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!