 Professor Ed Bullmore 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.
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By Jean Chen --- See Gustavo Deco's keynote OHBM2018 lecture here: https://www.pathlms.com/ohbm/courses/8246/sections/12540/video_presentations/115830 --- Dr. Wilder Penfield once said that “the brain holds within its humming mechanism secrets that will determine the future of the human race.” And yet, most of us would agree that the brain remains the least understood organ. How do we start to understand how the brain works? Prof Gustavo Deco’s approach, one of our OHBM2018 keynote speakers, is to try to build one.
In 2001, Gustavo was awarded the Siemens "Inventor of the Year" prize for his contributions to statistical learning, models of visual perception, and fMRI based diagnosis of neuropsychiatric diseases. He has published 4 books, more than 258 journal publications and 34 book chapters. He has also filed 52 patents in Europe, USA, Canada and Japan. He was awarded an “Advanced ERC” grant in 2012 and he is member of the Human Brain Project (EU flagship). Jean Chen (JC): As far as I know, you completed your PhD in atomic physics. How did you enter the field of neuroscience? How did these two fields come together for you? Gustavo Deco (GD): When I got my first PhD in Physics in 1987, I thought that I would dedicate my research career to this field. However, after a postdoc at the University of Bordeaux in France and a two-year (1988 to 1990) postdoc from the Alexander von Humboldt Foundation at the University of Giessen in Germany, I found my focus shifting. I was absolutely fascinated by neuroscience and neuropsychology and decided to change my focus. Very broadly, I was drawn to these fields and to the simple question of how the brain works. I want to understand how the brain processes information. I wanted to understand how the brain works. I was, and I am now, convinced that a good formation in physics, especially in theoretical physics, is absolutely an advantage for investigating the brain. For example, in my research I have used a lot of tools from Physics, such as statistical physics, nonlinear dynamics, etc. I went to Munich, and began working for Siemens in their research center. It was there that I started my career in Neuroscience. At Siemens, I created one of the first Computation Neuroscience groups in Germany. In 1997, I received a PhD in Computer Science from the Technical University of Munich (Dr. rer. nat. habil.). In 2001, I received a PhD in Psychology (Dr. phil) from Ludwig-Maximilian-University of Munich. JC: Your interests are broad, and you have made important contributions to computational neuroscience, neuropsychology and psycholinguistics, to name a few. How would you describe the importance of mathematics to neuroscience and psychology research, in the present and future? GD: We cannot build models of the brain without math. We cannot model cognitive processes without math. To sum it up, I'm absolutely convinced of the necessity of mathematics for being able to express in a quantitative and systematic way the laws that regulate the functioning of the brain. The main reason or intuition, is that we are dealing with a huge, complex, nonlinearly coupled and stochastic system (involving billion of neurons and synapses that are coupled, stochastic and nonlinear). It is impossible to intuitively "speak" or "describe" such systems (even a simple system of two feedback-coupled neurons is difficult!), but we can understand and study them by expressing and investigating explicitly the equations, math, describing the brain. If we renounce that, we do only phenomenology… and we know what we can expect from that… nothing. JC: I also understand that your most cited research focuses on computational modeling of spontaneous neural activity, the foundation of resting-state networks, and this work is incorporated into the Virtual Brain Project. What is the Virtual Brain Project, and how did it get started? GD: Yes, I was very active in modeling the whole brain (now not only spontaneous activity but also task and different brain states, like sleep and anesthesia). The implementation of those models in a public, easy-to-use platform is fundamental for making the models available to the community, and especially to interested researchers without a strong computational background (eg. clinical researchers). The Virtual Brain Project was a fabulous initiative that started thanks to the McDonnell Foundations and the team working out of many enthusiastic labs. The initiative is led by Randy McIntosh (Toronto), with strong contributions from the labs of Giulio Tononi, Michael Breakspear, Olaf Sporns, Viktor Jirsa, my lab and many others. JC: What is the next step or the main challenge in improving the ability of your computational models to predict biology and behaviour in brain diseases? GD: Neuroscience, especially computational neuroscience, is a new field, and now is the most exciting time for the field. There is everything to discover! We have many of the required elements to create the first theories of computational neuroscience. I'm very interested in whole-brain dynamics and modeling. Neuroimaging has opened an unprecedented window on human brain activity, raising great expectations for novel mechanistic insights into brain function in health and disease to emerge. Unfortunately, the largely correlational findings have not delivered the anticipated outcomes yet. In contrast, a computational framework will allow for causal manipulation of models of multimodal neuroimaging data, opening up for characterisation of biomarkers of disease subgroups and a better understanding of underlying mechanisms. Furthermore, adding a coupled neuromodulator system using receptor binding data will pave the path for novel methods for rational drug discovery in silico. I think the next challenge is to go from correlational neuroimaging studies to what we call, together with Morten Kringelbach (Oxford), causal neuroimaging. So in my view, the challenges are: 1) to develop and refine our novel framework for Causal Whole-brain Neuroimaging Modelling using sophisticated whole-brain dynamical models of multimodal neuroimaging data which can be manipulated off-line in silico to accurately describe causal mechanisms underlying human brain activity; 2) to apply the framework to the diagnosis of neuropsychiatric diseases, and to the design of therapies and their monitoring. In particular, one can use the model to exhaustively stimulate a realistic subject specific fitted whole-brain model in order to detect which type and locus of stimulation is more effective to re-establish a healthy dynamic of the whole brain. JC: What are the main projects that your lab is focusing on currently? GD: The main projects we are working on are the Human Brain Project, many other team projects of the EU, a large project from Germany together with Max-Planck in Leipzig (collaborator: Angela Friderici), and many others… The main issue that I see is to extend whole-brain models beyond just resting state as I described above. JC: Can you provide a few pieces of advice for junior scientists in our field? GD: As I said before, our field is a relatively new field, and now is, in my view, the most exciting time for the field. Junior scientists should study what they want. Don't be influenced by anyone. They should really investigate what motivates them. At this stage in their career, when they are learning how to be good scientists, it is an exciting time and they should take full advantage of it and study what really interests them. But I'm convinced, and so I tell my students, that the 21st century is the century of Neuroscience and Genetics (but especially the former). I left physics and Quantum Mechanics. Although those fields were extremely interesting, challenging and mathematically sophisticated, all the main elements and basic concepts were already developed at the beginning of the 20th century. I always felt jealous of the scientists that were working during those times … Schrodinger, Pauli, Bohr, amongst others, they developed everything!!! I tell my students that I really felt a kind of “romantic nostalgia” for that time. When I switched to Neuroscience, I felt (and still feel) that we are now reliving those same exciting years. We do not have theories, but we have millions of interesting questions and the experimental technology for accessing the right data… So, our task is incredibly important, namely to develop a theory of the brain… I would recommend all the junior researchers to work on that!  The Deco Lab -- Theoretical and Computational Group, Universitat Pompeu Fabra / ICREA Mentorship Program: an ongoing initiative by the OHBM Student and Postdoc Special Interest Group5/21/2018 “In order to be a mentor, and an effective one, one must care. You must care. You don’t have to know how many square miles are in Idaho, you don’t need to know what is the chemical makeup of chemistry, or of blood or water. Know what you know and care about the person.” — Maya Angelou The online mentorship program is an ongoing initiative launched by the OHBM Student and Postdoc Special Interest Group in early 2017. In this international initiative, mentors and mentees from around the globe are matched on the basis of their experience and expectations. The mentor supports the mentee’s growth by providing advice on topics such as - but not limited to - academic development, grant writing, and work-life balance. What is unique about this program is that every member of the OHBM community can be mentored and can also be a mentor. For example, the program has early career principle investigators (PIs) who seek mentoring by more established PIs, as well as senior PhD students who mentor trainees just starting out. As a rule of thumb, the program maintains at least 3 years of “experience difference” between mentors and mentees, with mentor-mentee pairs often being close in career stage. Currently, there are 424 participants in the program. In this blogpost, we compare statistics from two successive rounds (Round 1, 2017 and Round 2, 2018) of the mentorship program: 252 participants signed up in Round 1, and an additional 172 participants signed up in Round 2.
 Figure 2: Gender distribution. female (red), male (green) Relative to Round 1, geographical distribution of brain mappers joining the mentorship program in Round 2 remained largely unchanged, with two notable exceptions: gain in members from the Middle East, and drop in new members from South America. Distribution of participants with respect to career stage was similar in both rounds, with PhD candidates being the most prevalent.  Figure 3: Career stage distribution Round 2 observed a decrease in the fraction of mentees who declared an interest in starting a lab, relative to mentees who were either undecided, or planning to move to industry. This effect might be associated with constantly decreasing percentage of faculty jobs as opposed to PhD jobs, which is a strong trend in academia since the 80s. In line with the above observation, Round 2 of the mentorship program saw a drop in the demand for advice related to starting a lab, and a small increase in the demand for advice related to transitioning into industry from mentees.  Figure 4: Long term goals Looking at the summary statistic of all participants in rounds 1 and 2 coming from USA and Canada, Europe, Australia and Asia, an outlook on mentorship was found to be similar globally (Figure 6).  Figure 5: Advice sought by mentees In both rounds, mentors declared similar areas of expertise, mostly related to building a research career. This included taking career opportunities, finding postdoc jobs, developing relationships with coworkers and general advice on career development. Only a handful of mentors indicated expertise in coaching mentees on transitioning to industry.  Figure 6: Long term career goals and expertise sought around the globe  Figure 7: Advice sought by mentees  Figure 8: Expertise listed by mentors In summary, participants were gender balanced, and while geographically they hailed from around the globe, the vast majority were from North America and Europe. Over 25% of participants in the programme were willing to take on a double role (i.e. both as a mentor and a mentee), thus indicating a willingness to give back to the OHBM community. While the program saw an increase in requests for mentoring on non-academic career options (e.g. transition to industry), this was not followed by an increase in mentoring capacity in these areas. We would thus like to reach out to mentors with experience in industry and entrepreneurship to join the mentoring initiative. Overall, the expectations and competencies declared by participants around the globe were similar, thereby indicating that an online mentorship platform is necessary and useful for the OHBM community. Note: In addition to the online mentorship program, the OHBM Student and Postdoc Special Interest Group will be holding its second “Annual Mentoring and Career Development Symposium” at the annual OHBM meeting this year. Hope to see many mentors and mentees at the event on Tuesday, June 19th!  Prof Leah Somerville Professor Leah Somerville is an associate professor of psychology and director of the Affective Neuroscience and Development lab at Harvard university. She was recently awarded the Early Career award by the Social & Affective Neuroscience Society. Here we find out more about her academic career path, and what we can expect from her keynote speech at OHBM2018 in Singapore.
Nils Muhlert (NM): First, can you tell us about your career path – how did you get into neuroimaging? Leah Somerville (LS): I started working on brain imaging research as an undergraduate at the university of Wisconsin. I was working in a couple of different brain imaging labs, right when the first research dedicated scanners arrived at the university. I was one of the first people to have the opportunity to run experiments on it – along with a team, of course, of other researchers in the labs I was working in. I got that little thrill moment of seeing a person’s brain image pop up on the screen. Maybe others have had a similar experience. I still have that feeling every once in a while, it hasn’t completely gone away! I find neuroimaging so fascinating and powerful. From there I tried to orient my training towards continuing my brain imaging research, and in particular, fMRI-based research. I’ve studied emotion and anxiety-related processes. I’ve also studied motivation and cognitive control. Now in my lab we’re focused on understanding how those processes change with ongoing brain development through adolescence. NM: What would you say is so special about adolescence in the context of human development? LS: There’s a lot I could say here - I’ll try to keep it short! Adolescence is a time of life that on the surface level is associated with a number of important challenges, that individuals are facing sometimes for the very first time. Adolescents are people who are faced with independent choices about how to act, who to affiliate with, what kind of goals they like to hold for themselves. At the same time there’s increasing demands on their self-control. They’re becoming more and more self-guided in the way that they’re interacting with the world. We can sometimes think of them as novice independent people who are still developing the toolkit that can support mature independent actions. We find that ongoing brain development facilitates a number of great achievements at this time of life. But it also places a number of constraints on the way in which adolescents might optimize their behavior in certain situations. We’re very interested in understanding the interplay in that – thinking about adolescence as a very adaptive and useful time of life but also one that differs from adults in a number of important ways. One insight that has fascinated me is looking at brain development measures and asking “when does a person become fully mature?” It may seem like an easy question or one that could be measured using a single modality. In fact, the answer you get really differs when it comes to brain structure or function or network properties. It’s especially surprising that on certain measures – including measures of white matter – the developmental changes continue to play out throughout the twenties and perhaps even through the thirties. So one thing that’s interesting, as an extension of that, is thinking about how we decide when a person is mature from a societal standpoint. NM: In your work you also discuss socioaffective circuitry – how do changes in that circuit map on to the behaviors we see in adolescence? And what have you found out about that over the last decade? LS: In our lab we tackle this from different angles – so I’ll let you know about one in particular that I’ll be talking about in OHBM. We’re very interested in the intersection between motivation and cognitive control. That is, the degree to which motivational cues in the environment – potential rewards and punishments for example – can shape the way in which a person is able to optimize their cognitive control in a given context. We’re interested in the shift across development, in which individuals across the ages can recognize situations that hold different motivational values. They might want to perform better in certain conditions than in others – either to avoid punishment or to obtain rewards. All of the detection and assignment of values seems to be very consistent in early development. But the degree to which we can take that information and use it to guide our goal-directed actions in the moment, seems to be continuing to develop well throughout adolescence. One arm of our work is in trying to understand how the dynamic interactions in cortico-striatal circuitry (including the dorsal and ventral striatum and lateral prefrontal cortex) coordinate and give rise to motivation-guided cognition. This is something that we’ve seen play out and continue to change and refine well throughout adolescence and into early adulthood. This is one area of work that we’re excited about. Another area we’re interested in is adolescent attunement to their social environment. This is a time of life that’s associated with dramatic changes in daily life; individuals are forging new independent relationships for the first time and there’s a lot of volatility in adolescent relationships. They are falling out of favour with one another more frequently than adults would be, giving them lots of opportunities to get feedback about how they’re doing socially. Another arm of our work is therefore to understand how adolescents learn from feedback and use positive and negative social feedback as learning cues to inform how they should feel about themselves in a given situation and how they should feel about other people. We’ve seen in a couple of studies that when adolescents are on the receiving end of negative social feedback they tend to take that as a very strong cue to influence how they feel about themselves. This would result, for example, in a reduction in the momentary feelings of self-worth or self-esteem. Adults actually show a bias in the opposite direction. They have different strategies in place that allow them to offload or buffer themselves from negative feedback and maintain a positive self-concept, even in the face of very opposite social information. We’re really interested in understanding how learning processes – again subserved by striatal-based systems – might be biased towards learning from negative or positive information in the social domain at different points of life. NM: And how does this system seem to change from early to late teenage years, or even people's early twenties? LS: Well we carried out a study of individuals from age 10 to 25, and found that there is a period from early to mid-adolescence, perhaps from 12 to 15, that negative feedback had a strong negative impact on their self views. Whereas individuals of college age seem to have a lot of strategies in place already to buffer themselves from negative feedback. So this is one time period when a few years of age makes a large difference in terms of how these cues are incorporated into learning about themselves and other people. NM: Thinking about how social media might tap into this, and perhaps exacerbate the concerns that adolescents have: as social media has become a more integral part of their everyday lives, has this had negative and positive consequences? LS: Great question and one that I don’t have a scientific answer for but I’m happy to speculate! This is a very hot issue now – thinking about how developmental stage might manifest the influences of these kinds of media processes differently. It’s only in very recent generations where people have taken up a lot of social interactions online. This is something that has not been subjected yet to empirical study. There is a lot of speculation that perhaps social media is detrimental to adolescent development. Adolescents themselves are quite happy at having the option to socialize over the phone and over the internet. They say it helps them maintain strong social bonds, it gives them lots of information. They can stay attuned to the goings on of all of their friends more easily. There is also the potential for social media to have certain negative and perhaps unintended consequences. One that has been suggested by our work is that social media has been almost designed to elicit and deliver feedback to people – by getting friended, getting thumbs-up or the absence of a like or lack of response from somebody. This can be interpreted as negative by someone or by people on social media. The way we see it is that there can be very positive interaction on social media but there’s also the potential for a higher frequency of negative feedback, or the absence of positive feedback being interpreted as negative feedback. We’ve shown that negative feedback has a very potent influence on adolescent self-views, so that very high frequency of receiving negative feedback online could have a more detrimental effect during adolescence than other ages. Developmental scientists have often had concerns about the effects of new technology influencing self-views. When I was a kid this would have come up with video games – suddenly people would have a Nintendo in their house, there was a wave of concern about that. At this point we just don’t know enough to have a definitive evidence-based account about whether social media is a good or bad thing for adolescence. NM: Turning to your other work, what would you say are the scientific achievements that you’re most proud of during your career? LS: I’m not sure if I’d call this a scientific achievement but I’m most proud of having had the opportunity to run my own lab. I never thought I’d be a PI. It has been one of the most challenging and rewarding things I have ever done. I feel proud and gain a lot of reward from it, particularly when I interact with my trainees. They conduct great work, are great people and are becoming great mentors in their own right! It makes science very fun to do in our group. Fostering an atmosphere that makes science fun and exciting and collaborative is something I’m very proud of, and is down to the efforts of my whole lab. NM: And to reflect the quality of your mentoring you were awarded the Everett Mendelsohn excellence in mentoring award. When you look back at your own career, which people could you point to that offered you good advice during your career, and how has that affected how you interact with your own trainees? LS: I’ve been very fortunate to have had a number of wonderful mentors throughout my training. They’ve helped me bridge gaps into the next steps of my career – giving me advice, and sometimes tough love when I needed it! This includes my graduate mentor and my postdoctoral mentor, BJ Casey. I would point out BJ in particular – she was a big part of me discovering this very strong interest in developmental neuroscience, particularly after trialling out a number of different topics of study. That one fit for me in very large part because of the support in mentoring from her. It’s important to mention that at first I didn’t realize that every trainee needs something different from a mentor. You need a lot of flexible thinking when you’re mentoring to understand what each person needs at different points in time. This of course evolves at different points of training. They might start by needing more hands-on help and more topically-focussed advising. But watching a person beginning to strive for independence and allowing for independence is something that I work hard to detect and accommodate. When I became a PI I didn’t realize that I would still benefit from mentoring myself. I still have mentors who guide me and I don’t think anyone is ever quite finished in needing mentoring, advice and guidance. I have a number of colleagues – both peer-age going through similar career stages, as well as more senior mentors – who are still helping to guide me. I am very appreciative of that. NM: And finally, your OHBM 2018 talk – can you give us a sneak preview? Which gems from your research career have you decided to focus on? LS: Well I’m very excited about being invited to speak at OHBM and having the chance to go to Singapore. I’ll be talking about two main themes: adolescence as a phase of the lifetime associated with ongoing and dynamic brain development, in particular in development of functional brain connectivity. I’ll also specifically focus on understanding the interactions between motivations and cognition as a test bed to think about how ongoing brain development would lead to important shifts in behavior. In doing that I’ll present some specialized studies that were conducted in my lab in Harvard, as well as some broader projects that we’re currently working on. Most notably we’re one of the groups completing the human connectome project on development – a large scale ‘big data’ style project - that will ultimately collect brain imaging data on over 1,300 5-21 year olds. This is an ongoing study that we are about half-way through collecting data for. It’s partly longitudinal and partly cross-sectional, and it’s designed to help us really understand both fundamental patterns of brain connectivity that are changing at the basic neuroscience level as well as the implications of those connectivity changes for behaviours including motivated behavior and cognitive control. So I’ll be discussing how we approach these problems from a broad, big-data standpoint and how this can complement the more specialized work that we’re doing. NM: We’re definitely looking forward to that – many thanks for taking the time to speak to us and we’re looking forward to your talk in Singapore. By Elizabeth DuPre and Kirstie Whitaker This month we continued our Open Science Demo Call series by speaking to Ariel Rokem, Dora Hermes, and Tammy Vanderwal about three initiatives they’re involved with that promote openness in neuroimaging research.  Ariel Rokem Ariel introduced us to NiPy--short for NeuroImaging in Python--which is a large community-of-practice to support using python for neuroimaging. He explained that NiPy exists within the broader SciPy--short for Scientific computing in Python--community, and it unites many individuals who use Python in their scientific analyses. As open communities, Ariel pointed out that anyone is welcome to use the NiPy and SciPy software as well as to participate in its development. If you’re interested in hearing more, he encourages you to check out the NiPy mailing list or the annual SciPy conference!  Dora Hermes Dora told us about iEEG BIDS extension proposal, a proposed extension to the BIDS standard for structuring human intracranial electroencephalography (iEEG) data. She explained that to date, current challenges with iEEG data sharing include the large variability in both electrode locations as well as data formats across sites. The proposed extension will create a standardized structure to store iEEG data and metadata, allowing for novel, multi-model analyses via integration of iEEG with MRI, MEG and EEG. To contribute to the development of the iEEG BIDS extension, Dora encourages checking out the current draft or commenting on the BIDS mailing list.
By Valeria Kebets, Csaba Orban, Thomas Yeo on behalf of the OHBM 2018 Local Organizing Committee (LOC) As we’re swiftly approaching June, we thought we would follow-up our previous blogpost with 10 practical tips to help you make the most of OHBM 2018 in Singapore.  1. CLIMATE: Singapore has a hot and humid tropical climate. The air temperature remains in the mid-twenties (~75°F) even at night, so don’t be surprised if you break a sweat after only a 10 minute walk. For daytime walks, sunscreen is recommended as the UV index can reach extreme levels. Buildings tend to be heavily air-conditioned, so you may also want to pack a sweater for the conference. Also note that the weather is unpredictable, and heavy thunderstorms can develop in just a few minutes, so your weather app is unlikely to be helpful. 2. FOOD: If you want to grab a quick bite during the conference, there are many cafes and restaurants in the same building (listed here). There are also plenty of dining options at walking distance from the conference centre such as Gluttons Bay, Chijmes and Bussorah street. Shoppers will be glad to know that most stores in the city are open until 10pm, including on Sundays. 3. TRANSPORTATION: The best way to take advantage of Singapore’s public transportation system is by purchasing an ez-link card (same concept as Oyster card in London). Ez-link cards are sold at the airport and at most MRT (subway) stations for a $5 (3.75 USD) deposit. Ez-link works on all buses, MRT lines, and can also be used to pay in some stores, e.g. 7-11s, and some taxis. Pro tip: Remember to tap out with your card when alighting buses to avoid getting charged the maximum fare. 4. MAPS: Google Maps or Citymapper are great for figuring out the best combination of MRT/bus/walking to get anywhere on the island, including expected travel times, when to alight buses (stops are not announced), and the fastest way to exit MRT stations. Follow this link for directions to the conference centre. 5. TAXI/RIDESHARE: All Singapore taxis operate based on metered fare. There is no Uber, but Grab provides a similar service. There are separate pick-up points for metered and Grab taxis at Changi Airport. Pro tip: If you want to keep costs low avoid the Chrysler Cab (black taxis) in the airport taxi queue. 6. GRATUITY: Tipping is generally not expected in Singapore. Most restaurants automatically add a 10% service charge and a 7% Goods and Services Tax on the bill. 7. PAYMENT METHODS: Most places in Singapore will accept credit card payment (VISA/Mastercard, though usually not AmEx). However, do keep some cash for dining in hawker centres. ATMs are widely available in the city and airport. 8. LIQUOR TAX: Singapore imposes an excise duty on all liquor, so expect to pay between $10 - $14 (~ $9 USD) for a small bottle of beer in restaurants or bars. Pro tip: Duty free stores inside the airport terminal are exempted from the liquor tax. 9. MEDICATION RESTRICTIONS: Singapore has a strictly enforced no tolerance policy with respect to possession of illicit substances. Note that certain prescribed psychotropic medications (e.g. sleeping or anti-anxiety) may require you to apply for a license at least 10 days before your arrival. You can read more about this here. 10. RELATED EVENTS: Be sure to check out the satellite events before and after OHBM. The events kick-off with PRNI (June 12-14), OHBM Hackathon (June 14-16; Sold out) and BrainStim (June 15-16). The Chinese Young Scholars Meeting takes place June 19. There are also three post-conference workshops organized by the local brain imaging community: Multimodal Neuroimaging for Mental Disorders (organized by yours truly; June 22), Brain Connects (June 22) and Nonstandard Brain Image Analysis (June 22-23). Attendance is free but make sure to register early--while there are still seats! If you haven’t already, we highly recommend you to check out the brain in SINc website for more in-depth information on food, sights & attractions in Singapore curated by the Local Organizing Committee. We look forward to welcoming you next month in the Lion City! |
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