little difficult.
But that’s the interesting story. In my experience and the experience of all of my colleagues who also teach MOOCS, the thing that people find hardest is the thing that you feel you know best. So for example, I know quite a bit about linear models and so I think that I can explain the GLM really well, but maybe I’m not as good at explaining pulse sequences for fMRI. But it seemed like it was the opposite, they found my explanations of pulse sequences more understandable than the material that I was an expert in.
CB: Do you think it may be because once you do attain expert knowledge you kind of forget what it’s like to not have it?
ML: Absolutely. I think that’s what’s so interesting because I thought “Oh, I’m really good at that, why don’t they understand it?” Then you have to take a step back and realise it’s probably because I’m too deep in the weeds and I’ve sort of lost track … you have to remember what it’s like. And sometimes if you think about something all the time then it’s hard to remember that.
CB: One more question about your most recent work, you just published a paper about how to properly power fMRI studies. Researchers are increasingly aware about lack of power, but of course including more participants always comes with extra costs. What do you think are the implications of this?
ML: Often it will depend on the research question. In other fields like genomics, they need pretty big sample sizes and people went together in consortia etc. So possibly for certain questions we need to do similar things . But at the same time there are many big databases coming out, like the UK Biobank and the HCP. Being at Hopkins, you see that there is this tension, as there are also people who are more interested in single subject analysis. So I’m fascinated by the question of whether we can use these bigger databases to inform small samples or single subject analysis and I think that’s going to be important.
CB: We talk about increasing the number of participants all the time, and coming from a small institution myself, this may hinder these small institutions that want to work. However, there are now many open datasets, could these be the solution?
ML: Sure, there are all these big open datasets, but they are not acquired for any targeted purposes. Then you have these smaller studies that have a very specific hypothesis, and I think you need both. And figuring out a way to get both is going to be a very important question moving forward.
CB: What final advice would you have for budding neuroscientists?
ML: Be curious, look outside the box, be willing to do crazy things and fail, and have fun!
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The OHBM Publishing Initiative Committee (TOPIC) will be introducing a new journal, called Aperture. The roadmap above is tentative, and it was presented to council in 2017 to illustrate the various aspects of the project led by the OHBM Publishing Initiative Committee (TOPIC). Credit: Agah Karakuzu
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By Elizabeth DuPre; Edited by Aman Badhwar
What exactly is “open science”? As open science has become increasingly central to discussions of scientific practice, publishing, and policy, it’s become harder to provide a precise definition that encompasses all of its aims. The ubiquitous nature of open science is at once its greatest strength and deepest weakness -- it’s broadly useful, but difficult to distill as a clear set of values or prescriptions. It’s been said that one way to get a better sense of a movement is to talk to its supporters, so I turned to some of the newest advocates for open science within OHBM: the newly elected members of the Open Science Special Interest Group (OSSIG) committee. I asked for their thoughts on what open science means, how they got involved in promoting open science initiatives, and why they’re so passionate about increasing its reach within our community.
Camille Maumet, research scientist at Inria and Chair-Elect, confirmed the current state of affairs: “open science is not a monolith.” Indeed, the backgrounds of newly elected OSSIG members support this idea. With training ranging from art, to physics, to cognitive science, to software engineering, these are a diverse group of individuals with multiple perspectives and skills. A far cry, it seems, from the myth of open scientists as fitting a single mold.
Their reasons for first getting involved in open science initiatives aligned with each OSSIG member’s background and seemed to echo the three overarching aims of open science in practice, publishing, and policy. For Tim van Mourik, an fMRI methods scientist and Open Science Room Chair, it was a realization that open science could address many of his concerns about the practice of science and the methods commonly used in functional neuroimaging. “In the wake of the reproducibility crisis I started to get a more complete picture of the situation and learned about publication bias, analysis flexibility, and publish-or-perish factors,” he said. “When I got a null-result rejected from a journal despite better methods and more subjects than previously published, positive results, I became even more determined to try and change the system.”
For Ana Van Gulick, a library faculty at Carnegie Mellon University and Secretary-Elect, it was the open publishing of data and code that motivated her to fully embrace open science. “I wanted to keep track all of the emerging open source tools”, she said, “to help students and faculty maximize transparency, efficiency, and reproducibility in their workflows.” She pointed out the importance of open information given “how fast these tools and software are being developed.” Accessible information on social media sites like “Twitter is great for learning about new developments,” Ana added, “and so are preprints.”
For Chair-Elect Camille, it was the the idea of fundamentally re-imagining science policy and restructuring the incentives for how we as scientists work together. “Open science brings us closer to a collaborative research,” she pointed out, “where we can share our results earlier, capitalize on each other’s experience and design research together.”
Despite their varied pathways into open science, all the committee members I spoke to echoed the same idea for why they’ve stayed involved: the community. Sara Kimmich, graduate student and Treasurer-Elect, put it this way: “This may sound corny, but I’m still impressed with how supportive the community around open science is. It's filled with people who are genuinely interested in seeing the best science get done, and they'll go out of their way to help you on your own path.” This sense that the open science community directly improved their science and careers was echoed by Katja Heuer, PhD candidate at Max Planck and Hackathon Co-Chair. “For my very first paper, I found collaborators on Twitter that I have never met,” she said. “Through these collaborations, I received additional data that we’ve made available to the entire community, and I can incorporate all feedback into the final journal version of the paper – how fantastic!”
Now that they’re advocates for open science, they also share a similar set of concerns and hopes for the future. Greg Kiar, PhD candidate at McGill University and current Treasurer, pointed to the difficulty of pushing for change in the current system. “Established labs and institutes often have practices or procedures for data collection and tool development that have been streamlined and relied on to be engine of their scientific achievements for years,” he said. “Interrupting existing solutions that are closed for equivalent open ones is a tough sell -- it can be a lot of work, and the gains are not immediate.” Treasurer-elect Sara echoed this idea, and said she often feels as though she’s “waiting on a cultural climate shift in the larger scientific community to fully incorporate open science frameworks into existing institutions and our educational systems”. Despite this, everyone seems optimistic about the possibility of change.
After speaking with the newest members of the OSSIG, I feel as though we may not have a single-definition of open science, but we have a new generation of scientists working together for broad and lasting change. Roberto Toro, group leader at Institut Pasteur and Hackathon Co-Chair, summed up this vision for the future as realizing that “the difference between Science and Open Science is wrong. The real difference we should make is between Siloed Science and Science. Siloed Science describes a type of science where the evidence and methods supporting research cannot be fully evaluated and discussed. Now, what we call Open Science, that’s just Science, there is no need for extra adjectives.”