By Elizabeth DuPre and Kirstie Whitaker
This month we continued our Open Science Demo Call series with a focus on this year’s OHBM annual meeting in Singapore, and the many ways that OHBM members promote open science at the meeting. We heard from Michele Veldsman, Ayaka Ando, and Aki Nikolaidis from the student and postdoc special interest group; Greg Kiar and Anisha Keshevan from the open science special interest group; and Nils Muhlert from the communications committee.
We first heard from Aki about the Monday Night Social, featuring beautiful views of the Singapore skyline and an announcement of the winners of this year’s Brain Art competition. All OHBM members are encouraged to attend! Ayaka gave us a sneak peak into this year’s career development symposium, 'The Secrets Behind Success' with presentations from Tom Nichols and Lucina Uddin. Michelle told us about the mentorship program, which seeks to support professional growth at all career levels, from masters students to principal investigators. Although mentors and mentees meet at the OHBM annual meeting, you don’t have to be attending to get involved! Aki, Ayaka, and Michelle encourage everyone interested in any of these initiatives to stop by one of their events or reach out on twitter!
Greg and Anisha then told us about this year’s OHBM BrainHack and TrainTrack. Formerly known as the OHBM hackathon, this is a collaborative event which brings together researchers and scientists from across the neuroimaging community to work together on projects, learn new skills, and engage with the community. While registration for the BrainHack and TrainTrack is sold out, the Open Science Room at OHBM will be hosting demos, workshops, and an open working space throughout the annual meeting. Greg and Anisha encourage everybody to come and find out about the great initiatives and individuals in our open science community!
Nils introduced us to the amazing work the communications committee is doing to spotlight open science at the meeting and throughout the year. They were involved in revamping the OHBM website and maintain a blog highlighting initiatives within the OHBM community (including these demo calls!), as well as educational materials such as the OnDemand courses. Nils pointed out that this provides a great way to stay involved year-round (e.g. workshops on analysing diffusion MRI), even for those who cannot attend the annual meeting. The communications committee also helps promote the excellent brain mapping work carried out around the world, and provides a unique window into the stories of many successful neuroscientists through their keynote interview series. Nils encourages anyone interested in contributing to the blog to sign up!
We’ll be taking a break next month for the annual meeting, but look forward to starting back up at the end of the summer! If you’d like to nominate yourself or someone else to be featured on these monthly calls, please add their information at this github issue, or email the host of the calls Kirstie Whitaker at firstname.lastname@example.org. You can also join the OSSIG google group to receive reminders each month.
BY NIKOLA STIKOV AND JEAN-BAPTISTE POLINE
(IN CONSULTATION WITH THE OHBM PUBLISHING INITIATIVE COMMITTEE)
The current academic publishing norms impose many constraints on how and what we publish without fully embracing the new web-enabled dynamics. The emergence of the internet as the de facto publication medium, and the availability of open source technologies for handling the hosting and peer review process, have made it possible for organizations such as OHBM to experiment with innovative publishing platforms and 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. While initially our focus will be on reviews, tutorials and educational materials, we foresee using this format to incorporate computational notebooks, interactive plots, software, data, and post-publication peer-review to create living, reusable and reproducible research objects. We hope to have a beta version of Aperture in time for the Rome meeting in 2019.
Coko has extensive experience developing open source publishing components, some of which are used by Elife and other open-access publishers. Their framework could give the Open Science SIG and the broader OHBM community the opportunity to participate in the construction of Aperture. We look forward to establishing even more collaborations with like-minded partners.
Most importantly, we want to hear from our members! For that purpose, we invite you all to attend the Publishing Round Table, to be held at the annual meeting in Singapore on Monday June 18 at noon (Room: 324-326). Please join us for what we hope will be a fruitful discussion about the future of Aperture!
By AmanPreet Badhwar
“In a forest of a hundred thousand trees, no two leaves are alike. And no two journeys along the same path are alike.” ― Paulo Coelho
My first OHBM annual meeting experience was in 2015. I did not know many researchers in the field, having just started my postdoc in human brain imaging. On top of that, I was attending OHBM 2015 without my postdoc supervisor in tow (who knew the community well), and worried about not finding my place in the human brain mapping community. Luckily, I had met Daniel Margulies a few months prior to OHBM 2015. Not only did he make it a point to introduce me to the community at this particular annual meeting, but I also found myself happily involved in the many grassroot initiatives of the Neuro Bureau: ranging from brainhacks to sci-art exhibits to open science initiatives. Fast-forward to today, I have developed my own unique voice in the OHBM community, and it is in large part due to guidance from Daniel and his free-spirited compatriots during those formative moments in time. I have had the opportunity to collaborate with Daniel on several projects, both scientific and sci-artistic, and recently had the pleasure of interviewing him at the inaugural BrainHack School 2018 in Montreal.
AmanPreet Badhwar (AB): How would you describe your research to a random person on the street?
Daniel Margulies (DM): When explaining my research to a random person on the street, I usually gesture to my head to illustrate that I study the brain. If there is time for further elaboration, I explain that I study how areas are spatially arranged and connected to one another using MRI, and the consequences of this layout for the possibilities and constraints of cognition.
AB: What projects are you currently working on? Could you comment on some of the breakthroughs and bottlenecks you have encountered?
DM: I’ve recently moved my lab from Leipzig to Paris, which has provided a refreshing opportunity to set new research priorities and establish new collaborations. We recently identified a gradient in cortical organization that spans from primary cortical areas to the regions of the default-mode network, so my current projects are extending this observation to explore its consequences for cognition, cross-species comparative studies, and exploring how the gradient can be divided into zones of cross-modal integration.
AB: Can you tell me a bit about your career path?
DM: I studied humanities in undergrad, but ended up in neuroscience through a chance encounter years ago at a bus stop in New York that resulted in an invitation to join Xavier Castellano’s lab at New York University as a research assistant. I was soon introduced to neuroimaging data analysis by Mike Milham and imparted with a love for neuroanatomy by Michael Petrides. A similar twist of fate landed me in Berlin a few years later as a graduate student with Arno Villringer. I was very fortunate to have mentors that were immeasurably supportive and offered me opportunities to pursue my various interests. This all came together when I started my own lab in 2012 at the Max Planck Institute in Leipzig.
AB: What is your take on multimodal research? How have you integrated this within your own research project?
DM: The complexity of various fields in neuroimaging today has resulted in a level of specialization that makes it challenging to take a wider perspective. I believe one of the major challenges we face is in thinking across different methods and vocabularies to construct unified models that underlie these diverse, and at times divergent measures. As my core project aims to understand some basic principles of how features of the cerebral cortex are spatially arranged, perspectives from multiple modalities are central towards achieving that goal. We make use of the macaque monkey tract-tracing literature, high-resolution MRI, meta-analytic and task-based approaches… So much data is openly available these days that conducting multimodal studies is really becoming more the norm than the exception.
AB: If “like connects to like” in the brain, then tell us a bit about what makes the brain work as a unit?
DM: “Like connects to like” is a principle that has been introduced to describe preferential long-range connections between cortical areas that have similar degrees of laminar differentiation. It’s pithy and captures an elegant multimodal phenomenon of cortical organization. Nevertheless, various other principles are also critical to cortical organization, such as extensive connectivity between neighboring areas. Although there is a substantial focus in brain mapping of the differences and discrete boundaries between areas and large-scale systems, one challenge that your question illustrates is to also consider how these distinctions are integrated into a functional whole. There is little doubt that the brain is highly interconnected — a factor that is important to remember when delineating various subdivisions.
AB: You are a Neuro Bureau member. Could you tell me a bit about the Neuro Bureau?
DM: I started the Neuro Bureau with Cameron Craddock back in 2009 or so. When we first got going, all we had was the name, which we felt at the time was a solid enough starting point to merit a purpose. We developed the Neuro Bureau into a cross-institutional and cross-disciplinary support group for early career researchers with the aim of providing the neuroimaging community with projects and initiatives that weren’t traditionally credited. This includes the Open Science Gala at OHBM, the brain-art competition and exhibition, and the preprocessed data initiatives. The idea was to infuse our community with new perspectives, to render it more accessible to other disciplines, and to make it in some ways more playful. Towards those goals we also encouraged a spirit of open scientific practice, which grew into Brainhack a few years later. Early on I received the advice to help create the research community I wanted to be a part of — the Neuro Bureau is our way of doing just that.
AB: Could you comment on the Neuro Bureau’s role in mentoring trainees?
DM: I’ve never really thought of the Neuro Bureau as a mentoring-oriented organization. Mentoring implies a mentor and mentee, and the Neuro Bureau has always had more the spirit of a tree house, along with all the big ideas, camaraderie, shoe-string operations, and mischievousness that tree houses tend to have. Good mentorship is so critical when joining the neuroimaging community, but so is finding your own group of peers — a kind of research family. For us, the Neuro Bureau provides a space to try out new ideas, seek support when faced with the various challenges of research, and to feel that we have a place of our own in the wider community.
AB: Thank you Daniel for taking the time to sit down for this interview. Looking forward to your keynote at OHBM 2018.
By Shruti Vij
“I have always loved the idea of not being what people expect me to be!” - Dita Von Teese.
There isn’t another stalwart in neuroscience that this quote is better suited to describe. Be it her expertise in developmental cognitive neuroscience, her championing of novel techniques such as concurrent PET-MR scanning, her vocal demeanor or her punk rock persona! Bea Luna’s research persona lends an overwhelming sense of success in being not only innovative but also purposeful in her dogged pursuit of making one of the toughest periods in life - adolescence - better understood. She has to her credit hundreds of articles and many prestigious grants and awards, in addition to being the President of FLUX Society. With such lofty achievements, one might expect her to be a sombre intellectual but one is easily surprised by her bubbly and inviting personality! This article probably does not reflect my personal excitement at being able to interview such an amazing role model and to have so many take-aways from our conversation was definitely a bonus! What’s more, as you read on, you will discover that to be you in your own way is what leads you to success! Something that the world of academics needs to stop and think about while being in the rush to achieve the next big thing!
Shruti Vij (SV): The research focus of your lab at Pittsburgh is neurocognitive development. In particular, you are interested in brain maturation in adolescence. What motivated you to pursue this direction?
Bea Luna (BL): I was a crazy risk-taking teenager having fun and questioning everything, which probably contributed to being kicked out of school! But I remember being very aware that this was a really unique time of feeling free and invincible and that it was finite. I was very much into philosophy – thinking about what the mind means, what the brain means. It was during this time that I found out brain function could be measured with PET, and would fantasize about one day being able to use this to understand human consciousness.
As a grad student, I studied visual and attentional development longitudinally in premature infants who, due to their immature lungs, can have hypoxic ischaemic events in the brain. I was surprised and fascinated that it didn’t matter if half the brain was missing, or if there was a little dot of hypoxic ischaemia, it did not predict outcome in visual acuity and attentional processes. I thought, wow, how can that be? In adults it would be obvious what the outcome was likely to be. You could also think about diaschisis – where a small region could be injured but it compromises its connectivity to a whole bunch of other regions. So it became clear that what mattered, especially with regards to development, was the integrity of the functional brain, beyond structure, what the brain does with what it has as it is specializing. fMRI was just emerging and I was fascinated with the possibility of using this approach to look at development, which I concluded in my dissertation. However, I was discouraged from the ability to use it in pediatric populations, and I thought, ‘oh yeah, watch me’.
I did my postdoc in the Psychiatry department with John Sweeney where he saw that my developmental expertise could help us probe the prevalent theories that mental illness emerged in adolescence and neurobiological maturation could hold important clues. This was a perfect fit between my interest in developmental plasticity and brain functional integrity. From then on I just didn’t stop.
SV: And the field of cognitive neuroscience is thankful for that! What are some key questions that you think are going to be the big drivers in developmental cognitive neuroscience in the coming years and how is your research contributing to these questions?
BL: We are developing as a field ourselves. There has been great advances in mapping the regions and networks that show changes with age and their links to specific components of cognition. One of the areas that I have been a spokesperson about is the need to now understand the neural mechanisms underlying the developmental changes that we see with neuroimaging. Seeing pretty brain pictures is no longer enough. We need to speak to our colleagues that use animal models, those who are doing post-mortem work to build comprehensive models of development. This is how we can take the next step and make our work translational.
For example, we have an amazing project, that I’ll be talking about in my keynote, where we use a molecular MR machine that acquires MR and PET simultaneously. It’s very hypothesis driven and it considers different lines of evidence that suggests hyper-processing of the dopamine system in adolescence. Intuitively this makes sense but it’s very complex. I’ve literally stood up in front of big audiences and said “Hi I’m Bea Luna. I’m a developmental cognitive neuroscientist and I have overgeneralized the concept of dopamine function.” It’s a very complex system with pre- and post-synaptic processes and multiple types of receptors, all who could have their own developmental trajectory.
This is one of the ways that we are trying to understand mechanisms. I’ll also be talking about tissue iron as a proxy of dopamine. It’s difficult (though not impossible) to get testing pediatric populations with PET through the IRB so we’re finding this proxy for dopamine processing with MR-derived tissue iron. We are finding striking associations with tissue iron and PET markers of dopamine processing and how these are changing with age. You can see that talk for the punchline! I’ll also be mentioning a future direction with a new project that’s just started where we’re using spectroscopy at 7T using very complex acquisitions to look at changes in GABA and glutamate and the tissue iron proxy for dopamine. These three neurotransmitters are essential to understand plasticity. Animal models show molecular evidence for critical period opening and closing through puberty in association cortices, which motivates this new approach to understand critical period (vs. sensitive period) plasticity in association cortices in humans in vivo during adolescence. I find this tremendously exciting!
I come up with these crazy questions that compel me to probe them regardless of the complex technology it may require. Was I a PET expert? No. But I really want to understand what’s going on with dopamine. So, I go to my colleagues and say ‘elucidate me, tell me about how to use PET to answer my questions.’ It’s the same with MRS: ‘tell me how I can use this technology in the best way possible to answer my mechanistic questions’. I say, ‘come play with us’ and let’s use this fancy approach to answer some very cool questions. This is how we end up collaborating with people in other departments at Pitt and with other Universities such as MGH, and start discussions with Columbia and Stoney Brook to use new approaches. We also have some even newer projects looking at single-cell work in monkeys to further understand the actual neural basis of cognitive development.
SV: What do you think are the novel technological advances that will assist in uncovering brain maturation?
BL: Certainly PET/MR and MRSI have reached a level that can be applied to developmental questions. But there are tremendous advances in the analyses area including computational modelling, machine learning, and advanced statistics that can push the envelope as to what we can answer. We have been bringing that into the fore to make sense of these molecular mechanisms but also advancing what we can do with longitudinal neuroimaging data, resting state fMRI, and how to become informed by reinforcement learning. Resting state in particular is critically advancing in how we can control for head motion, a huge problem in developmental studies. Diffusion-weighted imaging has now advanced to a level that allows us to apply not just tensor models, but orientation approaches that afford us greater insight into the maturation of white matter connectivity making inferences beyond just myelination. For example, we know that glia dynamically influences myelination. Let’s not forget about the importance of behavioral assessments, which at the end of the day, we need to merge with our brain data to assess its relevance. Finally, Big Data approaches have had a huge impact in the power we now have to answer questions. We share our data as well as use other’s Big Data to replicate our findings. In sum, multimodal approaches to inform mechanisms, advanced analyses, and big data is where the field is showing great advancement.
SV: In today’s discussion on diversity and inclusion, what are your thoughts and how do you address these issues in your lab?
BL: First, I’ll tell you what my soapbox is. I was in the advisory council for the director of the NIH – this was in 2012. They’d just started to really speak about diversity. I was different to other minorities in the council. Understandably, they wanted to see enhancement and so on, but I thought ‘no, I don’t need your help because I’m a woman or I’m Hispanic. When I give a talk at SFN or OHBM I don’t want people to think ‘oh my god, look at the woman talking – and she’s Hispanic too.’ I said you know what Francis, white men have done a great job, and keep advancing things. That’s great, and we’re grateful – but I’m in a different place and am bringing insights that will never come to that closed club. So, actually, “you’re welcome!”. I’ve been in a lot of high level committees where I do feel in the minority, as a woman. I don’t blame the men, I have a husband, I have a son. I don’t think they mean ill – but I do notice that I have to prove myself, and it can take a while before I am finally listened to and my ideas be deemed critically helpful. White man are accepted much quicker. Again, I don’t think this is intentional at all and I get great satisfaction when I have won them over. I also don’t mind doing the extra work, it keeps me on my toes in science and keeps me humble. It is what it is, but I do see change. I see that men are really aware and trying to do better. Then again, my “dopamine” personality may be playing a role beyond typical diversity, ha ha.
SV: Your trainees speak very highly of you and the work environment that you have created in your lab. Is this purposeful? What are the things that you personally make an effort on to make things better and easier for your trainees?
BL: First of all, awesome! I am careful about selecting smart people that will fit into our dynamic cool atmosphere. Some may think “party lab” but in fact we are more about working hard and pushing each other in a respectful but humorous manner. Everyone in my lab loves what they’re doing and I don’t have to look over their shoulders. They’re self-driven, and that really works. I give them their space and I’ve been really lucky. Every grad student I’ve had has been amazing.
At the top of my list of favorite things is the one-on-one with my mentees. I talk to these young, super-brilliant people. They may not be familiar with these big questions that I have. So I have a discussion and there’s some theoretical aspects that take a while to understand – but I tell them not to worry. I’m planting seeds in their brain that will later grow. I tell them that they will deliberate and they will come up with the logical next step, which I am not interested in. Instead, I want to hear the other idea. I want to hear the one they’re embarrassed to tell me because it’s so outrageous, the one that makes you giggle. For me that’s great, because it’s usually in the context of bigger questions and leads to a bigger step forward. For them, it turns out that it grabs their passion, and then it’s their thing. They’re not following tightly in my footsteps. We go back-and-forth and think outside of the box with no limits. As a philosophy double major I loved to just boldly think beyond the obvious and now I can do this within my scientific questions with my brilliant students, who are so frikkin smart. But there’s a lot of laughter – sometimes too much!
There are a lot of bonds made in the lab – we’ve even had marriages. They are all discussing new methods and approaches and I love hearing how they all interact and help one another just for the joy of collaborating. I am careful not to bring in difficult people so as not to mess with our cool vibe.
SV: You are also the president of FLUX in addition to being an actively involved academic stalwart. How do you manage all this in the same 24 hours everyone gets?
BL: great question – I want to deliver an important message here. I think I can work intensely for a concentrated amount of time – but I try not to get into the office before 9 as I like my thinking space in the early morning before i go to the lab. Granted I’ll typically work until 7 or 7.30, and on the weekends I really try do minimal work. I think that’s super important. You need that space, doing other things, seeing your friends. Some of my friends have no idea what neuroscience is, a lot of them are artists and I think that gives you space to make the connections. If you’re always looking at the trees you sometimes struggle to see the forest, the bigger questions.
There are times when there are lots of deadlines, and it can be stressful. I’m in a medical school so you write grants, that’s what you do. Now I have an endowed chair so things are a bit easier but I always maintain a couple of R01s plus other collaborations and foundation grants. It used to be very stressful to know that you had to get a grant or sink. But for the last two grants, I thought enough, I’m going to love my the grant writing process. I now embrace and laugh at the innovative directions that I am willing to go. My first grants would take a couple of rounds to get funded with comments that the ideas were awesome but how could this be done, prove it! And we would! Now I have a track record and they get through more smoothly (knock on wood). I definitely never want to get a critique that everything is fine but “yawn”. When I have to write grants I get everyone involved and its a party with discussions going on everywhere and every white board filled with ideas and it’s intense and fun and we laugh a lot. I get home mentally exhausted but satisfied and my husband has a martini ready and feeds me. I don’t engage those who will be leaving the lab soon though since they will not be around, and I feel that they feel left out cause they want to play too.
By the way Flux is not an acronym, although everyone always capitalizes it. I made a great effort to not have an acronym. I chose the word Flux to always remind developmental cognitive neuroscientists that we are studying a dynamic process and we need to capture what is in Flux. I am so proud of the Society and the amazing conference that Brad Schlaggar, Silvia Bunge, and Bruce McCandliss and I made. We dreamed this up at a conference when we were all postdocs drinking at a pier late at night toasting to how one day we would bring developmental cognitive neuroscience into its own! By the way, that was another of those moments when people were saying no way that is too hard don’t do it, I didn’t even wince. Now as developmental cognitive neuroscientists we have a home and we bring people in to help unite us in advancing the field from David van Essen, Steve Petersen, Russ Poldrack, Michael Posner, John Gabrieli and on and on. We have intense days of hardcore science and then… party hard ending with crazy, and i am not kidding, crazy karaoke and people sweating and dancing from students to bigwigs. The idea being to break the science class system and for students to see that we are all in it together and for them to start forming bonds with each other that will end up in collaborations for great science.
SV: OHBM is largely made up of trainees at many different levels. They look up to people like you and would like your advice. What other advice would you like communicated to our large trainee audience?
BL: Whenever I find myself providing advice for people – and I see this a lot where people have to make choices, such as what lab to go to, what job, what country etc - some things that I think nobody ever tells them is to consider the whole package, to not put their personal life at the end of the list. For example, leaving the person they love to pursue what they think is the only path to career success. If you’re passionate about your science, you can make it work in many places. I personally chose to be where my husband and I both loved. That said, pursue the questions that you are passionate about, don’t compromise on that because this work is hard and can be stressful. But if you’re pursuing the questions that you are passionate about then you can deal with the annoyances. If you’re just going to do things because they’re convenient or they’re going to look good, then it’s not going to make you happy. For me the secret of success is to do what you really want to do, not what you think you should be doing.
Speaking of moving for work, I’m presently looking for postdocs, grad students, RAs to join the lab. If you’re into innovative thinking, great questions, and working with an awesome group doing new approaches then contact me, we’re actively looking for people. Feel free to contact me or my lab manager for further details!
SV: Thanks again for taking the time to share these wonderful pearls of wisdom with our audience! We look forward to your keynote next month!
The interview finished and as a trainee struggling to understand my personal growth within academia, I came away with a fresh look and a new mentor I felt I could seek out for advice! It also made me more confident that academics like Bea are what we are all fighting to be!
Martijn van den Heuvel heads the Dutch Connectome Lab, part of the Complex Traits Genetics Lab at the VU University in Amsterdam. The goal of his research is to understand the association between brain complexity and brain function in health and disease. We had the pleasure to interview Martijn and find out more about his career, and also get a sneak preview of his keynote lecture at OHBM 2018 in Singapore.
Tommy Boshkovski (TB): Can you tell us a bit about your background and your lab?
Martijn van den Heuvel (MvdH): I finished my undergrad and master’s in artificial intelligence, and Ph.D. studies in medical science at the University of Utrecht in the Netherlands. After finishing my Ph.D. I got a faculty position, and then my team and I recently moved to Amsterdam to the Center for Neurogenomics and Cognitive Research. There I built my lab on connectomics, we are quite a group of connectome enthusiasts; some of them are Ph.D. students, and some of them are postdocs now. The group is really multidisciplinary. We have a biologist, a physicist, mathematicians, psychologists, and engineers, and that's really my approach. I always try to make the group as interdisciplinary as possible, preferably with people that could do something other than what we can do.
TB: What motivated you to start a career in Connectomics?
MvdH: I’m not sure if anyone has intrinsic motivations to start a career in connectomics. The reason why I started to apply network science to brain imaging was actually by accident. I have a background in artificial intelligence, and I was trained in machine learning and neural networks, so to me that looked like a very obvious thing to do. I started to play around with small world network analysis, and then I got in contact with the big people in the field like Kees Stam and Olaf Sporns, and basically, that is how I rolled into the field.
TB: You are one of the keynote speakers at the upcoming OHBM annual meeting. Could you tell us how you got started with OHBM?
MvdH: I think OHBM was my first meeting that I have ever, attended, back when I was a master’s student. I was honored that my professor let me go (I think the meeting was in Budapest that year). It was awesome to be around all those people that were working on similar things. But I always had to choose between OHBM, ISMRM and SFN, and I think later on in my career I had the luxury to sometimes go to all three, and I really like that mix. The nice thing about OHBM is that it is very application-driven, so there are many people that are using the tools, but there is also (in more recent years) a growing interest in developing new tools in the field of network science and connectomics. I am part of a joint effort with Andrew Zalesky and Alex Fornito, organizing an (almost) annual educational symposium on graph theory and network science at the meeting, which is great.
TB: What can we expect from your plenary lecture?
MvdH: I’m going to tell quite a bit about the connectome. First I’m looking forward to providing a bird’s-eye view on the connectome field. I want to spend some time on “why are we doing this?” – so that’s the first part. Ideally, I want to put this into the framework of “if the brain is wired like it is, then does this also give certain vulnerabilities to brain disorders?” One of the ways that we are looking into this is by means of comparative connectomics. We got more and more interested in looking at connectome features shared across species. If these features are evolved through evolution and are present in many different species, then they might form the very core of the brain. I think it is really important to understand these ground rules of the brain because only then can we start understanding how the changes in the ground rules may lead to various different types of brain disorders. This will make up the second part of my talk, where I won’t focus just on one disorder, but, rather, I’ll talk about the wide range of brain disorders. Most of these studies are single disorders studies (i.e. we found property X or property Y to be involved in schizophrenia) but I think the field is ready now to start looking into more deep-rooted questions. I’m asking which of these effects are specific, and which of them are common across brain disorders. It is really important to understand the multidimensional aspects and multi-disease effect of connectomics.
TB: Which recent developments in the field excite you the most?
MvdH: There are quite a number of them, but what I really like is that the field moves a little bit away from just studying the connectome and starts combining this with other types of measurements. The connectome field is 10 - 15 years old, but it’s pretty nice to see that people already take it for granted. I am particularly excited by using for example gene expression data and combining them with connectome data, or using cell biology data, like cytoarchitectonics, and incorporating it into the connectome model. I think these developments are pretty cool to see from a multidimensional neuroscience perspective. What I also find exciting to see is that there is a lot OHBMof effort going on in improving the connectome field. Recent statistics papers show that it might be relatively easy to make a network, but then to study it in a very meticulous way, to have good case-control studies, it might actually be more difficult than we earlier thought. So there are great improvements in the last couple of years in new statistical methods that really also adopt the network perspective. Finally, there are so many efforts going into mapping connectomes of a wide range of species across different resolutions. A big part of the connectomics community is outside the MRI community, and I really hope that these communities will start to mix.
TB: Where do you see connectomics in the next 5-10 years?
MvdH: Connectomics is a young field, so looking into the future 5 or 10 years, or even 5 or 10 days is difficult. I do think that there will be more room for combining different types of data into the connectome model. We’re going to see more and more papers that combine EEG with fMRI, or diffusion with functional measurements or even beyond that, such as combining genetics with imaging, because the connectome alone is not going to give us the final answer. I hope that we are going to start using in-vivo connectomics, in a more clinical application. When we start to merge the machine learning field, the big data field, with the in vivo neuroimaging connectomics, there is room for very exciting discoveries: for example, some sort of personalized connectomics where we could use connectome imaging to make predictions on disease outcome, medications response, etc., and I think that could be feasible. It is going to be a rough road, and I’m not sure whether we are going to do this in 5 or 10 years, but I think it’s possible to use a clinical connectome approach on an individual level, like precision connectomics.
TB: What are your other interests besides connectomics?
MvdH: I’m Dutch, so obviously I cycle just like 17 million of my fellow countryman. I’m also quite interested in tech, like new gadgets or new developments in the machine learning and/or big data fields. Besides that, I’m a dad. I have two kids, which are of course the two nicest connectomes out there.
Professor Edward Bullmore has had careers in the clinic, academia and industry. He is the head of the department of Psychiatry at the University of Cambridge, the director of the Wolfson Brain Imaging Center and the head of a neuroimmunology research group at GlaxoSmithKline. His academic interests range from the clinical to the mathematical. He is perhaps most known for his work on analysing brain networks using the framework of graph theory while his current interest, described in his latest book, “The Inflamed Mind”, is neuroimmunology.
The understanding of psychiatric disorders is the thread that connects all of professor Bullmore’s diverse interests. The following interview probes into his past experiences and asks his advice for budding young scientists attending the OHBM 2018 annual meeting in Singapore.
Claude Bajada (CB): As a clinician, I found that the approach to thinking taught during a clinical course is very different to what is expected from a researcher. What are your thoughts on the differences between clinical medicine and medical research? And what enticed you to make the shift from clinical medicine to academia?
Ed Bullmore (EB): I agree the mindsets of a clinician and a biomedical scientist are often somewhat different. As a clinician you’re taught to convey a sense of calm certainty, or to reduce a complex situation to a much simpler diagnostic formulation or treatment recommendation. And at least when I was at medical school, in the 1980s, questioning the scientific basis for clinical wisdom was not always welcomed by senior physicians or surgeons! A scientific training, by contrast, is an education in learning to doubt or challenge everything, especially your own most treasured hypothesis or most precious results. There is certainly a tension between the reassuring bedside manner of a clinician and the oceanic scepticism of a scientist. Another very important difference between the two cultures is the status of numbers. Medicine was almost entirely non-quantitative when I was going through medical school; whereas in neuroscience and neuroimaging, mathematics is increasingly central. I think medical schools, at least in the UK, still need to do more to make doctors more mathematically competent and confident - and to provide proper career paths for non-medical scientists bringing their expertise from physics, maths and engineering into contact with the number-crunching challenges of modern biomedicine.
I switched to scientific training halfway through my clinical training in psychiatry. I was motivated by the idea that psychiatry was still at a relatively early stage of scientific development compared to other areas of medicine and I couldn't imagine being satisfied with a career solely dedicated to clinical practice in an area which I thought was very likely to see radical change. It wasn't a difficult decision for me in principle. But if I had been clinically specialised as a cardiologist, or some other area where the science base was already more evolved, it might have been a more debatable move. For people specialising in surgery or radiology, the number of training hours that must be dedicated to learning operational procedures is much greater than in psychiatry, and the financial rewards for focusing exclusively on clinical practice can be much greater than in psychiatry, so the decision to spend 3-4 years on a PhD is a much tougher choice. For any research-minded young doctors who might be reading this, I can say psychiatry is a highly recommended career move!
CB: When I hear the name Ed Bullmore, my semantic association goes: “Bullmore, Sporns, Graph Theory.” Were you always interested in the mathematical aspects of research? What first got you interested in Network Analysis? And how did your, now famous, collaboration with Olaf Sporns begin?
EB: My first research enthusiasm (aged 30) was fractal geometry, which I found intuitively very appealing as a way of quantifying the complexity of biological structures and processes, like MRI scans and EEG signals. However, my old-school medical education had left me completely unequipped with any quantitative skills. I was fortunate to find an excellent mentor, Prof Michael Brammer, at the Institute of Psychiatry in London, and applied to the Wellcome Trust for funding to do a PhD. I was interviewed at the Trust by Sir Stanley Peart, in 1992, who listened politely to what I had to say about fractals and then told me “of course what you’ll really find yourself working on is brain connectivity, isn’t it?”. I agreed with him immediately although that thought had not previously crossed my mind.
Karl Friston’s pioneering work on brain connectivity was very influential, and I also learnt a lot from Barry Horwitz, and through them I began to hear about Olaf Sporns. I admired the paper on complexity he wrote with Giulio Tononi and Gerald Edelman (PNAS 1994) but I didn't meet Olaf until we both attended the second Brain Connectivity Workshop, organized by Rolf Kotter in Dusseldorf in 2002. I liked his talk, about using graph theory to simulate computational networks that maximised the neural complexity measure from the PNAS paper; and several others at that meeting also opened my mind to the new physics of complex networks that was following from the seminal “small world” paper by Watts and Strogatz (Nature 1998) and the “scale-free” paper on network hubs by Barabasi (Science 1999). A few years later, in 2005, I met Olaf again at the Brain Connectivity Workshop in Boca Raton, where I presented some of the first results of using graph theory to measure topological complexity of human brain networks from resting state fMRI. That’s when we started talking more seriously about collaboration, which led to our first co-authored paper, a review of complex brain networks (Nature Reviews Neuroscience 2009) that has since been cited more than 5000 times.
CB: Your recent publications reveal a broad interest in psychiatric research, focusing on everything from developing methods to questions about the effects of drugs on the brain and much more. What are you working on at the moment? And what would you say is your current main interest?
EB: I am still working on brain network analysis or connectomics but since 2013 I have also become increasingly interested in the relationship between the mind, the brain and the immune system. The reason for this shift of focus goes back to my starting point as a psychiatrist. After 20 years of research, I couldn't help noticing that, although the field of neuroimaging and brain connectivity had grown tremendously, its real-life impact on mental healthcare was zero. By then I was edging into my mid-50s and I felt impatient to do something that might actually make a positive difference to the experience of people with depression and other mental health disorders in my lifetime. For various reasons, the strategy that appealed to me most was to pursue the idea that inflammatory responses of the immune system could cause depressive symptoms and, therefore, that anti-inflammatory interventions could provide a new therapeutic approach to depression.
I am not sure how many OHBM members will be acquainted with the immune system; I’m guessing not many. I knew a bit about it from my medical training in the 1980s but I was utterly dazzled when I took another look at immunology in 2013. We think neuroscience and neuroimaging has moved fast in the last 20 years, and it has, but scientific progress in immunology has been at least as rapid and its therapeutic impact has been much greater. The area I am working on is the interface between immunology, neuroscience and psychiatry – it’s called neuro-immunology or immuno-psychiatry and it’s growing rapidly. There are important questions for neuroimaging in this area: for example, how can we use MRI or PET to measure brain inflammation, especially microglial activation, more specifically and sensitively?
I have just published a book – called “The Inflamed Mind” – which summarises some of the background science for a general audience – and there are some short movies on YouTube which introduce the book in a brief and accessible way (here and here).
CB: What would you say to students, particularly medical students, who would like to start their research career? Particularly, what would you say to them if they were interested in technical subject matters but feel that they “come from the wrong background”.
EB: I always encourage students to recognise and pursue the interest that motivates them most deeply, almost regardless of any other consideration, because completing a PhD is a challenging process and you need to be highly motivated by your project if you’re going to get through it successfully. For medical students, there are some additional considerations, at least in the UK. There are basically two windows – you can do a PhD intercalated with your medical school training, so you graduate as MB/PhD. This works well if you are someone who knows what they want to do in research early on and if you have excellent time management skills. The other window is after completion of core specialist training – usually in general medicine or psychiatry – when there is an opportunity to take time out of the clinical training process to do a PhD, typically funded by a fellowship award from the Wellcome Trust or MRC. That is the route I took because I wasn't clear what kind of research I wanted to do until I was in my early thirties and had started specialist training in psychiatry. For UK medical students and recently qualified doctors, it is highly advantageous to get appointed to Academic Foundation Year (AFY) or Academic Clinical Fellow (ACF) posts because this will allow you to compete medical training and also spend a useful amount of time developing research interests and preparing a competitive application for a PhD training fellowship.
I think medical students with an interest in technical matters, like coding or statistics, should be encouraged. The world of biomedical science will increasingly need people who are both well-informed about the background biological and medical sciences and have the technical skills to handle big, complex datasets. So getting trained in both technical and biomedical skills can prepare you for an exciting career as a relatively rare and highly employable person! However, it is tough to learn technical skills from a low base and at the same time as keeping up with clinical training requirements. I think it is important to have an excellent mentor and also, in my opinion, to focus your technical learning priorities on solving the scientific problems that you are most motivated to address. A masters course in bioinformatics or image analysis could be a useful training step for some people but personally I found it easiest and most rewarding to learn technical skills when I could see immediately how they would help me answer the specific research questions I was interested in at the time.
CB: You also work for industry, do you see that as another career change or was the move to industry a natural progression? What are the difference between working in academia and working in industry?
EB: I started working half-time for GlaxoSmithKline in 2005. My original motivation was that I wanted to contribute to development of new treatments for mental health disorders and, much though I love(d) connectomics, I couldn't see that neuroimaging research in an academic setting was likely to have much impact on mental health practice in real-life.
I have really enjoyed the experience, for the last 12+ years, of working in two organizations with two rather different cultures. I have found it stimulating, refreshing, and I have learnt a lot that I would not have learnt if I had followed the more conventional path of staying fully embedded in academia.
Industry has offered me the chance to think and work broadly, across a wide spectrum of medicine and therapeutics, whereas the life of an academic tends to become proressively narrower and deeper in focus. Industry culture is also strong in terms of team-working and strategic planning, and the standards of statistical analysis and data management are high. In contrast, a tenured academic enjoys an extraordinary degree of intellectual freedom and the opportunity to work with highly talented younger people, as students or early career researchers. There are pros and cons to both organizational cultures. I would encourage people to keep an open mind about any opportunities that might arise to work in the private sector. It can be very exciting and, at least in the UK, there are increasing efforts to make it easy for people to move back-and-forth between industry and academia over the course of a career.
CB: OHBM 2018 will be held in Singapore in June, and is likely to be the first conference experience for many PhD and MD students. Such large events can sometimes be overwhelming. Can you remember your first big conference? And what advice would you give to newcomers?
One of my first big conferences was actually the first OHBM meeting in Paris in 1995. I thought it was electrifying to be in the same room as many people whose papers I had been reading for years but had never seen or met before. However, OHBM has got bigger since then and the scale can be intimidating. I would encourage newcomers to attend the educational program before the main meeting starts. The OHBM educational program has gone from strength-to-strength and is one of the best possible places to pick up on the state-of-the-art in neuroimaging methods. It is also a friendly atmosphere and a great opportunity to ask questions, introduce yourself to speakers, and connect with others who share your interests and are at a similar level of training. Once the main program starts, I would be sure to attend any smaller, early morning symposia that are focused on topics of personal interest. I would look through the poster schedule and make a point of visiting posters presented by people whose work you admire or you’d like to get to know. I would enjoy the social program for its own sake and also as another opportunity to get talking to the people you want to meet. Wear your name badge and consider using a business card so other people can easily remember your name. The key thing is to meet people and not to spend all your time sitting in the main hall passively listening to talks, or back at your hotel reading the abstracts! I am naturally quite shy so I don't find this particularly easy advice to follow myself. But I have discovered that if you have the courage to step up to someone, with a smile and a handshake, and say something like: “Dr X, I just wanted to introduce myself because I really liked your paper/talk/poster on Y…” then almost always you will find that Dr X is very open to starting a conversation.
CB: Finally, please be honest, are you reviewer 1 or 2?
EB: I hope I am not too often the legendary third reviewer who has a problem with the paper that nobody else recognises but can nevertheless be awkward enough to knock a good paper out of contention! My only advice for dealing with peer review is to remind yourself that it almost always improves the ultimate quality of the work to go through peer review, however uncomfortable it may be at the time, and it is an integral part of the scientific process to do so. I think you will generally have an easier ride if you respect the position your reviewer is coming from and try to deal with their points as constructively and clearly as possible. I usually recommend making changes to the text or supplementary material rather than writing long tracts in the rebuttal letter that do not change the paper itself. And take opportunities to be a reviewer yourself so you learn what kind of issues you should try to pre-empt when writing your own papers or responding to peer review.
By Jean Chen
Dr. Wilder Penfield once said that “the brain holds within its humming mechanism secrets that will determine the future of the human race.” And yet, most of us would agree that the brain remains the least understood organ. How do we start to understand how the brain works? Prof Gustavo Deco’s approach, one of our OHBM2018 keynote speakers, is to try to build one.
In 2001, Gustavo was awarded the Siemens "Inventor of the Year" prize for his contributions to statistical learning, models of visual perception, and fMRI based diagnosis of neuropsychiatric diseases. He has published 4 books, more than 258 journal publications and 34 book chapters. He has also filed 52 patents in Europe, USA, Canada and Japan. He was awarded an “Advanced ERC” grant in 2012 and he is member of the Human Brain Project (EU flagship).
Jean Chen (JC): As far as I know, you completed your PhD in atomic physics. How did you enter the field of neuroscience? How did these two fields come together for you?
Gustavo Deco (GD): When I got my first PhD in Physics in 1987, I thought that I would dedicate my research career to this field. However, after a postdoc at the University of Bordeaux in France and a two-year (1988 to 1990) postdoc from the Alexander von Humboldt Foundation at the University of Giessen in Germany, I found my focus shifting. I was absolutely fascinated by neuroscience and neuropsychology and decided to change my focus. Very broadly, I was drawn to these fields and to the simple question of how the brain works. I want to understand how the brain processes information. I wanted to understand how the brain works. I was, and I am now, convinced that a good formation in physics, especially in theoretical physics, is absolutely an advantage for investigating the brain. For example, in my research I have used a lot of tools from Physics, such as statistical physics, nonlinear dynamics, etc. I went to Munich, and began working for Siemens in their research center. It was there that I started my career in Neuroscience. At Siemens, I created one of the first Computation Neuroscience groups in Germany. In 1997, I received a PhD in Computer Science from the Technical University of Munich (Dr. rer. nat. habil.). In 2001, I received a PhD in Psychology (Dr. phil) from Ludwig-Maximilian-University of Munich.
JC: Your interests are broad, and you have made important contributions to computational neuroscience, neuropsychology and psycholinguistics, to name a few. How would you describe the importance of mathematics to neuroscience and psychology research, in the present and future?
GD: We cannot build models of the brain without math. We cannot model cognitive processes without math. To sum it up, I'm absolutely convinced of the necessity of mathematics for being able to express in a quantitative and systematic way the laws that regulate the functioning of the brain. The main reason or intuition, is that we are dealing with a huge, complex, nonlinearly coupled and stochastic system (involving billion of neurons and synapses that are coupled, stochastic and nonlinear). It is impossible to intuitively "speak" or "describe" such systems (even a simple system of two feedback-coupled neurons is difficult!), but we can understand and study them by expressing and investigating explicitly the equations, math, describing the brain. If we renounce that, we do only phenomenology… and we know what we can expect from that… nothing.
JC: I also understand that your most cited research focuses on computational modeling of spontaneous neural activity, the foundation of resting-state networks, and this work is incorporated into the Virtual Brain Project. What is the Virtual Brain Project, and how did it get started?
GD: Yes, I was very active in modeling the whole brain (now not only spontaneous activity but also task and different brain states, like sleep and anesthesia). The implementation of those models in a public, easy-to-use platform is fundamental for making the models available to the community, and especially to interested researchers without a strong computational background (eg. clinical researchers). The Virtual Brain Project was a fabulous initiative that started thanks to the McDonnell Foundations and the team working out of many enthusiastic labs. The initiative is led by Randy McIntosh (Toronto), with strong contributions from the labs of Giulio Tononi, Michael Breakspear, Olaf Sporns, Viktor Jirsa, my lab and many others.
JC: What is the next step or the main challenge in improving the ability of your computational models to predict biology and behaviour in brain diseases?
GD: Neuroscience, especially computational neuroscience, is a new field, and now is the most exciting time for the field. There is everything to discover! We have many of the required elements to create the first theories of computational neuroscience. I'm very interested in whole-brain dynamics and modeling. Neuroimaging has opened an unprecedented window on human brain activity, raising great expectations for novel mechanistic insights into brain function in health and disease to emerge. Unfortunately, the largely correlational findings have not delivered the anticipated outcomes yet. In contrast, a computational framework will allow for causal manipulation of models of multimodal neuroimaging data, opening up for characterisation of biomarkers of disease subgroups and a better understanding of underlying mechanisms. Furthermore, adding a coupled neuromodulator system using receptor binding data will pave the path for novel methods for rational drug discovery in silico.
I think the next challenge is to go from correlational neuroimaging studies to what we call, together with Morten Kringelbach (Oxford), causal neuroimaging. So in my view, the challenges are: 1) to develop and refine our novel framework for Causal Whole-brain Neuroimaging Modelling using sophisticated whole-brain dynamical models of multimodal neuroimaging data which can be manipulated off-line in silico to accurately describe causal mechanisms underlying human brain activity; 2) to apply the framework to the diagnosis of neuropsychiatric diseases, and to the design of therapies and their monitoring. In particular, one can use the model to exhaustively stimulate a realistic subject specific fitted whole-brain model in order to detect which type and locus of stimulation is more effective to re-establish a healthy dynamic of the whole brain.
JC: What are the main projects that your lab is focusing on currently?
GD: The main projects we are working on are the Human Brain Project, many other team projects of the EU, a large project from Germany together with Max-Planck in Leipzig (collaborator: Angela Friderici), and many others… The main issue that I see is to extend whole-brain models beyond just resting state as I described above.
JC: Can you provide a few pieces of advice for junior scientists in our field?
GD: As I said before, our field is a relatively new field, and now is, in my view, the most exciting time for the field. Junior scientists should study what they want. Don't be influenced by anyone. They should really investigate what motivates them. At this stage in their career, when they are learning how to be good scientists, it is an exciting time and they should take full advantage of it and study what really interests them.
But I'm convinced, and so I tell my students, that the 21st century is the century of Neuroscience and Genetics (but especially the former). I left physics and Quantum Mechanics. Although those fields were extremely interesting, challenging and mathematically sophisticated, all the main elements and basic concepts were already developed at the beginning of the 20th century. I always felt jealous of the scientists that were working during those times … Schrodinger, Pauli, Bohr, amongst others, they developed everything!!! I tell my students that I really felt a kind of “romantic nostalgia” for that time. When I switched to Neuroscience, I felt (and still feel) that we are now reliving those same exciting years. We do not have theories, but we have millions of interesting questions and the experimental technology for accessing the right data… So, our task is incredibly important, namely to develop a theory of the brain… I would recommend all the junior researchers to work on that!
“In order to be a mentor, and an effective one, one must care. You must care. You don’t have to know how many square miles are in Idaho, you don’t need to know what is the chemical makeup of chemistry, or of blood or water. Know what you know and care about the person.” — Maya Angelou
The online mentorship program is an ongoing initiative launched by the OHBM Student and Postdoc Special Interest Group in early 2017. In this international initiative, mentors and mentees from around the globe are matched on the basis of their experience and expectations. The mentor supports the mentee’s growth by providing advice on topics such as - but not limited to - academic development, grant writing, and work-life balance. What is unique about this program is that every member of the OHBM community can be mentored and can also be a mentor. For example, the program has early career principle investigators (PIs) who seek mentoring by more established PIs, as well as senior PhD students who mentor trainees just starting out. As a rule of thumb, the program maintains at least 3 years of “experience difference” between mentors and mentees, with mentor-mentee pairs often being close in career stage. Currently, there are 424 participants in the program. In this blogpost, we compare statistics from two successive rounds (Round 1, 2017 and Round 2, 2018) of the mentorship program: 252 participants signed up in Round 1, and an additional 172 participants signed up in Round 2.
Relative to Round 1, geographical distribution of brain mappers joining the mentorship program in Round 2 remained largely unchanged, with two notable exceptions: gain in members from the Middle East, and drop in new members from South America.
Distribution of participants with respect to career stage was similar in both rounds, with PhD candidates being the most prevalent.
Round 2 observed a decrease in the fraction of mentees who declared an interest in starting a lab, relative to mentees who were either undecided, or planning to move to industry. This effect might be associated with constantly decreasing percentage of faculty jobs as opposed to PhD jobs, which is a strong trend in academia since the 80s.
In line with the above observation, Round 2 of the mentorship program saw a drop in the demand for advice related to starting a lab, and a small increase in the demand for advice related to transitioning into industry from mentees.
Looking at the summary statistic of all participants in rounds 1 and 2 coming from USA and Canada, Europe, Australia and Asia, an outlook on mentorship was found to be similar globally (Figure 6).
In both rounds, mentors declared similar areas of expertise, mostly related to building a research career. This included taking career opportunities, finding postdoc jobs, developing relationships with coworkers and general advice on career development. Only a handful of mentors indicated expertise in coaching mentees on transitioning to industry.
In summary, participants were gender balanced, and while geographically they hailed from around the globe, the vast majority were from North America and Europe. Over 25% of participants in the programme were willing to take on a double role (i.e. both as a mentor and a mentee), thus indicating a willingness to give back to the OHBM community. While the program saw an increase in requests for mentoring on non-academic career options (e.g. transition to industry), this was not followed by an increase in mentoring capacity in these areas. We would thus like to reach out to mentors with experience in industry and entrepreneurship to join the mentoring initiative. Overall, the expectations and competencies declared by participants around the globe were similar, thereby indicating that an online mentorship platform is necessary and useful for the OHBM community.
Note: In addition to the online mentorship program, the OHBM Student and Postdoc Special Interest Group will be holding its second “Annual Mentoring and Career Development Symposium” at the annual OHBM meeting this year. Hope to see many mentors and mentees at the event on Tuesday, June 19th!
By Nils Muhlert
Professor Leah Somerville is an associate professor of psychology and director of the Affective Neuroscience and Development lab at Harvard university. She was recently awarded the Early Career award by the Social & Affective Neuroscience Society. Here we find out more about her academic career path, and what we can expect from her keynote speech at OHBM2018 in Singapore.
Nils Muhlert (NM): First, can you tell us about your career path – how did you get into neuroimaging?
Leah Somerville (LS): I started working on brain imaging research as an undergraduate at the university of Wisconsin. I was working in a couple of different brain imaging labs, right when the first research dedicated scanners arrived at the university. I was one of the first people to have the opportunity to run experiments on it – along with a team, of course, of other researchers in the labs I was working in.
I got that little thrill moment of seeing a person’s brain image pop up on the screen. Maybe others have had a similar experience. I still have that feeling every once in a while, it hasn’t completely gone away! I find neuroimaging so fascinating and powerful. From there I tried to orient my training towards continuing my brain imaging research, and in particular, fMRI-based research. I’ve studied emotion and anxiety-related processes. I’ve also studied motivation and cognitive control. Now in my lab we’re focused on understanding how those processes change with ongoing brain development through adolescence.
NM: What would you say is so special about adolescence in the context of human development?
LS: There’s a lot I could say here - I’ll try to keep it short! Adolescence is a time of life that on the surface level is associated with a number of important challenges, that individuals are facing sometimes for the very first time.
Adolescents are people who are faced with independent choices about how to act, who to affiliate with, what kind of goals they like to hold for themselves. At the same time there’s increasing demands on their self-control. They’re becoming more and more self-guided in the way that they’re interacting with the world. We can sometimes think of them as novice independent people who are still developing the toolkit that can support mature independent actions.
We find that ongoing brain development facilitates a number of great achievements at this time of life. But it also places a number of constraints on the way in which adolescents might optimize their behavior in certain situations. We’re very interested in understanding the interplay in that – thinking about adolescence as a very adaptive and useful time of life but also one that differs from adults in a number of important ways.
One insight that has fascinated me is looking at brain development measures and asking “when does a person become fully mature?” It may seem like an easy question or one that could be measured using a single modality. In fact, the answer you get really differs when it comes to brain structure or function or network properties. It’s especially surprising that on certain measures – including measures of white matter – the developmental changes continue to play out throughout the twenties and perhaps even through the thirties. So one thing that’s interesting, as an extension of that, is thinking about how we decide when a person is mature from a societal standpoint.
NM: In your work you also discuss socioaffective circuitry – how do changes in that circuit map on to the behaviors we see in adolescence? And what have you found out about that over the last decade?
LS: In our lab we tackle this from different angles – so I’ll let you know about one in particular that I’ll be talking about in OHBM.
We’re very interested in the intersection between motivation and cognitive control. That is, the degree to which motivational cues in the environment – potential rewards and punishments for example – can shape the way in which a person is able to optimize their cognitive control in a given context.
We’re interested in the shift across development, in which individuals across the ages can recognize situations that hold different motivational values. They might want to perform better in certain conditions than in others – either to avoid punishment or to obtain rewards. All of the detection and assignment of values seems to be very consistent in early development. But the degree to which we can take that information and use it to guide our goal-directed actions in the moment, seems to be continuing to develop well throughout adolescence.
One arm of our work is in trying to understand how the dynamic interactions in cortico-striatal circuitry (including the dorsal and ventral striatum and lateral prefrontal cortex) coordinate and give rise to motivation-guided cognition. This is something that we’ve seen play out and continue to change and refine well throughout adolescence and into early adulthood. This is one area of work that we’re excited about.
Another area we’re interested in is adolescent attunement to their social environment. This is a time of life that’s associated with dramatic changes in daily life; individuals are forging new independent relationships for the first time and there’s a lot of volatility in adolescent relationships. They are falling out of favour with one another more frequently than adults would be, giving them lots of opportunities to get feedback about how they’re doing socially. Another arm of our work is therefore to understand how adolescents learn from feedback and use positive and negative social feedback as learning cues to inform how they should feel about themselves in a given situation and how they should feel about other people.
We’ve seen in a couple of studies that when adolescents are on the receiving end of negative social feedback they tend to take that as a very strong cue to influence how they feel about themselves. This would result, for example, in a reduction in the momentary feelings of self-worth or self-esteem. Adults actually show a bias in the opposite direction. They have different strategies in place that allow them to offload or buffer themselves from negative feedback and maintain a positive self-concept, even in the face of very opposite social information. We’re really interested in understanding how learning processes – again subserved by striatal-based systems – might be biased towards learning from negative or positive information in the social domain at different points of life.
NM: And how does this system seem to change from early to late teenage years, or even people's early twenties?
LS: Well we carried out a study of individuals from age 10 to 25, and found that there is a period from early to mid-adolescence, perhaps from 12 to 15, that negative feedback had a strong negative impact on their self views. Whereas individuals of college age seem to have a lot of strategies in place already to buffer themselves from negative feedback. So this is one time period when a few years of age makes a large difference in terms of how these cues are incorporated into learning about themselves and other people.
NM: Thinking about how social media might tap into this, and perhaps exacerbate the concerns that adolescents have: as social media has become a more integral part of their everyday lives, has this had negative and positive consequences?
LS: Great question and one that I don’t have a scientific answer for but I’m happy to speculate!
This is a very hot issue now – thinking about how developmental stage might manifest the influences of these kinds of media processes differently. It’s only in very recent generations where people have taken up a lot of social interactions online. This is something that has not been subjected yet to empirical study.
There is a lot of speculation that perhaps social media is detrimental to adolescent development. Adolescents themselves are quite happy at having the option to socialize over the phone and over the internet. They say it helps them maintain strong social bonds, it gives them lots of information. They can stay attuned to the goings on of all of their friends more easily.
There is also the potential for social media to have certain negative and perhaps unintended consequences. One that has been suggested by our work is that social media has been almost designed to elicit and deliver feedback to people – by getting friended, getting thumbs-up or the absence of a like or lack of response from somebody. This can be interpreted as negative by someone or by people on social media.
The way we see it is that there can be very positive interaction on social media but there’s also the potential for a higher frequency of negative feedback, or the absence of positive feedback being interpreted as negative feedback. We’ve shown that negative feedback has a very potent influence on adolescent self-views, so that very high frequency of receiving negative feedback online could have a more detrimental effect during adolescence than other ages.
Developmental scientists have often had concerns about the effects of new technology influencing self-views. When I was a kid this would have come up with video games – suddenly people would have a Nintendo in their house, there was a wave of concern about that. At this point we just don’t know enough to have a definitive evidence-based account about whether social media is a good or bad thing for adolescence.
NM: Turning to your other work, what would you say are the scientific achievements that you’re most proud of during your career?
LS: I’m not sure if I’d call this a scientific achievement but I’m most proud of having had the opportunity to run my own lab.
I never thought I’d be a PI. It has been one of the most challenging and rewarding things I have ever done. I feel proud and gain a lot of reward from it, particularly when I interact with my trainees. They conduct great work, are great people and are becoming great mentors in their own right! It makes science very fun to do in our group. Fostering an atmosphere that makes science fun and exciting and collaborative is something I’m very proud of, and is down to the efforts of my whole lab.
NM: And to reflect the quality of your mentoring you were awarded the Everett Mendelsohn excellence in mentoring award. When you look back at your own career, which people could you point to that offered you good advice during your career, and how has that affected how you interact with your own trainees?
LS: I’ve been very fortunate to have had a number of wonderful mentors throughout my training. They’ve helped me bridge gaps into the next steps of my career – giving me advice, and sometimes tough love when I needed it! This includes my graduate mentor and my postdoctoral mentor, BJ Casey. I would point out BJ in particular – she was a big part of me discovering this very strong interest in developmental neuroscience, particularly after trialling out a number of different topics of study. That one fit for me in very large part because of the support in mentoring from her.
It’s important to mention that at first I didn’t realize that every trainee needs something different from a mentor. You need a lot of flexible thinking when you’re mentoring to understand what each person needs at different points in time. This of course evolves at different points of training. They might start by needing more hands-on help and more topically-focussed advising. But watching a person beginning to strive for independence and allowing for independence is something that I work hard to detect and accommodate.
When I became a PI I didn’t realize that I would still benefit from mentoring myself. I still have mentors who guide me and I don’t think anyone is ever quite finished in needing mentoring, advice and guidance. I have a number of colleagues – both peer-age going through similar career stages, as well as more senior mentors – who are still helping to guide me. I am very appreciative of that.
NM: And finally, your OHBM 2018 talk – can you give us a sneak preview? Which gems from your research career have you decided to focus on?
LS: Well I’m very excited about being invited to speak at OHBM and having the chance to go to Singapore. I’ll be talking about two main themes: adolescence as a phase of the lifetime associated with ongoing and dynamic brain development, in particular in development of functional brain connectivity.
I’ll also specifically focus on understanding the interactions between motivations and cognition as a test bed to think about how ongoing brain development would lead to important shifts in behavior. In doing that I’ll present some specialized studies that were conducted in my lab in Harvard, as well as some broader projects that we’re currently working on.
Most notably we’re one of the groups completing the human connectome project on development – a large scale ‘big data’ style project - that will ultimately collect brain imaging data on over 1,300 5-21 year olds. This is an ongoing study that we are about half-way through collecting data for. It’s partly longitudinal and partly cross-sectional, and it’s designed to help us really understand both fundamental patterns of brain connectivity that are changing at the basic neuroscience level as well as the implications of those connectivity changes for behaviours including motivated behavior and cognitive control.
So I’ll be discussing how we approach these problems from a broad, big-data standpoint and how this can complement the more specialized work that we’re doing.
NM: We’re definitely looking forward to that – many thanks for taking the time to speak to us and we’re looking forward to your talk in Singapore.
By Elizabeth DuPre and Kirstie Whitaker
This month we continued our Open Science Demo Call series by speaking to Ariel Rokem, Dora Hermes, and Tammy Vanderwal about three initiatives they’re involved with that promote openness in neuroimaging research.
Ariel introduced us to NiPy--short for NeuroImaging in Python--which is a large community-of-practice to support using python for neuroimaging. He explained that NiPy exists within the broader SciPy--short for Scientific computing in Python--community, and it unites many individuals who use Python in their scientific analyses. As open communities, Ariel pointed out that anyone is welcome to use the NiPy and SciPy software as well as to participate in its development. If you’re interested in hearing more, he encourages you to check out the NiPy mailing list or the annual SciPy conference!
Dora told us about iEEG BIDS extension proposal, a proposed extension to the BIDS standard for structuring human intracranial electroencephalography (iEEG) data. She explained that to date, current challenges with iEEG data sharing include the large variability in both electrode locations as well as data formats across sites. The proposed extension will create a standardized structure to store iEEG data and metadata, allowing for novel, multi-model analyses via integration of iEEG with MRI, MEG and EEG. To contribute to the development of the iEEG BIDS extension, Dora encourages checking out the current draft or commenting on the BIDS mailing list.
By Valeria Kebets, Csaba Orban, Thomas Yeo on behalf of the OHBM 2018 Local Organizing Committee (LOC)
As we’re swiftly approaching June, we thought we would follow-up our previous blogpost with 10 practical tips to help you make the most of OHBM 2018 in Singapore.
1. CLIMATE: Singapore has a hot and humid tropical climate. The air temperature remains in the mid-twenties (~75°F) even at night, so don’t be surprised if you break a sweat after only a 10 minute walk. For daytime walks, sunscreen is recommended as the UV index can reach extreme levels. Buildings tend to be heavily air-conditioned, so you may also want to pack a sweater for the conference. Also note that the weather is unpredictable, and heavy thunderstorms can develop in just a few minutes, so your weather app is unlikely to be helpful.
2. FOOD: If you want to grab a quick bite during the conference, there are many cafes and restaurants in the same building (listed here). There are also plenty of dining options at walking distance from the conference centre such as Gluttons Bay, Chijmes and Bussorah street. Shoppers will be glad to know that most stores in the city are open until 10pm, including on Sundays.
3. TRANSPORTATION: The best way to take advantage of Singapore’s public transportation system is by purchasing an ez-link card (same concept as Oyster card in London). Ez-link cards are sold at the airport and at most MRT (subway) stations for a $5 (3.75 USD) deposit. Ez-link works on all buses, MRT lines, and can also be used to pay in some stores, e.g. 7-11s, and some taxis. Pro tip: Remember to tap out with your card when alighting buses to avoid getting charged the maximum fare.
4. MAPS: Google Maps or Citymapper are great for figuring out the best combination of MRT/bus/walking to get anywhere on the island, including expected travel times, when to alight buses (stops are not announced), and the fastest way to exit MRT stations. Follow this link for directions to the conference centre.
5. TAXI/RIDESHARE: All Singapore taxis operate based on metered fare. There is no Uber, but Grab provides a similar service. There are separate pick-up points for metered and Grab taxis at Changi Airport. Pro tip: If you want to keep costs low avoid the Chrysler Cab (black taxis) in the airport taxi queue.
6. GRATUITY: Tipping is generally not expected in Singapore. Most restaurants automatically add a 10% service charge and a 7% Goods and Services Tax on the bill.
7. PAYMENT METHODS: Most places in Singapore will accept credit card payment (VISA/Mastercard, though usually not AmEx). However, do keep some cash for dining in hawker centres. ATMs are widely available in the city and airport.
8. LIQUOR TAX: Singapore imposes an excise duty on all liquor, so expect to pay between $10 - $14 (~ $9 USD) for a small bottle of beer in restaurants or bars. Pro tip: Duty free stores inside the airport terminal are exempted from the liquor tax.
9. MEDICATION RESTRICTIONS: Singapore has a strictly enforced no tolerance policy with respect to possession of illicit substances. Note that certain prescribed psychotropic medications (e.g. sleeping or anti-anxiety) may require you to apply for a license at least 10 days before your arrival. You can read more about this here.
10. RELATED EVENTS: Be sure to check out the satellite events before and after OHBM. The events kick-off with PRNI (June 12-14), OHBM Hackathon (June 14-16; Sold out) and BrainStim (June 15-16). The Chinese Young Scholars Meeting takes place June 19. There are also three post-conference workshops organized by the local brain imaging community: Multimodal Neuroimaging for Mental Disorders (organized by yours truly; June 22), Brain Connects (June 22) and Nonstandard Brain Image Analysis (June 22-23). Attendance is free but make sure to register early--while there are still seats!
If you haven’t already, we highly recommend you to check out the brain in SINc website for more in-depth information on food, sights & attractions in Singapore curated by the Local Organizing Committee.
We look forward to welcoming you next month in the Lion City!
By Jean Chen
As part of the OHBM International Outreach effort, we found about the experiences of Iranian trainees. Many of us in brain imaging have met and worked with Iranian trainees, who outnumber trainees from most other Middle-East countries. By hearing the trainees’ stories, we get a snapshot of the circumstances behind their decision to leave Iran as well as their aspirations in foreign lands. In this post, we speak to current and former trainees, including:
Jean Chen (JC): How much exposure to brain-mapping research did you have as undergraduate students in Iran?
Aras Kayvanrad (AK): I did not have much exposure to brain mapping research as an undergrad student. I completed my undergrad more than 10 years ago and at the time there was little brain-mapping research in the country. However, things have changed now and there are several research groups working in the area of brain-mapping. There are more opportunities for undergrad students to learn about brain mapping research through talks, workshops, seminars, etc.
Sana Nezhad (SN): During my undergrad in Electrical Engineering we had a course called " the Application of Electronics in Medicine". It was in that course that I received my first academic exposure to brain-mapping research, which actually motivated me to do a Masters in Bioelectronic Engineering in the University of Tehran. There we had one year of coursework, which exposed me to the use of EEG, MRI and CT for brain mapping. I also got to know about quantitative methods of analysing the data we acquire using these methods. For the second year of my Masters I was required to complete a research project on MRI data acquisition, and although my project was focused on body imaging, I had classmates doing fMRI and MRI projects on the brain. As a result of group meetings, I learned about their research.
Mahdi Khajehim (MK): My personal exposure to brain-mapping only started when I took the “introduction to biomedical engineering” course as an undergraduate student and for the first time got familiar with some methods like MRI and fMRI. However, I think as a result of multiple government-supported programs and increased interest to brain-mapping, this pattern has already started to change. Nowadays, undergraduate students in Iran have a much better opportunities to get familiar with this field through talks, workshops and summer schools, such as the Iranian Summer School of Cogntive Neuroscience. These are hosted by many different universities and institutions.
Arman Eshaghi (AE): During my undergraduate studies (Tehran University of Medical Sciences), I worked on at least two different projects in which we used advanced neuroimaging methods (DTI and fMRI) for patients with multiple sclerosis and neuromyelitis optica. My work was conducted with Professor Mohammadali Sahraian at the Sina Multiple Sclerosis Research Centre, which is affiliated with the Tehran University of Medical Sciences. I was also in active collaboration with UCL Institute of Neurology in London working with Prof. Olga Ciccarelli.
Mostafa Berangi (MB): During my undergraduate studies in Electrical Engineering, I took some courses in Biomedical Engineering, and they really interested me. As I became familiar with the multiple aspects of Biomedical Engineering, I was particularly interested in the field of MRI. That is the main reason for my decision to pursue brain imaging for my graduate degree.
JC: How would you describe the Iranian brain-mapping landscape? Are there major research programs or meetings that you were aware of as an Iranian student?
AK: Not as a student -- as I mentioned at the time I did my undergrad, there was not much brain mapping research going on. However, the growth of brain-mapping research has accelerated in recent years, and several research bodies have been established recently providing financial and/or technical support to researchers in this area, which can potentially further facilitate and expand brain mapping research in the country. Most notably, the Cognitive Sciences and Technologies Council (COGC) provides funding for brain-mapping research through a variety of research grants. Moreover, the recently-established National Brain Mapping Laboratory (NBML), equipped with state-of-the-art scanners, has further paved the way for brain mapping research in the country.
SN: There are several brain mapping groups specializing in advanced quantitative analysis of brain-imaging data generated through different modalities. I get the sense that In Iran there is a shortage of data-acquisition accessibility due to limited resources, however most active research groups overcome this problem through collaborations with universities abroad. For example, I had collaborations with a cancer centre based in the UK and received half of my data from there. This lack of imaging resources drives the research towards quantification methods rather than data acquisition approaches.
MK: In my perspective, the Iranian brain-mapping field has already started to grow at a promising pace. Thanks to increased government support through funding agencies like the Cognitive Science and Technologies Council (CSTC) and greater availability of required infrastructure that is an essential part of this field, there is now a rising interest to do research in brain-mapping. Moreover, some newly established institutions like the National Brain Mapping Laboratory (NBML) in conjunction with some older ones like the School of Cognitive Science are also playing a crucial role in expanding the field among the Iranian researchers and I personally benefited a lot from attending educational events hosted by these institutions. It all adds up to expect an even better future for this field in Iran.
AE: There have been active institutes working on animal neuroimaging (in addition to human) located in Tehran that are affiliated with top Iranian universities, including the Institute for Fundamental Physics and the Institute for Cognitive Science Studies. There are new centres such as the National Brain Mapping Laboratory, which did not exist when I left Iran in 2014. There are also groups working inside university hospitals including the Neuroimaging and Analysis Group. Therefore, in my opinion Iran can have a bright future in science and in particular neuroimaging in the Middle East.
MB: In Iran, the best students choose to go to Sharif University, University of Tehran, Amirkabir University of Technology, Iran University of Science and Technology, Khaje Nasir University and Shahid Beheshti Medical University (in that order). In terms of the field of brain mapping, from my perspective, the University of Tehran and Amirkabir University are the top institutions. I feel that these institutions have the largest and strongest faculties, and this quality is important for graduate students.
JC: For those of you abroad, what was your main motive for leaving Iran to pursue further training? For those in Iran, do you have plans to leave Iran for additional studies?
AK: I left Iran after my undergraduate degree. The reason was quite simple --- I left Iran to expand my horizons in a new environment doing cutting-edge research.
SN: My main motive was to have the opportunity to get more involved in MRI acquisition research, which is more feasible here in the UK. Also, I cannot rule out being adventurous and wanting to experience a different cultural environment!
MK: I imagine on one hand there are still some aspects of brain-mapping research that remained mostly untouched in Iran and those happen to be in the realm that I was mostly interested about and as such, leaving Iran made sense as there was not much expertise or experience available in Iran. On the other hand, in my opinion, one other thing still missing in Iran is the limited extent of the international collaboration that helps to accelerate the development and increase the quality of the brain-mapping in Iran. These two factors were my main motivations to go abroad for Ph.D. study.
AE: My main intention for leaving Iran was to expand my skill base in using larger databases, and in particular my quantitative skills. Moreover, working in a place such as the UCL Institute of Neurology, which is home to many renowned neurologists and neuroscientists, has enabled me to form more ambitious research plans with access to a wide range of patient populations.
MB: I would like to study in a foreign country, but it comes down to a personal decision, so I have not yet made up my mind. Certainly, most of our students would like to study abroad, and many of my labmates have left to pursue their PhDs. Our professors do not try to retain us. They actually encourage us to explore our options.
JC: How would you describe the career prospects of a highly trained neuroimaging researcher in Iran?
AK: With more groups working on neuroimaging and the availability of research funding and imaging facilities, the prospects seems very promising. In Iran many of the talented students are interested in engineering, in general, and medical imaging, in particular, which is an invaluable asset to principal investigators in these fields. Nevertheless, in spite of the recent progress, access to funding and imaging facilities is still very limited. Moreover, there is limited collaborative research between individual groups and between institutions. I hope the establishment of the new national research bodies, such as the NBML, will lead to collaborative research initiatives between research groups and institutions across the country.
SN: I would think a researcher with a good international network, particularly with countries with a strong neuroimaging landscape, can expect a promising future.
MK: I think for such an individual the available job positions could be in the academia or government-funded research institutions, however, in the private sector, there is only a limited range of options available. I imagine there would be several suitable faculty or research positions available in the capital city (Tehran), but not much so for the rest of the country. For the private research-based companies to grow and create more job positions in this field, there is still a lot that needs to be done.
AE: Compared to the developed world, there are very limited funding opportunities in a developing country like Iran. As a result, many students may prefer to leave the country to expand their skill base. However, despite these limitations, there has been an upward trajectory as is evident by the construction of new neuroimaging centres and availabilities of graduate (PhD) level university programme dedicated to neuroimaging.
MB: Medical Imaging is still a very new field in Iran, and frankly there are not that many jobs in this field, especially for PhD graduates.
Postamble (JC): As in any research community, trainees are the future of Iranian brain-mapping research. The trainees that you met here are some of the brightest among Iranian students; they are expanding their horizons voraciously and have ambitious future plans. Irrespective of their current locations, these trainees show their love of their home country and are obviously excited by the recent developments in the Iranian research arena. I have come to learn that > 40% of Electrical Engineering students as well as > 50% of Medical Physics students at the University of Tehran are women, numbers that exceed those of most western programs. We look forward to the transition of these trainees into independent scientists.
Although there is great need for brain-mapping expertise, there are currently few positions in Iranian universities for trainees, even those with often highly prestigious foreign training. In this regard, I have come to learn that the government has established paid postdoctoral fellowships (up to 2 years) for those returning to Iran and in search of faculty positions. In parallel, there are government programs that encourage highly-qualified individuals to return to Iran to establish tech companies, through both cash rewards (up to $40,000 USD) and low-interest (close to 0%) loans. These mechanisms will likely create jobs for future trainees in brain imaging.
We wish these trainees the best, and hope the OHBM community will be able to enhance outreach to those working and living in Iran as well.
BY THOMAS YEO, NICOLE KUEK
Professor Simon B. Eickhoff is the Director of the Institute for Systems Neuroscience at the Heinrich-Heine University Düsseldorf and the director of the Institute of Neuroscience and Medicine (INM-7) at the Research Center Jülich. Simon is a leading cartographer of the human brain, and his team utilizes a wide range of methods to map the organizational principles of the human brain. We had the opportunity to chat with Simon before his keynote lecture in the upcoming 2018 OHBM Annual Meeting in Singapore.
Thomas Yeo (TY): Today we have Prof Simon Eickhoff here, a keynote speaker at OHBM 2018. Simon, thanks for doing this. How would you describe your research to a random person on the street?
Simon Eickhoff (SE): I would say that I’m interested in how the brain is organized, how it varies between people, and how this variability relates to things like cognitive capacities. Then ultimately, I want to contribute to developing new tools for diagnosing and treating neurological and psychiatric disorders.
TY: That’s a rich set of activities – how did you end up on this research path?
SE: More or less by accident. I studied medicine in Aachen, and late at night at a party in my hometown, I met a friend from school who had started studying in Dusseldorf. He told me about brain research there and I thought it sounded quite interesting. So I called Karl Zilles’ secretary, met him, and was really fascinated. I started my doctoral studies there and never really managed to leave.
TY: Your lab is involved in several projects – but what is the most exciting thing you’re working on now?
SE: There are two things we are doing right now that I’m really excited about. One is brain-phenotype relationships. Can we actually infer complex phenotypes from brain imaging data? Can we predict personality traits or cognitive performance? And the key aspect here is --- given that there’s quite a lot of work on this already --- can we predict it in an interpretable fashion. What we need is good predictive performance, while also learning something about brain organization. This is one of the aspects that I really want to push, as it’s not highlighted enough in current discussions. It’s one thing to be on the data-driven side, and to get good compressions, good predictions. This is, without any question, awesome. But in the end, we also want to learn something about the brain – how the brain is organized. There’s a lot of work going on in our lab that really tries to combine the more data-driven work from a computer science perspective, with the more traditional neuroanatomical view.
The second part is more related to brain mapping. You can describe the brain through a lot of different features. So for each point in the brain you can say, what are the structural properties, what is the trajectory as we age, how is it disturbed or changed in people with Parkinson’s or schizophrenia. But also, what’s the functional connectivity profiles at rest and during task, what are the structural connectivity profiles, and so on. You can use each of these features to map the brain and to delineate brain areas. But how does all of that work together? That’s the critical question, and cracking this kind of topographical code, that’s something that we can hopefully get closer to. And it’s pretty exciting!
TY: Moving forward – what do you hope your research will accomplish in the next 5-10 years?
SE: Well if we manage to do the things we just mentioned, I’ll be quite happy! The predictive modeling, but also brain mapping, understanding organization and topographical complexity of the brain. That’s going to be fantastic!
Looking ahead, I’m not sure if it will be done in five years, but it will be really exciting if we can go outside of the academic field, outside of doing research to just get the next papers, and to mature enough to actually bring our research into clinical practice. Five years – I’m skeptical if we’ll get there, but over 10 years, I’d be more confident. If my team will be able to contribute to it, then that would be fantastic. And we’re working towards that goal.
TY: What do you think is the biggest obstacle right now?
SE: Towards clinical application? In the end there’s so much flexibility in the analysis of imaging data. We always hope that we live in a perfect world, where you acquire data, and then you do one single analysis, which is a priori planned, and that result gets published. But I’m not entirely sure that most labs do that.
The thing is, if you really want to go and measure yourself, say by the standards of clinical trials, that would need to happen. You’d have to have your analysis plan ready, deposit them, acquire the data and carry out one single analysis and report the outcome. Then if you want to have something that’s clinically useful, it needs to have a really high accuracy and predictive value. What I really value is the current push towards more methodological rigour. I’m really happy to see that it’s becoming slightly more easy to publish null results, and new methods are not just judged by “better” performance.
TY: So you’re saying that p-hacking is a problem, but I guess it’s a bit unclear to me if that’s the main problem. Even with the high quality Human Connectome Project data, the predictive accuracy is not that amazing.
SE: Right, we still have a lot more work to do. And since you’ve mentioned the HCP dataset, this is never what you’d get from a clinical setting, where the data is acquired in a short time by a technician who is less invested than, e.g., a PHD student. And also the patient may not be as motivated as a research subject to lie still, comply with instructions etc.
Most likely, what will happen is the field will be split into different domains. One that is very high resolution, very intense sampling, and a lot of valuable data for each individual. That will be great for understanding brain organization.
But there will also be the other side, that will deliberately say “I want to use low quality, clinical data.” These adjectives may be the same thing, though the latter just sounds better [laughs]. We are using standard clinical quality data and we know the data is bad, but we also know that we need to find something that works on such data if we want to make an impact beyond research as an academic discipline. So we have to be up for the challenge! One thing we are doing a lot now is to deliberately make our lives difficult, by combining data across many different sites, different scanners, different populations, different continents. That way, the dataset is diverse - often even bad - but we’re happy with some drop in performance, because we know this is genuine performance. We have to improve it, but at least we’re not tricking ourselves into believing that we’re doing extremely good predictions that don’t hold up in real life.
TY: We are at Whistler now and just had an exciting workshop held by Todd Constable. I’ve noticed that the talks from more senior professors seem to cover a lot of papers, whereas someone more junior (like me) will talk about two papers. Is this what I should be aiming for?
SE: I think it’s a matter of personal style. But maybe you are right, and personal style changes with age. You really have two choices when you’re giving a talk. You either take a rather high flight attitude and present an overarching picture. Or you are going to dive deeper into something and go into a lot more detail.
One of the explanations for your observation is that when you are younger, you just have fewer papers you really want to talk about. When you get to a certain stage, you have a lot of papers that you can talk about, so you need to make a decision to go deep or go broad. It really has to fit your style and what you’re comfortable with.
For me, usually I want to give an overview, as we’re making great efforts to put puzzles together. We have different studies that may not be particularly related to each other, but you can see the crosstalk, and you can see the connections that I’m so excited about. That’s why I talk about so many different things.
TY: I’m on twitter and see that you (@INM7_ISN) have strong perspectives on open science and the replication crisis. Do you want to comment on that?
SE: Yes – I’m a big chimera when it comes to that. On the one hand, I’m a big proponent of open science. Most of the work we’re doing depends on shared datasets. In fact, long before the term ‘open science’ became popular, I was sharing my software. Back in 2004, I was developing the SPM anatomy toolbox. That was just open matlab code. I was still an undergraduate at the time.
So I’m a big fan of open science, but what worries me at times are certain tones of the debate. Sometimes there’s a patronizing aspect to it, a moral argument “you have to” and “how can you not”. I think that in order for open science to grow, we need to take concerns seriously. Perhaps by virtue of being around the open science, computer science environments, but also around the very traditional German medical environment, I can see that there are two sides to the argument.
Basically, in the German medical environment, I don’t think any person that would review your grants or would hire you cares too much about whether your dataset is open, or whether your software has been released. There’s a more traditional focus on publications with a lot of focus on impact factor, and grant money. This is a completely different world.
An example – if you’re a software developer then your product, your outcome or claim to fame, is the thing (a software tool, a repository, any other resource) that you distribute freely on the internet. If you are someone who spent years collecting data on a rather rare disease, recruited patients, talked a lot to them, followed them up clinically, evaluated them repeatedly and put them in the scanner, then this data is a resource, an extremely valuable resource. And it comes with the assumption that this data will allow you to get enough out of it to carry you to the next step of your career. If you are then being told “well, you are unethical and doing something terribly wrong by not immediately sharing the data freely after the first paper”, this is not putting open science in a good light.
I think open science will succeed and it will be a tremendous accelerator of knowledge. But in order for that to really happen we need to take people’s concerns seriously. There will definitely be a development at different speeds, with things moving faster in some fields than others. It’s not the case that those fields that move faster can look down upon those that are not as fast. I don’t think anyone is opposed to open science from a personal conviction, but it’s more about needs and rewards and we need to take these views seriously.
Long-term - there has to be a better incentive structure. At the moment, we are conservative about it in the German medical system – one of our main criteria for hiring is based on a cumulative impact factor – from the sum of the journal impact factors of all your papers. H-index, citations and so on do come into play, but the fact that the cumulative impact is a major evaluation criteria shows you that different fields still evaluate contributions differently. At some point, open science practices will need to be rewarded not just morally, but also practically by selection committees. That will take quite a bit of time, though.
TY: So how do we change the minds of selection committees?
SE: Well, we just mentioned that we are getting older [laughs]. In some ways there is another generation before us, those who are really not used to it. This will perhaps change over time. Then at some point we have to find criteria for quantifying open science. You can show things on the internet to a review committee – e.g. you have 500 or so matlab scripts that you are sharing – but will that give you a job? If committee members are from another field, they might not get the value, so there’s needs to be some way of quantifying these contributions objectively. Then a committee made up of psychiatrists, dentists, or structural biologists (and they often as diverse at times) can refer to some numbers that give a assessment of your open science practices. That would be a big step forward.
TY: Have you heard of the idea that once you come up with a set of numbers, they will be gamed?
SE: Sure, but this will always happen. We would like a perfect world where all decisions about hiring or promotions are done by people who are experts in your field, spend several hours scrutinizing your CV or 10 most important papers for context and read related literature to compare to. But this just won’t happen. Maybe I’m part cynic, part realistic, but most people are overloaded with committee duties, so you need some easy summary of a person. Yes this will be gamed – there’s no way around that – and we hope there is on each committee someone, who can point out the gaming aspect, and spends time to know your work it more deeply. But usually you have to convey the importance of your work to people who are not familiar with your publications or your topics.
TY: Thank you so much for this interview!
We look forward to attending Simon’s exciting keynote on Monday June 18, 2018.
GUEST POST BY CHRIS CHAMBERS
The biomedical sciences are facing a rising tide of concerns about transparency and reproducibility. Among the chief concerns are inadequate sample sizes, lack of sufficient detail in published method sections to enable replication, lack of direct replication itself (and notable failures when attempted), selective reporting of statistical analyses in order to generate desirable outcomes, suppression of negative results, lack of sharing of materials and data, and the presentation of exploratory outcomes as though they were hypothesis-driven. Collectively these problems threaten the reliability of biomedical science, theory generation, and the ability for basic science to be translated into clinical applications and other settings.
Human neuroimaging in many ways represents a perfect storm of these weaknesses, exacerbated by the fact that two of the main techniques, MRI and MEG, are extremely expensive compared with adjacent fields. Researchers using these methods face tremendous pressure to produce clear, positive, publishable results, usually in small samples.
Until recently such issues were rarely discussed openly, perhaps for fear that it would bring a relatively embryonic discipline into disrepute and collapse funding opportunities. But they have been simmering below the surface for a long time. Years before irreproducibility was headline news, at one imaging centre where I worked we noticed that we were running out of data storage faster than we were acquiring new data. After some detective work we learned why. Researchers were repeatedly analysing and reanalysing the same datasets, and with every reanalysis they were inadvertently duplicating huge quantities of raw data. The incident was illuminating about normative research practices.
When I raise this scenario with colleagues, their typical response is “Well, duplication of raw data is a silly mistake but most fMRI research is exploratory and exploration is vital for science”. This is true, of course. There is a huge amount to gain from performing reanalysis of existing, complex datasets. But the key, then, is whether such exploratory research is documented transparently as exploration. In an exploratory field, and especially one that often relies on inferential statistics, shouldn’t publications faithfully report all analyses that were attempted before settling on the ones that drove the conclusions? And does this happen in fMRI? Of course it doesn’t. Pick up a copy of any neuroimaging or cognitive neuroscience journal and you’ll find article after article purporting to test hypotheses using complex analyses, each of which is presented as though it was planned in advance. Given the pressures on researchers to produce clean results and frame them as the outcomes of hypothesis testing, it comes as no surprise that virtually no two published fMRI studies report the same analysis pipeline.
There are many solutions to this quagmire, including greater sharing of data, materials and code, and I also believe one major piece of the puzzle is preregistration of hypotheses and analysis plans. Many in the neuroimaging community are skeptical of preregistration and what it might say about our scientific approach, which sits uncomfortably between confirmatory and exploratory modes and relies on massive investment to remain afloat. When your typical experiment involves hundreds of analytic decisions, each of which can be considered “legal” yet produce slightly different outcomes, there is a natural tendency to fear that pre-specification of any particular route through the garden of forking paths will lead to unpublishable, possibly confusing findings. We thus feel pressured to apply the “human element” to bring order to chaos. Researchers will routinely spend months poring over their data and analyses using sophisticated statistical methods but almost none appreciate the risks of their own biases in interpreting one outcome among hundreds or thousands of possibilities.
This is why I have pushed hard for neuroimaging journals to offer Registered Reports (RRs). The RR format eliminates the fear of preregistration producing unpublishable results because, for RRs, the editorial decision is made before the results are known. Detailed study protocols are reviewed before researchers commence the research, and following detailed review of the theory and methods, the highest quality submissions are accepted for later publication regardless of how the results turn out. Researchers can also report additional exploratory (unregistered) analyses, clearly flagged as exploratory, and are encouraged to include preliminary experiments to validate a proposed analysis pipeline.
This week sees the launch of Registered Reports as a new article option at NeuroImage as part of a two-year pilot initiative, co-edited by me, Birte Forstmann (University of Amsterdam), Rob Leech (Kings College London), Jeanette Mumford (University of Wisconsin-Madison), Kevin Murphy (Cardiff University) and Pia Rotshtein (University of Birmingham). In addition to the usual features of Registered Reports, we are also inviting proposals for secondary analyses of existing but unobserved datasets, innovative approaches using Bayesian adaptive optimisation to combine the strengths of exploratory and confirmatory science, and review/perspectives articles on the potential costs and benefits of preregistration in neuroimaging research. Submissions are invited in any area of human neuroimaging and readers can find detailed author guidelines here.
Preregistration in neuroimaging is a high stakes intervention. The combination of high analytic flexibility combined with high risk of bias and high expense of data generation means that it has the potential to yield perhaps the greatest scientific benefits of any field to which it has been applied so far. But because of this methodological complexity, preregistration also brings some of the greatest challenges.
One such challenge is power analysis. Many of the 103 journals that currently offer RRs require high prospective power to detect the smallest effect of theoretical interest (e.g. 0.9 at Cortex, 0.95 at Nature Human Behaviour), but we know that MRI in particular is underpowered to detect theoretically plausible effect sizes, and we also know that many researchers do not have the resources to fund large studies. At one level this problem can be solved by consortia projects. Initiatives such as the Psychological Science Accelerator, Study Swap and the ENIGMA neuroimaging consortium are blazing a trail to facilitate more definitive team-oriented science. However, the main benefit of RRs lies not in the support of big science but in the elimination of publication bias and selective reporting. Therefore, to make the format as accessible as possible to the neuroimaging community, the RR format at NeuroImage will not set a minimum required statistical power or sample size. Instead we will simply require authors to justify the sample size they are proposing.
A bigger question is whether preregistration in neuroimaging is even feasible. To what extent will researchers feel able to prespecify their analysis pipelines in advance? For a RR, if an exact pipeline cannot be prespecified then researchers will be given the opportunity to prespecify data-dependent contingencies (e.g. if the data look like this, then we will apply this filter, etc.). They may also propose a blinded analysis strategy or an adaptive design in which some decisions will be post hoc, but actively protected from bias. Can such approaches succeed? I believe they can but for me the most fascinating outcome of this particular RR launch will be to discover how a community of talented analysts tackles this challenge.
Chris Chambers is a professor of cognitive neuroscience at the Cardiff University Brain Research Imaging Centre and guest section editor for Registered Reports at NeuroImage
Since the first meeting of the Organization for Human Brain Mapping (OHBM) over twenty years ago in Paris, the Organization has evolved from a primarily European and North American organization, to an international organization that draws members from over 50 countries worldwide (Figure 1).
However, the European and North American leadership and educational roles within the organization have been slower to undergo a similar evolution. This is perhaps most noticeable in the geographic distribution of Council, of which apart from very sparse representation from Australia and Cuba, has consisted of primarily Europeans and North Americans (Figure 2).
The characteristics found in Council, are also seen in the chosen educational courses (Figure 3), while the symposia have slightly greater diversity (Figure 4).
The most striking omission from leadership and educational roles comes from our colleagues in Asia. The countries of China, Korea, Japan, Singapore, and Taiwan make up over 15% of attendees to the meetings and poster presentations (Figure 5) and similar rates (17%) within the OHBM membership; yet have to date no representation on Council*. The goal of the Diversity and Gender Committee (DGC) is to work with Council and the Nominations Committee to foster equity in representation both within the OHBM membership and meeting attendance.
*Note: there has been one member Council originally from China, however they are currently US-based, so was designated as representing the US. In addition, a former Council member also had a joint position in China, but was designated as representing Latin America.
How are we doing with Gender?
With three of the five most recent Council members being female, the gender distribution on council is 12 males and 3 females. While this tripled the gender distribution from one year earlier, it falls lower than the gender distribution within OHBM.
The gender distribution of attendees presenting posters is 50% male, 40% female, and 10% who provided no answer. Whether these 10% represent gender fluidity or allies for gender fluidity within OHBM is not known.
While the gender distribution for poster presentation is more balanced, there is a higher proportion of males for the educational courses and symposia.
Approaches to Foster Equity
There has been much productive discussion within the Diversity and Gender Committee regarding how to foster equitable representation within OHBM. There were a number of options that we discussed, including having ‘electoral votes’ for Council members to, in a sense, ‘force’ the leadership roles to match the membership demographics. However, we are a democracy, and the primary approach that we have adopted is to provide education (in the form of data) for our members and allow our members to vote. We therefore encourage all members to consider the above data and consider potential biases when voting for your OHBM leadership.
A member of the DGC also sits on the Nominations Committee, with the goal to keep diversity in mind during the decisions surrounding the nominations. Importantly, the Council, including the chairs and members of the Nominations Committee, are motivated to see equity in representation within leadership roles in OHBM. They have attended the DGC Meeting in Vancouver and echoed their support for the Committee’s work. This support is crucial!
Microaggressions and Bias
The DGC has been charged to address inequities in gender and geography, however, we have heard whispers of both macro- and micro-aggressions within the context of the OHBM meetings. OHBM is all about science and integrity in both science and behavior. Attendees should be able to attend the meetings free from any form of bias related to gender, ethnicity, sexual orientation, gender identity or handicaps. If events occur, whether overt or covert, it should be reported to the DGC who will then work within the OHBM leadership to assess the situation and, if indicated, to intervene. The DGC is currently working on the specifics of best practices to intervene in cases where it is warranted.
For some time now, intolerance at the political level has been propagated throughout the world. However, we as a scientific community subscribe to inclusivity from all cultures and nationalities, and value diversity. In this light, we would like to highlight some of the challenges faced by some of our international colleagues, some of their biggest achievements despite these challenges, as well as provide a platform to voice their opinions and concerns on scientific inclusion.
There are parts of the world that are far from our minds when considering brain-mapping research - Iran is certainly one of them. The last few decades have seen a massive Iranian exodus of highly trained individuals. As a result, this secluded country has produced a great number of researchers who now work and live abroad. In fact, many of us working in neuroimaging share frequent interactions with Iranian researchers and trainees, and these interactions have provided a glimpse into the state of science and education in Iran. I have come to understand that some of the top research-intensive universities in Iran in the field of brain mapping include Shahid Beheshti University, the University of Tehran, Institute for Research in Fundamental Sciences. When it comes to neuroimaging research, the University of Tehran, Shahid Beheshti University and AmirKabir University figure prominently.
Researchers who work in Iran, however, see not only the challenges but also tremendous potential in Iranian research. On the heels of the Persian New Year, we caught up with two Iranian imaging scientists who wish to share their distinct views and experiences with the OHBM international community.
Part 1: Dr. Mojtaba Zarei
Jean Chen (JC): Where did you receive your training, and what inspired you to study brain imaging?
Mojtaba Zarei (MZ): I was inspired to study brain mapping by my 3rd year high-school teacher and then by the work of Frank Duffy while in the early years of medical school. I completed my MD at Shiraz University of Medical Sciences in 1990, focusing on brain electrical activity mapping. Afterwards, I moved to King’s College London for my PhD in cortical electrophysiology, mapping out sensorimotor cortex of rat after embryonic neural transplantation. In 1996, I resumed my practice in Clinical Medicine and Neurology, first at London, then at Cambridge, Oxford and Birmingham (UK). In 1999, I restarted my research in cognitive neurology under Prof. John Hodges and later in Chicago with Prof Marsel Mesulam. In 2002, I became a postdoc in the Oxford Center for Functional Magnetic Resonance Imaging of the Brain (FMRIB) under Prof. Paul Matthews. I went on to become Senior Clinical Fellow at FMRIB in 2006. As part of this, I established the Imaging in Neurodegeneration Group in Oxford, which was later continued by colleagues. Following that, I moved to the University of Nottingham in 2012.
JC: Given your foreign training experiences, what inspired you to move back to Iran?
MZ: Iranians commonly maintain strong family ties even after moving abroad. I moved back to Iran during a time when the government was prepared to invest heavily in neuroimaging research. In 2013, I was invited to return by the Iranian Ministry of Health to establish the National Brain Mapping Centre. This negotiation included an equipment grant of $10,000,000 USD for the centre from the Office of Vice-President for Science and Technology. I was appointed Full Professor of Shahid Beheshti University, Senior Adviser to the Ministry of Health, and the Director of National Brain Mapping Centre based in Shahid Beheshti University of Medical Science. In the Ministry of Health, I designed and implemented a national Clinician-Scientist Program for the first time in Iran. I was also instrumental in founding National Institute for Medical Research Development (NIMAD), which was modeled from the Medical Research Council in the UK. This organization is now the main independent governmental grant awarding body with seven scientific committees.
JC: How would you describe the brain-mapping landscape in Iran? In terms of major infrastructure, labs, programs, universities involved in brain-mapping research?
MZ: The major labs are mostly located in the capital, Tehran. The major players in neuroimaging research include the University of Tehran, Shahid Beheshti University and AmirKabir University of Technology. There is a 3T GE MRI in the Iman Khomeni Hospital that is shared by researchers and clinicians. There are also two research-dedicated 3 Tesla Siemens MR scanners, one at the Institute of Research in Fundamental Sciences, and the other at the National Brain Mapping Lab. There are also 1.5 T Siemens Avanto systems in Iran that can be used for research but the most active one is at Kermanshah University of Medical Sciences.
JC: Are there formal national or regional-wide meetings or organizations devoted to brain mapping?
MZ: Indeed there are. Since 2014 I have been responsible for organizing the annual Iranian Human Brain Mapping Congress, involving an international audience with eminent scientists as speakers. In addition, in 2005, I invited my former colleagues from the UK, including Heidi Johansen-berg, Matthew Rushworth and Christian Beckmann to teach at the first Brain Mapping Workshop in Iran. There is also the Iranian Society for Cognitive Science and Technology, of which I am the current president. Furthermore, at the moment, our institution runs the only regular and long term hands-on brain mapping teaching program in the country.
JC: What are the biggest challenges facing Iranian brain-mapping researchers that you would like the OHBM to be aware of?
MZ: The obvious challenge is that due to travel restrictions, Iranian researchers are not always able to attend OHBM meetings. Perhaps with developments in web platforms, this difficulty could be somewhat circumvented. Within the country however, given the limited resources, funding is not necessarily distributed in the most productive way, and there has yet to be an effective plan to utilize the infrastructure that is in place. On top of that, competition for research funding is politicized, and I fear that the requirement for political connections may be hindering research and the development of a younger generation of researchers. Any international mechanism (financial or otherwise) to directly support young and enthusiastic scientists would be welcome.
JC: Does the Iranian education system foster research and encourage young people to enter research? For example, are there scholarships available to help students enter research?
MZ: Yes, there is a lot of encouragement but it translates little to financial support. Most MSc or PhD students do not get paid during their study, which makes life difficult for them during these years. Postdoc positions (12-18 months) have increased in the last few years, particularly for those who would have obtained their PhD abroad. There are a lot of workshops, but these are often aimed at raising income.
JC: Are there government funding bodies to fund research? If so, how difficult is it to obtain funding, albeit it limited?
MZ: There are a number of grant awarding bodies that provide funding for brain mapping research, including the National Institute for Medical Research Development (NIMAD), National Science Foundation, and the Cognitive Science and Technology Council.
JC: How did you build up your lab in Iran?
MZ: When I returned to Iran, I got official permission from the Ministry of Health to establish Brain Mapping Centre at the Tehran University of Medical Sciences. I then received additional permission from the Ministry of Health to establish the National Brain Mapping Centre in Shahid Beheshti University of Medical Sciences. However, after 2 years, with government changes, our funding was stopped. I obtained permission from the Ministry of Science and Technology to establish the Institute of Medical Science and Technology less than 2 years ago. Our researchers and labs are located in this Institute. We established international collaborations with the University of South Denmark, the University of Pennsylvania, University Nantes, and University of South California. The latter is where the ENIGMA Sleep project is. We are now leading the ENIGMA Sleep Group. More collaborations are being developed, and funding for these projects are often obtained from international bodies.
JC: What are the career prospects for your graduate students and perhaps for other foreign-trained Iranian brain-mapping researchers hoping to return to Iran?
MZ: Not much in Iran at the moment, many will leave the country for PhD positions and postdoc training. Some get recruited for teaching and research in Iranian Universities. I have written a curriculum for training PhD students specifically for brain mapping, but it has to be approved by the Ministry of Education before I can actually start the program. However, there are numerous upcoming opportunities for scientists who have been trained in the best western programs.
Part 2: Dr. Abbas Nasiraei Moghaddam
On a later occasion, I had the pleasure to speak with Dr. Abbas Nasiraei Moghaddam. Dr. Moghaddam is Associate Professor in Biomedical Engineering at Amirkabir University of Technology in Tehran, one of the top universities in Iran and a frontrunner in neuroimaging research. Dr. Moghaddam is one of the premier MRI physicists in Iran, and for the past 8 years, has been director of the Advanced Medical Imaging Lab at Amirkabir University. For most of that time, he has also been affiliated with the School of Cognitive Sciences at the Institute for Research in Fundamental Sciences (IPM).
Jean Chen (JC): I understand that you are the founder of the Iranian Chapter of the ISMRM (International Society for Magnetic Resonance in Medicine). Where did you receive your training, and what inspired you to study brain imaging?
Abbas Moghaddam (AM): I received my BSc in Electrical Engineering in 1995 from the University of Tehran, and completed my MSc at the same, under the guidance of Dr. Hamid Soltanian-Zadeh. Dr. Soltanian-Zadeh was the first person to teach MRI Physics in Iran (21 years ago), and he initiated me into the field of brain imaging. Afterwards, I went on to work at Washington University in St. Louis for two years (in cardiac imaging) before starting my PhD at the California Institute of Technology (Caltech). It was followed up by a few years of postdoc at the University of California in Los Angeles (UCLA).
JC: Given your foreign training experiences, what inspired you to move back to Iran?
AM: Iran is my home, where my parents, siblings and roots are. Prior to returning, I was in the US for a total of seven years, but for fear of travel restrictions, I did not visit Iran even once. It made me realize that I did not want to be away from my home for so long again. However, I retained a part-time appointment at the University of Southern California to allow me to facilitate my collaborations with my American colleagues.
JC: How would you describe the brain-mapping landscape in Iran? In terms of major infrastructure, labs, programs, universities involved in brain-mapping research?
AM: In Iran, most of the MRI systems are for clinical use. There is only one research-dedicated scanner (Siemens Prisma 3 Tesla), which is at the National Brain Mapping Lab (NBML). It is sited at the University of Tehran, which is where I first got into medical imaging. The NBML is not affiliated with any institution, but provides access to all researchers in Iran. The IPM system (Siemens Trio 3 Tesla) was purchased for the IPM, but due to regulations from the Ministry of Health, it was initially sited at the Imam Khomeini Hospital in Tehran. After 4 years, it was recently moved to the IPM, and is now essentially dedicated to research. As a result, we now have a unique opportunity to do MRI research at the IPM. The School of Cognitive Sciences at the IPM was directed by Dr. Hossein Esteky for over 15 years. Dr. Esteky is a world-renowned vision scientist that first drew the world’s attention to neuroscience research in Iran with his publication in Nature.
Currently, the research landscape in Iran is rapidly changing, allowing us to develop new areas of research. Amirkabir University is Iran’s leader in MRI Physics research, and its School of Biomedical Engineering is one of the oldest in the world (25 years old). Here at the IPM, we have about 40 students doing research in cognitive science. When I was at UCLA, I did sequence programming, but I did not have access to it when I first returned to Iran. Now we are in the process of negotiating a research agreement with Siemens that would allow us to do sequence development here as well. This is an exciting time.
JC: What are the biggest strengths and challenges facing Iranian brain-mapping researchers that you would like the OHBM to be aware of?
AM: We have excellent human resources. The students are well trained and eager for knowledge. Often, my students would have scored near the top during the Iranian University Entrance Exams. However, for many years, neuroimaging research in Iran was heavily focused on image processing, perhaps due to our lack of research-dedicated imaging infrastructure. We have labs that publish heavily on imaging processing algorithms. But without co-developing neuroscience and imaging physics, such a research program would lose its competitive edge. This is perhaps our biggest challenge. Since 18 months ago, the newly established NBML has been providing access to imaging facilities, including MRI, EEG, TMS and fNIRS, but researchers in Iran are still trying to adapt to a culture of doing their own data acquisition.
Of course, Iranian researchers suffer from travel restrictions. For example, we are glad that this year’s meeting of the ISMRM is in Europe (Paris). Had it been in the US, we would not be able to attend. I am thankful that my international collaborations have allowed to get around such challenges. Science should have no boundaries.
JC: I understand that research funding for brain imaging is limited in Iran. In this climate, how difficult is it for you to obtain funding?
AM: The funding levels are certainly nowhere near the levels in the developed world. However, nearly everyone I know has funding, and no one has had stress due to lack of funding. This is in strong contrast with my colleagues in the US. One thing that is not well understood by the west is that in Iran, research is not nearly as costly. Students do not typically receive stipends, and scanning is fully subsidized, therefore we only need funding for traveling, publishing and so on. This makes it possible to conduct relatively big studies with little funding. Having said that, there are multiple types of grants that we need to apply for. For instance, traveling is covered by a different type of grant from regular research expenditure. The system is actually much more relaxed than in the west.
JC: How did you build up your lab in Iran?
AM: Biomedical Engineering has attracted a lot of interest from students in recent years, and I have had many applicants. When I interview students, I emphasize that I do research in Imaging and not in Image Processing. They are still getting used to the concept, but drawn by the success of my previous students. In addition, I set high standards for my students and do not hesitate to reject students that do not meet the requirements. In my institution, we have also set up joint-degree programs with foreign institutions in the UK and Australia. I would really like to expand this field of research in Iran, but that too will take time.
JC: What types of research questions are you interested in?
AM: I am interested in developing both functional and quantitative MRI sequences to improve brain imaging. In terms of fMRI, we are interested in improving the neural specificity of the imaging technique as well as developing brain-connectivity processing methods. In quantitative MRI, we are developing new imaging technique for T1 and T2 mapping.
I first learned MR Physics at the University of Tehran, when I worked with Dr. Hamid Soltanian-Zadeh; this continues to be a big focus for my research. In the US, my research was in cardiac imaging, but when I moved back to Iran and started my affiliation with the IPM (at the recommendation of Dr. Soltanian-Zadeh), I started to do brain-imaging research. One of my recently graduated PhD students worked on developing a new MRI sequence. As we do not yet have a research agreement with Siemens, he did this work in collaboration with the group of Dr. David Norris in the Netherlands, and spent 15 months in the Norris lab. This resulted in a patent and 2 articles, and it was the first thesis on MR Physics in Iran. I have another student working on structural and functional brain connectivity. She worked with Patric Hagmann in Switzerland. This is mainly on image processing and neuroscience.
JC: Finally, what are the career prospects for your graduate students and perhaps for other foreign-trained Iranian brain-mapping researchers hoping to return to Iran?
AM: As I mentioned, we are hungry for MRI expertise, but the job situation in Iran is very uncertain. Brain Imaging is still a young field, and we certainly need more researchers to help us build up the programs. Meanwhile, I do encourage my students to see other places and gain other experiences. Many of my students have gone on to study in labs abroad, including Germany, the Netherlands and Canada.
Postamble (JC): As Dr. Moghaddam said, science should have no boundaries. What may seem to be challenges are also potential opportunities. Iranian scientists are passionate about their research as we are in the rest of the world. They are defying great odds to build up a research program and to provide their young generation with new opportunities. Also, although the current involvement of female scientists in brain-mapping research accounts for <10% of all users, the increasing dominance of female trainees at the graduate level will likely change this. In an installment about Iranian trainees, you will also hear the thoughts of early career researchers from Iran and around the world.
By Elizabeth DuPre and Kirstie Whitaker
This month we continued our Open Science Demo Call series by speaking to Tim van Mourik Eleftherios Garyfallidis and Malin Sandström about the communities they’re building and supporting to make everyone’s lives easier through better open source software tools.
After a few technical difficulties (Kirstie’s laptop inexplicably deleted the “broadcast” button so we were all chatting to each other without being able to include our viewers in the conversation!) Tim introduced Porcupine. Porcupine is a tool to visually program your analysis. By dragging and dropping modules that represent functions in your analysis, you can quickly build an insightful analysis and then Porcupine will provide the code that you or others need to run on your own data. All code and documentation is openly available at the project’s GitHub repository, and this is where you can also give any feedback or suggestions. Alternatively you can find Tim in the BrainHack Slack team (click here if you need an invitation to join) or via email at email@example.com.
Eleftherios told us about DIPY, a global, community-supported, software project for computational neuroanatomy, focusing mainly on structural and diffusion MRI. DIPY implements a broad range of algorithms for denoising, registration, reconstruction, microstructure, tracking, clustering, visualization, and statistical analysis of MRI data. You can get involved and help the DIPY team in many different ways, but Eleftherios particularly encouraged OHBM members to test their data with the DIPY algorithms and provide feedback on any challenges they have running the code. You can ask questions in the team’s live chatroom or send an e-mail to firstname.lastname@example.org.
Linking very nicely to Eleftherios’ call for student applicants to work on the DIPY team’s suggested projects was Malin Sandström, INCF’s community manager who manages the organization’s Google Summer of Code (GSoC) program. GSoC allows students to be financed with stipends for their work on open source software over the summer. Open source organizations in the project contribute project ideas and mentors. INCF is participating as a GSoC mentoring organization for the 8th year in a row, with mentors from the worldwide INCF community and a wide range of neuro-tool projects.
You can browse the INCF project list to learn more about the summer plans. If you were too late to take part this year, we encourage you to keep an eye on the INCF GSoC projects page for updates on future rounds. If you have a project idea you would like to mentor with INCF for next year, get in touch at email@example.com by 1st December 2018.
Our next call will be on Thursday April 26th at 7pm BST (check your local time zone). If you’d like to nominate yourself or someone else to be featured on these monthly calls, please add their information at this github issue, or email the host of the calls Kirstie Whitaker at firstname.lastname@example.org. You can also join the OSSIG google group to receive reminders each month.
by Souad El Bassam and Nikola Stikov
OHBM has members throughout the world. We used last year's meeting as an opportunity to interview some of them to find out about the international reach of OHBM.
In our Spanish language video, you can learn about LABMAN and the way developing countries try to keep up with the growing cost of brain mapping research. Maria Bobes, the president of LABMAN, speaks to Manuel Hinojosa about the importance of involving more Latin American researchers in brain mapping and the crucial role of LABMAN in raising awareness of the challenges facing researchers in this area of research in Latin America.
Our Dutch video features Emma Sprooten from Donders Institute for Brain, Cognition and Behaviour and Raissa Schiller from Erasmus MC – Sophia Children’s hospital, junior researchers who are interested in cognitive and behavioural research. They briefly talk about their impression of the conference before moving on to speak about Raissa’s PhD research on cognitive impairment in children who were critically ill as newborns.
Finally, the Balkan video features researchers from Macedonia (Nikola Stikov), Bulgaria (Kalina Christoff and Bogdan Draganski), Serbia (Bratislav Misic), Bosnia (Branislava Curcic-Blake) and Croatia (Lana Vasung) trying to communicate with each other in their respective languages. Among the topics discussed are work-life balance and the many reasons for attending (and skipping) the OHBM conference.
Our international outreach does not stop here. We have videos in 7 more languages, including Catalan, Czech, Greek, Hebrew, Portuguese, Slovak and Mandarin. If anybody reading this wants to help with the transcription, we will be very happy to add these videos to our YouTube channel. If interested, please get in touch with Nikola Stikov (email@example.com). Let's show our international community that the language of OHBM is universal!
P.S. A big "thank you" to Job van den Hurk, Amaia Benitez and Olivera Evrova for the transcription and translation of the videos.
Permutation methods are a class of statistical tests that, under minimal assumptions, can provide exact control of false positives (i.e., type I error). The central assumption is simply that of exchangeability, that is, swapping data points keeps the data just as likely as the original. With the increasing availability of inexpensive large-scale computational resources and openly shared, large datasets, permutation methods are becoming popular in neuroimaging due to their flexibility and ease of concern about yielding nominal error rates than parametric tests, which rely on assumptions and/or approximations that may be difficult to meet in real data. This becomes even more important in the presence of multiple testing, in that assumptions may not be satisfied for each and every test, and the correlation across tests may be difficult to account for. However, even exchangeability can be violated in the presence of dependence among observations, and it may not always be clear what to permute. The aim of this blog post is to emphasize the relevance of linking the null hypothesis and the dependence structure within the data to what should be shuffled in a permutation test. We provide a few practical examples, and offer some glimpses of the theory along the way.
Example 1: Permutation mechanics
Let’s begin by reviewing the mechanics of a permutation test. Consider a comparison between two groups, for example whether hippocampal volume is different between subjects with Alzheimer’s disease (AD) and demographically matched cognitively normal controls (that is, a group with similar age, sex, education level, etc). If we assume that in both groups the hippocampal volumes are independent samples from a Gaussian distribution, a classical parametric two-sample t-test can be used to test for a difference between means of the two groups. However, this distributional assumption may not be true, and departures from this assumption can potentially lead to incorrect conclusions. In these circumstances, permutation tests perform better than parametric tests by providing a valid statistical test with much weaker assumptions. Specifically, under the null hypothesis that the hippocampal volume has no actual difference between AD cases and controls, the group membership (or the label of case and control) becomes arbitrary, that is, any subject from one group might as well have been from the other.
While it may seem implausible that this would be the case for patients and controls, in fact this is what we are testing: all else being equal (that is, exchangeable), and any difference found must relate to the means, which is what we are interested in. In fact, a classical parametric two-sample test (with equal variance) makes not just the same assumption, but also further assumes that patients and controls come from the same Gaussian distribution. Permutation tests do not require Gaussianity; it suffices that the data are merely exchangeable. Exchangeability further relaxes another important assumption of parametric tests: independence. Data that are not independent may still be exchangeable, either globally or under certain restrictions, as presented in more detail in Example 3 below.
With exchangeability, we compute the t statistic under each permutation, and produce the permutation distribution of the statistic under the null. The permutation distribution is the empirical cumulative distribution function (cdf) obtained from the data themselves, as opposed to from some idealized distribution, as is the case with parametric tests. The observed test statistic can be considered a random sample from the permutation distribution because it is equally likely to have arisen from any case-control re-labeling given the null hypothesis.
The p-value is the probability of finding a test statistic for the group comparison at least as high as the one observed, provided that there is no actual difference (i.e., null hypothesis is true). So, the p-value can be calculated by randomly permuting the group labels many times, each time recalculating the test statistic; at the end of the process, we check how often a larger statistic was observed than the original (before any shuffling had been applied), and divide that by the number of permutations performed. Figure 1 shows an example in which there are three subjects in each group; before any permutation is done, the test statistic is t = +0.7361. After exhaustively computing all 20 possible permutations, we see that 4 of these (including the non-permuted) are higher than or equal to +0.7361. Thus, the p-value is 4/20 = 0.20. If we had decided beforehand that our significance level would be 0.05, we would say that the result of this test is not significant, that is, there is no significant difference in hippocampal volume between AD patients and controls.
Figure 1: Consider the hippocampal volume measured in 6 subjects, three with Alzheimer’s disease, and three cognitively normal controls. The values measured are shown in the boxes (ranging between 3498 and 3588), controls in blue, AD patients in green. The test statistic for a difference Controls > AD is t = +0.7361. If there is no actual difference between the two groups, then the group assignment can be randomly permuted. For each such permutation, a new test statistic is calculated. In this example, four t statistics (shown in red) computed after random permutations of the group assignments, out of the 20 performed, were equal to or larger than the observed, non-permuted statistic. The p-value is therefore 4/20 = 0.20.
Example 2: Permutation with the presence of nuisance
Suppose in Example 1 that there were other variables that could potentially explain some of the variability seen in hippocampal volume. Some of these variables could even be associated with diagnosis itself. For example, it may be the case that, in this particular study, AD patients were older than cognitively normal controls. To account for these nuisance variables, we can formulate the problem as a multiple regression, in which hippocampal volume is the dependent variable, whereas the case-control status, along with other potential nuisance variables, are the independent variables. We would then test whether the regression coefficient corresponding to the case-control label is significantly different than zero. Now it is less clear what should be permuted. If we permute just the group labels, what to do with the other variables in the model? It turns out that various approaches have been considered in the literature.
Systematic evaluations show that, among a host of permutation and regression strategies, the method attributed to Freedman and Lane provides accurate false positive control in the presence of nuisance variables and is robust to extreme outliers in the data. In the Freedman-Lane method, we regress out all nuisance variables from the hippocampal volume measurements to obtain the residuals of this nuisance-only model, and use the permuted residuals as the new dependent variable in the multiple regression, from which we construct the permutation distribution for the test statistic (i.e., the regression coefficient of interest). Intuitively, once the nuisance has been regressed out, what remains should be indistinguishable between AD patients and controls if the null hypothesis is true, and thus, can be permuted.
We note that whichever regression and permutation strategy is adopted, it is crucial that what is permuted is what would render the subjects different were the alternative hypothesis true. It is not relevant to permute aspects of the dataset that would not be affected should the null hypothesis be false, that is, should an effect actually exists. This is important because, when an experiment becomes complex (e.g., with multiple factors, levels, nuisance variables, and/or multiple response variables), it can be easy to permute aspects of the data that are not informative with respect to the null hypothesis. One should not lose sight of what is being tested, and permute the data accordingly.
Example 3: Permutation with the presence of dependence in observations
Data are not always freely exchangeable. It may be the case, for example, that there are repeated measurements from the same subjects among the observations. Or maybe some or all subjects are twins, siblings, or otherwise relatives. Cases such as these restrict the possibilities for permutations, but even so, permutation tests continue to be possible. They proceed in a similar manner as in the examples above, but care needs to be taken when selecting the permutations that are allowed. Exchangeability as defined above — that is, permuting the data keeps them just as likely as originally observed — must be preserved. More technically, it means that the joint distribution of all the data points must remain unchanged under the null. For example, in a twin study, one could permute the subjects within twin pairs, and pairs of twins could be permuted as a whole, but one sibling should never be mixed with the sibling from a different family; see an example in Figure 2. These restrictions, unfortunately, tend to reduce power compared to the analyses in which all subjects are independent and freely exchangeable. However, all other benefits of permutation tests are kept.
Figure 2: Observations that are not independent restrict the possible rearrangements of the data. In this figure, each white circle represent an observation (e.g., a measurement from a subject), the blue (+) or red (−) dots indicate whether the branches that originate at that dot are or are not exchangeable, respectively, and therefore indicate observations that can be permuted with each other. On the left, 10 unrelated subjects who are freely exchangeable. On the right, 18 subjects, some of which were recruited along with their siblings (FS), and/or with their monozygotic (MZ) or dizygotic (DZ) twin. Siblings must be kept together in every rearrangement of the data, which needs be performed in blocks; subjects within a sibship can be permuted; some families may have both twins and non-twins, which requires nested blocks. (Figure licensed under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/)
Consider a longitudinal extension of the AD patients vs. controls example, in which two measurements are obtained from each subject, one before and another after an intervention is applied. As per above, the measurements must stay together within subject. However, depending on what is being tested, we may permute the data only within-subject, or only the subjects as a whole while keeping the order of intra-subject measurements unaltered, or do both things simultaneously. Within-subject effects (that is, the effect of treatment) would require that permutations happen within-subject, whereas between-subject effects would require permutations of the subjects as a whole. Interactions in a mixed design (within and between-subject effects) could benefit from both types of permutation. Crucially, what needs to be permuted is what would be equal should the null hypothesis hold, and that would differ should the alternative hypothesis be actually true.
Example 4: Comparison between models
Now suppose that, in our AD example, in addition to hippocampal volume, we have also measured the amygdala volume for each subject, and are interested in investigating whether hippocampal volume is a better biomarker of AD than amygdala volume (for example, in terms of standardized mean difference between cases and controls as measured by the Cohen’s d statistic). It is tempting to permute the case-control label, but this strategy turns out to be wrong as it completely breaks the associations between the hippocampal/amygdala volume and disease status, which should be retained under the null hypothesis. In fact, in this example, it is unclear what to permute. As a second example, if we want to test whether the mean of hippocampal volume in AD cases is significantly different from a fixed value (e.g., the typical size of hippocampus in normal aging subjects), it can be seen that there is nothing to permute. In these circumstances where a permutation test is difficult to apply, we need to resort to other methods such as the bootstrap for statistical inference.
The bootstrap is an established data-based simulation method, which is often used to assign measures of accuracy, such as standard error, bias, and confidence intervals, to a statistical estimate. It essentially uses the observed data to define an empirical distribution that estimates the unknown underlying data-generation mechanism, and then generates bootstrap samples and bootstrap replications of the statistic of interest using the empirical distribution, from which measures of accuracy can be calculated.
Bootstrap can be applied to virtually any statistic and a wide variety of situations. For example, by sampling cases and controls with replacement independently, we can calculate the standard error or construct confidence intervals for the Cohen’s d statistic for hippocampal and amygdala volume, respectively, as well as for the difference of the two Cohen’s d. Given the strong connection between confidence intervals and hypothesis testing, a p-value can also be produced indicating whether the difference in Cohen’s d is significantly different from zero. In fact, bootstrap can be applied to hypothesis testing, including the questions described in Examples 1-3. However, unlike the permutation p-value, which is exact, the bootstrap significance is only approximate and thus less accurate.
Therefore, permutation is a natural and favorable choice when the null/alternative hypothesis is well defined and what to permute is clear. Bootstrap is useful when the primary goal is to quantify the accuracy of an estimate or when a permutation test is not available in a hypothesis test (e.g., nothing to permute). That said, we also caution that bootstrap relies on an accurate empirical estimation of the true underlying probability distribution. Thus the sampling procedure requires careful consideration in order to respect the data generation mechanism in the presence of complex data structures. For example, block bootstrap is often used to replicate correlations within the data, while variants of the wild bootstrap are used to capture heteroscedasticity in the sample.
Practical advice: It's easy to get started with permutation methods in brain imaging. Most software packages have some sort of permutation test implemented. AFNI's 3dttest++ now uses permutation by default for cluster inference with the -ClustSim option; BrainVoyager has a randomisation plugin (permutation tests are sometimes called randomisation tests); Freesurfer can do permutation with mri_glmfit-sim; FSL has its randomise tool; and SPM has the SnPM toolbox. Finally, PALM is a standalone tool for permutation that works with different types of input data and has various advanced features.
At its best, multi-modal imaging offers rich insight into a many aspects of brain structure & function. At the same time, its development has been thwarted by challenges, for example simultaneous EEG-fMRI has additional safety concerns, and the EEG data requires extra analysis steps to account for artifacts from the magnetic field and rapidly changing field gradients. Despite these issues, there is increasing attention to the merits of this approach, with high profile journals dedicating special issues to multi-modal data fusion.
To find out about the promises and pitfalls of multi-modal imaging, we sent a series of questions to members of the OHBM Multi-Modal Imaging Task Force. This team is comprised of experts in different imaging domains, and aims to promote and develop multi-modal imaging. We found out the state of the field from Alain Dagher, neurologist and PET/fMRI expert in the Montreal Neurological Institute, Urs Ribary, cognitive neuroscientist and EEG/fMRI expert in British Columbia, Gitte Knudsen, neurologist and translational neurobiologist at Copenhagen University, and Shella Keilholz, physicist and fMRI expert at Emory University and Georgia Tech.
OHBM: First, what advice would you give to those who are keen to get into multi-modal imaging?
Alain Dagher (AD): Make sure you have a strong grasp of both methods.
Urs Ribary (UR): First, focus on understanding the neurophysiological and biochemical aspects of the brain; then learn individual methods (MRI-fMRI, MEG/EEG, PET, or others…); finally, learn the additional technologies and techniques that will allow you to integrate these different sources of information.
Gitte Knudsen (GK): You need to train at a site where there is high-level expertise in both modalities, and preferentially integrated. If you cannot readily become attached to an academic site that masters true multimodality, do your master thesis/PhD in a centre where they master one or two of the modalities and then move on to another site with the complementary expertise.
Shella Keilholz (SK): Well I would tell them that if they want to do it, just go for it! It’s a great way to increase the impact of your research, especially when the additional modality allows you to make inferences about causality or fundamental mechanisms that you can’t obtain with a single methodology. Sometimes it seems overwhelmingly difficult to add another modality but we have always been able to find collaborators who generously help us get started.
OHBM: It seems the tools for collecting the data are more readily available (e.g. MRI compatible EEG setups). What is the biggest remaining hurdle in conducting multimodal studies? Is data-fusion between modalities improving?
AD: The increased cost and complexity is generally what holds this back. [Further note from Jean Chen, OHBM blogteam member: “For example, an integrated PET/MRI system is more costly than a regular PET or MRI system. Whilst it may not be as expensive as buying a PET and an MRI system separately, new money is often required to get into multi-modal imaging.”].
GK: The biggest hurdle is, first, to master more than one tool to perfection and second, to ask the right scientific questions that can only be addressed using a multimodality approach. Data-fusion between modalities is a challenge, but slowly improving.
UR: Yes, data fusion is improving, but not so much the underlying knowledge of neurophysiology (why to integrate). There are also clearly issues with money (more recordings are more expensive) and with time (it requires more knowledge and work, and everybody wants to publish quickly). On the other hand, data fusion is not something that has to be done alone, and can be done efficiently in collaborations.
SK: One of the biggest challenges in multimodal research is designing experiments and analyses that maximize the use of the information obtained from both modalities. It requires thinking beyond the conventional paradigms for each of the modalities involved.
OHBM: The increased use of simultaneous PET-MR scanners has clear advantages for cancer imaging. What benefits do you feel it may hold for other areas of neuroimaging?
UR: A clear benefit would be the ability to combine biochemical information with information about brain structure, function and dynamics.
AD: There are many benefits. For example if you take the combination of BOLD and neurotransmitter imaging, since neurotransmitter signalling fluctuates, simultaneous measurement of, for example, dopamine signalling and task-related BOLD has great potential. This then also allows powerful task designs with pharmacological manipulations.
GK: It allows us to measure neurotransmitter release and receptor occupancies and hemodynamic responses simultaneously. We can then use this with pharmacological, physiological or other stimuli. Another great advantage is that it saves time (becoming a one stop shop) for patients with neurological or psychiatric disorders, and so can be useful for those who are not able to tolerate multiple scanning sessions. Unfortunately, despite saving time and possibly resources, the simultaneous acquisition of these different types of information has not yet been truly exploited.
OHBM: The last decades have seen the development of a number of new radioligands for imaging tau and amyloid pathology, microglial activation with translocator protein, phosphodiesterases, and other exciting clinical markers. Are these helping drive multi-modal imaging research? Which emerging PET tracers are you most excited about and why?
AD: For me, the most exciting tracers have been those used to image tau and amyloid, providing otherwise unavailable information about neurodegenerative diseases. Previously we only had brain atrophy as a proxy of disease.
GK: If we’re still talking about hybrid scanners, then we are most interested in developing tracers that target components in the brain that are under rapid regulation. In these cases the methodology can capture these regulations and relate them to, for example, the hemodynamic responses. I’m currently excited about radioligands that are sensitive to neurotransmitter release, as well as emerging PET tracers that are informative of brain processes key to many different types of functions/pathologies. For example, tracers that indicate neuroplasticity or stem cells.
UR: Everything helps! I’ve been impressed with recent research relating imaging of neurotransmitters to cognitive functions in health and disease. In addition, the ability to image GABA as an inhibitory substance has been fascinating to see how it may contribute to, and even control, brain development and dynamic network functions. Last, it’s helped us understand the brain as a fine-tuned electrochemical system which controls all brain functions.
OHBM: Simultaneous EEG-fMRI offers high spatial and temporal precision - but how have labs coped with the challenge of integrating and analysing this wealth of data?
AD: This has been especially problematic for EEG. What we need is good open-source processing software for integrating this information, along with online tutorials and courses to teach people how use them.
UR: I believe that there’s still not enough work in this area. We need to have a much greater understanding of how structure, overall function and brain dynamics integrate in order to understand how typical/atypical brain networks function. Here the question is not so much about using information from different methods to prove each other but instead to complement each other.
OHBM: EEG-fMRI has clear benefits in conditions like epilepsy, for identifying seizure focus and spread. What applications has it had in other conditions - and what do researchers hope to achieve with it?
AD: Cognitive neuroscience can certainly benefit from the combination of higher spatial and temporal resolution in brain mapping.
GK: EEG-fMRI also has promise for use in sleep physiology, sleep disorders and coma.
UR: Any typical cognitive functions and any pathology which are ALL based on structure, function and dynamics....
OHBM: What do you think are the main strengths of multi-modal MRI work? Do you feel it offers hope for developing valid and reliable MR-biomarkers?
UR: Absolutely! Science is not a mystery, the more complementary information we have, the better we understand the human brain. It will help us to diagnose/monitor sub-types of pathologies and give much greater precision when tracking the effects of interventions....
AD: I do believe using multiple MR measures makes sense for biomarker development and understanding pathophysiology. Pathological processes (e.g. in Alzheimer’s Disease) can affect the brain in multiple but likely stereotyped ways. We can also Increase our power to detect pathology (e.g. inflammation, white and grey matter tissue loss, connectivity information) by combining multiple measures.
OHBM: What additional challenges do animal studies have in terms of sequence development or protocol considerations? How do you find these studies enrich those in humans?
AD: Clearly a major issue is the small size of animal brains. We also have to account for the animals typically being anaesthetised when scanned, which has implications for physiology.
GK: Sometimes data from preclinical studies can help optimize a project to be conducted later in humans.
UR: The real benefit of these preclinical studies is that it allows us to perform complementary invasive studies not possible on humans, such as MRI-histology studies. We do however need to continue developing better, or more realistic, settings in animal research in order to better correlate those findings with human brain research.
SK: One of the challenges that we’ve found is that tools that are available on human MRI systems (simultaneous multislice EPI, for example) are not easily implemented on animal systems due to hardware limitations. As Alain says, the other main issue is the use of anesthesia in animals, a special challenge for functional neuroimaging studies, as discussed in our review. Luckily, many of the basic properties of the brain remain relatively intact under light anesthesia, which has been critical of the validation of human neuroimaging methods against “ground truth” modalities like microelectrode recording. People talk of animal research as preclinical or translational, but we like to think of it more as circular. For example, one can take a neuroimaging finding in humans (e.g., fMRI response to tactile stimulation) and look at its basis in the rat using MRI and electrophysiology. Then perhaps one sees that this response is altered in human patients with a particular disorder (maybe stroke). One can then go back to a rat model of stroke and see if the same alteration is present, which helps to validate the stroke model. Then one can look for the neural basis of the alteration using MRI and electrophysiology and identify specific alterations in patients that may be detectable with EEG…etc, etc. We think that human and animal neuroimaging work should inform each other.
OHBM: Thanks all for your insight! We look forward to the multi-modal imaging symposium at OHBM 2018 in Singapore.
By Elizabeth DuPre and Kirstie Whitaker
This month we continued our Open Science Demo Call series by speaking to Anisha Keshavan, Yaroslav O. Halchenko, and Athina Tzovara about three tools they’re developing to improve openness and access in neuroimaging research.
Anisha introduced braindr, a project she’s developed to crowdsource quality control of large datasets such as the Healthy Brain Network data set. It builds off her previous work in creating MindControl but provides a fun, Tinder-inspired interface for image ratings. She encourages anyone interested to check out the app, remix it for their own data, or contribute to the conversation on how to do quality control of images!
Yaroslav told us about DataLad, a solution devised to allow for versioning data. We’ve already recognized the importance of versioning code, but it applies to data too! As Yaroslav pointed out, data can change or have “bugs” like the dreaded left-right orientation flip in MRI data, so understanding what version you’re working with is important. Using DataLad, Yaroslav demonstrated how to install datasets from sources like OpenNeuro and discussed how it can even be used for data sets before they are made publicly available. Interested contributors are welcome to check out the code!
Athina introduced a survey she’s actively developing to better understand how research treats underrepresented minorities. It aims to allow non-scientists --- particularly those belonging to traditionally underrepresented minorities --- to take an active role in the scientific process, bridging the divide between researchers and participants. Originally developed through the Mozilla Open Leadership program, the survey is still open to feedback from the community, and Athina encourages anyone interested to join the discussion on GitHub!
Our next call will be on Thursday March 22nd at 7pm GMT (check your local time zone). If you’d like to nominate yourself or someone else to be featured on these monthly calls, please add their information at this github issue, or email the host of the calls Kirstie Whitaker at firstname.lastname@example.org. You can also join the OSSIG google group to receive reminders each month.
"A brain scan may reveal the neural signs of anxiety, but a Kokoschka painting, or a Schiele self-portrait, reveals what an anxiety state really feels like. Both perspectives are necessary if we are to fully grasp the nature of the mind, yet they are rarely brought together".
-- Eric Kandel
Visual art can provide a glimpse into people’s consciousness. It works as a bridge, not only connecting us to each other, but also with the past, present, and future. The act of creating art is also therapeutic, and represents a powerful resource for mental and physical well-being. Yet, the mechanisms underlying the brain’s capacity to generate art remains largely elusive. While it has been commonly reported that the right brain (posterior parietal and posterior temporal) is dominant for artistic ability, emerging literature strongly indicates that the left brain is not a silent partner. Instead, it contributes to more of the symbolic/conceptual aspects of art. Moreover, the emergence of visual artistic skills in the healthy brain has been linked to plasticity in areas (in both hemispheres) responsible for cognitive processes. Which begs the question: how is visual artistic creativity affected by neurodegeneration?
In fact, art in the context of neurodegenerative diseases (e.g. Alzheimer’s disease, frontotemporal dementia) provides a unique window into brain anatomy and function. In this interview, I discuss the link between neurodegeneration and art with Bruce Miller, director of the Memory and Aging Centre at the University of California. Bruce also oversees the unique Hellman Visiting Artist Program, created to foster dialogue between scientists, caregivers, patients, clinicians and the public regarding creativity and the brain.
Q&A WITH BRUCE MILLER
AmanPreet Badhwar (AB): Can you begin by saying something about your background?
Bruce Miller (BM): I am a behavioural neurologist at the University of California, San Francisco. I focus a lot on degenerative disease: the clinical presentation, differential diagnosis of dementia, also deep dive into frontotemporal dementia. I think a lot about behavioral phenomena, particularly early in the course of these diseases.
I started realizing the importance of art and dementia very serendipitously. It was based on seeing a single patient (Jack). The son told me his father has become an artist in the setting of the illness. And I said “of course as the disease has progressed his work has gotten worse”, and he said “oh no it has gotten better”. So he sent me a series of pictures, and I was fascinated and really enchanted by the work that he did, and began to look in detail into the visual artistic process in that patient. Jack was preoccupied with creating purple and yellow art pieces, and a phrase I often heard from him was “ yellow and purple wave over me”.
I did not think it was a coincidence, although many people around me thought it was, and I was stubborn enough to pursue this, and continued to look for it in my frontotemporal dementia and progressive aphasia population. It does not take much time to hear about somebody, who they are, what they do etc. I would argue that this should be a mandatory part of any evaluation.
AB: How do the worlds of neuroscience and art combine?
BM: Art is unique to the human species. Other animals don’t spontaneously produce art and even our predecessors like the neanderthals and homo erectus made art. There are records of very sophisticated and complex cave paintings by homo sapiens that showed animals, had three-dimensional components and colours. So we developed this ability spontaneously, and without much formal teaching. The sense is that there is something really unique that happened, there was a change in the human brain, maybe a change in human circumstances that lead to this flourishing of art, and this continues to be a part of our ancient and modern societies.
Also looking at the human output around art: some people are extraordinary, and some never produce art. So I think art is a very interesting aspect of humanity and a very interesting aspect of the human brain, and that the two things cannot be more connected.
AB: You previously stated that “creativity is one characteristic that has been observed to improve with time, both in healthy older adults and people with age-related neurodegenerative disease”. Is the trajectory for artistic creativity different in normal aging and in age-related dementias?
BM: I think it’s a very interesting, complex question, tackling aging of humans and art. We are very interested in elder artists, there is no doubt about it. Picasso was in his eighties, he produced very different but interesting pieces, but they delighted people. There is no doubt that his work was exciting. Was it better when he was young, or was it more innovative, maybe not, but I think there is great variability in when an artist reaches his or her peak. Some artists may have a series of observations that become very important in their twenties, and don’t change very much over time, and in others there is a constant evolution. I think one thing that is clear is that it takes a while to master whatever artform that someone is working at, nobody picks up a pen and produces a perfect sketch of a face, it takes many, many iterations and practise over many times. I think this is what happens when someone is an art student, they are constantly working on these techniques, making their own observations and getting observations on their work made by teachers.
In disease, people who have never painted, made sculptures, or welded art pieces, suddenly become very interested in the process. Their first works are usually not as good as the ones they produce after they've had the chance to work at a specific media. They do things over and over again, and at some point they start to reach a mastery of their art. So I think there is often a period when they don’t produce something very interesting but there is a drive to do so. That drive pushes them to practise more and more and they reach some sort of a peak, until eventually the degenerative process and injury to circuits causes a loss of their abilities.
So we have this very beautiful but sad story of sometimes art heralding the onset of the degenerative disease process. Soon after the art has appeared the degenerative process gets worse, and eventually the ability to produce art is lost altogether.
AB: Do you think that this drive to produce art arises from disinhibition of certain brain networks, especially in patients who, earlier in their history, were never motivated to produce art? In other words is this artistic ability unveiled and perpetuated by the neurodegenerative process itself?
BM: I do. I think the fact that they never produced art before means that the circuits involved in this process had not been activated. Something about the degeneration, for reasons that we don’t completely understand, leads to an interest, an activation, an actual physical drive to carry out the artistic activities. The theme has been that degeneration on the left side of the brain (language based regions) releases functions on the right side, which are more visual.
AB: Have there been any fMRI studies done in these patients with relation to newly developed artistic abilities?
BM: There is quite a bit of fMRI data that we have collected on our artists. We are in the process of analysing that, but we don’t yet have a coherent story. We wrote about it. William Seeley did these analyses on a woman (Anne Adams) who became a visual artist in the setting of a non-fluent aphasia, and she showed on a blood flow scan increased activity in the right posterior brain region, and actually during that time an MRI was done and she had increased volume in that same area.
There are a number of theories, one being she was always like that (that is the bigger volume). But she was never much of an artist until the progressive aphasia emerged. We think there might have been slow remodeling in the early stages of the disease, with decreased activity in the left frontal insular regions allowing increased activity on the right posterior parietal area and actually some increase in volume.
AB: Does art created by people with brain disease or damage provide insight into brain anatomy and function? Could you provide a few examples?
BM: Surely Anne Adams was a paradigm shift for me to describe the phenomenon of art and dementia, but I had never really thought too much about the mechanism. But because she had undergone an MRI just before the onset of dementia, this really allowed us to look into the circuitry and mechanism. This also allowed me to broaden my thoughts about the topic, so seeing patients who had gardens with beautiful details, flowers, patterns. This is another form of visual creativity that I have become aware of.
AB: As a practising neurologist, how has your encounter with art influenced or changed your own conception about how the brain functions? Do you have specific examples? Did you have to overcome difficulties to promote this field?
BM: I think it has really humanised my approach to patients. It makes me realize that even though dementia is a relentless process, there are many pockets of preservation, and sometimes enhanced function. It is critically important that we recognize this in our patients. It is helpful in diagnosis. 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, and help design programs for them and have activities that are meaningful. If you have lost your visual spatial function profoundly, then probably working in art is not going to be satisfying. But if instead there are other areas that are preserved around music or singing or something else, these things have to be kept in mind while thinking about the future for the patient and their families.
I think this should be a routine part of our diagnostic process, that is not only what are the weaknesses, but what are the strengths, and has anything new emerged that is actually a new strength. We do this regularly now at UCSF (it has opened up a whole new side to the evaluation). This also makes me appreciate the unbelievable effort that every patient that we see is putting into their life. When blocked in certain domains, they activate others and use others. So I think about patients in a very different way since the story of art emerged. I think, to a fault, neurologists have often thought about deficits a lot, without really seeing the whole human being, and I think this has really forced me in a very good way to think about the entire human within the ecosystem that they live and interact with others, and some of the things they perceive that might be very important.
AB: I have had the good fortune of discussing both art and neurodegeneration on various occasions with Bruce. Not only do Bruce and I share similar scientific curiosities with regards to art and dementia, I have also found him to be an excellent mentor. He has taught me to follow my heart in the quest to figuring out the brain, and for this I shall be forever grateful!
“I think the next philosophers, the philosophers of the 21st century, are going to be neuroscientists.” - Bruce Miller
The OHBM is dedicated to understanding the anatomical and functional organization of the human brain using neuroimaging. But how to best use brain-activity measurements, including human neuroimaging, to understand computational mechanisms remains an open problem. “Mapping the brain does not by itself reveal the brain’s computational mechanisms” says Niko Kriegeskorte, past chair of the OHBM Communications Committee. “Therefore one of the strategic priorities in the OHBM Communications Committee has been to explore the interaction between computational neuroscience & human neuroimaging.”
Here, we had the chance to discuss the current state and future of computational neuroscience with Mark Humphries, senior research fellow at the University of Manchester, Chair of Computational Neuroscience at the University of Nottingham, and talented blogger. We found out about research environments in different countries, mindful language use in neuroscience, Mark’s outlook on the future of network neuroscience, and his top three tips for those starting out in computational neuroscience.
Nils Muhlert (NM): Can you tell us a bit about your career path - were you first interested in computing, or in neuroscience? Also, your work has seen you move between the UK and France - have you found different approaches to research in these countries?
Mark Humphries (MH): I’m of the generation that grew up programming their home computers - their C64s, Spectrums, and BBC Micros - so computing was always there. As a kid I also loved chemistry. Originally I wanted to do Chemical Engineering at university, but it turned out that A-Level Chemistry was both hard and boring. So when I came across the mysterious “Cognitive Science” degree, promising computing, AI, and the brain, I signed up like a shot. In effect, I’m one of the few who was trained in computational neuroscience from my first year at undergraduate level.
That degree was followed by a PhD and postdoctoral work at Sheffield, with the quietly wonderful Kevin Gurney. Not quite the straight run it sounds: disillusioned and exhausted by the end of the PhD, I went off to freelance web design and software engineering. That lasted a year before I was tempted back by the offer of a post-doc.
My long stint at Sheffield was followed by three years in Paris at ENS. Both teams of computational neuroscientists, with radically different approaches. Sheffield were neuroscience-first, circuit modellers: build a model of a brain region, study its dynamics, and infer its function. Paris were theoreticians first: propose and study general principles for how computations could be done by the brain (memory, inference etc), then worry about the details of specific circuits later, if at all.
In my experience, the French research system, dominated by the CNRS and INSERM, is essentially just part of their civil service system. So you can have a job for life, but getting financial support to do your research can be an absolute pain. Theorists in all fields can thrive, of course. (ENS has an extraordinary maths department: the Bourbaki group were based there, and they’ve had five Fields medalists). The UK research system more clearly supports fundamental science.
NM: In a recent blog post on connectomes, you highlight some of the many factors influencing the spiking of a single neuron. In human neuroimaging, we typically summarise activity at the scale of cubic millimetres, with each voxel containing tens or hundreds of thousands of neurons in different cortical layers. How much cross-talk do you see between cellular systems neuroscience and human neuroimaging, and how much do you think understanding at one level currently constrains understanding in the other?
MH: The neuroscience of detailed neuron types - their physiology, receptors, transmitters, gene expression, and so on - often has little constraint on systems neuroscience studies of large populations of neurons. Many multi-neuron recordings from cortical regions can only hazard a guess at what layer they are recording in, never mind whether the recorded neurons are Martinotti or ViP interneurons or whatever. I think this lack of identifying neurons has played a large role in driving the take-up of calcium imaging, where we can at least identify some subtypes of neurons (typically 1 or 2), despite the obvious disadvantage of recording something (calcium) that is only partially related to the thing we’re interested in (the spiking of neurons). What’s particularly missing is the constraints of anatomy - the wiring between individual neurons - on the activity we’ve recorded from those neurons.
But that will come. In a handful of specialised circuits, this information is being combined. For example, in studies of the mouse retina, the type and position of neurons has been used to constrain classifications of large population recordings. And in tiny animals, like Drosophila larvae (maggots to the rest of us) and C Elegans, the details of wiring and neuron types have been combined with large-scale imaging to reveal deep insights into how brains could work.
NM: Marsel Mesulam revealed that students requesting higher field strength MRIs are asked “what would you do if you could record from every neuron in the brain?” This thought experiment is now an ambition for international research projects. How do you feel network neuroscience could sensibly use this massive amount of data?
A question that has occupied much of my thinking, but to which I’m no closer to a good answer. We have passed the milestone of recording every neuron from a simple nervous system. But as I wrote at the time, it was a cool study from which we learnt very little of consequence.
That said, everything that brains do, they do through the collective action of hundreds to millions of neurons. And we lack well-established theories for what that collective action means, or how to interpret changes to it. In the absence of theory, the gotta-catch-them-all philosophy of recording every neuron is seductive: let’s get the data we think we will need one day, and wait for theory to catch up.
Fortunately, ideas are emerging about how we can sensibly use this data. There’s some great recent work on how we can tell whether there’s anything special about the joint activity of many neurons: whether it is just the expected result of lots of individual neurons tuned to different properties of the world; or if the joint activity really conveys more information than the individual neurons summed together. And we’re starting to get a handle on how to understand the dimensionality of that joint activity: how much redundancy there is between neurons, how that redundancy differs between brain regions (and between different brains), and what that means.
NM: In another of your blog posts, you criticize media misinterpretations of dopamine as representing the ‘reward system’ of the brain. How does your own work feed into this - and at what point did you feel a general education piece was warranted?
MH: The tipping point was seeing “Dopamine dressing” in The Guardian‘s Style section. As though dopamine neurons give a damn about what you wear. Endless publications call dopamine the “reward system”, when it is not. And it’s particularly embarrassing when such language routinely appears in august publications like Nature. So I thought that it’d be useful for everyone to have a simple, accessible, concise explanation that dopamine neurons signal an error, not reward. And then we can all just point our undergraduates, friends, family, and editorial staff at esteemed publications to that post, and save ourselves the trauma.
Dopamine has been around in my research since the first days of my PhD. For years my work was primarily on the basal ganglia, and the striatum - the massive input nucleus of the basal ganglia - is where the dopamine neurons send their dopamine. So we include the effects of dopamine in all our models. In Paris I spent a couple of years analysing dopamine neuron firing in a project that never saw the light of day. More recently, I helped Kevin Gurney achieve his mammoth computational account of how dopamine teaches the basal ganglia to select actions. Dopamine has haunted me for my entire career...
David Mehler (DM): Richard Feynman used to stress the difference between “Knowing the name of something and knowing something”. In a similar spirit, you have critically assessed whether we put too much faith in named brain structures, giving examples why these should not be taken at face value. What advice do you have for students and ECRs, whose experience of Neuroscience may consist wholly of learned brain regions with set functions?
MH: Read more than just about your brain region. And internalise the idea of degeneracy: brains have many solutions to the same problem.
If we work on only one brain region, it is easy to fall into the trap of thinking that one brain region does everything. Just being aware of the thinking about brain regions other than your own will help not take anything at face value. In my own fields, it is easy for basal ganglia researchers to fall into the trap of claiming that it is responsible for “action selection”. But this patently can’t be true: there are multiple systems that select actions in the brain, from spinal reflexes, up through the brainstem, midbrain, and other sub-cortical structures - the amygdala can select fear responses just fine on its own.
DM: A recent study from your lab, in collaboration with Angela Bruno & Bill Frost from the Chicago Medical School, provides fascinating insight into how neural populations orchestrate their activity when coordinating movement: while their combined output converges to a similar pattern (an attractor), activity of individual neurons is not stable over time. What does this finding imply in your view for our understanding of functional connectivity (e.g. between neurons or neural populations)?
It means that functional connectivity is an epiphenomenon. The correlations between individual neurons are imposed by the dynamics of the whole circuit in which they reside. Those dynamics obey certain properties that emerge from the wiring of the whole circuit and the excitability of the individual neurons.
But it is very useful to study functional connectivity of neurons: mapping the correlations between neurons is so much easier than trying to infer the underlying attractor, or other form of dynamical system. And changes to those correlations imply a change to the underlying attractor. Indeed, we use this approach all the time. We just need to be mindful that those correlations are a read-out, an observable property, of the circuit’s dynamics.
Functional connectivity at the level of whole brain regions, of MEG/EEG and fMRI, is a different kettle of fish, of course. On this scale, correlated activity is telling us something about the distribution of how things are represented across the brain in very large neural populations, with tens of thousands to millions of neurons in a single time-series. Instability of correlations over time for these time-series would suggest entire neural populations that wink on or off as needed. And dynamical systems analysis has long been applied to EEG data, but usually as a way of looking for changes in gross neural activity - as may precede an epileptic seizure, for example - than as a view of how the brain computes.
Seeing a spiral attractor in neural activity. Activity was recorded from 105 neurons in a sea-slug's motor network during three separate bouts of galloping. There are three lines plotted here. Each line is the low-dimensional projection of those neurons' joint activity during a 90 second bout of galloping, from its onset (grey circle). Each line traces a circular movement whose amplitude decays over time: a spiral. The three lines together trace the same region of this low-dimensional space, indicating that the neurons' joint activity is attracted to the same pattern: the spiral is an attractor.
DM: Your work increasingly focuses on dynamic changes in neural networks. What insight do you think this will bring to the field over the next 5-10 years?
MH: We’re going after the idea that the brain encodes information at the level of the joint activity of populations of neurons. In this view, each neuron is a read-out of the joint activity of all the neurons that project to it. That neuron, in turn, is just one small component of the populations projecting to other neurons. So only by looking at the dynamics of the neural network as a whole can we understand what neurons are seeing, and hence what the brain is encoding. A change to those joint dynamics are then the change in what is being encoded: be it a sound, a memory, or a movement. In short: the response of single neurons may be irrelevant to what the brain is doing.
DM: … and finally, computational neuroscience is gaining increasing popularity. But starting out may seem daunting. What are your top three tips to get into the field?
MH: First, learn to code, properly. To some, this may seem obvious. In my experience most people who’ve come to me with a genuine interest in getting into computational neuroscience have never coded, certainly not seriously. But coding is the day-in, day-out life of the computational neuroscientist, so you won’t get far without deep skills in coding. And by “properly” I don’t mean “you have to learn a proper programming language”, whatever that means. No: properly learning to code means learning the logic of how code is built, independently of the language used: of variable types, indexing, functions, control loops. And learn to comment your code. You know who will love you for commenting your code? You, in a year’s time.
Second, ask yourself: What type of computational neuroscience do I want to do? The choices are endless. We can work on scales across the actions of receptors at single synapses; plasticity at single synapses; the intra-cellular signals triggered by receptor activation; the dynamics of a single neuron in all its glory, dendrites and all; the collective dynamics of networks of neurons; of specific brain circuits; right up to the entire brain. And on to read-outs of mass activity, to EEG, MEG, and fMRI, and the functional connections between regions. We can work bottom-up, top-down, or middle-out. We can aim to ask what a specific brain regions does, work out what causes a disorder, or reach for general principles for how neurons compute. We can use algorithms, like machine-learning; simulations of dynamics using differential equations; or pencil and paper to solve equations. What is it you want?
Finally, take a Master’s course in computational neuroscience. Both so you can find out if this path is for you; and so that you can be taught the neuroscience by neuroscientists and the computation by computational neuroscientists. Get either wrong, and no one will take you seriously.
By Elizabeth DuPre and Kirstie Whitaker
The open neuroimaging community is great and growing every day. This month saw the first of a series of Open Science Demo Calls. Brought to you by the OHBM Open Science Special Interest Group, these live streamed calls are a chance to hear from the developers of open neuroimaging tools. We'll use these calls to build connections between all members of the OHBM Open Science community and to tell the stories of the people making outstanding and reproducible neuroscience happen.
For our first call, we spoke to Alejandro de la Vega, Cameron Craddock, and Guiomar Niso about three ongoing initiatives they’re spearheading to improve openness in neuroimaging research.
Alejandro spoke about NeuroScout, a new, cloud-based platform allowing for the flexible re-analysis of neuroimaging datasets with naturalistic stimuli, such as the Study Forrest dataset. To do this, Alejandro is actively working to develop tools such as pliers and pybids. If you’re interested in this line of research, make sure to check out and contribute to these tools!
Cameron discussed this year’s Brainhack Global. Building off the successes of Brainhack Global 2017, Cameron is organizing a globally based hackathon for this spring, where neuroimaging researchers around the world can come together online to learn about, develop, and improve open neuroimaging tools. He encourages anyone interested in attending the event to join the Brainhack Slack team.
Technical difficulties prevented us from seeing Guiomar in our call, so we recorded a supplementary video to hear more about her work with MEG-BIDS. This is a very big extension of the BIDS specification to cover MEG data. As Guiomar informed us, MEG does not have a standardized acquisition file format (like MRI dicoms), so the creation of an MEG-BIDS standard will make a huge difference to the community! Feedback is welcomed on the current draft of the specification, which is planned for release on February 14th.
Our next call will be on Thursday February 22nd at 7pm GMT (check your local time zone) and will feature Anisha Keshavan on Braindr, Yaroslav Halchenko on DataLad and Athina Tzovara discussing how research treats underrepresented minorities.
If you’d like to nominate yourself or someone else to be featured on these monthly calls, please add their information at this github issue, or email the host of the calls Kirstie Whitaker at email@example.com. You can join the OSSIG google group to receive reminders each month.
Professor Aina Puce is the Eleanor Cox Riggs Professor in the department of Psychological and Brain Sciences at Indiana University, Bloomington, and a senior editor at Neuroimage. She has followed a career path that is now becoming more common in human brain mapping, starting firmly rooted in the methods end but, over time, gradually shifting focus towards understanding complex patterns of behaviour. To do this, she has made use of a number of imaging techniques, exploring ways to extract converging lines of evidence.
Here, we find out how her interests changed throughout her research, the promises and pitfalls of multi-modal imaging, and why you should not be discouraged by rejections but instead focus on and be motivated by the paper acceptances and other highlights in your career.
Nils Muhlert (NM): You initially graduated with degrees in Physics/ Biophysics. Now, one of your lab’s key interests is specific applications - such as understanding social cognition - though clearly facilitated through your expertise in imaging methods. Can you tell us about how your research focus has changed throughout your career?
Aina Puce (AP): My undergraduate degree was in Biophysics and my Masters degree was in Physics. For my Masters I was already recording EEG/ERPs in the operating room under anaesthesia – generating a frequency response of the visual system using sinusoidal visual stimulation through closed eyelids. During my PhD, I recorded intracranial EEG/ERPs from the hippocampus and temporal lobe for the purposes of identifying the epileptogenic temporal lobe in presurgical patient assessments.
My interest has always been tied to the relationship between brain and behavior. Over the years it has evolved from consciousness under anesthesia, to hippocampal integrity, to recognition memory of objects, to face perception, to recognition of face, hand and body actions, to multisensory perception, and now to the implicit recognition of emotions and other non-verbal signals. Seems like a lot of topics perhaps, but the evolving theme is how we make sense of our world. I owe a lot to my colleagues from the humanities: over the years they have patiently taught me so much about psychology.
NM: Much of your work involves imaging across modalities. Alongside the higher temporal and spatial precision, multi-modal imaging often involves the challenge of combining very large datasets. How have you got round these issues?
AP: Important question. When you study brain function using only one imaging method you will look at the world with a set of (rose-colored) glasses that give you only part of the story. We tend to forget that. Using multiple methods (either across or within subjects) keeps you honest, as you might get different answers to a scientific question. Then the onus is on you to get to the bottom of those differences, which means taking more time to study a problem. This can be frustrating, because at times you feel you are not getting anywhere relative to others in the field. At the same time, I would rather generate work that is reproducible and replicable by others! The field needs a solid foundation, and this can only be achieved by paying attention to data quality and also fully understanding the methods we work with.
With respect to large multimodal datasets, the biggest challenge right now as I see is data quality control. Data will likely be analyzed by individuals who may not have expertise in data acquisition and artifact recognition/rejection. When multiple assessment modalities are involved, this problem becomes compounded.
Another challenge that I see relates to cloud computing and subject privacy. Increasingly, subjects in these big datasets will be patients. As more investigators around the world interact with these datasets there is an increased potential for hacking and accessing sensitive information. Having easy to use, but secure, user interfaces and procedures for interacting with big datasets is key.
Another critical component is user training on computer hygiene. I am continually horrified by what I see those who are not computer-savvy doing with data-archiving and sharing. We cannot blame these people as they have not been formally trained in this area, but these are the potential weak links in the chain. That said, user-training needs to be made meaningful and interesting and something that users view as important – and that is also a big challenge in my opinion.
NM: Where do you see multi-modal imaging going over the next 5 years?
AP: With respect to methods and scientific practice: these have been re-examined and will continue to do so. With respect to neuroscience in general: I think that meso-scale neural interactions will be a major focus, as this work is critical to building bridges between systems neuroscience and molecular/cellular neuroscience.
Finally, for social neuroscience, measuring/monitoring brain and bodily function will also become more important as science moves more and more from a lab-based focus to a real-life one. Smart clothing used with dry electrode portable EEG systems and smartphone applications to gather data will become more common. Exciting developments in MEG sensor technology will continue, with attempts to develop higher temperature MEG devices and also flexible sensor helmets to better fit any head shape or size. This is a really exciting time to be involved in neuroscience!
NM: In your work on social attention you have proposed a ‘socially aware’ brain mode of social information processing. Can you tell us a bit more about this. How, if at all, does this brain mode map onto specific resting-state networks?
AP: I have recently been interested in how we use social information that we access implicitly to make social judgments or decisions about the behavior of others. Most lab-based studies in social neuroscience use tasks where subjects make explicit social decisions about others. Yet, this is so unlike what we do in real-life. In our lab we use both implicit task (involving a ‘default’ mode, where there is an internal focus on achieving goals) and explicit tasks (requiring a ‘socially aware’ mode, where we make explicit social judgements), using the same stimuli in the same subjects. We found very different neurophysiology across tasks – explaining in large part the existing variability seen in the literature.
Relationship to resting-state networks? Excellent question! We have been looking at the EEG dynamics during these implicit and explicit tasks, but have not yet looked at resting state EEG in these same subjects. So this is something that I would like to look at in future work.
NM: What advice would you give an early career researcher to help them stand out in the hunt for competitive fellowships, grants and faculty positions?
AP: I usually tell everyone to find what their passion is. What topic of study really motivates you scientifically? Doing science is a perpetual set of ups and downs – often more down than up. If you follow that passion, you are more likely to be successful, because it will help you get through the bad times.
As for specific advice for early career researchers. First and foremost, find a mentor – a senior scientist who you trust, have a personal rapport with, and who can help you work on your desired career goals. They should be a good sounding board, but also be able to network you with other scientists and point out career opportunities you may not know about. OHBM has an excellent mentor-mentee matching service. I have been recently assigned to mentor two young scientists, and I am looking forward to interacting with them on-line and face-to-face at the OHBM meeting itself!
Second, network network network! Don’t be afraid to speak with senior scientists at scientific meetings – not just at your poster, but do it at the various social events. Getting to know someone can allow you to visit their lab (perhaps even on a short stay to analyze some data), and who knows what other opportunities that might lead to? Applying to competitive Summer schools can also give you this opportunity.
Third, seek feedback from peers and colleagues on your fellowship and grant applications. People do not do this enough. That said, it requires being organized – you need to allow time for people to read and give you feedback, so that you can make the edits before the submission deadline. Same thing applies for job talks or conference talks – in our lab no-one does a talk anywhere without doing a dry run first! This rule also applies to me, and I value the detailed and caring feedback I get from my trainees.
Fourth, you can stand out by being yourself – scientifically and personally. Scientists are by nature prone to eccentricities. I like to celebrate those. Your (hopefully positive) eccentricities make you who you are, and importantly make you distinctive and memorable to others. (I'll never forget a job candidate who told us that he had a pet tarantula. He got the job!)
NM: Next month, you’ll be a keynote speaker at the Brain Twitter Conference. Can you give us some insight into what you’ll be presenting - and what you think can be achieved through this online mini-conference?
AP: I will keynote tweet on the different modes of social information processing that I mentioned before.
What can be achieved with an online Twitter conference? A couple of things quickly come to mind. First, the conference builds a greater sense of community, allowing new connections between scientists around the world to be made through interactions generated in response to speakers’ tweets. (It is interesting to finally meet people at scientific meetings that you have been tweeting with.) Second, communicating one's ideas with a series of 10 Tweets makes one distill the absolute essence of the ideas to be presented. It allows the presenter, at least, to work out what is really important in the practice of their science.
NM: When did you become involved in Neuroimage - and how have you seen it develop over the years?
AP: I became a member of the Editorial Board in 2005, a Handling Editor in 2009, a Section Editor in 2011 and finally a Senior Editor in 2013. It has been wonderful to watch our field grow exponentially over the years and to work with so many dedicated and committed people in our NeuroImage family. Back in the early 1990s we had no outlet where (f)MRI-related work was welcomed, whereas work related to MEG and EEG was being published in well established neurophysiology journals. Today we have NeuroImage as well as Human Brain Mapping (which also began very early to meet the need to publish MRI-related work). It is terrific to see neuroimaging work so mainstream and regularly appearing in high-profile neuroscience journals. Indeed it is hard to keep up with it all right now!
NM: ...and finally, you’re currently serving on the program committee for OHBM. What does this role involve - and how can others contribute?
AP: OHBM is my tribe. As a post-doc I presented a poster at the very first OHBM meeting in Paris organized by Bernard Mazoyer in 1995. I have only missed a couple of OHBMs since then, due to issues related to visas... I have presented in Educational Courses, Symposia and given a Keynote, as well as chairing scientific sessions over the years. I was a member of Council from 1999-2002, where I was the Meetings-Liaison. Back then we did not have the wonderful Secretariat we have now, so the meeting organization was a bit different. Currently, together with Cyril Pernet I am Co-chairing a COBIDAS for MEEG committee for OHBM. I am also a member of the OHBM Scientific Program Committee – and right now this is busy time for the committee. I want to give a huge shout out to Michael Chee and his very capable team in Singapore. World-events forced the change of the meeting city at the last minute, and Mike and his team are making sure that OHBM 2018 will be just as successful as all of our other meetings. I am really looking forward to it!