Seven early-career pain researchers took part in the PRF Correspondents program during the 7th International Congress on Neuropathic Pain (NeuPSIG 2019), which took place May 9-11, 2019, in London, UK. This unique science communications training program provides participants with knowledge and skills needed to communicate science effectively to a wide range of pain researchers, and to patients and the broader public. In addition to blogging and writing summaries of scientific sessions, the Correspondents also conducted interviews with plenary speakers at the meeting. At NeuPSIG 2019, PRF Correspondent Nadia Soliman, a PhD candidate at Imperial College London, UK, caught up with plenary speaker A. Vania Apkarian, PhD, for an interview.
Apkarian is Professor of Physiology, Anesthesiology and Physical Medicine and Rehabilitation at the Feinberg School of Medicine, Northwestern University, Chicago, US, where he is also Director of the Center for Translational Pain Research, a National Institute on Drug Abuse-funded Center of Excellence for Chronic Pain and Drug Abuse. He is a pain researcher who uses non-invasive brain imaging techniques to study pain perception in people and also does work in animal models. He is also spearheading OpenPain, an open access data-sharing platform for brain imaging studies of human pain. In this interview, Apkarian discusses his research on brain imaging, OpenPain, and why he thinks letting chaos prevail is a good way to approach running a lab. Below is an edited transcript of the conversation.
Why do you use fMRI brain imaging to study pain?
My background is originally in electrophysiology and neuroanatomy, before fMRI technology came about. But after the first fMRI paper was published, I decided this is what we have to do. And so we put together the first fMRI brain imaging program specifically to study pain. That was quite fun; it was a real adventure. At first, I think we were collecting more noise than data. The scanner was not very good, and we didn’t really know what we were doing. But that’s how you start within a new field, with new technology.
Our initial studies explored what you see in the brain of a healthy human volunteer if you administer a painful stimulus. But very quickly I realized that we were essentially replicating what other people were doing in animal experiments. What is uniquely different about fMRI is that you can study the brains of patients. So it’s more than 25 years ago that I made a conscious decision that fMRI, or MRI technology in general, would be a lot more useful to open up a window that has never been available to us, which is the brains of people who have pain.
So that’s what we’ve been doing. It’s been, in many ways, really fun and exciting. Our initial papers were very hard to publish; no one wanted to even believe our findings, so it was a big battle. But in several respects, we won that battle and have moved on. We have identified what we think are fundamental mechanisms of chronic pain. Since I’m also a physiologist by nature, we’ve always conducted animal studies in my lab, too. But in contrast to other labs, we have the luxury of looking at the brains of patients. Our animal studies have always been reverse translational—we identify a hypothesis in the human brain and we test that in animal models. We’ve uncovered areas of the brain that classically were not considered to be important in pain. We now think, however, that they are fundamental to understanding what chronic pain is.
It’s a very interesting opportunity to use fMRI to investigate human patients and then reverse translate to animal models. Is that the only way in which you use animals? There is so much criticism about the use of animal models and their value.
In fact, we were part of that criticism against their use, saying, “No, no, what the heck are you guys doing?” The fun thing is that when you do reverse translation studies, you have an absolute benchmark against which you can test your results. Now, I am convinced that there’s nothing wrong with the animal models; what is often wrong is the choice of tests to prove or disprove a particular hypothesis. So if you only study peripheral nerve fibers, and if you only study the spinal cord, all you’re going to learn about them is in isolation.
On the other hand, what was surprising and exciting to us was identification of the role of high-level cortical limbic circuitry that we think is fundamental for chronic pain and beautifully translating that back to animal studies. This has given us a fundamentally new understanding of what’s going on in the brain.
Considering pain has an emotional component, how does that translate to animal models?
Pain—whether it’s in a rat or a mouse or a human—has an emotional component. And we think that in chronic pain, the emotional component is much more important than the sensory one. Our human studies have shown that brain limbic circuits are more important than cortical circuits, in general, and more important than neocortical sensory encoding regions. And those same areas are involved in animal models.
Is the emotional component of pain as important in animals as it is in humans?
It’s very hard to know what the level of emotional responses is—the animals don’t send us emails! But given those limitations, the animal models give us an understanding of mechanisms at the cellular and molecular level that seem to parallel the human studies.
You took a big risk when you first decided to study pain using brain imaging. What advice would you give to your younger self or today’s trainees who are considering going after something new and emerging?
Of course, the excitement of science is to explore what is not known, and not repeat the same experiment for the rest of your life, which some of my colleagues do. In my lab today, we’re still trying to debunk what we’re doing and see if, in fact, we can find an alternative way to think about it. So self-criticism is fundamental for the advancement of science, and it’s a very healthy exercise. Over the last 30 years of my research, the field of neuroscience has absolutely exploded. In my lab, we use essentially all available genetic/molecular techniques, including optogenetics and chemogenetics, to study these models. These techniques have an unbelievable future; there are a lot of incredible opportunities out there to mix and match them. For example, in some of our most recent animal studies, we have been brain imaging rats without anesthesia and combining that with chemogenetics.
And so, for the new generation, you just need to jump in there and say, “How many of these cookies can I taste all at the same time?” And, absolutely, yes, I’ve taken multiple risks in my career, and I think the risk was really to challenge the standards. We tend to all say yes to each other and pat each other’s backs, and that becomes sort of progress. I think the opposite is much more fruitful.
You have set up OpenPain, a data-sharing initiative. How did you go about it, what are the challenges, and has it been successful so far?
It’s been hard. We’ve had this project now for five years or so, and it’s been difficult to get our community to pitch in; we’re still trying. I’m hoping that as the times change, and as policies change worldwide, this will become the norm, but we’re very far away from it. I just did a PubMed search a few days ago. There are approximately 6,000-7,000 published fMRI pain studies. How many of them are in the public domain?
Very few—just yours?
Just mine. Now, a few labs have given us some datasets. But 99 percent of all the datasets out there are buried in people’s laboratories, and we cannot get them. On the other hand, I should say that our data are quite substantial. The dataset hosted on OpenPain is thousands of brains. We’ve made the data completely transparent—all you need to do is sign on, tell us who you are, and then you can do whatever you want with the data. We do not interfere. There have been two or three papers published by other people that we had nothing to do with, and who have been able to use the data in their research.
I very much believe that we must share this data with the world. It’s humans who have given their time and their brains for us to study, and the research is funded by national governments, so the data belong to society. We have to be able to share, mainly because it becomes a rich way of analyzing the data. A good example is the shared big datasets for Alzheimer’s disease. There are now hundreds and hundreds of papers being published from these shared datasets. In pain, we’re still hiding our data under the carpet—to what end, I have no idea.
Considering pain is, perhaps, one of the more challenging areas of research, it would certainly be more helpful to share data. In the next few years, do you feel that this open data sharing is going to increase?
Yes. In fact, in the last multicenter grant proposal we submitted, we insisted that the data have to be shared. They're going to be shared across the whole planet. This is the kind of thing that we’re pushing forward—it’s happening. I wish it was happening faster, but nothing happens as fast as one would like.
Of the groups that have done some analysis on the data that you’ve made available, what were their interesting findings?
They are findings that we have not even explored. The beauty of brain imaging is that it is such a rich dataset. A given subject gives us data in gigabytes. And at the end of it, we write one or two papers, maybe three if we’re really ambitious, and we analyze a few bits and pieces of the data. 90-95 percent of the data are still sitting there, and one just needs smart questions to ask and you’ll get answers. Brain imaging data are so rich in information content that it’s like having whole genome sequencing—it’s on the same scale of information. Yet we’re only looking at three SNPs, for example, in that whole sea of data.
I come from a very unorthodox background. After my degree, I joined the British Army, so I’m very interested in leadership in general, leadership in academia, how we prepare people for leadership roles, and how academia can be a very challenging working environment. As a leader of a large group, what are your thoughts about this and your advice for early-career researchers moving into leadership positions?
That’s great; we don’t have enough training in those things. I actually don’t think I have any leadership abilities myself; my leadership is mostly letting chaos prevail, have things happen, and enjoy the process of creative thinking—and not much more than that. I can hardly even balance my accounts for my grants; having a better organized, structured approach would be more efficient. But creativity comes from chaos, openness, and the free exchange of ideas, and my lab is really organized along those lines. Everybody in my lab—all of us—discuss all the experiments that we’re doing, all at the same time. And it’s complete chaos.
But I think that’s an example of good leadership—because you’re creating a very open and positive work environment in which people can share, feel valued, and be free to be who they are to achieve their maximum potential.
I’m completely enamored with the idea of science as a social event. It is not my intelligence that drives my science; it is my interaction with my students that drives my science. So any question and any exploration of weird directions is an opportunity to find something exciting. That’s about my extent of leadership. I always think of myself as an incompetent leader, basically, but I’m having a lot of fun doing what I’m doing.
I think good leaders would not consider themselves to be as such, since it’s very important for leaders to have that self-criticism and awareness. Finally, is there somebody that you really aspire to be more like, perhaps outside of science?
I would like to be so many things. I love science, but I also love art, and I like many other things. I write poetry in Armenian in my spare time. I don’t show it to anybody—I write it in a language very few people can even read. I’m also interested in the history of science, and I’m very excited about the history of mathematics. I read a lot of literature at the same time, and philosophy. I have lots of crazy energy that I point in many different directions.
That’s important for scientists, as it is easy to get focused on that one protein, that one ion channel, and lose sight of the world and everything else that goes on around us.
The world is an exciting place. It’s a short period we’re here, and I like it.
Nadia Soliman is a PhD candidate at Imperial College London, UK.