In this episode of NeuroSalience, Peter chats with Alex about connectomics, or the study of the brain’s networks of connections. We discuss Alex’s work leveraging the Allen Brain Atlas (https://portal.brain-map.org/) and fMRI to better understand the genetic basis of the network structure. He points out clear differences between network hubs and other network components, with hubs having important roles in resting state dynamics and in neurological disorders. We also discuss the ongoing challenge of removing physiological noise from the fMRI signal in the context of his new and powerful methods for dissecting it out. Last, we touch on the new iteration of the OHBM virtual platform that Alex was instrumental in developing.
Alex Fornito, Ph.D. is a leader, educator, and innovator in the field of brain imaging. A major emphasis of his work concerns understanding foundational principles of brain organization and their genetic basis; characterizing brain connectivity disturbances in psychiatric disorders such as schizophrenia; and mapping how brain networks dynamically reconfigure in response to changing task demands.
He is currently a Sylvia and Charles Viertel Foundation Senior Research Fellow, Professor of Psychological Sciences, and Head of the Brain Mapping and Modelling Research Program at the Turner Institute for Brain and Mental Health. He also leads his Neural Systems and Behavior Lab at Monash University.
He completed his Clinical Masters (Neuropsychology) and PhD in 2007 in the Departments of Psychiatry and Psychology at The University of Melbourne before undertaking Post-Doctoral training in the Department of Psychiatry at the University of Cambridge, UK, under the auspices of an NHMRC Training Fellowship.
He has co-authored an outstanding book (with Andrew Zalesky and Ed Bullmore) on network neuroscience, titled “Fundamentals of Brain Network Analysis.” He has collaborators all over Europe, the US, and Canada, and is an active member of OHBM, where he has been instrumental in establishing the virtual platform for the meeting.
The Neurosalience production team consists of Anastasia Brovkin, Katie Moran, Nils Muhlert, Kevin Sitek, and Rachael Stickland.
In this episode, Peter Bandettini meets with Tom Nichols, Remi Gau and Jack Van Horn to discuss the motivation for a set of best reporting and analysis practices. This provides insight into how the COBIDAS (Committee on Best Practice in Data Analysis and Sharing) in OHBM started. We talk about the reproducibility crisis in fMRI and how it is being addressed. We discuss how the culture of fMRI has changed from isolated scientists doing N=20 studies to a connected web of researchers collecting and contributing to fMRI databases of high quality data for the purpose of revealing ever more subtle information. Through this work, the field aims to achieve robust biomarkers that are clinically useful in diagnosing and treating diseases. We also discuss many of the issues and decisions made in analysis, and how this may contribute to irreproducible results. Last, we consider the ongoing and future global efforts to increase data transparency to make fMRI a more effective tool.
Remi Gau, Ph.D. is currently a postdoc at the Catholic University of Louvain in Belgium. He received his PhD. in 2010 in neurosciences from the University of Pierre and Marie Curie in Paris, and has studied fMRI methodology at Max Planck Institute in Tuebingen and University of Birmingham, UK. He has been active over the years focusing on the infrastructure of imaging data collection and sharing as well as more widely on the culture of neuroimaging, and most recently, created the COBIDAS (Committee on Best Practice in Data Analysis and Sharing) checklist in 2019 as well as eCOBIDAS. He also does neuroscience research, focusing on laminar fMRI to explore how the brain integrates and uses information.
Tom Nichols, Ph.D. is the Professor of Neuroimaging Statistics and a Wellcome Trust Senior Research Fellow in Basic Biomedical Science. He is a statistician with a solitary focus on modelling and inference methods for brain imaging research. He has a unique background, with both industrial and academic experience, and diverse training including computer science, cognitive neuroscience and statistics. He received his Ph.D. in Statistics from Carnegie Mellon University in 2001. After serving on the faculty of University of Michigan's Department of Biostatistics (2000-2006) he became the Director Modelling and Genetics at GlaxoSmithKline's Clinical Imaging Centre, London. He returned to academia in 2009 moving to the University of Warwick, taking a joint position between the Department of Statistics and the Warwick Manufacturing Group. Finally in 2017, he joined the Big Data Institute at Oxford. The focus of Dr. Nichols work is developing modelling and inference methods for brain image data. His current research involves meta-analysis of neuroimaging studies and informatics tools to make data sharing easy and pervasive.
Jack Van Horn, Ph.D. received his Ph.D. in Psychology from the University of London, and then received his Masters of Science and Engineering from the University of Maryland. He is currently a professor in the department of Psychology at the University of Virginia. He was a staff fellow at the NIH until 2000. He moved to Dartmouth College and while there - until 2006 - was instrumental in starting their databasing and data sharing efforts. In 2006 he moved to UCLA and contributed in a large way to their data repository efforts. In 2014 he moved to USC, and finally in 2020, moved to the University of Virginia. He has been an active member of OHBM and a proponent of data sharing since the very early days.
The Neurosalience production team consists of Anastasia Brovkin, Katie Moran, Nils Muhlert, Kevin Sitek, and Rachael Stickland.
by Roselyne Chauvin & Valentina Borghesani
We’ve freshened up!
After two years of existence as an official OHBM Special Interest Group (SIG), the BrainArt SIG has now proudly released its website, created by Anastasia Brovkin and Désirée Lussier, following brainstorming by all SIG officials. You can browse through all previous competitions and exhibits, as well as submit your pieces for the 2021 edition!
You can find out more about our SIG by checking out previous posts on how we came of age and how we consolidated our role within OHBM, but also about our prehistory and history. And we highly recommend having a listen to Neurosalience episode #8, where we had a blast chatting with Peter Bandettini.
We’re preparing a great BrainArt Exhibit for you!
The BrainArt SIG is busy preparing the annual brainart exhibit for OHBM 2021 meeting attendees. The SIG has confirmed artists from all over the world and...well, we don’t want to spoil your surprise but you are in for a treat! We will offer a broad representation of artists ranging from full time scientists, full time artists, and all creative souls in-between. Our 2021 theme is “Big Data & Me”. We wish to celebrate the achievements of Big Data neuroimaging projects, while acknowledging the suffering of individuals affected with brain disorders. Honoring the trees as well as the forest.
First, we will dive into large datasets while keeping in focus inclusivity, diversity, and the representation of populations.
Second, from big N to small N, what about the personal suffering of individuals? After all, we started the field with case study. We will explore the dimension of brain illnesses such as schizophrenia, depression, age-related neurodegenerative diseases, epilepsy, multiple sclerosis and autism in a single subject looking glass.
Finally, will host the pioneering ideas linking different levels of observation, as interpreted by artists who are encouraged to explore the reciprocal interactions between Big Data research and personalized treatment, i.e. breaking of the barrier between research findings and treatment.
We’re ready for your artworks!
So don’t wait another minute! Please go explore our website and the archives and get inspired. It’s time to create and participate in the BrainArt competition 2021 - now open to accept your masterpieces. On the website you will find a form to submit your art for one of the following five award categories:
This competition highlights an ongoing aim of the BrainArt SIG, which is to foster the dialog between artists and scientists, blurring the line between Arts and Science. We believe that the exchange of ideas and tools between these two disciplines encourages the development of novel approaches to scientific data visualization, and promotes the exploration of different perspectives on human brain structures and functions. Researchers, scientists, and everyone in between: you are all encouraged to submit your original work(s)! There are no limits to the number of submissions per participant, and both team and single-person entries are welcomed.
The Submission Deadline is 11:59 PM CDT, Saturday, June 6, 2021 and the award notification will take place during the Annual Meeting. For additional details, please check out our website.
This year, following the success of our campaign to provide a logo for Aperture, there is also a very special 6th category added to the BrainArt competition:
We hope that this teaser and the exploration of the new OHBM BrainArt SIG website will encourage you to participate or enjoy this year's BrainArt Competition & Exhibit.
With contributions from the BrainArt SIG:
In this episode Peter Bandettini meets Carolina Makowski, Michele Veldsman and Alex Fornito to discuss the OHBM Student–Postdoc special interest group (SIG), with particular emphasis on their mentoring scheme and meeting-related workshops. Carolina is a current member of the SIG, Michele previously served as its Chair, and Alex has been an active mentor to several junior OHBM members over the years through this group. They discuss the mentorship program, the workshops at the meeting, what good mentorship is, and why it’s needed more than ever, as the stresses and demands of students and postdocs increases within an ever more demanding professional climate.
Carolina Makowski, Ph.D. is the Career Development and Mentorship Director–Elect of the Student–Postdoc Special Interest Group. Dr. Makowski completed her PhD in neuroscience at McGill University and is currently a postdoctoral fellow at the University of California San Diego with Dr. Anders Dale and Dr. Chi-Hua Chen, with funding from the Canadian Institutes of Health Research, Fonds de Recherche du Quebec - Santé, and the Kavli Institute for Brain and Mind.
Michele Veldsman, Ph.D. is a previous Chair of the Student-Postdoc Student Interest Group and is currently a Postdoctoral Research Scientist in Cognitive Neurology, University of Oxford.
Alex Fornito, Ph.D. is the Sylvia and Charles Viertel Foundation Senior Research Fellow, Professor of Psychological Sciences, and Head of the Brain Mapping and Modelling Research Program at the Turner Institute for Brain and Mental Health. He leads his Neural Systems and Behavior Lab and has actively participated in the student-postdoc SIG.
The Neurosalience production team consists of Anastasia Brovkin, Katie Moran, Nils Muhlert, Kevin Sitek, and Rachael Stickland.
“It is precisely our plasticity, our long childhood, that prevents a slavish adherence to genetically programmed behavior in human beings more than in any other species.”
― Carl Sagan, Dragons of Eden: Speculations on the Evolution of Human Intelligence
I first learned about Ted Satterthwaite’s work when I started teaching about resting state fMRI and motion artifacts. His research showed how motion affects resting state connectivity measures, and I was thrilled that his group also compared the variety of effects with different preprocessing pipelines. In Mexico, every year we host a Neuroimaging Meeting where we invite neuroimaging researchers to visit the city of Guanajuato, [binge] eat Mexican food and talk to students, and so we were delighted to invite Ted to our 2019 meeting.
From our time together there, I got to know more about Ted and his research program. He is currently an Associate Professor in the Department of Psychiatry at the University of Pennsylvania Perelman School of Medicine, and the Director of the Lifespan Informatics & Neuroimaging Center. As a psychiatrist, he is highly interested in human development and building huge development datasets.
When I was asked to do this interview I knew it was going to be difficult to focus on a topic, but we managed to come up with a coherent chat, which I hope we can soon repeat with some beer and mezcal.
Eduardo A. Garza-Villarreal (EG): How did a psychiatrist end up writing these influential methodological papers such as the effect of movement on BOLD signal? And how did you go from psychiatry to methods? What was your career path?
Ted Satterthwaite (TS): I am a psychiatrist, and the reason I got into research was to try to develop tools that could be useful for the diagnosis and treatment of mental illness. That being said, I quickly learned if we ignore the methods, we probably can’t make progress on the ultimate clinical problems that we're interested in. I've primarily been working on large scale datasets, because to me, the only question in psychiatry is clinical heterogeneity. For example: someone comes to see me in the clinic and I diagnose them with depression. But clearly, depression is not one “thing” – depression is almost certainly many different things that we call one thing. It is pretty clear that to parse heterogeneity, we need large studies, because your ability to parse heterogeneity will be determined by both the noise in your signal and the size of your sample. Since we have noisy signals, we probably need very large samples. However, when having very large samples, although you get a lot of statistical power, you also become incredibly sensitive to confounding signals. There's a history in psychiatric imaging of being worried about medication confounds as well as other types of confounds. When we started doing developmental studies, that's right around the time my twins were born. Now, you don't need to scan thousands of kids to recognize that they don't sit still, you just need to come to dinner at my house. And it brought up this obvious question of “is movement going to impact our measures?”, and we started thinking about it because we could see the artifact in the time series. We were just very surprised. We assumed that this had already been solved. However, although there were papers from Karl Friston and others from a decade ago on motion in task-fMRI, there was nothing for functional connectivity. At that point it was just a practical question because we wanted to study brain development, we wanted to study psychopathology, and we know both age and illness are associated with in-scanner motion.
EG: Why are you so interested in development? And why do you think it's an important topic of research in psychiatry?
TS: The dominant paradigm in psychiatry now is that, when you see someone with their first onset symptoms like a severe mood disorder or psychosis, it's not like something went wrong in their brain right there that caused them to have the symptoms. It’s not like a switch that was flipped. Rather, there is accruing evidence for many years now suggesting that most mental illnesses are neurodevelopmental in origin. So, the goal is to understand how the brain develops normally, and then understand also how abnormal patterns of brain development are associated with different sorts of psychopathology. If you think about other fields of medicine, the way they've made clinical progress is by getting there earlier and unpacking heterogeneity. Think about cancer. We used to diagnose cancer only when we found huge tumors that had spread widely, and they were diagnosed on physical examination, like palpating the abdomen and saying, “you have a tumor”. Advances in oncology and in other fields, they have been predicated on both getting there earlier and unpacking heterogeneity; saying it's not just a tumor, but that it is a malignancy from this tissue, with this receptor profile and that genetics, and as a result it's going to be sensitive to this treatment. I think we're still at that “it's a tumor” phase in psychiatry. My hope is that by better understanding patterns of brain development and heterogeneity within the disorders we treat, we can get there earlier and ultimately achieve better outcomes for patients.
EG: As you say, psychiatric disorders are very heterogeneous, and there's a lot of overlap especially in symptoms. What do you think about the current disorder classification, about the Research Domain Criteria (RDoC), and about this overlap between disorders?
TS: There are two sides of the same coin: heterogeneity within disorders, and non-specificity across disorders. The RDoC framework is trying to map symptom profiles to brain systems, and I think that's a totally laudable approach. The one challenge of all this, though, is we don't have a ground truth. We don't have anything like postmortem pathology in neurodegenerative diseases, and so the challenge right now, for example in machine learning techniques, is that we don't know what the labels should be. You can have the best engineers and the best pattern recognition algorithms, but a lot of advances in machine learning have been based on supervised learning, and right now, we just don't have the best labels. Without good labels, it's kind of garbage in, garbage out. What I see is that the biggest challenge is using biological data like images to help us understand what those labels should be. I think it's a challenge and we're all still grappling with it.
EG: Do you think machine learning will have some influence on the future of psychiatry, or you think it's one more tool in the bag?
TS: I think both. Machine learning is going to have a big influence, especially as we have larger and larger training datasets, and the ability to generalize things across samples. However, part of the problem right now is that we don't have a lot of datasets where we can link multivariate patterns from machine learning tools to outcomes of interest, which I think is an essential step. Datasets like the Adolescent Brain Cognitive Development (ABCD) Study that follow 10,000 kids over 10 years are actually really important starting points because longitudinal prognosis is a very important clinical outcome to be able to predict. However, there is still a lot of room to make advances in terms of incorporating these methods using health system data where we have medical records on medications and hospitalizations, but we have a long way to go. Also, I think machine learning won't solve everything. Dani Bassett, one of my closest collaborators, makes a very cogent case that machine learning, while great, alone will not do it; you need a combination of good machine learning and sound theory. I agree. We should not forget that we know a lot about the brain from decades of basic neuroscience – we need to incorporate that knowledge as priors to inform these tools and help interpret the results.
EG: To you, what is your lab’s most interesting project and why?
TS: Right now, the one I am most excited about is a super ambitious project that I lead together with Michael Milham at the Child Mind Institute. We're trying to get a lot of the larger studies of brain development - around 11,000 samples in total – and make sure they are pristinely curated, processed, and QA’d ahead of a public data release on the International Neuroimaging Data-Sharing Initiative (INDI). The project is called the RBC - “Reproducible Brain Charts” – project. One thing that I've learned is that a lot of the sexy stuff in science is actually easier than the non-sexy stuff like data organization, curation, the really low-level things that are necessary for reproducible neuroscience. These things are often really time consuming. What makes me excited about this project with Mike and his team is that if we can do those things well, the dataset will be much more useful to everyone else, and everyone can just run faster by not having to recreate the wheel. I think that's super exciting. But it's been a huge challenge, because it's very heterogeneous data from different studies, which meant we've had to build new tools to handle it.
EG: Can you tell us a bit about the tools you're developing?
TS: Sure-- there’s a couple, which are at different stages. Sydney Covitz and Matt Cieslak have a really cool tool that is being presented as a OHBM poster at the meeting called “Cu-BIDS” for “Curation of BIDS”. It is a tool for curating Brain Imaging Data Structure (BIDS) formatted data at scale, designed for very large heterogeneous datasets -- we needed it to be able to handle the RBC data. I wish I had a time machine and we had that a couple years ago, because it makes life so much easier. And then once you get that data curated into BIDS, and this is all building off from Russ Poldrack, Chris Gorgolewski and Oscar Esteban’s work with BIDS and fMRIrep, we're focusing on running everything in containerized pipelines. For example, xcpEngine is our containerized post processing pipeline that was developed in the lab by Azeez Adebimpe (now) and Rastko Ciric (originally); it consumes fMRIPrep output to produce derived measures for studies of functional connectivity. Now, a lot of efforts are about moving beyond fMRI. For example, I am super excited about Matt Cieslak’s works to build QSIPrep, which is a fully containerized and highly generalizable BIDS-app for diffusion images. Lastly, Azeez is just finishing up ASLPrep for ASL MRI data. In the end, the goal of all this is to make it all super reproducible and open. If you talk to other neuroscientists, one of the biggest reservations they have about imaging is that it's so complicated, and there's so much data processing... that they just don't believe it. Over time, I’ve come to really agree with other people who have been doing open science for many more years than me -- I think the only answer that will get people to believe us is to just show them everything; sunlight really is the best disinfectant.
EG: In terms of your research achievements, what are you most proud of?
TS: You know, it's funny. What makes me most excited is not the individual projects, but the people I work with. I've just been super lucky to have amazing students who do really incredible work. This year’s keynote leverages work from many awesome people in the lab. The title of the talk is from a review from Valerie Sydnor, one of my grad students, who just dug into complex literature and produced a work of incredible scholarship. Similarly, some of the developmental data I am most excited about is from the hard work of another grad student, Adam Pines. What I'm most proud of is the trainees and how much heart they put into their work, and how much they teach me. That's the fun part for me, the people, not any individual finding.
EG: To me one key discovery was about motion artifacts and how it affects the signal. Even right now there is a huge debate on things like the global signal. I don’t think we have it figured out.
TS: Yes, it remains contentious. I think studies of motion artifacts however is a great example of how science should work. It was awesome – we had that paper in March of 2012, but in January of 2012, Koene Van Dijk and Randy Buchner published almost the same finding in an independent dataset. In February 2012 you got Jonathan Power and Steve Petersen with the same finding and also a method for handling it. So, three different labs, working independently with different data sets – all coming to the same conclusion -- producing three papers in NeuroImage at almost the same time. I think that's a great example of how science can work to provide convergent evidence.
EG: What do you think psychiatry should go for in the future? Where do you think is going? You mentioned looking for an actual ground truth, do you think we're going to get it at some point?
TS: People ask that all the time, “when is this stuff going to be useful?” And I think the first answer is: it's not useful now, to be honest. But that doesn't mean it's not a super important problem. If we do make progress, this sort of work could be incredibly impactful because psychiatric disorders are among the most common afflictions that humans get. But in the end, to really show something, we're going to need clinical trials and outcomes that matter. Some people who have been starting towards this, like Nicholas Koutsouleris and the PRONIA consortium – they are doing some really cool work. In the end we will not convince practicing physicians that this brain imaging in psychiatry matters until we show real results in clinical trials. And that's a challenge—perhaps a 10- or 20-year challenge. But I think we'll get there.
EG: Thank you Ted for taking the time to sit down for this interview. Looking forward to your keynote at OHBM 2021.
In this conversation, Peter Bandettini meets members of the BrainArt SIG to discuss its history from the NeuroBureau to its current formal SIG status. They discuss what brain art (or more generally science art) is, consider what the best features of brain art are, and how, essentially, any scientist trying to convey the essence of their findings can be considered an artist. You’ll discover the planned competitions and directions of the BrainArt SIG. The discussion also considers why diversity in this SIG, the field of Brain Mapping, and science in general is so important.
In the episode you’ll hear about the ‘Dream Catchers’ exhibit from OHBM2017 in Vancouver, and how those with dementia can discover new artistic creativity. You can also see some highlights from the OHBM 2020 exhibits below:
By Kevin Sitek
OHBM’s Annual Meeting is virtual again in 2021, following in the footsteps of 2020’s conference—but don’t expect it to look the same.
2020 was a year marked by challenges. For the Organization for Human Brain Mapping, that included quickly transitioning from the final stages of planning an in-person conference to putting together an entirely new format for its virtual meeting. In many ways, the 2020 Annual Meeting was a huge success. Understandably, though—given the short timeframe for creating and executing a brand-new conference format—not every part of the conference went off without a hitch.
For 2021, the OHBM Council sought to build the Annual Meeting on three core pillars of the OHBM community: Openness, Interactivity, and Accessibility. After months of deliberation by a dedicated task force, the OHBM 2021 Annual Meeting will run on a fully customized, open source platform designed and engineered by the Sparkle team. To help make this decision, Council created the OHBM Technology Task Force (TTF) in September of 2020. In an effort to ensure representation across the entire OHBM community, Council invited over twenty OHBM members to join the TTF, including representatives from the Open Science, Student–Postdoc, Sustainability & Environmental Action, and Brain Art Special Interest Groups (SIGs), multiple OHBM committees, and other diverse voices from OHBM’s membership around the globe.
“The goal of this group was to identify areas for improvement for the 2021 Annual Meeting, as well as to identify a virtual event platform which would meet all stakeholders’ needs for this year’s meeting,” says Mike Mullaly, a member of the OHBM Executive Office. “Over the course of several months, this group vetted various platforms via virtual demos.” To learn more about this process, we turned to TTF members themselves to hear directly about their experiences and hopes in selecting this year’s OHBM virtual platform.
The TTF looked not only at feedback regarding last year’s virtual OHBM meeting but also at other conferences. “We discussed which aspects of these meetings worked or did not work and which features we would like to incorporate into the OHBM platform,” says TTF Chair Professor Alex Fornito. “We also evaluated platforms used by other meetings that were considered to be effective by different TTF members. We then shortlisted different platforms and vendors, encompassing a broad range of open source and commercial options and met with reps for several of them.”
And TTF members came with strong, detailed expectations for the platform vendors. According to TTF member and Student–Postdoc SIG Social Chair Dr. Elvisha Dhamala, the ideal platform “enables real-time conversations and reactions to presentations. It has features that facilitate spontaneous and random interactions and conversations. It has an intuitive user interface that is easily manageable and navigable.”
Finding a vendor that could do all of these things (and more!) turned out to be easier said than done. Alex explains, “We quickly learned that there was no single platform that could do everything we wanted to the level that we desired. We had to make trade-offs.” For instance, some of the platforms that were considered had great search functionality and discoverability but couldn’t incorporate social interactions or work seamlessly across time zones. “In the end, we had to focus on identifying a platform that could do a good job of our priority features, while also having the potential to further develop other features in coming years.” Given the uncertainty of the past 18 months, flexibility will be an important feature moving forward.
Ultimately, the TTF settled on the Sparkle platform. Alex acknowledges that, “In many ways, this is a risky choice; Sparkle was originally developed for online concerts and I believe that we are the first scientific conference to take place on the platform.” Yet, the Sparkle team ultimately won over the TTF.
"Having reviewed in detail many different dedicated conference platforms, the TTF was nearly unanimous in their support for Sparkle.” Mike agrees. “We were looking for a vendor that was offering something completely customizable, open-source, and would improve social interaction. Sparkle was by far and away the front runner in this regard.”
There were a few central tenets that the TTF found attractive in the Sparkle platform. Most crucially, Sparkle demonstrated that virtual conferences could still be highly interactive, serendipitous, and fun. “While OHBM 2020 successfully presented the scientific content for the meeting, it lacked the features needed to socially interact with one another,” explains Elvisha, “whether that’s through mini one-on-one conversations about the ongoing presentation or in spontaneously formed small groups during a happy hour.” This year, the Sparkle platform “enables real-time conversations and reactions to presentations. It has features that facilitate spontaneous and random interactions and conversations. It has an intuitive user interface that is easily manageable and navigable.”
An early prototype of the platform's main map, showing various conference locations, sponsor visibility, and chat functionality. Specific stylistic elements and functions will likely be updated before the conference.
Secondly, the Sparkle team is fully dedicated to open source development: OHBM contracted Sparkle to build the conference platform, but the platform’s source code itself is open source. This means that OHBM can use the conference infrastructure beyond OHBM2021 and continue building in new features and technology. Indeed, “the Sparkle team was very willing to work with us to extend the platform and develop many of the essential features we required,” says Alex. “We did not encounter this openness with many other platforms.”
Finally, Sparkle understood our community’s need for accessibility and inclusion, working with the TTF to incorporate automatic text captioning and intuitive design elements. At last year’s conference, TTF member Professor Tilak Ratnanather used his own speech-to-text software during talks and poster presentations, but it was an imperfect solution to a very real problem. “Not having to think about this will make me more relaxed and focus on science.”
A page dedicated to OHBM SIGs is just off the main map. This is an early prototype—specific features unique to each SIG will be added for the conference in June.
However, as the meeting approaches, there are still a few high-priority items that OHBM and the Sparkle team are working on, including global accessibility. Testing is currently under way around the globe and Zoom has been integrated wherever possible. However, individuals concerned about the ability to connect to the Sparkle platform may try connecting via VPN and a list of Zoom links for the meeting will be available from the Executive Office upon request.
In addition, while real-time speech-to-text technology is advancing rapidly (for instance, 2020’s star app, Zoom, recently made live captioning an option for institutional accounts, and Google Chrome can now do live captioning automatically), in practice there are still significant limitations, especially for speakers with accents that the software wasn’t trained on, as well as for fast-paced, jargon-filled presentations. (We’re sure you can remember one or two of these.)
So while the conference platform is still being optimized for the meeting, TTF representatives from across the OHBM community are helping guide the platform’s development. And, according to Mike, OHBM "will be sending out a survey during the meeting (as we always do) looking for areas of improvement and member feedback" to improve the experience for the next meeting—whatever state that will be in.
Overall, excitement about the new platform is palpable across the board. Elvisha sums it up best: “The lack of space constraints and the endless features that Sparkle has really enables us to facilitate multiple activities simultaneously so we can cater to all interests and host a more inclusive social experience. I’m really excited about Sparkle and I can’t wait for the OHBM community to experience all that’s planned for the 2021 conference!”
OHBM Neurosalience episode 7: The Organization (Society) for Human Brain Mapping today. Some history, challenges and virtuality.
In this podcast we discuss some of the history and evolution of OHBM. We also talk about some of the challenges that it has faced in recent years with world events causing a last minute change in venue three times. We talk about the improvements in this year’s virtual meeting as well as the growth in the engagement of younger members of OHBM with all the chapters and SIGs.
Aina Puce, Ph.D. Aina has been active in OHBM since the beginnings. She is Chair of OHBM Council, otherwise known as President of OHBM and Director of the Indiana University Research Imaging Facility and the Eleanor Cox Riggs Professor of Psychological and Brain Sciences. After a Ph.D. from the University of Melbourne in 1990, she was a post-doc then an associate research scientist at Yale. She moved back to University of Swinburne in 1998, then back again to the states in 2002 to West Virginia University. Finally, she moved to Indiana University in 2008. She is an expert in visual neuroscience and EEG as well as fMRI.
Daniel Margulies PhD. Daniel started in the US, receiving his BS in 2005 from NYU then in 2008, earned his MS at the European Graduate School in Saas Fee, Switzerland and Ph.D. at Humboldt University in Berlin. From 2009 to 2011 he was a post-doc in the Department of Neurology at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig then a group leader of the Neuroanatomy and Connectivity group at Max Planck, Leipzig from 2011-2017. He received the 2018 OHBM Young Investigator (now called Early Career) award and received the Otto Hahn Award in 2010. He has been a pioneer in fMRI connectivity methods and has recently produced novel and penetrating work elucidating the organizational gradient that spans between sensorimotor and trans-modal areas.
The Neurosalience production team consists of Anastasia Brovkin, Katie Moran, Nils Muhlert, Kevin Sitek, and Rachael Stickland.
This week’s podcast is centered on physiologic fMRI. Generally, when people think of fMRI, they think of a way to map neuronal function, however there is so much information about neurovascular physiology in the signal. Many researchers who use fMRI may not realize all of the potentially untapped information—and confounds!—in the fMRI time series. Dr Jean Chen and Dr Molly Bright each run research groups that focus on this information in complementary ways. Both use physiologic manipulations and an array of acquisition methods to probe and characterize details of the hemodynamic response, though their two research programs focus on different aspects of the haemodynamic response function. In this podcast, they highlight the importance of physiologic fMRI for the field. They also consider the challenges facing women in male-dominated research fields and how the life of women scientists might be improved.
Jean Chen PhD. Dr. Chen received her MSc (2004) in Electrical Engineering from the University of Calgary, and her PhD (2009) in Biomedical Engineering from McGill University. She completed her postdoctoral work on multimodal MRI of brain aging at the Martinos Center for Biomedical Imaging and Harvard Medical School (2011), then joined The University of Toronto Medical Biophysics Program as faculty. She is a Senior Scientist at the Rotman Research Institute and Tier II Canada Research Chair in Neuroimaging of Aging.
Molly Bright PhD. Following a B.S. in physics from MIT in 2006, Molly received her D.Phil. from the University of Oxford in 2011 as part of a collaboration with the National Institutes of Health, working with Peter Jezzard at the Oxford Centre for Functional MRI of the Brain (FMRIB) and Jeff Duyn in the Advanced MRI group of NINDS. She completed postdoctoral training at the Cardiff University Brain Research Imaging Centre (CUBRIC). She then moved to Nottingham as an independent Anne McLaren Fellow, to develop ultra-high-field MR imaging methods for studying cerebral physiology in neurological diseases at the Sir Peter Mansfield Imaging Centre.
The Neurosalience production team consists of Rachael Stickland, Kevin Sitek, Katie Moran and Anastasia Brovkin
In this week’s podcast, you’ll hear about clinical applications of resting-state fMRI from Dr Michael Fox. You’ll hear some of the highlights of his research, from the beginnings at Wash U, including his early work on resting-state fMRI and the issue of global signal regression, to his more recent pioneering work on lesion network mapping. Through this, you’ll find out about how lesions can impact behavior through their effects on functional networks. This approach is a promising inroad of fMRI towards clinical utility.
Michael D. Fox, MD, PhD, is the founding Director of the Center for Brain Circuit Therapeutics at Brigham and Women’s Hospital and Associate Professor of Neurology at Harvard Medical School. He is also the inaugural Raymond D. Adams Distinguished Chair of Neurology and the Kaye Family Research Director of Brain Stimulation. He completed a degree in Electrical Engineering at Ohio State University, an MD and PhD at Washington University in St. Louis, and Neurology Residency and Movement Disorders Fellowship at Mass General Brigham. Clinically, he specializes in the use of invasive and noninvasive brain stimulation for the treatment of neurological and psychiatric symptoms. Dr. Fox’s research focuses on developing new and improved treatments for brain disease by understanding brain circuits and the effects of neuromodulation.
The Neurosalience production team consists of Rachael Stickland, Kevin Sitek, Katie Moran and Anastasia Brovkin
By Peter Bandettini & the OHBM Neurosalience production team
In this week’s podcast, Dr Catie Chang walks us through her thought process regarding pulling information out of the fMRI time series. After discussing some of the ongoing issues in fMRI, such as whether or not to use global signal regression to remove noise, she leads us into a commonly overlooked effect in fMRI—that of changes in arousal and vigilance. In particular, this has measurable effects on the resting state fMRI signal. She discusses the perspective that one person’s artifact may be another’s useful signal, depending on the goal of the study.
Catie Chang, Ph.D. received her B.S. in Electrical Engineering and Computer Science from MIT, and received her M.S. and Ph.D. in Electrical Engineering from Stanford University. While in graduate school, she opened up the field of fMRI by publishing a seminal paper using time-frequency analysis of resting state fMRI, showing that it was quite dynamic. Since then, she has been exploring the effect of basic physiological processes, such as cardiac function and respiration on the fMRI signal, and has recently been uncovering unique information regarding the influence that changes in vigilance have on the time series signal.
The Neurosalience production team consists of Rachael Stickland, Kevin Sitek, Katie Moran and Anastasia Brovkin
By Peter Bandettini & the OHBM Neurosalience production team
In this week's episode, Peter talks to directly to MRI scanner vendors. Together, they try to reconcile the importance of fMRI in research contexts with the market pressures of developing clinical applications. As fMRI has virtually no clinical market, does it really influence vendor decisions on pulse sequences and hardware? Could more be done aside from making fMRI more clinically relevant? In this discussion, you’ll hear some fascinating history into the early days of echo planar imaging and high speed imaging, as well as insight into the processes by which products are prioritized. You’ll also find out a possible future of how fMRI may begin to become more clinically useful.
R. Scott Hinks, Ph.D. is the Retired Chief Scientist from GE Healthcare's MR division. He received his PhD from the University of Toronto in 1985, where he began his studies of MR Physics and Imaging. For over 34 years Scott has pursued a career in MR research in both industry and academia, specializing in imaging and system physics. He was the principal developer of FSE and has led technical development of EPI for both fMRI and DWI. His work has resulted in numerous publications and over 34 patents. In his most recent role as Chief Scientist for GE Healthcare’s MR division,he is actively engaged in every aspect of MR imaging and works in close collaboration with leading academic researchers worldwide.
Franz Schmitt, Ph.D. is the retired chief scientist from Siemens’ MR division. He received his Ph.D. from the University of Munich and has worked for Siemens since 1983, overseeing development of EPI, gradient and RF coils, both 3T and 7T, as well as pTx imaging. He worked on site at the Martinos Center for a few years in the early 2000’s and has been actively engaged in academic research worldwide.
Ravi Menon, Ph.D. is a Professor of Medical Biophysics, Medical Imaging and Psychiatry at Western University, where he is also a member of the Graduate Program in Neuroscience and the Graduate Program in Biomedical Engineering and Scientific Director of Western’s Centre for Functional and Metabolic Mapping (CFMM), Canada’s only ultra-high field MRI facility. He received his Ph.D. in Medicine from the University of Alberta and performed his post doc in the laboratory of Kamil Ugurbil at the University of Minnesota where he helped to pioneer fMRI.
The Neurosalience production team consists of Rachael Stickland, Kevin Sitek, Katie Moran and Anastasia Brovkin
By Charlotte Rae, on behalf of the SEA-SIG
The Sustainability and Environment Action (SEA) SIG has formed three new Working Groups, to tackle the environmental impact of the annual meeting, assess environmental implications of neuroimaging research activities, and educate our community on these.
What are the new Working Groups?
In December 2020, we held two open meetings to talk about the priority actions for our new SIG with the OHBM community. We had colleagues attend from across the world, who shared fantastic ideas on how we should make OHBM activities more sustainable.
From these meetings, there was a pretty clear consensus that we needed to tackle three areas: the Annual Meeting, neuroimaging research pipelines, and education. So, we have set up three new Working Groups that will focus on these particular domains.
The Annual Meeting Working Group will assess the environmental impact of the Annual Meeting, investigate sustainable conference models, and make recommendations to the Council for how to create a more sustainable Annual Meeting beyond COVID-19.
The Neuroimaging Research Pipelines Working Group will assess the environmental impact of neuroimaging research pipelines, investigate how we could do our research more sustainably, and create resources and publications to support neuroimagers in greening their research practices.
The Education & Outreach Working Group will collaborate with the other two to educate our community about the impacts our research activities have, including putting on events around the Annual Meeting. It will also seek to collaborate with industry and sister neuroscience societies. In these collaborations and in guiding the SIG's own activities, it will use insights from psychological and neuroscientific work on how humans respond to communications about climate change and environmental issues.
How can I get involved?
We hope that there are lots of OHBM members who are interested in participating in these groups to help us achieve our sustainability objectives!
For example, in the Annual Meeting group, we want to comprehensively assess what the environmental impacts were of recent in-person (e.g. Rome, 2019) and online (2020, 2021) meetings. Looking forward, we hope to then investigate how much our meeting footprint would be reduced if we adopted potential alternative conference models, such as hybrid (with some in-person and some online content), hub-and-spoke (where we have several meeting locations and you travel to your nearest), or moving to a biennial meeting. Many other societies and conferences are considering such options (Figure 1). For the Working Group, we need colleagues who are interested in looking at these options and putting together a report for Council. We are very fortunate that Sepideh Sadaghiani, an experienced member of the Program Committee, has come on board to chair this Working Group.
In the Neuroimaging Research Pipelines Group, we need colleagues who are up for digging down into all the details of a neuroimaging workflow, from hardware and data acquisition to analysis and computing infrastructure. Ideally, we want to quantify the potential environmental implications of all these stages, so we can produce resources for the neuroimaging community that would allow researchers to plug in their pipeline protocol and get a measure of its environmental footprint. Of course the next step is then to provide resources for our community to enable them to go about changing this for the better - establishing what best practice looks like for sustainable neuroimaging. In this group, we will need colleagues from across OHBM disciplines who have experience across all sorts of neuroimaging processing streams. We might also seek to collaborate with external experts such as cloud computing providers.
The Education & Outreach Working Group will have quite a broad remit around educating our community about the impacts our research activities have, in concert with the other two groups. Here we need colleagues who have experience in (or want to get experience in!) areas such as putting on events around the annual meeting, like symposia and socials; interfacing with industry and sister neuroscience societies; and perhaps even bringing psychology-based knowledge of what works well when communicating about climate change, to make sure we are operating as effectively as possible in the SEA-SIG as a whole.
As well as general group members, we are looking for two individuals who might be interested in Chairing the Neuroimaging Research Pipelines and Education & Outreach groups.
I’m in! What are the next steps?
If you would like to participate in any of the three groups, or would like further information, please do get in touch with us at email@example.com. We welcome informal enquiries if you are not sure before you sign up to participate!
For further details on the aims and objectives for each group, see our new website at ohbm-environment.org
If you know a colleague who would be ideal to contribute to one of our groups, please do pass on our details. And you can retweet our Twitter post announcing the groups.
We look forward to sharing updates on the Working Groups’ progress soon!
By Peter Bandettini and the OHBM Neurosalience production team
In this week's podcast, Peter gets a birds-eye view of modeling of messy biologic systems, namely the brain, from Professor Danielle Bassett. They talk about the challenges of measurement accuracy and what scale might be most informative to modeling - and how to make do with what we have. On the clinical side, Danielle discusses network control theory for modulating networks for therapy and limitations in technology for modulation. They consider the limits of network modeling and the search for the equivalent of an idea as powerful as “natural selection” for the brain. In the second part of the podcast Peter and Danielle discuss bias in science and what Danielle is doing to help increase transparency to combat bias.
Danielle Bassett PhD, is currently the J. Peter Skirkanich Professor at the University of Pennsylvania with a primary appointment in the Department of Bioengineering and a Secondary appointment in the Departments of Physics and Astronomy, Electrical and Systems Engineering, Neurology, and Psychiatry. Dr. Bassett received her B.S. in 2004 in Physics from Penn State University. She received a Ph.D. in physics in 2009 from the University of Cambridge, UK as a Churchill Scholar, and an NIH Health Sciences Scholar. Following a postdoctoral position at UC Santa Barbara, she was a Junior Research Fellow at the Sage Center for the Study of the Mind. In 2013, she joined the University of Pennsylvania as an assistant professor, and in 2019, was promoted to full professor. She is also founding director of the Penn Network Visualization Program, a combined undergraduate art internship and K-12 outreach program bridging network science and the visual arts. Her primary work is towards developing network models towards deriving principles of brain function.
The Neurosalience production team consists of Rachael Stickland, Kevin Sitek, Katie Moran and Anastasia Brovkin
By the Neurosalience production team: Rachael Stickland, Kevin Sitek, Katie Moran and Anastasia Brovkin
OHBM has a new podcast: Neurosalience! You can listen to it in your car, while out walking, or just in the ever-present home office. Through Neurosalience, you’ll discover state-of-the-art topics and current controversies in brain mapping. The host for the podcast, Peter Bandettini, has lined up a stellar cast of interviewees ranging from brain scientists to hardware vendors and health professionals. This includes finding out about publication biases affecting gender and racial minority groups with Dani Bassett, network neuroimaging in neurological populations from Michael Fox, circuit based neuromodulation from Catie Chang and much more. Get all of this insight through your favourite podcast apps, including Spotify, apple podcasts, anchor and Google Podcasts.
We launch with a brief introduction to the podcast, a fireside chat between Peter and Rachael Stickland (one of the OHBM Communication Committee producers for the show). Then the first full episode explores Aperture, the new open-access publishing platform powered by the OHBM. Through discussions with founding members and the Editor in Chief, you’ll learn how Aperture came about and what it hopes to achieve.
OHBM Neurosalience episode 00: An introduction to the podcast
Peter Bandettini chats with Rachael Stickland, where they set out some of the exciting conversations you’ll hear on OHBM Neurosalience. The name ‘Neurosalience’ highlights the aim of this podcast - to put a spotlight on important developments, discoveries and controversies in the world of human brain mapping. Find out why this podcast was set up, what the main themes and topics will be, and what to look forward to with the first few episodes.
Peter Bandettini, Ph.D. is Principal Investigator of the Section on Functional Imaging Methods and Director of the Functional MRI Core Facility in NIMH. Recently he has also established the Machine Learning team and the Data Science and Sharing Team as well as the Center for Multimodal Neuroimaging within NIMH to help all intramural investigators with their neuroimaging studies. He has been working on fMRI methods for 30 years.
Rachael Stickland, Ph.D. is a postdoctoral fellow at Northwestern University in the Applied Neuro-Vascular Imaging Lab (ANVIL). Her work focuses on characterizing cerebrovascular function in healthy cohorts and in Multiple Sclerosis, using fMRI with breathing challenges and gas inhalation.
Aperture, a new open access publishing platform for neuroimaging research
Peter Bandettini introduces Aperture, a new open access publishing platform for neuroimaging research. Peter is joined by one of the co-founders, Jean-Baptiste Poline, along with the new Aperture Editor In Chief, Tonya White, and the journal manager, Kay Vanda. Together, they discuss the motive, history, steps for creation, and current status of Aperture. It was created with the strong support of the Organization for Human Brain Mapping, and aims to be a peer-reviewed platform for publishing not only papers, but also various other types of research objects that often do not find space in conventional journals, including data, educational tutorials and code. While there is still work to be done to be fully up and running, many insights into this process are shared and discussed.
Tonya White, MD, PhD is an associate professor in the Department of Child and Adolescent Psychiatry at Erasmus University Medical Centre in Rotterdam. Her primary research goals are to apply neuroimaging techniques to obtaining a better understanding of genetic and environmental factors associated with typical and atypical brain development in hopes that this will translate into either preventing or decreasing the morbidity of severe psychiatric disorders.
Jean-Baptiste (JB) Poline, Ph.D. is an Associate Professor in the Department of Neurology and Neurosurgery at McGill; the co-Chair of the NeuroHub and Chair of the Technical Steering Committee for the Canadian Open Neuroscience Platform (CONP) at the Montreal Neurological Institute & Hospital (the NEURO); and a Primary Investigator at the Ludmer Centre for Neuroinformatics & Mental Health.
Kay Vanda is the journal manager of OHBM Aperture, working out of the OHBM central offices in Minneapolis, MN. She is a key component of this entire effort as she handles all the organization as well as corresponds with authors, reviewers, and editors.
2020 was such an interesting year; it was certainly not the one I was waiting for. Due to several issues related to the pandemic, I unofficially took a leave from thesis work and had a chance to meet a lot of people virtually, collaborate, learn and grow. Although so many of us were stuck at home, open-science-driven events like NeuroMatch and BrainHack created opportunities to connect with colleagues and peers. This turned out to be hugely impactful for myself and other people like me—in other words students/early career researchers based in countries with limited resources.
And to think, it all started with a tweet….
Figure 1. Neuromatch 1.0 call for people (image credit: https://twitter.com/KordingLab/status/1239986383550365696).
Neuromatch 1.0 virtual (un)conference was held from 30th to 31st March, 2020. The organizers were so open and encouraging in setting up this collaborative experiment—I couldn't just sit back and wait for the event! I quickly volunteered to help with the scheduling, reaching out to the speakers before their talks, testing the setup and monitoring the chat during the talks. Even though it was entirely virtual I have learned a lot both academically and socially. It was so amazing to witness vibrant online community interaction. That time also coincides with my coming out, getting comfortable with my selected name and pronouns. It also felt so affirming to me to be accepted as who I am within the academic community. It was beyond my imagination!
But Neuromatch was just getting started. I was delighted to help organize the second Neuromatch conference, Neuromatch 2.0, held from 25th May to 3rdJune, 2020. It had more than 3000 registrants from all over the world. Although the same platform and structure were used as during the first Neuromatch, we saw even more talks, posters, presentations, and debates. This increased community interaction was encouraging, and showed how it was still possible to bring researchers together even without international travel.
Figure 2. Neuromatch 2.0 map of registrants all over the world. (image credit: https://twitter.com/KordingLab/status/1264998609411604480)
After my experiences at Neuromatch, I was overjoyed to discover another large-scale, explicitly collaborative event within the OHBM community: this year’s OHBM brainhack.
HBM Brainhack 2020 was my first Brainhack experience and one of my biggest encounters with the open science community after Neuromatch Conferences. It was truly phenomenal to see enthusiastic hackers, creative projects, inspirational training sessions and witness the facilitation of open science research virtually all around the world. It was an amazing privilege for me to serve the community as an event host. In addition to comprehensive guidelines, specific help channels and project specific coding places for hacking, a live virtual help desk at gather.town virtual space was dedicated for guidance and a socialization. There will be no exaggeration if I claim that OHBM Brainhack 2020 provided attendees a life-like event in the comfort of their homes. As the first virtual OHBM Brainhack, it was a stellar start. For the future events I believe that there would be more diverse representations and more opportunities from generally underrepresented neuroscience researchers all around the globe.
After OHBM brainhack I shifted my focus to Neuromatch Academy Summer School (NMA for short). It was a 3 week worldwide summer school, and, although all online, a tightly structured experience for 1757 interactive students and 191 teaching assistants (TAs). It was not only an intense course but also a huge mental challenge for students, TAs and organizers. It was extremely incredible that such a comprehensive course could happen at low/no cost for students. Diversity, Inclusion and Equity were core values of NMA that I championed in my involvement. For the preparation period of NMA, I took part in helping towards increasing diversity and inclusion besides enhancing student experience via extracurricular activities such as weekly karaoke times and daily yoga sessions. During the NMA, I helped some of the fellow TAs at my time zone towards mastering the material for teaching effectively, solving daily problems about the material and also served as a TA of a group of students in my time zone (pod)
From the interactions within NMA volunteers and TAs, I have learned that establishing an inclusive community is the key for a better and effective way of science, learning, teaching and academia.
One of the interesting things that I was involved in is meeting several artists to get some neuron doodles to increase virtual experience. I greatly enjoyed explaining to people without any STEM background, about the summer school, and about detailed yet basic concepts of the neuron. It is hard to find the words about my experience of witnessing the creative process of an artist. Seeing the sparkles of human creativity turning into an art piece was also very inspirational.
Figure 3. Neuromatch Academy, Mozilla Hub virtual space, student avatars interacting with neuron doodles (image credits: 1st image https://twitter.com/phant0msp1k3/status/1286102782890536960, 2nd image https://twitter.com/neurograce/status/1283047247299706880).
During the times I served as a TA, I realized that the best way of learning is teaching. I also witnessed that providing a safe space is the most important thing to do to enhance the abilities of students to flourish. CoC and automatic violation report system of NMA effectively helped us to preserve the provided safety.
Having gone from Neuromatch, to OHBM, to NMA, I was happy to return to Neuromatch with the Neuromatch 3.0 conference. Although largely the same format as Neuromatch 2.0, it significantly expanded again in size and in scope. I believe it was one of the most inclusive neuroscience conferences ever with six main themes of parallel tracks and a main stage events like lectures, panels, discussions, open affinity group sessions (black, queer, first generation) it gave the ambience of non-virtual big conferences. I served as a backend person mostly by clicking the necessary buttons, ensuring that every speaker runs their talk as smoothly as possible. It was wonderful to see some of the submitted NMA projects were turned into complete papers and submissions to NMC, it made me feel proud as a TA of NMA.
All of these experiences and the vibrant community of Brainhack encouraged me to organize (almost solely) and lead Brainhack Ankara on December 1st to 3rd, the first ever Brainhack in the Middle East! Taking part in both Neuromatch event series and OHBM Brainhack encouraged me to spread open science concepts and run a local event with the help of the global Brainhack community. Since the community here is new for Brainhack Ankara, it was convenient to have focus on learning the basics of open science tools and to explain why and how it can enhance our way of doing science. Overall attendance was about 30 people, it was a relatively small event comparingly. But I learned that to build a vibrant big community, starting small is important.
2020 for me was a year of meeting the neuroscience community virtually as well as demonstrating that how open science helps researchers, how the culture of collaborative science helps to enhance better practices and how vital it is to embrace diversity in research. I learned that listening, understanding and providing the needs of the people is necessary to maintain the collaborative culture and for the best possible approaches of ensuring inclusion. To take an action for this goal, I recently joined the OHBM Open Science Special Interest Group as an inclusivity officer for the organisation of the 2021 Open Science Room and OHBM Brainhack. In this role, I hope to provide a safe space for people to flourish, open space for underrepresented groups, and encourage initiatives that will enrich the community.
By Rachael Stickland & Nils Muhlert
Professor Helen Mayberg is a pioneer of neuroimaging and neurostimulation for depression. As a behavioral Neurologist she has helped to identify the brain circuits implicated in mood disorders, and then developed and refined effective treatments based on deep brain stimulation. She is a member of the National Academy of Medicine, The American Academy of Arts and Sciences and the National Academy of Inventors. As a founder member of OHBM we found out about her work, her experiences of seeing impact statements become reality and about holding on to the ‘OHBM train’.
Nils Muhlert (NM): I'm joined today with Professor Helen Mayberg, who is a professor of Neurology at Mount Sinai, as part of the OHBM oral history initiative. First, can you tell us how and why you became interested in neuroimaging?
Helen Mayberg (HM): I was a neurology resident in the early 80s. Imaging was in its infancy. In medical school, in the late 70s, we had our first CT scanner. MRI was relatively new during my residency training at Columbia, and it was an important diagnostic tool. I planned to train in behavioral neurology in Boston with Norman Geschwind. But in my last year of residency, he passed away suddenly, so I needed a change of plan.
My change in direction to imaging as a focus for further training was the result of a lucky coincidence. One of the first year neurosurgery residents on my team had just come to New York from Baltimore. He had written one of the first papers characterizing opiate receptor subtypes in the brain, and told me about this new research imaging method being developed at Hopkins where you could image chemistry in living people using positron emission tomography. I had always been interested in neurochemical abnormalities in psychiatric disorders but there was no way to study that directly in humans. Despite my intense interest in severe mental illnesses, I didn’t find the training in psychiatry to be a good fit for me, so I pursued my interest in behavior via neurology training. It wasn’t a perfect fit, but neurology seemed a better choice for clinical training than psychiatry where I just never felt comfortable with their vernacular. While it was still a stretch to understand chemical mechanisms of behavioral disorders it did provide foundation for thinking about structure-function relationships in the brain, an approach that really wasn't applied yet to psychiatric syndromes. So suddenly, here was this new scanner that might provide a way to do what I wanted: assess regional chemical abnormalities in patients with mental illness.
I started as a research fellow at Hopkins in the Nuclear Medicine Department, learning PET scanning in 1985. I was learning basic methods to map and model various neuroreceptor systems - mostly opiate receptors, but with some projects involving dopamine and importantly serotonin systems. The lab I was a part of did little to no behavioral mapping studies; it was a dedicated chemical neuroimaging lab. There was very little work on blood flow or glucose metabolism except as ancillary maps for receptor studies. At the same time, in LA, John Mazziotta, and Mike Phelps were working with glucose metabolism to map abnormalities in a variety of neurological disorders. At Wash-U, Marcus Raichle, Peter Fox, Mark Mintun and their colleagues were developing methods for behavioral mapping using cerebral blood flow. Nora Volkow was using various methods at the Brookhaven Labs. There was a relatively small group of teams with PET scanners worldwide that developed specific niches of expertise using this technology.
Whilst working within a chemical mapping group, I was reading about other methods and as my work evolved it was clear that my questions required tools that I didn’t have access to. Because there were so few groups, we were a very small and interactive community. Before OHBM, this type of research was discussed at the Cerebral Blood Flow and Metabolism meetings. The neurologists, like John Mazziotta, Marcus Raichle and Richard Frackowiak, would go to the Neurology annual meeting, and we’d have our own imaging sessions there. You’d also see different imagers at various meetings--Society for Neuroscience, American College of Neuropsychopharmacology, Society for Nuclear Medicine. The PET community was tight and continued to grow but it was relatively insular as there were few research PET scanners as you needed a cyclotron.
There was a point where the questions I wanted to ask about depression required methods that were not the focus of our lab. Peter Fox was setting up a new research imaging center in San Antonio; I had met him at a Cerebral Blood Flow meeting and heard what he was up to. It was a method that I didn't know but that would provide a set of tools to take my depression research in a new and potentially interesting direction.
In 1991, Peter invited me to be a founding member of this fledgling new center, so I moved from Baltimore to San Antonio in Texas. I was part of his team, setting up a research lab as part of the brain imaging group. We were very small, and focused on PET scanning but I was no longer doing any neuroreceptor mapping studies. By this point, I'd moved to studying depression exclusively. I worked as a clinical neurologist but did my research with collaborators in psychiatry using imaging.
In the early to mid nineties, The Research Imaging Center (RIC) was host to the original Human Brain Mapping workshops. As part of these workshops, there were ongoing discussions of how to develop common platforms to share data. The RIC team started compiling spreadsheets of coordinates of brain activation findings from the literature. These were the early days of imaging meta-analyses, done by hand. While the work was slightly peripheral to my own studies, I couldn't help but become involved. So I received an education in the world of mapping beyond my own area and saw a style of thinking that was way ahead of its time. We now take so much for granted with our contemporary approach to big data and data sharing. It was laborious work in those days.
NM: It sounds like a very exciting time - certainly within that lab.
HM: It’s funny. For every scientist you can never know when you're in the middle of something important, if anything you're doing will have long-term traction. We delude ourselves, write grants, make statements about the potential impact and how significant we think our work is. In fact, it's only when looking backwards that we can actually see how it all evolved. It's hard sometimes to be reminded that during my time in medical school, CT scanning was new. We studied dead brains, we had anatomical atlases, we had white matter maps from studies in nonhuman primates that we used to mentalize how brain regions were connected to each other. We mentalized a connectome in our head, by piecing together these various studies. We didn’t yet have access to multimodal mapping. I don’t think I could have even conceptualized such methods.
What was a paradigm shift of imaging, was that you could directly test your hypotheses rather than simply make interpretative inferences from pathology or animal models of behavior. Not only did technology allow visualization of the brain in action, but with time the choice of methods greatly expanded. My neurology professor in medical school said: “pick a topic, not a method.” At that time I was learning PET scanning. He said, “you'll reach a point where your current methodology no longer allows you to answer your question. So you’ll learn new methods and tailor your questions as methods evolve.” As a clinician and not a technical or methods developer, that was incredibly important advice - don’t just learn a method for method’s sake, but learn a method in service to your clinical question. That's been my approach since then. So what I know the most about, PET scanning, is something I don't even do much anymore. But the use of imaging as my experimental method has never changed, I have just learned to adopt new imaging tools to best address my next depression study. One can now pick and choose, and with that range of choices you can really go deep to answer your own questions.
NM: On that note, one highlight of your career is that you’ve helped identify the role of Brodmann area 25 in basic drives affected in people with depression. When did you start focusing on this area and how did that come about? Was it as a result of these meta-analyses?
HM: We didn’t go looking for it. We just followed the data and there it was. But it wasn’t on our radar with our early studies, which started with examining post-synaptic dopamine and serotonin receptor in post-stroke depression patients. These studies were complemented by studies of opiate receptor changes following electroconvulsive therapy as a model of epilepsy, to studying resting state abnormalities in basal ganglia disorder patients with and without depression. Our goal was to test the hypothesis that, regardless of etiology, there was a common set of regions affected in patients with depression. We were working to define a depression circuit. As a common pattern of limbic-cortical abnormalities emerged, we felt it was reasonable to move to study primary depression where clinical heterogeneity was well described. I presented that set of findings in 1989 at one of the Cerebral Blood Flow & Metabolism meetings. We did the next natural experiment: how the abnormalities change with treatment.
For the most part, we found what everybody else was finding in depression, low metabolism in the frontal lobes. When we treated people, the frontal activity normalized--it increased. By using the statistical methods Peter Fox had brought to San Antonio from Wash U-- change distribution analysis-- combined with new computer algorithms and higher resolution scans, we could further examine the whole brain instead of predefined regions of interest. I remember analyzing a specific set of data of depressed patients studied before and after successful antidepressant treatment. I was looking at the statistical change maps and I figured there must be some sort of misregistration artifact. I kept looking at the pictures. It was midnight and, all of a sudden, I realized the ventral parts of the brain were showing decreased activity while dorsal parts of the brain were showing increases. I squinted my eyes and looked at what I was seeing: there were brain regions that weren't abnormal at baseline that showed decreasing activity as people got better. When I looked up the brain regions, I found we were in the subgenual cingulate. I had my Talairach atlas, and I'm looking up with the ruler where I am - it was all done by hand, and I thought ‘what the hell is this BA25?’
Actually, the Talairach atlas I had misidentified it. So people thought for many years that it wasn't really in Area 25, and I say look I'm just following Talairach. I went to see who else had seen anything in this region. I found changes in this region in a study by Jose Pardo on mood induction. We tried looking at correlations, to see which part of this multi-node network went with which symptoms of depression, and I couldn't separate out the mood from the attention symptoms with the data I had. Peter suggested “Let's do a blood flow scan following a mood induction.” The intention was that if we induced a negative mood in healthy volunteers we would determine if you could dissociate the presumed limbic-emotional regions from the cognitive cortical regions in this presumed depression network. To our surprise, mood induction did indeed reveal limbic activations and they were in Area 25, but it also decreased activity in the prefrontal cortex--the same regions identified in the depressed patients. Area 25 and the prefrontal cortex were inversely correlated with each other in both experiments--depression recovery mapped over six weeks and sad mood induction over 2 minutes with the magnitude of behavioral changes correlating with both regions. In essence, our hypothesis was just wrong; we couldn’t induce solely change in limbic regions by focusing on mood. The two systems, limbic and cortical, could not be separated. Obviously a simplistic notion if viewed through today’s use of graph theory and dynamical modeling approaches to time series data. But at that time, these simple experiments using blood flow and glucose metabolism PET gave us one of our most important insights--these regions were yolked and worked as a synchronized limbic-cortical circuit to mediate the interaction of mood and cognition.
This pattern of reciprocal changes involving midline and lateral cortex regions was new. Today, we would look at this pattern and immediately see the default mode and executive networks displaying their typical anti-corrrelation with each other. But then, in the mid 90s, that concept was just developing. We looked at it thinking ‘what are these regions and what do they do?’
Area 25 had very little written about it. It was described in the animal literature as a visceral motor outflow area and not necessarily a mood area. You could even find references to its homologue in lizards, as it's a very old, highly conserved, part of the brain. I would get into fights with rodent anatomists by asking about the rodent equivalent: “is it infralimbic? Is it prelimbic?” Lots of opinions as to whether or not it is even a good idea to attempt to match rodent and human prefrontal cortices, if one is really interested in studying depression, a uniquely human clinical construct. That was sort of a turning point; if I wanted answers I needed to really learn to read the tract tracing studies done in nonhuman primates and learn the connections between regions by looking at combinations of anterograde and retrograde studies. Little did I know that I was laying the foundation for future work that would rely on maps of structural connectivity defined using DTI.
NM: And then you later moved into intervention studies, where you used targeted deep brain stimulation (DBS) of region BA25 to see how it affects symptoms. What was that like - setting that up and seeing the results from those first studies?
HM: In all honesty, I became an interventionist almost by accident. I wasn’t a trialist; I merely used treatments as probes to better understand depression and treatment mechanisms. I spent the first 20 years basically trying to prove that depression was a circuit disorder, first by identifying the nodes, and then the connections and making inferences about causal relationships using changes with various kinds of treatments. There became a point in Toronto where, findings in Area 25 were so consistent across all of our treatment studies, that we hypothesized that if you didn't downregulate this region then people didn't get better. It seemed to be really at the center of the antidepressant treatment response.
The idea to target Area 25 with DBS for treatment resistant patients was highly influenced by the neurosurgical literature and the evolution of ablation to DBS for Parkinson’s disease. The leading theory about DBS mechanisms at the time posited that high frequency stimulation resulted in a local depolarization block. As we had consistently demonstrated that effective antidepressants decreased or blocked activity in Area 25 and if you couldn’t block it you didn’t get better. We followed that logic to hypothesize that if you can't talk or drug or shock it down, maybe you could block it with targeted stimulation delivered very precisely at this node in the network.
Everything I knew about connectivity (even though at that point, there were no tractography tools available to us, so implied connectivity) was that if you downregulated a region such as BA25, maybe you would also get disinhibition of regions it was connected to. The DBS technology at this point, in 2002 or so, was well established and readily available. I had a surgeon that was willing to test my hypothesis. It was actually very much an imaging-driven idea. If I hadn't been doing imaging, would I have even thought about it? Probably not, but I was in the right place at the right time. We had the maps that pointed to a putative DBS target for treatment resistant depression, a surgeon with extensive experience with DBS for Parkinson’s disease and a team of investigators willing to learn about DBS and manage this group of extremely ill patients with this novel intervention. It was in some ways a natural next step for our ongoing studies. So that's why we did it - because we could. But the logic was basically built on that first mood induction depression recovery finding.
NM: It's as we discussed before - the impact statement becoming true over time, where you think ‘what areas are involved?’, then ‘what can you do about it?’ And here you've got a great example.
You were involved in the creation of OHBM. What was your role?
HM: Well, mainly I was involved because I showed up. I attended the first meeting in Paris, which was a natural extension of the Cerebral Blood Flow meetings I had been attending since starting my post-doc in PET imaging. With time, I became an officer; I was elected secretary in 2000, and served from 2000 to 2003. It's interesting that many of the originators of OHBM were clinician-scientists. Several of the key drivers - Mazziotta, Fox, and Evans all in North America - had a grant together, and joined forces with many key thought leaders and teams worldwide to make it happen. A shared vision. Again, being in San Antonio with Peter, I had a front row seat to the evolution of the organization. Timing and opportunity are a common theme here.
When I think back, how could anybody not participate? It was happening all around me. So you get on the train and hold on and see where the journey takes you. We all had a ringside seat, and saw an idea grow and mature. Like any diverse scientific community, building an infrastructure that requires not just expertise but buy-in and cooperativity is challenging. But like any democracy, there was a lot of trial and error and compromise seeing what worked, what the community, the stakeholders wanted; it evolved by taking great ideas and giving them space to evolve and mature. What was great was it was very inclusive - methodologists, clinicians, statisticians, engineers, all topics, all scan types, multimodal approaches, new science, courses, and great opportunities for networking. The multinational and multidisciplinary collaboration that established OHBM has continued to define it and foster its unique position among imaging meetings.
NM: And what have you found most rewarding about your experience in holding on to that train with OHBM over the years?
HM: Well, I've had the opportunity to collaborate with people world-wide and adopt a multimodal imaging approach to our team’s clinically-oriented research questions. Maximizing use of novel technologies is at the core of our work--with critical reliance on state-of-the-art engineering and statistics. OHBM is where I can always count on seeing the newest technical and analytic advances and where discussion is scholarly and collegial. Our own work is quite iterative, so it’s useful to see a new method used by others before jumping in ourselves. OHBM provides an important sounding board for our ideas and I have always found the meetings personally and scientifically rewarding.
OHBM has evolved beyond anything any of us could have imagined. Technological advances have been the critical catalyst but applications of the technologies have been important drivers. Perhaps I am biased, but imaging in one way or another has been at the center of many of the advances in neuroscience over the last 50 years.
NM: And what do you see as the most promising things that are coming out now?
HM: Like with anything, progress is not linear. Sometimes it seems like it's three steps forward, and then one steps backward or sometimes even sideways. I'm reminded of one of the first imaging meetings I attended prior to OHBM where we would sit and listen to thought leaders debate the advantages of their particular methodology. It was a curious sort of testosterone storm of statistical one-upmanship. It was as though one method had to defeat all others.
It has been fascinating to be part of our maturation as a field. Where our focus is on matching methods and technologies to a specific category of question rather than assuming one size fits all. How could it be otherwise? That's the natural evolution of any field… the first thing is you don't believe it, the second thing is it's obvious, then it evolves to be much richer because everybody starts to dig into working out the details.
Right now, I think we're going through a stage where there's so much data that we don't know how to parse it. We're at a time where doing experiments that people care about is expensive and hard. Early on, the focus was on ensuring the methods were valid and reliable. Like any broad field, people have different interests. I am grateful to know that there are people pushing the limits of the technologies and those using it to understand basic principles of brain function; the big data consortiums with multimodal data archives for general use are priceless resources for the community. As a depression researcher, I want to exploit the technology; ‘If I take on learning a new method, I need to decide if it's worth it.’ Then, ‘How is it going to help us test our next set of hypotheses?’ I don't think it's just because you're a physician that you want to do that. Everything is hard and time consuming - how do you have meaning in the way you spend your time, scientifically?
We're back to a plateau where we're “fighting” about ‘what's the right way to do it? Do we believe anything we know? Is it all an artifact? How do we replicate?’. We're learning that the brain is very adaptive. And when we think we control an experiment, we don't control it as well as we thought we did.
We're looking for big signals. We've gone from working with deforming the brain into a common space (which was to increase signal to noise and you didn't believe it until you saw it robustly across subjects) to trying to understand inter-individual variability (if you don't understand the individual, you know nothing). You can get dizzy, realizing these are natural evolutions. Everybody's right, just not at the same time.
This is the beauty of the OHBM culture: you can develop tools to answer the question in the way you want to. So for me, as a very specific example, we guessed based on a blob on a PET scan, where Area 25 was, stimulated with an invasive implant in that approximate spot, and made people better. It worked. And then we’ve spent the next 15 years trying to figure out what exactly we did, how to do it better, and why it worked.
Imaging has remained a key method towards these goals. For instance, Kisueng Choi in the lab had identified the critical white matter bundles that mediate the DBS treatment effects and developed tractography methods to reliably define the optimal surgical target in any individual patient. I love and use the data from the human connectome to test ideas, but at some point, we need to make a decision about an individual patient's brain. Where is the spot? Can it be visualized reliably? Can we hit it with millimeter precision? I’ve got to make a map for the surgeon to put the electrode where we say with a high level of accuracy. And then be able to show prospectively, that what we wanted to do, is in fact what we did.
I am envious, as we all are, of the amazing advances in circuit mapping techniques using cell-specific labeling, such as optogenetics, and CLARITY. While I can learn a lot from these exquisitely detailed maps in rodents and more recently non-human primates, I also need reliable lower resolution methods because I don't have the luxury of single cell stimulation. We inject a big amount of current into a pretty sizable brain area that contains a mix of many cell types and passing fibers. It remains a real mystery how such nonspecific stimulations work but it does. Obviously, more advanced methods will evolve. But for now, we work with what we have. That said, we’re always looking to see what new methods people are developing. When I was a kid, I used to hang out with my uncle who was a biochemist and nuclear medicine physician in the pre-PET era. He used low resolution detectors to measure radioactively tagged chemicals in the brain often injected through the ventricle during surgery and scanned later. The images were horrible, like looking at a fuzzy bowl of soup. You could measure changes in brain concentration of various compounds, but without the spatial detail. Still, it was really amazing we could do it at all. Now we're working to improve on 0.8 mm isotropic voxels. Looking to push the envelope further. All of this change in less than 50 years; I am just sorry my uncle missed all of this. He would have loved it.
NM: I could almost end there but one last question about your personal experiences attending OHBM. Are there any moments that stand out for you?
HM: There are so many. To hear the giants of imaging give the Talairach lecture is always a thrill. But I think like any meeting it’s the camaraderie, the openness of students who read your papers and want to get your opinion on their work; to both meet old and new heroes and to maintain relationships with colleagues over 30 years; to be able to sit down and talk or just hang out. I always enjoyed the grandeur of the big lectures and the rigor of the science as well. But it has always been the relaxed atmosphere that catalyzes new ideas and new collaborations. I can remember meeting the Oxford team -- Paul Matthews and Heidi Johansen-Berg and that short chat changed the course of my fledgling tractography work. I remember making sure to arrange meetings around world cup matches; Resting up for the dance party night; figuring out the train to Sendai. Just so many big and small wonderful memories.
NM: Professor Mayberg, I'd like to thank you very much for joining us. It was a fascinating insight into your experiences with OHBM.
HM: Well, thanks so much for including me. It's really an honor.
Ilona Lipp (Lead editor):
With the masked face and being on the beach in December in Connecticut (with 15 c), I feel it pretty much sums up the Pandemic and Climate Change ridden year - 2020!
In the coming year I look forward to more enlightening interviews as well as exploring new avenues for lay media blog posts with members of the Communication Committee.
Nils Muhlert (ComCom Chair):
This year has taught many of us a lot about ourselves. Personally I found out that I’m terrible at baking sourdough bread. Alongside all the awfulness there have been some real highlights. Inviting new people to join the blogteam and seeing their first contributions is definitely up there. As is passing the baton of blogteam lead and ComCom chair to Ilona Lipp; the OnDemand tutorials that she’s been leading have become a great resource for those wanting expert-led introductions to the many flavours of MRI. I hugely enjoyed interviewing some of the original founders of OHBM, including John Mazziotta and Helen Mayberg. More to come next year! As to next year, the prospect of an effective vaccine and a gradual return to an upgraded normality are certainly beacons of hope. With a bit of luck I’ll see many of you again at OHBM2022 in Glasgow, if not virtually at OHBM2021. Have a good holiday all, and hope you come back rested and recharged.
By Valentina Borghesani, Elvisha Dhamala, Niall Duncan, Marie-Eve Hoeppli, and Michele Veldsman, on behalf of the SEA-SIG
This month, OHBM announced the formation of a new Special Interest Group that will tackle sustainability and environmental issues around brain imaging.
Here, we talk with the Sustainability & Environment Action (SEA) SIG Chair Charlotte Rae to hear more about what the new SIG will seek to achieve.
Why do we need a new Sustainability & Environment SIG?
Awareness of the environmental impact of human activity has never been higher, and there is now strong international consensus that we urgently need rapid action to tackle multiple crises, including dangerous climate change and irreversible ecosystem degradation. Neuroimaging research activity plays a part in these crises - from liquid helium extracted through fossil fuel production, to the energy usage of big data. We all have a responsibility - especially as professional scientists - to address these issues and move towards a sustainable future.
We have set up the new SIG so that we can have a community conversation around how to enact the changes that are required. For example, we plan to do some work around measuring and assessing what the environmental impact of a neuroimaging workflow is, from data acquisition to data analysis and even publication. One back-of-the-envelope calculation puts the carbon footprint of a single MRI scan session at 160kg, and we know that server activity has a big impact - especially resource hungry approaches such as machine learning. Once we’ve quantified the size of the problem, we aim to provide a set of guidelines and recommendations for sustainable neuroimaging practises.
We are also really keen to work together with Council, the Executive Office, and colleagues across our community to decarbonise the annual meeting. There is growing recognition that 3000 of us flying across the globe annually isn’t compatible with a safe future on this planet: one transatlantic return trip generates nearly 2 tonnes of carbon dioxide. That’s the size of our annual individual personal ‘carbon budget’ if we’re going to limit warming to the 1.5C set by the Paris Climate agreement. We need to work up positive and practical alternatives that the whole of our neuroimaging community can get on board with, whether that’s ‘hub-and-spoke’ models, where you meet colleagues locally on your own continent, supporting hybrid in-person and online interactions, or reducing meeting frequency.
We have a lot of work to do! But our sister SIGs have shown that with international collaboration across our brain imaging community, we can achieve rapid change. The Open Science SIG has changed the way we think about open neuroimaging. The Diversity and Inclusivity Committee, set up in 2016, now has a dedicated symposium slot at every annual meeting. As has already happened for open science and inclusivity, we can aspire to drive rapid uptake of sustainability awareness and action amongst our community too.
How can OHBM members get involved?
We plan to hold regular open SIG ‘community meetings’ where any OHBM member can share their thoughts on what our priority actions should be for the SIG to take forward. This might be decarbonising the annual meeting - such as building on the 2020 and 2021 digital meetings to ensure we don’t simply return to 3000 members creating a huge travel footprint every June post-COVID. Or tackling the question of big data - how can we run our analyses sustainably when server manufacture has a huge ecological impact, and energy to perform computations often still comes from fossil fuels?
Once we know our priorities for action, we want to establish SEA-SIG working groups so that we don’t just ‘talk the talk’ about what the problems are, but ‘walk the walk’ to figure out what the changes are that need to happen. Ultimately, we want to be able to produce some guidance as to how neuroimagers can go about greening our research practises. We need OHBM members with expertise across MRI physics, computing, analysis practises, to all get involved!
It's also crucial that we have lots of input from early career researchers. Our current generation of trainees are going to have to live with the consequences of dangerous climate change for much of their lives - it is already happening, and is only going to get worse. We hope we can amplify the voices of ECRs, who we know often feel very strongly that rapid urgent action is necessary, but who are not always heeded by those in power.
If you would like to get involved with any of our activities or receive updates about what we’ve been doing then contact us at firstname.lastname@example.org.
You are also most welcome to come to our first community meeting, on Tuesday, 15th December via Zoom (with two sessions: 09.00 UTC and 18.00 UTC, to accommodate colleagues in different timezones). We will outline what the climate crisis and ecological emergency mean for us as neuroimagers, before we collaborate in small groups to determine priority aims for the SIG to pursue. Register to attend here: https://forms.gle/vVF3ydnJCyArobdj6
We are also looking for colleagues to join our Committee, in the posts of Webmaster, and Social Media officer. Please contact us on email@example.com if you are interested in taking on either of these roles.
Finally, you can follow us on Twitter, @OhbmEnvironment.
We hope to see you at a SEA-SIG community meeting soon!
By: Rosanna Olsen, Amanpreet Badhwar, Valentina Borghesani, Lee Jollans, Hajer Nakua, Laura Marzetti, Nils Muhlert, Pradeep Reddy Raamana, Tilak Ratnanather, and Lucina Uddin on behalf of the OHBM Diversity & Inclusivity Committee
In June 2020, OHBM made a statement condemning the murders of George Floyd, Breonna Taylor, and Ahmaud Arbery as well as ongoing actions of police brutality against Black Americans and underrepresented minorities around the world. During the conversations surrounding these events, there was a public recognition of the lack of support for Black and minority communities. We realized that at OHBM we have not done enough to support underrepresented minorities in science, and that we need to take concrete actions to make our organization a welcome and safe environment that educates and supports each and every member of our group.
To achieve this goal, we need to gain a better understanding of the experience of OHBM members and their sense of belonging within the organization. Hence, the Diversity and Inclusion Committee (DIC), with support from the OHBM Council, will perform a survey to learn how welcome and comfortable members feel within the organization, at the Annual Meeting, and other satellite events. This survey will also allow for anonymous reports of any experiences of discrimination based on race/ethnicity, gender, sexual orientation, religion, disability, or affiliation with any other marginalized group.
The DIC has developed an anonymous two-part survey: the “Survey of Member Views on Inclusivity at OHBM.” The first part of this survey will collect crucial information from OHBM membership and will eventually become a permanent resource for anonymous feedback for all of our activities. Survey responses will identify areas of concern, flag problems, and identify actions that OHBM can then work to improve. The second part of this survey will collect demographics and other identification characteristics of our membership. If you do not want your answers to this part of the survey linked to the first part, there will be a place to indicate this in the survey itself.
The Survey of Member Views on Inclusivity at OHBM will be sent to the OHBM members in December 2020. The survey will take around 10 minutes to complete, and your input will be incredibly valuable, as we aim for a complete picture of our membership’s unique experiences. A high response rate from our membership will provide us with a more representative picture of our diverse attributes and needs, which will provide a better basis for improving our organization. The survey will provide an opportunity for members to provide feedback regarding both what *is* and what *is not* working at OHBM and what do you think should be done to make OHBM more inclusive, for everyone . We also welcome any suggestions on how to improve our survey for subsequent data collection efforts.
by Claude Bajada
The GDPR is a new(ish) legislation by the European Union that regulates the processing of personal data when the person processing or controlling the data is in the EU, even if the actual processing occurs outside of the EU. Further, the GDPR also sometimes regulates the processing of personal data of people who are in the EU, even if the persons doing the processing are outside of the EU.
How does this affect neuroimaging? We sit down with neuroimaging expert and Open Brain Consent co-author Dr Cyril Pernet (CP) and Technology law expert Dr Mireille Caruana (MC) to discuss the implications of this law on our work.
The article flip-flops between the term “participants” and “data subjects” since ““data subject” is the term used in the GDPR but for the purposes of this article you can think of them as equivalent terms.
What follows is a summary of our conversation, edited for conciseness and clarity.
Who are our experts?
CP: I do a lot of method development in neuroimaging and in a clinical context. Data sharing is something that I have always been happy to work towards. Data sharing is like code sharing, we need it for good science. With the advent of the GDPR, we've got some extra constraints on what to share and how to share.
In the clinical context, the typical thing is to say is: “Oh, you know, we have patients’ data, therefore, privacy issues,” and people don't even try to share. This really annoys me because there are ways we can do it. It doesn't have to be completely open on the web so that everybody can download it. I've been working on all sorts of open science related projects and the Open Brain Consent is part of that line of work.
MC: I am the head of the Media, Communications and Technology Law Department within the Faculty of Laws at the University of Malta and my research has, since before the GDPR, focused on privacy and data protection issues. I would not contradict you that the GDPR is a relatively new law that has, from the start, been the subject to a lot of uncertainty and difficulty in implementation and application. It's well worth working our way through the legislation to seek correct interpretations of it.
Why is it important to discuss GDPR across disciplines?
CP: We are scientists, when we read the GDPR text, we don’t understand the implications. We do not know how judges will interpret the law. This means that we need lawyers to guide us on how to interpret what is written there.
MC: The problem is that in many instances there aren’t clear answers. In fact, while a lawyer may give legal advice, it may eventually be contradicted by a court. Nevertheless, scientists should behave as diligently and carefully as possible. If the perception of the GDPR ends up restricting research or not allowing researchers to do their work, that's a problem. It shouldn't be that way. But achieving this balance is very difficult.
Anonymous data are not governed by the GDPR. Do you think there's anything within neuroimaging that can be considered anonymous?
CP: In my opinion, one of the key points in GDPR that is relevant to neuroimaging is that neuroimagers are able to single out individuals from datasets, which makes the data identifiable. And I'm not just talking about brain structure data, I am also talking about EEG data, MEG data, etc.. With connectome matrices and a few tasks you can single out individuals, and we can thus consider that any imaging data should be considered identifiable. Others disagree with me and argue that singling out is not strictly identifiability, while I contend the opposite because GDPR indicates that singling-out is a prerequisite to identification.
This is a key difference between North American legislation and the GDPR. While North America differentiate between anonymised data, pseudonymised data and identifiable data, the GDPR only distinguishes between anonymised data or identifiable data. Pseudonymisation is just a process. Data can go through that process without changing their status as identifiable. That is we can remove the face, ID, etc ., but brain imaging data remain identifiable, in that we can potentially distinguish between individuals and even if we don’t have the metadata, link those data to someone by name.
Can we have an example?
CP: Imagine, for instance, that we have two independent datasets consisting of connectome matrices and tasks. There may be individuals who have been participating in each of those datasets. So we can now think about linking them and studies have indeed shown that it is possible to say that the same individual belongs to both datasets, because of the way connectomes look. Not only can we single out people within datasets, but we can also link datasets, and possibly by adding associated metadata we are getting even closer to identifying that person in the real world.
Are there any proposed solutions for this problem?
CP: The solutions that we have come up with are detailed in Open Brain Consent and involve two consent forms as well as a data user agreement for data collected in the EU. Of the two consent forms, one is the consent for the study and the other one is consent for people to share their data. The way you can legally share this is through a data user agreement, not through a licence, which means we ‘control’ who has access and to a lesser extent what can be done to the data. Now the control can be done in a way where people register to use specific datasets. For example, the Netherlands have a good system because every researcher is registered on a database. So for instance, if you log into the system of a particular institute, they know who you are, which institution you are affiliated with, and you can just download data, even if you're not part of the data-holding institute. This is possible because they can identify you. You can sign the data user agreement with a simple click.
A user agreement also helps researchers share data outside of the European Union. The GDPR refers to this as “standard contract clauses.” This allows you to get to a point where non-EU researchers can download the data and become the data controller. With the data user agreement, the downloader agrees with the terms of the GDPR. This way you can share data anywhere in the world, even outside the EU. But you cannot just put your data up on openneuro. This is important since openneuro servers reside in the US, and the US is special because it is not considered to be a “safe country” by the EU. Institutions can sign an agreement with the EU to become a safe repository. But that also means openneuro would have to change their infrastructure to support data user agreements.
Where does consent come into all of this? Could I just get consent from my participant to share all of my data in the US, and the rest of the world?
MC: In the GDPR, sharing or transferring data is considered to be a type of processing. Let's forget about how the original data were collected and focus on the sharing of these data. In this case, you should still have a legal basis for processing in the GDPR. I am also assuming that they're sensitive personal data, since I am assuming that they say something about an individual’s health status.
Article 9 of the GDPR has a legal basis specifically for research data processing. So perhaps you don't need to rely on consent to share data because there is another legal basis which speaks about the necessity for scientific research. However, this legal basis is somewhat unclear in its application because it speaks about individual member states laying down a law that provides appropriate safeguards.
With regard to data transfers to a third country such as the US, chapter 5 of the GDPR concerns transfers of personal data to third countries or international organisations. According to Article 45, transfer of personal data to a third country may take place where the EU Commission has decided that the third country, or one or more specified sectors within that third country, ensures an adequate level of protection. Such a transfer does not require any specific authorisation. In the absence of an adequacy decision, a controller or processor may transfer personal data to a third country only if the controller or processor has provided appropriate safeguards, and on condition that enforceable data subject rights and effective legal remedies for data subjects are available.
Under Article 49, in the absence of an adequacy decision, or of appropriate safeguards, a transfer or a set of transfers of personal data to a third country may take place only on one of a set of stated conditions, which include that “the data subject has explicitly consented to the proposed transfer, after having been informed of the possible risks of such transfers for the data subject due to the absence of an adequacy decision and appropriate safeguards”.
How do we deal with requests for deletion of data?
MC: Article 17, GDPR sub article 2 states that “Where the controller has made the personal data public and is obliged pursuant to paragraph 1 to erase the personal data, the controller, taking account of available technology and the cost of implementation, shall take reasonable steps, including technical measures, to inform controllers which are processing the personal data that the data subject has requested the erasure by such controllers of any links to, or copy or replication of, those personal data.” It talks about reasonable steps that would, by way of good practice, mean a record of people who accessed the data and contacting them to inform them about the request.
How long can we store data for?
CP: You are required to set a time frame within which you must review the need for continued storage of the data. However, if the data keep being necessary, the data can be kept indefinitely.
Is it true that under the GDPR, legally, you're not allowed to reuse your own data in your own lab to answer different questions than what it was originally collected for?
MC: The GDPR speaks about purpose limitation (“personal data shall be collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes”) and ‘specific’ consent (“‘consent’ of the data subject means any freely given, specific, informed and unambiguous indication of the data subject’s wishes…”). So ideally, I think even ethically, your research participants should understand how you're going to use their personal data; but no, research is treated in a particular manner under the GDPR. Research is not considered to be incompatible with the original purpose for data collection (“further processing for ... scientific ... research purposes ... shall, in accordance with Article 89(1), not be considered to be incompatible with the initial purposes”).
Furthermore, recital 33 of the GDPR clarifies “It is often not possible to fully identify the purpose of personal data processing for scientific research purposes at the time of data collection. Therefore, data subjects should be allowed to give their consent to certain areas of scientific research when in keeping with recognised ethical standards for scientific research. Data subjects should have the opportunity to give their consent only to certain areas of research or parts of research projects to the extent allowed by the intended purpose.” So, legally, you may be covered, even though the debate surrounding so-called ‘broad consent’ is not conclusive (cf. for example the Article 29 Working Party’s Guidelines on consent under Regulation 2016/679).
CP: In my opinion, the “purpose” research is not specific enough. But if you say the purpose is “memory” that's too specific because that way you could not even use a T1w image to create a template. So, we came up with a compromise. If you look at the Open Brain Consent GDPR edition, our solution is to say that, for instance, the purpose of conducting the study is one thing, but also that the data may be used for future research projects in the field of medicine and cognitive neuroscience, which strikes the balance.
MC: Article 5 (1) (b) of the GDPR states that “personal data shall be collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes; further processing for ... scientific or historical research purposes or statistical purposes shall, in accordance with Article 89(1), not be considered to be incompatible with the initial purposes”
This gives researchers quite a bit of flexibility. This is maybe one area where law and ethics overlap. The debate within research on genetic data that I have come across when dealing with biobanks, is that people speak of dynamic consent and they want to use dynamic consent to have more granular consent for specific projects. The thinking behind this is that certain people might object morally to particular research. So of course, you're being more respectful to the data subject if you don't use the data in ways that they would not approve of. Specific, granular consent is in line with the spirit of GDPR, but I don't think that the GDPR excludes such broader consent for scientific purposes.
The GDPR refers to data minimization? How do you guarantee that we don't collect data that are unnecessary?
CP: This is something that we also struggled with. On one hand, you would want to be able to collect participants' data and typically, in my lab, we go through a bunch of health questionnaires, handedness, medical history, language etc… because, of course, you can then reuse these data in a larger dataset. You've got 100 different studies, but for each participant, you have the common six questions, and you can do a nice big analysis. You could possibly connect these studies and perform richer analyses. What is the balance? We know that this may be the only way to aggregate enough data from multiple studies to then have a study that is powerful enough to look at the effect of some type of medication.
MC: Unfortunately, I think that this is an outstanding difficulty or problem because as a researcher you may not know exactly what you're looking for; for example, what analysing the patterns may reveal. It is a known tension in the GDPR that may also go against the purpose specification principle. So I think it's a tension that is real. I would however always emphasise in such contexts that the sole purpose for processing these data is in fact scientific research, that there may be uncertainty associated with research, but that there is also an important public good to be gained from such research that affects the balance to be achieved between the different competing interests, including the privacy and data protection rights of the data subjects.
What are the next steps?
CP: I think the next steps are twofold. One is for neuroimagers to engage with their own institutional repositories. We need to work with them and with data protection officers to come up with solutions for sharing data. You need to explain what systems need to be in place and how to implement them. We do have power because we do receive money from funders who often actively ask us to share the data. And it is the university’s job to provide us with the tools to be successful in funding applications and to comply with funders.
The other aspect is more ambitious. There are systems that work under the architecture of any repository to index them, so that for instance, every university in Europe could very well have their information connected. But this would necessitate that all universities cooperate with each other. It's more like a dream.
I am also very keen on making sure that everyone reading this interview knows about all the efforts of the Open Brain Consent project. I would like to highlight all of the hard work put in by many, in particular Chis Gorgolewski and Yaroslav Halchenko who started the project, Stephan Heunis and Peer Herholz who organize work on this during the Organisation for Human Brain Mapping (OHBM) hackathon, and all the people who helped sharing their consent, experience, and proposed translations (now available in 12 languages) thanks to the COST association support (GliMR2). Note that we are keen on having more people involved, in particular having and sharing more information about how these issues are dealt with in countries from the Global South that are currently under-represented.
You can find more details on the Open Brain Consent website.
Now is the time to submit your nominations for 2021 OHBM Awards. To inspire you, we are highlighting some of the outstanding winners from this year’s meeting.
This year’s annual meeting was unique in many ways. Uncertainty about whether the meeting would happen was followed by a remarkably fast reorganization in order to hold the meeting online with a complex time schedule. One event that was not missing in the program was the traditional award ceremony that recognized the work of individuals who have changed the scientific landscape of human brain mapping.
Inspired by their nomination letters, we honor OHBM 2020 award winners and their achievements:
2020 Early Career Investigator Award Winner: Danielle Bassett
Danielle Bassett received her PhD in physics in 2009 and after only 10 years, she is now a full professor at the University of Pennsylvania and has published over 240 peer reviewed articles. Her top-cited paper on small-world brain networks has received over 1,800 citations. In addition to the OHBM Early Career Investigator Award, she has also received the Erdos Renyi Prize in Network Science, a National Science Foundation CAREER award and a MacArther “genius” fellowship, amongst others.
Danielle Bassett’s laboratory, the Complex Systems Group at the University of Pennsylvania, combines theory and tools from bioengineering, physics, electrical engineering, neurology, psychiatry and philosophy. Her team’s translational, interdisciplinary research has enabled them to explore the human thought process through investigations of how we learn and how this is underpinned by the flexibility of brain network dynamics. Her interdisciplinary approach applies new physically-informed metrics and null models for spatially embedded systems to look at networks at different scales (from cellular to systems) in order to inform clinical medicine and societal interventions. Danielle and her lab also contribute to software packaging and science outreach events.
Danielle gave the opening lecture at OHBM2019 and participated in the mentoring symposium organized by the OHBM student/postdoc special interest group.
From musical notes to neural nodes, you can learn more about Danielle Bassett’s career and aspirations at ScienceMag.
2020 Education in Neuroimaging Award Winner: Robert Savoy
Educational programs are a key part of the success of OHBM. Before the OHBM meeting in 1996 in Boston, Dr. Robert L. Savoy organized an educational workshop on fMRI attended by 600 of the 900 meeting attendees. The success of this inaugural course showed the high demand for educational programs. These have continued annually with the still highly-attended workshops alongside each OHBM meeting. Robert’s very first course was offered at the MGH NMR Center in October of 1994, and it was envisioned that the market for an introductory fMRI course would soon be exhausted. In contrast, the continual advances in fMRI and the general excitement associated with the technology meant that it reached an ever-expanding range of disciplines, increasing the pool of interested students. As the field has grown, so too have Robert’s educational offerings. Since 2007, Robert has organized an annual two-week Multi-Modality course; this has in turn generated another short course on connectivity. Robert is a rare scientist who devotes almost all of his efforts to education. His courses have had a profound impact on the career trajectory of many of our colleagues, including many active and leading members of the functional imaging community around the globe.
A large fraction of the leaders in the field have attended his course – receiving their first instruction on fMRI and neuroimaging there. Peter Bandettini, Ph.D., Director of Functional Magnetic Resonance Imaging Core Facility (FMRIF) collected the following quotes:
2020 Mentor Award Winner: F. Xavier Castellanos
In her nomination letter, Lucina Q. Uddin describes Francisco Xavier Castellanos as “a winner with great mentoring values, guiding his lab members to become independent thinkers and scientists. He is a tireless mentor and teacher. He proposes clear goals with defined timeline and expectation along the way and he predicts correctly. He shows a clear vision of a career path and best opportunities that should not be to define a path for new lab members. He is able to teach the art of “grantsmanship”, one that every scientist must master. Xavier is always there for his trainees, current and past. Trainees can always count on Xavier to submit a letter of recommendation at a moment’s notice, which is a great aid to apply for fellowships, grants, and positions as the opportunities arise. He is always happy to comply with letter requests, no matter how frequent. He also remains, at every career transition, a sounding board, providing clear-headed, rational and thoughtful advice.”
Lucina mentioned one particular anecdote that represents her experience of being supervised by F. Xavier Castellanos: “One particularly salient example of Xavier’s unconditional and enthusiastic support for his trainees comes to mind. One day, in a conversation with Bharat Biswal, we were tossing around the idea of trying to collect neuroimaging data from a split-brain patient in order to test a theory we had about functional connectivity. Without a fuss, Xavier funded the trip for me and a colleague to fly across the country, collect data from this unique patient, and spend months analyzing the data (though this project was unrelated to any of his grants at that time). This spontaneous trip led to a number of interesting case studies (Nomi et al. 2019, Uddin et al. 2008), and remains one of my favorite Xavier memories. The fact that he has always been enthusiastically supportive of whimsical projects has made science fun over the years.”
What particularly distinguishes Xavier from other senior successful scientists is his generosity, intellectuality and personality. He clearly has had a positive impact on a number of young scientists. Indeed, it is worth noting that three of his previous mentees (Lucina Uddin, Mike Milham, and Daniel Margulies) have received the OHBM Early Career Award. Another example of the way in which Xavier exemplifies the values of collegiality and building community through acknowledgment and recognition is in his authorship practices. He never hesitated to add junior scientists as co-authors on manuscripts, and readily gave up senior authorship positions to his trainees, as he always practices the maxim of giving credit where credit is due.
Xavier has been a proponent of open science before open science was a thing. His lab was one of the earliest to get involved in grass-roots data sharing initiatives such as the Autism Brain Imaging Data Exchange, the ADHD 200 International Neuroimaging Data-sharing Initiative, and the Enhanced Nathan Kline Institute – Rockland Sample. In fact, he is acknowledging that so much data are collected and so many people are needed to analyze them, so he favors giving others the opportunity to use their expertise without worrying about authorship or credit or restrictions. This kind of radical data sharing has inspired countless researchers worldwide, who are beginning to follow a similar philosophy. Xavier’s lab and pioneering radical data sharing initiatives set the stage for the current climate of open science and collaboration that permeates the field today.
2020 Replication Award Winner: Andre Altmann
Andre received the replication award for his paper titled ‘A comprehensive analysis of methods for assessing polygenic burden on Alzheimer’s disease pathology and risk beyond APOE’, Altmann et al., Brain Communications (16th December):
In this paper, Andre Altmann and colleagues attempted to replicate results from “Polygenic hazard score, amyloid deposition and Alzheimer’s neurodegeneration”, published in early 2019 by Tan et al.. The original paper was proposing a link between a polygenic hazard score (PHS) and amyloid deposition (from amyloid PET) beyond APOE.
Andre Altmann and colleagues proposed to account for APOE4 status (carrying or not) instead of APOE4 burden (number of copies). Beyond this difference in analysis, Altmann et al. went further to show that their analysis better accounted for APOE4 than the initial study. While using subjects from the same database (ADNI), Altmann et al. were not able to replicate the results from Tan et al. (2019).
APOE4 is the strongest common genetic risk factor for the sporadic late onset of Alzheimer's disease and is known to be associated with amyloid deposition in the brain. Therefore, it is of importance to disentangle the effect of APOE4 from the polygenic hazard score in order to avoid correlation of no interest in the results. This would explain part of the previously observed strong link between their proposed Polygenic hazard score (PHS) and amyloid deposition. This reanalysis questions the conclusion from Tan et al. (2019) that PHS influences longitudinal cognitive decline in regard to the model.
Altmann et al. adjusted their linear mixed effects model and the replication study shows that small differences in modeling decisions have a dramatic impact on the results.
This study also rectifies a result that could have had a large impact in the field as PHS could have been used for follow up study in the Alzheimer's disease community without proper initial support.
2020 Open Science Award Winner: Michael P. Milham
Mike P. Milham’s efforts in open data, open resources and collaborations are numerous. They impact both clinical and basic science neuroimaging communities. In just a decade, starting with the aggregation and publication of the 1000 Functional Connectomes Project (FCP-1000), Mike has:
The above initiatives have had a substantial impact on the neuroscientific community both in terms of immediate/direct (e.g., publications) and sustained/indirect impact (e.g., cultural change).
Mike has been the driving and inspirational force of a host of important open science initiatives that have helped change the landscape of human and non-human primate neuroimaging.
Once again, we congratulate all the OHBM 2020 winners and nominees. We wish them a great year of science.
We hope we have inspired you to look around you, consider your own mentors, colleagues, trainees, friends and neuroimaging heros to might be an appropriate candidate for one of the 2021 OHBM Awards. The OHBM website has all the details regarding eligibility, and required information for each of the award categories; just select the award by name and there you will find the link to the submission webform. The nominating process is reasonably easy, all online and waiting for your submission. Remember, our ability to inclusively honor members of our diverse community is directly dependent upon you submitting deserving candidates!
Written by: Claude Bajada, Fakhereh Movahedian Attar, Ilona Lipp
Expert reviewers: Adina Wagner, Cyril Pernet
Newbie editors: Yana Dimech, Renzo Torrecuso
This post is about good neuroimaging practices. ‘Practices’ relates to all aspects of conducting research. By ‘good’, we mean beneficial to the field and neuroimaging community - but you’ll see that most of these practices also benefit the individual researcher. Here, we collected a number of tools, tips and tricks to do neuroimaging in the ‘best’ way possible. We aim to provide an overview and answer some questions you may have asked yourself about reproducibility and good neuroimaging practices. As usual, we refer to OHBM On-Demand videos from the educational sessions of previous annual meetings. OHBM has both a special interest group (SIG) for Open Science as well as a Best Practices Committee, where leading brain mappers promote and help implement Open Science and good practices in data analysis and sharing. Both the Open Science SIG and the Best Practices Committee regularly create invaluable resources, such as the annual Hackathon workshops, and the COBIDAS Best Practices in MRI and M/EEG data analysis papers.
Isn’t the main issue in our field reproducibility? Or the lack of it? Should I care about my science being reproducible?
Those are loaded questions. We think we just might not answer them because you are luring us into a trap that begins with seemingly innocent questions and then rabbit into an unending borough. There are so many terms to wade through that the novice neuroscientist can easily get lost in this bog!
In his video, Cyril Pernet clarifies the often used terms 'repeatability’ and ‘reproducibility’ (from min. 1:07). First, ‘repeatability’ means “simply” that redoing the same analysis with the same data should result in an identical result as the original analysis, which is not as trivial as it seems. The software version and the operating system can be variables that affect the output of your imaging analysis. That, however, is only step one. In his video, David Kennedy (from min. 3:54) highlights that ‘reproducibility’ is really a spectrum. We could use the exact same data and nominally similar analysis. Or, we may have nominally similar data with the exact same analysis. Or, we may have nominally similar data with nominally similar analysis. This way we can test the sensitivity and stability of our experiment.
Cyril explaining the different levels of reproducibility.
But this leads back to your question. Scientific findings should generalise. They should first be valid (repeatable) but should also be robust to various permutations of the data and analyses used. There is a great video by Kirstie Whitaker on YouTube that tackles these issues.
The reproducibility crisis is often associated with the field of psychology, is there anything different in the field of human brain mapping?
Ok, so here we are generally talking about the more general “reproducibility”, not just about being robust to permutations. We will assume that researchers have already ensured that their analysis is re-executable.
In 2005, John Ioannidis published a landmark article with the eye watering title of “Why Most Published Research Findings Are False.” If you are interested in understanding why many scientific articles are not reproducible we strongly recommend reading this article; it is an easy and insightful read. Notice that this article does not even specifically refer to psychology or to neuroimaging. This problem is general to, at least, the wider “medically-related” field.
The article points out that effect sizes in these fields tend to be low and that sample sizes are frequently lower than what would be needed to test for such small effects. In neuroimaging, there are many steps and expertise (and often money) involved in acquiring good data. As a result, our sample sizes tend to be typically small. Indeed, it was not too long ago when most neuroimaging articles were published on samples of approximately 20 participants. In 2020, studies with several hundred, up to a couple of thousand, participants are becoming more common, but these require a massive investment in resources and tight collaboration between sites.
In his video, Cyril provides an overview of cognitive biases that can contribute to limited reproducibility of neuroscientific research (from min. 7:18). He also explains how the analytical flexibility in neuroimaging research (such as fMRI analyses) adds an additional level of complexity (from min. 15:59). While papers with hot stories and “positive results” have it much easier to find a home in very high impact journals, the drawbacks of this trend are slowly starting to be recognized. Neuroimaging scientific societies are becoming aware of the importance of reproducible research and are incentivising the work. OHBM has a yearly replication award that was won by Andre Altmann this year. Also, initiatives, such as DORA, The Declaration on Research Assessment, aim to find ways of evaluating research and researchers that go beyond journal impact factors.
Pia Rotshtein discussing the conflict of interest between good science and researcher’s careers.
So what can we do to make neuroimaging research more reproducible?
Well, some things are harder to deal with than others. Running neuroimaging studies is time-consuming and expensive, there is very little that can be done about that, at least in the short to medium term. One thing we can do is to work towards using robust and valid measures from neuroimaging data. In his video, Xi-Nian explains how validity of our measures depends on reliability (from min. 5:40). He introduces reliability indices (the intraclass correlation coefficient) and gives an example of how they can inform the extent to which inter-subject variability (which is often what we are interested in, e.g. when investigating different groups of people or brain-behaviour correlations) exceeds intra-subject variability (which in these cases is unwanted variability in repeated measurements, often caused by measurement noise). He reminds us of this paper pointing out that brain-behaviour correlations are “puzzling high”, given the reliability of our cognitive measures and of our imaging measures. From min. 16:20 he goes through a variety of imaging measures and their reliability, and introduces CoRR (min. 21:30), the Consortium for Reliability and Reproducibility. The prerequisite to have reliable imaging measures is, of course, to have sufficient data quality.
How do I ensure that my data exhibits sufficient quality?
Quality assurance (QA) and quality control (QC) procedures are put forward to ensure and verify the quality of neuroimaging data, respectively. Although somewhat intertwined, QA and QC are slightly different. QA is process-oriented and aims to boost our confidence in the data via routine system checks, whereas QC is product-oriented and deals with verifying the quality of the final product in the pipeline. In his video, Pradeep Raamana briefly introduces QA and QC and outlines the different QC steps involved in the acquisition of neuroimaging data (from min. 3:47). Visualising and checking your neuroimaging data at all processing stages is absolutely essential. The most important yet basic tool you need is therefore an image viewer that allows simultaneous visualization of the three image planes, and of course, you as the observer! For more specialized QC, Pradeep presents a list of some of the available neuroimaging QC tools per neuroimaging modality here, where he also presents use-cases of some of the tools.
In order to conduct QC successfully, one would need to take care of the various common types and sources of artifacts encountered in neuroimaging data. Importantly, we need to keep in mind that QA and QC must be tailored to the specific nature of neuroimaging data in its various modalities, separately.
In the videos of the ‘Taking Control of Your Neuroimaging Data’ session, some of these procedures are presented. Pradeep introduces common sources of artifacts in anatomical MRI (min. 8:14) and presents some tips and tricks for detecting artifacts in T1-weighted images (min. 19:08). Then, Martina Callaghan presents key metrics to perform scanner QA for functional MRI, emphasising the need to look for subtleties (min. 3:53). Here, the key is to establish whether the system fluctuations inherent in the acquisition procedure and hardware are sufficiently low to allow detection of BOLD-related signal changes in task-based and resting-state functional MRI. Martina Callaghan then presents some of the online (i.e. real-time) QC procedures for functional MRI (min. 17:17).
Esther Kuehn then takes over and introduces artifacts in high resolution functional MRI acquired at high-field strength with particular emphasis on cortical layer imaging applications and presents some available means of artifact-correction (from beginning). In her video, Joset Etzel introduces a different aspect of QC for neuroimaging data - dataset QC - and talks about the importance of checklists and standard operating procedures (SOPs).
Dataset QC aims to verify whether a valid dataset (i.e. one that has already passed the various data QC steps) is also usable by different people at different times in different places, and intuitive data organisation alone is not sufficient. Finally, in his video, Alexander Leemans introduces common artifacts in diffusion MRI, presents strategies for checking the quality of data and common errors in this checking, and also correcting artifacts.
I’ve got so much data, how do I organise it?
Lots of neuroimaging data are acquired all over the world and the resulting datasets are organized in different ways according to the personal preferences of the users or the labs. With Open Data, so data that is publicly accessible, picking up momentum, there is growing need for standardization of neuroimaging datasets so that they are easy to use soon across a wide community of neuroscientists. The brain imaging data structure (BIDS) initiative aims to standardize neuroimaging data structures in order to make them interoperable under the FAIR data principle. In this tutorial, the BIDS data structure is introduced as a practical means for achieving FAIR data. Here, a number of BIDS resources and repositories and simple BIDS specifications are also given for an easy get-go (min. 27:27). Later, a hands-on session on how to create and validate a basic BIDS dataset is also introduced (min. 34:57). Also check out the TrainTrack session on BIDS of this year’s virtual meeting by Sam Nastase!
Jeffrey going through the benefits of the brain imaging data structure (BIDS).
Once you have nicely organised your data, they are also easier to use for other people. To make neuroimaging more reproducible overall, something else that can be done is to ensure that data does not get lost and forgotten. In short that our data are Findable, Accessible Interoperable and Reusable (or FAIR; see the educational course on FAIR data from min. 1:52 by Maryann Martone and Jeffrey Grethe).
This way, your science will be more robust, transparent and verifiable.
The problem is that making research FAIR as an afterthought is really tough. Indeed, generating or curating good quality data that abides by FAIR principles requires some forethought (FAIR workshop min. 12:36). Not only do a lot of steps and expertise go into acquiring good quality data, but your data need to be in a format and in a place that makes those data easy to use for your present self, your future self and for someone who is not yourself!
One tool to share statistical maps from your study is the platforms NeuroVault and Neurosynth. In his video, Chris Gorgolewski goes through the advantages that uploading your map has for you, such as the options for fancy visualisations of your maps (min. 4:37), cognitive decoding of your maps (min. 5:25), search to find similar maps in papers (min. 6:25), gene decoding (min. 7:04).
How can I make sure that my analysis workflow can be reproduced by others?
If you want all aspects of your study to be documented and reproducible, then this of course also includes your analysis. The BIDS structure can help with setting up a reproducible workflow, but it is not sufficient. It also needs to be clear which processing steps have happened, which analyses were done, with which software and which parameters, etc. There are a lot of tools out there to help you and the Center for Reproducible Neuroimaging Computation initiative (ReproNim) has held an extensive course at the 2018 annual meeting about this (and a whole Webinar series on best practices for neuroimaging, if you are interested).
Starting with the “computational basis”, Yaroslav Halchenko gives an introduction into the Linux shell, including the importance of environment variables (from min. 12:50) to ensure you are running the right version of software, how to use shell history (from min. 23:40) to check whether you indeed ran the right commands, and how to write shell scripts (min. 29:30). He also shows how Neurodebian can be used to search and download software (min. 41:21).
Most people have probably heard the name Git before. (Did you know the official definition is “stupid content tracker”?) Yaroslav explains the Git philosophy in 2 minutes (min. 58:01) and shows the most important commands (min. 52:50). While Git is useful to keep track of your scripts, get and provide code, a tool called DataLad (min. 1:03:17) can be used to do similar stuff with datasets. A hands-on session on this is provided in the Workflows for neuroimaging session from min. 47:20, and how this can be combined with specific statistical analyses is explained from min. 1:52:08.
Other tools to help you make sure you use consistent software within a study are containers and virtual machines. Dorota Jarecka gives a good overview of why these are very useful in research (from min. 7:39) and even guides you through some exercises (from min. 15:45). Jean-Baptiste Poline gives a short intro to Jupyter notebooks to demonstrate your code to others (from min. 2:43:51).
This year’s OHBM Hackathon also has a session on Git by Steffen & Saskia Bollman, on good coding practices with Matlab by Agah Karakuzu, on Datalad by Adina Wagner and on Containers by Tom Shaw and Steffen Bollmann.
You said that replicability also refers to other people being able to get the same outcome as my study, but if they test different participants, this is out of my control, right?
This is a good point, it is somewhat out of your control, but there are some ways in which you can help. First, being very transparent about what you did to your data will allow others to adapt methods as similar as possible to yours. As Celia Greenwood explains (from min. 2:24:01), the final statistical measure that one tries to replicate involves a lot more than just the statistical test, but includes all steps before, the processing, exclusion of outliers etc., which sometimes makes it hard to even work out what the null hypothesis is. She states that reproducibility in the statistical sense is about the final inference you make, so it is tied to the p-value. And this of course depends on your sample size and, to some extent, chance. In a demonstration (from min. 2:34:24) she shows that if you draw different samples from the same population, there is huge variability in the p-values and effect sizes that you get across samples (even with sample sizes of N > 100) , which are purely a result of random sampling.
Celia illustrates the effect of random sampling on estimated effect sizes.
Is this why “most published research findings are false?”
Are you insisting on going back to things we have already discussed?! I suppose it is fair to say that there is more to it. A measure called “predictive value” is the probability of the alternative hypothesis being true given your test result. In his video, Jean-Baptiste (from min. 2:47:14) uses a Jupyter notebook to explain the Bayesian math behind this value and shows that this measure depends on the power of your study as well as the odds ratio of the alternative hypothesis over the null hypothesis being true. So the lower the power in your study, the more unlikely that the alternative hypothesis (usually what you are interested in) is true, even if you have a significant result. And most neuroscience studies do not have much power, as shown by Katherine Button.
Well, you may say now, how do I know what my power will be? And is there even a point in doing my experiment or will it just produce another - false - research finding!?.
Good question. Doing power analysis for neuroimaging studies is not straightforward, but luckily, some packages, such as fmripower and neuropower, have been developed to at least get an educated guess of what your power might be. As Jeanette Mumford explains in her video (from min. 4:53) doing a power analysis has many benefits. She also gives some tips on how to assess other people’s power analyses (from min. 7:08) and what to consider when estimating effect sizes based on the literature (from min. 9:18). Jeanette also explains why the difficulty of doing power analysis increases with difficulty in model (from min. 11:59).
Jeanette talking about the power of different statistical models.
What else can I do to ensure best practices in neuroimaging?
Thorough reporting of what you have been doing in your data acquisition and analysis is always a good idea. Guidelines have been created by the Committee on Best Practices in Data Analysis and Sharing (COBIDAS; also see Tonya White’s video for the idea behind COBIDAS) for MRI and MEEG.
Various tools are available for testing your code. Also, if you publish your code on sites such as github, then other researchers can try it out and help further develop it.
Preregistration and registered reports are becoming more and more popular for neuroimaging, meaning that more and more journals accept and encourage them. In her video, Pia Rotshtein explains the philosophy behind and principles of registered reports (from min. 11:06) and shows some examples (from min. 22:55).
Tonya telling us about the Committee on Best Practices in Data Analysis and Sharing.
If I get into all these things, will I still have time to do research?
That is why there are 36 hours in every day! Seriously though, this is all part of doing research! Often, however, efforts on good practices in neuroimaging are not publishable by themselves and have not been well respected. There are good reasons and incentives to follow Open Science practices as individual researchers (for examples see this summary) and with the new OHBM initiative Aperture (see video and website), a new room for unconventional research objects (such as software and documentation) is being created.
If this still all seems overwhelming and time consuming, don’t worry. Most of the tools presented here have been developed to save you time and resources in the long run while making your research more sustainable. Think about the time that one would spend re-acquiring a data set because of a previously unnoticed problem with the scanner, trying to make sense of not intuitively organised data or trying to find a mistake in a long, badly structured code. Putting in place some of these preventative measures, does not seem like such a big investment anymore.
If you’re hooked, stay tuned. The numerous emerging Open Science initiatives keep coming up with new ideas and tools for how to make research as a whole more reproducible and trustworthy, and help us brain mappers, conduct neuroimaging research in more robust and applicable ways.
Guest post by Hiromasa Takemura
International diversity is essential for organizations like OHBM. Through my experiences attending recent OHBM Annual Meetings, I have found myself asking why so few researchers from Japan have visible roles. To find out whether this was indeed the case, and possibly why, I worked with the OHBM Executive Staff, Diversity & Inclusivity Committee, and Communications Committee to analyse membership and attendance data from the annual meetings. By collecting and analysing this demographic data we can gain insight into why some countries (in this case Japan given my background, but the findings may extend to others) may be underrepresented at OHBM.
Japan is the 11th most populous country in the world, with an estimated population of 126 million (m) people in 2020. For comparison, Mexico has the most similar population with 128m people and Germany, Europe’s most populous country, has 83m. Japan has, over the years, substantially contributed to the OHBM community: for instance, the 2002 Annual Meeting was held at Sendai, Japan and Dr. Kang Cheng, a pioneer of high-resolution fMRI studies at a founding lab for RIKEN's Brain Science Institute, is heavily involved in organization of OHBM meetings.
To get a picture of recent involvement of researchers from Japan, we examined data summarizing attendance and presentations at the OHBM Annual Meeting between 2017-2019 (Table 1). We defined Japanese members as those affiliated with Japanese institutions. Using this definition we found that Japanese members comprised 3.6%, 5.4% and 3.9% of all attendees for 2017, 2018, and 2019 respectively, with the fluctuation reflecting the location of the annual meeting (OHBM 2018 was held in Singapore). We found a lower proportion of abstracts submitted by Japanese members: 2.6%, 3.6%, and 3.6% of the total number of abstracts for each of these years.
We then examined the proportion of Japanese members giving oral presentations. These numbers included both regular oral sessions and symposia. The proportion was 1.7%, 3.0%, and 0.9% for 2017, 2018, and 2019 respectively. The low number at the 2019 Annual Meeting was striking, given the proportion of attendees and abstract submissions.
To determine potential contributors to these statistics, we examined the number of Japanese members who selected “talk preferred” at abstract submission, but were not accepted for talk presentations. Surprisingly, these numbers were very small: 3, 1, and 2 for 2017, 2018, and 2019, respectively. A major reason for underrepresentation of Japanese members at the OHBM meeting appeared to be a reluctance to present data in the form of a talk. It is true that certain types of presentations work better as posters than talks, but we wanted to find out why so few researchers from Japan opted for oral presentations. We wanted to find out why the community would miss opportunities to highlight and benefit from their work.
Why do Japanese members hesitate to give talks at the OHBM annual meeting?
To find out, we surveyed 86 Japanese scientists working in human brain mapping (Figure 1). First, we asked whether they would choose oral or poster presentations at domestic conferences: 58% answered “oral”. Then we asked whether they prefer oral or poster presentations at international conferences. In this case, only 35% answered “oral”. The trend to favor posters in international conferences was common across both junior and senior scientists.
Next, we asked why they would opt for a poster presentation (Figure 2). For domestic conferences, researchers chose poster presentations when the topics were specialized, or the data wasn’t ready to present to a broad audience. For international conferences, 32.6% of respondents were dissuaded due to the challenge of presenting in English. Indeed, for Japanese researchers the most common deterrent for oral presentations at international conferences like OHBM was the language barrier.
Figure 2. Survey on the reason for choosing a poster presentation for a domestic (left) and an international conference (right). Multiple choices were allowed for this question. While there are common reasons between a domestic and an international conference, people raised a difficulty in English presentation as a reason to prefer poster presentation in international conferences.
The challenge of presenting in English is not unique to Japanese members of OHBM. Instead, this case study serves to demonstrate the extent to which language barriers can limit scientific communication. It is, therefore, worth considering ways to organize an international conference that help enable non-native English speakers.
There are several actions we can take as a community. First, we could promote and encourage junior Japanese members (and other non-native English speakers) to apply for oral presentations, symposia proposals and educational courses. My own experience speaking at the 2019 Annual Meeting greatly increased my enthusiasm and experience of the conference (see photo below).
Second, as an international community, we can promote a friendly, open-minded environment for scientific presentations and debates across members, irrespective of their English proficiency. I appreciate that OHBM has made a clear Code of Conduct prohibiting harassment based on the accent of speakers. Since I believe that OHBM members are mutually respectful, I hope that non-native English speakers feel able to discuss their scientific work and ask questions during annual meetings.
Third, we can devise conference formats that reduce language barriers. OHBM 2020 was a virtual event. This allowed members to communicate using live chat features that will be much less affected by spoken language proficiency. OHBM 2021 will now also be virtual, so we have time to consider further digital features to aid communication. Looking forward to the return of physical conferences, we can use features like mobile apps to ask questions, as we did at OHBM 2019. There may be no single solution, but we can benefit from technologies tested in virtual formats in future physical conferences to encourage broader active participation in OHBM meetings. We could ensure that new scientific advances are communicated widely, and not hindered by the lingua franca.
Finally, it is worth restating that these issues are likely not specific to Japanese members. We hope that by shining a light on the challenges faced by my local community, we can increase accessibility for OHBM members from a variety of non-English speaking countries around the world.
Addendum (from the Diversity & Inclusivity Committee)
To examine the breadth of underrepresentation, the Diversity & Inclusivity Committee examined the geographical distribution of speakers at OHBM 2020. We calculated the number of speakers (at regular oral sessions and symposia) as a proportion of current OHBM members (see figure below).
Our findings paint a complex picture: most Asian countries are certainly underrepresented but researchers from central European countries, including non-native English speakers, are well represented. However, the Romance or West Germanic languages of these latter countries share typology with English, and so are considered by the Foreign Service Institute to be easier for an English speaker to learn. In contrast, Japanese, Arabic, Cantonese and Mandarin are considered to be ‘exceptionally difficult’ for English speakers to learn, and vice versa.
Other factors likely influence whether researchers submit abstracts as oral presentations. For example, Spain and Mexico, despite their Romance language, had relatively few speakers. Historical ties to OHBM from individual labs and other economic, local, and macro-cultural factors are likely at play. By considering what causes barriers - language or otherwise - and exploring how we can break them down, we can promote a culture of greater diversity and inclusivity at OHBM.
By Elizabeth DuPre
The OHBM 2020 Annual Meeting was a year of many firsts. The move to an all-online event reflected the severity of the COVID-19 pandemic, with work, travel and schooling routines already in disarray for researchers across the globe. As many of us had been out-of-office or away from our university campuses for months before the Annual Meeting, the chance to connect with the broader human brain mapping community became especially important.
Traditionally, the Annual Meeting offers a chance to interact formally and informally with other researchers to make both scientific as well as interpersonal connections. Replicating these spontaneous conversations was perhaps the biggest challenge for this year’s meeting. First, there were the issues of timing. With OHBM members participating from their home countries, one member’s afternoon in North America would be the middle of the night for another member in Asia. The meeting was therefore set on a rotating schedule, with day-blocks favoring Asia-Pacific, European and African, or North and South American working hours.
Once the timing was set, the second hurdle was developing a virtual space for interactions. Large online platforms—like those necessary to run a conference for thousands of members—often lean towards structured, lecture-style environments rather than organic interactions and impromptu discussions. From the available infrastructure options, OHBM Council decided in April to adopt the 6connex platform. Council’s intention was to allow time for all presenters, committees, and special interest groups (SIG) members to adapt their content; however, the time pressures of the COVID-19 pandemic meant that many were still unclear how this new platform would work in practice in June.
Expectations were thus high for the 6connex platform—possibly higher than could be reasonably met. The platform did well in delivering pre-recorded content, such as the excellent selection of keynotes lectures, symposia and oral sessions, but the space for spontaneous interaction was woefully lacking. As one example, many members noted the challenges of using the chat feature, such as when 1000+ attendees simultaneously participated in a single-threaded chat room. This lack of functionality created particular frustration in poster presentations and interactions, where presenters and attendees were unclear how to contact one another or how to provide on-the-spot poster walk-throughs.
OHBM members enjoying one of the poster sessions on the GallOP platform.
Although the official platform did not provide an outlet for interaction, it did create a galvanizing effect for the community to create such a space. Attendees, such as Yaroslav Halchenko, Soichi Hayashi, and many others, came together to openly develop the OHBM 2020 GallOP (Gallery of Open Presentations) platform. GallOP provided an easy interface to search for poster authors, titles, or keywords, creating more chances for researchers to find relevant work. But perhaps most importantly, it created individual video conferencing rooms for each poster, allowing attendees and presenters to directly interact during presentation time slots or to leave one another notes outside of official meeting times. Although GallOP was only created after the first poster presentation time, the community response was enthusiastic, and it was quickly accepted and shared by the OHBM leadership and incorporated into the official platform.
Interactions in the Open Science Room (OSR) Gather.Town, a virtual space where OHBM members could gather throughout the conference.
This spirit of creativity and connection swept through OHBM2020 and was perhaps the defining feature of the conference. Other important community-driven initiatives that arose included the BrainWeb poster viewer and the first-ever virtual OHBM Club Night, both of which created online spaces that mimicked many of the social features of an in-person meeting, albeit with fewer spilled drinks. All of these community-driven initiatives were linked together by emergent discussions in the Open Science Room (OSR); this central hub seemed to catalyse interaction across the conference. The OSR hosted emergent discussions on everything from software containerization, to correcting for confounding, to even the structure of the virtual conference itself. In a year in which our idea of community has been redefined by political, social, and cultural reckonings, this space to have conversation with other brain mappers about the important issues of our science—both in terms of research topics and lived experience—proved a highlight of the conference for many attendees.
Alongside these experiences, the official OHBM program also provided attendees the chance to consider the direction of our field. As always, the OHBM Talraich, Glass Brain awardee, keynote, and symposium speakers provided an inspiring vision of the future of our society and the work we can do together. The OHBM 2020 Hall of Fame celebrated individuals that uphold many of the values important to the membership (e.g. education, replication, open science, mentoring), as well as this year’s award-winning abstracts. Uniquely, the community-driven efforts of this year’s event also provided a glimpse into just how important more grassroots efforts are to the structure and functioning of our academic society. As a result of this work, the SIG chairs were invited to sit in on Council meetings and increase interaction between official and grassroots initiatives. This is an exciting next chapter for OHBM leadership, and it suggests that we will continue to see more innovation in the years to come.
Although the 2020 Annual Meeting was our first all-virtual event, it is clear that its lessons will shape the structure of OHBM moving forward. We now know that the OHBM 2021 annual meeting will also happen virtually; this decision was made in advance such that all community members have more time to prepare. These preparations include creating a dedicated ‘Technology Task Force’ to translate the lessons learned in the 2020 meeting into next year’s experience. Altogether, it’s clear from the 2020 meeting that the OHBM community is vibrant, responsive and collaborative. We look forward to seeing how these attributes can be further advanced in coming years, starting with the 2021 Annual Meeting!