by Nikola Stikov
As we are getting ready to announce the 2020 OHBM Replication Award winner, here is a brief flashback to 2019 and our interview with Richard Dinga from the Department of Psychiatry at the Amsterdam University Medical Centers in the Netherlands. Richard led the effort to replicate a study published in Nature Medicine in 2017 about the relationship between resting state connectivity and the neurophysiological subtypes of depression.
In the lead up to the OHBM Annual Meeting, I had the pleasure of speaking to one of the keynote speakers, Dr. Biyu He, an Assistant Professor at New York University. Dr. He has made many valuable contributions to the field of neuroscience, combining diverse imaging methods and analytical techniques to tackle big questions relating to perceptual processing, spontaneous activity and consciousness in the human brain.
Rachael Stickland (RS): Thanks again for joining me. It's nice to - virtually - meet you.
Biyu He (BH): Pleasure to meet you as well.
RS: I'm getting used to having many video calls every day now. I'm sure you are as well. How have recent months been for you, adapting to working remotely and only connecting to most people virtually?
BH: It's been okay. I miss the face to face interactions with people. But I think we've been very adaptive in my lab. As you know, in human brain imaging, we do a lot of data analysis. So we have been working on reading, writing and data analysis. And I think we've been able to weather the strange situation we live in pretty well.
RS: You're currently based at New York University (NYU) as an Assistant Professor in the Departments of Neurology, Neuroscience & Physiology and Radiology. Do you mind telling me about your research path and your route into science?
BH: Sure. I was a biology major in college, and really liked maths and physics when I was young. I wasn't sure what I was going to do in college initially but once I found neuroscience I was immediately hooked. It is just so absolutely fascinating. I felt like I couldn't ever be bored again. And it's also one of the most interdisciplinary fields in science. It's challenging and fascinating and very, very intellectually engaging. I did my PhD in neuroscience at Washington University in St. Louis. From there, I was looking for postdoc positions at the end of my PhD and unexpectedly got offered two positions to set up my own lab. One at the National Institutes of Health (NIH) and one at the University of Konstanz in Germany. I decided to go to the NIH and spent about five and a half years there. It was a wonderful time — I learnt new techniques, made new friends, found new mentors, and started a new line of research, which is what I'll be talking about in my [OHBM] keynote talk. Then, I moved to NYU a few years ago.
RS: You mentioned how neuroscience is very interdisciplinary. That might be why it’s hard to explain what we do! If a non-scientist asked you what your research is about, and also why it's important, what would you say?
BH: Broadly speaking, I’m trying to understand how the human brain generates conscious awareness and conscious experiences. And how neural mechanisms underlying conscious awareness differ from, and interact with, unconscious processing. From decades of research in psychology, we know that sensory input impinging on the brain can be processed by the brain consciously, giving rise to all the experiences that we enjoy, but also unconsciously. So things that you don't consciously perceive can nevertheless influence your behavior. We don't really know what neural mechanism gives rise to conscious experience and how that differs from unconscious processing. Understanding the neural underpinnings of these processes and their differences is very important for a lot of clinically and societally important questions. For example, we'll be able to better treat disorders of consciousness, including minimally conscious states and vegetative states, as well as many clinical conditions with disordered perceptual awareness, such as hallucinations in schizophrenia, tunnel vision in autism. These are cases where you have disturbed conscious perception. In addition to applications in the clinical and societal domains, addressing this question also satisfies a fundamental human curiosity that is ‘Who are we? Why are we sentient beings? How are we different from robots?’
RS: That’s fascinating. I think scientists and nonscientists alike find the topic of consciousness very interesting. So do you think that fMRI has a key role in helping us understand consciousness?
BH: Absolutely. It's the best method for non-invasively measuring whole brain activity and finding out where in the brain some type of information is. In my mind, it is especially powerful when we combine fMRI with other techniques with higher temporal resolution, like MEG, ECoG or EEG. In human brain imaging, we have a lot of complementary techniques that are very powerful and can give us a view of whole brain activity or large-scale brain network activity, which you could say some of the more traditional animal research techniques haven't been able to get at. But, obviously, there's a lot of push to do large-scale simultaneous recording of many neurons and across many brain areas in animal models now as well.
RS: So your own research combines many of these techniques you just mentioned - invasive and non-invasive methods of studying the brain, including many different human neuroimaging methods. What are the main challenges with integrating such diverse methods, in terms of the experiments themselves but also in the interpretation of findings?
BH: Probably the main challenge is to grasp a lot of literature that's grounded in different techniques, because, when I was a PhD student, I realized that for the same question there is parallel literature, depending on if you use fMRI or EEG/MEG and then the insights are different. The questions and the debates people care about are also different. Each technique is like a window into the brain with its own vantage point. So if you only look through that one window, your field of view is somewhat limited. When combined, the knowledge and the insights from multiple techniques to understand the same biological question can provide a much broader view and you can get at the mechanisms better. Ultimately, we want to understand the mechanisms of how something works in a computational sense: how do neural circuits do the information transformation that allows certain perception and cognition to happen. And for that reason, simply mapping where or when would not be sufficient. We need to combine the insights from these different angles to build a full answer that addresses the mechanisms.
RS: Yeah, that makes sense. So, non-neuroscientists may be surprised just how much our prior knowledge and experience can shape how we perceive something in the present moment, and your research has advanced the scientific understanding on this topic. Related to that, what scientific finding have you found most surprising in your career? Has there been something that particularly surprised you about the brain?
BH: What you just mentioned was a finding that was actually very surprising to me. Me and my lab, when we made the discovery, we actually literally scratched our heads for several months before things started to make sense. You're absolutely right that past experiences and prior knowledge have a profound impact on perception. And it's very interesting because there are certain clinical disorders, including schizophrenia, autism, PTSD, where we know that this process is abnormal. There has been a lot of behavioral and neuroscience research done on this topic. What was really surprising in our findings was the spatial extent of the prior knowledge's impact on perceptually relevant processing across the brain. It used to be thought that visual perception, for example, is basically solved by visual regions. But what we found was that when you go to the really higher-order regions in the brain, even the so-called default network (that is the most remote from sensory input and the apex of the cortical hierarchy) they are involved in this process of prior knowledge guiding visual perception. It's not just that their activation magnitude changes, but their activation pattern changes as well. The voxel-wise activity pattern in those regions reflected the content of prior knowledge and the content of perception. So, that was very surprising. I think, in retrospect, it made sense because this process of prior knowledge guiding perception really requires many different brain networks to work together, from those processing sensory input to those mediating memories. We are still working on the exact mechanisms involved in this. But in the broader picture, it suggests that in real world vision, real world perception, where past experiences continually guide our perception, much more of the brain might be involved than we initially thought.
RS: Your research has brought new insights into the best ways to measure, categorize and model brain activity. Moving forward, what do you think are the most important questions that need addressing, or the most important technological advances, in order to progress understanding in your field?
BH: I have two thoughts here — one one is broader than the other one. The first one is that we need to integrate resting state approaches and task-evoked approaches. There's a huge amount of insight that has been learned, and to be learned, from both approaches. But each approach alone obviously won't be able to resolve how the brain works. I think we have made a lot of progress with both of those approaches, but exactly how we integrate the insights and their analysis methods, that is something that has a lot of room to be developed in the coming years. For example, related to my research topic, conscious perception: I don't think a system without spontaneous activity will have conscious perception; I think it will solve perceptual tasks, but it will not have perceptual awareness. Currently, we have a wonderful, beautiful field of knowledge based on resting-state studies but there is a gap between these insights and what we know about the neural mechanisms underlying perception and cognition. I think at the junction between those two fields, there is a lot of progress to be made.
And the second is something that I alluded to earlier (I think this is where the field is already going), which is to go beyond the mapping of where and when to get at the computational mechanisms. And there are many different ways of getting at the mechanisms — it probably requires leveraging multi-facetted analysis techniques to understand exactly the computational mechanisms as embodied in neural circuits and networks that underlie perception and cognition.
RS: What was the best piece of scientific or career advice you've received? What has helped you to get to the position you are in, carrying out brilliant research?
BH: Thank you. Something that comes to mind is when I was doing my PhD, my PhD advisor, Marcus Raichle, often told us that “Science must be done for its own sake, for any other harvest is uncertain.” It is important to enjoy the science you do. If not, you probably should do something different. That advice has propelled us to pursue questions we are passionate about.
RS: Your OHBM keynote talk is titled “From Resting State to Conscious Perception”. Can you give us a teaser or sneak preview of some of the interesting topics you will cover?
BH: It’s kind of a personal journey of how my scientific career has evolved, and how my work continues to make connections between these two areas. As you can see, from what I alluded to earlier, I think understanding the neural basis of conscious perception requires us to take into account the role of spontaneous brain activity and past experiences that persist through the resting brain. I've been to OHBM almost every year since I was a student, so it's very gratifying for me to be able to tell this personal journey through the different scientific questions I've investigated.
RS: Well, that's great. I look forward to tuning in and hearing it online.
Lee Jollans and the OHBM Diversity and Inclusivity Committee.
Edited by AmanPreet Badhwar
At the 2020 virtual meeting, OHBM will, for the second time, host a Diversity Round Table. This year the round table will feature discussions on the intersection between Neuroscience and the Lesbian, Gay, Bisexual, Transgender, and Queer (LGBTQ+) community. The four speakers will outline the specific challenges LGBTQ+ individuals face working in STEM (Jon Freeman), insights into the possible developmental bases of sexuality and gender (Doug VanderLaan), the current body of research into transgender identity (and its limitations), and the challenges and considerations that are crucial for carrying out good sex and gender research (Grace Huckins and Jonathan Vanhoecke).
Jon Freeman, New York University (top left), Doug VanderLaan, University of Toronto (top right), Grace Huckins, Stanford University (bottom left), and Jonathan Vanhoecke, Humboldt University (bottom right)
While studies suggest that the percentage of students interested in pursuing a doctorate is significantly higher among LGBTQ+ students (Greathouse et al., 2018), LGBTQ+ individuals have been shown in numerous studies to face unique challenges in STEM. Although specific data about Neuroscience and related fields is lacking (which is part of the problem), LGBTQ+ people are less represented in STEM fields than statistically expected, more frequently encounter non-supportive environments, and leave STEM fields at a high rate (Freeman, 2018). Moreover, one study suggests that more than 40% of LGBTQ+ people in STEM are not open about their LGBTQ+ identity with colleagues (Yoder & Mattheis, 2016). In his talk “LGBTQ Challenges in STEM: The Need for Data and Policy change”, Jon Freeman will outline how bias, harmful stereotypes, and unwelcoming environments can result in LGBTQ+ scientists leaving STEM, and will propose steps and policy changes we can implement to counteract these effects.
With a disproportionately low percentage of LGBTQ+ researchers, and rigid and outdated norms used to assess sex, gender, and sexuality, research about LGBTQ+ individuals has historically suffered from flawed data collection, and oversimplified, inaccurate, or outright harmful framing of research findings. In her talk “Trans Neuroscience: Stuck in 1995”, Grace Huckins will explain how studies examining the brains of transgender individuals are stuck in an outdated paradigm and why it is so crucial that this paradigm change.
Gender and sexuality are complex and interconnected, and attempting to examine them in isolation ignores the lived experiences of LGBTQ+ individuals. Cultural perceptions of masculinity and femininity, and social visibility and acceptance affects not only how LGBTQ+ people are treated and perceived, but also how research is conducted in different cultural contexts. Doug VanderLaan will describe findings from a neuroimaging study of LGBTQ+ individuals in Thailand, highlighting clues as to the relationship between early brain development, gender and sexuality in his talk “Sexual Orientation and Gender Identity Development: Insights from Thai gay men and sao praphet song”.
Research about marginalized groups by necessity always has a societal dimension – not only regarding the different experience of the world which marginalized individuals encounter, but also regarding the implications that findings might have for policy, stereotypes, and lived experience for the entire society. How can we disentangle ‘otherness’ from sociobiological variety? How to distinguish brain effects from effects of sociological background? Jonathan Vanhoecke will outline in their talk how brain research in the transgender community provokes sociological questions about sex and gender in other neuroscience fields. “The gap between neuroimaging of gender and gender studies of the brain: New perspectives from transgender research”.
We hope you’ll join us for this topical and thought-provoking roundtable, and we look forward to an interesting discussion!
The Diversity and Inclusivity Committee focuses on a different topic for their symposium each year. Topic and speaker suggestions for upcoming meetings are welcome.
In preparation for OHBM 2020, we talked to Dr Tomas Paus, who will be giving a keynote lecture on Friday, June 26th. Dr. Paus is Director of the Population Neuroscience & Developmental Neuroimaging Program at the Holland Bloorview Kids Rehabilitation Hospital, and Professor of Psychology and Psychiatry at the University of Toronto.
Roselyne Chauvin (RC): Thank you for taking the time to chat with us. In your talk you will be speaking about “population neuroscience and the growing brain.” There are a few ongoing longitudinal big data initiatives, such as ABCD or generation R. Those projects are now starting to think about the current pandemic situation. On one side, the situation is affecting everyone without discrimination; on the other, government responses create different experiences (from full to partial lockdown, to no restrictions), and of course, individuals show different stress responses. How do you think this might affect longitudinal datasets? And what are the questions that will need to be investigated out of this situation with regard to psychiatry and genetics?
Tomas Paus (TP): You can look at COVID as a natural disaster. There are studies where natural disasters have been used in the past as pseudo-experimental designs, i.e., to study the effects of a perturbation, because in most of our observational studies, we can really only look at associations between x and y and so cannot infer causality. In most cases, we don't know anything about the directionality of those relationships. But natural disasters provide an opportunity to study before and after and try to attribute the observed changes to those events.
A key component in the context of brain development and psychiatric disorders is social distancing and what has happened with social relationships. For children in particular there are two elements that I think really stand out. One is homeschooling, which, depending on a country, may last for several months. I don't know how it is in the Netherlands, but in Canada, it will last for at least three more months, if not more. And then the other element is the family, so it depends on what's happening at home. Unfortunately, in some cases, that means bigger exposure to adversity, adversity as bad as family violence. So there the stressor may be huge for some children.
Studies that have acquired detailed phenotypes, whether it's behaviour or brain phenotype on children before the event are in a unique position to go back when it will be possible and study the change in behaviour or in the brain. Generation R is certainly one such cohort, ABCD is another one. There are others. Even birth cohorts that may not be at the most relevant age from the perspective of child development but able to study the relationship between exposure to COVID-19 and events related to the disease and health in general. Of course, UK Biobank is the biggest one of all, right?
Now, one more thing in terms of children. Unfortunately, we do know that the most vulnerable segment of the population in terms of mortality are older people. And so there will be an increase in the number of grandparents dying. That is again, of course, a highly stressful life event and that will, one way or another, affect those children. Finally, we know already that at the level of mortality, COVID-19 is more frequent in disadvantaged populations, mostly in the context of socio-economic position. So there may also be an interaction between the pre-COVID conditions of those children and COVID-related stress.
RC: You’ve been involved in many different types of big data projects, from the acquisition and study of local communities like the SYS (saguenay youth study, ~1000 adolescents and their parents, from the genetic founder population of the Saguenay Lac St Jean region of Quebec, Canada) to the ENIGMA consortium (ENhancing Imaging Generic through Meta Analysis, a worldwide collaboration with more than 40 countries involved). How have you found carrying out these projects, and what advice would you give for those wanting to carry out these big data projects?
TP: It's a very good question and makes me reflect on my own path from the Saguenay to now. Over time I have increasingly become involved with collaborative work in the context of Enigma, and CHARGE, the other consortium that we work very closely with. I started this Saguenay study with my wife, Dr. Pausova, and others almost 20 years ago. That gives our team a lot of hands-on experience in carrying out big data projects. We learned what it takes to set up a cohort, to set up the protocol, to carry out quality assurance. All those different steps, on a relatively small scale. Even though 20 years ago, 1000 individuals was a fairly large scale for us. But I think that hands-on experience with a cohort is very, very important once you enter collaborations with others, and also once you start using data that had been produced by others. Of course, in a consortium, you share that experience and that's a currency.
In the CHARGE Consortium we have weekly conference calls. It's amazing how much you learn during one hour given there are between 20 to 40 people on the call. In one hour, we pick a topic, usually a study that is being carried out, and it's being discussed from the beginning to the end. You benefit, of course, from the expertise of people who have done many of those studies before. And you benefit from informal expertise that is very hard to get from reading the paper. In the same way that I can share my 20 years of experience with the Saguenay study with this group, every member of CHARGE group shares her or his experience back. So that's a huge plus.
In these consortia, it's not only about accessing data, you're really sharing knowledge; not only expertise in designing studies and acquiring data, but you’re also learning about the latest in genetics, epidemiology and statistics. So you’re keeping up-to-date with developments across many different fields. That's a huge benefit of working within a consortium.
The last point is about the diversity of the group. The group is diverse not only in terms of the disciplines, but also cultural backgrounds: it includes researchers from different countries, different educational systems. So for us, it means that there is a diversity of perspectives and I think that that's what you want. If you want to create new knowledge, you don't want everyone to have exactly the same background; you want to see things in many different ways and from many different perspectives.
RC: That also makes me think of sharing experience and trying to find the best way to maintain high quality. I mean, there are many initiatives to standardise scientific practices, for example using the BIDS format to organise data - that type of knowledge came from a consortium. Do you think we could extract some guidelines to help big database initiatives?
TP: I'm not sure about that. I mean there is a whole science of data harmonization of origin - there are experts who work on that. I'm somewhat sceptical about coming up with guidelines or toolboxes to be imposed on investigators when they are starting a new study. I think that there is a danger there. Yes, it would get easier then to harmonize across cohorts, but there is a danger that it would stifle innovation and new discoveries. If everyone is doing everything the same way, then where is the novelty? Where is the potential for new knowledge?
What I've seen is that, basically, it's a democracy of the scientists and the trainees voting indirectly by adopting certain tools more often than others. And then all of a sudden that tool emerges as the most commonly used tool. Freesurfer is an example of that, right? There are different ways to extract information about cortical thickness and surface area, but I must say that in the majority of studies Freesurfer became the main tool that everyone uses and so now you have a sort of natural emergence. So harmonisation has emerged in a natural way.
RC: In a similar vein, neuroimaging has faced a reproducibility crisis, just like genetics did before. There is increasing recognition that studies need to use larger sample sizes to produce more representative and reproducible findings. OHBM sessions have reflected these improvements in working, creating best practices for methods, promoting transparency via open publications, code, and data. The OHBM open science room grows every year and now the announcement of Aperture, their publication platform. What has been your experience and your change in practice? What advice do you give your lab members or early career researchers to improve the quality of their science?
TP: Well, that's a difficult one. I think that the starting point is critical thinking and that's what I'm trying to convey to my students. We need to question conclusions, to question reliability and that's maybe one of the reasons that even though we do use functional imaging, I do put more emphasis on multimodal imaging of brain structure because we know that structural imaging has higher reliability. Even though I started with imaging with PET with blood flow activation studies, I moved into that field from my interest in brain behaviour relationships, in a way. The relatively low test-retest reliability of functional measures and behaviour in general made me shift my focus to features that are easier to measure, such as the structural properties of the brain. That's probably one of the reasons why I changed my way of doing science in those large numbers - test-retest reliability becomes crucial if you are interested in a trait and if you are doing genetics, if you are running epidemiological studies where you are interested in influences of environment, you need to have that measure with a quality of a trait. That is, if I measure a trait today, and I measure it again two weeks later, I get more or less the same number. That's really crucial. I started by saying that one has to be critical, and I think, that that's kind of the simplest advice.
Another key for quality of science is replication. Let's say functional imaging studies, split the sample, analyse the data in one half and then see whether you find the same thing in the other half. Don't trust p-values. That would be my other advice. P-values will not guarantee reproducibility; replication would.
R.C.: So you said, you started with PET and then moved more towards structural MRI. Now that we are on the advice side, what do you think would be the next big topic in neuroimaging? Would you advise a young neuroscientist to follow the trend or look for their own niche? If you had to start something new, what would you go for?
TP: I wasn't really thinking too deeply about what I want to do in five years. I went with the flow and was always driven by curiosity, by novelty, by something unexplored. Often I was critical of a finding that I didn't believe and that triggered a line of thoughts: “I don't believe it's this way. Let's prove that it's the other way and what do I need to prove it.”
I do like to combine different levels of analysis. That's partly because of my initial educational background in medicine, human physiology, anatomy, etc, combined with deep interest in behaviour and psychiatric disorders. So you have both the systems level and molecular level, and integrating across systems, across levels, and I think it did work for me.
If I was going to do it again, I would probably again try to get a broad education that gives me at least some understanding of the different levels, rather than one very deep understanding of a particular approach, like the details of DNA structure. That just doesn't work for me, but it may work for someone else.
RC: Multidisciplinarity is at the core of cognitive science.
TP: It wasn't like that when I was starting! The fact that I got that broad education really prepared me for that interdisciplinarity and for working in large teams. When I was starting, the labs were small and there was little data sharing, even in genetics, and particularly in genetics of Mendelian traits. There were fierce competitions between people in terms of discovering disease genes, so people did not share. They competed with each other and that is a dramatic change over the past 30 years, possibly the biggest change I've seen in science and the social aspects of science.
Now, even with the amount of sharing there is always competition. Competition is good, we need it. But the competition doesn't interfere, as it did in the past, with generating data, with access to data because open science puts everyone on an equal playing field. So now it's not about someone having access to these data and blocking us from having access. It's not the case any longer. You really have to share data in some form.
RC: Yes. The evolution of the field is towards being open, being collaborative and getting experience from those that know how to acquire data and those that have strong expertise in methods.
TP: Also, when you look at institutions that support this kind of approach - they are successful. Institutions that are supporting open science and developing platforms for data sharing and open science in, for example, bringing different bioinformatics databases to communicate with each other, etc. An example is MIT Broad Institute in genetics.
RC: What are the findings that you are most proud of?
TP: There are two different types of things that I am proud of. I told you that I like innovation. I like doing things in a new way. In that context, I'm proud of two innovations. One is when we put together brain imaging and brain stimulation, our combined studies with transcranial magnetic stimulation and PET. Technically it was quite a challenge and I think we did it the right way. That approach eventually did not take off on a large scale. But I think in the mid 90s, when I worked on it, it was really exciting to be putting together TMS and PET in the way that we did. I'm definitely proud of that aspect.
Then, I think about what I'm doing now in terms of the combination of epidemiology, genetics, and neuroscience. I'm glad that I was able to put it together into that framework and I wrote a little book about it. I'm happy about it.
In terms of findings, I think two, for me, stand out. One goes back to the late 80s, to my PhD when I noticed some very interesting deficits associated with lesions of the anterior cingulate and then I followed up those findings with my first PET studies in Montreal. I came up with some discoveries about the function of the anterior cingulate cortex and its role in the interface between intention and motor control. Those early studies I still like.
The second finding is more recent and relates to what we have done in teenagers. The observation that testosterone has something to do with the radial growth of the axon. So, basically, the thickness of the axon, in particular in male adolescents, and how this may relate to axonal transport. That is a slight shift away from myelin and toward axon and I think it's important. We are pursuing that finding. I think that it's the axonal transport element that becomes very important for function. I personally believe that the link between axonal diameter and axonal transport will inform new studies of individuals, also mental illness. So that's the second finding that made a difference in my research.
RC : Are you going to talk about that during your OHBM lecture? Can you give us a sneak peak?
TP: I will talk mostly about big data and some findings from our work in the context of ENIGMA and CHARGE consortium, relating to the developing brain. This will illustrate the power of big data. But I will start with a bit of history on how we got where we are now and how important observations are, going back to my mentor Brenda Milner.
RC: Thank you for your time and for chatting with me!
TP: Thank you, it was really enjoyable.
RC: I am really looking forward to your lecture. This year is going to be a different format, as OHBM is happening online. So I hope this teaser will attract a lot of digital attendees and that everyone will enjoy your lecture and the meeting safely from home.
By Nils Muhlert
Professor Michael Fox is a neurologist at Harvard Medical School and director of the Lab for Brain Network Imaging and Modulation. His research into brain network imaging to define targets for brain stimulation holds considerable promise for new and improved treatments for a wide range of neurological and neuropsychiatric conditions. Here we found out how his academic career started through a chance meeting with Mark Raichle, about his plans for clinical translation of network neuroimaging, and his advice for early career researchers:
Nils Muhlert (NM): Thanks for meeting with us. We'll start by finding out about your background. How did you become interested in neuroimaging?
Michael Fox (MF): Good question. I didn't start off life as a neuroimager. I was an electrical engineer as an undergrad and then went to Washington in St. Louis for my MD and PhD combined. I wanted to do something at the intersection of engineering and medicine. My interest in neuroimaging came when I was walking through the neuroimaging facility at Washington University in St. Louis, on the way to a meeting. I saw a poster hanging there in the hall by Mark Raichle looking at brain imaging and the default mode network. I stopped, and I read the poster and thought, wow, that's fascinating. I had no idea who Mark Raichle was, but I subsequently knocked on his door and said, “Hey, I'm Mike - I just read a poster out here that I think is really interesting.” And that's how I got interested in neuroimaging.
NM: And how have you found the challenges of balancing your clinical work with your academic work?
MF: It's a challenge! There's always time constraints. On the side of getting out papers and getting grants, your clinician-scientists have to compete with full-time scientists. And with the challenge of taking care of patients, our clinical care has to be up to the same standard as full-time clinicians. It's like you're doing two jobs at once, and you have to be really good at both of them.
But with that challenge comes enormous opportunity. I wouldn't be doing both clinical and research if I didn't feel that it was valuable, and that one inherently informed the other. I don't feel like I'd know what the relevant research questions are to ask or to go after if I'm not seeing patients. Similarly, I won't know how to take care of my patients as best as I could, if I am not up to date on what the research is telling us about how to think about the brain.
NM: A lot of your work uses network neuroscience to understand how lesions in different locations in the brain can lead to similar symptoms. Can you tell us about this lesion-network mapping, how it works and how it can translate into the clinic?
MF: You asked me earlier: "how does research inform clinical care and clinical care inform research?". Well this entire field came from a patient. Aaron Boes, who was a fellow of mine at the time, saw a patient that walked into the clinic with acute onset visual hallucinations. Radiology acquired a brain scan on that patient and they found a focal lesion in the medial thalamus. Aaron Boes was fascinated by this patient. Why is it that a lesion in this particular location could result in this very impressive rapid onset severe visual hallucinations?
Aaron did what any good neurologists would do: he went through the literature and found other similar cases of patients with brain lesions that caused acute onset visual hallucinations. He mapped out where all of these lesion locations were, and then was left scratching his head.
All these different cases that cause symptoms very similar to what his patient had, were all in different locations across the brain. That's when he had his critical insight. When I'm trying to understand this patient's symptoms and I map out all the locations of brain damage, they don't line up. They don't intersect a single brain region.
Aaron literally came and knocked on my door and said, "Mike, I hear you do some kind of brain connectivity thing; could that brain connectivity stuff could help us understand how all these lesions in different locations are causing the same symptom."
Aaron's insight, which was in retrospect really brilliant, was that you can take a map of brain connectivity, overlay the lesions on a brain network and test the hypothesis that lesions causing the same symptom map to a single connected brain network rather than a single brain region. He was right for visual hallucinations. And subsequently, I think he's been right for every other neurological or psychiatric symptom that we've tried to investigate.
It's not really a new idea. Neurologists have known for a long time that symptoms probably mapped to brain networks or brain circuits. But before we had a wiring diagram, it was very hard to test that hypothesis or figure out what the network or circuit was in a data driven manner.
NM: How does it work in practice?
MF: In practice, you derive the network for each lesion location. So when you have a lesion that causes a certain symptom, you map it onto a brain atlas. You then turn to a connectome database and say, "Okay, I know where the lesion location is, but what I think is relevant for symptoms is everything that lesion location is connected to." So you turn the lesion into a lesion network, and you do that for every single lesion that you're interested in. Now, every lesion is going to be connected to hundreds of different brain areas, right? But if you take 40 lesions that all cause the same symptom, each one of those 40 lesions is a very different brain network or different set of connections. But the one thing that those 40 lesions share should be the connections that are relevant to the one symptom. And that's how you're able to then pull out the circuit that's relevant for that symptom shared by those 40 lesions.
NM: That's great. So this is a great example of how open science, through the human connectome project, has the potential to influence clinical practice...
MF: Very, very much so! I often feel a lot of gratitude for the field of neuroimaging as a whole and all the people out there that work so hard to build these connectome databases. If we didn't have things like Randy Buckner's genomics superstruct project, which is the connectome that I use for most of my work, if we didn't have the Wash U connectome, if we didn't have the MGH DTI connectome, then we wouldn't have the wiring diagram that allows me to do all the work that I do. So I'm very grateful to neuroimaging and grateful to these large scale projects that gave us these wiring diagrams. I'm just a user of this amazing resource that other people built.
NM: That's great to hear. Right now it's tricky to carry out clinical research projects so I imagine these large open databases are being well used. One topic that people have debated, particularly over the last couple of years, is clinical applications of fMRI. Your work seems to allow that - using functional brain networks to identify the targets for deep brain stimulation. How did you find the process of convincing people of the suitability of that approach?
MF: You're getting really to the heart of it. My PhD was focused on neuroimaging, and so when I moved into the clinic, and in my residency focused on trying to help people with brain problems, there was a disconnect. The field of functional neuroimaging does not have a lot of success stories. The idea was: "Hey, if we can see the brain at work, and identify areas that light up, if we can see the brain's connectivity, if we can look at the anatomical connectivity based on things like diffusion mapping, that all this will lead into better clinical care, better diagnosis, better outcomes, better treatments.” We don't have a lot of successes to hang our hat on. Even preoperative mapping with functional MRI is only used by a handful of centers. There's still debate as to how valuable it actually is. And that's probably our number one success story of clinical translation of functional neuroimaging.
So I've spent a lot of time thinking through why is that? One reason might be that we're on the right path but we need higher cohort sizes, better scanners, the next greatest imaging technique to show us something in the brain that we couldn't see before.
The other possibility is that we're approaching how we use neuroimaging to improve clinical care in the wrong way. I don't know the answer to it, but there's a couple of shifts that I've made in how I use neuroimaging and how I think about it. One big shift has been away from correlation imaging to causal mapping of human brain function. What I mean by this is that if you want to understand where a symptom lives in the human brain, neuroimagers have typically approached that by taking a bunch of patients with that symptom, and identified neuroimaging correlates of that symptom, which might be atrophy, PET metabolic patterns, resting state connectivity changes, and so on. But the problem is that in the end, that's just a correlate, not a therapeutic target. It doesn't tell you whether that neuroimaging correlate is causing the problem, compensating for the problem, or just a risk factor for the problem. We've started focusing on brain lesions and brain stimulation sites as a way to get at this causality. The idea is that the causal mapping of symptoms and brain function might be a more direct path to a treatment target.
The other big shift that I've made is a move away from focusing on single subject neuroimaging data to group neuroimaging data like the connectome. It's almost like I'm going in the opposite direction of where a lot of brilliant people are going: they're focusing on the individual and getting massive amounts of data on each single subject. That research is very valuable and might get us where we need to go with the clinical utility of single subject imaging data. In the meantime, as they improve the methods and technologies for single subject imaging, what I found is that the group connectome is already ready to be applied clinically. It's robust and reproducible and the wiring diagram is the wiring diagram of the average human brain.
NM: So we've very high hopes for your work targeting sites for stimulation to reduce symptoms in patients.
MF: Well, I don't want to overstate the success of my approach either. What we have right now is a lot of retrospective observations. So when we administer transcranial magnetic stimulation, for example, to try and reduce people's depression, what we see reproducibly is that people that are stimulated at a certain brain circuit or a certain site that's connected to a certain circuit, those are the people that are getting better. That is a reproducible, retrospective observation to explain why some people are getting better and some people are not. What we haven't done is taken the next step, where we change our clinical practice and directly target that circuit to improve clinical outcomes. We're just now reaching that precipice, the point where we're convinced that the retrospective observation is real and reproducible. But now we've got to actually prospectively apply it and find out if we can improve clinical outcomes, but we haven't done that yet.
NM: So what would you say are the most exciting things that your lab is working on now?
MF: I'd say, twofold. One is I'm very, very excited that we're reaching the point where we can take some of these retrospective observations and actually prospectively test them clinically. Now, those are bigger grants and take a lot more money. But I believe those resources are going to be coming. So I'm very excited to find out whether we can prospectively confirm our results and make treatment better.
The other is focusing on symptoms that are in huge need of better treatment. We recently submitted a paper, for example, on lesions that get rid of addiction (for a similar paper see here [NM]) and what brain circuit do those lesions map to? Does that identify a therapeutic target for addiction that can help constrain ongoing trials trying to make addiction treatment better?
In the field of depression, we've worked on brain lesions associated with depression, TMS sites that are associated with depression relief, and then some deep brain stimulation data that either can relieve depression or cause depression. What happens when you link up all three sources of causal information? Does it all converge on a single circuit target for depression across all these different modalities?
On the science side, we're even working on lesions that manipulate measures of spirituality or religiosity. Is there a human brain circuit that we can link to spirituality in a causal way? And is that a therapeutic target?
We're having a lot of fun these days, looking at very interesting questions both from the scientific side of things and social side of things, but also going towards the greatest therapeutic need. And then going towards clinical validation of all these observations that we're coming up with.
NM: Finally, what is your advice for early career researchers and those who are interested in network neuroscience? What would you say is a good training pathway for them?
MF: One piece of advice is follow your passion. If you're passionate about a particular brain problem or symptom or imaging technique, or brain circuit, follow that passion because your work is going to be better if you're following something that you're passionate about, not just what your advisor is passionate about.
Two, look at where the herd is going, and then intentionally go in a different direction. If everybody believes that the next big advancement is this imaging technique or application, then go the direction they're not going. Because there's plenty of people that are already doing what the herd is doing. That's why the herd is going there. It's an obvious need and a lot of smart people will fill that need. Go the opposite direction, find a way that people are not thinking about it. And that's where you feel like you add value to science, above and beyond what the community can generate. Think about it differently.
The last piece of advice is one I always tell my students. In my particular lab, we're focused on clinical translation and clinical application. So whenever my students come to me with brilliant ideas (and they come to me with brilliant ideas), I try and play it out. I say "Okay, let's say you're right, let's say that the experiment works out or that you're able to map it. Where does that go? What do we do with that information?" Oftentimes, you realize when you play that out is that the experiment, although it might be interesting, has no pathway towards clinical translation. There's no way that you can turn that information into a better treatment or a therapeutic target. Now, not everybody's interested in clinical translation, identifying therapeutic targets, but for my lab, thinking ahead three steps, we want to know 'Where does your research go? What do you do with that result? And how does that result translate into something important and meaningful, in my case for taking care of patients?' Again, it's a different way of approaching things then maybe in other neuroimaging labs.
NM: That's great advice. Professor Fox, thank you very much for your time today. We really look forward to your talk.
MF: Thank you so much for your interest.
By the OHBM Diversity and Inclusivity Committee (and endorsed by OHBM Council)
We share the deep sadness, outrage, and frustration that many around the world have felt in reaction to the murders of George Floyd, Breonna Taylor, and Ahmaud Arbery, and too many other innocent Black people over the years. As an international organization that strives to represent a diverse and vibrant global community of researchers studying the human brain, OHBM itself has struggled over its 25 years to incorporate initiatives and policies that reflect our values of inclusivity, tolerance, and respect.
The events of these past weeks are a grim reminder that words alone are not enough to combat the systemic racism that plagues societies across the world, and we recognize that we have not done enough to support Black, Indigenous, and People of Color. To this, we also add other groups, such as people with disabilities for which as an organization we may not have provided sufficient support. The past few days have been a period of inward contemplation for us. The events of last week enraged us, and our first urge was to publicly denounce them. Cautious voices advised us against issuing a statement that is not followed by concrete actions. They were right. Since then we, on the Diversity and Inclusivity Committee have had detailed discussions about how we can meaningfully contribute to the conversation and truly make a difference to make our organization a welcome, safe environment that educates and supports each and every member of our group.
In the spirit of openness, we share with you a non-exhaustive list of concrete actions OHBM plans to undertake over the coming months to affirm our commitment to creating and maintaining a supportive environment for all OHBM members, especially those who are historically underrepresented and marginalized. Our goal is to particularly support the Black community within OHBM, increase its representation, and address anti-Black racism. We understand that we have not done enough for this community yet, but we would like to change that in the near future.
The OHBM Council, the Diversity and Inclusivity Committee, and the Program Committee will work together with various other OHBM special interest groups and committees to implement the following:
By the OHBM Communication Committee
By now you've heard that the OHBM Annual Meeting will be virtual! The 26th Annual Meeting of the Organization for Human Brain Mapping is happening from June 23 - July 3, Saturday and Sunday excluded, and will take place entirely online.
This is new for many of us so we’ve put together a short Q&A. Here we address a number of questions you may have, and provide a taste of what you can expect from this unique OHBM Annual Meeting experience.
What can I expect from a virtual OHBM?
To start, this is not just going to be a massive Skype or Zoom meeting. After searching through many options for virtual meeting applications, OHBM council decided on a ‘real-feel’ conference provider that has previous experience with Neuroscience conferences and other very large meeting events. This conference may not feature wafts of espresso from the Rome cafes, or access to the delicious hawker markets of Singapore, but it will have almost everything else that you expect from a conference location: a lobby with signposts to navigate your way around, auditoriums for talks, a poster hall, an exhibit hall, engagement lounges for networking, the art exhibits, the open science room and even a help desk.
What about the different time zones, will I need to get up at 3am to not miss my favourite speaker?
OHBM is an international Society (as can be seen in the distribution of our members in the map below) and in looking at the Annual Meeting schedule and time zones, there are relatively few overlapping “humane” work hours in each day for all continents. To account for this, all sessions will be available 24/7 once presented and the schedule for this year’s Annual meeting has been carefully crafted by Program Committee to allow fair access to the live Q&A portions for all participants. The content has been spread out across two weeks, and the start time for sessions will vary. For three days, sessions will alternate between three major time zones as follows: 1) New York (North/South America) 2) London (Europe) and 3) Hong Kong (Asia/Australia). In addition live sessions will happen for only a few hours a day - so no need to spend long stretches glued to your computer screen! See the most up-to-date meeting schedule here.
A look on the bright side of meeting virtually:
No jet lag! With the OHBM Annual Meeting going virtual, there are no long plane rides, no cramped seating and no battles for middle arm rests. Instead of having three espresso shots and struggling to stay awake during a keynote lecture, you can attend after a good night’s sleep or even outside while getting some vitamin D and fresh air.
See everything! No choosing between talks in parallel sessions and running from the slightly overrun talk 1 in session A to talk 2 in session B. You can swap between auditoriums with just a mouse click and you will always have a front seat for each presentation!
No need to find pet sitters! Nor somebody to water your plants! And if you have older kids at home, there will be links to activities to get them engaged in neuroscience, such as printable brain hats and colouring sheets: give them insight into what it is you actually do.
A much reduced carbon footprint! As a community, we will produce less air/rail/car travel emissions from travel and also less onsite paper, plastic and food waste. And no need to argue with the airline staff about whether you can take your poster or not!
Home-made food, no queueing for the toilet and predictable Wifi connection at all times!
Still, internet connections are sometimes unstable, so what happens if the keynote speaker drops out during his or her talk?
Mindful of this, almost all sessions will be pre-recorded, but during the allocated time slots for the “live program”, the sessions will be chaired and the speakers will be available for Q&A. Pre-recording with professional audio visual support means that there will be a minimum of glitches. Plus, this means that almost everything in the meeting will be available for viewing on the Communiqué platform for four months after the meeting (using your registration) and, in time, via the OnDemand system (for OHBM members).
For a list of keynote lectures, symposia and oral sessions see the meeting website.
What I always loved most about OHBM are the poster sessions and interacting with people at their posters. How do I do this online?
One of the joys of attending OHBM meetings is pouring a coffee (or beverage of your choice) and ambling through the poster hall. So it’s a relief that there will still be poster sessions, albeit in a virtual poster hall. There are stand-by times as usual. For this you’ll have live chat functions, so you can respond to questions in real time (or leave questions outside the stand-by times for the presenter to respond to later). Even better, you can ‘stand’ in front of the same poster for as much time as you want without fear of blocking somebody else’s view. Contact options at each poster allow you to ask questions to the presenter and a virtual poster reception lets you interact with presenters and other poster hall attendees.
Some things you may want to think about when preparing your poster
The only restriction is that your poster has to be in PDF format, so be creative! But keep in mind that your audience may view it on their tablet or laptop, so make the layout easy to read. You might want to include links for more information, larger figures or your preprint paper on the work. Why not record yourself presenting the poster and add a link to the video in your poster? There are some great recording options (e.g. using Zoom); you could even ask somebody to be a pretend audience and ask tricky questions about your work!
Since this year you cannot attract people to your poster using funny dances or by handing out chocolates, advertise your poster or poster video on Twitter using #OHBM2020Posters!
A main aspect of OHBM is socialising. Are there options to do that?
Yes, it’s always Happy Hour somewhere! There will be Happy Hours/Coffee Hours taking place at various times in the schedule to accommodate many different time zones. We are currently working on solutions to make these Happy Hours as interactive as possible by having chat and video options available.
I usually don’t get enough sleep during OHBM due to the packed program and all the social networking. I don’t think I can handle being online so much!
We all know that being hunched over a desk for long periods is bad for our eyes, our backs and for our concentration. As stated above, the annual meeting has been split from four full days into eight half days, spread over two weeks. In addition, the meeting will now run only on weekdays to minimise any disruption with other activities, and all material will be available throughout the duration of the meeting (and beyond, as described above).
What about the educational courses?
The educational courses are always a particular draw, and extremely useful for both early career researchers and seasoned PIs alike. This year we have some great offerings on deep learning in neuroimaging, advanced functional and structural imaging of the cerebellum, EEG data acquisition and pre-processing, and many others (full list here). These educational sessions will now run after the annual meeting beginning July 13, when you can begin watching the pre-recorded lectures. Later that week there’ll be interaction times when you can ask questions of the educational speakers.
You can also prepare for some of these educational courses by reading through our ‘OnDemand’ tutorial series of blogposts, on resting-state fMRI, diffusion MRI, machine learning and anatomy in neuroimaging.
Will there be virtual brain art this year?
Definitely! Do not miss NeuroDiversity, the exclusively online 2020 OHBM Brain-Art Exhibit & Competition brought to you by the OHBM Brain-Art SIG.
NeuroDiversity is being developed along three axes. Axis 1 aims to give underrepresented groups in neuroscience a voice. Axis 2 will showcase art pieces by neurodiverse populations - for whom art can be a means of communication, an instrument for therapy, or a source of solace and pleasure. Axis 3 is designed to highlight the geographic, ethnic and cultural richness within the OHBM community - the Brain-Art SIG will put together a ‘brain collage’ from postcards provided by OHBM members. Check out our artworks and videos, chat with artists, and engage in our art-guided meditation session.
If you would like to showcase your art at the conference, then our annual Brain-Art competition is now open for submissions! We are accepting pieces for the following categories: 2D art (i.e., digital images such as drawings, photos, paintings); 3D art (i.e., sculptures & installations); Failed attempt/bug/artifact; and Special category on Neurodiversity & Hope. For the Special category we encourage all OHBM members to download one of the provided brain postcard outlines and fill it with a pattern/image that they like and feel represented by. Submit your art before Friday, June 20, 2020, 11:59 PM CDT.
You’ll be able to see this Brain-Art and use our online family-friend brain-art activities throughout the annual meeting. You can also engage with a train track session on brain visualization at the OHBM Hackathon. In addition, we’ll be announcing our competition winners at the virtual Student/Postdoc SIG and Neuro Bureau Networking Social. So yes, definitely lots of virtual brain art this year.
What about a virtual Open Science Room?
The Open Science Special Interest Group (OS-SIG)’s Open Science Room (OSR) will be hosted using the same interactive virtual platform as the OHBM meeting, and also broadcasted live to a zero-cost registration platform for accessible global access. The OSR will provide opportunities for networking and informal discussion alongside the formal hosting of nearly 40 talks, including keynotes, lightning talks and software demonstrations. OHBM members will also have 24h access to ‘Open Research Advisors’ in the main exhibition hall, who will be on hand to answer all your open research practice questions and signpost where necessary.
The Open Science room content will be repeated 3 times over 24 hours, at times suitable for individual members of our global community. For the first time, we are also actively engaging with the community to help us build the OSR, so we can deliver a professional and accessible program which works for everyone. Interested volunteers can still sign up to contribute here. As one of our community volunteers has said, “The OSR is the place to witness the practice of open science in action”, and we can’t wait for you to be a part of it. A call for OSR talks is also open; please consider contributing! We are also open for talk submissions (schedule space permitting) until 1st June. Please do submit your talk abstract via our website as soon as you can!
The OHBM Brainhack - the collaborative hackathon organized by the OS-SIG - will be held online from June 16th-18th. For the first time, the OHBM Brainhack will be run as a global online event organized around 3 hubs ('Africa, Middle East and Europe', 'Americas' and 'Asia and Pacific') that will foster collaborations across countries while making it possible for participants worldwide to attend during working hours. Registration is now open for an unprecedented capacity of 500 attendees. This year we are putting special care into building a welcoming environment for those who have never attended a hackathon before. We will provide educational TrainTrack sessions tailored for beginners and opportunities to directly apply new skills by joining a hackathon project.
What other events organised by the SIGs and committees can I expect?
For the family-friendly activities planned by the Diversity & Inclusivity Committee see our recent blog post. The Diversity & Inclusivity Committee is also organizing the second Diversity Roundtable on the topic of Neuroscience and the LGBTQ community. The four speakers of this year's roundtable will elaborate on challenges faced by LGBTQ scientists, and will familiarize the audience with research (and lack thereof) on LGBTQ individuals, with a focus on how increasing awareness around issues faced by this community can impact academic careers.
The Student Postdoc SIG are planning an annual symposium themed Success in Academia: A road paved with failures. There are 3 sub-themes: 1- Sharing/normalizing experiences of failure; 2- How to be a good mentor; 3- How to handle your own failures. A series of workshops are also being planned (e.g., career transition, coping with COVID-19 and trauma, life and work balance, working with industry from academia), stay tuned!
OHBM’s new open access publishing platform – Aperture – is set to launch in June! Aperture will host an informational booth during the OHBM 2020 Annual Meeting where you can learn more about the platform, the submission and review processes, and meet the Journal Manager and members of the Aperture Oversight Committee (AOC). You are invited to participate in an Aperture round table discussion that will also be offered during the Annual Meeting to get your question answered and learn more about the platform. In the meantime, if you have questions, please contact Kay Vanda, Aperture Journal Manager at email@example.com or visit the Aperture website.
Will all of this influence future meetings?
Going completely online will clearly take a little of the magic away from this year’s Annual Meeting. But the silver lining to that cloud is that this 2.0 version of the event addresses a number of recent concerns brought up by OHBM members: It makes attendance much easier for those less able to leave their home countries (for instance due to visa issues, dependents or mobility restrictions). It doesn’t require sometimes expensive travel and accommodation budgets, and reduces our carbon footprints. It also allows the use of innovative interactive elements that may not have been easy to implement at the physical conference. If you haven’t registered yet, you can do that now here.
The current situation has forced many scientific organisations to ramp up remote attendance options. In doing so, it has fostered innovative solutions that can improve online user experiences. In future years, these options will be tried and tested, making them easy to apply to supplement our physical meetings.
Overall, this year’s Annual Meeting is certainly going to be different. It will however remain consistent in that it will provide a thorough update on the latest findings, current trends and promising avenues of brain mapping research. It will provide learning opportunities for those wanting to train up in new skills. It will also provide opportunities for networking and socialising that may be sorely missing during early summer. The staff and Committees at OHBM have worked to ensure each part of the Annual Meeting is thought of and included in the virtual version.
And for those not yet convinced about whether a virtual meeting will offer the same communal or educational experience, there will be ways to increase the realism of the event in your workspace. Brew some coffee before the poster session; you don’t even need to drink it, just get the smell wafting through the house. Set your Zoom or Skype background to the streets of Montreal. Plan to “attend” with some of your friends or colleagues at the same time or the same talks/posters. You can even make your own event shirt using the OHBM 2020 logo, or design your own version! Or just show up and chat to new and old friends. We look forward to welcoming everyone to the OHBM 2020 Virtual Annual Meeting and hope to “see” you there.
Of course we all know that the brain functions as a network, but it is not straightforward to model it as such. One person who works very hard for us to be able to do so is Alex Fornito. He is a professor at Monash University and one of the leading forces in MRI-based network neuroscience. As he is also one of this year’s virtual meeting’s keynote speakers, I had the pleasure to invite Alex to a virtual meeting to ask about his scientific life.
Ilona Lipp (IL): Thanks for joining me during these crazy times. Apart from OHBM going virtual, what else has changed in your scientific life in the last few weeks?
Alex Fornito (AF): Yes, these are unusual times. Probably the biggest change in my life has been the intimate relationship that I've developed with Zoom! But, seriously, I feel fortunate that not much has actually changed for me from a professional or scientific standpoint. A lot of the work that we do in the lab focuses on data analysis and modelling, which is reasonably straightforward to do from home. We have two young kids at home and so our regular rhythm has been disrupted because we're juggling homeschooling and work. But at the same time, it's nice to spend more time with the family, and see and help the kids learn. Relative to the disruptions that other people have had to deal with, I think I have been very lucky. I guess the main challenge is really in trying to maintain a sense of connectivity, communication, and cohesion within the lab. But I'm very fortunate to work with a fantastic team of people that make that really easy.
IL: A main component of your research has been on connectomics, developing metrics to describe the whole brain as a network and applying them to psychiatric diseases, such as schizophrenia. How did you end up in this research niche?
AF: Well, that’s a bit of a long story! I did my PhD in a psychiatric lab focused on structural brain imaging where I was working on mapping cortical thickness changes in psychotic disorders. At that time, surface-based approaches were only beginning to be applied to MRI. And so this was a very exciting new way of looking at structural brain changes. But in my spare time I read a lot of fMRI work. At the time, classic voxel-wise activation mapping was the main approach that was being used. This work was really giving us a lot of insights about how brain regions respond to different tasks, but I always felt like it provided an incomplete picture, because we know that the brain is essentially an interconnected network. I was a really big fan of those early seminal papers on connectivity by people like Olaf Sporns and Rolf Kötter, Klaas Stephan, Karl Friston, and Randy McIntosh. But the applications of network methods to imaging data were limited.
Then, as I was nearing the end of my PhD, I came across a paper by Sophie Achard and Ed Bullmore, which was one of the first that generated these whole brain maps of connectivity using fMRI data. I remember a figure in that paper that had a tangled graph showing how different brain regions connected to each other, and I just thought to myself, perhaps naively at the time, “that looks more like how the brain works! I want to learn how to do that!” And so I got in contact with Ed and he was gracious enough to host me for a postdoc fellowship. It was really great timing to be there, as Ed was developing his Networks Group and I was able to work with and learn from some really great people like Dannielle Bassett, David Meunier, Manfred Kitzpichler and Aaron Alexander Bloch. That experience really ended up shaping the trajectory that I ended up taking
IL: Can you explain how looking at the brain’s connectome with MRI can help us gain a better understanding of psychiatric diseases and develop hypotheses about disease mechanisms and treatment options?
AF: I guess for me, it's a simple chain of logic. If we think about the brain as a network, then an important first step is to map and understand how different parts of the brain connect with each other. That's not to say that generating a map of connectivity on its own is sufficient; we also need to understand the dynamics that unfold on connectomes. But I do think that generating such a map is a necessary and important first step.
The first wave of connectomics studies that we've seen have been really useful for mapping where connectivity differences are between a given patient group and healthy controls. Now, in general, in psychiatry we do need to be a bit smarter about the way we define our clinical phenotypes in the first place, but we are starting to build a picture of how different brain systems are disrupted in different disorders. So now as we move into the next phase, the challenge is going to be to use this knowledge to generate new mechanistic insights and develop new treatment strategies. We're starting to see some success already, such as with the development of connectivity-guided brain stimulation protocols for mood disorders.
An advantage of the connectomic approach is that it can be coupled with biophysical models of brain dynamics, like neural mass models, which allow us to generate whole brain simulations of neural activity. This is an area that is still very much a work in progress, but, in principle, these models will allow us to test different mechanistic explanations for different disorders by tuning model parameters and seeing if the model can reproduce the activity changes seen in a given patient group. I think there's a lot of promise in this regard.
IL: You have been studying schizophrenia a lot. Why is the connectome particularly interesting in this disease?
AF: That's a good question. So the name itself–– schizophrenia––implies a splitting or a breakdown of the mind's thought processes. And so then an obvious neurobiological hypothesis would be that this disorder emerges or arises from a disruption in the way different parts of the brain communicate with each other. This is not a new idea –– It was first suggested by Carl Wernicke over 100 years ago.
Personally, I think there's a natural alignment between this idea and the phenomenology of the disorder, given that it really does seem to involve a breakdown in the brain's ability to think coherently and in an organised way. We now have the tools available to really interrogate these connectivity disruptions across the entire brain. You can see how, as these approaches have developed, the thinking in the field has changed. When I was doing my PhD, most of the literature focused on the role of individual brain regions like the dorsolateral prefrontal cortex or the striatum or hippocampus; and now we see a greater emphasis on trying to understand how all these regions interact in a connected system. The hope is that these network-based understandings provide a more accurate description of what's actually happening in the disease.
But this doesn’t just apply to schizophrenia. We know from a lot of imaging and lesion studies that there's no single causal lesion for psychotic illness, which then leads to the idea that there is something happening at the level of interconnected circuits. This tends to be a recurring theme in a lot of psychiatric disorders. We now know that most of them can't be explained by focal damage in any single part of the brain. It is possible that at least some of these disorders might have a focal onset of pathology in one part of the brain that then spreads to affect other areas over time. Other disorders might have a truly multi-focal origin. It’s also possible that many psychiatric disorders are the result of subtle neurodevelopmental changes in brain wiring. But these are still open questions.
IL: The microstructure and gene expression of cortical regions seems to play a large role in determining inter-cortical connections. Can you tell us a bit about the recently trending transcriptomic brain atlases and why and how you have been using them in your research?
AF: Well, I don't want to speak for other people, but I feel like if you spend enough time doing brain imaging, you eventually get to a point where you start to question what it is that you're actually measuring. And I mainly work with MRI, which is a fantastic tool, but it often provides indirect measures of the underlying physiological processes that we're interested in. This poses two major problems. The first is that it can be difficult to disentangle neural contributions from other contributions to the signal, including different noise sources, and that can make it difficult to interpret our findings. The second problem is that, even if we can rule out measurement noise, we often don't know what the underlying molecular mechanisms are that are driving our results. For me, the gene expression atlases provide an opportunity to try and move beyond just using the imaging data to develop some hypotheses about those underlying mechanisms.
That's not to say that the expression data are some kind of gold standard. In our lab, we've concentrated a lot on trying to understand some of the issues associated with gene expression data and developing workflows for how they can best be integrated with imaging measures. But I do think that if we put those issues aside and we do get a correlation between an imaging measure and the expression profile of a gene or a set of genes, then we can limit the range of possible explanations and identify candidate mechanisms that we can then pursue in further work. So it's really a way of moving beyond just mapping so that we can say: “Of all the possible molecular mechanisms that could explain what I see in this map, the expression data now allows me to narrow my search down to this set of mechanisms or pathways."
In our own work, we've looked at how gene expression profiles relate to brain connectivity. Other groups have done some really interesting work looking at transcriptional correlates of the effects of normal development or different types of disease. I find this work interesting because it does allow us to move past simply mapping where changes are occurring to start developing some plausible hypotheses about the specific molecules or pathways that might be involved.
IL: Recently, the usefulness of relating variants of candidate genes to brain and behavioural phenotypes in the context of psychiatric disease has been heavily questioned. Could you tell us a bit about where this debate is coming from? What do you think are the consequences and alternatives for researchers trying to understand the genetic underpinnings of individual differences in brain structure and function?
AF: More or less two decades ago, the main way to identify risk variants for disease was through linkage analysis. This required people to recruit extended pedigrees and it worked well for Mendelian traits, but a lot of psychiatric disorders are not Mendelian. So researchers started to hypothesize which specific genes might be involved based on what they knew about the physiology of those disorders. And then the idea was to identify a specific variant in that gene that was known to be functional and to examine how that variant relates to some imaging or behavioural measure.
After a little while people started to question the plausibility of that approach, for a few reasons. One is that the prior probability of correctly choosing a causal variant is quite low if you think about all the possible genes and variants that could contribute to complex phenotypes like schizophrenia. And we also know relatively little about the molecular mechanisms of what might be causing variation in these phenotypes. We then started to see a number of well powered studies fail to replicate earlier findings that had been published in smaller samples.
The solution to these and other problems in the field of genetics - because they had a false positive problem with candidate gene associations - involved shifting towards conducting large scale genome wide association studies, or GWAS, where the idea is that you compare allele frequencies between, say, a patient or control group, at hundreds of thousands or even millions of markers scattered throughout the genome. And given that you're doing so many tests and that you're often doing a Bonferroni correction over a million comparisons, you need huge sample sizes to be able to identify anything as being significant with a decent degree of power. So the sample size is generally in the order of tens of thousands of people.
We've had a wave of these studies now, and they've been really important in showing us that psychiatric disorders, and even brain imaging phenotypes, have a complex genetic basis. The effects of any individual variant, at least if we're talking about common variants in the population, are pretty small, at around 1%. The upshot of these developments is that if someone's interested in identifying genetic variants related to a phenotype, they probably need to conduct a GWAS as a first step. The ENIGMA consortium has really led the charge in this space with respect to imaging phenotypes, and I'm sure we'll start to see more of this kind of work as large open datasets like UK Biobank become increasingly available and used widely.
Personally, I view these analyses as a first step to identify which variants are related to a phenotype. But then the next step is to identify the biological effects of those variants and imaging can be helpful in addressing this goal. There are also some other really nice resources, such as data made available by the GTEx and PsychENCODE consortia, which allow people to identify which variants impact gene expression in the brain. These can be combined with data from gene expression atlases to develop a more comprehensive picture of the relationship between genes and brain. This approach aligns with the strategy we've been using in our own lab. We've tried to combine these and other sources of information to try to understand how genes influence brain connectivity.
IL: Your research combines expertise from various disciplines, including brain imaging methodology, modelling, psychiatry, genetics etc. What are the challenges when doing so and what recommendations do you have for people who want to pursue highly interdisciplinary research?
AF: It's a good question. I think the interdisciplinary nature of my research probably stems from my inability to focus on a single topic! But my personal view is that the mapping between brain and behaviour is so complex, and our measurements are so imprecise, that any single approach on its own is not going to be sufficient to really tackle interesting neuroscientific questions in a comprehensive way. So the main thing that motivates and excites me about science is the opportunity to learn new ideas and get exposed to completely different ways of thinking. And so I guess I just like to explore my interests and see where they lead me.
I feel that the main challenge is that you always feel like a novice. Each new area or new field has its own jargon and concepts and methods and conventions, and these can take time to learn. And so I guess the best advice I could give would be to learn to be comfortable with the discomfort of not being an expert and having to start from scratch. Something that can really help with that is to team up with people who are experts in the domain and to learn as much as you can from them. Try to cultivate a good working, respectful relationship and to not be afraid to ask dumb questions, which I happen to do a lot of. Especially in the beginning of a collaboration with someone from a different discipline, you might be speaking completely different languages and it can take some patience and time to navigate those differences. But personally I find that the end result is always worth it.
IL: We previously talked about how important a healthy work-life balance is to stay productive. Leading your own research group, how do you encourage the people in your team to sometimes work less?
AF: I think the most important thing that someone can do is to set aside some time each day to try and do something pleasurable that is unrelated to work. That could be playing a sport or a musical instrument or doing gardening or sketch art or stamp collecting or whatever. For me, daily exercise is really important, but for others, it could be something completely different.
The first thing I always suggest is to create that time each day. But everyone is different, and some people struggle with that. So ultimately, I'll let people decide what's going to work for them. But sometimes, when I suggest it, people think, ‘Oh, my God, I can't do that. It's impossible. How can I spare an hour a day?’. But you never realise that you're able to do it unless you actually do it. I always think of a line from the movie The Matrix, where one of the characters says ‘You cannot ever have time if you do not make time’. And I think that's very true. Once you create that headspace, it allows you to think a bit more rationally about how you're using your time effectively. And even more broadly, where you want to go with your career, what are the things you want to focus on. Creating that healthy space can help you get a bit more perspective.
IL: Somebody once told me that one should have a 10 year career plan ready. What are your plans for the next ten years?
AF: I guess it is challenging to develop a detailed 10 year plan, but I do think it is good to have long-term goals and a 10-year horizon can act as an anchor for more detailed shorter-term plans. I like to work in five-year increments.
If you press me, I'd have to say that there are two big questions that I want to focus on over the next 10 years. The first question is: why is the brain connected the way it is? We know that connectivity between brain regions is not random, so what are the underlying principles that govern how different parts of the brain connect to each other? Are these principles instantiated through genetic factors or other mechanisms? Do these principles or wiring rules have any bearing on our understanding for mental illness? So that's one area.
The other is a little more clinical and is really focused on whether we can develop an empirically-guided alternative to the DSM (Diagnostic and Statistical Manual of Psychiatric Disorders). Thinking about questions like how can we best draw the line between mental illness and health? What's the underlying latent structure of psychopathology? If we had an alternative to DSM, would it allow us to generate better insights into the biology of mental illness? These are the two big picture areas that I'm interested in, and which will frame my work over the next 10 years.
IL: Last but not least, do you want to give us a little teaser about your OHBM virtual keynote lecture?
AF: I'll be talking about work we've been doing in the lab, trying to understand brain network hubs. Hubs are highly connected parts of the brain, and it's thought that they play a really important role in promoting integrated brain function. In our lab, we've been focused on trying to understand why they get wired in the way they are, so I'll talk about work we have been doing on how to map and describe properties of brain network hubs, some of the insights that we've gained from generative models of network wiring, and what these models reveal in terms of what can and can't explain hub connectivity. I'll talk about some more recent work we've been doing focused on the genetics of hub connectivity, and I’ll present some data that suggests that there really is something quite unique and special about hubs at the level of genes. This is work that we've done across mouse, human and C. elegans, and it's trying to bring together imaging, genetics, and modelling, so hopefully there will be something in there for everyone!
IL: Thanks a lot, I am looking forward to seeing your keynote lecture!
by Athina Tzovara, Julia Kam, Valentina Borghesani, AmanPreet Badhwar
‘If you never did you should. These things are fun and fun is good’ - Dr. Seuss
Live Review for Kids
The OHBM Diversity and Inclusivity Committee is exploring an exciting and new direction this year: we will be engaging kids in the scientific review process! We asked five prominent scientists in the field of brain mapping and neuroscience to write a short article explaining their research to kids. The articles are written for the Frontiers for Young Minds (https://kids.frontiersin.org/, a journal dedicated to young readers of 8-15 years old. Once written, the articles are assigned to five young reviewers, who will work together with a neuroscientist mentor to critique the articles and prepare questions for the scientists.
During the virtual OHBM meeting, the five scientists will give a short presentation on their article. Following this, young reviewers will have 5-10 mins to grill the scientists with questions, based on the review they prepared with their mentors. Finally, the panel of young reviewers will decide whether to accept the paper or not. Thanks to the enthusiasm of our scientists, we have gathered five engaging topics: Caitlin Mills will talk about the neural correlates of boredom, Tonya White will explain why some kids are more easily frustrated than others, Fady Girgis will walk us through the use of brain surgery to treat epileptic seizures, AmanPreet Badhwar will present on biomarkers of Alzheimer’s disease, while Christoph Korn and Gabriela Rosenblau will talk about social learning in adolescence. After the virtual meeting, all accepted papers will be published online in a joined OHBM-themed release of Frontiers for Young Minds. All articles will then be combined in the form of an e-book, with the common theme of brain mapping.
Our goal with this activity is to communicate science to kids, develop their critical thinking ability, and nurture their inherent curiosity towards understanding the functions and anatomy of the human brain. Join the live review online and witness how Caitlin, Tonya, Fady, Aman, Gabriela and Christoph will handle the grilling by these critical kid reviewers!
Online family-friend brain-art activities - A collaboration with the OHBM Brain-Art SIG
At last year's meeting in Rome, a drawing corner was provided where children in attendance could freely craft. Their art pieces were progressively displayed on a panel within the BrainArt exhibit (see figures below). For this special virtual edition of OHBM, we will be providing links to online resources that kids can explore and enjoy at home such as printable activities (e.g., build your own brainhat) and coloring sheets (e.g., online coloring books).
Example of the kids drawing corner at OHBM 2019 in Rome
Kids and their caregivers can also dive into the exciting world of TEAM REMARKABLZ - an exciting world of diversity and inclusion aware science superheroes (https://www.theremarkablz.com/). Free Educational Resources include: printable colouring pages, science experiments, and topics that combine science and art (https://www.theremarkablz.com/freeresources).
Some examples of Science Superheroes waiting to welcome kids of all ages!
Images courtesy of www.theremarkablz.com
The Diversity and Inclusivity Committee is committed to supporting all families within the OHBM Community. We strive to offer activities and spaces (however virtual) where parents and kids can develop a shared passion for science: no matter their age, language, or educational system.
This year, we hope to engage them with our renewed collaboration with the Brain-Art SIG and adding the format of the live scientific review, but we wish for these initiatives to keep growing and diversifying as the Community they serve. To this end, we welcome comments and suggestions to ensure that any future OHBM annual meeting, whether online or in-person, will be a great experience for grown-ups as well as kids!
by Ekaterina Dobryakova
In preparation for this year’s Annual Meeting, we spoke to one of the keynote speakers, Dr. Claudia Buss. Claudia is an Associate Professor at the University of California, Irvine and a Professor of Medical Psychology at the Charité University Medicine in Berlin. In a virtual meeting, I sat down with Dr. Buss to discuss her captivating research in the field of developmental programming and newborn infant neuroimaging.
Ekatarina Dobryakova (ED): Dr. Buss, thank you for dedicating your time for this interview. Before we get into more specific questions, I was wondering whether you mind sharing a bit about how you came to do the work that you're doing, and what got you to follow this passion in research.
Claudia Buss (CB): Since I started studying psychology, I have always been very interested in the interface of the mind and the brain. Specifically, in how stress can affect the brain, and then, consequently, health and disease, specifically psychiatric disease. My mentor during my doctoral training was Dirk Hellhammer, who unfortunately recently passed away. He was really the one who stimulated my interest in stress biology and fetal programming of health and disease. He also taught me that if you want to understand the origins for disease susceptibility, you have to go back to the very early period of life, when an individual develops. The brain is highly plastic at that time and therefore can integrate information about the environment during development. Dirk introduced me to the concept of developmental programming.
The origins for basically all common complex disorders, including psychiatric disorders, can be traced back to very early life, when the susceptibility for these disorders is laid. I'm particularly very interested in the developing brain, because there is no other organ that develops over such a protracted period of time. More specifically, I am interested in how cues about maternal stress during and before pregnancy, and even during mothers’ own development, can affect the developing fetal brain. I investigate which biological signals provide information to the fetus about maternal stress.
ED: This is fascinating. What are the challenges of this research area given that you want to study individuals in their prenatal stage.
CB: Gaining information about the fetal origins of risk for psychiatric disorders in humans is best achieved by prospective longitudinal studies. These start during pregnancy and, ideally, follow up the offspring during critical developmental periods from fetal to infant to child to adolescence, with serial measures of brain, cognitive, and affective development. This ideally requires large study samples, which we usually don't have. However, our studies to date have provided a first proof of principle that there are associations between variation in the prenatal environment and alterations in the neonatal brain. At this point, postnatal events will have had minimal influence. Longer term changes and risks for psychiatric disorders have to be studied in large samples. Therefore, multicenter studies such as, the HEALthy Brain and Child Development Study and the Lifespan Baby Connectome Study are extremely valuable.
Further, to gain information about prenatal origins of susceptibility for psychiatric disorders, it is crucial to record many aspects of the prenatal environment and then serially assess the brain during the period of most rapid development. This is especially important during the first two years of life.
It is very important to have a neonatal baseline measure and then characterize the developmental trajectory from this point. Because we acquire MRI scans from neonates and young infants during natural sleep (so we never sedate the children), scan acquisition is extremely laborious and requires very, very committed and patient staff, as well as the cooperation of parents, because sometimes children take a long time to fall asleep and scans need to be repeated. We often discuss this amongst our collaborators, that study sites are only successful if there are people who are committed to this being done. It needs to be a priority because it's so laborious.
In terms of other challenges, the developing and immature brain is very different from an adult brain. Common data processing tools that have been developed and optimized for adult scans cannot be used. I'm very fortunate to collaborate with leading experts in the field, specifically Damien Fair from OHSU and Martin Styner from UNC, who have greatly contributed to developing methods that address these specific challenges.
Of course, another challenge common to all observational human studies is that inferences about causality cannot be drawn. This is why animal models that allow experimental manipulation is a very crucial complementation to the human observational studies on fetal programming.
ED: It’s obvious that there are a lot of layers to your research. Longitudinal studies by themselves present a lot of challenges, without adding a layer of scanning such a young pediatric population and having additional layers of technological challenges. So you already touched upon this before, but when you just started in this area of research, what was the most inspiring or motivating scientific work that sparked your interest even more aside from the inspiration you got from your mentors?
CB: The field received a lot of attention when the first papers on fetal programming of health and disease came out from David Barker’s lab. They showed that there were associations between lower birth weight and risk for cardiovascular disease in later life. Then, more and more epidemiological studies found associations between adverse birth outcomes, such as lower birth weight and shorter length of gestation, and basically all common complex disorders, including psychiatric disorders.
The idea was that it's not the low birth weight per se that's increasing the risk for later disease. Lower birth weight was found to be an indicator of an adverse prenatal environment which affects later disease susceptibility. So people started thinking: what kind of environmental conditions (i.e., nutrition, smoking, stress) during fetal life can program the organism in a way to predispose that individual for later disease? Through which pathways do these risk factors affect the developing fetus?
Another study that was really interesting to me was a study by Gilbertson et al. published in Nature Neuroscience in 2002. In a very elegant study design they found that a smaller hippocampal volume is a risk factor for developing Post Traumatic Stress Disorder (PTSD) after combat trauma exposure. Before that, it wasn't really clear whether the smaller hippocampal volume resulted from the trauma and due to the neurotoxicity of stress, or whether these patients had a smaller hippocampal volume to begin with, predisposing them to developing PTSD after trauma exposure. This study actually showed that it was smaller hippocampal volume predisposing them to PTSD. This made me want to find out what might be the origins of smaller hippocampal volumes.
I was pretty sure that genetics wouldn't explain hippocampal volumes, but that it rather would be an interaction with the environment and environmental factors, especially in early life and especially during critical periods of brain development. The Gilbertson paper made me want to study what might lead to these neuro-phenotypes that then increase vulnerability and susceptibility for psychiatric disorders.
ED: So are you seeing lower hippocampal volumes in pediatric populations, given the environmental factors during fetal development later in life.
CB: Yes, there are associations between adverse birth outcomes and smaller hippocampal volume, and lower birth weight and shorter length of gestation. Also, something we haven't published yet but we're just about to publish, is an association between maternal stress during pregnancy and smaller hippocampal volumes in newborns. Other groups have shown this as well. There's quite a bit of work now on alterations of the limbic system in association with prenatal stress.
ED: Stress is a hot topic now. When you're talking about stress, how do you define it in your research? Is it more chronic stress or a particular type of stress? One can say that even exercise is a stress to your body, for example.
CB: That's a very good question. I mainly define stress as an increase in stress-sensitive biological markers, specifically endocrine markers like cortisol or immune markers like proinflammatory cytokines, but also metabolic markers. What we know is that there are many different stressors, like stress at work, anxiety, depressive symptoms, death and sickness of someone close, lack of social support. All these factors have the potential to alter maternal biology. And the fetus needs to receive a biological cue to be able to adapt its development. The fetus doesn't care whether the mother is stressed because her boss is stressing her out, because she has conflicts with her partner, or because someone is sick. The fetus cannot interpret that; the fetus only gets biological cues through the placenta. Thus, stress of the mother has to translate into a biological signal, so that the fetus can respond to it.
Whether stress-associated biological changes occur depends on many things in the maternal constitution, such as maternal genetic makeup, social support, coping strategies, all these will determine whether the mothers’ stress that she experiences actually translates into a biological signal that the fetus can then receive. This is why I would refrain from calling certain stressors more harmful than others. There are some colleagues who think there's some evidence for that, but I don't think there is strong evidence. What is pretty clear is that acute stress and alterations in biological mediators of stress are very unlikely to alter fetal development. So it would have to be chronic stress exposure and chronic elevations of these stress mediators. I would even go as far as saying that maternal acute stress, from time to time, is good because the fetus gets exposed to certain variation in stress-sensitive biological mediators, which prepares him for extrauterine life.
Even when talking about the long-term neurodevelopmental consequences of chronic stress, you may view these as harmful because they increase risk for mental health disorders but you could also look at them from an evolutionary perspective and consider them adaptive because the changes may increase chances of survival in a more stressful environment (for example altered neural circuitry that supports high vigilance may on the one hand increase risk for anxiety disorders but may also serve an important purpose in a dangerous environment).
ED: Is there any research where you follow the kids who were exposed to chronic stress and how they fare later in life, even when they do develop psychiatric disorders?
CB: The studies that we have done mainly characterized newborn neuro-phenotypes based on MRI studies in association with different types of stressors. We have looked at elevated cortisol concentrations, and then also inflammation during pregnancy. There, we do see associations with newborn brain anatomy and also connectivity, and these neuro-phenotypes predict behavior at the age of two years. We have followed up these kids again at five years. But it's a rather small sample size; our sample size wasn't that big to begin with. We started out with about 120 mother-child pairs, where we had complete data during pregnancy of three time points during pregnancy and then the newborns. So by the age of five, the sample size was quite small: around 70. But in early childhood we also see associations between chronic systemic maternal inflammation during pregnancy and neuro-cognitive function, for example. We are now participating in the ECHO initiative, which may better allow answering questions about prenatal origins of psychiatric disorders because ECHO integrates many prospective longitudinal US studies into a common research protocol to answer questions related to developmental programming of health and disease.
ED: This is so interesting, and again, shows how multilayered your research is. What would you say are the most pressing methodological issues in your field of research?
CB: Improving processing pipelines for neonatal MRI data is a pressing issue. As I said, my collaborators are at the forefront of working on this and great advances have already been made in the context of the developing Human Connectome Project and the Baby Connectome Project. When I started this work 10 years ago there were very few groups who actually did newborn infant neuroimaging. Now, more and more people have become interested in this field and there are big consortia focusing on MRI-based characterization of early brain development. So a lot of progress has been made compared to 10 years ago. But I think there's still a lot of work ahead of us to be at a comparable state as we are for adult image processing.
Then, I think there is still room for optimizing acquisition protocols based on recent experiences. Weighing resolution and signal-to-noise ratio to scan time is important, as we always have to be very cognizant that, usually, newborns sleep pretty well for about 40 minutes and then after that, they start waking up. My experience, if you can keep the protocols below 40 or 45 minutes, you're good. Thus, my recommendation is to stay below 45 minutes and if you want to scan different modalities, you really have to weigh what is important to you.
I think what is also a pressing issue is to harmonize measures of the prenatal environment because, as you are pointing out, what people mean when they refer to stress and the way they define stress may differ a lot. Further, evaluating the quality of biological assays is very important. I'm not sure that this receives enough attention. I think there is this notion that biological measurements are more objective and valid than psychological self-report measures. However, there are problems and inaccuracies associated with biological assays as well, which deserve attention to obtain reliable and comparable study results. Right now, there is a lot of heterogeneity in the way prenatal stress is being defined in studies studying its neurodevelopmental consequences and it's therefore hard to say whether there is one study that specifically replicates what another study has found, as there are all these nuances between how the prenatal environment was characterized and then what aspects of the offspring brain was looked at. That really complicates the picture and I think there we can definitely improve.
ED: Absolutely. Hopefully such initiatives like OHBM’s Replication Award will start the wave of researchers trying to replicate previous findings and remove the barriers for replication studies. Another topic I would like to talk about is mentorship. What do you think are the most important things to do as a mentor?
CB: I feel that continuous communication and support for my students and regular meetings are crucial. Only if you're constantly communicating, you can monitor progress and detect barriers to progress. I also think it's crucial to foster intrinsic motivation for students’ work because I feel it is important to burn for what you are doing and what you're researching. Only then can you conduct these laborious studies and stay on top of things and stay motivated. I think one way of keeping students motivated is a very early introduction to international experts in the field, having research exchanges, visiting international laboratories and conferences, and being able to present one’s own work to peers.
What I also learned from my mentor, Pathik Wadhwa at UCI, is that having a well-grounded conceptual framework in the context of which research questions are being developed is very crucial. This is why I like to begin the training of my students by developing concept and perspectives papers in the respective fields that they are working on. I think that helps them get established in the field and provides a good basis and conceptual framework for doing their own empirical studies and developing their own research questions.
ED: Indeed, intrinsic motivation is very important. To round out our conversation, I was wondering whether you have thoughts about the OHBM conference going virtual in 2020. What are the silver linings given the current situation that hopefully will improve?
CB: Well, with the OHBM conference going virtual, potentially even more people can attend the conference because it will be more accessible, it doesn’t include traveling, which is good from an environmental perspective and also saves time. I think it could also be an advantage to take the time and listen in more detail to presentations that you're really interested in, and being able to go back and listen to them again. What we will be missing, of course, is the Q&A after the talks and interacting with peers during coffee breaks that's also really important at conferences. But I would assume that if there are specific questions, the speakers can be contacted, so I don't see too much of a downside. We just have to make the best of the situation that we're in. And I think having a virtual meeting is definitely better than not having a meeting and not hearing about the advancements in our field.
ED: Absolutely. The conference should still be very interesting and engaging. Thank you so much for your time.
Authors: Claude Bajada, Nils Muhlert, Ilona Lipp
Infographic: Roselyne Chauvin
Expert editors: Alfred Anwander, Jurgen Gatt
Newbie editor: Caroline Jantzen
Neuroanatomy is one of the most exciting topics in neuroscience! Some readers may disagree, but for now, humor us and read along. With the help of this On-Demand post, we will convince you not only that anatomy is a useful endeavour but that it is one where much beauty is found.
Our journey starts with the fundamental notion that the structure and the function of objects are tightly coupled; sometimes in ways that are not obvious. Understanding the complexity of the brain’s structure, hopefully, allows researchers to build more accurate models of brain function.
Neuroscience has however become such a transdisciplinary subject that it is not unexpected to meet a top scientist who has never seen a cadaveric brain. Indeed, while most neuroscientists have acquired a basic understanding of brain anatomy, learning about the main gyri, sulci and nuclei, few remember the function or location of the mysterious substantia innominata or of the periaqueductal grey.
If you are one of such scientists, fear not, you are not alone. Neuroanatomy is a vast and somewhat arcane subject, steeped in history. As such, for the sake of our sanity and yours, in this singular blogpost we decided to restrict our dealings to the topics that are most commonly tackled within the neuroimaging community. This is an overview of the major landmarks and structures that one can expect to see on an MRI scan and the ongoing conundrum of how to subdivide the brain into useful (sub)regions for further analysis - parcellation (See OHBM How-To Machine Learning on performing data-driven parcellation of MRI data).
Why is understanding anatomy important?
Getting from MRI DICOM files to a statistical map of significant clusters that shows differences between two experimental groups requires no anatomical knowledge whatsoever. But coming up with sensible brain based hypotheses, interpreting findings and communicating them to your fellow colleagues relies on a common language in the field: anatomy.
In his video, David Van Essen (from min. 0:50) details the basic features of human brains, such that we have two hemispheres of about 1000 cm2 volume and a 2-4mm thick cortex that is highly convoluted. While providing an overview of the developmental mechanisms leading to the adult brain as we know it, he points out the huge individual variability in brain anatomy that requires us to apply flexible approaches to neuroimaging studies. These approaches include advanced image registration and the use of atlases, but also functional localisers, individual tractography, individual parcellations and last, but not least, knowing our anatomy.
A knowledge of anatomy can make your life as a neuroscientist a lot easier. For example, it can aid placement of volumes of interest in MR spectroscopy, it can allow you to evaluate the output of automated segmentation pipelines, and, of course, help you quickly identify patterns of activity before receiving confirmation from atlases. Interested in doing some fancy high field, layer-specific, fMRI (see Noam Harel’s video)? Anatomical knowledge is vital! Also, recent evidence suggests that it can help increase the reproducibility of findings between labs, by improving accuracy and reducing noise when carrying out tractography in diffusion MRI. Understanding anatomy has clear implications for neuroimagers!
Nomenclature, Etymology and Orientation in the Brain
One of the first battlegrounds for the new student of anatomy is understanding the cryptic vocabulary of experts. Throughout the videos presented in the OHBM on-demand anatomy courses, various experts refer to different parts of the brain on the assumption that we all share a similar understanding of the language. To bring everyone to the same page, we will review the major terms that are crucial to understand before diving into any neuroimaging study.
Anatomy uses many words borrowed, butchered, and stolen from Latin and Greek. Cerebrum is Latin for “that which is carried toward the head.” Cephalon is ancient Greek for head. Encephalon is the substance that is found inside the head, the cauliflower-resembling organ we now know as the brain. Any word encountered that has elements of these words refers to the brain. For example, the word cerebellum is the diminutive of cerebrum. The diencephalon (across the brain) is made up of the thalami (chambers), hypothalamus (below the thalamus) and epithalamus (above the thalamus). Knowing the etymology of the words makes remembering these ludicrously named structures easier. Readers are referred to this amusing article for more.
Orientation is also often done in Latin. In their talks, Svenja and Julian Caspers regularly use two different ways of describing orientation in the brain. The terms superior, anterior, inferior and posterior (SAIP) versus the terms dorsal, ventral, rostral, and caudal (DVRC). While elite neuroanatomists would have no difficulty using these terms, for the neophyte they can be confusing. They actually refer to two completely independent coordinate systems. The SAIP approach is a real world orientation system. The terms themselves are intuitive and most people need little more explanation than the terms themselves. DVRC is another story! This is an orientation system that depends on the organism itself, the terms relate to parts of the body, once again, in Latin. Dorsal means towards the back, ventral is towards the belly, rostral is towards the beak (or nose), while caudal is toward the tail. Our bipedal nature makes this orientation system unintuitive - at the level of the spine, brainstem and cerebellum, rostral means towards the top of our body (upwards towards the nose), but in the cerebrum, rostral means towards the front of our body (forwards towards the nose). If it is still murky in your heads we would advise dipping into an introductory neuroanatomy textbook for some pretty pictures of the two orientation systems.
When you hear the term medial, this means “towards the middle of the brain”, whilst lateral indicates “towards the sides”.
The frames of reference in neuroanatomy change for the cerebrum compared to the cerebellum, brainstem and spinal cord. For quadrupeds, like sheep, no such change is seen. Walking on two feet is great for reaching things, but not so great for keeping neuroanatomy simple!
Another important set of terms relate to the way one can slice a brain. This is generally what we do when viewing MR images. Axial, or horizontal, slices allow you to scroll through the brain from top to bottom through the axis of the brain; sagittal slices from left to right derived from the latin word for arrow (think of the way an archer holds their bow and arrow). Finally, coronal sections provide a view of the brain as if it were cut through with a burning hairband shaped crown from ear to ear, slices moving anterior to posterior.
(Yawn) Thank you for that primer, but when I look at an MRI scan, I still feel completely lost!! How do I get my head around it?
MRI scans are tricky, they are often viewed as two dimensional slices and depending on the cut, and on the individual, it can still be very difficult to orient yourself, especially when the person was not lying straight in the scanner. As Svenja states in her video (min. 1:20), there are some general organising gross anatomical features that remain relatively consistent across individuals. These are called landmarks.
So, what are the major landmarks in the brain?
Some aspects of anatomy are pretty clear. Every healthy human brain has a cerebellum, brainstem and cerebrum... and a substantia innominata, of course. It has ventricles filled with corticospinal fluid, white matter that looks homogeneous on a conventional MRI scan, easily spottable subcortical gray matter nuclei, and cortical gray matter. Looking at a whole human brain from the outside shows a cortical folding pattern with specific sulcal and gyral structures, which have been labelled and can be used to orient yourself around the occipital, temporal, parietal and frontal lobes. In his video, Julian explains the main sulcal and gyral landmarks and how to find them in structural MR images. For example, if you spot an ‘M’ and ‘U’ in a lateral sagittal section, you have most likely found the central sulcus and precentral sulcus (from min. 11:36). An omega sign in a superior axial section indicates that you have found the motor cortex, while the cingulate sulcus appears as a bracket sign (from min. 14:40). The Figure below summarizes Julian’s guide to landmark spotting strategies.
How to find the major landmarks in the brain on MRI scans (compilation of Julian Casper’s slides)
These landmarks are definitely useful, but they seem quite vague for reporting the spatial locations of my findings!
A wise anatomy professor once complemented one of the authors of this piece (CJB) by stating that he was the owner of a “gelatinous mass of a brain.” Despite the gyral formations, the brain does indeed look like one amorphous clump of jelly.
Notwithstanding the repeated news headlines claiming that Neuroscientists have found the region of the brain responsible for X, it is notoriously difficult to consistently define brain regions across different individuals. While the macrostructure of the brain, such as the main sulcal and gyral pattern, is useful to orient yourself on a whole brain or MRI scan, its macrostructure does not necessarily relate very well to the underlying brain function, which might be more closely related to the neuronal microstructure of a cortical brain area. To decide how to define brain areas based on cell anatomy, we first need to think about what neural features to use in order to separate them (also see this paper for cortical parcellations).
Academic Journal Articles often refer to specific brain regions such as Brodmann area 17. Is this parcellation based on neural features?
Yes! During the late 19th and early 20th century, anatomists started discovering that, while the cortex looks fairly homogeneous to the naked eye, it consists of various layers that differ in their cell type, cell composition, and function. Anatomists such as Cecile and Oskar Vogt, Constantin von Economo, and Korbinian Brodmann spent their time observing microscopic features of the cerebral cortex and classifying it according to similarity. These areas have become known as parcels. Undoubtedly the most famous parcellation scheme is Korbinian Brodmann’s 1909 atlas.
In her video, Nicola Palomero-Gallagher shows some of the main historical cortical parcellations. She points out that the parcellation you get depends largely on how boundaries are defined. She then explains how more quantitative and objective approaches are used for not only finding parcellations in individual post-mortem brains, but also how this can be taken further into generating population maps that reflect individual variability in the boundaries of the areas. Cytoarchitecture is not the only feature that was used to parcellate the brain. At the same time that Brodmann was using cytoarchitecture, the Vogts tended to use myeloarchitecture to define regions. In his video, Matt Glasser explains how myelin-sensitive MRI contrasts can be used to study cortical myeloarchitecture and how that helps align cortical surfaces across individuals. But there is no reason to simply stop there, why not use the distribution of receptors for neurotransmitters to delineate brain areas?
Nicola explaining the cytoarchitectonic profiles of the primary and secondary visual cortices.
In fact, while the cyto- and myeloarchitecture of the cortex tells us something about the type of processing happening in a cortical region, neuroreceptor density can also tell us quite a bit about different neural functions and how they become impaired in disease states. However, as Karl Zilles explains in his video, while cortical regions differ in their receptor fingerprints, there does not seem to be a clear relationship between the parcellations based on cyto- and myeloarchitectonics and those based on neurotransmitter receptor maps.
Karl showing neurotransmitter receptor profiles of different cortical regions
How can I link my imaging research to the histological parcellation of the brain?
Functionally, the cortex is often divided based on the order of information processing: primary sensory areas are the first ones to receive sensory information, secondary areas do further processing, and association areas integrate information from different sensory modalities. Some functional units clearly match the microstructural organisation of the brain. For this reason, Brodmann’s atlas is often used to report the location of activation in functional imaging studies.
Particularly high correspondence between function and microstructure has been reported for primary areas. In her video, Katrin Amunts explains how to identify the primary areas cytoarchitectonically, including the primary motor cortex (from min. 3:00), the primary auditory cortex (from min. 8:20), the primary visual cortex (from min. 9:20), and Broca’s area (from min. 12:37). However, how well can these cytoarchitectonically distinct areas be localised based on the anatomical landmarks visible on conventional MRI scans differs. In her video, Nicola Palomero-Gallagher provides examples for brain regions where the gyrification patterns nicely coincide with the microstructure and where it does not. Practical examples of how to make use of the correspondence in the visual system are given by Kalanit Grill-Spector in her video. She explains the anatomical localisation and microstructural features of place-selective regions within the so-called collateral sulcus (from min. 5:30) and face-selective regions in the so-called mid-fusiform sulcus (from min. 09:53). (For a good overview of the visual system also see Rainer Goebel’s video (from min. 3:12)). Primary areas are also characterized by specific mesoscopic organisation called topography (explained in Daniel Margulies’s video from min. 1:10). How high resolution fMRI can be used to study such organisation is explained by Rainer with examples of retinotopic mapping (min. 6:30).
In order to be able to spatially localize results from your imaging study to parcellations based on the underlying cortical microstructural profile, findings from detailed postmortem characterisation have to be somehow transformed into usable atlases for in vivo imaging. In his video, Simon Eickhoff explains how probabilistic cytoarchitectonic mapping based on large-scale histology can aid with the spatial identification of MRI findings (from min. 7:28). He also goes into details on how to practically go about the question “Where is my blob?” (from min. 14:00).
But how many brain regions are there now and how should I define them?
How many regions there are depends on how you parcellate the brain (indeed experts often can’t agree on how many lobes there are! To limbic, or not to limbic?). To aid the localisation of findings and the definitions of regions of interest, brain atlases have been created. These atlases represent parcellations of a representative template brain, made to help you define your regions. When using these atlases (described in more detail here), we need to understand where they come from and what their limitations are, to decide which is the best atlas for our purpose.
The different available atlases are based on various features of the brain (e.g. see Paula Croxson’s video on parcellation based on histological and microstructural features or Danilo Bzdok’s video on functional parcellations), and as you will find out there is no simple way of defining ‘brain regions’. There is also no reason to restrict oneself to a single feature of interest. Multimodal parcellations are becoming more popular! In her video, Paula Croxson explains that a robust parcellation of the brain has various advantages, such as help with localisation of function and also for understanding individual variation.
In some contexts, parcellations into individual brain regions may not even be the way to go. For example, higher cognitive functions rely on large-scale networks and a complicated interplay of different regions. Functional connectivity is often done to tap into these networks (also see On Demand post about that). In his talk, Daniel explains how some local changes in functional connectivity even correspond to cytoarchitectonic boundaries (from min. 7:14).
Finally, of course, there is no cortical hegemony in the brain, even though reading the neuroimaging literature seems to imply it. Hence, all the concepts and approaches discussed for cortical parcellations also apply for subcortical parcellation.
We have discussed the cortex and the grey matter, can you tell me something interesting about white matter anatomy?
Of course! Like everything else in anatomy, to speak about white matter deserves a little bit of time travel to the nineteenth century. This is the era where all (or most) of the “great tracts” were first described. We say described rather than discovered because there is nothing intrinsic to a tract that requires it to be so!
This century (and parts of the previous) is the home to German and French giants such as Johann Christian Reil (did you know that the insula is sometimes referred to as the “Island of Reil?”, which is the word for island in Latin), who first described the arcuate and uncinate fasciculus, Karl Friedrich Burdach who identified the inferior longitudinal fasciculus, as well as Joseph and Augusta Dejerine, and Heinrich Sachs who all made contributions to confusing and contorted the white matter lexicon that we all currently know and love. A good resource on white matter tracts is this paper.
In his video, Marco Catani gives an excellent introduction to the different types of white matter tracts that you may encounter during your research (from min. 1:30). He explains that ascending and descending projection fibres connect subcortical with cortical regions, that commissural fibres connect left and right hemisphere, and that association fibres serve feedforward and feedback connections. He also goes into detail about how to evaluate the anatomical plausibility of diffusion MRI tractography, which is currently the only approach that we have to investigate white matter non-invasively (See OHBM How-To Diffusion MRI). If you are specifically interested in the white matter tracts of the occipital lobe, Svenja’s talk guides you through this area of the brain. She goes into details on projection fibres (from min. 1:30), such as the optic radiation, association fibres (from min. 9:27), such as the inferior longitudinal fasciculus, the inferior fronto-occipital fasciculus and the superior longitudinal fasciculus, intralobar association fibres (from min. 16:15) and transcallosal fibres (from min. 21:00).
Can we assign functions to white matter?
No and yes. Tracts are not really thought of primarily as processing regions, therefore the naive labeling of tracts with specific functions may be misguided. However, knowing which tracts tend to be associated with certain functions (particularly their disruption secondary to damage - a disconnection) is crucial for any neurosurgeon’s work. In his video, Hugues Duffau explains this beautifully. He describes the fascinating procedure behind intraoperative direct electrical stimulation of white matter (from min. 3:00) and the way that this is used in neurosurgical mapping. Also check out this paper on disconnections and dysfunctions.
Where is anatomy research heading with MRI developments?
While anatomy seems like an old thing, there is still a lot we haven’t agreed on. With developments in high resolution imaging (described in Noam’s video), we have new ways of understanding how the brain is structured. Noam gives an overview of how moving to high field strength allows to obtain images with higher resolution and more sensitive studying of functional anatomy. Focussing on the visual system, Rainer gives examples for specific developments in ultra-high-resolution functional imaging that allow to study the meso-scopic functional organisation of neurons in vivo.
Rainer Goebel on the latest developments and aims of high resolution MRI.
While many functional imaging findings have been superseded as the resolution and complexity of processing improve over the decades, neuroanatomical findings tend to be less dependent on the specific technique. As Marcel Mesulam said in an interview with the OHBM Blog: “The beauty about neuroanatomy is that it changes over millions of years. So once you discover something, it’s true for a few million years. And I have made some discoveries in neuroanatomy that were published maybe 30 to 35 years ago and are as true today as they were then.”
David van Essen, Alumni Professor of Neuroscience at Washington University St Louis School of Medicine, has been a pivotal figure in non-human and human neuroimaging. David is the principal investigator on the Human Connectome Project, and has made substantial contributions to brain parcellation methods, functional neuroimaging, and data sharing initiatives. Here, we find out about his early work in cortical cartography and his early experiences with OHBM.
Nils Muhlert (NM): I'm joined here today with Professor David van Essen for the OHBM oral history initiative, celebrating 25 years of OHBM. Thank you, David, for joining us. First, can you tell us about your background and how you became interested in neuroimaging?
David van Essen (DvE): I started as a vision neuroscientist. I got interested in the visual cortex because I was working with professors Hubel and Wiesel, who received the Nobel Prize for their studies on vision. That hooked me on that general area for many years.
When I did postdoctoral work in University College London, I started working on the visual cortex of macaque monkeys. That got me into making maps of the cerebral cortical surface. It was an important transition because the tradition of those early days was to draw results on slices of the brain. But I realized that the same way that Earth maps help us navigate the earth, flat maps of a monkey brain would help us navigate that terrain. So that became a driving theme. I became a cortical cartographer, as it were.
I continued as a cortical cartographer for many years while on the faculty at Caltech. I realized that the maps I was making of monkey brains could be extended beyond the visual cortex to the entire cerebral cortex. In principle, they could be extended to the human cerebral cortex. I wanted to get my feet wet in human neuroimaging, but Caltech at that time had no opportunities.
I moved to Washington University in St. Louis, and connected with Mark Raichle, Steve Peterson, and other colleagues there. That got me rooted in that community and I started not doing neuroimaging myself, but analyzing results that others had generated in the context of cortical maps that I was familiar with. A very important event right after I arrived at Wash U was meeting Peter Fox, who had been a collaborator and colleague of Raichle. He had moved to San Antonio and started organizing the winter Brain Map events. We had conversations about having a different annual meeting that would be open rather than closed in invitation.
It was one of those meetings in December of 1994 that the commitment was made. Bernard Mazoyer stuck his neck out to commit to organizing the meeting in Paris. At that time, I was still working mainly on monkeys, not doing neuroimaging myself, not sure whether I would attend. Fortunately, I was invited to give a lecture. Per Roland came up to me at the Paris meeting and said: "We need some animal models. The title is human brain mapping but it should be broader than that." I was more than happy to join in.
In Paris, I participated in the excitement of the very first HBM meeting, where no one knew what to expect. It turned out to be spectacularly successful - even though the one vivid minor memory I have is that the poster boards were extremely hard to attach pins to. I had a tremendous struggle just doing that! Later, at the town hall meeting, there was a general enthusiasm for making this an annual event. But also a gradual realization that to make that happen, somebody's got to get their act together. It turned out that Peter Fox's San Antonio meeting was a focal point for a number of us, all volunteering to spontaneously, in an ad hoc way, become a working group or committee that would draft a plan.
Alan Evans, Peter Fox, Steve Peterson, and a number of others put our heads together and drafted a plan for what would be discussed at the Boston meeting. We set out the boundary conditions, what it would be called and how it would operate. That led to the vigorous, now entertaining, but hotly debated at the time, discussion of whether this should be the Organization for Human Brain Mapping or what became known as the SHABOOM, the Society for Human Brain Mapping. It's been noted that, in retrospect, that probably wasn't as important an issue as it seemed at the time. In any event, it helped frame the project. It helped constructively engage the community to think hard about what it was they wanted, and to listen to the discussion and debate that was held in Boston.
The meeting in Boston is where Alan Evans pulled out his helmet for mock protection against the angry mob. That took the heat out of the situation and the net result was a resounding enthusiasm for establishing an entity and, on balance, an organization. Another part of that discussion was setting out the mechanics for a more stable entity. At this year’s annual meeting David Kennedy brought out several pages of paper that had been passed around for people to vote on: what they wanted, who the board of directors would be - and who would later become the OHBM Council. I was fortunate enough to be one of those who were voted in. Then, lo and behold, I was asked to become chair of that committee. During the following year, we set up proper bylaws, not just name who's running the show, but the process, who would be the officers, etc.
These standard operating procedures are not too complicated but important to get right. That was put forward to the membership at the Copenhagen meeting and resoundingly approved. As they say, the rest is history. Once the Copenhagen meeting occurred, all the ducks were lined up in a row to make the organization run smoothly and evolve. It's been great fun to see that evolution, both in terms of growth of the community, growth of the field and transitions. The organization certainly now feels more like a society.
NM: And from those early beginnings, what do you see happening with neuroimaging now, particularly in the US?
DvE: It's fascinating to see the explosion, not just in terms of the number of investigators, but in the richness of the portfolio of tools and approaches that start at the front end with data acquisition. That's still a hot issue, because we need to take out as much high quality data as possible from MRI and other methods to give us the best shot at exploring how the brain works and functions.
But it certainly doesn't stop there. The way I frame it is the field as a whole needs to move towards a coordinated effort. Project by project, whether small or large, to get the best possible data to preprocess it as carefully as possible, to analyze it as thoroughly as possible, to interpret it as rigorously as possible, including being aware of over-interpretation or false positives and false negatives. These are hot button issues to this day. Another important part of it is the neuroinformatics side, the data storage and management, and data sharing.
Data sharing has been one of my passions for more than two decades now. In the early days, Peter Fox was another of those who were pounding the drum for data sharing. But there was, to some degree, a sense that we were voices in the wilderness, because there wasn't a whole lot of attraction from the majority of neuroimaging investigators who felt more like hoarding the data that is being mined. And that's now changed dramatically and that's wonderful to see.
NM: And so you've touched on some of the areas that you've been involved in, but what contributions would you say you're most proud of in your career?
DvE: I'll admit to being proud of a number of things, and part of it is just being a cortical cartographer broadly read who's interested in structure, function, development and evolution of the brain and particularly the cerebral cortex. As one example, cortical development is not what I spent most of my working hours on, but I developed a theory of tension based morphogenesis that can account for how the cortex gets its folds. I first presented that at OHBM and got very warm, enthusiastic reception and that helped me appreciate the opportunity to bring that to an audience and get some focus on issues of development. In another sense, the opportunity to work on a very broad framework came up in a very significant way for me when the Human Connectome Project came on the radar screen. I led a consortium along with me a little gerbil to drive that effort. And that has been very rewarding to carry out the process and very gratifying to see. Even today, many talks and many posters are taking advantage of the Human Connectome Project with freely shared data sets and use it for analyses of structure, function, and even development and, obviously, connectivity as well.
NM: And, and so, going back to which OHBM, what memories would you say particularly stand out from the annual meetings that you've attended?
DvE: The early days do stand out: the Paris meeting, the Boston meeting. The excitement of being part of an enterprise that was just getting its feet on the ground. And then I remember the main meetings in Sendai, in Beijing, and Vancouver. Just a wonderful collection.
NM: And so walking around the auditorium here in Rome, how do you find that it's changed in terms of the makeup of people or the types of events that are on offer?
DvE: It's great to see the diversity in terms of age and international composition, although that has been a strength of the OHBM from the early days. I don't know that it's changed so much but the fact that it's very broad. It's great seeing lots of women present and on the stage, front and center; that's been a definite notable feature, particularly at this meeting.
One of the things I attend to when I go around posters is how many capitalize on the use of surface based representations, going back to what I was mentioning earlier. As a cortical cartographer, the best way to make maps from my view, is to make surface models rather than volumetric slices. And it was, frankly, painful in the early years to again be feeling somewhat of a voice in the Wilderness in those early days and only a smattering of posters would make use of surface models. Today, almost half of the posters in some sections are doing that. To me, that's an extremely important transition to capitalize on making best use of the data in neuroimaging studies.
NM: Related to that, how was your kind of anatomical training, were you involved in teaching neuroanatomy in the early days? Or has this been something that's developed over the years?
DvE: When I came to Wash U, I agreed to be a course master for the first year medical neuroscience course. So I've taught hardcore neuroanatomy medical students style for well over two decades and learned a lot in the process myself. Fundamentally, even before that, I was rooted in the tradition that anatomy and function are intimately interlinked and you can't make sense of one without the other. Thus, I have a very strong anatomical grounding,even when I'm thinking about other aspects of functional organization.
NM: Great! And what do you see as the future of neuroimaging?
DvE: I think it is generally fairly bright. The challenges remain daunting. I think the optimism of the early years that we're on the verge of identifying biomarkers for brain disorders has been sobering to realize how hard a problem that is. That's for reasons that we're now getting a better appreciation for why it's so hard. I think we have to take the long view and realize that the past two major successes are in better understanding and diagnosing brain disorders. But to me, we're still in the infancy stage because we desperately need progress on that front. To achieve that progress, I think the best way to make headway on major scientific achievements is to do the best possible analysis, collect the best possible data, analyze it in the most thorough way possible, and then freely and openly share that data so that the community can dive in and help further analyze and interpret it. If that mindset continues to thrive in the OHBM environment, I think it will continue to do a major service to the field.
NM: Professor van Essen, thank you very much for joining us.
DvE: My pleasure.
Aperture, OHBM’s exciting new open-access publishing platform, is on track for a June launch, just in time for the 2020 Annual Meeting. The Aperture Oversight Committee, consisting of Jean-Baptiste Poline, Peter Bandettini, Michael Breakspear, Nikola Stikov, David Kennedy, and Jessica Turner, has been hard at work finalizing a plan of operations that will help guide all of Aperture’s submission, editorial, and review processes.
Aperture was created by and for members of the OHBM community to share and promote their research. Aperture is unique in that it offers a platform for publishing traditional papers, code, data, and data papers.
The platform will operate as an open-access journal with a double-open Peer Review system. However, Peer Reviewers will have the option to opt-out of having their identity known to the author at any stage in the review process. Research will be published under a Creative Commons Attribution License. There are no submission fees, but authors will be asked to pay an Author Processing Charge upon acceptance for publication. We’ve worked to make our fees very competitive, and the fee is reduced for OHBM members.
Recently, the platform put out a call for an Editor-in-Chief. The selected candidate will serve as Aperture’s first Editor-in-Chief and will play a significant role in shaping the platform’s editorial and publishing policies. Any qualified individual is welcome to apply. If you know someone who would be a good fit, please help Aperture get the word out. For more information on the application requirements and to apply, please visit the online submission form. The closing date for applications is Friday, May 1st.
Opening up nominations for an Editor-in-Chief isn’t the only milestone Aperture has reached. We’ve also begun site testing with the platform developers.
With all the progress made, Aperture’s development is well underway, and we hope to be ready for submissions in June for the virtual OHBM annual meeting. Stay tuned for information on Aperture’s submission guidelines and launch.
If you have any questions about the platform or Aperture’s progress, please direct them to the Journal Manager, Kay Vanda, at firstname.lastname@example.org.
By Lisa Nickerson
The current Chair of the OHBM is Jia-Hong Gao, who brings a fresh perspective being the first Chair elected from Asia. Jia-Hong received his Ph.D. from Yale, followed by post-doctoral work at MIT and faculty positions at San Antonio and Chicago. Since 2013, Jia-Hong has been in Beijing as the Director of the Beijing City Key Lab for Medical Physics and Engineering and a Principal Investigator at the McGovern Institute for Brain Research at Peking University. We interviewed him about his experiences as Chair of OHBM, what excites him most about neuroimaging, and the rapid expansion of neuroimaging research in China.
Lisa Nickerson (LN): What has been your experience as Chair of OHBM?
Jia-Hong Gao (JG): The OHBM is like a family for people interested in the human brain, and provides an open and lively forum where various ideas can converge, collide and create. Since my election, I have felt more connected to every corner of the community than ever before. I’ve had the opportunity to directly interact with researchers to understand their opinions, perspectives, suggestions and critiques across multiple disciplines and across different countries. Previously, I was a researcher connected to the OHBM, but now I have been given a panoramic view that shows how amazing the bonds established by OHBM are in linking scientists in the brain mapping community. More importantly, I feel obliged to protect and promote this tight-knit community, which has tremendous potential to impact academia, industry and society.
LN: What do you hope to accomplish as Chair of OHBM and in what areas do you see opportunities for growth and impact for the OHBM?
JG: Generally, I hope the OHBM will evolve as a multidisciplinary interface where people from all backgrounds can communicate efficiently and sufficiently. Currently, human beings in modern society are facing many challenges and problems. With decades of development, the science of and imaging technology for studying the human brain have evolved to a stage where we need to collaborate with many other fields to gain fresh perspectives, such as artificial intelligence, genetic biology, environmental sciences, engineering, social sciences, and so on. In addition, different parts of the world are confronting different demands and problems, from which novel perspectives and values will arise. If the OHBM plays a central role in facilitating effective collaboration and dialog between different regions and disciplines, more researchers and professionals will work together to help discover solutions to these issues. This new knowledge and technology will, in turn, benefit human brain research and our organization. I also consider the OHBM to play a key role in the linkage of brain research with medical or societal applications, for example, how functional imaging can impact diagnostics or public policies.
LN: What excites you the most about neuroimaging research today?
JG: There are so many exciting aspects of neuroimaging. As a neuroimaging researcher, I am particularly excited by the advances in brain imaging technologies, such as the new generation of wearable MEG and high field (7T+) MRI. These revolutionary technologies offer new information and perspectives about the human brain, which is still incredibly mysterious in many ways. I also find it very exciting that I have witnessed many advances that improve our efficiency for studying the human brain, such as increasing computational capacity and automatic data processing pipelines. Another exciting development is that there are now numerous open access big datasets (such as HCP and UK Biobank) that are accessible to the public and will lead to enormously fruitful advances. Studies based on these open access data will generate comprehensive results that reflect various facets and dimensions of the human brain. Hopefully, we can piece these accomplishments together to finally unlock the ‘black box’ of the human brain.
LN: What do you consider to be your greatest scientific achievement?
JG: I am trained as an MRI physicist and now I study brain science. I am very excited that my background offers opportunities to work in an extremely multidisciplinary field. My projects include research on diverse topics, including cerebellar function, obesity, sleep, high-field MRI technology and new MEG technology. I enjoy all of these research areas tremendously.
LN: What direction do you see your own research going in the next few years?
JG: Currently, my interests span basic neuroscience research, as well as development of MEG and high field MRI technology. One particular interest is to address problems related to sleep, including why we have to sleep and how our cognitive systems are functioning during sleep. Also, in my lab, we are building a next generation MEG system based on optical pumping magnetometer technology, and will apply this new system to study challenging clinical issues such as epilepsy and neuropsychiatric diseases.
LN: Neuroimaging in China seems to be developing at a very fast pace. What is driving these advancements and how do you think neuroimaging in China will evolve over the next few years?
JG: Rapidly increasing funding support from Chinese central and local governments has driven the fast pace and prosperity of neuroimaging research in China. In addition, China will launch the China Brain Project shortly. The central goal of this project is to understand human cognition and to devote resources and research capabilities to address urgent societal needs given that the increasing social burden of major brain disorders are calling for new preventive, diagnostic and therapeutic approaches. To realize or even surpass its goals, the Brain Project will progress hand-in-hand with neuroimaging advances.
LN: What do you see as compelling questions neuroimagers should be focusing on and what are your thoughts on the future of neuroimaging? Are the questions and priorities the same in China as in Europe and the US?
JG: While many neuroimagers are dedicated to understanding complex human cognitive functions and brain disorders, I think there is an urgent call to bring functional neuroimaging technologies and research output from laboratory to ‘bed-side’ applications, namely linking neuroimaging research to medical applications. For example, fMRI technology was invented nearly 30 years ago and has revolutionized neuroscience research. However, the integration of fMRI into routine clinical practice is really non-existent in most hospitals worldwide. Furthermore, confronted with a new era of big data, scientists need to harness the ever-increasing amount of information by revolutionary brain-inspired computing methods and systems that are essential to achieve stronger artificial intelligence. As for priority, these questions should not be ranked dramatically different between countries, but there will be minor differences provided that different countries and regions have their own situations. In China, the physicians and medical system are under huge pressure in terms of how to better serve a large population with greatly diverse demands. Thus, I think it is incredibly urgent for China to clearly link neuroimaging research to clinical usage.
LN: Are there effective opportunities in China to help young investigators become successful neuroimaging researchers that are not widely available for young scientists in the US? For example, my graduate student from Dalian Technical University is receiving a stipend from the Chinese government for him to spend two years studying in my lab.
JG: From my perspective, there are great opportunities around the world for young neuroimaging researchers to boost their early career development. The Chinese government is putting more and more emphasis on scientific research. Over the last few years, it has become much easier for young Chinese investigators to receive ample government sponsorship for education or training in other countries, and an increasing number of Chinese researchers with an international education are returning to China. In addition, generous funding support and friendly open policies from the Chinese government have attracted many extraordinary non-Chinese scientists across different fields (including neuroimaging) from other countries, including the US, Germany and Japan, to start their labs as full-time PIs in China. Further, China has a large population with a diverse pool of brain diseases and disorders that makes it easier (in terms of patient recruitment) for scientists to find solutions to both common and rare clinical conditions. Last, neuroimaging-related enterprises are emerging in China so that neuroimaging researchers can have closer links to industry and greater opportunities to convert academic research to medical or societal applications.
LN: Many thanks.
By Nils Muhlert
“The times, they are a-changing.” Dylan’s lyrics from the early 1960s reflected that era’s mass societal upheavals. But now, with increasing realization of the impact of climate change, with a once-in-a-generation response to the dangers of a pandemic, those words ring truer than ever. Life is definitely moving apace. But our work in trying to understand the structure and function of the human brain continues. These efforts to improve scientific methods, make more accurate insights and accelerate the communication of that work remains.
In this vein, this week sees the inaugural OHBM equinox (OHBMx) twitter conference. On Friday 20th March, scientists from across the globe will present their work. This twitter meeting evolves from the Brain Twitter Conference, which has run annually from 2017. It complements the annual OHBM meeting, but with talks delivered by a series of tweets under the conference hashtag #OHBMx.
There will be keynote speakers, including Lucina Uddin, Michael Breakspear and Laura Lewis. There will be abstract presentations on the genetic origin of structural covariance, the neural correlates of olfaction, using fNIRS to study mother-child problem solving, and examining whether deep learning outperforms standard machine learning techniques in brain imaging, amongst many, many others. And the best thing – attendance is not only free, but the conference can be accessed from anywhere with internet connections. That could be your office, a local café, or wherever your current social distancing policy allows.
OHBM is a community. Through OHBM, and academia more generally, we meet and work with people from all corners of the Earth. As we face unprecedented times, seeing that community in action will offer both a welcome distraction from world events and genuine insight into the state-of-the-art from many different brain mapping modalities. We hope you join us there.
Follow the virtual twitter conference using the #OHBMx hashtag from 2am (UTC) on Twitter. For more information on the what, when and how of OHBMx, please see this blogpost from the key organiser of OHBMx, and OHBM Chair-Elect, Aina Puce.
Balancing the budget requires trade-offs
At OHBM 2019, Council decided that it would be beneficial to the membership to provide a window into the decision-making process regarding finances. If you’ve ever wondered why our support for Special Interest Groups (the SIGs) changes from year to year, or how we decide on the location, venue, and registration costs for a meeting—we hope to demystify some of the many thought processes that go into how Council makes its financial decisions and prioritizes requests for funding.
Responsible financial stewardship of OHBM has always been a priority of the Society. This includes the maintenance of adequate financial reserves that are needed for a society to function. For OHBM, this requires that our financial reserves are equal to at least 50% of the average annual costs, averaged over the previous three years. This is consistent with professional investment advice, and how many societies run their finances.
Each year, Council makes budget projections, taking into account future meetings and their likely attendance and venue, to be able to fund the OHBM meeting and activities while maintaining the financial reserves. At the moment, our primary sources of revenue for the Society are membership dues and meeting registrations. Expenditures include the cost of the conference and its activities, as well as the year-round support of other OHBM-related efforts.
Summary of income and expenditures
Every year we do a projected budget based on a number of assumptions, such as the expected number of attendees for the yearly meeting, as well as expected costs based on historical data. Last year we had projected a $61,293.34 surplus by this time next year. In fact, the record turnout at Rome’s meeting helped to offset the shortfall from previous years, and thus the surplus turned out to be $166,963.59. So, with our conservative financial planning we came out ahead. That said, this doesn’t mean that we have an extra $166,063.59 for the next meeting, but rather that this can be applied towards building back up partially depleted reserves (see below).
Current assets? Presently we have $828,507.86 in assets. After all anticipated funds come in from activities related to the 2019 meeting, we expect to have $995,471.45. We have an investment account for part of this money, allowing us to earn interest, so that we can continue to grow our reserve.
Current financial reserves? Council’s policy is that OHBM should always have at least 50% of average (of 3 years) yearly expenditures in reserves. Over the past 3 years, our yearly expenditures have been $1,963,020.00 in 2017 (Vancouver), $2,117,551.72 in 2018 (Singapore), and $2,492,525.00 in 2019 (Rome); this comes to a yearly average of $2,191,032.24. Thus, for us to maintain at least 50% in reserves, we’d need at least $1,095,516.12. Therefore, to meet that goal, we currently are $100,044.67 short.
Why are our reserves down? The purpose of reserves is to protect OHBM from unexpected fluctuations in either meeting attendance or expenditures. In fact, over the past couple of years, OHBM has weathered a couple of such ‘storms’, each of which required a change of the annual meeting venue at short notice. Those last-minute changes meant that deposits that were made for conference facility rental were not refunded for the cancellation of the meeting (a standard part of the rental agreement) and new costs were incurred to rent the new venue. In spite of these increased expenses, we were able to continue our mission both years—because of our healthy reserves, which did exactly what they were meant to do. However, because the reserves are now slightly depleted, for the next year or two we need to focus on building them back up.
There are three general ways to address this deficit: increase revenue, decrease expenses, or change reserve requirements.
For OHBM, there are really only two practical ways to increase revenue: 1) increase registration and/or membership fees; or 2) fundraise via industry sponsorships. Registration has different tiers, each of which can be tweaked independently. We are sensitive to the fact that our membership is heavily dominated by students and post-doctoral fellows and have prioritized keeping registration costs low for trainees. With respect to fundraising, we are grateful for the continued sponsorship of manufacturers of neuroimaging related equipment and tools.
In terms of decreasing expenses, review of our budget identifies five options that would have the strongest effect: 1) select cheaper locales and entertainment (disregarding the desires of many attendees for going to cities that are also tourist destinations, which can be a difference of up to $300K/year in expenses, 2) cut back on complementary registrations to OHBM Council members, invited speakers, or individuals running educational programs and/or symposia, which currently comprise lost revenues of $191K/year, 3) explore ways to reduce credit card charges or possibly add a service charge to cover these costs, 4) cut back on support to SIGs and other OHBM initiatives, including community outreach programs (currently about $60K/year).
Options like cutting credit card fees (at least partially by providing alternative payment mechanisms for some members) are relatively non-controversial. However, other choices are less clear cut, and thus Council has tried to exercise responsible judgement in making decisions that respect and honor the guiding principles on which OHBM is based. So, what are our priorities? We champion the education and training of the neuroimaging scientists of the future. Therefore, we aim continue to support our SIGs as much as possible, but we also now require them to cover their OHBM administrative costs via a fee and will assess support each year based upon degree of involvement. We have discussed and modified the policy with respect to complementary registrations for the meeting. We have discussed increasing membership fees—structuring cost-adjustments so that trainees are impacted the least. We are actively discussing alternating between popular (but also expensive) locales with less popular, but also operationally cheaper locales for future meetings. With respect to corporate sponsorship, manufacturers of neuroimaging-related equipment already provide support for the meeting, for which we are very grateful. However, given the current controversies in our field relating to for profit scientific publishers, we have hesitated to pursue sponsorship from this corporate area, as we feel that this might be a divisive issue within our community.
As mentioned above, current reserve requirements are based upon a moving average over the previous three years. However, Rome was not only exceptionally popular (a record-breaking 91% of members attended this meeting), but also exceptionally expensive, and thus might be considered an anomaly. If so, it may be a viable option to violate our 50% reserve maintenance policy, if we include a more ‘representative’ three-year moving average, as well as ensure that our meetings for the next few years are lower in costs.
There are also other ethical considerations that Council has discussed. We already have worked hard to ensure that OHBM investments are socially responsible and avoid conflicts of interest. But what about green conference facilities that do not use large amounts of plastic? What about the carbon footprint of the meeting? This affects the choice of venue, and potentially there may need to be a balance between face-to-face activities versus online activities going forward. What about maintaining diversity and accessibility to the meeting? This interacts directly with venue choice, as there may be scientists in middle-to-low income countries who cannot realistically come to OHBM meetings if they only alternate across Europe and North America.
The members of OHBM elect Council to run the society and make such decisions by balancing the requirements and needs of our members and attendees, and we on Council, in turn, try our best to meet these expectations. Ultimately, all of us want the same thing: for OHBM to offer the most benefits to the global neuroimaging community—whether offering cutting-edge talks, offering childcare at meetings, or starting a new open-access open science-journal whose scope includes scientific papers, teaching material, and the highlighting of best practices for different areas of neuroimaging—while ensuring that our organization remains financially sustainable.
By Valentina Borghesani & AmanPreet Badhwar
And here we are, January 2020! A new decade is starting, with end-of-the-year reflections giving way to wishes and resolutions for the future. As Chair and Secretary of the BrainArt Special Interest Group (SIG), the youngest SIG within OHBM, we will here outline our inner structure to introduce you to our members, briefly cover our past endeavors, and finally sketch our plans for the coming year. Buckle up!
The coming of age of OHBM Brain & Art initiatives
For us, 2019 has been the year of the transition from a rather informal group of aficionados, supported and sustained by the Neurobureau, to a structured team within our Society. Last year's meeting in Rome thus signed not only the 25th anniversary of our community get together, but also our graduation, so to speak [you can read more about our transition here].
For this second year of activity, our board consists of eight members:
Chair: Aman Badhwar, PhD, Université de Montréal
Secretary and Diversity & Inclusivity Committee Liaison: Valentina Borghesani, PhD, University of California San Francisco
Art Exhibition Manager: Ting Xu, PhD,Child Mind Institute
Treasurer: Sridar Narayanan, PhD, McConnell Brain Imaging Centre, McGill University
Past Chair: Alain Dagher, MD, McGill University
Council liaison: Cameron Craddock, PhD,University of Texas at Austin
Advisory committee: Daniel Margulies, PhD, ICM - CNRS, & Pierre Bellec, PhD, Université de Montréal
Would you like to be constantly updated on our activities? Follow us on twitter and help us reach colleagues and friends across continents and disciplines!
Are you ready to take one step more and help us out more directly? Join our Slack workspace! Anything can be useful: from simple brainstorming of ideas to actual volunteering during the conference)
Finally, do you wish to join the decision making process and take on some of the great responsibilities that come with great power? Consider applying for the next cycle of board members! We will soon be accepting candidature for:
3. Art Competition & Onsite Exhibit Manager-elect
4. Social Media & Communications Manager-elect
Feel free to reach out to our current members to know more about what each position would entail.
Ars Cerebri - the 2019 Exhibit & Competition in Rome
Most art historians would agree that the Ancient Romans had a very peculiar approach to the Arts: artistic expressions had to be somewhat useful, purposeful. They could be means of celebration (arco di trionfo), or expression of devotion (statues in temples). Never something aesthetically pleasing for pleasure itself. The second key aspect of the Art in Ancient Rome was its stylistic eclecticism: elements and themes were constantly borrowed from other mediterranean cultures such as the Greeks, Etruscans, and Egyptians.
Is our community only using (visual) Arts to improve visualization of our neuroimaging findings? No, not really. Or at least not in our view of a symbiotic and mutually beneficial exchange. We believe that Scientists can improve how results are represented and distributed, but also that Artists can be inspired by them. Ultimately, both paths allow society at large to better digest scientific discoveries and their implications. Crucial in this regard is to respect the talent and expertise of both sides.
Like the Romans, our Society is definitely open in spirit, with OHBM striving to promote open science with awards, our sister OpenScience SIG, and the new open publishing platform, Aperture (for which we helped selecti the new logo - spoiler alert: it’s wonderful!).
Our exhibit and Competition during OHBM 2019, Ars Cerebri, aspired to embody this message. Because we all know that while words only spoken will fly away, words put into writing will remain, and words turned into Art will live in perpetuity. Here is a taste of what we offered in Rome, but you can also enjoy the full catalogue of Ars Cerebri and check out all pieces submitted on the dedicated gallery!
NeuroDiversity - the 2020 Exhibit & Competition in Montreal
For OHBM 2020, our exhibit theme will be NeuroDiversity, developed along three axes. First, we will invite selected artists to present their takes on neurodiversity. These works will range from giving underrepresented groups in neuroscience a voice, to the idea that some neurological differences should be seen as non-pathological forms of human variation. All our abilities and faculties range along a continuum and we all just represent one dot in a wide, diverse, spectrum.
In a complementary fashion, we will be showcasing art pieces by neurodiverse populations, for which Art can be a means of communication, an instrument for therapy, or a source of solace and pleasure. From neurodegenerative diseases to epilepsy, various communities have been contacted and will take part in the exhibit.
Finally, we will include pieces reflecting the (above-and-beyond-neuro) diversity within the OHBM community. Our Society houses scientists from all over the world and we want to celebrate the geographical, ethical and cultural richness this brings us.
To this end, we encourage all members of OHBM to download from this folder their favorite brain outline, and fill it with whichever (colored) pattern they like and feel represented by. Personalized postcards should then be sent to ohbm[dot]brainart[at]gmail[dot]com. We will be collecting them and displaying them during the conference in Montreal as an artistic collaborative collage.
As in previous years, the competition will be held online relying on the support of the Neurobureau. Submissions will be accepted starting in February until mid-June (stay tuned to our social media for prompt updates!). Awards will be given for the following categories:
All artworks submitted to the competition will be exhibited digitally at the Exhibit, as well as the Student and Postdoc SIG and NeuroBureau Networking Social when the winners will be announced. In keeping with the tradition, the winners will be announced during the Open Science SIG party.
Our last words are to acknowledge all the artists and OHBM members who, through the years, supported our endeavors.
BY NILS MUHLERT (Lead editor)
The OHBM blog is now entering its fifth year. In that time it’s moved from primarily an interview-based format to embrace a diverse range of educational and entertaining posts. In 2019 this culminated in the introduction of the popular ‘How to’ series. This leveraged the rich content of previous OHBM lectures to teach novices, intermediates and experts about setting up and analysing resting-state fMRI and diffusion MRI studies, and using machine learning in neuroimaging. Spearheaded by Ilona Lipp, our Chair Elect for the Communications Committee, these painstakingly crafted posts offer one of the best freely available resources for those wanting to get to grips with these techniques. We also managed to shine the spotlight on neuroimaging efforts in China and Iran, and plan to continue highlighting brain mapping across the globe throughout the ‘roaring 20s’. Finally, my personal highlight from 2019 was to interview Bruce Rosen. He offered not only deep insight and historical perspectives of fMRI but did so with good humour. Looking forward to more next year.
Although I've contributed to the blog before, this was my first official year on the comms team. I'm happy to have spent the year focussed on open science efforts in our own community, such as the OHBM open science room and the Aperture survey results. Now I'm looking forward to a new year (and decade) of open, inclusive, and exciting brain science here on the OHBM blog!
My main contributions in 2019 were about Open Science (OS) initiatives, engaging the public as well as other science communities. So it was an honour to work with Teodora Stoica to narrate short fairy tale about Registered Reports for the Scientific American. It was also a great experience t 12o work with other OS enthusiasts on a blog post about our study preregistration hackathon, held at the last meeting of the Society for the Improvement of Psychological Science (SIPS) in Rotterdam. For 2020 I look forward to writing more about the clinical relevance of neuroimaging.
2019 was not only a fun but also a very educational year of blogging for me. The posts that I have been working on essentially aim to gather and summarize experience and advice from the very smart people in our field on various topics. This year we covered keeping a healthy work-life balance, how to apply machine learning in neuroimaging, hot to carry out highly clinically applicable neuroimaging (Interview with Gil Rabinovici), how to teach MRI stats (Interview with Jeanette Mumford) and how to do diffusion MRI. Stay tuned and get ready for more in 2020!
In my final year as part of the OHBM Blog Team, I wanted the chance to tackle a topic close to my heart --- work-life balance in academia. Fortunately, we had enthusiastic participation from scientists around the globe, volunteering their experiences to guide a up-and-coming generation of young scientists. I enjoyed partnering with Ilona on this piece, and am proud of it as my final contribution to the OHBM blog.
This year I greatly enjoyed interviewing neuroscientists for the OHBM blog. One of the interviews was with the OHBM 2019 keynote speaker, Tianzi Jiang. The second was for a blog on understanding neurological conditions such as multiple sclerosis, reaching out to various experts in the multiple sclerosis MRI community for their take on where the field is moving and the value of old and new approaches.
My mission on the OHBM Communications Committee has been to help OHBM extend its communications to Chinese audiences. In my time on this team I I have created a Wechat Official Account for OHBM, on which we translated some popular OHBM blogs and news from OHBM. More importantly, working with the OHBM China Chapter, we have hosted three Annual Chinese Young Scholars events for OHBM (2017, 2018, 2019). These events aim to bring together Chinese researchers with diverse backgrounds to discuss and collaborate on cutting edge neuroscience research topics and methods. As my term at the Committee comes to an end, I hope to continue working with the Committee to organize these events and help improve collaborations between the Chinese brain imaging community and OHBM.
This year I had the pleasure and honor of interviewing one of the OHBM 2019 keynote speakers, Dr Armin Raznahan, on his career path and exciting research combining genetics, neuroimaging, and child brain development. My next blog post echoed key themes from the OHBM conference in Rome with that of the first Israeli Human neuroimaging conference took place in Jerusalem focusing on unique neuroimaging acquisition methods, pathologies and child brain development. Last but not least, we are currently working on an exciting blog article exploring the relations between screens exposure and brain development. Can’t wait for the topics we will cover in 2020!
CLAUDE JULIEN BAJADA
2019 has been a whirlwind! I spent most of my time writing two “How-to” blogs focused on machine learning in neuroimaging and, my favorite topic, diffusion MRI. I also interviewed one of the 2018 OHBM keynote speakers; Thomas Yeo where he described his path from electrical engineering to neuroscience. The experience collaborating with colleagues to write the educational blogs has been amazing, and the response from the community heartening. I look forward to more “How-to” in 2020!
A special thanks to all the contributors, editors and members of the Communications Committee for their dedication and effort on the OHBM blog. Happy New Year to all. If you are interested in contributing to the OHBM blog in 2020, please complete a contributor interest form!
By Nils Muhlert
Bruce Rosen is a physicist and radiologist who, for the past 30 years, has been instrumental in the introduction and development of functional MRI. Bruce currently serves as the director of the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital. Here, we found out about the exciting early stages of putting a team together to discover and develop the principles of fMRI, and helping to found OHBM in the process.
By Ilona Lipp
Every year, the OHBM gives out an Education in NeuroImaging Award, acknowledging significant contributions to education and training in our field. This year’s award went to Jeanette Mumford. Jeanette is a well-known fMRI stats guru who spreads her knowledge not only through her published papers but also through YouTube and Facebook, and in the handbook for fMRI analysis. Many of you may also have tried her fMRI power analysis tools. I had the pleasure of meeting Jeanette in Rome and interrogating her about how she became such an enthusiastic educator and her views on neuroimaging research.
Ilona Lipp (IL): Your YouTube videos must have contributed to you winning this award. Your channel has over 2600 subscribers - and without any cat videos! What was the motivation to start youtubing about brain stats?
Jeanette Mumford (JM): I had just moved to the University of Wisconsin Madison and I loved my new job, but one thing I had done at University of Texas was teach an fMRI analysis course, and I kind of missed it. At the same time that I was missing teaching this class, I was doing yoga on YouTube with ‘Yoga with Adrienne’. She has all these free classes, and specifically every year she has a 30 day yoga challenge where you do yoga every day. I thought ‘Oh, this is really cool for yoga, well, I could do free fMRI statistics classes online’. So I decided to put the two together and take my semester long course and break it up into digestible little 10 to 20 minute long videos and roll that out at the end of the summer so people who are starting graduate school could get a head start on fMRI analysis.
IL: When someone asks me how to get started with their fMRI analysis, I say: “Go watch Jeanette’s videos!” So tell me, how did you learn all your brain stats back in the day - without having such youtube tutorials at hand?
JM: Luckily, I had an amazing graduate advisor. That was Tom Nichols, so I obviously had to read all the fMRI statistics papers, which was tough, if you ever read… oh I’m not gonna say that. But if I got stuck on those, he'd help me out. He's a really good teacher as well; I watched him teach fMRI statistics at the University of Michigan summer course. Most of my fMRI stats knowledge I learned through him, and then more through taking the FSL summer course in the University of Michigan. I started following the mailing list for FSL - which tends to be the software package I use. A great way to learn for anyone starting out is just follow the emails and read them every once in a while and you learn a lot of new things.
IL: You trained as a biostatistician. Most people in our field, and here at OHBM, are not statisticians, but psychologists, biologists, physicists, physicians, engineers etc. How do you approach teaching something like stats in a comprehensive way to people coming from such different backgrounds?
JM: I actually made a conscious choice when I was graduating to do a postdoc that wasn't in biostatistics. Because it is all these people working together from these diverse backgrounds, so I wanted to work with somebody either a biomedical engineer or a psychologist, so I ended up with Russ Poldrack at UCLA. I think working with him and working with his lab, I learned how to explain things so that they understand, and then I of course learned a lot from them in turn. And especially Russ' lab are really good at giving feedback. So you can quickly learn and learn how to teach.
IL: Have you developed strategies and do you have any teaching advice?
Yeah, definitely to be okay with making mistakes and being ok with not knowing the answers to all the questions. I remember the first time I taught fMRI statistics at the NITP summer course, some of the questions I hadn't heard before and I hadn’t really thought about the answers. So a lot of times I had to just say ‘I don't know’. And after saying ‘I don't know‘ a few times, I thought ‘oh my God’. But then one of the other speakers came up to me afterwards and said that it's refreshing to hear someone just simply say they don't know. As opposed to another strategy I see, which is to answer a related, but different question to the question that was asked, and just use a lot of words and confuse the person who asked the question. I don't know if it's necessarily done intentionally, but I think it's fine to say ‘I don't know, but I'll look into it for you’. I do that a lot even now.
Another mistake I’ve made teaching is if I make an error to dwell on it. I don't know if it's something women do more than men, but I don’t think that’s true. But I’d think ‘Oh, I can't believe I did that’ and I’ll say that and say ‘sorry’ and I'll keep apologizing. After my first year of teaching, when I was reading at Wisconsin - different class - I was reading the comments, and I was like ‘Oh, it's that thing I got hung up on’. You're basically feeding them the information to criticize you with. If you're criticizing yourself, then they're going to do that. So, now if I make a mistake, I correct it – obviously, I'm not going to pretend I didn't make a mistake because I'm human - so, be human, correct the mistake and move on. ‘Oh, I made a mistake. Here's the right answer, we're moving on now’. They're not going to be as devastated as you are because you just said something totally wrong.
Also, if you teach something for a while, add something new that you don't know and try to teach it, because it makes it more exciting. And if you're not excited to teach something, don't teach it because you're going to give a boring lecture. If you're bored, everyone else will be bored. So I always add new materials to my courses every year.
IL: This is a controversial question: Doing stats properly – if that is even possible – is already complicated, brain stats are even harder to grasp, and even the experts can fail to reach a consensus about issues like false positive rates. Would it be better to have a few, specifically qualified people who just do analysis and have the time to keep up to date with the best practices, rather than educating the masses on how to do their own MRI analysis?
JM: So when they build cars, they have an assembly line, right? They have a bunch of people that do one specific task and at the end you get a car. It works really well for cars. But I think it's a horrible idea for scientific papers. If you have one person who did the data collection and then a different person does the data analysis, there's a gap in the communication. Things may have gone wrong during data collection and the analysis and then if somebody else is interpreting the analysis results and writing the paper, I just think it's not really helping move our science forward, so I actually prefer teaching folks. First of all, most neuroscience people are really smart, their statistics background is pretty strong compared to other graduate students. I think they can pick up things pretty quickly, and they can learn enough to do a good job at it and then they can do all the analyses and then the papers are more likely to be coherent. So, yeah, I'd rather train 10 people to do data analysis than do 10 data analyses myself.
IL: The Anders Eklund’s PNAS paper three years ago created quite a panic in our field, suggesting a vast amount of false positives in neuroimaging research. What is your personal standpoint on this and what recommendations do you have for minimizing the risk?
You know, the paper - for better or worse - had a pretty big splash. I think a lot of great things came out of it: software is better, people have a better understanding, they paid attention. Unfortunately, some other people used it to try to tear down our field a little bit.
A lot of people now say ‘Do I always have to run permutation test? Because they take so long’. I mean, for most of what we do, you can set it running on a cluster and it doesn't take that long. I think permutation tests are great and a lot of people I work with use them. But I'm still okay with the parametric approaches, as long as they use a higher cluster forming threshold, because, at least - that was in the paper as well - Type I error control was better as long as the cluster forming threshold wasn't low, so now the default in FSL is that higher threshold, so that's one of the improvements.
IL: Registered reports are slowly taking off. One concern I have heard several times now is that they are hard to do with brain imaging studies because they require thorough power analysis. You have been working on power analysis for fMRI. Do you think there are reliable ways to estimate power for fMRI studies?
Actually, my stance on requiring power analysis has changed a lot over the years. I used to think everybody needs to do them, but then I realized if people are forced to do them, they're just going to make up numbers - and they're already kind of made up anyway. We’re using pilot data that might be really noisy and not correct, and there are other issues with power analyses of course. But in terms of registered reports, I think they are a little more lenient with that. I don't want to cheap in the power analysis, because then people aren't going to take it seriously. And you get a lot of benefit out of doing it anyway, because you think about your hypotheses more clearly, connecting those with what the data are going to look like, and connecting the data with what the models are going to look like. Usually when I'm helping a PI with a grant it could take two or three meetings to make that transition from their idea to the hypothesis test we're actually going to run. And when I read protocols that people have written, you can always tell when they've actually done a decent power analysis, because the methods section is better, even though the power analysis is a little hand-wavy. But I think we can go with our guts too, if you have 30 subjects and you're looking at a correlation between BOLD activation and behavioral measures, it's not enough.
IL: So far, we have mostly been talking about brain stats. Apart from the statistical challenges, what do you find most exciting about brain imaging?
I think how the communities has changed over the years. I've been coming to this conference almost every year since 2003. And it's so different now, all the changes with the attention that's been given to diversity, and all the new special interest groups and I feel like there's a lot more support for postdocs and graduate students. Improving the community, the attention to open science, people working together more.
And I’m trying to get more into machine learning, that'll be my next thing on the YouTube channel. People keep asking and I haven’t thought of a good collection of papers to cover on it that would go together, but I think I got some now.
Many thanks Jeanette and congratulations on the award!
By Bin Lu and Niall Duncan
Recent years have seen a number of important themes come to the attention of the global neuroimaging community. The robustness of findings reported in the literature have been questioned as people begin to focus more on reproducibility and other statistical issues. At the same time, more attention is being paid to the variability between individuals, not least as efforts to develop diagnostic tools for different brain diseases advance. Databases of imaging data from very large samples have come to the fore as one way of tackling these issues and have already led to some striking results.
Researchers working in China are leading a number of these large-scale initiatives. In all, several thousands of participants have been scanned to acquire various MRI image types. These have been used to produce resources that are openly available to all. Here, we provide a brief overview of some of these resources to bring them to the attention of the community and let people know what is available to work with now, and what will be coming out in the near future.
Investigating the changes in the brain across the lifespan is a difficult endeavour but will help us understand how these changes affect us in health and disease. Large datasets are particularly useful in this context as they can capture the variability in developmental trajectory seen across the population. Understanding the brain in later life is a particularly prominent question within countries, such as China, that have rapidly aging populations.
The Southwest University Adult Lifespan Dataset (SALD) includes data from 494 individuals spanning an age range of 19 to 80 years. Each person has a T1-weighted anatomical image and a resting-state functional scan, along with rich phenotypic information available for download. This represents the largest raw data resource currently available involving participants living in China.
Two other large aging and development related initiatives are currently ongoing. The Beijing Aging Brain Rejuvenation Initiative (BABRI) project has been running for over a decade and has so far obtained multimodal imaging data from several thousand people over 50 years of age in the Beijing area. Each person also completes a battery of neuropsychological tests and various psychological questionnaires. The project, run by Beijing Normal University, aims to scan a total of 5000+ people. The Colour Nest Project, run by the Chinese Academy of Sciences Institute of Psychology is a longitudinal MRI project of participants aged between 6 to 84 years, and aims to scan up to 1200 people three times between 2016 and 2022.
Testing this sort of measurement reliability is also the aim of the Southwest University Longitudinal Imaging Multimodal (SLIM) dataset. This is a test-retest resource obtained from 241 young participants. Each person was scanned three times over a three and a half year period, with each session including anatomical, diffusion-weighted, and resting-state fMRI scans. It is also the aim of the global Consortium for Reliability and Reproducibility (CoRR) to which researchers based in China have been contributing and which has been partly led out of the Chinese Academy of Sciences. This dataset includes a large number of anatomical, diffusion weighted, rs-fMRI, and cerebral blood flow images from centres in China and around the world.
Hosting MRI data can be expensive and complicated due to the large amount of storage space required, especially as one gets to subject counts in the thousands. The R-fMRI Maps Project, run out of the Institute of Psychology at the Chinese Academy of Sciences, seeks to reduce this problem by hosting the final indices calculated on resting-state data, rather than the data itself. Standardised pipelines are applied to the data by researchers to produce these indices and then the relatively small resulting files can be easily uploaded, along with other data such as demographics or cognitive test scores. This approach also has the advantage of reducing some of the privacy concerns associated with publicly sharing raw data.
One of the sets of indices hosted at the R-fMRI Maps Project is the REST-meta-MDD dataset. This represents one of the largest major depressive disorder (MDD) patient and control resources in the world with 2428 participants included (1300 patients) from sites all over China. The same processing pipeline was applied to all the participants and the resulting indices then uploaded to the central server. This resource is likely to be of great use in efforts to understand the variability contained within the MDD diagnosis.
Finally, the standard brain templates used in most neuroimaging analyses are made from one person or from small samples of people of European descent. There may be morphological differences between these templates and many of the people living in China that could affect the results of analyses. To address this problem the Chinese2020 project obtained anatomical images from 1000 people in China and Hong Kong to create a brain template for the majority population in that region. The template is freely available for use, as is a conversion between it and MNI space.
As can be seen, there are many exciting projects going on in China, generating large amounts of data that is (or will be) available to researchers to investigate. These datasets are targeted at some of the main questions neuroimagers are currently focused on and have the potential to greatly advance our understanding of, amongst other things, brain development, aging, and psychiatric disorders.
“Bringing Great Minds Together and Signaling to OHBM in Rome”, Human Brain Mapping Israel 1st Conference
Israel is a small country, approximately 400 km long north to south and 25 km width at its narrowest point. Despite its small size, Israel is home to six large universities and this year hosted the 1st Human Brain Mapping conference. This inaugural conference aimed to bring together neuroimaging researchers from each of these universities, to share ideas and methods. The conference unites those working on a number of different modalities - as was shown by the diversity in over 70 talks and posters, with research using MRI, fNIRS, MEG, EEG and brain stimulation, studying populations across the lifespan.
The conference covered a wide array of computational tools to analyze neuroimaging data (deep learning algorithms, multi-variate pattern analysis, variability quenching etc), unique sequences for structural mapping, and applications of the above methods to clinical and healthy populations. Researchers presented studies on the therapeutic effect of TMS, for example, to reduce alcoholism symptoms, as well as other brain stimulation techniques such as tDCS, multi-unit electrodes, and deep TMS.
As a preview for the OHBM conference in Rome, a special session was dedicated to the developing brain. This session focused on functional MRI studies during reading and screen exposure in children. The researchers discussed the neural networks related to changes in the use of visual and language-related regions during development with the exposure to reading (Dr Bitan), the critical changes in neural circuits supporting memory along development (Dr Ofen) and the “competition” on these neural networks while exposed to screens in childhood (Dr Horowitz-Kraus). The session also highlighted the importance of mother-child joint attention for social and emotional development and the effect this interaction has on babies’ neural activity coherence patterns during rest (Dr Frenkel). These topics were expanded in the Neurobehavioral basis of Development session, chaired by Luna Beatriz in OHBM in Rome. In this session Niko Dosenbach demonstrated exciting new fMRI analysis techniques that could estimate functional connections within and between neural networks at the single subject level in children. Using this technique, he was able to reveal several networks, previously seen at group-level, including cingulo-opercular and fronto-parietal networks. His talk was followed by fascinating presentations by Drs Satterthwaite and Beatriz on the conjunction of behavior with structural (diffusion) and neurochemical (spectroscopy) neuroimaging data in relation to mental health and development. This, combined with a large sample of data (ABCD database, Damien Fair), left the audience with the feeling that this is just the tip of the iceberg.
Other intriguing topics presented in the Israeli conference included several unique methods applied to structural neuroimaging data: from differentiating the six layers of the cortex (aka cortical layering), and using an MR sequence that provides the caliber of the axons in humans, presented by Dr Yaniv Assaf and his students, to quantitative T1 mapping presented by Dr Mezer. Some of these methods were extended to a discussion about structural plasticity in the session in Rome, chaired by Dr Monika Schonauer, which focused on changes in diffusion weighted measures (Dr Brodt), plasticity of diffusion weighted measures in relation to motor learning (Drs Maggiore and Johansen-Berg) and to the dynamic of the connectome (Dr Assaf). Both topics of developmental neuroimaging and innovative structural neuroimaging methods were merged in a fascinating keynote given in Rome by Dr Armin Raznahan, discussing sex-related differences in structural neuroimaging data (anatomical T1 data) in children.
Israel, one of the leading countries in applications and industry development, is also known as the “start-up nation”. With several developments related to brain stimulation, machine learning algorithm applications to human brain mapping, a strong hub of human brain mappers across populations, ages, and techniques may mutually fertilize both researchers in academia and industry. “These annual meetings, which will continue occurring before the official OHBM conference, allow a unique opportunity to students and researchers with a variety of specialties focusing on the human brain, to interact, collaborate and comment on each other’s work” says Dr Porat. As a small geographical area with many stimulating brains, the ability to bring these brains together to make more than the sum of their parts during this conference was welcome, and we look forward to more exciting developments in human brain mapping in Israel. For more information see https://elsc.huji.ac.il/events/718
By Johannes Algermissen, James Bartlett, Remi Gau, Stephan Heunis, Eduard Klapwijk, Matan Mazor, Mariella Paul, Antonio Schettino, David Mehler
The neuroimaging field has recently seen a substantial surge in new initiatives that aim to make research practices more robust and transparent. At our annual OHBM meetings you will have likely come across the Open Science room. While many aspects fall under the umbrella term Open Science, for this post we focus on research practices that aim to make science more replicable and reproducible. These include non peer-reviewed study preregistration, peer-reviewed registered reports that reward researchers’ study plan with in-principle acceptance before data collection, but also code and data sharing tools such as NeuroVault and OpenNeuro.
As neuroimagers, we work closely with and learn from other disciplines, including Psychology. One place where a lot of grassroot development has come to fruition in recent years is the annual meeting of the Society for the Improvement of Psychological Science (SIPS). SIPS breaks with the traditional conference format and focuses on practical work, peer projects and solving concrete problems in groups. The SIPS experience can feel a bit like a playground for research practice geeks: participants sit in the driver's seat and can pick from a variety of so-called unconferences where they pitch and debate ideas to reform research practices, hackathons where everyone can contribute their “bits” and thoughts, and workshops where you can catch up on learning to use the latest R packages or Bayesian analysis. In this vibrant setting we embarked as a group of enthusiastic neuroimagers on an expedition to intermingle with other open science crowds. We wanted to find out how study preregistration and registered reports could be tailored more towards neuroimaging studies. Prepared with a list of challenges that we learned about through our informal survey, we felt determined to provide more clarity around adequate statistical power in our field, and strived to ultimately come up with a potential user-friendly template for preregistration of neuroimaging studies. We completed some initial steps at the hackathon and the immediate aftermath with a focus on tools that help researchers preregister their studies. Here, we summarize our group projects and provide you with some (interim) outcomes.
Collection of preregistrations and registered reports in neuroimaging
Preregistration and registered reports are ways to state in advance what your hypothesis is and how you are planning to run and analyze the study. They are meant as tools to prevent researchers’ own cognitive bias (e.g., hindsight bias or confirmation bias) hijacking their investigation. They are not meant to stifle exploration but to make very explicit what part of a study was confirmatory and what part was exploratory (see http://cos.io/prereg/ and http://cos.io/rr/ for more details). Preregistration protocols have been around for a while for clinical trials but they have only started in the past few years to be on the radar of psychology researchers. The uptake seems to have been much slower in research involving (f)MRI, EEG, or MEG. Apart from the large amount of methodological and analytical detail needed to preregister neuroimaging studies, one reason may be the lack of examples of what a preregistration in those fields could look like. Those M/EEG and fMRI preregistrations and registered reports scattered on the internet are also hard to find. Therefore, during the hackathon, we started a list of all the openly available neuroimaging preregistrations and registered reports. This resulted in a spreadsheet, accompanied by keywords to make it easier to select relevant ones you are interested in. This document is still a work in progress and we welcome contributions to this potentially ever-growing list, especially if we missed one of your own preregistrations! Simply use this form to add an entry. We hope that such an easily accessible list of preregistrations will inspire many more neuroscientists to preregister their studies and will help to establish best practices.
BrainPower: resources for power analysis in neuroimaging
Every planning phase of an empirical neuroimaging research project should consider sample size and statistical power: How big is the effect that I am interested in? How likely am I to observe it given the resources (number of participants, number of trials) at my disposal? Power analysis should provide clarity on these questions. It might appear relatively easy for simple designs with one-dimensional behavioural variables, especially with the help of programs such as G*Power and standard effect size measures such as Cohen's d. However, the high-dimensional nature of neuroimaging data and designs (processing three-dimensional data over time with mass univariate and multivariate approaches) requires additional steps, e.g., cluster correction, to prevent false-positive inference. And our understanding of "effects" based on these data and methods is not necessarily as intuitive: how strong should the level of activation be, or how large should the cluster be?
One important approach to power analysis is simulations: When taking resting-state data and adding an activation of a certain size and extent, can I reliably find the effect? This approach has been facilitated by advances in computational power and new software in recent years, allowing researchers to have full control of the ground-truth. Alternative approaches to estimate effect sizes is relying on past literature (which may provide biased estimates) and re-using existing, or even open data sets.
For both approaches, experts have created primers, tools, and software. Unfortunately, their use may not always seem intuitive. Further, researchers might have a hard time recognizing which tools best suits their specific needs. We thus collated a variety of such tools, compared these different approaches, and described their use (“how to”) to empirical researchers. Overall we gather collection that provides:
This list of resources is openly available and still growing in content. The immediate future goal is to expand the resources with tutorials and work examples of conducting power analyses on real and simulated fMRI data. We then plan to formalise these resources into a website. We invite and welcome any and all contributions from the community!
A new way to calibrate the smallest effect size of interest (SESOI) for neuroimaging, using an fMRI example
Adequate sample size planning is crucial to make good use of resources and draw valid inferences from imaging data. One-size-for-all recommended sample sizes are slowly being replaced by power analysis procedures that are based on effect sizes that seem reasonable. In the more common approach, effect sizes are estimated based on available data or previous studies. However, this approach does not account for the ability to necessarily detect a meaningful effect size. An alternative approach is to power studies sufficiently to detect the smallest effect size of interest (SESOI), thereby increasing the chance to find an effect that is meaningful for the research question (e.g., for practical, or theoretical reasons). Also, in the event of a non-significant (i.e., “null”) finding, this approach increases the chances to reject negligible effect sizes, rendering “null findings” more informative. Hence, while this approach is more rigorous, it often requires larger samples, especially when studying higher order cognitive functions where group effect sizes are known to be small. On the other hand, running too many participants also comes with a cost: scanner time is an expensive resource of limited availability. Identifying a procedure that can balance this trade-off would thus be desirable and potentially help researchers to implement a sampling plan that is based on a SESOI.
We thus started with the following thought experiment: in an attempt to optimize sample sizes for specific experiments and statistical tests, one can capitalize on the fact that neuroimaging data is rich and affords numerous statistical tests that are statistically orthogonal. It is safe to assume that some sources of noise are shared between contrasts, within a participant (for example, a participant that moves a lot in the scanner will have more noisy parameter estimates), and that other sources are shared between participants within the same lab (for example, the quality of the scanner). Based on these two points, we envision a dynamic procedure for sample size specification that is sensitive to the noise in the specific sample of participants. Implementing such a procedure seems fairly simple: data acquisition stops exactly when a group-level contrast that is orthogonal to the ones of interest reaches a pre-specified significance level in a pre-specified region of interest.
A preregistration template for EEG
Analyzing neuroimaging data involves a myriad of decisions that researchers often consider only after data collection. When preregistering a neuroimaging study, thinking of each detail of the analysis can be challenging, especially because current available preregistration templates are generic and do not ask for the relevant technical details and specifications that are relevant for EEG experiments. For example, preprocessing EEG data involves many decisions - including resampling, filtering, and artefact rejection - that can have a profound impact on the results.
As part of the hackathon, we started to create a preregistration template for EEG studies that highlights such decisions during preprocessing and statistical analysis. For instance, the user is reminded to describe the electrode type and brand, data import, resampling, filtering, epoching, artefact detection/rejection/correction procedures, baseline correction, and averaging. The current version of the template is a text document based on the standard OSF preregistration form where we added specific questions about preprocessing and analysis steps for event-related potentials (ERPs). This EEG preregistration template is an ongoing project. If you have worked with EEG data or preregistrations before, your input would be highly appreciated! Ultimately, we aim to include the finished template on the OSF list of preregistration forms and extend the preregistration template to other analyses of EEG data (e.g., time-frequency analyses).
To wrap up, SIPS certainly provides a great opportunity for neuroimagers to intermingle with others and contribute to projects related to scientific practices in an open, inclusive, and dynamic environment. Anyone can pitch a session ad-hoc for the next day and the outcome of each project is openly documented on the OSF. This ensures that projects like ours on preregistration and neuroimaging can develop and live a happy after-conference life.
By Claude Bajada & Ilona Lipp
Infographics: Roselyne Chauvine
Expert editors: Tommy Boshkovski, Nikola Stikov
Newbie editors: Alina Serbanescu, Adriana Oliveira, Andreia Meseiro
For the budding cerebronaut, the term diffusion MRI evokes images of fancy red, green or blue fibre coursing across the brain; pretty enough to find their way onto a musical album cover or to be the standard stock image for anyone giving a public communication lecture about the brain. While the pictures are appealing, the terminology associated with diffusion MRI is often confusing and hard to disentangle. Any PhD student about to embark on a diffusion MRI project has had to grapple with a sea of acronyms such as DTI, HARDI, FA, RD, ADC, CHARMED and many more! If you have ever got frustrated by these terms and how they relate, this “how-to” post is for you.
What is diffusion MRI?
Keeping things simple, diffusion MRI refers to the collection of magnetic resonance imaging data that is sensitive to the direction of water diffusing in a tissue. Let us imagine that the brain were the fishbowl that Christian Beaulieu shows in his video (see below); devoid of any tissue. Any single water molecule in this “fishbowl brain” will, depending on the temperature of the water in the bowl, vibrate and move in a seemingly random fashion, colliding with neighboring molecules. This motion is called Brownian motion and it will only be restricted at the limits of the bowl.
However, as Christian shows so beautifully in his video (min. 2:20), the brain is not like a fishbowl (not even remotely like one - luckily!), but full of neurons and other cellular structures that act as potential barriers to water diffusion; hence water molecules can be used as a microscopic probe. If we can measure the average rate of displacement of water in all directions in every single brain voxel (a volume element - in the same way that a pixel is a picture element), then we have a measured profile of water diffusion in each voxel of the brain. To measure “displacement of water in all directions” we need to take many diffusion-weighted images. Diffusion MR data consists of these diffusion-weighted images and non-diffusion weighted images. A single diffusion weighted image can be thought of as an MRI volume that is sensitive to diffusion of water in one single direction. If you are wondering why we would want to do this … stick around!
Ok, now we know the basics, but how do we do it in practice?
In her video, Jennifer Campbell introduces the concepts of hindered and restricted diffusion in biological tissue and how they relate to the most basic diffusion MRI-based measure, the apparent diffusion coefficient (min. 2:50). She then explains how the generation of diffusion-weighted contrast in an MRI machine requires the application of a pair of equal and effectively opposite magnetic field gradients in a particular direction. These gradients disrupt the phase of proton spins in water molecules, and if there is random diffusion along the gradient direction, lead to signal loss, as compared to when no such gradient pair is present (from min. 4:25). In his video, Zoltan explains how this random movement differs from bulk flow (from min. 7:20) and how the diffusion-induced signal loss can be used to estimate diffusion constants (from min.11:00). How strong the signal loss is depends on the diffusion-weighting that is applied in your sequence, which often is parameterized with the so-called b-value. You will learn what the b-value means and what the famous signal equation looks like in Zoltan’s video (from min. 34:00). Importantly, as Els Fieremans explains in her video (from min. 3:00), diffusion-weighting can be manipulated either by changing the strength of the diffusion gradients or by altering the diffusion times, which is important for microstructural imaging (from min. 24:15, also see Christian Bealieu’s video from min. 23:45), a concept that we will discuss later. You would also always acquire some non-diffusion-weighted volumes (often called b0 images). A rule of thumb is that these are about 10% of your volumes. If you spread them out across your acquisition, this can later help you to correct for potential signal intensity drifts across your scan time (as explained in Alexander Leemans’ video from min. 14:25).
Jennifer explains that if you acquire diffusion-weighting along many directions in a voxel, you get a diffusion-weighted signal profile, which depends on the underlying fibre orientations (around min. 13:50). The more directions we have, the higher the angular resolution (around the center of the sphere) we sample. In fact, scans with over 60(ish) directions are called High Angular Resolution Diffusion MRI (HARDI). Ideally, the directions you sample should be spaced out evenly around the sphere, and optimized using an electrostatic algorithm (this and other principles of diffusion acquisition are explained here). Luckily, most of the time you do not have to optimize these yourself, there are various standard gradient sets around that you can use in your HARDI acquisition.
The diffusion MRI vocabulary is large, and this also holds true for diffusion sequences. In her video, Jennifer clarifies often used terms related to the most frequently used diffusion sequence, Stejskal-Tanner, such as little and big delta, effective diffusion time and all these funny letters b, k, n, and q (from min. 12:00). She also introduces other diffusion sequences designed to allow longer or shorter effective diffusion times, to reduce artifacts or to facilitate the quantification of compartment-specific or microscropic anisotropy (from min. 22:00).
With all the options available, how do you choose how to acquire your data? Jennifer gives tips for how to design your protocols, depending on aim that you want to achieve with your experiment (from min. 16:00), which could be just estimating the apparent diffusion coefficient (ADC), reconstruction of the tensor, inferring multiple fibre orientations, or applying specific microstructural models such as Neurite Orientation and Dispersion Density Imaging (NODDI), Composite hindered and restricted model of diffusion (CHARMED) or AxCaliber. Zoltan gives some additional insight into optimising your acquisition, e.g. through triggering (from min. 40:30) and how to use a water bottle to test how linear your gradients are (from min. 19:45).
Ok, now we have some data, what’s next?
The first step that is always recommended with any kind of imaging data is to look at your images in different planes (axial, sagittal, coronal). During the first 14 minutes of his video, Alex shows us pretty much anything that could be wrong with diffusion data, before and after processing, including movement artifacts (from min. 2:00), distortions induced by eddy currents that occur due to fast gradient switching in diffusion sequences (from min. 19:15), distortions in the phase-encoding direction (from min. 19:50), vibration artifacts (from min. 4:50), physiologically plausible signals caused by Gibbs ringing (from min. 10:30), having done the calculations with wrong diffusion gradient directions (from min. 3:15), having applied unsuitable models (from min. 7:10), and artifacts that are not actually artifacts but truly abnormal ingenious brains (from min. 09:10).
Luckily, most of the artifacts can be corrected with the vast amount of processing techniques that have been developed. Alex explains how to correct for signal intensity drifts (from min. 14:25) and why you famously have to rotate your b-matrix when doing subject motion correction (from min. 17:20). He illustrates the effects of eddy current correction (from min. 19:15), corrections for EPI deformations (from min. 19:50), and encourages the use of robust diffusion model estimations (from min. 22:15).
If you have checked all these things and your data or results still look odd, don’t worry, there are at least 25 pitfalls with the analysis of diffusion MRI, and there is a variety of software packages that allow you to implement all sorts of algorithms and tricks.
Everyone keeps saying “diffusion tensor imaging” - what on earth is a tensor anyway?
This is a great question and one that diffusion MRI newbies (who do not come from a maths or physics background) will almost certainly ask. It is also one of those questions that seemingly has one of the most unsatisfactorily unintuitive answers ever to be found. Google’s dictionary defines a tensor as “a mathematical object analogous to but more general than a vector, represented by an array of components that are functions of the coordinates of a space.” But what does that have to do with the spheres and ellipsoids that we often see in diffusion tensor imaging? It may be more helpful to actually focus less on the idea of a tensor and more on the idea of a local diffusion model.
We want to have a model that adequately describes the diffusion of water (or in some models, the fiber structure) in a given voxel and can explain the image intensities in our weighted images. One of the easiest ways of doing this is assuming that the diffusion of water can be described by a Gaussian distribution of displacement. At a particular time point that Gaussian can be described as a sphere (if diffusion is similar in all directions) or an ellipsoid (if diffusion is preferential in one direction).
In any case, this idea that we have just described is called a local model - with diffusion tensor imaging, our local model is the ellipsoid. It is a model because it is a simplified way of describing the data that can be used (as we will see later) to extend our knowledge. It is local because the model is fit in every voxel.
I still don’t know where the tensor comes into play!
The tensor is a mathematical formulation of this simple local model. If you are super keen, keep on reading, otherwise, swiftly move to the next section. Since we are thinking in terms of spheres and ellipsoids, imagine that you can populate every voxel in an MRI volume with a unit sphere. This is a start, but it is not the best model of diffusion in every voxel. If we think of the corpus callosum we would have to apply tension to the ball to stretch it into an ellipsoid. You can think of the diffusion tensor as the transformation matrix, estimated from the data, that warps each sphere into an ellipsoid that describes the direction and magnitude of diffusion. So the tensor is not the ellipsoid itself, it is the instructions needed to warp a sphere into an ellipsoid. How to turn this mathematical intuition into a usable formulation is explained in Zoltan’s video. He explains how Fick’s law of diffusion relates to the diffusion constant we want to measure along one direction (from min. 12:50) and how to extend this concept to the 3D scenario (from min. 17:30). He nicely illustrates the tensor and explains the meaning of the off- and on-diagonal values (from min. 18:30).
Is DTI just another way of saying diffusion MRI?
No, in fact the terms diffusion MRI, diffusion-weighted MRI, and diffusion tensor imaging (DTI) all refer to subtly different things (also see Christian Beaulieu video from min. 16:45). Diffusion MRI is a very general phrase that can refer to any MRI sensitive to diffusion processes. Diffusion-weighted MRI refers to MRI sequences that are specifically designed to be sensitive to diffusion. Diffusion tensor imaging aims to specifically reconstruct the “tensor local model.”
Do we always use the tensor as a local model?
No, the diffusion ellipsoid (or tensor) is one of the simplest models that we can use when doing diffusion MRI but it is definitely not the only one. In his video, Flavio Dell’Acqua explains that the limitation of using an ellipsoid “model” is that it can only adequately capture the diffusion profile that results if our voxel is only occupied with one fibre population (when people say fibre population, they mean fibres travelling the same way, usually because they belong to the same white matter “tract”). However, an estimated 70-90% of voxels have more than 1 fibre population (from min. 3:25). Therefore, more advanced approaches try to capture the orientation distribution of fibres in each voxel, either by looking at the diffusion orientation distribution function (ODF) or by applying spherical deconvolution to get down to the fibre orientation distribution (Flavio walks us through the various ways to do that, from min. 10:00). In fact, almost every diffusion software package will implement their own local model. For example, the popular software package FSL, besides also having the option to fit a tensor, can model each voxel in terms of a “ball and sticks”, where the “free water” in the voxel is modelled by an isotropic sphere and every “white matter fiber” is modelled by a “stick” - which is a super skinny ellipsoid. This is why such a model is sometimes referred to as a “multi-tensor” model. Other software packages such as MRtrix and StarTrack use Spherical Deconvolution (explained in this review paper). The type and complexity of the local model that you choose depends on your application. We will later describe two main applications of diffusion MRI, tractography and microstructural imaging.
What do I do after I have successfully estimated my local model?
Now that we have a local model at every voxel we reach a fork in our pipeline. So far, all of the explanation was linear. But now there are multiple routes to go down and some of them potentially interact down the line, so writing about them becomes tricky!
The two main prongs of the fork are “tracing” diffusion within white matter through tractography to reconstruct anatomical pathways, and extracting scalar maps from the local model which can potentially be given biological meaning related to brain microstructure.
Let us first go down the tractography route: what can diffusion MRI teach us about white matter anatomy?
Diffusion MRI is most frequently looked at in the context of WM. This is because WM consists of bundled fibre tracts that significantly impact on the diffusion anisotropy and directionality. The measured diffusion profiles can allow to reconstruct and delineate WM pathways in individual brains. This allows us to investigate an individual’s connective anatomy rather than us having to rely on expert or group based delineations of white matter pathways. For a nice review on the applications and challenges related to tractography see here.
To do tractography, the process of “tracking” the WM pathways, we need voxel-wise estimations of fibre orientation. How to get these is explained in Flavio’s video (he also illustrates the limitations of tensor-based tracking in min. 24:30). Based on the peaks in the estimated diffusion or fibre orientation distributions, so-called streamlines are generated.
In his video, Maxime Descoteaux explains how this can be achieved and all the various parameters that need to be set, such as how streamlines are seeded, how large each step size is etc. (from min. 3:00). An important point is the ability to resolve crossing fibres while not being tricked by kissing fibres (Flavio min. 4:50), and establishing constraints to ensure that the results are reliable (in min. 22:50 Flavio gives example for what happens when we don’t have enough constraints).
The set of all streamlines is often called “tractogram”. Maxime introduces the methods of deterministic and probabilistic tractography (from min. 6:15) and how different software packages differ with regards to considering uncertainty (from min. 9:00). He also introduces the recent concepts of anatomically constrained tractography (from min. 13:00) and microstructure-informed tractography (from min. 25:00) to make results more anatomically accurate.
Tractography results strongly depend on the methods used (and there are a lot of options, see this paper for a nice overview), something which has been recently demonstrated in a large-scale tractography challenge, where a large number of groups have applied their methods to the same dataset. In this and a previous challenge, it turned out that a main challenge with tractography is the large amount of false positive tracts produced by the currently available algorithms. Understanding WM anatomy helps with doing and validating your own tractography results. In his video, Marco Catani explains the different types of fibres in the brain (from min. 1:30) and provides some basic rules that help evaluate your tractography results, such as that projection fibres do not cross the midline and that association fibres do not enter core WM (from min. 8:30). He also emphasizes that the probability in probabilistic tractography, which is related to the certainty of the local fibre orientation estimates used to generate a streamline, is not an indication of anatomical certainty (from min. 12:20) and talks about the difficulties related to validating fibre tracts post-mortem (from min. 13:30).
Since tractography allows us to study how cortical regions are connected through WM pathways, diffusion imaging has frequently been used to try to parcellate the cortex. In his video, Alfred Anwander points out that some of these parcellations, such as Brodmann parcellation or gyrification, are not useful to predict connectivity of cortical regions (from min. 4:00). He shows how probabilistic tractography seeded in individual voxels can be used to find parcels with similar “tractograms” (from min. 6:00). He also discusses the challenges of finding a parcel size that balances interindividual variability and specificity of parcel (from min. 3:00), and the problem that in some areas there is a smooth rather than sharp change in connectivity profile (from min. 11). In his video, Michel Thiebaut de Schotten explains how diffusion-based parcellation is based on a combination of connectivity matrices with clustering methods and the methodological challenges related to tractography-based parcellation, one being that the further the distance between two points, the fewer streamlines can be found (from min. 12:00).
We will now back track and discuss what diffusion MRI can teach us about microstructure
In her video, Els explains how diffusion MRI can be used as an in vivo microscope for probing the brain’s microstructure. This means, even though we are looking at large voxels, there is information in the diffusion data that tells us about the tissue composition of the voxels. More complex local models (than e.g. the tensor) can be used to obtain more biologically meaningful information. Els emphasizes the difference between mathematical representations of the diffusion signal and biophysical models (from min. 10:00).
The most widely used mathematical representation of the diffusion signal (loss) is the tensor, as already discussed above. There are various parameters we can extract from it that tell us something about the microstructure, such as mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) and fractional anisotropy (FA; Els’ video from min. 4:40). FA is probably the most widely used microstructural parameter in application studies, and quantifies the extent of diffusion anisotropy in a voxel. What this means in terms of biology, is discussed by Christian in his video. He concludes that anisotropy is mostly driven by the axonal membranes, with myelin having a comparatively smaller impact. FA is known to be sensitive to various tissue properties, so interpreting individual differences or changes over time can be challenging.
As diffusion is influenced by various things, to gain more specificity of our measures we can apply biophysical models (review paper on diffusion-based microstructural models). Commonly used examples are NODDI, neurite orientation dispersion and density imaging, CHARMED, the composite hindered and restricted model of diffusion, and many … many …….. many more.
In these models, the geometry of the brain tissue is simplified, for example by assuming a limited number of compartments which differ in their diffusion behaviour. The most common distinction is the intra-axonal compartment - modelled as impermeable cylinders or sticks and extra-axonal space. In her video, Els (from min. 11:50) explains the different parameters that can be derived from such models, such as the proportion of the tissue belonging to intra-axonal vs extra-axonal space. To estimate such parameters, biophysical models mathematically formulate what aspects of microstructure affect the diffusion signal in what way, and then find out what is the most likely microstructure to give rise to the observed signal. Be aware that the more complicated the model and the more parameters are estimated, the more data you need and the less robust your fit will be. Els explains that this is challenging, and illustrates how such nonlinear models are ill-posed (from min. 14:45). For this reason, constraints need to be established, e.g. in the famous NODDI model, department diffusivities are assumed (from min. 16:50). Using biophysical models, it is always a good idea to understand where the constraints and assumptions are, so you can decide for your specific application, whether these are reasonable or may be problematic (see this paper for an example).
Now that I have my microstructural parameter maps, what do I do?
Ultimately, we want to apply all these methods to learn something about the brain. Often, we want to compare different groups of people, find brain-behaviour correlations or understand brain plasticity. In his video, Anton Beer walks you through the various methods to do group analysis on microstructural maps, including region of interest (ROI)-based (from min. 6:25) and whole-brain approaches (from min. 11:00). One frequently used statistical method for diffusion data is tract-based spatial statistics (TBSS), which is not actually based on tractography (as the name may imply). How TBSS uses FA maps to obtain a skeleton is explained by Anton (from min. 15:35). As he and Alex (from min. 30:45) point out, one limitation that needs to be kept in mind is that due to the alignment errors and the fact that the analysis is limited to local maxima of FA, there is no guarantee that anatomical structures do overlap across individuals (for other methodological considerations related to TBSS check out this paper).
Anton explains how your results from tractography can be useful to get ROIs (from min. 8:20) and for surface-based analysis (from min. 19:15). An application of how a set of complementary tract-specific microstructural measures can be used for studying brain development is illustrated in Jason Yeatman’s video. Recent developments even allow fibre-population specific metrics (see Flavio’s video from min. 27:30).
What do I do if I am completely confused or super motivated to do my own diffusion MRI study?
Diffusion MRI is a large, complicated topic and there are a lot of things to get your head around. Luckily, there are also various resources other than this blog post that can help you with that. If feel like watching more educational videos, check out our friends at ISMRM with their diffusion without equations post and their interactive course on diffusion, this also includes quizzes to test yourself. If you are interested in the history of diffusion MRI from its earliest days to the latest developments, check out the presentations from the A Spin Thro’ the History of Restricted Diffusion MR workshop. If you prefer reading, a number of textbooks cover a wide range of aspects, such as Diffusion MRI: From Quantitative Measurement to In-vivo Neuroanatomy, Diffusion MRI: Theory, Methods and Applications, Introduction to Diffusion Tensor Imaging and Higher Order Models.
Last but not least, if you have any questions that came up while reading this post, questions about things you have never really understood or questions that you have always wanted to ask the “experts” in the field, please send them to us (via email, or tweet). We are aiming to write a follow-up post to give you the answers.
By Cyril Pernet, Dora Hermes, Chris Holdgraf
We are happy to announce that the Brain Imaging Data Structure (BIDS) now supports all of the major electrophysiology modalities in human neuroscience. This means that EEG, MEG, and iEEG researchers can all store their data in a BIDS-compliant manner, making these datasets more shareable, understandable, and re-usable. This post describes the BIDS standard in general and the community around it, as well as recent changes that have brought support for electrophysiology.
The Brain Imaging Data Structure: BIDS
BIDS is a standard that specifies how to organize data in different folders, how to name files and how to document metadata (i.e. information about the data). It does this using community standards and dictionaries enabling efficient communication and collaboration between data users. Details about BIDS can be found at http://bids.neuroimaging.io/.
BIDS is an initiative that arose as a specific action taken in response to deliberations of the INCF, NeuroImaging Data Sharing Task Force (NIDASH), along with the NeuroImaging Data Model. NIDM is a Semantic Web-based metadata standard that helps capture and describe experimental data, analytic workflows and statistical results that complement BIDS.
With seeds planted in January 2015, BIDS started in September after being presented at the OHBM (June) and INCF (August) annual conferences and has rapidly been taken up by our community – starting with a specification related to sharing MRI data (basic structural, functional and diffusion) submitted in December 2015 and followed by a growing number of extensions into various modalities. The analysis of a recent survey done by the Stanford Centre for reproducible neuroscience lead to a current estimate of over 65000 subjects’ data stored and/or shared using BIDS.
Building bridges with the electrophysiology communities
In 2016, MEG BIDS was published describing how to organize and share MEG data and metadata. Right after that, Cyril Pernet used the open science space during the annual OHBM meeting in Vancouver (2016) calling for an EEG-BIDS. The first draft was done the following week with the help of Robert Oostenveld during an EEGLAB workshop. Simultaneously, the iEEG community had a need to organize and share data in a standard that matches MRI, MEG and EEG data, so Dora Hermes and Chris Holdgraf developed the iEEG-BIDS extension. For almost 2 years, the two teams developed the standards, while checking with others for consistency, with some help from the MEG-BIDS team. This work culminated with two papers published in the journal Scientific Data (Holdgraf et al., 2019, Pernet et al., 2019). Concretely, this means human brain electrophysiology data sharing is fully harmonized thanks to the effort and collaborative spirit of all involved. It also means that about 2/3 of all functional imaging data can now be organized, documented and shared efficiently (with the exception of PET, NIRS, TMS and dTCS, 34.7% of publications since January 2018 according to our PubMed search).