The Annual Event of Chinese Young Scholars for Human Brain Mapping was held on June 19th, during the 2018 OHBM Annual Meeting in Singapore. This was the second annual event, and continued the success from the inaugural meeting in Vancouver. The theme for this year’s event was “The Road to Independence”. Around 200 young scholars from universities around the world participated.
The annual event is committed to bringing together young Chinese researchers from a wide variety of backgrounds to share and discuss their professional expertise and career experiences, as well as any challenges they may have faced. This offered a platform for young researchers to build collaborations on cutting-edge neuroscience topics and methods, and also to learn from senior researchers on the route to a successful scientific career.
This year’s schedule commenced with a brief review of the annual event by one of the organisers: Professor Chaogan Yan. Then, Professor Yan introduced the two guest speakers: Professor Jiahong Gao (Director of the MRI Research Center of Peking University, Chair Elect of OHBM), and Professor Xinian Zuo (Director of the MRI Research Center, Institute of Psychology, Chinese Academy of Sciences, Program Committee Chair elect of OHBM).
Professor Jiahong Gao gave the first talk, entitled “ Brain Imaging in China: Opportunities and Challenges”. He summarized the fast development of human brain mapping research in China, and shared his vision on future directions of this field in a humorous way. Taking the latest advances on Magnetoencephalography development in his lab for instance, Professor Gao discussed the challenges and opportunities we face in brain imaging, and encouraged young scientists to seize the opportunities and bravely climb to the scientific peak.
The second speaker was Professor Xinian Zuo from the Institute of Psychology at the Chinese Academy of Sciences. In his talk titled “From Mathematics to Brain Sciences”, Professor Zuo shared his own career experiences, from a PhD in mathematics to becoming an outstanding independent researcher in human brain science. He particularly emphasized the importance of reliability and reproducibility in brain imaging studies, and briefly introduced several ongoing projects by his team, including the Chinese Color Nest Project and the Traveler Project.
After the two keynote talks, Professor Juan (Hellen) Zhou from Duke-NUS Medical School, and Professor Ning Liu from the Institute of Biophysics at the Chinese Academy of Sciences joined the guest speakers for a panel session. Professor Chaogan Yan moderated the discussion, and introduced several topics under this year’s focus “The Road to Independence”, including relationships with tutors, necessity of career planning and recovery from failures. Each senior researcher shared their insights on these questions.
Professor Jiahong Gao provided advice on these topics based on his own experiences. He pointed out that the extent of independence of a young scholar largely depends on the mentors’ style. Professor Gao encouraged young scholars to develop their skills with support from mentors, and to prepare themselves to become independent researchers. Young scholars should set spiritual goals, make plans to achieve them, and learn lessons from their consistent efforts.
Professor Xinian Zuo shared his insights based on his personal experiences of switching from mathematics to neuroimaging, and echoed Professor Gao that young scholars would better seek support from their mentors and develop the ability for independent research in projects with their mentors. He also shared his “failure” stories about manuscript writing and paper submission during his very early projects. He summarized that failure is not terrible, and that one should learn lessons from what he/she had experienced, and aim to improve from them.
Professor Juan (Hellen) Zhou shared her personal study experiences, and emphasized the importance of independence, as well as hard work and persistence in order to become a successful researcher. She provided the example of her public speaking training during her PhD, emphasizing the critical role of hard work for acquiring professional skills. She also advised that one could obtain power and motivation from setbacks, and should move forward towards one’s ultimate goal.
Professor Ning Liu provided her thoughts based on how she got along with her own students. She pointed out that unstructured ‘light-touch’ supervision would not be suitable for all students, and she suggested to supervise each student with specific proper strategies. She also discussed the special difficulties associated with animal studies, and encouraged young scholars to actively adapt to any difficulties or potential failures, and keep being positive towards their goals.
Professor Chaogan Yan talked about his personal “failure” when attempting to switch from neuroimaging studies using fMRI to animal studies, and how he subsequently adjusted his research direction back to human neuroimaging. He pointed out that it could be a big challenge to move to completely new fields for a PhD or postdoc. But he believed that it may still be worth trying, especially if you are keen on the new questions and are still young. Even if there was a high chance of failure, one could learn valuable lessons from these unforgettable experiences.
Towards the end of the panel session, Professor Jiahong Gao provided his answers to the questions from audience on how to get international impact as local scholars in mainland China and how to publish papers in high-impact journals. He encouraged young scholars to perform high-level studies in the field, and to actively communicate research results with international researchers and journal editors. He mentioned that “the point is not that we cannot publish high-impact papers, instead it’s that we have not yet achieved high-impact research results.” He continued, “we should cherish our time, and work hard, to pursue critical questions in the field. Only in this way, can we achieve influential results, and publish papers in high-impact journals, which will lead others to recognize our research capability.”
At the end, the audience thanked the speakers for their informative presentations and discussions with hearty rounds of applause. We took group pictures to conclude this inspiring and memorable event. After the meeting, we enjoyed a group dinner and more informal discussions on both science and life as a scientist.
Organizing Committee of the Annual Event of Chinese Young Scholars for Human Brain Mapping:
Chao-Gan Yan, Institute of Psychology, Chinese Academy of Sciences
Yuan Zhou, Institute of Psychology, Chinese Academy of Sciences
Rui-Bin Zhang, Department of Psychology, The University of Hong Kong
Xiang-Zhen Kong, MPI für Psycholinguistik
Chun-Yu Liu, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Xiao Chen, Institute of Psychology, Chinese Academy of Sciences
Heidi Johansen-Berg interviews Charlie Stagg about GABA-MRS, neurostimulation and medical and scientific careers
By Nils Muhlert
In much of biomedical science the questions dictate the methods. This often means we have to draw on knowledge from different disciplines, or combine data from different modalities to converge on a likely solution. In a first for the OHBM blog we asked a senior PI to interview a more junior PI within their institution. This was always going to lead to interesting discussions - but when Heidi Johansen-Berg, director of the new Wellcome Centre for Integrative Neuroimaging in Oxford, agreed to interview the multi-modal brain mapper Charlie Stagg, it became clear that we would be acquiring a full spectrum of insight into scientific and career-related issues.
What follows is a wide-ranging discussion on moving from medicine to pure research, combining information about neurotransmitters from MR spectroscopy with neurostimulation techniques and the potential benefits of mapping from preclinical to clinical imaging.
Heidi Johansen-Berg (HJB): Charlie, you initially trained as a medic, but then decided to go down the pure science route – what persuaded you to take on a life of research rather than being in the clinic?
Charlie Stagg (CS): Yes – I did medicine as an undergraduate in the UK. As part of that, in Bristol University, I had the chance to do an extra year halfway through, at which point I did an undergrad degree in physiology and fell in love with the subject. I’ve always been very interested in the brain and wanted to be a neurologist, then through my undergrad research and in my clinical years I realised that I wanted to do something more interventional and that there weren’t many treatments for chronic stroke recovery – something I was interested in at the time.
It’s very common in the UK for clinicians in training to do a PhD some years after their medical training, so I came to Oxford to do that. I fell in love with it and didn’t want to stop! I’ve been an academic ever since.
Quite a lot of our work is clinical research, I work with a number of clinicians, and I think the training is useful in dealing with the medical side and dealing with patients. It’s way too much fun to go back to doing just clinical work.
HJB: So you’ve not regretted that decision – you’ve not wished you could do a bit more.
CS: No. There are certainly days where you wonder what you’re doing – but overall, no!
HJB: I think we all get that [laughs]. In terms of your research, a lot of it has focussed on the role of GABA in behaviour. As we know, this neurotransmitter is receiving increasing attention, particularly as the methods to measure it have improved over the years. What most excites you about GABA, what have been the recent breakthrough findings in the role of GABA in healthy and disordered brain function?
CS: It’s a really exciting question – as there are a lot of recent papers on this. When I started there wasn’t much work on it – it was a real niche subject. I used to have to start all of my talks by having to explain what spectroscopy was. I remember people coming up at the end of talks saying “I didn’t know you could do that, that’s amazing.” And now, I never have that – and that’s fantastic.
Much of our work has been on primary motor areas and motor systems in particular. That’s been very interesting from my point of view. But there’s been a lot of recent work taking it out of the motor system that’s been really exciting.
James Kolasinski’s paper (who’s now in Cardiff at CUBRIC) took a really simple hypothesis from the animal literature, that local inhibition and cortical organisation should relate to behaviour. He really beautifully showed that it did, in absolutely the way we’d expect. That’s a really nice, elegant study as it hadn’t been measured in humans – but showed that what we’re measuring with GABA has at least some relevance to what we see in animals.
You then get people starting to answer interesting questions, not only about immediate behaviour within that region but how it relates to networks. For instance, there’s a paper on overlearning from Watanabe’s lab where they showed increases in GABA as memories stabilise. That’s really exciting, as we’d been previously looking at decreases in GABA as we learn. If our understanding of physiology is correct then we should see increases as the memories stabilise – so that was really exciting to see the first demonstration of that in humans.
All of this work has been done in primary regions – such as primary sensory regions or the motor system – and people like Helen Barron in Tim Behrens lab have done some really cool things, asking really abstract questions. You imagine that lateral inhibition is really important for somatotopy or retinotopy but linking it to memory and more advanced cognitive processes is really cool. It asks a lot of questions about what is going on.
HJB: And I guess that’s where the ability to ask these questions in humans really does make a difference. One of the limitations of MRS has perhaps been the lack of precision, compared to manipulating GABA in animal models. Potentially that’s been a criticism about why you would do it imprecisely in humans when you can do it precisely and specifically in animals. Presumably you feel the fact of doing it in humans makes a difference?
CS: Absolutely – it’s something that I think about a lot. It is an indirect method and we can use multi-modal approaches to triangulate what we’re seeing, but it’s still not as direct as doing invasive recordings and never will be. So we need a good reason for doing it in humans – and the complicated cognitive work that Helen Barron’s doing is absolutely one of those reasons.
I’d also argue that human hand control, and particularly the relearning of that in the timescales after stroke, is very difficult to model in animals. The use of the hand is incredibly complex, the separation between primary motor and somatosensory cortex is pretty unusual in primates. We’re beginning to believe that the primary motor cortex works quite differently in terms of the physiology compared to the primary somatosensory cortex. That distinction is important, so there aren’t ready animal models that recapitulate all of that. There are certainly arguments for carrying this work out in humans.
HJB: Relatedly, the ability to measure neurotransmitter levels using MRS has been around for decades but has never really had the popularity of other structural and functional techniques. What do you think has held it back – and why do you think this may be changing in recent years?
CS: It is true. I do remember people finding out that we could measure neurotransmitter levels and wondering why we don’t do it more often. It’s really hard – particularly at lower field strengths. The signal we’re dealing with is 10,000 times smaller than the water signal we use for fMRI, so we’re dealing with much poorer signal-to-noise. So trying to get measures of chemicals at the millimolar range even at 3T is challenging and takes a long time. The advent of 7T being more widely available has massively boosted that SNR and made it much more achievable to get reliable measures within a sensible time frame of a few minutes.
If you look at our early work on the 3T, it took 20-25 minutes to get a sensible measurement. This is possible, but actually quite difficult in the context of studying learning. It limits the questions you can ask and makes it more difficult for the patients. This is perhaps why there has been less work in neurological and psychiatric groups – the timescales are just not clinically feasible. But suddenly, because the timeframes are shorter with ultra-high field MRI you now don’t need to have a big team of skilled physicists before you can do this. You still need a good physicist and quite a lot of time and a 7T but it’s becoming much more feasible to take these approaches and use them in a similar way that you would with fMRI.
HJB: One thing that strikes me as a bit unusual with MRS on the analysis side is that other MR modalities have seen massive amounts of research into signal processing – but in MRS there seem to be few, relatively simple, approaches. But the field of signal processing has changed massively over decades. The analysis of MRS doesn’t seem to have changed a lot in that time. For something like the OHBM community you’d think there would be a wealth of talent of people who could develop much more sensitive measures for extracting useful information from noisy spectra – why hasn’t that happened?
CS: I think it’s chicken and egg. There haven’t been that many people using it, there hasn’t been that drive and the simple spectra that you get, the edited spectra, are reasonably easy to analyse with simple approaches: you just fit a Gaussian. Now people are doing interesting things, more complicated things at 7T; we’re dealing with much more complicated signals. There are gold standard methods out there but people are developing their own.
To some extent, some of the questions that are important for fMRI just aren’t important for MRS. You usually get just a single voxel, so all the issues around clustering and thresholding, all those issues are less important, but yes, it’s a field that’s wide open right now.
HJB: Yeah, you’re on the lookout for talent! There are now methods with multi-voxel MRS, there will be a spatial component. People are starting to acquire functional MRS over time. It’s getting a lot more multi-dimensional than it has been. It’s certainly an exciting topic.
CS: It is! One of the things we’re getting excited about is work with Uzay Emir, who’s set up some fantastic sequences that we’re playing with and getting to grips with. One of them is a combined fMRI-fMRS. So for each TR you get BOLD signal and a single voxel decent spectra. That gives us the temporal resolution that we’ve never had before, so we’re beginning to ask new questions. It feels very much, from talking to people who were there, that’s it’s like the beginning of fMRI. We have this new thing and no-one’s quite sure what to do with it. We’re amazingly lucky to be situated here in Oxford, where the FSL guys are sitting next door and we can just go and talk and work with them. But there’s a lot of room for improvement. If you’re interested, then there’s an entire career there.
HJB: And how have you found the challenge of fusing information from MRS and non-invasive brain stimulation, techniques like TMS, tDCS. What do you think are the opportunities in bringing those approaches together?
CS: Partly, my first ever study on the MR was combining the two as a first year PhD student. I didn’t realise that this was difficult.
HJB: It was cutting-edge!
CS: Yeah, it was the thing to do, so I went and did it! But it is technically challenging. The reason that I just went and did it as a first year student is a massive testament to the support that we have. The physicists and radiologists are amazing at setting up sometimes weird bits of kit – and there’s a load of expertise around on the brain stim side. Doing it is one thing and we’ve now worked through it enough to be confident and happy with it. But interpreting the results is a whole different question! That’s ongoing and lots of people are becoming interested in it.
HJB: It’s quite an exciting possibility – one of the big limitations of brain imaging is that we’re stuck with correlations. You put someone in the scanner and see what lights up and you can correlate activity with behaviour but you never get causal inference. So to be able to perturb the system and observe the consequences does certainly add to the toolset. So it’s powerful for asking causal questions.
CS: Yes, I think so. But there are obvious caveats to what we can assume about the specificity – and we use tDCS rather than TMS for lots of good reasons, but it does have slightly more questions about the spatial specificity. With all the techniques there are questions if you’re thinking about physiology. Quite a lot of our work a while ago was trying to ascertain whether tDCS, and TMS to an extent, affect the brain in a similar way to the naturally occurring changes when we learn something. Are we engaging the same systems and doing the same thing to induce plasticity – or are we doing something completely different? It looks like there are very similar mechanisms involved, which makes sense but that’s a key assumption that we’re making. We’re still doing it and I still think it’s a really important thing to do.
Looking at the OHBM symposium this year, it’s really encouraging to see how much brain stimulation work is being presented – how many novel techniques are presented. There’s also a satellite event, so clearly people are beginning to realise that BOLD is brilliant and can tell us many things but, as you say, it’s very correlational and doesn’t tell us that much about the physiology. Once we understand the regions and networks that are important we can then go in and look much more specifically at given nodes within that using MRS and using stimulation to get a feel of what’s happening.
HJB: And it feels like the brain stimulation field is evolving, getting more sophisticated and more nuanced. There has been scepticism about some of those techniques because of things like variability or lack of replication and I think there’s an acknowledgement that these effects are very variable, and that needs to be taken into account, but it could actually be incredibly interesting; there might be interesting reasons for that variability. Trying to prove our experiments, capture that variability at the individual subject level and understand it, could really increase the use of those techniques for studying healthy brains. It could also help explain the cases where you get the completely opposite response for the same stimulation. For some people that’s a reason to shy away from the technique altogether but for others they’re asking “why is that?”. It does seem that there are interesting reasons for the variability, which could include genetics or brain geometry, that could help us understand the responses.
CS: Yes, and it has been an interesting time over the last few years in the brain stimulation literature. We hope we’re coming to a conclusion where we’re saying that it is variable but that could be interesting. As with any technique we need to be able to use it properly – we need to control it properly, carry out double-blinded, placebo-controlled trials.
HJB: It’s just like the early days of brain imaging, there were a lot of non-perfect (shall we say) imaging studies in those days but that doesn’t mean that imaging is a flawed technique, you just have to do the experiments right.
CS: And it’s like 10 years ago people were tweeting about the dead salmon – now there are other things coming up – the field is very similar.
HJB: Here in Oxford, as you know we have core-funding from the Wellcome Trust to create this new centre for neuroimaging, the Wellcome Centre for Integrative Neuroimaging (WIN), building on the success of places like FMRIB and OHBA over the years. With that, we’ll have access to new facilities – ultra high-field MR but also new facilities for animal MRI. What kind of things are you most excited about, once you have all this equipment at your disposal?
CS: The WIN is really exciting for many reasons. One of the big differences it has made already is in people – having a lot more physicists around to develop these techniques, getting the brain stimulation working in all of the scanners and also to get the sequences to work and be reliable and trouble-shooting. Having physics-support to do that, which is provided by the Wellcome Trust, is just amazing. Oxford is also wonderful and has a huge number of very, very good people working on similar things. But it’s spatially spread out across the site. While that’s still true of the WIN, having one centre has begun to get people to talk to each other in ways that they haven’t before. There are people here that I’ve never spoken to – so it’s been good to get together and see what we could do.
One of the things I’ve been interested in for a while is that, while human MRS is important, there’s no doubt that it’s an indirect measure, and there are some key questions about what it is that we’re picking up in terms of the underlying physiology. It would be fantastic to look at that, if we can do very specific interventions. Having a small bore animal scanner which allows us to do similar things to what we do in humans – the same sequences, as well as complicated behaviour and genetics – is just very exciting.
HJB: Yes, that’s what I’m particularly excited about – being able to use imaging as a bridge between lab-based neuroscience and the things we do with patients. All of us using neuroimaging have been frustrated at the lack of specificity in what’s going on in physiological terms. Having the techniques that allow us to bridge that gap actually allows us to carry out the manipulations in animal models, but then relate those signals to what we see in humans. That’s something that I think is really exciting.
CS: We’ve been working very specifically with Jerome Sallet on ultrasound modulation and have a grant to develop it in humans. He’s doing it in primates. We’ve been working really hard on that, and it’s been fantastic to work with him to see what it actually looks like – what behaviour changes he’s getting, what imaging changes he’s getting. And it’s starting to inform what we can expect to see. That’s concrete evidence that’s already coming out.
HJB: Yes, bringing together people across those species boundaries – and starting to train junior people to carry out cross-species experiments, those like Helen Barron, individual scientists who are doing fMRI, but then understanding those signals in terms of electrophysiology, and using optogenetic manipulations. You then have the macaque work from people like Jerome, Rogier Mars, and so bringing them together with people with imaging analysis expertise like Mark Jenkinson, Saad Jbabdi, will help us build tools to seamlessly move from rodent space, to macaque space, to human space will make it all much easier for people to cross those species boundaries.
To finish up, what advice would you have to early career researchers who are about to start their careers in brain imaging or multi-modal brain mapping?
CS: I think it’s difficult – and it’s a case of do what I say and not what I do [laughs].
It’s all about the questions. You have to work out what the question is that you find exciting and interesting – I made a joke about it earlier but it is hard and you have days when you’re wondering why you’re doing it and it has to be something that really excites you.
HJB: Yes, it has to be something you care about and want to know the answers to.
CS: That you really, really want to know the answers to!
And then you need to work out what techniques you need to be able to answer that. For me, that did and does involve multi-modal neuroimaging. We’re starting to use MEG to look at brain oscillations, which I think is the key mechanism by which we’re getting links from inhibition, GABA MRS measures, to the functional connectivity changes we see in plasticity. I think oscillations are really important, probably the route through which that happens. Having worked out that that is what we’ll need to do did it. Working, again, with excellent people within Mark Woolrich’s group has certainly helped.
So you need to work out the question, what techniques you need to be able to answer it and then make sure you’re somewhere that can support that. One of the downsides of doing the multi-modal work that my group do (we do MRS, MRI, brain stimulation, MEG) is that you end up as a jack-of-all-trades and not an expert in any given one of them. Working somewhere that you have genuine experts in all of those, and where they’re happy to help, is really important and one of the reasons for me being here – is because we have all that expertise covered and we can do those tricky experiments.
HJB: Yes, that two-way interaction where you have impressive experts developing methods, which can then inspire researchers to ask new questions. But you need them there as well to know the pressing questions – whether those are clinical questions or neuroscience questions. That can hopefully steer method development to answer particular questions. I think it’s always tempting for some of us to get wooed by a particularly cool method or new analysis approach and lose sight of why we’re doing it, or what question we’re trying to answer. So I think your point that the question needs to come first, particularly for those of us in the more neuroscience side, is really important.
CS: Yes I think it may be different for the methods-development people.
HJB: Yes, but even then you need to keep in mind what question this method answers that can’t already be answered. Not just what cool engineering or mathematical principles can I implement. Really, what’s the point of it? It’s important for people to keep that in mind and use that as a way of prioritising and steering your work. Ideally you get a combination of something that uses cool cutting-edge computer science, but also allows people to do something that they couldn’t do before.
CS: And my other standard advice for early career researchers is to move between labs – but I’m very conscious that neither of us did! Though we did travel strategically, I spent a little time in Florida and UCL and you spent 6 months in Montreal.
HJB: [laughs] Yes, I completely agree, the standard advice is to move around and see various different labs, travel the world. That’s absolutely something that benefits people’s career development but for me personally it wasn’t the right thing to do at different stages of my career. So I’ve pretty much been here in Oxford throughout. I’ve tried to get that experience through collaborating very widely locally here in Oxford and elsewhere in the UK and further afield. We can get inspiration and avoid going stale in the same location through collaboration and meeting people in that way. So if you can’t move round, don’t worry too much about it.
(or, How I became an advocate of Open Science in 5 days.)
It’s the final day of OHBM 2018, and I’m tired. I’m also excited, enthusiastic and exhilarated. I’ve had my first real taste of the Open Science community, and I want more! What follows is an honest account of how I went from feelings of scepticism and ambivalence about Open Science, to a flag waving advocate. My hope is that you might read my story and find it in some way relatable. Then maybe, just maybe, you’ll join me in the Open Science Room next year.
When I registered for OHBM, I intended to sign up for the “Hackathon”. I wanted to improve my python programming skills and hoped I might make a few buddies along the way. Alas, the Hackathon was sold out, and if I wanted to know more I’d have to “make do” with attending the one-hour introduction session: Brain Hacking 101. “But do stop by the Open Science Room while you’re here!” the organiser said. Open Science wasn’t a priority for me, so I wasn’t sure if I’d find the time.
I started the conference with some educational sessions. People kept mentioning their “GitHub” pages, but I didn’t take much notice. Vince Calhoun presented some work using dynamic functional connectivity (dFC), and I felt the rumblings of a new project brewing. I knew I could apply this method to some data I had sitting in a drawer and it might turn out to be interesting. And then there was a link to his GitHub page. I scribbled in my notebook: “dFC toolbox available, with documentation and examples!” The toolbox was written in MATLAB (phew!), and was an extension of something I was already familiar with.
Next I headed to Brain Hack 101, to fill the lunch-hour void. There was an unassuming looking guy stood at the front, and he was clearly the super-programmer sort. This was Greg Kiar. He was patient with the attendees and our entry-level questions, and in one hour he explained a few terms that I had heard being thrown around but not really understood:
BIDS (Brain Imaging Data Structure) got triple underlined in my notebook. I had been looking for a way to better structure the masses of data I’d inherited, and here was a fully-fledged and well organised system which I could use. “One less job for me!”, I thought. Clearly a lot of effort had gone into defining BIDS, and who was I to try and reinvent the wheel? Someone else has already done a fantastic job in creating the system and implementation, and they were giving it to me for free!
fmriprep got triple underlined as well. This was a tool for doing some kick-ass preprocessing, made by the masters and shared freely with a ton of documentation and support. I was hoping to come away from the meeting prepared to build a tool to improve the efficiency of preprocessing the data collected by our group. In fmriprep, I’d found another fully-fledged tool to do exactly what I was looking for, and do it significantly better than I could with the time and resources available to me.
At the end of the session I was a bit more confident in my understanding of what was available and how I could make use of it. More importantly, I had ticked two of my “OHBM Outcome” boxes in discovering BIDS and fmriprep. I thanked Greg after the session, specifically because he had suggested I came here and I had found it really useful. He seemed grateful for the feedback.
The OHBM meeting continued and I found myself in the Open Science room again and again. This was the place where I could find out about real, tangible “things” which could elevate my research. They also had bean bags and power sockets, and there was an atmosphere of generosity which was very pleasant when you’re surrounded by some of the most eminent people in your field.
The real turning point was attending a session described as “Discussion: How to improve Multi Echo denoising”. I expected a panel, with me taking notes. What I got was an invitation to join a (physical) circle of 8-10 very impressive people as they attempted to make something (TEDANA) better, by working together in a friendly, communicative and organised way. The session started, as these circle things do, with each of us saying who we were and what we did with multi echo. Most people seemed to know each other already, at least by association, but the Chairs of the sociocratic circle (Elizabeth DuPre and Dan Handwerker) made a point of ensuring everyone was on the same page and that there were no assumptions. I was clearly the least technically experienced person in the room, but they were nice and attempted to actively engage me in the discussion. Susan Bookheimer had told us that women in science need to be outspoken, so I made an effort to speak out. To my surprise, my opinion was welcomed. I learned that people like them really do need to hear from people like me; I assisted in the development of this tool through virtue of my inexperience.
My original motivation for attending the session was not altruistic: ”if the tools are going to develop I want to be early on the curve to adopt them”. I had intended to be a passive recipient of their talent and hard work. At some point it became clear that this wasn’t going to be the case. I was skilfully guided into volunteering my services in improving the documentation of the tool, but I wasn’t too aggrieved about the extra workload ‘imposed’ upon me. I was probably going to read the “how to” guides anyway, to get TEDANA working with my data, so all I’d have to do is ‘track changes’ and make a few suggestions. This seemed a pretty reasonable exchange, but they weren’t taking my commitment lightly. They wrote my name down next to the action on the google doc which was being typed up for all to see. I have no doubt, however, that if I had kept my head down the group wouldn’t have push me to be involved. In truth, I wanted to be a member of this group, but I didn’t feel like I had anything to offer.
When the allotted time for discussion was over, I approached Kirstie Whitaker to thank her for her contributions in different sessions, and for saying out loud things which I was too nervous to say. She spoke with strength, clarity and precision. She seemed to embody all the things I want to see in the field, and I wanted her to know I was appreciative. Turns out we have some shared research interests and she was keen to work with me. She treated me with respect and encouragement, and seemed willing for me to benefit from her experience.
I was starting to notice a theme: The people in the Open Science Room were really nice. They were kind, considerate, generous, and welcoming. They were also really skilled scientists. The kind of top-of-your-game people who anyone would be honoured to work with and learn from. They genuinely cared about empowering others to foster more efficient, more effective and higher quality research. They cared about non-science things that are important to me, like gender equality, privilege and dubious political decisions. I wanted to work with them, and they wanted to work with me.
Though my initial motivations for engaging with Open Science were purely selfish, I came to appreciate that if I go a little bit further and contribute back to the system we can all do more effective research. Ultimately, this is good for advancing our understanding. I am reminded that the reason I love this job is not just because of the creative freedom and intellectual stimulation is provides, nor the opportunity to go to cool places and meet awesome people. It all boils down to helping those individuals whose brains work differently, or maybe not quite as they should, and trying to help them exist happily in this confusing and sometimes dangerous world. I have come away with the firm belief that Open Science is Social Justice, and that is something I want to be a part of. I am also reaffirmed of the good that we can do as a community, if we are kind and generous with each other.
Before this week, I was not a fan of Open Science. It felt like a bucket of extra work in my already stretched schedule. It also played on my insecurities as an early career researcher, to meet the demands of publishing and the fear that I may be exposed for being no good (I believe that’s called Imposter Syndrome). What I’ve come to learn is that I’ve already benefited massively from Open Science by using a long list of tools generated through many hours of skill, labour and dedication (see below for a list which grows longer the more I think about it). All of these have been free to me and required little more than a citation, if anything.
After this whirlwind of a conference I am now a strong advocate of Open Science, and I think you should be too. You don’t have to take my word for it, just come to the Open Science room at the OHBM Annual Meeting in Rome next year and see how you get on. They have bean bags and power sockets, and you might even have some emotions which help you do research.
Ways I have personally benefited from Open Science, without being aware of or appreciating:
3) OBART (NITRIC’s Online Brain Atlas Reconciliation Tool)
4) Chris Rorden’s mricron, mricro, dcm2nii, and fmri simulator
5) The 1,000,000 times when I’ve searched the internet for “how do I do x in MATLAB/Linux/bash?”