Professor Edward Bullmore has had careers in the clinic, academia and industry. He is the head of the department of Psychiatry at the University of Cambridge, the director of the Wolfson Brain Imaging Center and the head of a neuroimmunology research group at GlaxoSmithKline. His academic interests range from the clinical to the mathematical. He is perhaps most known for his work on analysing brain networks using the framework of graph theory while his current interest, described in his latest book, “The Inflamed Mind”, is neuroimmunology.
The understanding of psychiatric disorders is the thread that connects all of professor Bullmore’s diverse interests. The following interview probes into his past experiences and asks his advice for budding young scientists attending the OHBM 2018 annual meeting in Singapore.
Claude Bajada (CB): As a clinician, I found that the approach to thinking taught during a clinical course is very different to what is expected from a researcher. What are your thoughts on the differences between clinical medicine and medical research? And what enticed you to make the shift from clinical medicine to academia?
Ed Bullmore (EB): I agree the mindsets of a clinician and a biomedical scientist are often somewhat different. As a clinician you’re taught to convey a sense of calm certainty, or to reduce a complex situation to a much simpler diagnostic formulation or treatment recommendation. And at least when I was at medical school, in the 1980s, questioning the scientific basis for clinical wisdom was not always welcomed by senior physicians or surgeons! A scientific training, by contrast, is an education in learning to doubt or challenge everything, especially your own most treasured hypothesis or most precious results. There is certainly a tension between the reassuring bedside manner of a clinician and the oceanic scepticism of a scientist. Another very important difference between the two cultures is the status of numbers. Medicine was almost entirely non-quantitative when I was going through medical school; whereas in neuroscience and neuroimaging, mathematics is increasingly central. I think medical schools, at least in the UK, still need to do more to make doctors more mathematically competent and confident - and to provide proper career paths for non-medical scientists bringing their expertise from physics, maths and engineering into contact with the number-crunching challenges of modern biomedicine.
I switched to scientific training halfway through my clinical training in psychiatry. I was motivated by the idea that psychiatry was still at a relatively early stage of scientific development compared to other areas of medicine and I couldn't imagine being satisfied with a career solely dedicated to clinical practice in an area which I thought was very likely to see radical change. It wasn't a difficult decision for me in principle. But if I had been clinically specialised as a cardiologist, or some other area where the science base was already more evolved, it might have been a more debatable move. For people specialising in surgery or radiology, the number of training hours that must be dedicated to learning operational procedures is much greater than in psychiatry, and the financial rewards for focusing exclusively on clinical practice can be much greater than in psychiatry, so the decision to spend 3-4 years on a PhD is a much tougher choice. For any research-minded young doctors who might be reading this, I can say psychiatry is a highly recommended career move!
CB: When I hear the name Ed Bullmore, my semantic association goes: “Bullmore, Sporns, Graph Theory.” Were you always interested in the mathematical aspects of research? What first got you interested in Network Analysis? And how did your, now famous, collaboration with Olaf Sporns begin?
EB: My first research enthusiasm (aged 30) was fractal geometry, which I found intuitively very appealing as a way of quantifying the complexity of biological structures and processes, like MRI scans and EEG signals. However, my old-school medical education had left me completely unequipped with any quantitative skills. I was fortunate to find an excellent mentor, Prof Michael Brammer, at the Institute of Psychiatry in London, and applied to the Wellcome Trust for funding to do a PhD. I was interviewed at the Trust by Sir Stanley Peart, in 1992, who listened politely to what I had to say about fractals and then told me “of course what you’ll really find yourself working on is brain connectivity, isn’t it?”. I agreed with him immediately although that thought had not previously crossed my mind.
Karl Friston’s pioneering work on brain connectivity was very influential, and I also learnt a lot from Barry Horwitz, and through them I began to hear about Olaf Sporns. I admired the paper on complexity he wrote with Giulio Tononi and Gerald Edelman (PNAS 1994) but I didn't meet Olaf until we both attended the second Brain Connectivity Workshop, organized by Rolf Kotter in Dusseldorf in 2002. I liked his talk, about using graph theory to simulate computational networks that maximised the neural complexity measure from the PNAS paper; and several others at that meeting also opened my mind to the new physics of complex networks that was following from the seminal “small world” paper by Watts and Strogatz (Nature 1998) and the “scale-free” paper on network hubs by Barabasi (Science 1999). A few years later, in 2005, I met Olaf again at the Brain Connectivity Workshop in Boca Raton, where I presented some of the first results of using graph theory to measure topological complexity of human brain networks from resting state fMRI. That’s when we started talking more seriously about collaboration, which led to our first co-authored paper, a review of complex brain networks (Nature Reviews Neuroscience 2009) that has since been cited more than 5000 times.
CB: Your recent publications reveal a broad interest in psychiatric research, focusing on everything from developing methods to questions about the effects of drugs on the brain and much more. What are you working on at the moment? And what would you say is your current main interest?
EB: I am still working on brain network analysis or connectomics but since 2013 I have also become increasingly interested in the relationship between the mind, the brain and the immune system. The reason for this shift of focus goes back to my starting point as a psychiatrist. After 20 years of research, I couldn't help noticing that, although the field of neuroimaging and brain connectivity had grown tremendously, its real-life impact on mental healthcare was zero. By then I was edging into my mid-50s and I felt impatient to do something that might actually make a positive difference to the experience of people with depression and other mental health disorders in my lifetime. For various reasons, the strategy that appealed to me most was to pursue the idea that inflammatory responses of the immune system could cause depressive symptoms and, therefore, that anti-inflammatory interventions could provide a new therapeutic approach to depression.
I am not sure how many OHBM members will be acquainted with the immune system; I’m guessing not many. I knew a bit about it from my medical training in the 1980s but I was utterly dazzled when I took another look at immunology in 2013. We think neuroscience and neuroimaging has moved fast in the last 20 years, and it has, but scientific progress in immunology has been at least as rapid and its therapeutic impact has been much greater. The area I am working on is the interface between immunology, neuroscience and psychiatry – it’s called neuro-immunology or immuno-psychiatry and it’s growing rapidly. There are important questions for neuroimaging in this area: for example, how can we use MRI or PET to measure brain inflammation, especially microglial activation, more specifically and sensitively?
I have just published a book – called “The Inflamed Mind” – which summarises some of the background science for a general audience – and there are some short movies on YouTube which introduce the book in a brief and accessible way (here and here).
CB: What would you say to students, particularly medical students, who would like to start their research career? Particularly, what would you say to them if they were interested in technical subject matters but feel that they “come from the wrong background”.
EB: I always encourage students to recognise and pursue the interest that motivates them most deeply, almost regardless of any other consideration, because completing a PhD is a challenging process and you need to be highly motivated by your project if you’re going to get through it successfully. For medical students, there are some additional considerations, at least in the UK. There are basically two windows – you can do a PhD intercalated with your medical school training, so you graduate as MB/PhD. This works well if you are someone who knows what they want to do in research early on and if you have excellent time management skills. The other window is after completion of core specialist training – usually in general medicine or psychiatry – when there is an opportunity to take time out of the clinical training process to do a PhD, typically funded by a fellowship award from the Wellcome Trust or MRC. That is the route I took because I wasn't clear what kind of research I wanted to do until I was in my early thirties and had started specialist training in psychiatry. For UK medical students and recently qualified doctors, it is highly advantageous to get appointed to Academic Foundation Year (AFY) or Academic Clinical Fellow (ACF) posts because this will allow you to compete medical training and also spend a useful amount of time developing research interests and preparing a competitive application for a PhD training fellowship.
I think medical students with an interest in technical matters, like coding or statistics, should be encouraged. The world of biomedical science will increasingly need people who are both well-informed about the background biological and medical sciences and have the technical skills to handle big, complex datasets. So getting trained in both technical and biomedical skills can prepare you for an exciting career as a relatively rare and highly employable person! However, it is tough to learn technical skills from a low base and at the same time as keeping up with clinical training requirements. I think it is important to have an excellent mentor and also, in my opinion, to focus your technical learning priorities on solving the scientific problems that you are most motivated to address. A masters course in bioinformatics or image analysis could be a useful training step for some people but personally I found it easiest and most rewarding to learn technical skills when I could see immediately how they would help me answer the specific research questions I was interested in at the time.
CB: You also work for industry, do you see that as another career change or was the move to industry a natural progression? What are the difference between working in academia and working in industry?
EB: I started working half-time for GlaxoSmithKline in 2005. My original motivation was that I wanted to contribute to development of new treatments for mental health disorders and, much though I love(d) connectomics, I couldn't see that neuroimaging research in an academic setting was likely to have much impact on mental health practice in real-life.
I have really enjoyed the experience, for the last 12+ years, of working in two organizations with two rather different cultures. I have found it stimulating, refreshing, and I have learnt a lot that I would not have learnt if I had followed the more conventional path of staying fully embedded in academia.
Industry has offered me the chance to think and work broadly, across a wide spectrum of medicine and therapeutics, whereas the life of an academic tends to become proressively narrower and deeper in focus. Industry culture is also strong in terms of team-working and strategic planning, and the standards of statistical analysis and data management are high. In contrast, a tenured academic enjoys an extraordinary degree of intellectual freedom and the opportunity to work with highly talented younger people, as students or early career researchers. There are pros and cons to both organizational cultures. I would encourage people to keep an open mind about any opportunities that might arise to work in the private sector. It can be very exciting and, at least in the UK, there are increasing efforts to make it easy for people to move back-and-forth between industry and academia over the course of a career.
CB: OHBM 2018 will be held in Singapore in June, and is likely to be the first conference experience for many PhD and MD students. Such large events can sometimes be overwhelming. Can you remember your first big conference? And what advice would you give to newcomers?
One of my first big conferences was actually the first OHBM meeting in Paris in 1995. I thought it was electrifying to be in the same room as many people whose papers I had been reading for years but had never seen or met before. However, OHBM has got bigger since then and the scale can be intimidating. I would encourage newcomers to attend the educational program before the main meeting starts. The OHBM educational program has gone from strength-to-strength and is one of the best possible places to pick up on the state-of-the-art in neuroimaging methods. It is also a friendly atmosphere and a great opportunity to ask questions, introduce yourself to speakers, and connect with others who share your interests and are at a similar level of training. Once the main program starts, I would be sure to attend any smaller, early morning symposia that are focused on topics of personal interest. I would look through the poster schedule and make a point of visiting posters presented by people whose work you admire or you’d like to get to know. I would enjoy the social program for its own sake and also as another opportunity to get talking to the people you want to meet. Wear your name badge and consider using a business card so other people can easily remember your name. The key thing is to meet people and not to spend all your time sitting in the main hall passively listening to talks, or back at your hotel reading the abstracts! I am naturally quite shy so I don't find this particularly easy advice to follow myself. But I have discovered that if you have the courage to step up to someone, with a smile and a handshake, and say something like: “Dr X, I just wanted to introduce myself because I really liked your paper/talk/poster on Y…” then almost always you will find that Dr X is very open to starting a conversation.
CB: Finally, please be honest, are you reviewer 1 or 2?
EB: I hope I am not too often the legendary third reviewer who has a problem with the paper that nobody else recognises but can nevertheless be awkward enough to knock a good paper out of contention! My only advice for dealing with peer review is to remind yourself that it almost always improves the ultimate quality of the work to go through peer review, however uncomfortable it may be at the time, and it is an integral part of the scientific process to do so. I think you will generally have an easier ride if you respect the position your reviewer is coming from and try to deal with their points as constructively and clearly as possible. I usually recommend making changes to the text or supplementary material rather than writing long tracts in the rebuttal letter that do not change the paper itself. And take opportunities to be a reviewer yourself so you learn what kind of issues you should try to pre-empt when writing your own papers or responding to peer review.