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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.
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 Biyu He 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. OHBM 2020 Diversity Round Table: Intersection between Neuroscience and the LGBTQ+ community6/18/2020 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.  Tomas Paus 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  Michael Fox 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.  Draft image of the virtual OHBM Annual Meeting lobby 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.  The distribution of OHBM members in June 2020 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.  Draft image of OHBM Auditorium 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.  The ‘entrance’ to the OHBM poster hall  Join the poster discussion and view posters in the category 'rooms' 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 aperture@humanbrainmapping.org 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. Final thoughts 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.  Alex Fornito 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! |
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