By Claude Bajada
OHBM is a community of neuroscientists interested in neural cartography. It draws upon the traditions of 19th century neural mappers such as the Vogts, Brodmann and von Economo. While the spirit of the society is still based in the biological brain, the conference itself is multidisciplinary. Although still a place for biologists, anatomists, physicians and surgeons, thanks to the development of Magnetic Resonance Imaging the field has become increasingly computational.
Thomas Yeo is an assistant professor at the National University of Singapore where he leads the Computational Brain Imaging Group. His lab develops machine learning algorithms for MRI data. His work is well known to brain imagers who are familiar with the “Yeo” brain networks. Ahead of his keynote lecture, I met Thomas and learned how he made the switch from engineering to neuroscience, what led him to working on the topics he is now well known for, and what the exciting new topics in his field are.
Claude Bajada (CB): From studying electrical engineering and computer science to getting into neuroscience what was the path? Or perhaps draw the graph.
Thomas Yeo (TY): To tell you the truth, my path was actually quite random. There was no sudden epiphany, no single life-changing event that led me down this path. As a kid, I was generally interested in the brain, but I was also interested in mathematics and physics. The best way to describe my path is that things just sort of happened. When I was deciding on PhD supervisors, I was debating between computer vision or medical vision. I ended up pursuing medical vision mainly because my PhD supervisor (Polina Golland) expressed the most interest in me joining her lab. At the time, I did not want to work with fMRI because it seemed too difficult. Consequently, I ended up working with both Polina and Bruce Fischl during my PhD, developing machine learning algorithms for registering and segmenting brain data on cortical surfaces. When my PhD was nearing its end, I was looking for a postdoc position, but I also wanted to try something new. I could either move closer towards image acquisition (MR physics) or towards the “end users” (neuroscientists/clinicians). Bruce mentioned that Randy Buckner was putting together a big dataset. At that time, there was not as much data sharing and not as many large datasets like today, so I was to join Randy’s lab, learn some neuroscience and analyze some large datasets. From then on, I was hooked onto neuroscience research, but with a computational bent because of my PhD training.
CB: As someone with a solid STEM background what are your experiences interacting with clinicians, psychologists and other health professionals?
TY: Interactions with clinicians, neuroscientists, and psychologists are extremely important to what I do. I have found that problems, which neuroscientists think are important, are often quite different from what I was interested in as a PhD student. Back then, I thought I was developing algorithms that were very helpful to neuroscientists/clinicians/psychologists. But after joining Randy’s lab, I realized that my algorithms are often not immediately relevant to what neuroscientists need. In engineering/computer science, there is the pressure to develop novel, beautiful, fast algorithms. However, at the Martinos center, where there is a very nice big computing cluster, speed is often not a pressing issue. Most neuroscientists also do not care about novelty or how elegant an algorithm is. They care about whether an algorithm can help to answer their question or help their patients. They don’t really care whether the algorithm involves lots of equations or just simple correlations. In fact, they prefer a simple algorithm to a complex algorithm unless I can demonstrate the complexity is really necessary. So working with neuroscientists has really changed how I think about problems.
On a day-to-day basis, I like to think about what interesting neuroscience problems can be formulated as machine learning problems. For example, around 2012, I became intrigued by Russ Poldrack’s 2006 paper on reverse inference. He had this beautiful figure showing that tasks recruit unobserved cognitive processes, which can then be observed with brain imaging, behavioral and other kind of data. I realized that the figure can be mathematically expressed using a hierarchical generative model. I then applied this model to real data to estimate the unobserved cognitive processes and discover new insights into brain organization. Throughout this project, I received a lot of inputs from quite a number of neuroscientists, who brought with them their own unique expertise and insights to the project. In fact, I met Simon Eickhoff and Maxwell Bertolero because of this project and we have since collaborated on many more projects. Later on, I realized that the same class of hierarchical generative model can be applied to understanding disease nosology: in this case, the model would encode the idea that different disorders or disorder subtypes share multiple disease processes, which can then be observed with brain imaging and behavioral data. This has in turn led to projects on disorder nosology with quite a number of folks. Thus, one project led to new collaborations, which led to even more collaborations.
CB: Your name is now almost synonymous with the 7 and 17 resting state “Yeo” networks. How did that work come about and how did it influence your subsequent career?
TY: As I was saying before, I ended up joining Randy’s lab as a postdoc because he was amassing a large dataset in collaboration with a large number of PIs in the Boston area. At that time, there was already a lot of work showing that resting-state fMRI can be used to extract different networks. Given that my PhD advisor (Bruce Fischl) is one of the creators of FreeSurfer, I ended up re-processing Randy’s data and projecting them onto the surface coordinate system to visualize the data. I then used a clustering technique developed by a fellow PhD student (Danial Lashkari) of my other PhD advisor (Polina Golland) to parcellate the cortex. Frankly speaking, most of the networks we found were already known in the literature, so to this date, I am not 100% sure why this paper became such a hit. Perhaps it was the large number of subjects. Or the surface coordinate system allowed us to see some very exquisite topography that were less obvious in the volume. For example, we showed that the existence of multiple parallel-distributed and interdigitated association networks. Or perhaps it was the comprehensiveness of the paper – 40 pages long. I like to joke that it’s my second thesis.
Without a doubt, the paper has been incredibly helpful for my career. I have a few students, who continue to push the frontier on this topic. Our work probably gets a disproportionate amount of attention, so my lab continues to benefit from the original paper. In some sense, I was very lucky. The technical aspects of the 2011/2012 papers (e.g., surface processing, clustering) were possible because of my PhD training. And I arrived in Randy’s lab at exactly the right time. If I came a year earlier, the data would not be ready. If I came a year later, the impact of the work might be overshadowed by similar papers (e.g., Jonathan Power’s work), which would have been published a lot earlier. I was lucky to have worked with super talented people in Randy’s lab, including Fenna Krienen (who was co-first author on the paper), Hesheng Liu, Jorge Sepulcre and of course Randy!
CB: What would you say is the most exciting topic in computational brain imaging at the moment?
TY: Given the large quantity of public data out there, I think this is an exciting time for human neuroscience. This is especially the case for computational scientists like me. I have found the big data to be very helpful for developing algorithms and applying them to discover new insights into the brain.
Given the large public investments in these datasets, I am also thinking a lot about how we can use these big data for useful applications, e.g., helping patients, etc. Consequently, I have become less interested in problems, such as classifying controls versus schizophrenia, which are useful for benchmarking algorithms, but not really useful for clinicians per se. There are definitely machine learning problems with real clinical value, e.g., predicting best treatment in depression, but there’s not that much big public data on that (although I can’t really complain since I am just a data leech).
Furthermore, the vast majority of machine learning algorithms only allow us to find associations. So no matter how “deep” the algorithms are, we are just finding glorified correlations, even if it’s out-of-sample prediction! Do these big data only allow us to find associations or can we gain mechanistic insights into the brain? On this front, I think biophysical modeling and causal modeling are potentially promising and exciting.
CB: You played an integral role in COBIDAS, what was the motivation for that and what influence do you think it has had?
TY: WelI, I wouldn’t say I played an integral role. I was one of many folks who contributed to the report. It was really Tom Nichols who had the unenviable task of “herding cats”! The OHBM Council initiated COBIDAS to develop recommendations and consensus on best neuroimaging practice. But soon it became clear that “neuroimaging” would cover too many things, so we ended up focusing on MRI. EEG/MEG COBIDAS is now spearheaded by Aina Puce and Cyril Pernet.
Unfortunately, in my opinion, the COBIDAS report has not been as influential as I hope. We recommended a checklist of items that researchers should consider and report, but I think it’s safe to say that the vast majority of papers (including from my lab) do not really do so. I am speculating here, but one reason might be that many researchers do not know sufficient details of their preprocessing pipelines or analysis algorithms to actually complete the checklist. The checklists are also very long, so researchers might balk at the work of filling them. I think the best way for this to happen is to try to automate the process. I can imagine some software that keep track of the preprocessing/analysis one performs on the data. These metadata can then be shared. I believe Tom Nichols and others might be working on this. This could be promising.
In the case of my lab, we mostly perform analysis of open datasets and we often develop our own algorithms. Unfortunately, I do not believe that there is a checklist long enough to completely specify an algorithm without access to the original code. Thus, my lab is more focused on sharing our code. Even then, replication is not so easy of course. While we work on open datasets, many datasets (e.g., UK Biobank) might not allow us to re-distribute the data. Thus, replicating our results is not so easy. If you explore our github (https://github.com/ThomasYeoLab/CBIG), you will see that our wrapper scripts often reference data on our server. But we have tried to make the code user friendly, so hopefully users can easily apply our code to their own data.
CB: What can we expect in the future from the Yeo lab?
TY: We have some new exciting individual-specific brain parcellations stuff coming out! We are also working on using machine learning and GPU to invert neural mass models; right now, these biophysical models mostly require hand-tuning of critical parameters. Finally, we are also working on using machine learning to understand disease nosology.
GENES, ENVIRONMENT, THE DEVELOPING BRAIN, AND EVERYTHING IN BETWEEN
By Tzipi Horowitz-Kraus
One of the most interesting questions when researching the developing brain is the level of impact of defined genetic and environmental factors. Dr Armin Raznahan, a Neuroscientist and a child psychiatrist, who serves as Chief of the Developmental Neurogenomics Unit in the National Institute of Mental Health (NIMH), examines patterns of brain development in health and in groups with known neurogenetic disorders. His unique blending of basic and clinical neuroscience may help to identify risk pathways towards common psychiatric presentations, in addition to the insights it provides regarding the specific rare developmental disorder subtypes his clinical research protocols are focused on. I had the honor of interviewing Dr Raznahan, a keynote speaker in the upcoming OHBM 2019 conference, to find out more about his work.
Tzipi Horowitz-Kraus (THK): What is developmental neurogenomics, and what motivated you to go into this area of research?
Armin Raznahan (AR): I see Developmental Neurogenomics as a discipline that is concerned with brain development, and emphasises the role of genetic factors in patterning brain structure and function over development. Usage of the term Developmental Neurogenomics has increased in recent years, and for us, there is an additional emphasis within what I’ve just described on thinking about how genetic influences on the developing brain can contribute to psychiatric disorders. Coming from the perspective of my initial training as a child psychiatrist, there is that clinical element to what I do as well as the basic science questions about spatiotemporal patterning of the brain over development, and how genetic variation can contribute to that.
By Roselyne Chauvin
Recently, a Brain-Art Special Interest Group (SIG) was created within OHBM. This SIG will be officially managing the Brain-Art competition and exhibits that have been organized for several years by the Neuro Bureau. Each year the Brain-Art competition receives numerous submissions; the winners of this competition are then announced during the Student and Postdoc SIG and Neuro Bureau collaborative social evening at the OHBM annual meeting. Since the first exhibition in 2011, Brain-Art exhibitions have always been a great success. I was really happy to learn about the creation of the Brain-Art SIG and curious about the aim and perspective of development of its board. By officializing a Brain-Art dedicated group, art might start to take a bigger place in the OHBM scene.
I’ve always valued the interaction between Art and Science. It’s an amazing way to reach out to the general public and scientific pairs, and thus to promote scientific content. Every type of art can be used as a vessel to talk about science, such as music, dance, theatre, literature or painting. It can come directly from researchers or from their collaboration with artists. Programs such as Artists in Residence or Artists in lab promote that artist-scientist interaction by proposing to artists to stay few months in a lab in order to learn and get inspired for their art. The other way around works as well, researchers might see their work with another angle by doing art or interacting with artists, revealing new perspective.
As the Brain-Art competition 2019 just opened, let’s discover a bit more about this Brain-Art SIG and review the past editions of the competition and exhibits. You might even find inspiration on the way to participate in this year’s edition of the competition. To get to know about the Brain-Art SIG mission, I asked the board their personal experiences with Brain art and how this SIG came about.
Brain-Art SIG vision for OHBM: Interview with the board
Roselyne Chauvin (RC): So what is the view of Brain-Art SIG’s officials on art and science interactions and, personally, what motivated you to join the board of this SIG?
Alain Dagher, Chair (AD): My answer is roughly half-way between “Because Daniel M asked me” and “because art and neuroscience both often seek to explain the human experience.”
Valentina Borghesani, Secretary (VB): I agree with Alain: human beings are way too complex to be tackled only from a scientific perspective, one needs to embrace diverse forms of expression/investigation. Personally, while being totally void of any artistic talent whatsoever, I love being involved in the scientific community and do…the leg work! One day I saw on the brainhack slack workspace that they were looking for volunteers for this to-be-established SIG and… First lesson learned: things do escalate quickly around these folks!
Aman Badhwar, Chair-Elect (AB): As far back as I can remember, I have been fascinated by art, and started painting as a child. In my view, science and art both seek to observe, record and explain the world around us, just using different means. Both have their theoretical frameworks, evolving techniques, and schools of thought. Above all, both scientists and artists need to be creative and insightful in order to make meaningful contributions to their respective fields.
In one direction, I use painting as a means of communicating ideas from my scientific work to the public using the more visceral, emotional language of art. In the other direction, when grappling with a thorny scientific problem, the distinct focus required while painting frees my subconscious mind from conceptual boundaries and dogmatic ideas, and allows me to return to my scientific work with fresh eyes. Some people have told me that one cannot be a scientist and an artist at the same time, and that it is necessary to choose. Personally, I find that art synergizes with my academic endeavours, and provides me with a clarity that is sometimes hard to find in the barrage of scientific data.
I was first told about Neuro Bureau and its OHBM art competition by Pierre Bellec. It was 2014, and I was having my first solo art exhibit at CRIUGM, University of Montreal. The next thing I know I was having this intense conversation with this “highly energetic, mile a minute person” (well compared to me as I internalize my energy, and Pierre, I would say, is the opposite), who was convinced that I needed to submit my art to the Neuro Bureau art competition. I did not know who Pierre was, had never been to the OHBM, but the art was submitted, and a couple of months later, again a very enthusiastic Pierre informed me that I had won one of the categories. Life has a funny way of working sometimes, because the next year (2015), I found myself being a postdoc at Pierre B’s lab, going to my first OHBM meeting, and being intensely involved in the Neuro Bureau/OHBM art activities.
RC: Your Brain-Art SIG page states that you aim to:
Outside of the exhibit and competition, what other tasks would you like to start to reach these goals? I know that there is always multiple BrainHack projects related to new data-visualization tools. Will you consider proposing a special BrainHack on data visualization mixing scientist and artists? Or will we see the start of graphical abstracts for OHBM?
AD: I think that is a great idea. At the prosaic level, better data visualization can improve communication of scientific results, and ease the work of reviewers (making your paper more likely to be published).
But just as we need to emphasize the aesthetic side of brain imaging visualization, we also need to incorporate concepts of openness and reproducibility, i.e. make sure the data-to-image generation process is transparent.
VB: Improving our current scientific visualization practices is clearly one of the expected, let’s say, side effects. Graphical abstracts and cross-disciplinary hackathons sound great way to enrich our SIG activities! However, I would like to point out that our concept of Art cannot be resolved in visual arts, as we clearly stress with this year’s award categories. Only embracing the heterogeneity of tools and perspectives the Arts can offer us, we will appreciate the full potential of this dialogue. Neuroscience can definitely exploit this diversity when it comes to outreach, both within (interactive graphical abstracts? performative poster presentations? Why not!) and outside our community (e.g., reach the general public honoring the different ways information can be assimilated and digested). But the benefit of integrating more insights from the Arts will also be seen in how it will unleash scientists’ creativity and divergent thinking. It’s not only about finding new ways of showing our results, but also exploring new point of views.
RC: For now, you have a board that is composed of:
RC: I guess you might need more manpower to go on with these tasks, are you looking for more people in the SIG?
AD: Yes. We always need people to assist during OHBM for setting up the exhibition and helping during the conference. Also, always happy to see new ideas to take the project in new directions.
RC: I think the transfer of Brain-Art activities from the Neuro Bureau to this Brain-Art SIG is a great initiative to get more attention on the exhibit and to be able to communicate specifically about art. What motivated and when did you take the decision of creating the Brain-Art SIG?
VB: Over the years, the community of OHBM members interested in art-related initiatives kept growing. Giving it structure within an official SIG seemed like the best option to support its evolution. One key aspect is that the SIG promotes an open and transparent process allowing every OHBM member to contribute, e.g., joining our Slack community, following our activities online, volunteering to help, or joining as one of the SIG officials.
Retrospective of Brain-Art Competitions and Exhibits
The Neuro Bureau fostered the Brain-Art competition yearly since 2011. Every year, everyone can participate to the competition by submitting art pieces to specific categories. There is no limit in the number of submission per person.
The constant categories are:
In addition, one or two special topics are proposed every year and reflected trendy topics in the field of neuroimaging:
As for the main contributors and most consistent over the years, we can find several submissions from Katja Heuer, AmanPreet Badhwar, Roberto Toro, Michel Thiebaut de Schotten, Benedicte Batrancourt or also Lucina Uddin.
It’s only recently that the Brain-Art exhibit was proposed. The first exhibition called “Crossing fibers: A retrospectroscopic view” was proposed at OHBM 2015 in Hawaï and later displayed in Germany (2015,honolulu, berlin and Leipzig). This exhibits featured the best art from the Brain-Art competition and, to support the initiative, people could buy posters of their favorite piece.
In OHBM 2017, a new exhibit was proposed to present the new art from the Brain-Art competition (read more about the OHBM 2017 exhibit) and presenting an art piece called Dream Sessions. Created by Nathalie Regard and Roberto Toro, this dream log of 101 nights was not only a piece of art but also a tool to study the EEG recordings done during these nights.
In OHBM 2018, together with the Brain-Art competition best art pieces, the exhibition featured a local singaporean artist, Shubigi Rao (2018), inspired by her knowledge of neuroscience. Conference attendees were able to discover mesmerizing representations of creatures with complex nervous system.
The Brain-Art SIG is currently working on setting up the exhibit of OHBM 2019 that is entitled: “Ars Cerebri : Creativity stemming from, and at the service of, neuroscience.”
Inspired by the ancient Muses, this year exhibition will feature pieces covering multiple domains of the Arts sharing one common denominator: they are the fruits of the creativity that stems from or is inspired by neuroscientific research. Whether established or emerging, different artists and scientists will contribute their personal and unique works produced under the Muses' power of inspiration. Static as well as dynamic pieces will be exhibited during the main conference (June 9-13, 2019) in the heart of the Auditorium. In addition, a special evening event showcasing live performances will be held on Monday the 10th.
The SIG just opened The Brain-Art Competition 2019 and this year, we see a renew of categories with an emphasis on different types of art and more dimensions. A major novelty is the dedicated categories for text and live performances. This year's exhibit will go further than visual arts.
The categories for this are:
You can submit your art pieces/illustration/representation before the 11:59PM CDT, Wednesday, May 29th, 2019.
To stay updated and participate in the Brain-Art SIG activity, join their slack workspace!
Twitter handle: https://twitter.com/OHBM_BrainArt
Slack workspace: ohbmbrainart.slack.com
Peter Fox is a Professor of Neurology and has been a director of the Research Imaging Institute at the University of Texas Health Science Centre, San Antonio since 1991. He’s a co-founder of the journal Human Brain Mapping (with Jack Lancaster), a founding member of the International Consortium for Brain Mapping and has consistently been listed as one of the top 100 most cited neuroscientists since 2004.
Peter Fox has played an integral role in the founding and development of OHBM, serving as Chair in 2004-05. We found out about his major academic achievements and experiences with OHBM.
About that time, articles started appearing in journals and being covered in Scientific American about what the Danes were doing with single photon studies. They started off with language studies, identifying that during language listening, there was a lot going on in the frontal lobes, and that the right hemisphere was involved, two points that nobody had anticipated. At that point, I knew that I wanted to study people, and I figured the only real way to do that was to go to medical school and to become a clinical neuroscientist. Then I could do this kind of work.
What do you see happening with neuroimaging in the US these days?
PF: In the area that I'm most involved in, and the sort of grants that I review, what I'm seeing as a strong trend, pushed both by the investigators and by the funding agencies, is using neuroimaging as a demonstration of the neurobiology of treatments. And the expectation is that if you're going to test a new treatment, if you're going to do a clinical trial in a neurological disorder or psychiatric disorder, you won't be funded unless you can establish the neurobiological mechanism. Imaging is the way to do that. So it's moved from being really basic science to clinical neuroscience, and the interface between the treatments and theory. I think that's a really powerful and appropriate role, and a way of moving neuroimaging into demonstrably helping humankind at large. So I think that's a very important and powerful direction that the field is going in.
What research or other contributions are you most proud of in your career?
PF: Two areas that I was very pleased to have been involved in were both at about the same time. One is doing the original studies demonstrating that blood flow and metabolism are uncoupled or are engaged in a very complicated relationship. Those observations gave rise pretty much immediately to the development of functional MRI. And in particular, the prediction of the BOLD signal. The people who described it, predicted it, and cited the work that we had just published, said, “If Fox and Raichle are correct then that would predict this, and we should get a signal like this”, and that was correct. Now BOLD fMRI has become the dominant technique for brain mapping. And so the lineage there is really quite clear. And so I'd say that was a lot of fun.
Another area that I've been really pleased with how well early ideas evolved, and were adopted, is introducing standardized coordinates. And so when I started doing mapping studies, right away, I was unsatisfied with the ability to say where we were. And I looked around, and there weren't many examples, but people were mostly naming things by gyri. That, to me, seemed not enough. So I spent time looking for alternatives and came across Talairach's 1967 Atlas and some papers referencing that. So we developed a way of putting the images I was acquiring into Talairach space, and published that method, and encouraged people to adopt it. So ultimately, that has become the standard. And so really, everything is published in standardized coordinates, originally Talairach coordinates, but now the 1988 Talairach and MNI, and there's various versions, but still, they are translatable from one to another. And so the format that we all publish and analyze our data in, I had the opportunity to introduce and so that's real fun, I enjoyed that.
You played a part in the creation of OHBM. What was that like? And how did you imagine OHBM would be like?
PF: When I was just starting out in San Antonio, I'd been working on the brain map database for a few years. We were trying to develop a data sharing mechanism that used standardized coordinates, to give people a way of sharing their data, or at least sharing their results, if not their original data. I received funding for quite a few years to bring people to San Antonio. I focused on bringing people that were having the most influence on methods development. We had two days of methods talks, always in the same organization. There were algorithms for data analysis; Karl Friston always ran that as a half day session. And there was a session on databases that I ran. There was a session on spatial normalization and [Jack] Lancaster ran that, and there was a session on merging different imaging modalities. We did that year after year and after about the third year, this was a meeting of about 200 people. I got grants to bring people and the people that kept coming year after year, said, we should open this up to a bigger community.
I said, that's fine with me. And they said, you do it Peter, but I thought 'No, I like doing meetings this size. I'll do one later, but I don't want to do the first big meeting.’ Bernard Mazoyer and Per Roland said they'd do it, but they wanted guaranteed support because they didn't know how to do it. And so everybody there, [John] Mazziotta and [Karl] Friston and [Richard] Frackowiak and Leslie Ungerleider all said, we'll bring our labs. So with that kind of agreement, Bernard and Per went forward and did it. And then that just kicked it off. And it's rolled since then.
What have you found most rewarding about your involvement with OHBM?
PF: I've been to many different meetings. I think it's a very widely held opinion that the standard of science at OHBM is the best of any meeting that I've ever been to. It's very sophisticated, and has very high expectations. Clinical meetings are not this good by a long shot, they are not. Another thing is the inclusivity of this meeting; it's been that way from the start. We have mathematicians and statisticians and physicists and engineers, psychologists, psychiatrists and neurologists, everybody comes together. That's very unusual. The third thing that I think is really fun about this meeting is the international scope of it. And the gender balance. Many clinical meetings, many clinical studies are not very gender balanced. They're male predominant a lot of them, particularly imaging. So for instance Radiology is 80% males. And at this OHBM meeting in Singapore it’s pretty much 50:50, male to female. That's very unusual. So I think there's a lot of unusual things about this meeting.
What memory stands out when you think about your experiences with OHBM?
PF: I remember being the council chair in Toronto and that was amazing. That was really a lot of fun. And I definitely remember we got sponsored to host a party. Just the council chair hosted a party; that was a spectacular party. So we've been to a lot of really outstanding social events involved with OHBM.
What changes have you seen in OHBM over the years?
PF: I'm as impressed with what doesn't change as what does change. Certainly, the recent changes have been that there's much more social media outreach, and proactive engagement of young people, trying to attract people into this field and making a very complicated field as approachable as it can be. High marks for that - that's really an excellent initiative. And honestly, I think OHBM is doing that better than any other organization that I've been exposed to. It's very proactive, it's very positive.
But, to me, equally impressive, is that the overall concept of the meeting, the organization of the meeting, how the program committee approaches the meeting, just the style of the meeting, was created early. If you went to the first OHBM and you went to the current OHBM you would see strong similarities. The intention of the meeting and what is attempted, what's being done, is giving you the most cutting edge applications, the most cutting edge methods, trying to span cognitive and clinical neuroscience, that was present from day one. And that really creates an outstanding feel, flavor and content for this meeting. And so I'm just as happy with what has persisted as with what has evolved.
So what do you see as the future for neuroimaging?
PF: I mentioned earlier that I think neuroimaging has a huge role in treatment development. I expect that to continue. Another direction that I really think the field will have to push on and be open and kind of aggressive in bringing people in, is pushing down into the basic neurobiological mechanisms underlying the imaging signals that we have. And so collaborating with people working in animal models, and working with techniques that are more invasive than our techniques, such as optical techniques that we need to do. And we need to really encourage scientists working at that level to bring their work to OHBM. So I think those are the directions that are important for us to go.
Professor Fox it's been great. Thank you very much.
PF: Yeah. Thank you.
By Shruti Vij & Nils Muhlert
Functional MRI has been in use for over 25 years. Despite providing us with a breadth of methods developments and exciting findings about how the brain works, there has been a dearth of clinical applications. The OHBM Alpine Chapter has been keenly focussed on ways in which we can translate fMRI and other neuroimaging modalities to the clinic. Founded in 2014, the Alpine Chapter has provide a forum for like-minded brain mappers, both basic scientists and clinicians, throughout Austria, Switzerland, Germany and neighbouring countries to discuss new methods, new projects and to collaborate on programs of research. Here, Shruti Vij spoke to the past and current Chairs, Roland Beisteiner and Christoph Stippich respectively, to find out how the Chapter has developed and its directions for growth.
by Aina Puce & Bernard Mazoyer, OHBM Program Committee
In the late 1980’s, neuroimagers were a ragged band of multi-disciplinary researchers with no real home. In search of their scientific interests, they attended meetings covering radiology, nuclear medicine, neurophysiology, engineering, image processing and computer science. Starting in 1992, a small group of internationally well-known neuroimagers had attended a series of 8 annual BrainMap Workshops in San Antonio devoted to promoting the development of standard space as an analysis and reporting standard, with discussions also related to development of open-access neuroimaging archives. These meetings were organized by Peter Fox [USA] and funded by NIH [USA] R13 awards. After one such meeting in 1994, the crying need for a home of their own was the central issue discussed around a table of 25 scientists who became the driving force behind what would become OHBM. At the meeting, Dr. Bernard Mazoyer [France] volunteered to host a first launch of such an international conference, with a second meeting in Boston, USA to be held in 1996 and organized by Jack Belliveau and Bruce Rosen. The rest is history.
Mazoyer and colleagues Per Roland [Sweden] and Rudiger Seitz [Germany] hosted the meeting in Paris, France in June 1995. Incredibly 820 attendees came to the first meeting – greatly exceeding the organizers’ expectations! The meeting consisted of talks and poster sessions. The inaugural Talairach keynote lecture was given by Dr Jean Talairach – the French neurosurgeon who pioneered the use of a standardized stereotactic grid system for neurosurgery.
OHBM officially became an Organization in 1997 with ratified by-laws and the potential to elect office bearers [OHBM Council, OHBM Program Committee]. Indeed, many of the first OHBM Council Chairs were scientists who had participated in the original BrainMap Workshops. Over the past 25 years, the OHBM has taken on multiple new responsibilities, effectively functioning as a Society while retaining its original name. Therefore, it finally became a Society in 2018 – ratified by the OHBM membership at the annual meeting in Singapore – allowing the official sanctioning of year-round activities of ‘Chapters’ in different international communities.
In the mid-1990s, the neuroimaging zeitgeist was such that Positron Emission Tomography [PET] was an established neuroimaging modality, with activation studies of cerebral blood flow and glucose metabolism being performed in both humans and animals. The requirement of a nearby cyclotron meant that PET was largely confined to the largest institutions with clinical and/or research imaging centers. The 1995 Paris neuroimaging meeting was actually a satellite meeting for the Brain PET meeting in Cologne. At the time, only a few groups were performing functional magnetic resonance imaging [fMRI] studies. Analysis software was vestigial – the first generation of Statistical Parametric Mapping [SPM] software for PET data analysis was available – with the first methods papers being published by Karl Friston in 1990/1991 [see https://www.fil.ion.ucl.ac.uk/spm/doc/history.html]. Software packages for fMRI were being developed e.g. Analysis of Functional Images [AFNI] by Bob Cox at the Medical College of Wisconsin began in 1994 [see https://afni.nimh.nih.gov/afni_history], and SPM for fMRI came about from a number of attempts at implementing data analysis from Friston’s group in 1995. Magnetoencephalography [MEG] and electroencephalography [EEG] were already established neurophysiological methods in the mid-1990s, with their own specialized smaller scientific meetings. High-density MEG/EEG recordings were still not that common. Most of the book of 404 abstracts for the Paris meeting was devoted to brain activation studies, with 27% devoted to fMRI methods, 6% to the nature of the BOLD response, and 9% to MEG-EEG.
The OHBM has been a hub for the neuroimaging community, gradually incorporating additional MRI-based methods such as quantification of grey matter and white matter, formulation of anatomical atlases. Efforts to encourage the involvement of more basic and clinical researchers performing MEG and EEG studies are also being made. Right from the outset, OHBM has recognized the importance of having an educational program [initially organized by Peter Bandettini from 1998-2000], with weekend education sessions being added as early as 1998, and morning education sessions commencing in 2000 for OHBM in San Antonio. In 2000, Peter Fox, obtained a 5-year NIH R13 grant whose $25,000/year proceeds were devoted to 25 travel awards for OHBM trainees, based on abstracts with the highest peer-reviews. This grant was extremely helpful in kickstarting engagement from new scientists just starting out in functional neuroimaging and launched the OHBM Trainee Travel Award Program. In 2005, Peter Fox succeeded in obtaining a renewal for this 5-year grant with an increased budget of $50,000/year. After 10 years of NIH travel awards to the tune of $750,000 and increasing attendances at OHBM meetings, OHBM had enough financial reserve to continue the travel award program and the NIH-grant was allowed to lapse. Additionally, the neuroimaging journals NeuroImage and Human Brain Mapping were spawned for this community. NeuroImage was an existing Elsevier journal that was transformed to be a forum for [mainly human] PET and fMRI studies by Editors Art Toga, Richard Frackowiak, and John Mazziotta , whereas Human Brain Mapping was started de novo by Peter Fox for Wiley . Both Human Brain Mapping and NeuroImage were the source of OHBM abstract books for the first few years. Additional journals for neuroimaging and related disciplines have been added since those times e.g. Brain Connectivity [Christopher Pawela & Bharat Biswal] and Brain Structure and Function [Laszlo Zaborszky & Karl Zilles]. All of these senior scientists have been active in the OHBM community. Indeed, Editors for all of these journals continue to come largely from the OHBM community. In addition to journal-based activity, early efforts to standardize data formats and data sharing were occurring at the time. For example, in the early ‘90s, workshops for the International Consortium on Brain Mapping [beginning in 1992 and co-ordinated by John Mazziotta] and for the European Computerized Human Brain Database [beginning in 1994 and co-ordinated by Per Roland] were run in addition to the San Antonio BrainMap Workshops.
A set of awards recognize the achievements of OHBM Members. An award devoted to recognizing excellence in early career neuroimagers began as the Wiley Young Investigator Award [first awarded to Karl Friston in 1996]. In 2016, it became the OHBM Early Career Investigator Award. Other OHBM awards include the Education in Neuroimaging Award [first awarded to JB Poline in 2013], the Replication Award [first awarded to Wouter Boekel in 2017]. In 2014 OHBM awarded the Glass Brain Award to Karl Zilles – created to recognize the lifetime achievements of scientists in the field of human neuroimaging. From 2005, OHBM has also been very fortunate to have the Editors-in-Chief of the journals Human Brain Mapping and NeuroImage also announce their Editor’s Choice Award for the best paper in their respective journals at the opening ceremony of each OHBM meeting.
OHBM is a Society that is known to be inclusive and to change with the times. Its Council and Scientific Program Committee have existed from the early years . In response to current issues, committees such as a Diversity & Gender Committee, a Communications Committee, and the OHBM Publishing Initiatives Committee, among others, have been more recently constituted. The Communication committee has its hands full improving the OHBM website - providing ‘on demand’ education program  consisting of resources such as videoed lectures from previous meetings and educational materials, running a blog , among other things. OHBM also is an inclusive Society as indicated by its Code of Conduct Statement [see https://www.humanbrainmapping.org/i4a/pages/index.cfm?pageid=3912 ]. Three special interest groups [SIGs] devoted to Students & Post-docs, Open Science and Brain-Art are also now part of OHBM. As OHBM has grown, a professional secretariat soon become necessary, which has helped to preserve institutional knowledge and to increase professionalism. Initially, in the early 2000s Lori Anderson and her team [from a US-based company called L&L] fulfilled that role. Nowadays these greatly expanded functions are fulfilled by the OHBM Executive Office, based in Minneapolis, USA.
Over the years the OHBM Annual Scientific Meeting has alternated between the European, Asian and North American continents, with occasional detours to places such as Australia. Attendee numbers have steadily grown over the years – first surpassing 3000 in 2005 when the meeting was held in Florence, Italy. Indeed, the 25th anniversary of scientific neuroimaging meeting in Rome, Italy promises to be a bumper year – with over 3700 abstract submissions and attendee numbers expected to be around 4000! This year’s meeting will be an exciting one – not only for the new science being presented, but also for the nostalgic look back at the previous 25 years of meetings being prepared by members of OHBMs Scientific Advisory Board – individuals who have been part of the history of OHBM.
We look forward to seeing you at OHBM in Rome on June 9-13, 2019!
By Nils Muhlert
Resting-state fMRI has seen increasing attention over the last decade. The majority of these studies have focussed on static resting state networks, often considering the spatial topography or extent of components. A number of researchers are however considering how these networks change over time - dynamic changes - and what these temporal shifts in networks tell us about cognition and behaviour. Catie Chang, an assistant professor of computer science and electrical engineering at Vanderbilt University, has focussed on this question since her PhD - with her work exploiting signal analysis techniques to understand what drives and affects these dynamic changes in fMRI signals and networks.
As our first keynote interview for OHBM 2019, we found out about how Catie honed her craft, what we can gain from investigating these signals, and her experiences of life as a new PI.
Nils Muhlert (NM): I'm here today with Catie Chang, one of our keynote speakers at OHBM2019. Thanks Catie for joining us.
Catie Chang (CC): Thank you so much.
NM: First, can you tell us a bit about your background? What turned your work towards functional connectivity?
CC: My first experience in a human neuroimaging lab was at Stanford, working with Vinod Menon and Michael Greicius. They were pioneering ideas about brain networks, dynamics, resting state connectivity, and applications to neurological and neuropsychiatric disorders back around 2005, 2006 and earlier. I was very influenced by their perspectives and found this an interesting and exciting research field. That got me considering many ideas about brain connectivity, about resting state.
Then, I went to work with Gary Glover for my PhD in the Radiological Sciences lab at Stanford, and the emphasis in that lab and environment was on the physiology and the physics of imaging signals. This led me to questions like, what is the physiological basis of the signal changes that we're measuring? Can we better acquire these signals, improve our analysis and post-processing? And can we combine signals from different modalities to improve our interpretations? So I really found my home at the intersection of these different worlds.
NM: Did you spend a lot of time looking at noise? Trying to work out where the signal was actually coming from?
CC: I started out looking at, I guess what we kind of consider noise, which is the influence of systemic physiology on fMRI signals. So when you take a deep breath, for example, this induces a very large BOLD signal change.
But we were interested in it not only from the perspective of how this introduces noise into our signals, but also how it can introduce new information into the data. The first research question I started working on with Gary was, how can we use the fact that there's this large, systemic influence on the BOLD signal to calibrate for hemodynamic timing differences between different regions that may not be related to underlying neural activity? Can we use a breath holding task, and if we find timing delays across the brain in the breath-holding BOLD response, can this help us pinpoint fMRI timing differences between brain regions that are not neural in origin, but may be more vascular, hemodynamic related.
Throughout, my work has been looking at two sides of the same coin - noise on the one side and trying to clean up the data, but on the other side, looking at the discarded component, which is often very valuable for a different purpose. And if we can disentangle these influences on the signal, then we have the power to use those components in different ways.
NM: You also mentioned a few people there. So Mike Greicius, who we've interviewed before for the blog - he came across as thoughtful. Do you think that's influenced how you supervise your own students, now that you're building your own lab in Vanderbilt?
CC: Yeah, Mike was a really influential mentor to me. He was always giving me great advice about for instance, not getting too caught up in certain details, instead seeing the big picture and the more interesting questions.
To be honest, I am very detail oriented, so I keep this advice in mind when I mentor students. I try to be very involved in the details, but on the other hand, I also try to step back and say, are we providing an important message? Is this research going in the right direction? And so having many complementary mentoring styles throughout my work from Mike Greicius, Gary Glover, and Jeff Duyn and David Leopold, who I worked with as a postdoc -- they've all shown me very different but very valuable perspectives.
NM: How have you found that process - moving to becoming a PI, having your own lab?
CC: I really love it. There's so many interesting things that come with starting your own lab: working with my students, and the collaborators here at Vanderbilt, that's one of the best parts of being here. They're just brilliant, great collaborators, great colleagues. But it's also been very busy. So I'm not even one year in to this new position, and the past year has been a blur. Many new things to get used to in this environment. For example I've taken on some teaching. And I've discovered I really like teaching.
NM: Don't let anyone know - they'll pull you in for loads of it!
One thing that's come up in your work is the idea that you could use the dynamics of functional connectivity as a biomarker for cognitive and clinical studies, and clinical trials. Do you think this is feasible over the next 5-10 years? Are there steps being made towards that? How's the validation process going?
CC: I think that looking at dynamics is very promising for studying cognitive and clinical questions. The idea here is: can we get more information from the signal if we open up the dimension of time, and aspects of the signal that may change over time? This notion opens the possibility that we can look at features of the data that reflect state changes and cognitive processes that may be really relevant markers of different disorders.
But there are still many challenges that we have to address at the same time as we do this exploratory research. It's hard to go from having a hypothesis about brain dynamics to knowing exactly what metrics and features of the signal we should isolate to test for these questions. We (as a field) are also working out how we carry out the statistical testing, for example, to see if “dynamics” is really the core element that's disrupted in a given disorder, or if, perhaps, some of those apparent signal dynamics are just an offshoot of some other, simpler phenomena. We're at least starting to dig into that. There's a lot of exciting progress being made by many research groups and it's interesting to see where that will go.
We also face a lot of challenges in the dynamics world, because fMRI has a low signal-to-noise ratio, with many different things that can cause fluctuations within a voxel other than neural activity. And so trying to interpret and clearly link the phenomena that we observe to a conclusion about brain function is challenging.
NM: So what would you say you're most proud of in your career? What kind of work, would you say, stands out?
CC: Whatever I can do that's helpful to researchers, I feel proud of. And so when people ask for code to isolate physiological signals, for example, then I'm really happy I can share that. My deep interest is trying to understand signals and mine them for information. So I'm really excited about the work that goes toward resolving particular influences on the fMRI signal. For instance, a subject’s level of alertness is one factor that can change fMRI signals, but on the other hand, it's also something very interesting we can study in itself. Our recent work examines how we can detect natural changes in alertness from fMRI spatiotemporal dynamics, which I also find to be a fascinating direction.
NM: So this is maybe going back a little bit over what you've already said. But one issue that some people have been struggling with is about the underlying physiological basis of resting state functional connectivity networks. And people are starting to look at whether there are particularly high densities of neurotransmitter receptors within the hubs of these networks that might aid coordination of this activity. Do you think we're moving closer towards having that understanding of how these networks emerge?
CC: I think we're moving closer. I mean, it's hard for me to say how close we are -- but for example, many researchers are combining fMRI with other techniques to perturb neural activity in specific ways and understand how that impacts resting state networks, which I believe is an important direction. I think that a bridge between non-invasive human imaging and more invasive animal or patient studies is really helping to provide that link. In animals, of course, there are many more flexible manipulations that we can do to try to understand the precise impact of activating or inhibiting certain brain regions on large scale connectivity. And so that'll be really important to bridge these types of research.
NM:What can we expect from your lab over the coming years, then?
CC: One direction is that in Vanderbilt, there's close collaboration between engineering and the medical center. So I'm really excited about the collaborations that we're forming with the medical school as well as the imaging center here. And so I've started to work together with Vicky Morgan and Dario Englot here, for example, forming ideas for how we can use fMRI methods to understand epilepsy. They've been carrying out this type of research for a long time, and I'm really excited to be collaborating with them.
Another area that we're trying to push is carrying out multimodal studies to understand changes in alertness, and how that relates to changes that we see in fMRI signals. We're developing ways of performing more detailed characterization of the effects of these kinds of state changes on fMRI data.
We're collecting, I guess, “mega scans”, where we have fMRI together with EEG, eye tracking, cardiac and respiratory signals, and behavioral measures. So my subjects may not like me very much [laughs], and now we have so much data, all these different data types, how do we combine them? But the more information that we have, the more that we can start to piece together the puzzle of what moment-to-moment fMRI signal changes reflect, and the signatures of specific ongoing neural and physiological processes. We're asking whether we can better capture and understand that. If we can figure out ways to integrate these external measurements, which are all complementary measures of humans and what state they're in, then it'll be really exciting.
NM: And so last, can you give us some insight into what you'll be discussing in your keynote lecture?
CC: The main theme is along the lines of what we've been talking about - the more that we can understand and disentangle the sources of signal or network fluctuations, the more rich and clear information we can extract from our data. That can lead us to more sensitive biomarkers, and sensitive measures and inferences of neural activity from fMRI. And when we combine fMRI with other modalities, such as EEG, then it can help us draw that information out of the data.
NM: That sounds very comprehensive!
CC: I'm going to make it more specific, and I have this terrible habit of changing my talks the night before, so who knows [laughs].
NM: Thanks for joining us here and we look forward to your talk!
By Shruti Vij & Nils Muhlert
Peter Bandettini has been a key figure in neuroimaging for over 25 years. His career started with earnest, in a PhD working with James S. Hyde and R. Scott Hinks in Wisconsin, where he pioneered the development of functional MRI. Now at the NIH, Peter’s work has examined the sources of functional contrast and noise in BOLD, the temporal variability of resting-state fMRI and, more recently, layer-dependent activity in fMRI.
We found out about his history working alongside other founding members of OHBM, his advice for early career researchers and the unique challenges of working at the National Institutes of Health.
Shruti Vij (SV): I would like to start by asking you about your background and why and how you became interested in neuroimaging.
Peter Bandettini (PB): I started out as an undergraduate in physics at Marquette University. I was interested in the brain the entire time there. Even from high school, I remember reading a famous Scientific American article showing the first functional CT and PET scans. That was inspiring. I was always interested in the brain. At the same time, I wanted to study a “hard” science like physics. I thought that the integration between the two would be useful. Although I wasn't quite sure what I wanted to do. I could have been an engineer, I was potentially interested in medicine, but then I decided to go into grad school in a biophysics department, and luckily, it led me to brain function research.
SV: Great! What do you see happening with neuroimaging in your country? What kinds of research and what kinds of advances?
PB: I think that the Brain Initiative is the latest big thing in the United States. In Europe, there’s the Human Brain Project and I think together, they are really trying to step back and understand the brain at a more fundamental, a more mechanistic level. Right now, the exciting thing is that both programs are focusing on methods. There's methods development that is leading into more brain modeling, and then there’s the development of a more integrated structure for sharing data. In fMRI there's a lot of work on things like data pooling, machine learning, things like extracting information about individual subjects, as opposed to group studies. Actually trying to get more clinical traction from the data.
SV: Awesome. What research or other contributions are you most proud of in your career?
PB: I'm really proud of a lot of things, many of them from really good collaborations. I was lucky enough to be in the right place at the right time at the biophysics department at the Medical College of Wisconsin, to be working with Eric Wong who was developing the hardware for echo planar imaging. That was the right place at the right time to get going quickly as a graduate student in helping to start functional MRI.
I was a grad school student, and I submitted my first first-authored paper ever - and on fMRI - which happened by luck to be the first paper ever published on fMRI (by a week) - to MRM as a communication. They published it quickly.And so I'm very proud of that. That said, our group was, by most accounts, the third group that successfully performed fMRI - behind MGH and Minnesota. We might have been the first group to perform fMRI of motor cortex activation - as shown in this paper.
I was also part of pushing the initial use of correlation analysis for fMRI data. That was our second paper. I’ve been told that, in that paper was the very first use of the term “FMRI”.It’s important to emphasize that neither of those would have been possible without the incredibly rich environment of colleagues and resources. In particular, Eric Wong, a fellow grad student, was most fundamentally important.
I'm very proud of pushing the concept of embedded contrast in fMRI data like multi-echo or simultaneous spin-echo, gradient-echo, pushing the temporal and spatial resolution. But right now, I'm proud of being able to lead and direct a group. They do all the work, I now feel more like an enabler of everything from multivariate assessment, to pattern effect assessment to resting state. I'm very proud of helping my grad students achieve great things as well.
Recently I've been going to very high resolution and looking at layer-dependent fMRI, so you can actually start to untangle input and output connections from layer fMRI activation. So I'm proud of being able to integrate everything from the acquisition side with physics, to the basic neuroscience and also the data analysis as well. So to try to bring it all together.
SV: Great! You played a part in the creation of OHBM. What was that like?
PB: So that was really exciting. It's interesting that it first started out even before OHBM. Peter Fox had a regular meeting. And I think a number of people came together and thought, this can be bigger. It was exciting to be part of that process at the very beginning. I remember trying to get everything organized and trying to figure out: “okay, so we're going to have a council and we're going to have a program committee, and this is what we’re going to do.”
Another memory is back in 2001, in Brighton, we hired a meeting management company called L&L, which we still use today (under a different name). It was a big decision then. And we were like, yeah, I think we should go with them. And it's amazing what impact it had. Also, back when the meeting was in San Antonio, one of the early OHBMs, around 2000, I remember sitting in council, and we had the idea of having a separate day for education courses, and having education courses in the morning as parallel sessions. That was very exciting. I was the chair of the education committee at the time, and back then I had to organize every single parallel morning session for two years - that was really challenging! Ed Bullmore took over after that, so there are a lot of good memories of that.
I knew OHBM would grow. At the time, it seemed to be just another meeting - as I was young and had limited perspective on these things. At the same time, we all knew there was always something special about it. OHBM seemed united around the methods, and as opposed to just a cognitive neuroscience meeting or neuroscience or ISMRM, It’s about the brain imaging methods. And that was effective for bringing the community together. And there are many people who've attended every single year, and after about five years, everyone ended up knowing everyone else. And so it's become this really large, nice, extended sort of family in some regard where we all kind of know each other and know what everybody does. And that’s a good feeling.
SV: And what did you imagine it would be like?
PB: I thought it would likely go more towards the neuroscience direction. I didn't think it would go in the direction of the methods, continually improving. I always thought the methods would get better but now they're getting qualitatively much better, and they're becoming integrated. And I didn't imagine it would maintain the same cohesion. I mean, before it was small and cohesive. And it's somehow both grown and scaled but that cohesiveness has scaled with the times as well, which I think is really unique. So I didn't imagine that.
I also didn't quite imagine that it would be as respected. It was always a grassroots movement of a meeting, but now it's a really respected meeting. And people look at it as their main meeting that they go to. I think that respect and that reputation is still growing. So that's been a nice thing that I didn't really completely expect.
SV: What have you found the most rewarding in your involvement with OHBM?
PB: The most rewarding thing has been that we really did get to invent it from the ground up. That was rewarding, to be able to figure things out as you're going along. But it's also rewarding, that it’s been a catalyst for so many people: to make science more than just doing the science asking the questions, presenting your paper. Instead it was about having a real community, knowing the people involved in the field and looking forward to going to the meeting, not just to give a talk and exchange information, but to get a better appreciation of what's going on in other people's groups and actually catch up with old friends. That I find really satisfying.
SV: What advice would you give to young investigators?
PB: I think people need to be a little more bold, because even with the established literature, there's a lot of room for complementary information. And I wouldn't be afraid to have data that contradicts results, because everything is relatively new. And we're still trying to figure out what's going on and how to interpret things.
Another thing is to try to always think in an integrated way. Never be afraid of not being an expert. I'm coming from a physics background. Many of the physicists who I work with, sometimes aren't just physicists. I think that the people who really become successful are those who are not afraid to think outside their domain and gain confidence. Right now, I feel more like a neuroscientist than a physicist, even though my background, and training and PhD, were in physics departments. It's interesting how the tendency is to lock in and say, I'm a physicist, but for the last 20 years, I've been doing more neuroscience, processing and physiology. So I try to think of myself more in those domains as I expand out. So never limit yourself. That would be another main piece of advice.
SV: Until recently you were the editor in chief of Neuroimage. How was that experience? And what would you tell trainees who are looking into going into editorial jobs?
PB: When I started this work, I wasn't thinking of getting into an editorial job. I said yes to everything. I think that we're all really lucky to be in a situation like this, where there's so many opportunities. So I said, “Yes!” And I never took that for granted. I still try not to take it for granted. So I said yes to always reviewing papers and I would always do the best I can reviewing them. Then my number of reviews caught editor's attention. They said, “Oh, he says yes to all our papers, and he really likes to review papers, let's make him an editor.” I enjoyed that. And as you get older, it's probably good to say no, to just manage your time. But still, I still haven't figured that out to be honest. So what I would tell people is, when I agree to review papers, I look at it less like, "Oh, it's a duty i'm doing". Instead I feel like I would read the paper anyway so I might as well review it.
So my advice to people is that getting into editorial work takes a certain mindset, it takes time, it takes a certain amount of confidence in making decisions about papers, but you learn about what's a good paper, what's a bad paper. You learn all the processes of sending back feedback and doing reviews and what a good review actually means. So that's helpful and it helps you write your own paper.
The whole editing process has helped me so much in terms of my own writing. And it broadened my horizons a lot, especially being editor in chief. I got 10 to 20 papers in a day that I had to assess quickly and then send out to the senior editors. And so that gives you a very broad perspective and a very up to date perspective on the field because you're given the latest things. It's a good four to six months before they're published. So that's what you gain. Maybe some advice to people, I think I would just say “yes” to review as many papers as possible.
I think it's also important to not just find flaws in papers. Finding flaws is good, but it's also too easy to dismiss papers as bad just because it has this, this and this flaw. I would recommend that people be more accepting. The goal is to help the author get it published, it's not to stop them. Usually a reviewer should, as much as possible, look at themselves as trying to help the process if it's above a threshold, as opposed to trying to stop the paper from being published.
SV: Over all these years, has it been hard to maintain a work-life balance?
PB: Yeah! [laughs] There's certain things though, like I have to go running every day or do 30 minutes of aerobic exercise, but that's like an addiction anyway. So that's easy to maintain that balance.
And I'm really lucky to have a great job at the NIH, where I can turn it off when I need to. But to have a good work-life balance, you do need to develop a certain amount of discipline. It's so easy just to have your work spill over. And when I was a graduate student, I had no balance. I lost track of what day of the week it was. I'd be working all night, working whenever. But now I have a family, three boys and a wife, and sometimes there's a need to compromise on both ends. You know, even going to a meeting like this, it's a week and a half away and all kinds of craziness is happening at home. But as long as you have a good schedule, and compartmentalize enough, then it's good.
I think that's the key, to be disciplined and to do certain things no matter what. I know I'm going to go running, to try to get a certain amount of sleep. And I know that I'm going to try to spend this much time with my family. I don't meet these goals all the time but I really try to work when I’m at work and focus on my family when I’m at home. The more you can compartmentalise in a disciplined way, the easier it is to achieve this balance. But that said, I fail all the time with this.
SV: So you've been at the NIH for a while now. Are there specific challenges at the NIH that other people would not be aware of?
PB: NIH is a unique place. It's so good in the sense that all the researchers there have a certain budget and don't have to write grants - they have incredible resources. At the same time you're working for the government, which is different, in the sense that there's certain rules that apply to government employees that I'm still learning - even after 20 years. That limits you in certain things. If you're a PI in the extramural world, I always think that you can write grants and be an entrepreneur, because you can build an empire, depending on how successful you are at getting grants. You can collaborate with industry, you could have your startup company, whatever. At the NIH, you're more confined. It frees you to do other things, but there are conflicts of interest - like for the flight here, I had to pick a flight on a government contract carrier that I didn't have a frequent flyer program on or whatever. You have to because you're working for the government.
And also you have your budget. And in the way you get approval, you're assessed scientifically every four years. But it's not like you have a grant review. So there's less uncertainty. And it's potentially easy to coast if you want to, but luckily there's a good enough environment, really motivated professors and people. I've been so well supported at the NIH; all the good stuff outweighs the quirky, government stuff.
SV: Thank you so much, Peter, for this oral history. I'm sure everybody will really enjoy hearing about it. Thank you.
PB: Well, thank you.
By Claude Bajada, Simon M. Hofmann and Ilona Lipp
Edited by: Thomas Yeo and Lisa Nickerson
Machine learning, deep learning and artificial intelligence are terms that currently appear everywhere; in the media, in job adverts… and at neuroimaging conferences. Machine learning is often portrayed as a mystical black box that will either solve all our problems in the future or replace us in our jobs. In this blog post, we discuss what the term machine learning actually means, what methods it encompasses, and how these methods can be applied to brain imaging analysis. Doing this, we refer to the OHBM OnDemand material, which contains some great videos explaining machine learning methodology and we provide examples for how it has been used in a variety of applications. If you are curious about machine learning tools, but are not really sure whether you want to jump on the bandwagon, then we hope that this post is right for you and will help you get started.
What is machine learning?
Machine learning is a broad term that goes beyond deep learning and incorporates many other methods that are discussed in this post. Many of these methods you may already be familiar with and have heard about in the context of classical statistics, such as linear regression. While machine learning ‘is built on the foundations of statistics and has absorbed much of its philosophy and many of its techniques over the years’ (Max Welling, 2015), a main focus of machine learning lies in generalization, i.e. finding patterns in data by training a computational model such that it can predict unseen data of the same or similar nature. Here, a balance between “overfitting” (fitting a model that is too complex and will only work well for the data used to train it) and “underfitting” (fitting a model that is too simple and works poorly even with the data used to train it) needs to be found, aiming at high sensitivity and specificity when applied to new cases. This is generally attempted by splitting the data into various sets, training model parameters on one set, choosing the best model by evaluating it in another set, and testing it in yet another set. Sometimes, the term “statistical learning” is used for machine learning methods that have their foundation in statistics. Introduction to statistical learning and The Elements of statistical learning are two great textbooks introducing some important concepts.
What about Artificial Intelligence? Is that something different?
Artificial Intelligence is a high-level, conceptual term that describes the movement to emulate “natural intelligence” in machines. Machine learning can be thought of as one set of statistical tools that can make machines more “intelligent”.
What types of methods are there?
The most popular machine learning techniques applied to neuroimaging can be split into two general groups: supervised and unsupervised learning. Supervised learning requires labelled data (e.g., data that has been labelled, classified, or categorized), whilst unsupervised methods detect patterns in unlabelled data. Different machine learning methods also differ with regard to their complexity. Both types of machine learning approaches can range from fairly simple linear models to much more complicated, non-linear, algorithms. The more complex the models get, the more computational power is required.
Even though machine learning has been around for a long time, it has experienced a recent boom. In his OHBM OnDemand video, Vince Calhoun (6:30 min) explains why: it is not only because more and more data are available, but also because there has been an immense improvement in computational power (note that training neural networks can sometimes still take weeks!) and in better algorithms being developed and implemented in open source tools.
Below we will discuss some important methods that deploy both supervised and unsupervised learning. We will also discuss some approaches that are unique to neuroimaging (such as multivoxel pattern analysis).
As described by Christophe Phillips in the OHBM 2017 course on pattern recognition (4:34 min), the idea of supervised learning is to train a machine to find a mapping between observed data, such as your fMRI images, and an explanatory variable, which could be a disease label or a cognitive score. We can then find new, unlabeled subjects and predict the disease label or cognitive score.
Christophe further explains (7:10 min) that supervised machine learning problems can be further subdivided into discrete classification predictions and continuous, or regression, predictions. Either way, supervised learning (10:50 min) ultimately relies on a mapping function between input and target variables, the specification of the shape of this function and the optimization of its parameters. The following are some examples of various commonly used algorithms:
Most of you are familiar with linear regression as a classical statistical technique. However, this old staple has refashioned itself as a supervised learning technique. We can think of regression as a predictive technique that uses one (or many) features in order to predict a response as one continuous value (7:35 min). The main difference between using regression as a standard statistical tool and as a machine learning tool is that in machine learning we test the predictive power of the linear model on unseen data that did not contribute to the training of the model.
The idea behind logistic regression is, at its root, exactly the same as linear regression. The only difference is the function that is fitted to the data. While in linear regression we fit a line (or some generalisation of it in n dimensions – e.g, a plane or a hyperplane), in logistic regression we fit a logistic function. The logistic function is that “S-shaped” curve that often pops up in many biological sciences. The logistic function has the very nice property of being bounded (often these boundaries are set to 0 and 1) and hence can be used to express a probability. By having a cut-off, usually half way, we can use logistic regression to categorise our sample, for example into patients and controls.
Support Vector Machines (SVMs):
Support vector machines (SVM) are a type of classification algorithms, where the aim is to draw a decision (or classification) boundary between sets of data points, so as to maximize the “separation” (or margin) between the sets. While this sounds fairly straightforward, it is often the case that the data points are not easily separable by a line or plane, for instance, if two circles are embedded into one another. Kernel SVMs use “kernels” to transform the data into an alternative space where it might become much easier to separate the two instances. Christophe describes kernels and SVMs (from 17:00 min) in his introductory lecture. There are additional parameters such as regularisation parameters, gamma and the margin, that are important to define how well the line separates the training data. For a more generic discussion of SVMs, this medium post does a good job at explaining the basics.
Deep learning is one of the most talked about classes of machine learning algorithms and the one that most excites the public’s mind. Despite all the hype, deep learning models are often treated as a black box, since their input-output-mapping is both analytically and intuitively hard to grasp. In Vince Calhoun’s OHBM educational lecture on deep learning approaches applied to neuroimaging, he explains that the foundation of deep learning lies in artificial neural networks. In fact, despite experiencing a boom in popularity in recent years, neural network modelling dates back to the 1950s when there was a lot of interest in creating a mathematical model of a biological neuron (This paper by Hassabis et al. (2017) provides a stimulating discussion on the relationship between neuroscience and artificial intelligence). This neuronal model became known as a perceptron. The most basic type of network is the multilayer perceptron (MLP), with artificial neurons (perceptrons) organised in hierarchical layers. The input to the network is propagated layer-by-layer, first through activation-functions in each node, and then through connections (weights) to the successive layer. The “deep” part of deep learning refers to the number of multiple hidden layers, i.e. the layers between the input and output of the network. In recent years, computational advances have allowed the training of deeper and deeper networks. Some types of neural networks that Vince describes are Restricted Boltzmann Machines (7:10 min), deep belief networks (8:20 min), convolutional neural networks (16:35 min) and others.
As with other supervised learning algorithms, deep learning needs a training set and a test set. Furthermore, the more layers you have, the more (labeled) data and computational resources you usually need. In fact, deep learning increased in popularity once computing power increased to the point that deep networks were feasible, particularly after the availability of graphical processing units (GPUs), which are hardware chips originally developed for accelerated processing of digital videos and graphic rendering (3:10 min).
Multi-voxel pattern analysis (MVPA): A common application of ML in brain imaging?
In the classical analysis of structural and functional MRI, i.e. the application of a general linear model (GLM), each voxel is considered separately. Due to its linear equations, the approach is mathematically neat and tractable, however, this “massively univariate” approach disregards the interdependencies between multiple voxels (see Robert Cox’s talk about fMRI analysis methods at 4:16 min, Mike Pratt’s talk at 0:35 min). In light of dynamic brain processes that engage entire networks of the brain, the independence assumption of single voxels is controversial. In order to address this issue, a more recent class of statistical models, known as multi-voxel pattern analysis (MVPA), has been introduced to account for the joint contribution or ”combinatorial code“ of multiple voxels across the brain to the phenomenon of interest (see Janaina Mourão-Miranda’s talk at 6:08 min). That is, MVPA describes a class of pattern-recognition techniques, which are presented in Mike Pratt’s talk on MVPA (3:33 min), and in a session devoted to MVPA at OHMB 2017 (the corresponding videos can be found here).
MVPA draws from algorithmic strategies commonly used in machine learning. First, the data are split into a training set and test set. Then the classifier of choice (e.g., SVM) is trained on the former to discriminate various multi-voxel patterns corresponding to the experimental conditions, and validated on the latter. Validation is done by using the trained model to predict the conditions in the test set based on the multi-voxel input, which is often referred to as decoding (see Bertrand Thirion from 5:38 min, and Mike Pratt’s talk at 8:04 min). In decoding, we try to predict from multi-scale neural processes its representational content, such as percepts or cognitive states, mostly induced by experimental conditions (Pratt’s talk at 11:55 min). Classifiers can be linear or non-linear in nature, each having their own limitations. Linear classifiers (e.g., linear discriminant analysis, LDA) are considered easier to train and to interpret, however, their sensitivity depends on the individual contribution of each voxel in the observed pattern (see Jo Etzel’s talk at 18:00 min). Whereas non-linear classifiers (e.g., artificial neural networks, see Vince Calhoun’s talk) are able to find more complex relationships between patterns of voxels, they require training on large datasets.
The term MVPA was coined by Norman, Polyn, Detre, and Haxby (2006), who introduced it within the framework of fMRI analysis. However, considering a broader definition of the term, most of the methods that MVPA encompases are not restricted to fMRI and can be equally applied to structural imaging (e.g., Zhang et al., 2018; or Cole et al., 2017; and see James Cole’s talk at OHBM 2017).
In supervised learning, in addition to the input data (for example, fMRI images), we also need the ‘ground-truth’ output, which may be labels (e.g. healthy vs condition) or scores (some sort of cognitive or behavioural scores). However, frequently we either do not have appropriate labels, or the labels that we do have are unreliable, for example, in psychiatric imaging, as explained by Verena Kebets in her video. In this case, unsupervised machine learning methods open new doors.
In the neuroimaging community, the unsupervised machine learning technique of clustering is best known by its application to estimating brain parcellations. Brain parcellation is not a new problem and neither is it one that necessarily involves machine learning. All neuroimagers have heard of the 19th century neuroanatomist Korbinian Brodmann who labeled brain regions according to their cytoarchitecture -- the original brain mapping! As Simon Eickhoff explains in last year’s keynote, cytoarchitecture is not the only feature by which to parcellate the brain; there are others, such as receptor architecture, cortical myelin structure, and connectivity structure.
Unsupervised clustering methods are ideal for data with known differences based on features of interest where we want to automatically group brain regions according to these features. The simplest, and probably most widely used technique available, is k-means clustering. In neuroimaging, this is done by creating a feature vector per voxel in a region of interest, for example, structural or functional connectivity information. These voxels can now be thought of as points in an n-dimensional feature space. The k-means algorithm then attempts to maximize within-group similarity. Unfortunately, k-means clustering requires an a priori knowledge of the amount of groupings (k) one is interested in (although there are some iterative techniques to try to establish the number of k).
Other approaches to clustering, such as hierarchical clustering or spectral clustering, have the same basic idea of splitting up data (in this case brain voxels) into discrete groups, or parcels, but have slightly different assumptions or tricks. For example, hierarchical clustering assumes that the data have a hierarchical structure and so you could split the brain into two groups, each of which can be split into another two groups, until we reach the level of individual voxels. Or you could start from individual voxels and work your way up. On the other hand, spectral clustering has an additional step (the spectral transformation), which allows you to disregard weak similarity. Sarah Genon, in her educational course lecture, describes how to perform such analyses using diffusion MRI data.
Laplacian EigenMaps / Diffusion Embedding:
Sometimes you may not be interested in grouping voxels into a fixed number of parcels, but rather explore the relationship of voxels in a region of interest based on a feature of interest. In his educational talk, Daniel Margulies describes techniques that can be used to investigate the connectopies, or connectivity maps, of the brain. The initial approach is similar to the one described above, where you create a feature vector for every voxel in the brain. These features are then compared to each other using a measure of similarity to create a similarity, or affinity, matrix. This matrix is then decomposed and new vectors are obtained which describe the principal gradients of similarity across a region of interest, or indeed the whole brain. Daniel’s keynote describes how such types of analyses can be used to elucidate topographic principles of macroscale cortical connectivity.
Associative models, such as partial least squares (PLS) or canonical correlation analysis (CCA), are not exactly supervised or unsupervised. In supervised learning we generally have a multivariate input (e.g. brain images) and a univariate output (labels). In unsupervised learning, we only have one set of multivariate input data, such as the connectivity information for brain parcellation. In PLS or CCA, we want to discover relationships (associations) between two sets of multivariate inputs (e.g., brain images and behavioral/clinical scores).
As Janaina Mourao-Miranda explains in her video (2:25 min), psychiatric conditions often have unreliable labels. To deal with this, she uses associative models (e.g., PLS), trying to find a linear combination of neuroimaging predictors that are most strongly associated with a linear combination of multivariate clinical and behavioural data. This provides a data-driven way to generate summary labels that can possibly shed new light on clinical conditions.
It is possible to do significance testing on associative models to make inferences. Valeria Kebets describes (11:20 min) how to perform permutation tests in order to determine which components are significant, how to determine whether components are expressed differently across groups, and, finally, which variables drive the extracted components. In her video, Janaina also goes into the details about how her group applies a multiple hold-out validation framework in partial least squares analysis (16:50 min).
What do I need to consider when using machine learning tools for brain imaging analysis?
As explored in the previous paragraphs, machine learning techniques open many doors for brain imaging. They can help make predictions that depend on complex interactions, help find patterns in our data that we have been previously unaware of, and also automate time consuming manual tasks, such as segmentations (e.g. see Pim Moeskop’s video). However, there are also pitfalls that must be considered. First, the more complex and powerful machine learning techniques really need large datasets. In his video, Andrew Doyle (25:30 min) discusses how neuroimaging applications differ from classical image processing problems, with brain imaging data usually being very large and high-dimensional data, while sample sizes are comparatively small. For some applications (e.g. image segmentation or MVPA), smaller sample sizes may not be a big issue, but for others (such as patient classification) they may. A recent publication by Arbabshirani et al. (2018) explores the reason for why making individual predictions from brain imaging data is challenging. Another paper by Varoquaux (2018) focuses on the challenges with model cross validation on small sample sizes.
Of course, the noisier the data, the more data points are needed, and brain imaging data are renowned to be noisy. Additionally, if no reliable labels can be provided, the best supervised learning algorithms will not be able to succeed. Another problem, in particular with the more complex methods such as deep learning, is the challenge of assessing how biologically meaningful the resulting models are. Recent efforts have gone into better understanding and evaluating what is actually happening in the deep layers (e.g. watch Alex Binder’s video). However, resulting models may not teach us anything about biological or pathological mechanisms, and they may actually represent biases that exist in our training data, limiting their generalisability to other data. For example, this year’s replication award was awarded to a study that showed lack of generalisability of some published models.
Until these issues are fully resolved by the community, as individual researchers the best we can do is to understand the algorithms we are using and their limitations. That way we can choose the most suitable techniques, and rigorously apply them on suitable sample sizes and avoid overfitting. Luckily, there is a vast amount of online resources on machine learning techniques, including textbooks (e.g. Bishop, 2006), Andrew Ng’s famous Coursera courses on machine learning and deep learning, and online blogs and forums. Numerous papers from the MRI community provide overviews of machine learning tools for neuroimaging, or more specific examples, such as how machine learning is shaping cognitive neuroimaging, and how to use machine learning classifiers for fMRI data. OHBM’s OnDemand has an extensive archive of videos from education courses and talks on machine learning applications for neuroimaging that we’ve included in this article and, we also expect many exciting new educational and symposium talks on the use of machine learning techniques in brain imaging at this year’s OHBM in Rome, so watch out for those, too!
Professor David Kennedy is a Professor of Psychiatry at the University of Massachusetts. He was a key contributor to the development of functional MRI and diffusion MRI, working in MGH during the late 80s and 90s. His current work reflects his interests in Neuroinformatics and data sharing - indeed he is a founding editor of the journal Neuroinformatics. We found out about his experiences with OHBM, and some of the deep and lasting friendships he made along the way.
By Kirstie Whitaker
Open science means different things to different people. It includes open data, open source code, preprints, preregistrations, and open access publications. Getting started with open science practices can be overwhelming, and there is considerable variability in their adoption across the OHBM community. I sat down with Tibor Auer to learn about the survey he has developed to capture different attitudes towards open science practices in order to better support everyone in doing the best research they can.
Hi Tibor, let’s get started with an easy question: who are you?
I am a Research Fellow in MRI at the Department of Psychology, Royal Holloway University of London, where I facilitate neuroimaging by contributing to methods innovation, as well as training and education. Neurofeedback is one of my main research interests, as it offers the opportunity to follow neural development during the training process, thus satisfying interest in both theory and application. I received my PhD in clinical neuroscience and implemented various neuroimaging techniques in a clinical environment. Then, I focused on the implementation and the optimization of fMRI-based neurofeedback, and investigated assumptions and mechanisms underlying a neurofeedback training.
Why do you care about open science?
I am probably not the only person who has found a cool paper, and tried out its methods on my own data…. and fabulously failed! There are so many steps to reproducing a paper. If you are lucky you can find the corresponding author’s current e-mail address. They may bother to reply to your questions. The authors need to be able to locate the corresponding version of their workflow. It has to be documented well enough that you can decipher the code and adapt it to your data. Those are a lot of “ifs” and most of us aren’t that lucky. The process isn’t transparent.
Transparency, defined as unambiguous description of the data and the approaches, is not only beneficial to reproducibility but also to productivity in the first place. Automated pipelines, such as automatic analysis, allow quick exploration of the analysis space. Once you set up the workflow on pilot data, it can be applied on the real data right away. The pipeline’s documentation, based on standards such as the Neuroimaging Data Model, ensures the longevity of the project by making transitions smoother for current and future users.
What is this survey and what does it cover?
I am conducting this survey to investigate the knowledge and adoption of open research practices, including sharing of data and materials, study preregistration and related activities. It has been largely inspired by a recent survey in Cardiff. I want to know how people think about Open Science practices, their influence (positive or negative) and how the perception of Open Science might depend on experience with actual solutions and tools. Probably the most important aspect is what people see as the greatest barriers to the uptake of open research practices, and whether/how they can be ameliorated by (local and global) training and support. The awareness of challenges and solutions might vary across career levels, fields, and sectors (e.g. public and private), and I would like to be able to capture this variance, because we can only achieve change if we understand and resolve our differences.
Why did you want to create the survey?
Open Science is not just a (better) way to do science. One day, hopefully, we can omit the ‘open’, because all science will be done this way. Getting to that point cannot be an authoritarian process. It can only happen based on consensus and as a bottom-up initiative. We must understand what Open Science means for each of us, what encourages or discourages us to be engaged with it, what kind of support may be the most effective. The survey itself may raise awareness and give some hints and ideas by mentioning specific solutions in the questions.
Who do you hope will fill out the survey?
My guess is that many responses will be from methods experts who already appreciate the benefits of Open Science practices, but I would really like to also hear from the broader community of neuroimagers. Answers from people who do not use any Open Science practices are particularly valuable; especially if they tell us their reasons. It is important to know which aspects of Open Science have the biggest reach at the moment, and how they are perceived by a broad range of people in the OHBM community.
Early career researchers and senior scientists have different, sometimes even conflicting, priorities and motivations which may often explain the slow implementation of Open Science practices at an institutional level. Efficient resolution of these conflicts is possible only if we understand and harmonise the different incentives faced by these disparate groups.
How long will the survey be open for?
I have two stopping criteria for the survey: either we reach one thousand participants, or we get to 30 April 2019, whichever is earlier. I would like to receive responses from at least five hundred people but ideally one thousand!
I really appreciate the support of International Neuroinformatics Coordinating Facility (INCF) and OHBM in disseminating the survey to their members. It is important to note, however, that you do not have to be in either of these communities to respond. Everyone is welcome to submit their opinions.
What will you do with the results when you have them?
A summary of the results will be shared within the UK Network of Open Science Working Groups and other professional platforms, including the related Special Interest Groups of the OHBM and the INCF, to feed into the formulation and harmonisation of our Open Science strategy. The main aim of the survey is to capture different points of view, but I also hope it will prompt people to talk with each other, and think about and understand perceptions other than their own.
Every such survey has an educational angle, as well. For example, a recent survey on data management and sharing lets people know about several data handling approaches they might have not thought of before but they may try out after filling out the questionnaire. I hope this survey will encourage people to consider solutions unfamiliar to them and understand how they could benefit from additional tools.
Fantastic, I’m so looking forward to reading the results!
Thank you! Here’s the link again
By Amanpreet Badhwar and the Diversity and Gender Committee
The OHBM Diversity and Gender Committee is performing a series of interviews to better understand and address the issues of implicit and explicit biases in academia. The ultimate goal is to promote gender and geographic balance and create a more inclusive brain mapping community. Over the next months we will be interviewing social psychologists and social neuroscientists to get multiple perspectives on the topic. We start this series by interviewing Uta Frith.
Aman Badhwar (AB): How did you get into social psychology and neuroscience? Are there any personal experiences that motivated you?
Uta Frith (UF): When I left school for university, way back in the 1960s, in provincial Germany, I had never heard of neuroscience, and I wasn’t sure that psychology was a respectable subject to study. I more or less drifted into psychology and one reason was that I was curious to learn about myself and about other people. But that, I soon gathered, was considered the worst possible motivation for taking up psychology! Instead I could learn about memory, perception and attention. It sounded a bit dreary, but I persevered. I was not disappointed.
At the University of Saarbrücken, I discovered that there are millions of questions about the mind and the brain, but the most interesting ones came from some vivid case demonstrations in the psychiatric clinic. I therefore decided to take up training in clinical psychology and was lucky to get a place at the Institute of Psychiatry in London. But it was by chance that I met an autistic child right at the beginning of my training. This five-year-old boy was supremely uninterested in me, but very interested in bricks and puzzles. I was captivated by the strange contrast of abilities and disabilities. It made me determined to find out what might go on in the mind/brain of this little boy.
I never tired of doing research on autism and it taught me a lot about myself. As a child of my time, I had believed that it was my social environment that had turned me into a person keenly interested in other people. Now I had to entertain the notion that social interaction is possible only because of a built-in part of the human brain, and that if this part does not work, the result is autism.
Autism provided a way to probe the question ‘what makes human communication special’. In the 1980s a group of us tested the theory that a cognitive mechanism, we termed mentalising, enables us to attribute mental states to ourselves and others, and is crucial for reciprocal communication. It still feeds my sense of wonder that this cognitive mechanism is at the basis of human cooperation and reputation, as well as competition and deception.
AB: Women haven’t always received appropriate recognition for their work. How have you seen opportunities for women change? A change in climate? Is there a similar trend for women of colour?
UF: I grew up at a time of strong gender stereotypes and I was thoroughly imbued with them. My mother, born 1907, did not have a higher education, - it simply wasn’t even considered at the time. However, she managed to educate herself. At age eleven or so I begged my parents to send me to an academically challenging school that was then aimed at boys who would go on to University. The general view at the time was that girls should get a good all-round education at secondary school, then do something useful before getting married, such as becoming teachers or nurses. A few girls at my school were happily tolerated as oddballs or ‘bluestockings’. Since we were not conforming to the prevailing gender stereotype, we were having to shape our own role and station in life. I was diligent and ambitious, and I was sure that I would be able to take up any career I wanted. I don’t know where this confidence came from. Perhaps one reason was that my parents had confidence in me and always supported my choices.
I am very aware of the long history of the struggle for women’s rights. I am proud that in the last 100 years women in Western societies have won rights that previously were reserved for men. The fact that the inequality and inferiority of women has been seen as unsupportable, morally and economically, has changed the cultural climate. Now we need to work towards this change for every part of the world, not just for a few privileged parts.
However, there are still barriers even in privileged societies. For example, it is harder to enter an academic career if your social background leaves you ignorant or suspicious of the world of academia. And then there is still the glass ceiling! While women in the UK now make up almost a quarter of professors, when we take all universities and all disciplines together, women of colour make up a vanishingly small fraction. What is remarkable, is that women of colour have provided some of the most inspiring role models for women in science and technology in recent times – think of the success of the film Hidden Figures.
AB: What aspects related to gender differences within the work setting have not changed, but should?
UF: The first thing that comes to mind is the gender pay gap. Many organisations are now committed to close this gap, and they are deeply embarrassed if the gap is large. It would be a great injustice if women would not get the same pay, the same status, the same rewards for doing the same jobs as men. But this is a remarkably complicated issue, and it is not always easy to know what is the ‘same job’. I remember being surprised on discovering, when I became a professor, that my salary was lower than that of other professors. But then, I loved my job as a Medical Research Council scientist. It allowed me to dedicate myself entirely to research. I did not envy the administrative responsibilities or teaching duties that the higher paid professors had to do.
There are differences in our individual preferences and also abilities. I suppose these might correlate with gender differences to some extent. But individual differences trump gender differences. If you take only averages and forget about distributions and individual differences, then gender differences will emerge in some more or less relevant variables. Their importance depends on culture and context. I like to quote the example of a highly significant gender difference that exists in the ability to throw a ball. Women are far worse at this than men.
In situations where women compete with men, strengths and weaknesses associated with gender stereotypes often come to the fore. These can be quite ambiguous, sometimes harmless and sometimes treacherous, and therefore very suitable for sexist jokes. Q: “Is Google male or female?” A: “Female, because it doesn't let you finish a sentence before making a suggestion.” This makes me smile because it refers to a well-known stereotype but also contains a backhanded compliment – interrupting boring talk is surely better than meekly listening. But I can also see the exasperating side of making clever suggestions.
Of course, mostly, prejudices are not funny and can have serious consequences, for example in the selection of people given awards, or grants, or simply being invited to give talks. One thing we have learned from social cognitive neuroscience is that we cannot help identifying ourselves with a group, our ingroup. The reverse side of the coin is disrespecting an outgroup. The image we build of ourselves, and the confidence we have in ourselves feed on group alliance and group discrimination. None of us want to be seen as outsiders. We relish the approval of our ingroup and we perfectly understand if we are not exactly loved by the outgroup.
In the work setting there continues to be prejudices about gender differences that keep women down, or in their place, as some would have it. Early career researchers are necessarily at a stage of their life when they have to build up a family, and this almost always means a bigger burden on women than men. I am very glad to note that the younger generation of men are now taking up more of the burden. But this is still far from being the norm.
Is it possible to get rid of prejudices? Not really. I think that getting rid of one prejudice may well mean acquiring another. We have to use shortcuts when making fast decisions, or else we may never decide anything.
AB: Should there be more attention to microaggressions in the workplace, or at conferences? For example, ignoring or minimising ideas, claiming others’ ideas, assuming stereotypic roles. Have these reduced over time? Or increased with decrease of overt aggression?
UF: Wouldn’t it be great if we all became nicer people, more kind to each other and more tolerant? But that is a pipedream. We are shaped by evolution to be aggressive as well as benign, to be selfish as well as altruistic. I personally find it a satisfying side effect of getting older that competitiveness declines, and that with it the related emotions, such as pride, envy, revenge, or triumph, seem to decline as well. Sadly, I see less sign that my propensity for discriminating ingroups and outgroups declines with age.
Forms of aggression and selfish behaviour are expressed differently in different cultures, and there are subtle expectations that women should be less aggressive. They are also suspected of feeling offended more easily. Women can work against these stereotypes, and in particular not being afraid of giving and taking criticism. Academic life involves a lot of critique and judgment. It is hard to take justified critique and easy to take it for unjustified aggression. The main danger in my view is to feel offended when we shouldn’t be. Above all, we should not get drawn into retaliation. Empirical studies show that retaliation leads to escalation.
Hence my advice would be that monitoring should be less zealous when registering aggressions, including micro-ones, and more zealous when gauging the appropriate reactions to aggressions. Any monitoring is best done in diverse groups. With different backgrounds we have different perspectives, and we can see each other’s flaws much better than we can see our own.
AB: What tips would you give for young investigators, irrespective of their own gender to better understand gender biases, and to make changes?
UF: My main tip is to discuss and to argue freely any differences of opinion about whether some things are more suitable for men or for women. Take the opposite view and try the argument from the other side. Arguments are a good thing for scientists. We need diversity or else we can easily get stuck in a dead end.
I tend to think that complementarity of social roles and fairness to all should be celebrated over a relentless quest for equality. We are all different! Research needs different types of people, different skills, different outlooks and collaboration as well as competition. There are conflicts, and research tells us that transgressions need to be punished, or else cooperation collapses. But punishment is a dangerous thing. The danger is retaliation and escalation. So what to do? Rules of politeness have been set up for good reason and I recommend anyone to value them and not throw them aside as old fashioned. My advice goes further: try to be more than polite and try to be kind and forgiving! Of course, there will always be people who will do something that has rightly offended you or disadvantaged you in some way. But remember others may experience the same from you, even if you feel that you never gave cause for offence. Our self-bias makes this so.
AB: Are there any specific gender biases that earlier were advantageous for women, that are now gone?
UF: I can’t think of any.
AB: Thank you Uta for taking the time to provide such insight into the various topics. I really look forward to seeing you at OHBM 2019 in Rome!
Note: AB is also a member of the OHBM Diversity and Gender Committee.
By: Elizabeth DuPre with the Aperture working groups
The Aperture survey has closed, and we’re excited to share the results! Here, we summarize our initial conclusions and outline some next steps for moving the conversation forwards. If you’re interested in diving into the full dataset, anonymized responses are available here.
Aperture is an OHBM initiative to develop a new publishing platform. Envisioned as an open platform to publish novel research objects, Aperture was created by TOPIC (The OHBM Publishing Initiative Committee) and received support from the OHBM Council in Winter of 2017. To better understand publishing needs within the OHBM community, we launched a survey in December 2018 to capture feedback on several dimensions of the publishing process. After advertising on the blog, social media, and the OHBM mailing list, we received nearly 200 responses. Here, we report on the results for three of the surveyed dimensions: publishable research objects, reviewing models, and paths to financial sustainability. If you are interested in examining these conclusions yourself or diving into other aspects of the data, be sure to check out our github repository. There, you can access the anonymized data and these initial analyses as well as an interactive environment to explore them in your web browser using Binder.
In analyzing the survey results, our first concern was whether respondents wanted an official OHBM publishing platform. From this sample of the OHBM membership, the answer was a clear ‘Yes,’ with over 85% of respondents in favor of developing Aperture. A majority of respondents hoped that Aperture would publish cutting-edge research objects such as data descriptors and code in addition to traditional empirical papers. These results strongly support our initial vision and solidify our commitment to developing this new publishing platform.
By Michele Veldsman & Gabby Jean
Australia has been steadily increasing its output in the field of neuroimaging. It hosts a number of leading imaging centres, including the Melbourne Brain Centre, the Brain & Mind Centre in Sydney and the Herston Imaging Facility in Brisbane. Professor Amy Brodtmann, Stroke Neurologist in Melbourne University and Inaugural Chair of the OHBM Australian Chapter, has been witness to and helped drive these developments. As a clinician-scientist she has made significant scientific contributions to our understanding of stroke, such as documenting grey matter changes and amyloid depositions in the months and years following an incident.
To celebrate the first meeting of the OHBM Australian chapter we managed to interview Amy, and provide an overview of the events at this meeting. First, Michele Veldsman demonstrated her multitasking skills by interviewing Amy with her infant daughter attached (and thankfully mainly sleeping) in a baby sling.
Michele Veldsman (MV): Can you tell us how you became a neuroimager?
Amy Brodtmann (AB): I started carrying out Neuroimaging research back in 2000-01. I did my training as a neurologist at the Austin Health medical school in Melbourne. I wanted to learn how to understand cognitive disorders and brain plasticity and to build those skills. I was very fortunate to know Aina Puce, who many of you will know as one of the founding members of OHBM, She recommended I did an fMRI PhD, and since I’ve returned to Melbourne I’ve used imaging in almost all my projects. Sometimes it makes me wonder why I try and combine this with stroke populations as there’s a lot of pain that comes with that [laughs].
MV: And so what are the advantages of being both a researcher and a clinician?
AB: The huge advantage is that I see patients every day, and questions arise every day from the people that I see. I work with both caregivers and patients. In my world that makes research easier. I’m lucky and get inspiration all the time. Then I have to think about what I’m going to do next. The disadvantages are that you’re split: I have two days a week that I’m full time clinical and the rest of the week in theory I’m part-time because of my children’s needs and running my family. I’m running around the wards as well as looking after three kids.
MV: As a stroke neurologist your work has clear links to brain plasticity. What do you think are the big findings in that area and do you think there will be a lot of translation into the clinic?
AB: I looked initially at a series of stroke patients who were in their mid-80s and saw enormous changes in their brains. Our brains are quite phenomenal. We saw changes in an area of the brain that, according to the textbooks, was supposed to be very static and isn’t supposed to exhibit much experience-dependent plasticity. That was in primary visual cortex: striate. What was so interesting is that these heteromodal cortices are always the areas that kick in – we were seeing activations in the same hemisphere, ipsilesionally and then contralesionally, and after the plasticity we wondered if the brain then goes on to degenerate. I think that most of that degeneration is due to stroke being just another example of a risk factor. Thinking about the effects of physical activity, we found that the percent of the day spent active was associated with better retention of function.
MV: You were also interested in the link between stroke and dementia. What do you think neuroimaging is going to tell us about that?
AB: We’ve got a number of different factors at work. Patients who have had a stroke have already got the risk factors that can affect their brain prior to the stroke. Then we’ve got changes due to the stroke itself. Then we’ve got potential deafferentation or disconnection from both the stroke and white matter hyperintensities. So I think that it’s really different – we need to understand which areas are affected first, old fashioned structural imaging. The second thing we have to understand is how does that affect networks, thinking of Bill Seeley’s work and how others have explained how networks are affected. It’s really critical because I don’t think it’s going to be quite so straightforward in stroke. I think that there are so many areas that are potentially affected, so we’ll need to very carefully unpack what’s happening in the frontal lobes. I think we’ll have to think about how we factor in both structural and functional connectivity and how they can interact. There are so many ways that imaging is going to help us understand this process – we can not only chart observationally but we can start to look at the grey matter in terms of cortical thickness and the thalami and hippocampi and understand regional vulnerability. Critically we can also understand the processes going on in the white matter, and finally we’ve got methods that allow us to do that, which is really exciting.
[Michele’s child wakes up contentedly – and allows the interview to continue]
MV: So the Australian OHBM Chapter has recently started, what are the aims of the chapter and where do you see it going in the next five years?
AB: This was something that Michael Breakspear suggested to us last year. He’s on council for OHBM. Michael suggested to a number of us that come to the meetings regularly that we should make an application for a chapter. So we got a group together and made a pitch and OHBM were keen to get local chapters up and running.
I know that within Melbourne and outside Melbourne there are plenty of projects that share interests or methods. We’re uniquely situated to do that in Australia given our population. The big focus will be on getting more people connected and people interested and getting our PhDs and postdocs to get together. OHBM has always had that youthful drive and focus and I want that to stay the same. We start to get a bit more concrete as we get older – it must be that vascular brain burden and it’s really good to have people say ‘why not?’. I love a ‘why not?’.
MV: Thank you!
Gabby Jean then provided us with an update on events at the OHBM Australia Chapter meeting:
On 12 October 2018, the first inaugural meeting of the OHBM Australian Chapter was held at the Melbourne Brain Centre. Our aim was to introduce the neuroimaging community in Australia, to maintain current and form new networks of regional and international collaborations across all disciplines of neuroscience and neuroimaging.
Our first meeting had a great turn out, with nearly 200 registered attendees. The day was kicked off by a brief overview of the OHBM Australian Chapter and its mission, followed by an inaugural keynote by Professor Michael Breakspear, past OHBM treasurer. A second keynote presentation by Professor Caroline (Lindy) Rae detailed what we are exactly mapping in ‘human brain mapping’. We heard about impressive and inspirational work presented by PhD students in a ‘PhD data blitz presentations’ session and by junior postdocs in the ‘post-doc presentations’ session. Sila Genc won the prize for best PhD blitz presentation and Steffen Bollmann won the prize for best post-doc presentation, although all PhD and postdoc presentations were of exceptionally high quality.
In addition to hearing about great neuroimaging work carried out throughout Australia, delegates broke into smaller workgroups to discuss ideas for a potential large collaborative research effort similar in scope to the UK Biobank initiative.
This brainstorm session was introduced by Dr. Lianne Schmaal who presented an overview of current international and national ‘big data’ initiatives. Amy Brodtmann then presented an overview of potential funding opportunities for such a big data initiative in Australia.
After lunch, each workgroup presented their proposal for a big data initiative in Australia, which included many interesting and partly converging, partly unique ideas. We aim to further follow-up with the OHBM Australian Chapter community on these great ideas for a large-scale data collection initiative in Australia (for further details check us out on our chapter site).
Overall, we believe the inaugural meeting was a success and we hope it was productive and interesting for all attending. We would like to thank everyone for their attendance and great contributions to the discussions. Please stay tuned for upcoming events in 2019!
In this second installment of the OHBM Oral History series we had the chance to speak to Professor Susan Bookheimer. Susan is a Clinical Neuropsychologist and Professor-in-residence at UCLA. She has played a leading role in our understanding of the brain basis of language, and pioneered the use of functional MRI and PET in clinical samples. Her recent work has explored the causes of social communication deficits in children with Autism.
Susan has been a significant contributor to OHBM throughout its history having taken on the role of meetings liaison (2002-03), Chair of the Scientific Advisory Board (2015-16) and Chair (2012-13). We found out about her early clinical work using functional imaging, her excitement about big data and how she overcame a bout of laryngitis to give a talk at the first OHBM meeting in Paris.
By the OHBM Diversity & Gender Committee
Nearly three years ago, a young woman approached the microphone at the “Town Hall Meeting” in Geneva during OHBM’s 20th Annual Meeting and pointed out that all the newly elected Council members were men. While there were women on the ballot for the 2016 Council elections, the results ended in a composition of 14 men to 1 woman. Given a binomial probability of < 0.0005 for this outcome, the coin is clearly weighted.
The Council meeting in Geneva took place the day following the Town Hall Meeting, and there was unanimous agreement that not only was the male/female ratio a problem, there were other aspects of diversity, including geographic representation on Council, that needed to be addressed. For example, demographic research showed that approximately 15% of the OHBM membership is from Asia, however, at that time, there was no Asian representation on Council.
By Tim van Mourik; Edited by Elizabeth DuPre
The abstract reviews are in, and we're getting excited for OHBM's 25th annual meeting. Tim van Mourik has been chatting with Cameron Craddock about the history of the Open Science Room. Here he shares that history, his vision for Rome 2019, and asks for your priorities and interests via this survey.
As a member of the Special Interest Group on Open Science, I will be chairing the OHBM Open Science Room (OSR) this year. This is a room in which, during the main OHBM conference, topics related to open science are being discussed. The way I like to see it is that the main OHBM conference primarily focuses on the results of our scientific work, whereas the OSR is a place where we can focus on the process of doing science. In addition, it is a place for community discussion. Only last month a preprint on time-varying functional connectivity came out that had its roots in the OSR. After a Twitter conversation during the conference in Vancouver, the discussion was quickly moved to the OSR and eventually resulted in a collaborative manuscript.
Although today the OSR is a place for new analysis methods and tools, discussions, and collaboration, it started out as a small space for hacking on code together. To learn more about this transformation, I interviewed Cameron Craddock on how the OSR came about and where it could go. Cameron is one of the founders of Brainhack, which he envisaged as a space for neuroscience researchers to code together and learn collaboratively. “After the first Brainhack in 2012, we wanted to bring this to a central place, to OHBM. The first Hackathon in 2013 was competitive in nature and took place during the conference.”
Following this initial success, the Hackathon was reconceived as a separate event preceding the conference. Making the Hackathon into a separate event encouraged a natural evolution: “We wanted to bring in more of the educational aspect to the conference. From this, the OSR emerged. It started out as an educational course: Brainhack 101. Besides lectures that provided handles for doing more reproducible science, there were ad hoc software demonstrations of open source tools.” The OSR started out with many ‘unconferences’: spontaneous and unrehearsed talks about personal ideas, questions, and suggestions. The low barrier-of-entry has always made it accessible to a wide range of people and has facilitated a better learning experience. The talks had a strong educational focus and highlighted the collaborative power of new open source tools.
The mindset of the OSR has always been one of collaboration and empowerment to do open science. Answering questions like: “How can I best share my data?” and “What factors are most important for reproducibility?”. But as the OSR grew bigger, it started to get harder to maintain its spontaneity. I asked Cameron if the fact that it was so low-key is a bug or a feature: “It’s a shame to learn that you missed a talk because you didn’t know it was there, or that it overlapped with talks from the main program. It would be really useful to have this more organised in advance. But indeed, it is important to try and retain the same atmosphere. Spontaneity can be important for having better conversations.”
As OSR chair, and with support from all the members of the Open Science Special Interest Group committee, I will be organising this year’s OSR at OHBM 2019, Rome. We have been thinking a lot about what we would like the OSR to be; how to be of value to the OHBM community as a complement to the main conference. It is clear that the OSR has an important educational role and that there is a strong focus on the adoption of open science best practices and technical solutions as a means of improving science. There is also an aspect of constructive discussion and community building to it.
Parallel to the main conference, the OSR could be a place for discussing the problems of scientific work we encounter in our daily lives. Questions like: what are reasonable demands in terms of data and code sharing without it becoming a burden? What are the considerations if you want to publish ethically? How can we make sure that science is open for all?
But maybe an even broader perspective could be beneficial. The process of doing science also concerns topics beyond just Open Science. Could the OSR also be a platform for discussing a wider range of topics, such as the ways in which science is done, evaluated, and funded, career perspectives in and outside a traditional university setting, and mental health challenges in academia. By covering these topics, the OSR could add a platform to the initiatives from the Student-Postdoc SIG as well. This way we can make the OSR the best experience for all.
When I asked Cameron if there was any one thing he would want people to know about the OSR, his response was: “I firmly believe that everybody could get something out of the OSR & Hackathon!” And that is my personal mission for this year’s OSR.
Within the Open Science Special Interest Group we have many ideas on what we could feature in the OSR. But we are also really interested in hearing what you would like to see. We would much appreciate if you could give us brief feedback about your experiences, hopes, and expectations for the OSR by completing this survey.
Invitation to project
Natalia Bielczyk & OHBM Student and Postdoc Special Interest Group,
Edited by AmanPreet Badhwar
Early career researchers in different parts of the world face similar challenges, but not everyone has the same access to mentoring and career development resources. While online mentoring programmes, such as the OHBM International Online Mentoring Programme, are available, it is hard to cover the needs of the whole population of early career researchers in the natural sciences.
In order to tackle this issue within the OHBM Student and Postdoc Special Interest Group, we are developing a set of advice relevant to early career researchers in the natural sciences. The aim of this project is to empower early career researchers to positively influence their future career opportunities on a daily basis - regardless of the circumstances. The main points which we aim to cover are the following:
The project will take the form of a full-length manuscript. The draft working paper has been posted under the link https://osf.io/53yrv/.
We would, however, like to further develop the manuscript before submitting this work for peer review. Therefore, to provide advice that can be generalizable to a wide variety of situations, demographics, and countries, we are seeking contributions from the OHBM community. We would like to welcome everyone willing to participate to join us and discuss this subject on the associated Google group: https://groups.google.com/forum/#!forum/effective-self-management-for-ecrs
We look forward to your contributions. The manuscript is dedicated to early career researchers but we welcome the expertise and experience of seniors researchers on this topic as well. We hope the group endeavour will not only make for a better manuscript but also serve as a platform where early career researchers can give each other support and advice, and make new friends! Depending on the traffic, the Google group might be sustained after the final form of the manuscript is formed. The official starting date of the Google group is Monday, January 14th 2019, and we close collecting contributions on Thursday, February 28th 2019.
Active participation in the Google group will be interpreted as an academic collaboration. Therefore, we will bring together feedback received through the group, invite the significant contributors to co-author this work and reupload the working paper as a preprint with a new, extended list of authors. Invitation will be determined on the basis of: (1) constructive comments / additions to the manuscript and (2) being active and helpful to other members of the Google group. This material will be subsequently submitted for peer review.
You can find all the details about the project, together with the Code of Conduct and the rules for establishing authorship, on the Google group. We hope to meet you soon and chat about careers together!
By Ilona Lipp and Jean Chen
Edited by: Nils Muhlert
In science, the term “work-life balance” may seem like a holy grail for some and a conundrum for others. Its easy matter-of-factness belies deep self-examination. Today’s research communities are larger and more competitive than ever with regard to permanent positions and funding, with the success rate for many grants being as low as 5%. For this reason many leave academia after finishing their PhDs (according to a recent report by the Royal Society). For those who choose to stay, the clock starts ticking from the very moment one starts a job, and the counting begins --- for grants, for journal articles, for trainees, for experience in international labs, etc. So who are the people that, despite everything seemingly being against the odds, persevere and manage to stand out in a world of stressed early-career researchers? Do they purposely dedicate their lives to science? Do they have a life outside of work? Are they even human?
To find out, we talked to a diverse group of seven early-to-mid-career researchers, all highly successful for their career stage in terms of their funding situation, publication list and professional recognition (below, ordered by first name). We asked them how important work-life balance is to them and what strategies they take to achieve it, and have summarized their answers for you.
By Chris Gorgolewski & Ekaterina Dobryakova
Reproducibility and transparency are core to all branches of science. Two years ago, OHBM established the Reproducibility Award. The purpose of this award is to honor researchers who conducted a ‘successful’ or ‘unsuccessful’ replication study, while adhering to rigorous standards of study design, data collection and analysis. The second recipient of the Reproducibility Award is Benedikt Sundermann, who received the award during OHBM 2018 in Singapore for his study that was published in the Journal of Neural Transmission. Chris Gorgolewski, one of the initiators of the OHBM Reproducibility Award, interviewed Benedikt about his experience related to this replication study.
Chris Gorgolewski (CG): I am joined here today by Benedikt Sundermann, the recipient of 2018 OHBM Replication Award. Benedikt, thank you for joining us and congratulations on the award.
Benedikt Sundermann (BS): Thank you.
CG: The first question I want to ask you is how would you describe the study if you met a stranger a bar?
BS: In previous studies, people have tried to apply artificial intelligence technologies that are frequently used in face recognition to functional brain imaging data in order to try and diagnose people, for example, with depression. In our study, we wanted to see whether this replicates in a larger and more clinically realistic sample, featuring various comorbidities, heterogeneous age, sex etc. Surprisingly, most of the previously reported results did not replicate in this larger, more heterogeneous and clinically realistic sample. Only when we looked at a subgroup of people could we replicate some models but, still, at a diagnostic accuracy that would not be clinically useful.
CG: I see. So that is quite a controversial statement, especially considering how much we discuss about the application of neuroimaging methods to the clinic. Did you experience challenges in publishing this work?
BS: Yes, definitely. If I remember correctly, it was the fourth or fifth submission of this work that we finally managed to publish. Generally, when you try to publish a replication study with a negative result, you should be prepared to be interrogated much more critically by the reviewers, about the sample, about the methods and so on. But there was also criticism that our work was not scientifically solid. For example, there were comments like “We know from previous studies that this works, the fact that you couldn’t replicate it means that you must have made a mistake.”
CG: I see, but you must have, in a way, anticipated that it’s going to be somehow challenging. I want to understand a bit more what motivated you to do this wonderful work to begin with.
BS: I have a clinical background in radiology and I am mostly interested in actual diagnostic tools and to expand the spectrum of diagnostic tools that we can use in the clinic. In major disorders, we are usually limited to the exclusion of larger structural lesions but we cannot really get the diagnostic information about the actual disease mechanisms and disease correlates in these people. So my main motivation was to work on these technologies to improve these diagnostic imaging techniques based on functional imaging and machine learning.
CG: So after having completed the study - you know they say ‘hindsight is 20/20’ - if you were to take the trip back to the past in a time machine, what would you do differently? What advice would you give to people who are planning to do a replication study?
BS: First, you need to expect frustration. We did not expect that much frustration that we experienced during the study and when we tried to publish it. So just be prepared and then it will probably be easier, I think. The other thing is, in a replication setting you might be interrogated more strictly about your sample and about your analyses. Having a good structure of your analysis and your data will make it a lot easier. This is also one of the points that you are also working on in your initiative, so I think this is pretty important and we should focus more on that.
CG: Great. So what’s next for you? What are you excited about? What are you working on right now?
BS: One thing that I am working on right now is more clinical than scientific. I will be working on project optimisation/project standardization from a clinical point of view. Scientifically, I am currently interested in multi-modal integration of functional and structural imaging data in diagnostic models.
CG: So do I understand correctly that the main implication of your finding is that resting state fMRI is not ready for clinical use?
BS: I think it is currently not ready for clinical use. But I think it is important to realize that this is not an endpoint. Our findings do not suggest that there is zero information about depression in these data. We looked at it with machine learning techniques that were available two to three years ago and there has been a lot of improvement in this field since. Also, the current classification system of patients into major disorders has room for improvement. For example, just saying whether somebody has unipolar depression or not, may not be sufficient to capture the wide spectrum and large heterogeneity of these patients.
CG: So you think that the main reason for the lack of diagnostic ability are noisy labels and poor definitions of the phenomena that we are trying to predict?
BS: I think that it is at least one major determinant.
CG: So as a clinician how do you improve classification of methods?
BS: Currently, the main classifications are based on clinical interviews. You have to ask standardized questions, and you have a checklist to see whether certain criteria are fulfilled or not. But maybe we can use biological information itself to further sub-categorize patients’ mental disorders, and to see some commonalities or links between different mental disorders.
CG: That sounds very exciting! The OHBM replication award will run next year so I encourage everyone to submit their replication studies and see you next year.
Please note, the submission deadline to be considered for the Replication Award is January 11th, 2019.
This year marks the third full year of the OHBM blog. In 2018 we’ve published over 40 blogposts, covering topics as broad as diversity in brain mapping, neuroimaging in Iran, art and science and of course our interviews with the Annual meeting keynote speakers. We’ve seen changes in our editorial team, with new faces Claude Bajada and Ilona Lipp bringing fresh energy to our blogposts, but also saying farewell to two of our original blogteam members, Panthea Heydari and Thomas Yeo. We are proud to see Thomas becoming one of the keynote speakers at OHBM2019 in Rome, and Nikola Stikov, the original blogteam lead editor, taking over for Jeanette Mumford as Chair of the OHBM Communication Committee. Here, we share our favourite posts of the year.
The holidays are fast approaching, and I would like to celebrate it by shining a light on the many nuggets of wisdom I have gained via my participation, both as a writer and an editor, on the blog team. I have had the opportunity to interact with many brilliant brain mappers, and have had many memorable conversations and exchanges. So here it goes:
Daniel Margulies taught me to gesture to my head to illustrate that I study the brain. Trust me, it is so much more effective and easier to understand than my usual repertoire of explanations about what I do. But what he really hooked me with was the following: “Although there is a substantial focus in brain mapping of the differences and discrete boundaries between areas and large-scale systems, one challenge is to also consider how these distinctions are integrated into a functional whole”. Bruce Miller provided some key advice on things to concentrate on when the “functional whole” that Daniel mentioned is falling apart in the face of neurodegeneration. Bruce pointed out how important it is to concentrate on “not only what are the weaknesses, but what are the strengths, and has anything new emerged that is actually a new strength… What is preserved is telling us something about where in the brain the bad molecules are not accumulating. But it also allows us to think about the patients, about things that are important to them”. From the career development blog by the Student and Postdoc SIG, I was relieved to learn that every career path is different, and that there is still hope for me to start my own institute (and it just might be modeled after a treehouse, taking inspiration from Daniel Margulies here). Finally the Open Science SIG blog post reinforced for me that we are all part of the the scientific community, …..the sappy, corny and mushy reason we all stick around! Happy holidays everyone!!!
Being new to the blog team, I feel as though this little fish is now swimming in the big pond, gill to gill with some of the biggest fish in human neuroimaging research. My first interview was with Ed Bullmore, learning about his diverse experience in the clinical, academic, and industry worlds. Entering neuroscience from a medical background myself, I was particularly interested in his advice to medical students who may be interested in the technical side of research but feel they “come from the wrong background”. During the 2018 OHBM meeting I met and interviewed the instructor of a MOOC I had followed during my PhD! Martin Lindquist was the 2018 OHBM educational award winner and we had a great discussion about the challenges students and researchers face in neuroimaging. In his words, the best advice may be to “be curious, look outside the box, be willing to do crazy things and fail, and have fun!”. Finally, I worked with this year’s local organising committee in preparing a short blog post about the 2019 25th anniversary OHBM meeting. I’m looking forward to seeing everyone in Rome!
This has been my 3rd year on the OHBM blog team, and I am thoroughly enjoying the opportunities for shaping communications within our research community. This year, besides my OHBM Keynote interview with Gustave Deco, I mainly focused my efforts towards transcending geopolitical barriers of brain-mapping research, with the 2-part series on scientists and trainees from Iran. This initiative, though challenging, was very rewarding. Going forward, I hope to contribute more material to help guide the careers and lives of junior researchers.
Having worked in the media team before, I joined the blog team this year to broaden my science communication horizon. 2018 kicked off for me with an article about Open Science (OS) challenges; an idea that emerged after OHBM 2017 in Vancouver where so many of you had shown interest in OS. “Sharing is caring, but is privacy theft?”. It was also our first interview series on the PLOS Neuro blog and introduced a new format: several experts get to answer the same question. Together with media team captain Kevin Weiner we set out to find from Russell Poldrack, Jeanette Mumford, and 4 other OS pioneers! where they see the main challenges and solutions that will make our field more open and reproducible. The format worked well, the final post was rewarding, and I felt boosted for more Q&A. As a physician-scientist I am interested in the added value of neuroimaging for patients, and the difference it could make for neurology and psychiatry. And so my other favourite blogging experience in 2018 dealt with progress and challenges for imaging in depression. I hope that you will find the views of our translational brain mapping experts in “Closing the loop for brain imaging in depression: What have we learned and where are we heading?” as intriguing. Blogging in 2018 was great fun, and I am already so curious what 2019 will bring!!
I only joined the blog team a few months ago and kicked off with quite an elaborate post, the “OHBM OnDemand How-to: resting state fMRI analysis” guide. It’s been really fun being able to write for a scientific audience in a less structured way than I do with papers, and I’m also very excited about my next posts (spoiler alert: there may be one coming out just after new year).
Hard to believe that 2018 is behind us. This year I had diverse writing opportunities that I greatly enjoyed. Through my post for PLOS Neuro blog, I interacted with and interviewed many leading minds in the field of traumatic brain injury research and neuroimaging. While acknowledging the complexities of scanning a clinical population, in particular the multi-disciplinary skills needed, all of the interviewees were optimistic about the potential insight into disease offered by developing neuroimaging methods. These posts were great for networking and connecting to other scientists in the field. It was also gratifying to receive emails from researchers who are new to the field or topic, or even lay persons who want to know more about brain injury due to personal experience. Such communications are always very inspirational. I also had a change of pace, writing a blogpost on the art exhibit by Shubigi Rao. Interviewing this artist revealed the similarities between art and science in aspects such as inspiration, patterns of work on a project, and exchange of ideas. Now, I am looking forward to more exciting interactions as we enter 2019.
2018 was our busiest year so far as Blogteam editor. There were a number of standout points for me (alongside the sweet egg buns in Singapore). We heard Leah Somerville discussing the potential effects of social media on teenage brains, and Aina Puce highlighting the challenges of quality control in multi-modal imaging projects. I really enjoyed hearing Mark Humphries’ views on how findings from cellular neuroscience currently constrain systems neuroscience theories - or whether that’s even happening at all! Finally, our first double interview between Heidi Johansen-Berg and Charlie Stagg extended these discussions on ways in which preclinical and clinical scanning can be fruitfully combined but also considered major advances in measuring neurotransmitters like GABA using MR spectroscopy - a method that has so far received scant coverage in our blogposts.
Jeanette Mumford (ending note as Chair of Comcom)
I’m so glad I decided to join the Communications Committee three years ago and am honored that I was able to serve as chair over the last year. I’m amazed by how far we’ve come and how hard the members of the committee work as well as Stephanie McGuire, our fearless Communications Manager. For the first time since 2004, I wasn’t able to attend the conference in Singapore, but thanks to the blog posts, tweets and OnDemand materials, I don’t feel like I completely missed out. I’m a big fan of the posts related to the OHBM Open Science SIG and the OHBM Student and Postdoc SIG, because they’re such a great addition to OHBM and I wish they existed when I was a graduate student and postdoc. I also really like the Keynote series, since the interviews offer a new dimension about the speaker beyond what we’d get from their talk, reading their papers or their CV. I’ve even gotten to help out with a few of the posts this year, including the “OnDemand How-To: Resting State fMRI” piece, which featured some great videos in the OHBM OnDemand portal. Overall, it was a great year and I can’t wait to see what we do in 2019.
From all of us at OHBM Communications Committee, we wish you a happy and productive 2019!
OHBM 2019 in Rome next June will mark twenty-five years since the first meeting in Paris. During that time the organization has evolved from an annual meeting of like-minded brain mappers to a society with multi-national chapter meetings hosted throughout the year, early-career researcher led special interest groups and open science resources. To celebrate what has been achieved during that time, we asked some of the founding members how they became interested in neuroimaging, how brain mapping has changed, about developments in funding and opportunities within their country and about their memories from OHBM meetings.
This first OHBM Oral History video interview features Professor Alan Evans, a James McGill Professor of Neurology and Neurosurgery, Psychiatry and Biomedical Engineering at McGill University and researcher in the McConnell Brain Imaging Centre (BIC) of the Montreal Neurological Institute. We learned about Canada’s hugely impressive investment in neuroimaging, the incorporation of genetics and other sciences into the work presented at OHBM and the collegiate, youthful feel of the OHBM meetings themselves.
By Ayaka Ando & Natalia Z. Bielczyk, OHBM Student and Postdoc Special Interest Group,
Edited by AmanPreet Badhwar
Christmas is just around the corner and the deadline for the OHBM annual meeting abstract submission is fast approaching! The OHBM Student and Postdoc Special Interest Group (SP-SIG) also had a busy 2018 organizing the Secrets behind Success symposium in Singapore, launching the third round of the International Online Mentoring Programme (attracting an additional ~150 participants), and launching the new SP-SIG blog.
Considering a new year is upon us soon, we wanted to share with you some insights we have gained by interviewing researchers in academia and industry. In this blog, we present a collection of interesting insights from our 2018 interview series. For the full interviews, please visit our SP-SIG blog.
Leaving academia is not a failure
Leaving academia as a conscious career choice is often seen as a failure (Kruger, 2018). However, this is what Dr Anita Bowles, the Head of Academic Research & Learner Studies at Rosetta Stone in San Jose, California, had to say about her experience:
“People often feel like a failure if they are thinking about leaving academia. I talk to a lot of graduate students in this situation and I understand their concerns, because I felt the same way. I would like to tell these students that this is not the case at all once you are on the other side of the decision. You can do valuable and satisfying things outside academia, including research that can be applied to help people. And if you feel like trying, just go for it.”
How to find jobs in industry
If one decides to leave academia, the first impulse is to browse through job listings. We, however, found that our interviewees had varied approaches.
Dr Anita Bowles says:
“I found out about Rosetta Stone through networking: I knew someone else who was employed by the company and who also had moved from academia to industry. I am glad that it turned out this way for me, as what I am doing now has a lot of overlap with my past research, and I am passionate about this topic.”
Dr Ricarda Braukmann, a recent PhD graduate, and currently a Program Leader for Social Sciences at Data Archiving and Networked Services (DANS) told us:
“As I was already passionate about open science and knew of the work DANS was doing, I was proactive and contacted them explaining my wish to gain experience outside academia. I was very lucky, as I indeed got the chance to work for DANS during a four months part-time internship in my final, fourth year of the PhD. The internship was the starting point of the job I have now, which DANS offered me after I finished my PhD.”
Natalia Nowakowska, a PhD candidate at the University of Amsterdam and a freelance copy- and content-writer, also started her job as a freelancer by networking:
“How did I start freelancing? In a way, it was also a lucky strike - I met a person in a bar who was leading a course on how to become a freelancer. I joined the course and got some practical instructions on how to start and find my own place in this space.”
Every career path is different
No matter how much we try not to, we sometimes still compare our career trajectories with our peers. However, from Dr Aaron Clauset, an Associate Professor of Computer Science at the University of Colorado at Boulder and in the BioFrontiers Institute, we learned that career trajectories in science are highly nonlinear. In his research Aaron and colleagues analyzed career paths of thousands of computer science professors at US and Canadian universities (Way et al., 2017, Clauset et al., 2017). What they found was that the conventional research trajectory of a rapid rise in productivity to an early peak, followed by a slow decline, only emerges after averaging the individual trajectories of large groups of scientists. Even though the average number of papers per person per year is higher in highly prestigious institutions than in other institutions, the shape of this average trajectory is independent from the prestige of the affiliated institution. However, the average pattern conceals high inter-individual variability in the career trajectories to the extent that only one in every three faculty members follow the average trajectory (Fig. 1).
Fig 1. The average trajectory versus inter-individual variability in the trajectories; a reprint from Way et al. (2017). (A) Average publication count follows conventional narrative across prestige, with the division into 5 groups of affiliations on the basis of the prestige. (B) In fact, there are four different types of trajectories, and most of the subjects do not follow this averaged, conventional trajectory. Research conducted in a group of 2,300 computer scientists from the U.S. and Canada.
Furthermore, related work on citation to papers by physicists, carried out by Dr. Sinatra, one of Clauset’s collaborators and colleagues, revealed that groundbreaking discoveries seem to be equally likely at each career stage (Fig. 2, Sinatra et al., 2016).
The results from these studies are very optimistic - in a sense that there is not just one canonical way of pursuing a career in academia, and that success can come at every ‘academic age’.
Start your own institute!
Lastly, what do you do when you know deep inside that you should pursue a career in research, but the whole universe tries to prove you otherwise? We interviewed Jeff Hawkins, the founder of Redwood Neuroscience Institute & Numenta Inc., who finished his official research training with a BSc from Cornell University in 1979. Since then, Jeff never successfully accomplished a PhD because of constant rejections of his ideas that were ahead of his time.
So, Jeff developed an impressive career in mobile computing in the Silicon Valley instead, where he established Palm and Handspring, two mobile computing companies. However, his thoughts have always circled around science. Today, Jeff leads his own research institute, Redwood. Recently, he gave a keynote lecture at the Open Day of the prestigious Human Brain Project summit in Maastricht, where he introduced his theory of grid cells in neocortex (a.k.a. a Thousand Brains Theory of Intelligence) as the framework for cortical computation. He is very modest about his achievements, and, when asked about how he managed to create his own institute, Jeff answered:
“It was easier than you imagine. The idea came from several neuroscientist friends of my mine who said the field of neuroscience needs cortical theory. They encouraged me to start an institute. I agreed only on the condition that they help me, and they did. There were a number of scientists who, like me, wanted to work on cortical theory, so they signed up.”
Easy, right? :)
If you would like to get involved in the SP-SIG activities, give us an interview or perform an interview that can be featured on our website, please contact us!
Also, stay tuned for the roll-out of our next big project early next year! We are going to launch an open initiative, where any OHBM member is welcome to help us shape a set of guidelines (in a manuscript format) on effective self-management for early career researchers. We hope to meet you in this project!
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!
OHBM plans to create a new publishing platform, Aperture, to host high-quality research objects while promoting reproducible and open science. With Aperture, OHBM plans to open up to a more diverse approach in communicating academic research, bringing transparency and interactivity to the publishing process. We want to hear from you, the OHBM community, about what you would like to see in such a publishing platform.
Please complete the survey by clicking here.
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
For more information about the processes behind publications, read through this explainer by Michael Breakspear.