By Claude Bajada, Simon M. Hofmann and Ilona Lipp
Edited by: Thomas Yeo and Lisa Nickerson
Machine learning, deep learning and artificial intelligence are terms that currently appear everywhere; in the media, in job adverts… and at neuroimaging conferences. Machine learning is often portrayed as a mystical black box that will either solve all our problems in the future or replace us in our jobs. In this blog post, we discuss what the term machine learning actually means, what methods it encompasses, and how these methods can be applied to brain imaging analysis. Doing this, we refer to the OHBM OnDemand material, which contains some great videos explaining machine learning methodology and we provide examples for how it has been used in a variety of applications. If you are curious about machine learning tools, but are not really sure whether you want to jump on the bandwagon, then we hope that this post is right for you and will help you get started.
What is machine learning?
Machine learning is a broad term that goes beyond deep learning and incorporates many other methods that are discussed in this post. Many of these methods you may already be familiar with and have heard about in the context of classical statistics, such as linear regression. While machine learning ‘is built on the foundations of statistics and has absorbed much of its philosophy and many of its techniques over the years’ (Max Welling, 2015), a main focus of machine learning lies in generalization, i.e. finding patterns in data by training a computational model such that it can predict unseen data of the same or similar nature. Here, a balance between “overfitting” (fitting a model that is too complex and will only work well for the data used to train it) and “underfitting” (fitting a model that is too simple and works poorly even with the data used to train it) needs to be found, aiming at high sensitivity and specificity when applied to new cases. This is generally attempted by splitting the data into various sets, training model parameters on one set, choosing the best model by evaluating it in another set, and testing it in yet another set. Sometimes, the term “statistical learning” is used for machine learning methods that have their foundation in statistics. Introduction to statistical learning and The Elements of statistical learning are two great textbooks introducing some important concepts.
What about Artificial Intelligence? Is that something different?
Artificial Intelligence is a high-level, conceptual term that describes the movement to emulate “natural intelligence” in machines. Machine learning can be thought of as one set of statistical tools that can make machines more “intelligent”.
What types of methods are there?
The most popular machine learning techniques applied to neuroimaging can be split into two general groups: supervised and unsupervised learning. Supervised learning requires labelled data (e.g., data that has been labelled, classified, or categorized), whilst unsupervised methods detect patterns in unlabelled data. Different machine learning methods also differ with regard to their complexity. Both types of machine learning approaches can range from fairly simple linear models to much more complicated, non-linear, algorithms. The more complex the models get, the more computational power is required.
Even though machine learning has been around for a long time, it has experienced a recent boom. In his OHBM OnDemand video, Vince Calhoun (6:30 min) explains why: it is not only because more and more data are available, but also because there has been an immense improvement in computational power (note that training neural networks can sometimes still take weeks!) and in better algorithms being developed and implemented in open source tools.
Below we will discuss some important methods that deploy both supervised and unsupervised learning. We will also discuss some approaches that are unique to neuroimaging (such as multivoxel pattern analysis).
As described by Christophe Phillips in the OHBM 2017 course on pattern recognition (4:34 min), the idea of supervised learning is to train a machine to find a mapping between observed data, such as your fMRI images, and an explanatory variable, which could be a disease label or a cognitive score. We can then find new, unlabeled subjects and predict the disease label or cognitive score.
Christophe further explains (7:10 min) that supervised machine learning problems can be further subdivided into discrete classification predictions and continuous, or regression, predictions. Either way, supervised learning (10:50 min) ultimately relies on a mapping function between input and target variables, the specification of the shape of this function and the optimization of its parameters. The following are some examples of various commonly used algorithms:
Most of you are familiar with linear regression as a classical statistical technique. However, this old staple has refashioned itself as a supervised learning technique. We can think of regression as a predictive technique that uses one (or many) features in order to predict a response as one continuous value (7:35 min). The main difference between using regression as a standard statistical tool and as a machine learning tool is that in machine learning we test the predictive power of the linear model on unseen data that did not contribute to the training of the model.
The idea behind logistic regression is, at its root, exactly the same as linear regression. The only difference is the function that is fitted to the data. While in linear regression we fit a line (or some generalisation of it in n dimensions – e.g, a plane or a hyperplane), in logistic regression we fit a logistic function. The logistic function is that “S-shaped” curve that often pops up in many biological sciences. The logistic function has the very nice property of being bounded (often these boundaries are set to 0 and 1) and hence can be used to express a probability. By having a cut-off, usually half way, we can use logistic regression to categorise our sample, for example into patients and controls.
Support Vector Machines (SVMs):
Support vector machines (SVM) are a type of classification algorithms, where the aim is to draw a decision (or classification) boundary between sets of data points, so as to maximize the “separation” (or margin) between the sets. While this sounds fairly straightforward, it is often the case that the data points are not easily separable by a line or plane, for instance, if two circles are embedded into one another. Kernel SVMs use “kernels” to transform the data into an alternative space where it might become much easier to separate the two instances. Christophe describes kernels and SVMs (from 17:00 min) in his introductory lecture. There are additional parameters such as regularisation parameters, gamma and the margin, that are important to define how well the line separates the training data. For a more generic discussion of SVMs, this medium post does a good job at explaining the basics.
Deep learning is one of the most talked about classes of machine learning algorithms and the one that most excites the public’s mind. Despite all the hype, deep learning models are often treated as a black box, since their input-output-mapping is both analytically and intuitively hard to grasp. In Vince Calhoun’s OHBM educational lecture on deep learning approaches applied to neuroimaging, he explains that the foundation of deep learning lies in artificial neural networks. In fact, despite experiencing a boom in popularity in recent years, neural network modelling dates back to the 1950s when there was a lot of interest in creating a mathematical model of a biological neuron (This paper by Hassabis et al. (2017) provides a stimulating discussion on the relationship between neuroscience and artificial intelligence). This neuronal model became known as a perceptron. The most basic type of network is the multilayer perceptron (MLP), with artificial neurons (perceptrons) organised in hierarchical layers. The input to the network is propagated layer-by-layer, first through activation-functions in each node, and then through connections (weights) to the successive layer. The “deep” part of deep learning refers to the number of multiple hidden layers, i.e. the layers between the input and output of the network. In recent years, computational advances have allowed the training of deeper and deeper networks. Some types of neural networks that Vince describes are Restricted Boltzmann Machines (7:10 min), deep belief networks (8:20 min), convolutional neural networks (16:35 min) and others.
As with other supervised learning algorithms, deep learning needs a training set and a test set. Furthermore, the more layers you have, the more (labeled) data and computational resources you usually need. In fact, deep learning increased in popularity once computing power increased to the point that deep networks were feasible, particularly after the availability of graphical processing units (GPUs), which are hardware chips originally developed for accelerated processing of digital videos and graphic rendering (3:10 min).
Multi-voxel pattern analysis (MVPA): A common application of ML in brain imaging?
In the classical analysis of structural and functional MRI, i.e. the application of a general linear model (GLM), each voxel is considered separately. Due to its linear equations, the approach is mathematically neat and tractable, however, this “massively univariate” approach disregards the interdependencies between multiple voxels (see Robert Cox’s talk about fMRI analysis methods at 4:16 min, Mike Pratt’s talk at 0:35 min). In light of dynamic brain processes that engage entire networks of the brain, the independence assumption of single voxels is controversial. In order to address this issue, a more recent class of statistical models, known as multi-voxel pattern analysis (MVPA), has been introduced to account for the joint contribution or ”combinatorial code“ of multiple voxels across the brain to the phenomenon of interest (see Janaina Mourão-Miranda’s talk at 6:08 min). That is, MVPA describes a class of pattern-recognition techniques, which are presented in Mike Pratt’s talk on MVPA (3:33 min), and in a session devoted to MVPA at OHMB 2017 (the corresponding videos can be found here).
MVPA draws from algorithmic strategies commonly used in machine learning. First, the data are split into a training set and test set. Then the classifier of choice (e.g., SVM) is trained on the former to discriminate various multi-voxel patterns corresponding to the experimental conditions, and validated on the latter. Validation is done by using the trained model to predict the conditions in the test set based on the multi-voxel input, which is often referred to as decoding (see Bertrand Thirion from 5:38 min, and Mike Pratt’s talk at 8:04 min). In decoding, we try to predict from multi-scale neural processes its representational content, such as percepts or cognitive states, mostly induced by experimental conditions (Pratt’s talk at 11:55 min). Classifiers can be linear or non-linear in nature, each having their own limitations. Linear classifiers (e.g., linear discriminant analysis, LDA) are considered easier to train and to interpret, however, their sensitivity depends on the individual contribution of each voxel in the observed pattern (see Jo Etzel’s talk at 18:00 min). Whereas non-linear classifiers (e.g., artificial neural networks, see Vince Calhoun’s talk) are able to find more complex relationships between patterns of voxels, they require training on large datasets.
The term MVPA was coined by Norman, Polyn, Detre, and Haxby (2006), who introduced it within the framework of fMRI analysis. However, considering a broader definition of the term, most of the methods that MVPA encompases are not restricted to fMRI and can be equally applied to structural imaging (e.g., Zhang et al., 2018; or Cole et al., 2017; and see James Cole’s talk at OHBM 2017).
In supervised learning, in addition to the input data (for example, fMRI images), we also need the ‘ground-truth’ output, which may be labels (e.g. healthy vs condition) or scores (some sort of cognitive or behavioural scores). However, frequently we either do not have appropriate labels, or the labels that we do have are unreliable, for example, in psychiatric imaging, as explained by Verena Kebets in her video. In this case, unsupervised machine learning methods open new doors.
In the neuroimaging community, the unsupervised machine learning technique of clustering is best known by its application to estimating brain parcellations. Brain parcellation is not a new problem and neither is it one that necessarily involves machine learning. All neuroimagers have heard of the 19th century neuroanatomist Korbinian Brodmann who labeled brain regions according to their cytoarchitecture -- the original brain mapping! As Simon Eickhoff explains in last year’s keynote, cytoarchitecture is not the only feature by which to parcellate the brain; there are others, such as receptor architecture, cortical myelin structure, and connectivity structure.
Unsupervised clustering methods are ideal for data with known differences based on features of interest where we want to automatically group brain regions according to these features. The simplest, and probably most widely used technique available, is k-means clustering. In neuroimaging, this is done by creating a feature vector per voxel in a region of interest, for example, structural or functional connectivity information. These voxels can now be thought of as points in an n-dimensional feature space. The k-means algorithm then attempts to maximize within-group similarity. Unfortunately, k-means clustering requires an a priori knowledge of the amount of groupings (k) one is interested in (although there are some iterative techniques to try to establish the number of k).
Other approaches to clustering, such as hierarchical clustering or spectral clustering, have the same basic idea of splitting up data (in this case brain voxels) into discrete groups, or parcels, but have slightly different assumptions or tricks. For example, hierarchical clustering assumes that the data have a hierarchical structure and so you could split the brain into two groups, each of which can be split into another two groups, until we reach the level of individual voxels. Or you could start from individual voxels and work your way up. On the other hand, spectral clustering has an additional step (the spectral transformation), which allows you to disregard weak similarity. Sarah Genon, in her educational course lecture, describes how to perform such analyses using diffusion MRI data.
Laplacian EigenMaps / Diffusion Embedding:
Sometimes you may not be interested in grouping voxels into a fixed number of parcels, but rather explore the relationship of voxels in a region of interest based on a feature of interest. In his educational talk, Daniel Margulies describes techniques that can be used to investigate the connectopies, or connectivity maps, of the brain. The initial approach is similar to the one described above, where you create a feature vector for every voxel in the brain. These features are then compared to each other using a measure of similarity to create a similarity, or affinity, matrix. This matrix is then decomposed and new vectors are obtained which describe the principal gradients of similarity across a region of interest, or indeed the whole brain. Daniel’s keynote describes how such types of analyses can be used to elucidate topographic principles of macroscale cortical connectivity.
Associative models, such as partial least squares (PLS) or canonical correlation analysis (CCA), are not exactly supervised or unsupervised. In supervised learning we generally have a multivariate input (e.g. brain images) and a univariate output (labels). In unsupervised learning, we only have one set of multivariate input data, such as the connectivity information for brain parcellation. In PLS or CCA, we want to discover relationships (associations) between two sets of multivariate inputs (e.g., brain images and behavioral/clinical scores).
As Janaina Mourao-Miranda explains in her video (2:25 min), psychiatric conditions often have unreliable labels. To deal with this, she uses associative models (e.g., PLS), trying to find a linear combination of neuroimaging predictors that are most strongly associated with a linear combination of multivariate clinical and behavioural data. This provides a data-driven way to generate summary labels that can possibly shed new light on clinical conditions.
It is possible to do significance testing on associative models to make inferences. Valeria Kebets describes (11:20 min) how to perform permutation tests in order to determine which components are significant, how to determine whether components are expressed differently across groups, and, finally, which variables drive the extracted components. In her video, Janaina also goes into the details about how her group applies a multiple hold-out validation framework in partial least squares analysis (16:50 min).
What do I need to consider when using machine learning tools for brain imaging analysis?
As explored in the previous paragraphs, machine learning techniques open many doors for brain imaging. They can help make predictions that depend on complex interactions, help find patterns in our data that we have been previously unaware of, and also automate time consuming manual tasks, such as segmentations (e.g. see Pim Moeskop’s video). However, there are also pitfalls that must be considered. First, the more complex and powerful machine learning techniques really need large datasets. In his video, Andrew Doyle (25:30 min) discusses how neuroimaging applications differ from classical image processing problems, with brain imaging data usually being very large and high-dimensional data, while sample sizes are comparatively small. For some applications (e.g. image segmentation or MVPA), smaller sample sizes may not be a big issue, but for others (such as patient classification) they may. A recent publication by Arbabshirani et al. (2018) explores the reason for why making individual predictions from brain imaging data is challenging. Another paper by Varoquaux (2018) focuses on the challenges with model cross validation on small sample sizes.
Of course, the noisier the data, the more data points are needed, and brain imaging data are renowned to be noisy. Additionally, if no reliable labels can be provided, the best supervised learning algorithms will not be able to succeed. Another problem, in particular with the more complex methods such as deep learning, is the challenge of assessing how biologically meaningful the resulting models are. Recent efforts have gone into better understanding and evaluating what is actually happening in the deep layers (e.g. watch Alex Binder’s video). However, resulting models may not teach us anything about biological or pathological mechanisms, and they may actually represent biases that exist in our training data, limiting their generalisability to other data. For example, this year’s replication award was awarded to a study that showed lack of generalisability of some published models.
Until these issues are fully resolved by the community, as individual researchers the best we can do is to understand the algorithms we are using and their limitations. That way we can choose the most suitable techniques, and rigorously apply them on suitable sample sizes and avoid overfitting. Luckily, there is a vast amount of online resources on machine learning techniques, including textbooks (e.g. Bishop, 2006), Andrew Ng’s famous Coursera courses on machine learning and deep learning, and online blogs and forums. Numerous papers from the MRI community provide overviews of machine learning tools for neuroimaging, or more specific examples, such as how machine learning is shaping cognitive neuroimaging, and how to use machine learning classifiers for fMRI data. OHBM’s OnDemand has an extensive archive of videos from education courses and talks on machine learning applications for neuroimaging that we’ve included in this article and, we also expect many exciting new educational and symposium talks on the use of machine learning techniques in brain imaging at this year’s OHBM in Rome, so watch out for those, too!
Professor David Kennedy is a Professor of Psychiatry at the University of Massachusetts. He was a key contributor to the development of functional MRI and diffusion MRI, working in MGH during the late 80s and 90s. His current work reflects his interests in Neuroinformatics and data sharing - indeed he is a founding editor of the journal Neuroinformatics. We found out about his experiences with OHBM, and some of the deep and lasting friendships he made along the way.
By Kirstie Whitaker
Open science means different things to different people. It includes open data, open source code, preprints, preregistrations, and open access publications. Getting started with open science practices can be overwhelming, and there is considerable variability in their adoption across the OHBM community. I sat down with Tibor Auer to learn about the survey he has developed to capture different attitudes towards open science practices in order to better support everyone in doing the best research they can.
Hi Tibor, let’s get started with an easy question: who are you?
I am a Research Fellow in MRI at the Department of Psychology, Royal Holloway University of London, where I facilitate neuroimaging by contributing to methods innovation, as well as training and education. Neurofeedback is one of my main research interests, as it offers the opportunity to follow neural development during the training process, thus satisfying interest in both theory and application. I received my PhD in clinical neuroscience and implemented various neuroimaging techniques in a clinical environment. Then, I focused on the implementation and the optimization of fMRI-based neurofeedback, and investigated assumptions and mechanisms underlying a neurofeedback training.
Why do you care about open science?
I am probably not the only person who has found a cool paper, and tried out its methods on my own data…. and fabulously failed! There are so many steps to reproducing a paper. If you are lucky you can find the corresponding author’s current e-mail address. They may bother to reply to your questions. The authors need to be able to locate the corresponding version of their workflow. It has to be documented well enough that you can decipher the code and adapt it to your data. Those are a lot of “ifs” and most of us aren’t that lucky. The process isn’t transparent.
Transparency, defined as unambiguous description of the data and the approaches, is not only beneficial to reproducibility but also to productivity in the first place. Automated pipelines, such as automatic analysis, allow quick exploration of the analysis space. Once you set up the workflow on pilot data, it can be applied on the real data right away. The pipeline’s documentation, based on standards such as the Neuroimaging Data Model, ensures the longevity of the project by making transitions smoother for current and future users.
What is this survey and what does it cover?
I am conducting this survey to investigate the knowledge and adoption of open research practices, including sharing of data and materials, study preregistration and related activities. It has been largely inspired by a recent survey in Cardiff. I want to know how people think about Open Science practices, their influence (positive or negative) and how the perception of Open Science might depend on experience with actual solutions and tools. Probably the most important aspect is what people see as the greatest barriers to the uptake of open research practices, and whether/how they can be ameliorated by (local and global) training and support. The awareness of challenges and solutions might vary across career levels, fields, and sectors (e.g. public and private), and I would like to be able to capture this variance, because we can only achieve change if we understand and resolve our differences.
Why did you want to create the survey?
Open Science is not just a (better) way to do science. One day, hopefully, we can omit the ‘open’, because all science will be done this way. Getting to that point cannot be an authoritarian process. It can only happen based on consensus and as a bottom-up initiative. We must understand what Open Science means for each of us, what encourages or discourages us to be engaged with it, what kind of support may be the most effective. The survey itself may raise awareness and give some hints and ideas by mentioning specific solutions in the questions.
Who do you hope will fill out the survey?
My guess is that many responses will be from methods experts who already appreciate the benefits of Open Science practices, but I would really like to also hear from the broader community of neuroimagers. Answers from people who do not use any Open Science practices are particularly valuable; especially if they tell us their reasons. It is important to know which aspects of Open Science have the biggest reach at the moment, and how they are perceived by a broad range of people in the OHBM community.
Early career researchers and senior scientists have different, sometimes even conflicting, priorities and motivations which may often explain the slow implementation of Open Science practices at an institutional level. Efficient resolution of these conflicts is possible only if we understand and harmonise the different incentives faced by these disparate groups.
How long will the survey be open for?
I have two stopping criteria for the survey: either we reach one thousand participants, or we get to 30 April 2019, whichever is earlier. I would like to receive responses from at least five hundred people but ideally one thousand!
I really appreciate the support of International Neuroinformatics Coordinating Facility (INCF) and OHBM in disseminating the survey to their members. It is important to note, however, that you do not have to be in either of these communities to respond. Everyone is welcome to submit their opinions.
What will you do with the results when you have them?
A summary of the results will be shared within the UK Network of Open Science Working Groups and other professional platforms, including the related Special Interest Groups of the OHBM and the INCF, to feed into the formulation and harmonisation of our Open Science strategy. The main aim of the survey is to capture different points of view, but I also hope it will prompt people to talk with each other, and think about and understand perceptions other than their own.
Every such survey has an educational angle, as well. For example, a recent survey on data management and sharing lets people know about several data handling approaches they might have not thought of before but they may try out after filling out the questionnaire. I hope this survey will encourage people to consider solutions unfamiliar to them and understand how they could benefit from additional tools.
Fantastic, I’m so looking forward to reading the results!
Thank you! Here’s the link again
By Amanpreet Badhwar and the Diversity and Gender Committee
The OHBM Diversity and Gender Committee is performing a series of interviews to better understand and address the issues of implicit and explicit biases in academia. The ultimate goal is to promote gender and geographic balance and create a more inclusive brain mapping community. Over the next months we will be interviewing social psychologists and social neuroscientists to get multiple perspectives on the topic. We start this series by interviewing Uta Frith.
Aman Badhwar (AB): How did you get into social psychology and neuroscience? Are there any personal experiences that motivated you?
Uta Frith (UF): When I left school for university, way back in the 1960s, in provincial Germany, I had never heard of neuroscience, and I wasn’t sure that psychology was a respectable subject to study. I more or less drifted into psychology and one reason was that I was curious to learn about myself and about other people. But that, I soon gathered, was considered the worst possible motivation for taking up psychology! Instead I could learn about memory, perception and attention. It sounded a bit dreary, but I persevered. I was not disappointed.
At the University of Saarbrücken, I discovered that there are millions of questions about the mind and the brain, but the most interesting ones came from some vivid case demonstrations in the psychiatric clinic. I therefore decided to take up training in clinical psychology and was lucky to get a place at the Institute of Psychiatry in London. But it was by chance that I met an autistic child right at the beginning of my training. This five-year-old boy was supremely uninterested in me, but very interested in bricks and puzzles. I was captivated by the strange contrast of abilities and disabilities. It made me determined to find out what might go on in the mind/brain of this little boy.
I never tired of doing research on autism and it taught me a lot about myself. As a child of my time, I had believed that it was my social environment that had turned me into a person keenly interested in other people. Now I had to entertain the notion that social interaction is possible only because of a built-in part of the human brain, and that if this part does not work, the result is autism.
Autism provided a way to probe the question ‘what makes human communication special’. In the 1980s a group of us tested the theory that a cognitive mechanism, we termed mentalising, enables us to attribute mental states to ourselves and others, and is crucial for reciprocal communication. It still feeds my sense of wonder that this cognitive mechanism is at the basis of human cooperation and reputation, as well as competition and deception.
AB: Women haven’t always received appropriate recognition for their work. How have you seen opportunities for women change? A change in climate? Is there a similar trend for women of colour?
UF: I grew up at a time of strong gender stereotypes and I was thoroughly imbued with them. My mother, born 1907, did not have a higher education, - it simply wasn’t even considered at the time. However, she managed to educate herself. At age eleven or so I begged my parents to send me to an academically challenging school that was then aimed at boys who would go on to University. The general view at the time was that girls should get a good all-round education at secondary school, then do something useful before getting married, such as becoming teachers or nurses. A few girls at my school were happily tolerated as oddballs or ‘bluestockings’. Since we were not conforming to the prevailing gender stereotype, we were having to shape our own role and station in life. I was diligent and ambitious, and I was sure that I would be able to take up any career I wanted. I don’t know where this confidence came from. Perhaps one reason was that my parents had confidence in me and always supported my choices.
I am very aware of the long history of the struggle for women’s rights. I am proud that in the last 100 years women in Western societies have won rights that previously were reserved for men. The fact that the inequality and inferiority of women has been seen as unsupportable, morally and economically, has changed the cultural climate. Now we need to work towards this change for every part of the world, not just for a few privileged parts.
However, there are still barriers even in privileged societies. For example, it is harder to enter an academic career if your social background leaves you ignorant or suspicious of the world of academia. And then there is still the glass ceiling! While women in the UK now make up almost a quarter of professors, when we take all universities and all disciplines together, women of colour make up a vanishingly small fraction. What is remarkable, is that women of colour have provided some of the most inspiring role models for women in science and technology in recent times – think of the success of the film Hidden Figures.
AB: What aspects related to gender differences within the work setting have not changed, but should?
UF: The first thing that comes to mind is the gender pay gap. Many organisations are now committed to close this gap, and they are deeply embarrassed if the gap is large. It would be a great injustice if women would not get the same pay, the same status, the same rewards for doing the same jobs as men. But this is a remarkably complicated issue, and it is not always easy to know what is the ‘same job’. I remember being surprised on discovering, when I became a professor, that my salary was lower than that of other professors. But then, I loved my job as a Medical Research Council scientist. It allowed me to dedicate myself entirely to research. I did not envy the administrative responsibilities or teaching duties that the higher paid professors had to do.
There are differences in our individual preferences and also abilities. I suppose these might correlate with gender differences to some extent. But individual differences trump gender differences. If you take only averages and forget about distributions and individual differences, then gender differences will emerge in some more or less relevant variables. Their importance depends on culture and context. I like to quote the example of a highly significant gender difference that exists in the ability to throw a ball. Women are far worse at this than men.
In situations where women compete with men, strengths and weaknesses associated with gender stereotypes often come to the fore. These can be quite ambiguous, sometimes harmless and sometimes treacherous, and therefore very suitable for sexist jokes. Q: “Is Google male or female?” A: “Female, because it doesn't let you finish a sentence before making a suggestion.” This makes me smile because it refers to a well-known stereotype but also contains a backhanded compliment – interrupting boring talk is surely better than meekly listening. But I can also see the exasperating side of making clever suggestions.
Of course, mostly, prejudices are not funny and can have serious consequences, for example in the selection of people given awards, or grants, or simply being invited to give talks. One thing we have learned from social cognitive neuroscience is that we cannot help identifying ourselves with a group, our ingroup. The reverse side of the coin is disrespecting an outgroup. The image we build of ourselves, and the confidence we have in ourselves feed on group alliance and group discrimination. None of us want to be seen as outsiders. We relish the approval of our ingroup and we perfectly understand if we are not exactly loved by the outgroup.
In the work setting there continues to be prejudices about gender differences that keep women down, or in their place, as some would have it. Early career researchers are necessarily at a stage of their life when they have to build up a family, and this almost always means a bigger burden on women than men. I am very glad to note that the younger generation of men are now taking up more of the burden. But this is still far from being the norm.
Is it possible to get rid of prejudices? Not really. I think that getting rid of one prejudice may well mean acquiring another. We have to use shortcuts when making fast decisions, or else we may never decide anything.
AB: Should there be more attention to microaggressions in the workplace, or at conferences? For example, ignoring or minimising ideas, claiming others’ ideas, assuming stereotypic roles. Have these reduced over time? Or increased with decrease of overt aggression?
UF: Wouldn’t it be great if we all became nicer people, more kind to each other and more tolerant? But that is a pipedream. We are shaped by evolution to be aggressive as well as benign, to be selfish as well as altruistic. I personally find it a satisfying side effect of getting older that competitiveness declines, and that with it the related emotions, such as pride, envy, revenge, or triumph, seem to decline as well. Sadly, I see less sign that my propensity for discriminating ingroups and outgroups declines with age.
Forms of aggression and selfish behaviour are expressed differently in different cultures, and there are subtle expectations that women should be less aggressive. They are also suspected of feeling offended more easily. Women can work against these stereotypes, and in particular not being afraid of giving and taking criticism. Academic life involves a lot of critique and judgment. It is hard to take justified critique and easy to take it for unjustified aggression. The main danger in my view is to feel offended when we shouldn’t be. Above all, we should not get drawn into retaliation. Empirical studies show that retaliation leads to escalation.
Hence my advice would be that monitoring should be less zealous when registering aggressions, including micro-ones, and more zealous when gauging the appropriate reactions to aggressions. Any monitoring is best done in diverse groups. With different backgrounds we have different perspectives, and we can see each other’s flaws much better than we can see our own.
AB: What tips would you give for young investigators, irrespective of their own gender to better understand gender biases, and to make changes?
UF: My main tip is to discuss and to argue freely any differences of opinion about whether some things are more suitable for men or for women. Take the opposite view and try the argument from the other side. Arguments are a good thing for scientists. We need diversity or else we can easily get stuck in a dead end.
I tend to think that complementarity of social roles and fairness to all should be celebrated over a relentless quest for equality. We are all different! Research needs different types of people, different skills, different outlooks and collaboration as well as competition. There are conflicts, and research tells us that transgressions need to be punished, or else cooperation collapses. But punishment is a dangerous thing. The danger is retaliation and escalation. So what to do? Rules of politeness have been set up for good reason and I recommend anyone to value them and not throw them aside as old fashioned. My advice goes further: try to be more than polite and try to be kind and forgiving! Of course, there will always be people who will do something that has rightly offended you or disadvantaged you in some way. But remember others may experience the same from you, even if you feel that you never gave cause for offence. Our self-bias makes this so.
AB: Are there any specific gender biases that earlier were advantageous for women, that are now gone?
UF: I can’t think of any.
AB: Thank you Uta for taking the time to provide such insight into the various topics. I really look forward to seeing you at OHBM 2019 in Rome!
Note: AB is also a member of the OHBM Diversity and Gender Committee.
By: Elizabeth DuPre with the Aperture working groups
The Aperture survey has closed, and we’re excited to share the results! Here, we summarize our initial conclusions and outline some next steps for moving the conversation forwards. If you’re interested in diving into the full dataset, anonymized responses are available here.
Aperture is an OHBM initiative to develop a new publishing platform. Envisioned as an open platform to publish novel research objects, Aperture was created by TOPIC (The OHBM Publishing Initiative Committee) and received support from the OHBM Council in Winter of 2017. To better understand publishing needs within the OHBM community, we launched a survey in December 2018 to capture feedback on several dimensions of the publishing process. After advertising on the blog, social media, and the OHBM mailing list, we received nearly 200 responses. Here, we report on the results for three of the surveyed dimensions: publishable research objects, reviewing models, and paths to financial sustainability. If you are interested in examining these conclusions yourself or diving into other aspects of the data, be sure to check out our github repository. There, you can access the anonymized data and these initial analyses as well as an interactive environment to explore them in your web browser using Binder.
In analyzing the survey results, our first concern was whether respondents wanted an official OHBM publishing platform. From this sample of the OHBM membership, the answer was a clear ‘Yes,’ with over 85% of respondents in favor of developing Aperture. A majority of respondents hoped that Aperture would publish cutting-edge research objects such as data descriptors and code in addition to traditional empirical papers. These results strongly support our initial vision and solidify our commitment to developing this new publishing platform.
By Michele Veldsman & Gabby Jean
Australia has been steadily increasing its output in the field of neuroimaging. It hosts a number of leading imaging centres, including the Melbourne Brain Centre, the Brain & Mind Centre in Sydney and the Herston Imaging Facility in Brisbane. Professor Amy Brodtmann, Stroke Neurologist in Melbourne University and Inaugural Chair of the OHBM Australian Chapter, has been witness to and helped drive these developments. As a clinician-scientist she has made significant scientific contributions to our understanding of stroke, such as documenting grey matter changes and amyloid depositions in the months and years following an incident.
To celebrate the first meeting of the OHBM Australian chapter we managed to interview Amy, and provide an overview of the events at this meeting. First, Michele Veldsman demonstrated her multitasking skills by interviewing Amy with her infant daughter attached (and thankfully mainly sleeping) in a baby sling.
Michele Veldsman (MV): Can you tell us how you became a neuroimager?
Amy Brodtmann (AB): I started carrying out Neuroimaging research back in 2000-01. I did my training as a neurologist at the Austin Health medical school in Melbourne. I wanted to learn how to understand cognitive disorders and brain plasticity and to build those skills. I was very fortunate to know Aina Puce, who many of you will know as one of the founding members of OHBM, She recommended I did an fMRI PhD, and since I’ve returned to Melbourne I’ve used imaging in almost all my projects. Sometimes it makes me wonder why I try and combine this with stroke populations as there’s a lot of pain that comes with that [laughs].
MV: And so what are the advantages of being both a researcher and a clinician?
AB: The huge advantage is that I see patients every day, and questions arise every day from the people that I see. I work with both caregivers and patients. In my world that makes research easier. I’m lucky and get inspiration all the time. Then I have to think about what I’m going to do next. The disadvantages are that you’re split: I have two days a week that I’m full time clinical and the rest of the week in theory I’m part-time because of my children’s needs and running my family. I’m running around the wards as well as looking after three kids.
MV: As a stroke neurologist your work has clear links to brain plasticity. What do you think are the big findings in that area and do you think there will be a lot of translation into the clinic?
AB: I looked initially at a series of stroke patients who were in their mid-80s and saw enormous changes in their brains. Our brains are quite phenomenal. We saw changes in an area of the brain that, according to the textbooks, was supposed to be very static and isn’t supposed to exhibit much experience-dependent plasticity. That was in primary visual cortex: striate. What was so interesting is that these heteromodal cortices are always the areas that kick in – we were seeing activations in the same hemisphere, ipsilesionally and then contralesionally, and after the plasticity we wondered if the brain then goes on to degenerate. I think that most of that degeneration is due to stroke being just another example of a risk factor. Thinking about the effects of physical activity, we found that the percent of the day spent active was associated with better retention of function.
MV: You were also interested in the link between stroke and dementia. What do you think neuroimaging is going to tell us about that?
AB: We’ve got a number of different factors at work. Patients who have had a stroke have already got the risk factors that can affect their brain prior to the stroke. Then we’ve got changes due to the stroke itself. Then we’ve got potential deafferentation or disconnection from both the stroke and white matter hyperintensities. So I think that it’s really different – we need to understand which areas are affected first, old fashioned structural imaging. The second thing we have to understand is how does that affect networks, thinking of Bill Seeley’s work and how others have explained how networks are affected. It’s really critical because I don’t think it’s going to be quite so straightforward in stroke. I think that there are so many areas that are potentially affected, so we’ll need to very carefully unpack what’s happening in the frontal lobes. I think we’ll have to think about how we factor in both structural and functional connectivity and how they can interact. There are so many ways that imaging is going to help us understand this process – we can not only chart observationally but we can start to look at the grey matter in terms of cortical thickness and the thalami and hippocampi and understand regional vulnerability. Critically we can also understand the processes going on in the white matter, and finally we’ve got methods that allow us to do that, which is really exciting.
[Michele’s child wakes up contentedly – and allows the interview to continue]
MV: So the Australian OHBM Chapter has recently started, what are the aims of the chapter and where do you see it going in the next five years?
AB: This was something that Michael Breakspear suggested to us last year. He’s on council for OHBM. Michael suggested to a number of us that come to the meetings regularly that we should make an application for a chapter. So we got a group together and made a pitch and OHBM were keen to get local chapters up and running.
I know that within Melbourne and outside Melbourne there are plenty of projects that share interests or methods. We’re uniquely situated to do that in Australia given our population. The big focus will be on getting more people connected and people interested and getting our PhDs and postdocs to get together. OHBM has always had that youthful drive and focus and I want that to stay the same. We start to get a bit more concrete as we get older – it must be that vascular brain burden and it’s really good to have people say ‘why not?’. I love a ‘why not?’.
MV: Thank you!
Gabby Jean then provided us with an update on events at the OHBM Australia Chapter meeting:
On 12 October 2018, the first inaugural meeting of the OHBM Australian Chapter was held at the Melbourne Brain Centre. Our aim was to introduce the neuroimaging community in Australia, to maintain current and form new networks of regional and international collaborations across all disciplines of neuroscience and neuroimaging.
Our first meeting had a great turn out, with nearly 200 registered attendees. The day was kicked off by a brief overview of the OHBM Australian Chapter and its mission, followed by an inaugural keynote by Professor Michael Breakspear, past OHBM treasurer. A second keynote presentation by Professor Caroline (Lindy) Rae detailed what we are exactly mapping in ‘human brain mapping’. We heard about impressive and inspirational work presented by PhD students in a ‘PhD data blitz presentations’ session and by junior postdocs in the ‘post-doc presentations’ session. Sila Genc won the prize for best PhD blitz presentation and Steffen Bollmann won the prize for best post-doc presentation, although all PhD and postdoc presentations were of exceptionally high quality.
In addition to hearing about great neuroimaging work carried out throughout Australia, delegates broke into smaller workgroups to discuss ideas for a potential large collaborative research effort similar in scope to the UK Biobank initiative.
This brainstorm session was introduced by Dr. Lianne Schmaal who presented an overview of current international and national ‘big data’ initiatives. Amy Brodtmann then presented an overview of potential funding opportunities for such a big data initiative in Australia.
After lunch, each workgroup presented their proposal for a big data initiative in Australia, which included many interesting and partly converging, partly unique ideas. We aim to further follow-up with the OHBM Australian Chapter community on these great ideas for a large-scale data collection initiative in Australia (for further details check us out on our chapter site).
Overall, we believe the inaugural meeting was a success and we hope it was productive and interesting for all attending. We would like to thank everyone for their attendance and great contributions to the discussions. Please stay tuned for upcoming events in 2019!
In this second installment of the OHBM Oral History series we had the chance to speak to Professor Susan Bookheimer. Susan is a Clinical Neuropsychologist and Professor-in-residence at UCLA. She has played a leading role in our understanding of the brain basis of language, and pioneered the use of functional MRI and PET in clinical samples. Her recent work has explored the causes of social communication deficits in children with Autism.
Susan has been a significant contributor to OHBM throughout its history having taken on the role of meetings liaison (2002-03), Chair of the Scientific Advisory Board (2015-16) and Chair (2012-13). We found out about her early clinical work using functional imaging, her excitement about big data and how she overcame a bout of laryngitis to give a talk at the first OHBM meeting in Paris.
By the OHBM Diversity & Gender Committee
Nearly three years ago, a young woman approached the microphone at the “Town Hall Meeting” in Geneva during OHBM’s 20th Annual Meeting and pointed out that all the newly elected Council members were men. While there were women on the ballot for the 2016 Council elections, the results ended in a composition of 14 men to 1 woman. Given a binomial probability of < 0.0005 for this outcome, the coin is clearly weighted.
The Council meeting in Geneva took place the day following the Town Hall Meeting, and there was unanimous agreement that not only was the male/female ratio a problem, there were other aspects of diversity, including geographic representation on Council, that needed to be addressed. For example, demographic research showed that approximately 15% of the OHBM membership is from Asia, however, at that time, there was no Asian representation on Council.
By Tim van Mourik; Edited by Elizabeth DuPre
The abstract reviews are in, and we're getting excited for OHBM's 25th annual meeting. Tim van Mourik has been chatting with Cameron Craddock about the history of the Open Science Room. Here he shares that history, his vision for Rome 2019, and asks for your priorities and interests via this survey.
As a member of the Special Interest Group on Open Science, I will be chairing the OHBM Open Science Room (OSR) this year. This is a room in which, during the main OHBM conference, topics related to open science are being discussed. The way I like to see it is that the main OHBM conference primarily focuses on the results of our scientific work, whereas the OSR is a place where we can focus on the process of doing science. In addition, it is a place for community discussion. Only last month a preprint on time-varying functional connectivity came out that had its roots in the OSR. After a Twitter conversation during the conference in Vancouver, the discussion was quickly moved to the OSR and eventually resulted in a collaborative manuscript.
Although today the OSR is a place for new analysis methods and tools, discussions, and collaboration, it started out as a small space for hacking on code together. To learn more about this transformation, I interviewed Cameron Craddock on how the OSR came about and where it could go. Cameron is one of the founders of Brainhack, which he envisaged as a space for neuroscience researchers to code together and learn collaboratively. “After the first Brainhack in 2012, we wanted to bring this to a central place, to OHBM. The first Hackathon in 2013 was competitive in nature and took place during the conference.”
Following this initial success, the Hackathon was reconceived as a separate event preceding the conference. Making the Hackathon into a separate event encouraged a natural evolution: “We wanted to bring in more of the educational aspect to the conference. From this, the OSR emerged. It started out as an educational course: Brainhack 101. Besides lectures that provided handles for doing more reproducible science, there were ad hoc software demonstrations of open source tools.” The OSR started out with many ‘unconferences’: spontaneous and unrehearsed talks about personal ideas, questions, and suggestions. The low barrier-of-entry has always made it accessible to a wide range of people and has facilitated a better learning experience. The talks had a strong educational focus and highlighted the collaborative power of new open source tools.
The mindset of the OSR has always been one of collaboration and empowerment to do open science. Answering questions like: “How can I best share my data?” and “What factors are most important for reproducibility?”. But as the OSR grew bigger, it started to get harder to maintain its spontaneity. I asked Cameron if the fact that it was so low-key is a bug or a feature: “It’s a shame to learn that you missed a talk because you didn’t know it was there, or that it overlapped with talks from the main program. It would be really useful to have this more organised in advance. But indeed, it is important to try and retain the same atmosphere. Spontaneity can be important for having better conversations.”
As OSR chair, and with support from all the members of the Open Science Special Interest Group committee, I will be organising this year’s OSR at OHBM 2019, Rome. We have been thinking a lot about what we would like the OSR to be; how to be of value to the OHBM community as a complement to the main conference. It is clear that the OSR has an important educational role and that there is a strong focus on the adoption of open science best practices and technical solutions as a means of improving science. There is also an aspect of constructive discussion and community building to it.
Parallel to the main conference, the OSR could be a place for discussing the problems of scientific work we encounter in our daily lives. Questions like: what are reasonable demands in terms of data and code sharing without it becoming a burden? What are the considerations if you want to publish ethically? How can we make sure that science is open for all?
But maybe an even broader perspective could be beneficial. The process of doing science also concerns topics beyond just Open Science. Could the OSR also be a platform for discussing a wider range of topics, such as the ways in which science is done, evaluated, and funded, career perspectives in and outside a traditional university setting, and mental health challenges in academia. By covering these topics, the OSR could add a platform to the initiatives from the Student-Postdoc SIG as well. This way we can make the OSR the best experience for all.
When I asked Cameron if there was any one thing he would want people to know about the OSR, his response was: “I firmly believe that everybody could get something out of the OSR & Hackathon!” And that is my personal mission for this year’s OSR.
Within the Open Science Special Interest Group we have many ideas on what we could feature in the OSR. But we are also really interested in hearing what you would like to see. We would much appreciate if you could give us brief feedback about your experiences, hopes, and expectations for the OSR by completing this survey.
Invitation to project
Natalia Bielczyk & OHBM Student and Postdoc Special Interest Group,
Edited by AmanPreet Badhwar
Early career researchers in different parts of the world face similar challenges, but not everyone has the same access to mentoring and career development resources. While online mentoring programmes, such as the OHBM International Online Mentoring Programme, are available, it is hard to cover the needs of the whole population of early career researchers in the natural sciences.
In order to tackle this issue within the OHBM Student and Postdoc Special Interest Group, we are developing a set of advice relevant to early career researchers in the natural sciences. The aim of this project is to empower early career researchers to positively influence their future career opportunities on a daily basis - regardless of the circumstances. The main points which we aim to cover are the following:
The project will take the form of a full-length manuscript. The draft working paper has been posted under the link https://osf.io/53yrv/.
We would, however, like to further develop the manuscript before submitting this work for peer review. Therefore, to provide advice that can be generalizable to a wide variety of situations, demographics, and countries, we are seeking contributions from the OHBM community. We would like to welcome everyone willing to participate to join us and discuss this subject on the associated Google group: https://groups.google.com/forum/#!forum/effective-self-management-for-ecrs
We look forward to your contributions. The manuscript is dedicated to early career researchers but we welcome the expertise and experience of seniors researchers on this topic as well. We hope the group endeavour will not only make for a better manuscript but also serve as a platform where early career researchers can give each other support and advice, and make new friends! Depending on the traffic, the Google group might be sustained after the final form of the manuscript is formed. The official starting date of the Google group is Monday, January 14th 2019, and we close collecting contributions on Thursday, February 28th 2019.
Active participation in the Google group will be interpreted as an academic collaboration. Therefore, we will bring together feedback received through the group, invite the significant contributors to co-author this work and reupload the working paper as a preprint with a new, extended list of authors. Invitation will be determined on the basis of: (1) constructive comments / additions to the manuscript and (2) being active and helpful to other members of the Google group. This material will be subsequently submitted for peer review.
You can find all the details about the project, together with the Code of Conduct and the rules for establishing authorship, on the Google group. We hope to meet you soon and chat about careers together!
By Ilona Lipp and Jean Chen
Edited by: Nils Muhlert
In science, the term “work-life balance” may seem like a holy grail for some and a conundrum for others. Its easy matter-of-factness belies deep self-examination. Today’s research communities are larger and more competitive than ever with regard to permanent positions and funding, with the success rate for many grants being as low as 5%. For this reason many leave academia after finishing their PhDs (according to a recent report by the Royal Society). For those who choose to stay, the clock starts ticking from the very moment one starts a job, and the counting begins --- for grants, for journal articles, for trainees, for experience in international labs, etc. So who are the people that, despite everything seemingly being against the odds, persevere and manage to stand out in a world of stressed early-career researchers? Do they purposely dedicate their lives to science? Do they have a life outside of work? Are they even human?
To find out, we talked to a diverse group of seven early-to-mid-career researchers, all highly successful for their career stage in terms of their funding situation, publication list and professional recognition (below, ordered by first name). We asked them how important work-life balance is to them and what strategies they take to achieve it, and have summarized their answers for you.
By Chris Gorgolewski & Ekaterina Dobryakova
Reproducibility and transparency are core to all branches of science. Two years ago, OHBM established the Reproducibility Award. The purpose of this award is to honor researchers who conducted a ‘successful’ or ‘unsuccessful’ replication study, while adhering to rigorous standards of study design, data collection and analysis. The second recipient of the Reproducibility Award is Benedikt Sundermann, who received the award during OHBM 2018 in Singapore for his study that was published in the Journal of Neural Transmission. Chris Gorgolewski, one of the initiators of the OHBM Reproducibility Award, interviewed Benedikt about his experience related to this replication study.
Chris Gorgolewski (CG): I am joined here today by Benedikt Sundermann, the recipient of 2018 OHBM Replication Award. Benedikt, thank you for joining us and congratulations on the award.
Benedikt Sundermann (BS): Thank you.
CG: The first question I want to ask you is how would you describe the study if you met a stranger a bar?
BS: In previous studies, people have tried to apply artificial intelligence technologies that are frequently used in face recognition to functional brain imaging data in order to try and diagnose people, for example, with depression. In our study, we wanted to see whether this replicates in a larger and more clinically realistic sample, featuring various comorbidities, heterogeneous age, sex etc. Surprisingly, most of the previously reported results did not replicate in this larger, more heterogeneous and clinically realistic sample. Only when we looked at a subgroup of people could we replicate some models but, still, at a diagnostic accuracy that would not be clinically useful.
CG: I see. So that is quite a controversial statement, especially considering how much we discuss about the application of neuroimaging methods to the clinic. Did you experience challenges in publishing this work?
BS: Yes, definitely. If I remember correctly, it was the fourth or fifth submission of this work that we finally managed to publish. Generally, when you try to publish a replication study with a negative result, you should be prepared to be interrogated much more critically by the reviewers, about the sample, about the methods and so on. But there was also criticism that our work was not scientifically solid. For example, there were comments like “We know from previous studies that this works, the fact that you couldn’t replicate it means that you must have made a mistake.”
CG: I see, but you must have, in a way, anticipated that it’s going to be somehow challenging. I want to understand a bit more what motivated you to do this wonderful work to begin with.
BS: I have a clinical background in radiology and I am mostly interested in actual diagnostic tools and to expand the spectrum of diagnostic tools that we can use in the clinic. In major disorders, we are usually limited to the exclusion of larger structural lesions but we cannot really get the diagnostic information about the actual disease mechanisms and disease correlates in these people. So my main motivation was to work on these technologies to improve these diagnostic imaging techniques based on functional imaging and machine learning.
CG: So after having completed the study - you know they say ‘hindsight is 20/20’ - if you were to take the trip back to the past in a time machine, what would you do differently? What advice would you give to people who are planning to do a replication study?
BS: First, you need to expect frustration. We did not expect that much frustration that we experienced during the study and when we tried to publish it. So just be prepared and then it will probably be easier, I think. The other thing is, in a replication setting you might be interrogated more strictly about your sample and about your analyses. Having a good structure of your analysis and your data will make it a lot easier. This is also one of the points that you are also working on in your initiative, so I think this is pretty important and we should focus more on that.
CG: Great. So what’s next for you? What are you excited about? What are you working on right now?
BS: One thing that I am working on right now is more clinical than scientific. I will be working on project optimisation/project standardization from a clinical point of view. Scientifically, I am currently interested in multi-modal integration of functional and structural imaging data in diagnostic models.
CG: So do I understand correctly that the main implication of your finding is that resting state fMRI is not ready for clinical use?
BS: I think it is currently not ready for clinical use. But I think it is important to realize that this is not an endpoint. Our findings do not suggest that there is zero information about depression in these data. We looked at it with machine learning techniques that were available two to three years ago and there has been a lot of improvement in this field since. Also, the current classification system of patients into major disorders has room for improvement. For example, just saying whether somebody has unipolar depression or not, may not be sufficient to capture the wide spectrum and large heterogeneity of these patients.
CG: So you think that the main reason for the lack of diagnostic ability are noisy labels and poor definitions of the phenomena that we are trying to predict?
BS: I think that it is at least one major determinant.
CG: So as a clinician how do you improve classification of methods?
BS: Currently, the main classifications are based on clinical interviews. You have to ask standardized questions, and you have a checklist to see whether certain criteria are fulfilled or not. But maybe we can use biological information itself to further sub-categorize patients’ mental disorders, and to see some commonalities or links between different mental disorders.
CG: That sounds very exciting! The OHBM replication award will run next year so I encourage everyone to submit their replication studies and see you next year.
Please note, the submission deadline to be considered for the Replication Award is January 11th, 2019.
This year marks the third full year of the OHBM blog. In 2018 we’ve published over 40 blogposts, covering topics as broad as diversity in brain mapping, neuroimaging in Iran, art and science and of course our interviews with the Annual meeting keynote speakers. We’ve seen changes in our editorial team, with new faces Claude Bajada and Ilona Lipp bringing fresh energy to our blogposts, but also saying farewell to two of our original blogteam members, Panthea Heydari and Thomas Yeo. We are proud to see Thomas becoming one of the keynote speakers at OHBM2019 in Rome, and Nikola Stikov, the original blogteam lead editor, taking over for Jeanette Mumford as Chair of the OHBM Communication Committee. Here, we share our favourite posts of the year.
The holidays are fast approaching, and I would like to celebrate it by shining a light on the many nuggets of wisdom I have gained via my participation, both as a writer and an editor, on the blog team. I have had the opportunity to interact with many brilliant brain mappers, and have had many memorable conversations and exchanges. So here it goes:
Daniel Margulies taught me to gesture to my head to illustrate that I study the brain. Trust me, it is so much more effective and easier to understand than my usual repertoire of explanations about what I do. But what he really hooked me with was the following: “Although there is a substantial focus in brain mapping of the differences and discrete boundaries between areas and large-scale systems, one challenge is to also consider how these distinctions are integrated into a functional whole”. Bruce Miller provided some key advice on things to concentrate on when the “functional whole” that Daniel mentioned is falling apart in the face of neurodegeneration. Bruce pointed out how important it is to concentrate on “not only what are the weaknesses, but what are the strengths, and has anything new emerged that is actually a new strength… What is preserved is telling us something about where in the brain the bad molecules are not accumulating. But it also allows us to think about the patients, about things that are important to them”. From the career development blog by the Student and Postdoc SIG, I was relieved to learn that every career path is different, and that there is still hope for me to start my own institute (and it just might be modeled after a treehouse, taking inspiration from Daniel Margulies here). Finally the Open Science SIG blog post reinforced for me that we are all part of the the scientific community, …..the sappy, corny and mushy reason we all stick around! Happy holidays everyone!!!
Being new to the blog team, I feel as though this little fish is now swimming in the big pond, gill to gill with some of the biggest fish in human neuroimaging research. My first interview was with Ed Bullmore, learning about his diverse experience in the clinical, academic, and industry worlds. Entering neuroscience from a medical background myself, I was particularly interested in his advice to medical students who may be interested in the technical side of research but feel they “come from the wrong background”. During the 2018 OHBM meeting I met and interviewed the instructor of a MOOC I had followed during my PhD! Martin Lindquist was the 2018 OHBM educational award winner and we had a great discussion about the challenges students and researchers face in neuroimaging. In his words, the best advice may be to “be curious, look outside the box, be willing to do crazy things and fail, and have fun!”. Finally, I worked with this year’s local organising committee in preparing a short blog post about the 2019 25th anniversary OHBM meeting. I’m looking forward to seeing everyone in Rome!
This has been my 3rd year on the OHBM blog team, and I am thoroughly enjoying the opportunities for shaping communications within our research community. This year, besides my OHBM Keynote interview with Gustave Deco, I mainly focused my efforts towards transcending geopolitical barriers of brain-mapping research, with the 2-part series on scientists and trainees from Iran. This initiative, though challenging, was very rewarding. Going forward, I hope to contribute more material to help guide the careers and lives of junior researchers.
Having worked in the media team before, I joined the blog team this year to broaden my science communication horizon. 2018 kicked off for me with an article about Open Science (OS) challenges; an idea that emerged after OHBM 2017 in Vancouver where so many of you had shown interest in OS. “Sharing is caring, but is privacy theft?”. It was also our first interview series on the PLOS Neuro blog and introduced a new format: several experts get to answer the same question. Together with media team captain Kevin Weiner we set out to find from Russell Poldrack, Jeanette Mumford, and 4 other OS pioneers! where they see the main challenges and solutions that will make our field more open and reproducible. The format worked well, the final post was rewarding, and I felt boosted for more Q&A. As a physician-scientist I am interested in the added value of neuroimaging for patients, and the difference it could make for neurology and psychiatry. And so my other favourite blogging experience in 2018 dealt with progress and challenges for imaging in depression. I hope that you will find the views of our translational brain mapping experts in “Closing the loop for brain imaging in depression: What have we learned and where are we heading?” as intriguing. Blogging in 2018 was great fun, and I am already so curious what 2019 will bring!!
I only joined the blog team a few months ago and kicked off with quite an elaborate post, the “OHBM OnDemand How-to: resting state fMRI analysis” guide. It’s been really fun being able to write for a scientific audience in a less structured way than I do with papers, and I’m also very excited about my next posts (spoiler alert: there may be one coming out just after new year).
Hard to believe that 2018 is behind us. This year I had diverse writing opportunities that I greatly enjoyed. Through my post for PLOS Neuro blog, I interacted with and interviewed many leading minds in the field of traumatic brain injury research and neuroimaging. While acknowledging the complexities of scanning a clinical population, in particular the multi-disciplinary skills needed, all of the interviewees were optimistic about the potential insight into disease offered by developing neuroimaging methods. These posts were great for networking and connecting to other scientists in the field. It was also gratifying to receive emails from researchers who are new to the field or topic, or even lay persons who want to know more about brain injury due to personal experience. Such communications are always very inspirational. I also had a change of pace, writing a blogpost on the art exhibit by Shubigi Rao. Interviewing this artist revealed the similarities between art and science in aspects such as inspiration, patterns of work on a project, and exchange of ideas. Now, I am looking forward to more exciting interactions as we enter 2019.
2018 was our busiest year so far as Blogteam editor. There were a number of standout points for me (alongside the sweet egg buns in Singapore). We heard Leah Somerville discussing the potential effects of social media on teenage brains, and Aina Puce highlighting the challenges of quality control in multi-modal imaging projects. I really enjoyed hearing Mark Humphries’ views on how findings from cellular neuroscience currently constrain systems neuroscience theories - or whether that’s even happening at all! Finally, our first double interview between Heidi Johansen-Berg and Charlie Stagg extended these discussions on ways in which preclinical and clinical scanning can be fruitfully combined but also considered major advances in measuring neurotransmitters like GABA using MR spectroscopy - a method that has so far received scant coverage in our blogposts.
Jeanette Mumford (ending note as Chair of Comcom)
I’m so glad I decided to join the Communications Committee three years ago and am honored that I was able to serve as chair over the last year. I’m amazed by how far we’ve come and how hard the members of the committee work as well as Stephanie McGuire, our fearless Communications Manager. For the first time since 2004, I wasn’t able to attend the conference in Singapore, but thanks to the blog posts, tweets and OnDemand materials, I don’t feel like I completely missed out. I’m a big fan of the posts related to the OHBM Open Science SIG and the OHBM Student and Postdoc SIG, because they’re such a great addition to OHBM and I wish they existed when I was a graduate student and postdoc. I also really like the Keynote series, since the interviews offer a new dimension about the speaker beyond what we’d get from their talk, reading their papers or their CV. I’ve even gotten to help out with a few of the posts this year, including the “OnDemand How-To: Resting State fMRI” piece, which featured some great videos in the OHBM OnDemand portal. Overall, it was a great year and I can’t wait to see what we do in 2019.
From all of us at OHBM Communications Committee, we wish you a happy and productive 2019!
OHBM 2019 in Rome next June will mark twenty-five years since the first meeting in Paris. During that time the organization has evolved from an annual meeting of like-minded brain mappers to a society with multi-national chapter meetings hosted throughout the year, early-career researcher led special interest groups and open science resources. To celebrate what has been achieved during that time, we asked some of the founding members how they became interested in neuroimaging, how brain mapping has changed, about developments in funding and opportunities within their country and about their memories from OHBM meetings.
This first OHBM Oral History video interview features Professor Alan Evans, a James McGill Professor of Neurology and Neurosurgery, Psychiatry and Biomedical Engineering at McGill University and researcher in the McConnell Brain Imaging Centre (BIC) of the Montreal Neurological Institute. We learned about Canada’s hugely impressive investment in neuroimaging, the incorporation of genetics and other sciences into the work presented at OHBM and the collegiate, youthful feel of the OHBM meetings themselves.
By Ayaka Ando & Natalia Z. Bielczyk, OHBM Student and Postdoc Special Interest Group,
Edited by AmanPreet Badhwar
Christmas is just around the corner and the deadline for the OHBM annual meeting abstract submission is fast approaching! The OHBM Student and Postdoc Special Interest Group (SP-SIG) also had a busy 2018 organizing the Secrets behind Success symposium in Singapore, launching the third round of the International Online Mentoring Programme (attracting an additional ~150 participants), and launching the new SP-SIG blog.
Considering a new year is upon us soon, we wanted to share with you some insights we have gained by interviewing researchers in academia and industry. In this blog, we present a collection of interesting insights from our 2018 interview series. For the full interviews, please visit our SP-SIG blog.
Leaving academia is not a failure
Leaving academia as a conscious career choice is often seen as a failure (Kruger, 2018). However, this is what Dr Anita Bowles, the Head of Academic Research & Learner Studies at Rosetta Stone in San Jose, California, had to say about her experience:
“People often feel like a failure if they are thinking about leaving academia. I talk to a lot of graduate students in this situation and I understand their concerns, because I felt the same way. I would like to tell these students that this is not the case at all once you are on the other side of the decision. You can do valuable and satisfying things outside academia, including research that can be applied to help people. And if you feel like trying, just go for it.”
How to find jobs in industry
If one decides to leave academia, the first impulse is to browse through job listings. We, however, found that our interviewees had varied approaches.
Dr Anita Bowles says:
“I found out about Rosetta Stone through networking: I knew someone else who was employed by the company and who also had moved from academia to industry. I am glad that it turned out this way for me, as what I am doing now has a lot of overlap with my past research, and I am passionate about this topic.”
Dr Ricarda Braukmann, a recent PhD graduate, and currently a Program Leader for Social Sciences at Data Archiving and Networked Services (DANS) told us:
“As I was already passionate about open science and knew of the work DANS was doing, I was proactive and contacted them explaining my wish to gain experience outside academia. I was very lucky, as I indeed got the chance to work for DANS during a four months part-time internship in my final, fourth year of the PhD. The internship was the starting point of the job I have now, which DANS offered me after I finished my PhD.”
Natalia Nowakowska, a PhD candidate at the University of Amsterdam and a freelance copy- and content-writer, also started her job as a freelancer by networking:
“How did I start freelancing? In a way, it was also a lucky strike - I met a person in a bar who was leading a course on how to become a freelancer. I joined the course and got some practical instructions on how to start and find my own place in this space.”
Every career path is different
No matter how much we try not to, we sometimes still compare our career trajectories with our peers. However, from Dr Aaron Clauset, an Associate Professor of Computer Science at the University of Colorado at Boulder and in the BioFrontiers Institute, we learned that career trajectories in science are highly nonlinear. In his research Aaron and colleagues analyzed career paths of thousands of computer science professors at US and Canadian universities (Way et al., 2017, Clauset et al., 2017). What they found was that the conventional research trajectory of a rapid rise in productivity to an early peak, followed by a slow decline, only emerges after averaging the individual trajectories of large groups of scientists. Even though the average number of papers per person per year is higher in highly prestigious institutions than in other institutions, the shape of this average trajectory is independent from the prestige of the affiliated institution. However, the average pattern conceals high inter-individual variability in the career trajectories to the extent that only one in every three faculty members follow the average trajectory (Fig. 1).
Fig 1. The average trajectory versus inter-individual variability in the trajectories; a reprint from Way et al. (2017). (A) Average publication count follows conventional narrative across prestige, with the division into 5 groups of affiliations on the basis of the prestige. (B) In fact, there are four different types of trajectories, and most of the subjects do not follow this averaged, conventional trajectory. Research conducted in a group of 2,300 computer scientists from the U.S. and Canada.
Furthermore, related work on citation to papers by physicists, carried out by Dr. Sinatra, one of Clauset’s collaborators and colleagues, revealed that groundbreaking discoveries seem to be equally likely at each career stage (Fig. 2, Sinatra et al., 2016).
The results from these studies are very optimistic - in a sense that there is not just one canonical way of pursuing a career in academia, and that success can come at every ‘academic age’.
Start your own institute!
Lastly, what do you do when you know deep inside that you should pursue a career in research, but the whole universe tries to prove you otherwise? We interviewed Jeff Hawkins, the founder of Redwood Neuroscience Institute & Numenta Inc., who finished his official research training with a BSc from Cornell University in 1979. Since then, Jeff never successfully accomplished a PhD because of constant rejections of his ideas that were ahead of his time.
So, Jeff developed an impressive career in mobile computing in the Silicon Valley instead, where he established Palm and Handspring, two mobile computing companies. However, his thoughts have always circled around science. Today, Jeff leads his own research institute, Redwood. Recently, he gave a keynote lecture at the Open Day of the prestigious Human Brain Project summit in Maastricht, where he introduced his theory of grid cells in neocortex (a.k.a. a Thousand Brains Theory of Intelligence) as the framework for cortical computation. He is very modest about his achievements, and, when asked about how he managed to create his own institute, Jeff answered:
“It was easier than you imagine. The idea came from several neuroscientist friends of my mine who said the field of neuroscience needs cortical theory. They encouraged me to start an institute. I agreed only on the condition that they help me, and they did. There were a number of scientists who, like me, wanted to work on cortical theory, so they signed up.”
Easy, right? :)
If you would like to get involved in the SP-SIG activities, give us an interview or perform an interview that can be featured on our website, please contact us!
Also, stay tuned for the roll-out of our next big project early next year! We are going to launch an open initiative, where any OHBM member is welcome to help us shape a set of guidelines (in a manuscript format) on effective self-management for early career researchers. We hope to meet you in this project!
Many fledgling neuroscientists who are eager to dive deep into the statistical analysis of functional MRI data know of Martin Lindquist. Martin is a professor in the department of biostatistics at the Johns Hopkins University. His popular “Principles of fMRI” Massive Open Online Course (MOOC) and associated book have reached an audience of more than 80,000 students worldwide.
Our interview with Martin follows his path through various academic disciplines, eventually leading him towards educating the current generation of neuroimagers and winning the 2018 OHBM Education in Neuroimaging Award.
Claude Bajada (CB): Prof. Lindquist, you're a statistician by training but according to your CV your first academic foray was in the “History of Ideas” at Stockholm University. Can you tell us a bit about that?
Martin Lindquist (ML): When I was a highschool student, I didn't really know what I wanted to do with my life and what direction to take. At that time Sweden had compulsory military service and since I was a bit younger than my peers, I had to wait. So I decided to have a go at some history and philosophy. I’ve always enjoyed it, and still do, but I realised that it wasn’t the career for me.
CB: How did you link that to statistics? Was that a smooth progression?
ML: Well, after my military service, I applied to the Royal Institute of Technology’s Statistics program - it was really more like an engineering program, a STEM Science and technology type thing. So at the time I still didn’t know that I was going to work in statistics, but I was now focused on science and technology.
CB: Was this like a masters degree?
ML: Yes, it was a masters.
CB: How did you get into neuroscience?
ML: I actually did my master’s thesis on neural spiking. I then went to do a PhD at Rutgers University in the US. My PhD was in statistics doing fMRI-type analysis.
CB: Neuroimaging is a multidisciplinary field, there are psychologists, physicians, statisticians. Would you consider yourself a neuroscientist or a statistician if someone had to ask? Or a historian?
ML: Not a historian (laughs). I like to consider myself a little bit of everything. I think if I was pressed, I’d say I was a statistician because that’s what my degree is in. I have a PhD in statistics, I’m in a biostatistics department and the colleagues that I interact with on a daily basis are statisticians, so it feels like my home.But at the same time I’m excited about applying statistics to neuroscience. It just doesn’t feel as honest to say that I’m a neuroscientist because I don’t have that training and background. So I’d probably say I’m a statistician, but I like the idea of being a little bit of everything
CB: We are conducting this interview because you won the 2018 OHBM Education in Neuroimaging Award. Last year you interviewed the previous winner Mark Cohen. I’m curious, how does it feel to be on the other side this year?
ML: Awesome. It was great to help interview Mark last year, he really deserved the award. And it is very humbling to have been chosen to win the award myself.
CB: You have taught a lot of statistics to budding neuroscientists and have also changed fields from history, which is not a STEM subject, to engineering. How difficult do you think is it to understand the needs of students who don’t necessarily have a STEM background but come into neuroscience?
ML: I think, as any educator, you have to remember what it felt like to not know anything. What it felt like to enter a discipline or to learn about a new subject. It can be hard. I like trying to break down information into the smallest possible components that I can. Then I try to re-build things from these components and that seems to work with people. Sometimes people get lost but at least they have something to hold on to. My goal is that everyone gets something out of it. My strategy is to make content as small and manageable as possible, then sort of grow it, and you know, if you understand 80% of it, that’s ok! Everyone will take a different amount home.
CB: You’re talking about modular structures, these little chunks that people can build on, and I suppose in that sense MOOCS are really great because they do come in little bite sized pieces.
ML: This is true.
CB: I remember I took your Coursera course while doing my PhD and I found MOOCS amazing to fill little gaps in my knowledge. How did you you get into teaching MOOCS and do you think that they will be the future of education?
ML: That’s actually a pretty interesting question. My department, which is the department of biostatistics at Johns Hopkins University, was one of the pioneers in MOOCS. A few of my colleagues created a data science programme. It’s a set of 10 courses and they started about 3 or 4 years ago and they’ve had 5 million students, it’s pretty amazing! Those were the first real MOOC blockbusters and people saw what was happening and how exciting it was, so a lot of people in the department started playing around with MOOCS. We had some expertise and we kept saying “let’s try this.”
At one point, our department was running more MOOCs than most universities - in fact, had it been a university, our department would have been ranked fourth! At this point we probably had 50 MOOC classes. Finally we hired a videographer from a local arts school and she helped us with a lot of the editing and we streamlined the process. It became the culture of the department. So I guess I was at the right place at the right time! I mean had I been somewhere else, it might not have been considered possible, because it is a lot of work.
CB: How do you gauge what your students are finding hard to grapple with and more than that, what are the things that they find hard?
ML: Remember your earlier question “how do you teach people who may not be so STEMie”? If you are teaching a workshop, you see it on their face. If you are looking and interacting with your audience you can see when people are puzzled, you can see when they are nodding their heads and you can feed off that. That really helped me figure out what worked and what didn’t work. With the MOOCS this was really not clear, so I tried to use some of the knowledge from previous workshops. In Coursera you had student feedback, but it just didn’t have that same personal touch, so it was unclear exactly what needed to be tweaked. That was a little difficult.
But that’s the interesting story. In my experience and the experience of all of my colleagues who also teach MOOCS, the thing that people find hardest is the thing that you feel you know best. So for example, I know quite a bit about linear models and so I think that I can explain the GLM really well, but maybe I’m not as good at explaining pulse sequences for fMRI. But it seemed like it was the opposite, they found my explanations of pulse sequences more understandable than the material that I was an expert in.
CB: Do you think it may be because once you do attain expert knowledge you kind of forget what it’s like to not have it?
ML: Absolutely. I think that’s what’s so interesting because I thought “Oh, I’m really good at that, why don’t they understand it?” Then you have to take a step back and realise it’s probably because I’m too deep in the weeds and I’ve sort of lost track … you have to remember what it’s like. And sometimes if you think about something all the time then it’s hard to remember that.
CB: One more question about your most recent work, you just published a paper about how to properly power fMRI studies. Researchers are increasingly aware about lack of power, but of course including more participants always comes with extra costs. What do you think are the implications of this?
ML: Often it will depend on the research question. In other fields like genomics, they need pretty big sample sizes and people went together in consortia etc. So possibly for certain questions we need to do similar things . But at the same time there are many big databases coming out, like the UK Biobank and the HCP. Being at Hopkins, you see that there is this tension, as there are also people who are more interested in single subject analysis. So I’m fascinated by the question of whether we can use these bigger databases to inform small samples or single subject analysis and I think that’s going to be important.
CB: We talk about increasing the number of participants all the time, and coming from a small institution myself, this may hinder these small institutions that want to work. However, there are now many open datasets, could these be the solution?
ML: Sure, there are all these big open datasets, but they are not acquired for any targeted purposes. Then you have these smaller studies that have a very specific hypothesis, and I think you need both. And figuring out a way to get both is going to be a very important question moving forward.
CB: What final advice would you have for budding neuroscientists?
ML: Be curious, look outside the box, be willing to do crazy things and fail, and have fun!
OHBM plans to create a new publishing platform, Aperture, to host high-quality research objects while promoting reproducible and open science. With Aperture, OHBM plans to open up to a more diverse approach in communicating academic research, bringing transparency and interactivity to the publishing process. We want to hear from you, the OHBM community, about what you would like to see in such a publishing platform.
Please complete the survey by clicking here.
The OHBM Publishing Initiative Committee (TOPIC) will be introducing a new journal, called Aperture. The roadmap above is tentative, and it was presented to council in 2017 to illustrate the various aspects of the project led by the OHBM Publishing Initiative Committee (TOPIC). Credit: Agah Karakuzu
For more information about the processes behind publications, read through this explainer by Michael Breakspear.
By Elizabeth DuPre; Edited by Aman Badhwar
What exactly is “open science”? As open science has become increasingly central to discussions of scientific practice, publishing, and policy, it’s become harder to provide a precise definition that encompasses all of its aims. The ubiquitous nature of open science is at once its greatest strength and deepest weakness -- it’s broadly useful, but difficult to distill as a clear set of values or prescriptions. It’s been said that one way to get a better sense of a movement is to talk to its supporters, so I turned to some of the newest advocates for open science within OHBM: the newly elected members of the Open Science Special Interest Group (OSSIG) committee. I asked for their thoughts on what open science means, how they got involved in promoting open science initiatives, and why they’re so passionate about increasing its reach within our community.
Camille Maumet, research scientist at Inria and Chair-Elect, confirmed the current state of affairs: “open science is not a monolith.” Indeed, the backgrounds of newly elected OSSIG members support this idea. With training ranging from art, to physics, to cognitive science, to software engineering, these are a diverse group of individuals with multiple perspectives and skills. A far cry, it seems, from the myth of open scientists as fitting a single mold.
Their reasons for first getting involved in open science initiatives aligned with each OSSIG member’s background and seemed to echo the three overarching aims of open science in practice, publishing, and policy. For Tim van Mourik, an fMRI methods scientist and Open Science Room Chair, it was a realization that open science could address many of his concerns about the practice of science and the methods commonly used in functional neuroimaging. “In the wake of the reproducibility crisis I started to get a more complete picture of the situation and learned about publication bias, analysis flexibility, and publish-or-perish factors,” he said. “When I got a null-result rejected from a journal despite better methods and more subjects than previously published, positive results, I became even more determined to try and change the system.”
For Ana Van Gulick, a library faculty at Carnegie Mellon University and Secretary-Elect, it was the open publishing of data and code that motivated her to fully embrace open science. “I wanted to keep track all of the emerging open source tools”, she said, “to help students and faculty maximize transparency, efficiency, and reproducibility in their workflows.” She pointed out the importance of open information given “how fast these tools and software are being developed.” Accessible information on social media sites like “Twitter is great for learning about new developments,” Ana added, “and so are preprints.”
For Chair-Elect Camille, it was the the idea of fundamentally re-imagining science policy and restructuring the incentives for how we as scientists work together. “Open science brings us closer to a collaborative research,” she pointed out, “where we can share our results earlier, capitalize on each other’s experience and design research together.”
Despite their varied pathways into open science, all the committee members I spoke to echoed the same idea for why they’ve stayed involved: the community. Sara Kimmich, graduate student and Treasurer-Elect, put it this way: “This may sound corny, but I’m still impressed with how supportive the community around open science is. It's filled with people who are genuinely interested in seeing the best science get done, and they'll go out of their way to help you on your own path.” This sense that the open science community directly improved their science and careers was echoed by Katja Heuer, PhD candidate at Max Planck and Hackathon Co-Chair. “For my very first paper, I found collaborators on Twitter that I have never met,” she said. “Through these collaborations, I received additional data that we’ve made available to the entire community, and I can incorporate all feedback into the final journal version of the paper – how fantastic!”
Now that they’re advocates for open science, they also share a similar set of concerns and hopes for the future. Greg Kiar, PhD candidate at McGill University and current Treasurer, pointed to the difficulty of pushing for change in the current system. “Established labs and institutes often have practices or procedures for data collection and tool development that have been streamlined and relied on to be engine of their scientific achievements for years,” he said. “Interrupting existing solutions that are closed for equivalent open ones is a tough sell -- it can be a lot of work, and the gains are not immediate.” Treasurer-elect Sara echoed this idea, and said she often feels as though she’s “waiting on a cultural climate shift in the larger scientific community to fully incorporate open science frameworks into existing institutions and our educational systems”. Despite this, everyone seems optimistic about the possibility of change.
After speaking with the newest members of the OSSIG, I feel as though we may not have a single-definition of open science, but we have a new generation of scientists working together for broad and lasting change. Roberto Toro, group leader at Institut Pasteur and Hackathon Co-Chair, summed up this vision for the future as realizing that “the difference between Science and Open Science is wrong. The real difference we should make is between Siloed Science and Science. Siloed Science describes a type of science where the evidence and methods supporting research cannot be fully evaluated and discussed. Now, what we call Open Science, that’s just Science, there is no need for extra adjectives.”
By Niall Duncan
The rich scientific program enjoyed each year at the OHBM conference is the product of persistent hard work by the program committee. They take the raw material of the abstracts and proposals submitted by scientists all over the world and craft it into the finely polished end result that we all see. That means deciding which symposia get the green light, which abstracts become oral presentations and which posters only, and which researchers will be given the distinction of presenting their work in a keynote address.
This year the committee was chaired by Prof. Guillén Fernandez of the Donders Institute. We met with him to find out how the process went this year, to learn about his scientific path, and to hear his thoughts about the brain and how we study it.
Niall Duncan (ND): Professor Fernandez, welcome. You’re the program chair this year. In that process were there any particular unexpected challenges that came up? Any surprises?
Guillén Fernández (GF): There’s a pretty regular operation that you do every year. You get all the abstracts, the proposals for the keynotes, and so forth, and then you get together the program committee who meet in person and by teleconference. Then we just put together a nice program that fits the interesting topics together while considering some factors of diversity of gender and geography. Sometimes there is a surprise like a keynote is not available, so then you have to look for another one - that sometimes makes the balancing out in the end difficult. We were also interested to get certain topics that are currently of particular interest, large cohort studies, for example, into the program. It was all done quite smoothly.
ND: You started out as a medical doctor and then made the switch to what we could call basic science. Why did you make that switch?
GF: I actually started doing science while in med school, and that continued throughout my residency as a neurologist. At that time I initially did electrophysiology, then later also neuroimaging. It was hard to see how I could use these methods in my clinical practice - there was a gap in understanding. That was something that interested me so I worked on it and then you’re automatically away from clinically applied science, from science that is useful for clinical application.
A second point was that I liked clinical work and scientific research but I saw that it was difficult to do both at a good level. To be a good clinician and a good scientist is just difficult at the same time. Some people are able but I thought it was a stretch for me. I wanted to avoid being a kind of mediocre clinician and a good scientist, or vice versa, so then I decided to go for science only. Then there was this position at the Donders which I got and so the decision was made.
ND: And the rest was history... So, starting out as a physician, and then moving into brain science only, do you think that background has shaped the way that you think about the brain and how to study it?
GF: Yes, I think the disadvantage is that at med school and residency you are not that well trained in carrying out science. I think there is a deficit which you have to compensate for. But, on the other hand, as a clinician you have a very good overview of all kinds of things. You are quite pragmatic in your approach and I think you can more easily see the relevance of things sometimes. So, if you are too theoretical, too conceptual, then I sometimes in interactions with colleagues have the idea that it’s easier for physicians to be pragmatic in some aspects.
ND: A lot of your research has focussed on stress – both current stress and developmental stress. What was it that lead you into that area of study?
GF: I worked on, and my work is still quite focussed on, memory. I’m interested in states where memory formation - establishing a new memory trace, or retrieving that or stabilisation of it - is either impaired or improved. Stressful states are quite unique in the sense that they improve memory formation and subsequent stabilisation, but impair retrieval. And that’s a nice approach.
The second point is that I think neuroscience, and in particular neuroimaging, can bring something to understanding mental disorders, and I’m quite interested in why and how traumatic experiences are so well remembered that the memories become maladaptive to the individual. That is something about mental disorders that I am in the long term interested to understand more about. I’m trained as a neurologist but now my research might be more relevant for psychiatry. That’s something that I developed and is the reason why I research the effect of acute stress on memory formation and retrieval.
Developmentally, the human brain - the brain in general - is a very plastic organ and therefore is shaped by the experiences one has. These might make the brain later on more susceptible to, for example, negative memories. This memory bias and how that develops over the lifetime is something I am interested in.
ND: You’ve published many great papers but do you have a favourite paper or research project?
GF: I think that with our stress work there are some quite different studies that fit together nicely. There we have developed a model - that we also described in a review paper - that I think is particularly nice because we manipulated cortisol, we manipulated norepinephrine, and even with the genetic studies it all fitted together nicely. That makes the model quite nice and changed the perception, in my view, of the effect of cortisol in the brain. It’s usually just the bad boy but if I understand the more recent literature well then it appears to be that it is quite helpful in the acute state to get back to a normal state. So that’s more a dampening and normalisation function of cortisol, which is a different view. If you look into the literature twenty years ago then it’s always the bad boy. In the chronic state it probably still is, but in the acute not. That’s the most interesting.
ND: Similarly, you’ve taken what could be called emotions and applied it to what some people might call a cognitive function in memory. Do I understand that correctly, and if that interaction between emotions and cognition is correct what do you think that tells us about how the brain works?
GF: Sometimes I have trouble distinguishing between what an emotion is and what a cognition is. Sometimes they might be more or less the same. I think that there are states in the brain, for example acute stress, arousal, or threat perception, that affect a whole set of cognitive processes. We have to understand that a bit better. There are the second by second cognitive processes that are going on, the computations, and there’s more slowly modulated states that go rather in minutes, and sometimes also more rapidly. Trying to understand the interaction between these is something we are not doing often. We are usually lucky, we are happy, that we can kind of get something done on just the cognition, or just on the state, and I think we have to look more into that interaction. These states have different timescales and different spatial distributions. In neuromodulatory terms they are processes from norepinephrine, or serotonin or dopamine, that have an effect all over the brain. We have to capture these slowly modulated states in the brain and how they affect specific processes.
ND: And finally, if you were the program chair in five years time, which topics do you think will be the most exciting for everyone?
GF: Predictions are difficult! I can at least express my hope, whether it will be fulfilled in five years I don’t know. I hope that we will have bigger systematic studies, on the one hand. Not only them as I hope we will also keep the small hypothesis testing experiments, but at the same time we should have larger systematic studies that go after more complex interactions between the different cognitive levels, or emotions and cognitions, the different brain states, in a more systematic way. I think that will be there.
I think that we will still see new methods for analysis. We are already getting to see machine learning and artificial intelligence used for data analysis. I think it will help us with more complex patterns that we are currently having difficulties to grasp. And, probably not in five years but hopefully soon, we will get useful biomarkers from neuroimaging in mental disorders, so that they are really informative for diagnostics, for treatment selection or prediction. These I hope for in five years.
ND: Here’s hoping! Dr. Fernandez, thank you very much for your time!
By Claude Bajada, Emiliano Ricciardi, Pietro Pietrini and the Rome LOC
As you might know, the 25th OHBM Congress will come back to Italy and this time we will be in Rome. The capital and the largest city of Italy, Rome is one of the most visited cities in the world and is famous for its extensive, rich history. Delegates will travel from all corners of the world, all nooks and crannies to visit the eternal city for a week of neural cartography.
The 25th anniversary meeting will feature the most up to date research in the field of neuroimaging, using multimodal data and cutting edge analysis techniques with an increasingly strong focus upon machine learning and ‘big data’ approaches. OHBM also proudly promotes an increasingly open science environment.
The conference caters for all levels of researchers. This includes educational sessions for PhD students, postdocs and early career researchers, as well as the annual OHBM Hackathon, now a staple event that welcomes both new and established open science enthusiasts.
Given its long tradition in neuroscience, neurophysiology and psychology, Italy is well qualified to host such an important gathering of scientists who come from every corner of the globe. Indeed, it was the conclusion of Italian physiologist Angelo Mosso that brain circulation changes selectively with neural activity that is the basis of the powerful methodologies that we now employ to explore neural correlates of mental function. Currently, Italy has a rapidly expanding neuroimaging community distributed across the whole country and the 2019 Local Organizing Committee gathers together ‘brain mappers’ from the major Italian research centers, covering all methodological approaches of neuroimaging.
Rome was called “the Eternal City” by the ancient Romans, first of all because they believed that no matter what happened in the rest of the world, the city of Rome would always remain standing, and also because when the Roman Empire was new, Rome was already very old! Rome's history spans over two and half thousand years. During this time it transformed from a small Latin village to the center of a vast empire, through the founding of Catholicism, the Italian Renaissance and into the capital of today's Italy.
The historic center of the city is a UNESCO World Heritage Site with wonderful palaces, thousand-year-old churches, Romantic ruins, opulent monuments, ornate statues and graceful fountains. Rome has a rich historical heritage and cosmopolitan atmosphere, making it one of Europe's and the world's most famous, influential and beautiful capitals.
Today, Rome has a growing nightlife scene and is also seen as a shopping haven, being regarded as one of the fashion capitals of the world. Modern Rome is captivating with its heady mix of haunting ruins, awe-inspiring art and a vibrant street life.
There are so many things to do and places to visit that your week in Rome will be intense!
Ancient Rome aficionados cannot miss the great Colosseum, the Circus Maximus and the Roman Forum. Those who would like to discover Baroque Rome have to visit Piazza Navona with its great fountains and the world-wide famous Fontana di Trevi.
You cannot leave Rome without visiting the Vatican City with its majestic museums, Saint Peter’s Basilica and the Sistine Chapel.
And what about the beautiful gardens of Villa Borghese? A great opportunity to switch-off and take a stroll ending your visit with the entrance at the Borghese gallery Museum!
Then you can spend a great time shopping in the city center; you can go to Via del Corso for the major brands, to Via Condotti for the luxury brands and to Via del Boschetto for the independent boutiques. And it goes without saying, you cannot have shopping in Rome if you don’t experience one of the weekly markets in the city!
Italians, and Romans, often boast that their food is the greatest in the world; from the most known and iconic Italian foods, as pizza or ice-cream, to the more local dishes as “pasta all’amatriciana” or “supplì” or “maritozzi con la panna”… are you curious? We will not tell you what they are because you have to come and taste them to discover how great could be the real Roman food!
Reaching us is very easy. The Leonardo Da Vinci international airport operates daily flights to over 300 destinations throughout the world. The airport is also well connected to Rome's city center. There is the Leonardo Express, a train exclusively for airport passengers to/from Rome Termini railway station, leaving every 15 minutes with a journey time of 32 minutes.
The conference will be held at Auditorium Parco della Musica, a big multi-functional art complex designed by the most important Italian Archistar, Renzo Piano, and located in the heart of Rome.
We encourage you to submit your abstracts as soon as possible (the deadline is 11:59pm EST Wednesday, December 19). and what more can we say except… see you in Rome!!
For even more information, visit Rome’s official tourism website: http://www.turismoroma.it/?lang=en
The OHBM have negotiated better rates for local hotels - we welcome you to take advantage of them.
By Ekaterina Dobryakova
Shubigi Rao, the Singapore-based artist whose works were presented at the OHBM 2018, grew up surrounded by science. As a child, she owned and was fascinated by rare books from the 17th-20th centuries that explored science and natural history. Neuroscience has always mesmerized her --- something she shares with brain mappers. Now Shubigi is a self-taught neuroscientist, with a neuroscience theory under her belt and art installations that often depict primordial ocean creatures with a complex central nervous system that are also reminiscent of sprouting dendrites and stained neurons.
We reached out to Shubigi Rao to get a behind-the-scenes look at her artistic thought processes:
Ekaterina Dobryakova (ED): You use many different mediums in your artwork and installations. Do you have a favorite technique and art form?
Shubigi Rao (SR): This is a great question - the reason I have employed diverse media is because for me the idea or concept is paramount, and if necessary I will teach myself a new medium or discipline if the idea demands it. This has been a lot of fun, but challenging sometimes when working with deadlines, as I don't have the luxury of getting lost in the wonders of a new form or field of knowledge. Since my current 10-year project involves the study of cultural destruction - its history and also why our species has a hostile relationship with knowledge - I've re-trained myself as a solo film-maker, and have been travelling around the world to document sites, events, people and oral histories. In terms of artistic medium I've also loved drawing (such a primal impulse and one that predates verbal/written language) and printmaking, especially intaglio and etching, but my current love is definitely film-making. I've written a fair bit about the relationships between these media, and I enjoy reading the neuroscience behind the drawing impulse etc.
ED: What was the most fascinating thing that you have learned during your studies of neuroscience?
SR: Almost everything is fascinating to me - from the very first human articulation to know the working of the brain, to studies on sea-slugs. I even find the politics behind the institutionalization of R&D and corporatization of research, and the problematics of it all to be very urgent and important issues. I'm endlessly fascinated by current work in language acquisition in infants (and in other animals as well), and interspecies communication. To answer your question with a single example, I suppose it would be my first encounter with neuroscience, when, as a young adult, I wanted to understand how we see, especially how our brain processes visual information and can make 'sense' of abstract art for instance. I still remember my sense of amazement at the decoding from V1 to the inferotemporal cortices (I was reading Hubel and Weiss, I think). Also, Cajal's studies, of course, appealed to me greatly, (as I grew up reading books on natural history and the science of the natural world from sometimes outdated books of 18th-19th century naturalists and scientists), and I devoured his work, and biographies.
ED: When I look at the works presented at OHBM 2018, I see ocean inhabitants such as the octopus and the jellyfish but these works also make me think about brain cells. What was your inspiration to create the works showcased at OHBM 2018?
SR: I've been particularly interested in interspecies communication, and also the way anthropomorphism occurs in popular retellings of scientific breakthroughs. The octopus is of course a subject of much current study and interest for its unique neuroanatomy. I've also been enjoying how it has been reimagined in popular imagination - all the way from Viktor Hugo's infamous 'devilfish' in Toilers of the Sea, which created an indelible image of the octopus as monstrous, to its appearance at the famed Great Exhibition at the Crystal Palace (London) in 1851. Our human imaginations (so essential to the artist) also make us invariably anthropomorphic and often unable to extrapolate from observed animal behavior without affixing human attributes. It's also why the life cycle of 'immortal' Turritopsis dohrnii has so seized public imagination. Invertebrates have often been classed as lower life forms, yet their neurological systems are amazing - the surprisingly complex nerve nets of siphonophores, their radial symmetry.
I make my work ambiguous and open, to allow the brains of the viewer to fill in the blanks, rather than passively look at an image. I hope the viewer will enter it, get lost in it, reimagine or re-contextualize it. For OHBM, I mixed fact and fiction. I was inspired by the way the human brain attempts to understand 'alien' or radically different intelligences and neuroanatomy, and the way we confabulate those gaps. This is also because of my lifelong study of how we look at nature.
I grew up in a forest - my parents left the city and took us to live in the jungles of northern India, where we learned how to 'read' interspecies communication between prey species, for instance, so we knew when a predator was on the move by the types of alarm calls of birds, monkeys, even insects. We developed a very intuitive appreciation for the lowliest of creatures, often disregarded in conservation efforts, for instance.
It's only recently that the cause of bees have been taken up, yet one has to only read Karl von Frisch's brilliant work from 1973 on bee communication to see the incredibly complex nature of its dance and the way that communication can only occur because of a social agreement of its codes. So, the social aspects of information processing is what I unconsciously and intuitively imbibed growing up in the wild.
All these elements feed back into the way I process disparate information, make connections, and interpret - which is what eventually led to my seeking out neuroscientific studies as a youngster, despite being disallowed from studying science at a higher level because of my gender.
ED: One can say that, just as artists, researchers have to start with an idea, an inspiration, that subsequently culminates in writing of a publication or a work of art. Do you get ‘writer’s block’?
SR: Yes, I do, sometimes, but once I start I don't stop. My writer's block is often because of the sheer enormity of the subject and its associated bodies of knowledge, that I am paralyzed into being unable to decide where to begin. Of course, like most people, once I start then it's off to the races, and I work in a fever of hyper-focus to the exclusion of everything, often forgetting to eat. I recently finished 65000 words in under 10 days, after being paralyzed with indecision for 7 months. So, a very uneconomical way of working!
ED: Many thanks Shubigi!
By Elizabeth DuPre
The Open Science Special Interest Group (SIG) is a relatively new organization within OHBM; however, it is responsible for several increasingly popular community initiatives including the hackathon and the open science room. As the Open Science SIG assumes new leadership this month, I sat down with the incoming chair, Kirstie Whitaker, to hear about her hopes for the upcoming year.
Elizabeth DuPre (ED): Today I’m here with Kirstie Whitaker, Chair of the OHBM Open Science SIG. Kirstie, can you first tell us about yourself?
Kirstie Whitaker (KW): I’m a research fellow at the Alan Turing Institute – the UK’s national research institute for data science and artificial intelligence. There’s a lot of research going on there, but one of the projects I work on is trying to incentivise reproducible research across all of data science. I’m a neuroscientist by training, and I did my PhD in UC Berkeley, followed by a postdoc in Cambridge at the department of Psychiatry. I then had a one year fellowship with the Mozilla Science Lab before I transitioned to working in the Turing Institute.
ED: It sounds like you’ve seen many aspects of neuroscience and data science, both in academia as well as in industry through your fellowship with Mozilla. Those can all lend very different perspectives on the thing we’re both passionate about: open science. Can you tell us your thoughts about open science following from those experiences?
KW: Open science, as you’ve said, can mean different things to different people. You can imagine our friends in the library sciences are extremely passionate about open access. We should all be passionate about open access and being able to read our colleague’s work. There’s also a lot of work going on at OHBM using open data. That’s making science more efficient and allowing us to answer more interesting questions with different types of techniques – by harnessing different peoples’ data and sharing that with our colleagues.
There’s another aspect which is pretty prominent in neuroscience, with huge influence around the world, which is open source code. I write some analyses and importantly I allow other people to use it – so in that sense it’s similar to open data – but they’re also able to see it and interrogate it. So instead of building a black box we’re building tools that you can look inside.
There’s also an additional angle of making sure that science is open to all people. This includes citizen science – and one of our hackathon organisers this year is Anisha Keshavan, who’s one of the coolest and most exciting citizen science people that I’ve ever worked with – which means breaking out of the ivory tower, and allowing everyone who’s interested in helping us understand the brain to productively take part.
It also means making sure that there are scientific career paths for people with diverse experiences and opinions. That means we allow women to succeed as well as men. We ensure that people from different cultural backgrounds, different races, different countries who speak different languages, are all given a fair shot at expressing their goals, and completing the analyses that they want to do.
So for me, open science is just doing science, and doing science well. But my particular passion is to ensure we are being diverse and inclusive.
ED: Over this past year you’ve served as chair elect while I’ve been secretary elect – and we’ve gotten to see the leadership do some amazing things. Anisha was the co-organiser for our hackathon. And this was the first time that the hackathon has sold out – so it was really exciting to see all the enthusiasm that the open science events are generating. We also had Felix Hoffstaedter organizing the open science room at the annual meeting, where we even decided we needed a bigger space.
And of course our current chair Chris Gorgolewski and secretary Matteo Visconti di Oleggio Castello have done a great job about communicating to the community what we’re so excited about. Given all this, now that you’re taking over as chair where would you like to take the SIG?
KW: I know, it’s such a brilliant and terrible problem to have sold out the hackathon! The other person we should mention is Greg Kiar who co-organised the hackathon. He liaised with ethnographic researchers who specifically do research on hackathons to create a survey that asked attendees what they gained from the event, how they felt it accommodated more junior members, and importantly, how these events could be improved in the future. I’m so glad Greg conducted that survey - before we closed out the room on Saturday we all had 30 minutes to fill in our survey and answer our questions - and we’ll see the fruits of that survey in next year’s hackathon. He gave a brief overview and one of the biggest themes was people being so excited and grateful that there were so many skills available – and that there were so many different levels of people that were there.
I think that the event selling out reflected that excitement. But selling out means we’ll have to confront some issues; in particular, we’re going to have to figure out if we want to keep the hackathon small and intimate or let everyone who wants to come attend. One of the big sells of a small event is that you can easily make some connections with individual people who can share their expertise with you or point you in the right direction. Once you get larger you effectively start building OHBM [laughs]. I mean, we’re the hackathon, we’re not trying to take over the entire conference, so we’ll have some interesting challenges about how we include everyone.
My goal is to think about culture change, and making sure we give credit to early career researchers that are doing excellent work that supports others. Historically, the incentive structure in academia has been to encourage very sharp elbows and making sure “To get to the top I’ve got to be number one. I’ve got to be uniquely better than everyone else.” One thing that really impressed me at this year’s OHBM conference was a presentation by JB Poline where he talked about the work that the community has brought together for a publishing platform where you don’t just publish traditional papers, but you might also publish code, data or tutorials. These are things that we all know are very useful, but that aren’t fully recognised. I’d love to see early career researchers get a bit more credit for that sort of thing.
I also think that the wider community should take back that spirit of the hackathon – the feeling in the open science room of these really helpful conversations and try and take that out into the OHBM community all year long. We have a Slack channel where you can get in touch with people, by pinging questions out. But I think it would be really interesting to see if we can solicit ideas from our community and actually get our members involved. It doesn’t have to be the SIG that puts on an event – it could be that we help our members make the connections (and we perhaps help out with a little funding).
One of the initiatives [Elizabeth] and I have been doing is the demo calls. There, we reach out to people and I sit on YouTube live and I ask people about their experiences with open source and their projects, and how others can get involved. Maybe those demo calls are useful and we can take them forward and keep them going. But maybe there are better ideas and that’s what I’d love to explore – how we can generate more ideas and bring them to light.
ED: I’m really excited to see where that goes. That leads into our recent round of elections…
KW: Yes! Traditionally there were just two members of the committee and they’ve done a lot of work. Thank you to the previous leadership of the OHBM hackathon and the Open Science Room and the brain hack and everything - all the people who have run so many of these initiatives. It was a lot of work! I was very happy that we created quite a few more positions to bring more people in that were passionate and wanted to help nurture the open science community. For example, this year we realised that we didn’t have a treasurer position, and keeping track of all this money and paying for these things was a lot of work, so we’re introducing a new role to cover this need.
We’ve talked about my vision and my passion for open science. But one of the things that is so fun, and frightening, about open science and diversity is that you have to eat your own dog food; that is, to practice what you preach. The success of open science in general and the SIG in particular relies on bringing in new people, new points of view, and I’m looking forward to it.
ED: Yes, I’m looking forward to seeing everything that happens and our new initiatives. Thanks so much!
After our conversation took place, we concluded the most recent round of elections. We’re now excited to announce the new leadership joining the Open Science SIG:
Greg Kiar - Treasurer
Camille Maumet - Chair elect
Ana Van Gulick - Secretary elect
Sara Kimmich - Treasurer elect
Roberto Toro and Katja Heuer - Hackathon co-chairs
Tim van Mourik - Open science room organizer
Cameron Craddock - Council liaison
Look for a follow-up post where we find out more about their pathways into open science!
Danilo Bzdok heads the section for “Social and Affective Neurosciences” at the Department of Psychiatry, Psychotherapy and Psychosomatics at RWTH Aachen University in Germany. Using his dual background in neuroscience and data science, Danilo tries to reframe psychological questions as statistical-learning questions to generate new insights. His work on social cognition and psychiatry has led to innovative data-led perspectives on how humans navigate the social world and its neural substrates. In 2017, he was designated “Rising Star” by the Association for Psychological Science (APS) in the USA. He is also a self-proclaimed potato chips gourmet and excessive consumer of especially electronic and classical music.
My first encounter with Danilo was unilateral, over the pages of Nature Methods’ Points of Significance section, where he published several introductory pieces on machine learning. His way of boiling down a complex topic into an accessible explanation was also at the heart of our next meeting. At ICM in Paris, he gave an institute lecture about the relation between mainstream statistics and emerging pattern-learning techniques in brain-imaging neuroscience. This led to a longer discussion afterwards, this time face to face... And this discussion is revealed here, where Danilo gives his views on big data, the changes in how we answer questions with data in everyday science, and some speculations on the future of neuroscience.
Tal Seidel-Malkinson (TSM): First, can you tell us about your career path. You started in medicine, then moved into basic research. What made you shift?
Danilo Bzdok (DB): It actually started when I was in middle-school – my first intellectual passion was in programming and computer science. I really liked composing logic using computer code. At roughly 15 I was fluent in half a dozen programming languages, such as 32-bit assembler, Pascal, and C++. But I felt early on that I was also intrigued by various other, completely different, things like philosophy,foreign languages, social sciences and neuroscience.
At that time, being mostly focused on natural sciences felt like somewhat of a limitation to me. One thing I really liked about the way of thinking in philosophy, and still appreciate very much, is the close interplay between logic and language. I was however not fully convinced this was a very pragmatic career choice. At least in Germany, a degree in philosophy is not always something that keeps many doors open for the next steps in life. That’s why I eventually decided to study a conservative area that would give me a solid foundation. Medicine seemed to be a safe choice, provided such a general education, and also gives you a lot of options. You go through an intense learning experience that shapes your work ethic. I wanted to go towards becoming active in scientific research, determined to move into brain science in particular. I therefore spent my early University years concentrating on neuroscience and psychiatry.
In the middle of my studies I wanted to get involved with research as soon as possible. This led me to work with Simon Eickhoff at the Institute of Neuroscience & Medicine at the Research Centre Juelich, who was an incredible mentor to me, and I also reached out to the department of psychiatry at the RWTH Aachen. I was lucky enough to be funded by the German Research Foundation (DFG) and to be part of an international research and training group (IRTG1328 on “Schizophrenia and autism”) with UPENN, USA. This particular department of psychiatry at RWTH Aachen University turned out to be active in brain-imaging research. Due to a series of lucky coincidences, I had the opportunity to go through an authentic research experience.
During the second half of Medical School I spent less and less time attending lectures, and instead tried to min-max the exams. Towards the end of my medical studies I was barely studying anymore. It then felt like a smooth transition into being a full-time researcher. At that point, I wasn’t ready to commit another >5 years to clinical specialization in psychiatry, which takes ~50-60 hours of your time per week and leaves less time for research.
I also learned a lot during a fantastic research stay working with Peter Fox and Angela Laird in San Antonio, Texas, USA, and I launched several collaboration projects with social cognition enthusiasts here in Germany, including Kai Vogeley, Leonhard Schilbach, and Denis Engemann. Together we conducted a series of neuroimaging studies on whether or not there are brain regions that may be uniquely devoted to social-affective processing - a direction of research which later pushed me to pursue always more general systems neuroscience questions. In 2013, I had become convinced that whether human-specific neural systems exist -- particularly ones that might be devoted to human social interaction -- was at its heart a methodological and statistical question. Whether or not scientists can go beyond the cognitive terms that we have been used for decades in social and affective neuroscience, such as “theory of mind”, “affect”, or “empathy”, is a question that can be more readily wrestled with certain data-analysis toolkits than others.
TSM: It’s clear that neuroscience nowadays increasingly requires an interdisciplinary set of skills. In your unique path you have acquired a broad set of skills from your degrees in medicine and maths and your PhDs in neuroscience and in computer science. How did you choose this path and, given this can’t be common training, what do you think early career researchers should focus on?
DB: I went through a journey of sometimes unconnected interests. It wasn’t always a conscious choice at a particular point. Essentially, I just went through several bouts of intense interest, getting absorbed in specific topics. That’s why, in retrospect, I am happy I somehow made it all the way through medicine. Despite changing areas of interest, at least I have an official degree that could help me give something back to society.
For years, I was not really sure how to cultivate and usefully combine my skills in language, logic and algorithms. When neuroscience later turned out to be such a vibrant interdisciplinary field, it was quite a relief to me. I found an opportunity to combine several different, what I like to call, thought styles. In neuroscience you can interface between diverging thought styles and approaches, and really get something out of it. That’s perhaps why I have a weakness for fuzzy topics like higher-order cognition, what domain-general function the TPJ may subserve, and what the “dark matter” of brain physiology - the default-mode network - may tell us about the nature of the human species. Several of these topics have a decent amount of soft-scienciness, at least to me – I then try to be principled and get at these research questions with algorithmic approaches that “let the data dominate”.
One thing that appears obvious to me in my activities as a supervisor, mentor and speaker: the data science revolution will depend on better quantitative literacy of the next generation of ambitious neuroscientists. We live in an increasingly quantified world. There are more quantifiable aspects about how we live and what we do; in normal life as well as when things go awry. There is a rapidly increasing opportunity to use algorithmic and computational tools, to generate quantitative insight and reach rigorous conclusions from the increasing amount of data at our hands.
Such modern regimes of data-analysis may look disturbingly different from the traditional goals of statistics and how statistics is taught at the university for many empirical sciences. In the data-rich setting, some traditional methods may have difficulty approximating the truth. That’s why I tried to structure my scientific education not only towards a solid neuroanatomical and neurophysiological understanding, in which I was much influenced by Karl Zilles and Katrin Amunts, but also a sense of probabilistic reasoning and quantitative methodology, in which I was much influenced by Bertrand Thirion, Gaël Varoquaux, and Olivier Grisel.
As almost every PI will tell you, most of their students will ultimately not end up in academia. I therefore believe that, at a more pragmatic level, getting an education with a solid data-analysis component can avoid pigeonholing PhD students or Post-Docs for a career as a scientist, and offer a broader portfolio of options to find jobs in industry and government after leaving academia.
TSM: Big data is a new opportunity for neuroscience, but equally it’s a new challenge. How do you see this development?
DB: In general, many scientific disciplines show a tendency to diversify into ever more specialized subdisciplines over time. So just because there are new opportunities doesn’t mean that the more established ways to conduct research and older techniques are rendered obsolete. Meticulously designed, hypothesis-guided experiments in carefully recruited participant samples will most likely remain the workhorse to generate new insight in neuroscience. What appears to be happening right now, is that we are extending the repertoire of questions that can be asked and are quantifiable.
Let me give one particular example. The increasing availability and quality of brain measurements will soon allow learning description systems of mental operations in health directly from data themselves - a cognitive taxonomy directly extracted from brain measurements, and nomenclatures of disturbed thinking in mental disease. Such goals are likely to require combinations of massive amounts of richly annotated brain data and innovative pattern-learning approaches.
TSM: There’s a tendency towards moving from group analyses to predicting outcomes for individual participants, are our current tools reliable enough for that?
DB: Broadly, I can see two distinct and promising trends – on the one hand, scientists bring in a small number of subjects into the lab several times and acquire hours of brain scanning, which allows accessing a finer granularity of neural processes at the level of densely sampled single individuals. There are several well-known labs that now seriously go into this direction with a lot of success...
TSM: Do we need so much data on individuals because of variability of cognition or the SNR of fMRI?
DB: There are several aspects at play. Often, resting-state scans are still just 5-10 minutes. I think that may not be enough to robustly describe *all* aspects of neural activity changes in the brain that investigators may find interesting. This is the first trend: one pocket of the brain-imaging community now tries to go always deeper in terms of subject specificity. It nicely complements the dominant agenda of conducting statistical tests on differences between pairs of experimental conditions or participant groups.
The completely other way to go beyond binary comparisons that I see is progress towards population-scale neuroscience. There is an increasing tendency for extensive data collections with hundreds and thousands of indicators like demographic, neuropsychological and health-related items, from a maximum of individuals. Such population neuroscience approaches will probably shed new light on variability patterns of brain biology, across distinct brain-imaging modalities, and bring into contact previously unconnected research streams. These people try to acquire as much information as possible that characterizes as many people as possible. The approach avoids strict a-priori choices as to the type of person or disease category to be distinguished and studied. One hopes that coherent clusters of individuals emerge in massive data. That again is a completely different perspective. This is a good setting, for example, to discover, quantify, and ultimately predict subclinical phenotypes in people - individuals who deviate from the normative population in some coherent way, without being “dysfunctional” in society.
It is my impression that both highly-sampled single participants and richly phenotyped participant populations are two exciting upcoming directions that hold a lot of promise. Both these research agendas can probably complement and inform experimental studies of ~30 people with well-chosen hypotheses and dedicated experimental designs.
From a more statistical perspective, there is an orthogonal aspect. For the majority of the 20th century, researchers in biomedicine have acquired and analyzed “long data”, with fewer variables than individuals. Today neuroscientists need to tackle always more often “wide data”, some call it “fat data”, with sometimes a much greater number of variables than individuals. Having extensive “found” or observational data from general-purpose databases is where machine-learning algorithms and data science come into play. Such more recently emerged statistical tools offer new strategies to search through abundant yet messy data. It is an exciting future perspective to integrate both – the highly sampled subjects and population neuroscience.
TSM: As you said both approaches require collecting, logging and archiving big datasets – this requires a lot of resources. Do you think this might increase the gap between well-funded and less well-funded labs?
DB: That’s a bit political, I’ll try to give a neutral answer. When you look at the Human Connectome Project (HCP) – there was a lot of excitement when it established itself as a trusted reference dataset for the brain-imaging community. That allowed new methodological approaches to be compared against each other in a more principled fashion. Yet, looking at the many thousand imaging neuroscientists on the planet, how many of those have really published a paper with the data from the HCP project? Actually, not that many.
Many of the existing HCP publications appear to often be methods-focused papers. I’m not saying that’s not interesting. But I think many scientists would perhaps have expected more discoveries on brain structure and function based on this unique data resource. One reason why this is surprising to me is that many of the classical software libraries still scaled fairly well to the HCP 500 release; just having to wait a bit longer for the results. Even with the full 1,200 subjects you could still scale to the higher sample size using essentially identical software and analysis pipelines that were already set-up in the lab.
We now have the UK Biobank Imaging, CamCAN, ENIGMA, and many other rich datasets. Given that HCP data were not primarily used by labs to answer cognitive neuroscience or neurobiological questions on brain connectivity, I expect that there will probably be an even bigger gap between the majority of imaging neuroscientists and those people who capitalize on the new generation of complex datasets. There will be even fewer labs that have a vested interest in and a daily exposure to methodological techniques needed to leverage these burgeoning data repositories.
TSM: This transition to big data requires a change in our methodologies and ways of thinking. How do you think this cultural shift should be achieved?
DB: Let’s go back to the two larger trends we discussed before – using densely sampled participants and population neuroscience to understand the healthy and diseased brain. Big-data methodologies are likely to play an important role in gaining this insight. We’ll need a shift in our everyday data-analysis practices and how we design and run our labs. We’ll need more computational savoir-faire and more people from STEM backgrounds. But that’s not enough. There also needs to be a more organic and fluid conversation between analysts and the PIs who have these people on their payrolls. More exchange in both directions will help us to negotiate between the research questions and optimal algorithmic methods.
A big issue, for instance, already is and will increasingly become the “big-data brain drain”: many people with quantitative aptitude and a proven data-analysis skill-set are highly sought after and may be aggressively headhunted by companies for several times higher salaries than what we in academia can offer. For instance, one of my students with a background in physics recently got recruited by McKinsey Analytics in London.
To tackle some of the ambitious questions we mentioned, we’ll also need better infrastructure than many universities today offer us neuroscientists. We simply need more money for this expensive computational architecture and its sustained maintenance. Now, some people may ask why we don’t just use cloud computing. And sure Amazon AWS and other cloud-based solutions are attractive options. But it’s worth considering two problems: first, you have data-privacy issues where you have personal data from individuals. In many research institutions, researchers may not be allowed to upload detailed information of individuals to servers in a different country. Second, there is a bureaucratic problem: you cannot easily estimate in advance how much money you need for your particular cloud-computing jobs. Many finance departments are however allocating money on a per-year basis, at least at German universities.
Last but not least, there’s the educational issue: how should we train young neuroscientists? It’s not clear how in this already very interdisciplinary teaching schedule, with theory of neuroscience, molecular biology, anatomy, physiology, classical statistics, genetics, brain diseases, and so forth, we could add multi-core processing, high performance programming, and so on. There are so many things that a 21st century neuroscientist is expected to absorb. It’s not clear where you’ll find people with such a multi-faceted mind who can be incentivized to, and are able to, embrace this breadth.
TSM: So perhaps we need to be collaborative? It’s perhaps not realistic to expect single people to have all these skills.
DB: It’s probably not realistic, but still, we will need some of these “glue people”. It’s not clear to me where we should expect them to come from. That’s why my feeling is that the shape and form of scientific education may play an increasingly important role in neuroscience.
TSM: Big data has been seen by some as a solution to the replication crisis – and another approach has been to use meta-analysis. You’ve recently published a meta-analysis on theory of mind. What did you learn from this, and what should we be careful about when applying meta-analysis?
DB: Several decades ago, there was a similar crisis in the social sciences, as we experience now in the current replication crisis. Many people weren’t sure how to go forward as there was a lot of uncertainty about how robust and valuable the abstract constructs were that these empirical scientists were studying. An important contribution to provide justification for these mental and social constructs came from quantitative meta-analysis.
Quantitative analysis is a very useful tool to identify convergence across isolated findings and thus solidify scientific areas. Especially if you know you will be facing small effects and a lot of noise; which is true for social and psychological sciences, and probably not wrong for brain-imaging. So you can either shift to a different area of research with more tractable problems or adapt to the situation that we have, where meta-analysis is one key solution to cope with the idiosyncrasies of a broad range of studies. It will unavoidably mask some subtle effects from single experiments. But you can see through the noise – distinguish the forest from the trees.
TSM: Presumably it also helps to collaborate with multi-centre studies.
DB: Sure. Many young students getting into neuroscience may perhaps still envision the lonely genius who is knowledgeable about so many areas of neuroscience. The biggest steps forward may come from *teams*. Sets of people who learned to genuinely work together; not despite but because they are drastically different in their knowledge and thought styles. If they succeed in aligning their thinking and efforts towards a common goal in neuroscience research, non-linear progress probably becomes much more likely.
In terms of data-collection, it’s worth comparing brain-imaging to genetics or genomics. Several trends in imaging neuroscience today may have been preceded in a similar form already 5-10 years ago in genomic research. There, many data collection collaborations were foundational and helped the research community to see through the noise more clearly. Imaging neuroscience is becoming larger and more international with increasing numbers of labs, so there is greater potential for people to work together. Intense and bidirectional collaboration between drastically different disciplines may be a prerequisite to render some of the ambitious questions actionable that we had the pleasure to discuss today. It also means you need people skills, on top of everything else!
TSM: I want to thank you for the nice chat – and it’s definitely an exciting, interesting era in neuroscience!
By Danka Jandric, Jeanette Mumford & Ilona Lipp
Planning a resting state study and analysing resting state data can feel overwhelming. There seems to be an endless number of options regarding all stages of the experiment. Decisions need to be made about how to acquire data in an optimal way, what preprocessing and noise correction pipelines to employ and how to extract the most meaningful metrics. Many strategies have been published and are available in software packages. However, there seems to be little consensus about what works best and even more importantly, about how to judge whether something “works” or not. The choice of method often depends on the specifics of the data and addressed research question, but can equally often seem arbitrary. To help guide you through this jungle of rs-fMRI, we walk you through all stages of a resting state experiment. We do this by addressing questions that researchers are likely to have… or should have! While we do not provide definite answers to these questions, we try to point out the most important considerations, outline some of the available methods, and offer some valuable video resources from recent OHBM education courses, to help you make informed decisions.
What do I need to consider when planning my experiment?
Running a rs-fMRI experiment seems easy enough. Technically, all you need is to put your participant in the scanner, tell them to rest and run a standard BOLD sequence. However, it may be worth thinking about your analysis strategy beforehand, so that once you start analysing your data you do not suddenly wish you had…
How do I know my data quality is good?
One of the most common questions asked when evaluating data is how to tell if the data are “good” or not. The answer to this question, regardless of the data, is to actually look at your data. Although this task is somewhat easy with behavioral data, when faced with hundreds of thousands of time series for a single subject, it is less clear how we can do this. Luckily Jonathan Power has not only developed tools we can use with our own data but also takes us through data inspection in his educational talk from 2017, “How to assess fMRI noise and data quality”.
How do I improve my data quality?
fMRI data are noisy and this is not going to change any time soon, so we have to deal with it somehow. Acceptance and hoping for the best is a strategy, but could lead to problems further on in your analysis. If there is a lot of noise compared to the signal of interest, then individual subject’s resting state networks will not look clean, and the power in detecting group-level effects may be low, so you might not find anything interesting in your group-level analysis. However, as importantly, if there are systematic differences in noise sources between the cohorts you are studying, then seemingly interesting effects can be simply a result of group differences in noise, such as head motion. Having ignored the noise problem, you might end up spending days writing a paper with a game-changing title, being hit by reality when the annoying reviewer then asks you to quantify group differences in your noise. Better to be aware of and account for noise to start with, right? But this is easier said than done…
What causes noise in rs-fMRI data?
Resting state analysis generally deals with correlations in time courses between voxels. If a noise source affects several voxels in similar ways, this can lead to temporal correlations which are independent of neural co-fluctuations. For this reason, the aim of noise correction is to get rid of as much variance in the BOLD signal as possible that is related to noise. To figure out what the best possible noise correction strategy may be, we first have to be aware of what the sources of noise in BOLD time series are.
In his video, Cesar Caballero Gaudes gives a comprehensive overview of the most common sources of noise, such as head motion (from minute 05:11), respiratory and cardiac variation (from minute 05:53), and hardware (from minute 11:11), and their effects on the data. Cesar also gives an overview of some of the denoising strategies that are available to tackle different types of noise.
How can I correct for noise when I have information about the noise sources? The nuisance regression approach:
One denoising approach is to record information about some of the potential noise sources during the scan, such as physiological recordings or head motion parameters. These can then be used to figure out to what extent our BOLD time series can be explained by the noise sources, by including nuisance regressors in a general linear model. Generally, we probably all agree that the more high-quality information we have on what happened during our scan, the better. One may also think that the more nuisance regressors we employ to regress out from our BOLD time series, the better our clean-up… but is that so? In her video, Molly Bright gives us deeper insight into the nuisance regression approach to clean up noise.
In some smart simulation analyses (from minute 12:30), Molly shows that simply adding as many nuisance regressors as possible may not be the best strategy, as we may accidentally remove a lot of signal. Also, we need to be careful about time-lagging our regressors in order to account for the delay between a physiological change and the BOLD response. Molly explains why trying to identify that delay using the rs-fMRI data can be tricky, and why adding a breath-hold at the end of your acquisition may be a good idea (from minute 20:16).
Molly also demonstrates that very commonly applied preprocessing steps, such as bandpass filtering, can have effects on our data that we might not have predicted (from minute 16:30). While introducing a few strategies to make the nuisance regression approach for noise corrections more valid – such as prewhitening (from minute 12:00) - she stresses the fact that there is not one optimal strategy and that it is very difficult to tell whether noise removal “has worked”. The take-home message here is probably that as a field, we need to work towards a better understanding of the BOLD profiles of different noise sources. Additionally, integrated strategies are needed to deal with the complicated interplay between different noise sources, such as between head motion and physiological noise.
How can I correct for noise when I do not have information about the noise sources? The ICA approach:
While the success of nuisance regression depends on having good quality nuisance regressors in the first place, data-driven approaches are available that can be applied to any dataset, the most common strategy being independent component analysis (ICA). ICA for noise removal is based on the separation of the BOLD time courses into spatial components, and classifying each component into signal vs. noise. This is typically done on a subject-by-subject basis. The time courses of the noise components can then be regressed out or accounted for during further analyses.
Ludovica Griffanti gives a comprehensive introduction to ICA for noise removal and highlights the difficulty that often lies in the signal vs noise classification that is performed by “experts”. Whilst semi-automated and automated approaches are under development in order to make this classification more objective, Ludovica makes the strong point that ultimately these algorithms or at least their validation are based on “gold-standard” manually labelled data. While there is no clear consensus yet on what signal and noise components look like, Ludovica provides us with some guidance and rules that can help with classification and are a first step towards this consensus.
How can multi echo data help with noise correction?
The vast majority of BOLD data has been acquired with a single echo time, optimised to the average T2 across grey matter. However, if you have not started your experiment, you might want to acquire data with several echo times. Prantik Kundu explains why: BOLD and non-BOLD related signal have different sensitivity to echo time, so having information about the actual decay can help distinguish signal of interest from noise (from minute 05:10).
Prantik provides a few beautiful examples on how multi-echo fMRI data can be combined with ICA-based approaches for noise clean-up, calculating parameters that objectively inform about how similar the components’ behaviour is to BOLD vs non-BOLD related signal (from 11:43). In the grand scheme of things, the multiple echo times used are still quite short, so acquiring this extra information would not necessarily increase your total acquisition time. On a side note, even data from one additional short echo time can provide information about some noise sources, as described in a study by Bright and Murphy (2013). Be aware that certain noise sources, such as slow physiological changes yield ‘BOLD-like’ noise (which we can treat as noise or as signal of interest, depending on our perspective), as they interact with the cerebrovascular system. Multi-echo data does not help with correcting for this type of noise.
Why go through all that pain? Can I not just do a simple global signal regression for noise correction?
A cheap and easy (and still very widely used) way for performing ‘noise correction’ is global signal regression. Here, the average signal across the whole brain (or all gray matter voxels or all cortical voxels) is calculated and regressed out from each voxel time series, with the underlying assumption that the global signal mostly reflects combined noise from various sources. The advantage of this approach is that it is able to remove artifacts that are hard to get rid of with other noise correction methods. However, global signal regression is highly controversial in the field, with the main points of criticism being that the global signal has neuronal contributions and that global signal regression shifts the correlation coefficients and induces negative functional connectivity. In her video, Molly Bright briefly touches on this (from minute 24:43), and refers to a recent 'consensus paper'. An alternative to regressing out the global signal are using the signal from white matter or CSF, as briefly described in Cesar’s video (from minute 20:00). If you are interested also see his recent paper.
How should rs-fMRI data be preprocessed?
Resting state fMRI data can largely be preprocessed in the same way as data from a task-based fMRI acquisition (for a refresher on steps we recommend the slides from the educational course from OHBM 2016). As Molly pointed out, some of the “standard” preprocessing steps, such as bandpass filtering, can have unexpected effects on rs-fMRI data. As rs-fMRI data does not have strong task-driven signal changes, it is generally more susceptible to noise and probably to anything we do to the data, so be wary of that.
As described above, there are strategies for tackling noise, such as physiological artifacts, in the preprocessing pipeline. Some good pointers, including Cesar Caballero Gaudes’s video on denoising, have been outlined in the previous section. In addition, in 2016 Rasmus Birn, an expert on the influence of physiological noise on the BOLD signal, gave a thorough overview of physiological noise and approaches to remove it.
How can I analyse the data to find meaningful resting state networks?
Once your data is preprocessed, denoised and you are confident that it is in good shape, you will want to get on with the exciting part – identifying resting state networks. When done properly, resting state data can show us large-scale networks in the ‘brain at rest.’ What defines them are the correlated temporal patterns across spatially independent regions. Each network has a distinct time course from other resting state networks, but one which is consistent across its regions.
The aim of rs-fMRI analyses approaches is to use the time courses of brain regions to decompose the brain into resting state networks. Several techniques exist, with the two most common being seed-based correlation analysis (SCA) and independent component analysis (ICA).
In his video, Carl Hacker gives a nice overview of both SCA and ICA. He introduces the two methods (from minute 1:12) and identifies the main differences between the approaches (from minute 4:15). Carl also discusses how to identify RSNs from seed-based mapping (from minute 6:25), and how the brain can be parcellated using ICA (from minute 13:35). While SCA uses the time series of an a priori selected seed region in order to identify whole brain functional connectivity maps of that region, ICA decomposes data from the whole brain into the time courses and spatial maps of the resting state signals, called independent components (ICs). SCA is a useful method to answer questions about the functional connectivity of one specific region. However, the drawback is that it only informs about connectivity of this region. On the other hand, the numerous ICs that you get from ICA are defined as a collection of regions which have maximal spatial independence but co-varying time courses, thus showing networks across the whole brain that have synchronous BOLD fluctuations when the brain is not performing a task.
In healthy subjects, SCA and ICA have been shown to produce moderately corresponding functional connectivity information, and the choice between them is likely to be guided by the specific research question. Note that the focus of Carl’s video is parcellation of the brain. However, many concepts and principles also apply to other types of analyses. Read more about these two methods in Cole et al. (2010) and Smith et al. (2013).
How do I interpret ICA components?
If you have run ICAs on your resting state data, your next task will be to interpret the output. The output consists of a number of spatial maps showing regions with spatial independence but co-varying time courses, called independent components (ICs). How many ICs you get depends on the parameters you set when you run the ICA, but it is typically a few dozen.
The first step when interpreting the ICs is to determine whether they are signal or noise. Because ICA is data-driven, it does not ‘filter out’ noise, but it can separate neural signal from non-neural signal, i.e. noise, so it is important to classify the components correctly as either signal or noise.
So how do I distinguish between signal and noise in extracted ICs?
In her video, Ludovica Griffanti discusses how RSNs and noise can be distinguished. She provides an overview of component classification approaches, including manual and automatic classification approaches (from minute 2:58). Importantly, Ludovica describes the characteristics of signal and noise components and gives examples of both (from minute 5:20). Ludovica’s key message is that the aim of classification is to retain as much signal as possible, so if you are unsure if a component is signal or noise, keep it in as signal. She also makes the point (from minute 19:00) that a number of factors relating to participants, MR acquisition and preprocessing affect IC characteristics and discusses these briefly. The classification approach discussed in Ludovica’s video is very similar when classifying ICA outputs from single-subject data and group level ICA, but there are differences. For an outline of these and for a more thorough discussion of manual classification of ICA components, please see Griffanti et al. (2017).
How do I identify RSNs from ICs classified as signal?
There are a few approaches to determining what networks signal components correspond to. Some ICA toolboxes will have spatial templates that can be compared to the ICs. But perhaps the most common approach is manual labelling based on known anatomy. The spatial patterns and time courses of many common resting state networks (RSNs) have been described. (e.g. for labelling RSNs from group-level data see Beckman et al. (2005) and De Luca et al. (2006)).
A further option for IC classification is the use of automated RSN classification techniques. In his video, Abraham Snyder gives an overview of how machine learning can be used to classify RSNs based on pattern recognition (minutes 28:50-33:00).
What is this thing called dual regression?
ICA is typically done with group data and produces spatial maps that reflect the group average functional connectivity. However, the individual variability of IC topography is often useful, for example to make comparisons between groups of individuals. A process called back-reconstruction is therefore used to obtain the individuals’ time courses for the ICs obtained from the group-level ICA, which are then correlated with each voxel to obtain subject-specific spatial maps. Dual regression is one available back-reconstruction method. In his video, Carl Hecker gives a brief overview of how it works (from minute 19:38).
If you are interested, Erhardt et al. (2011), describe the principles of several back-reconstruction methods, including dual regression.
What metrics can I extract from the rs-fMRI analyses?
Local activity metrics:
Even before running a network analysis on the rsfMRI data, such as SCA or ICA (see above), two useful metrics can be derived from the data, ALFF and ReHo.
Amplitude of Low Frequency Fluctuations (ALFF) measures the magnitude of low frequency oscillations (0.01-0.1 Hz) in the BOLD signal in neural regions. The fractional ALFF, a complementary metric, is a measure of the contribution that a specific low frequency oscillation makes to the whole frequency range recorded. Both metrics give a measure of the amplitudes of brain activity in specific regions. However, the interpretation of these measures is difficult. Fractional ALFF has been shown to be dependent on the vascularisation of the brain, similar to the resting-state fluctuation amplitude (RSFA), which is a very similar measure to the ALFF, and available from any rs-fMRI scan, but has often been interpreted differently. Physiological mechanisms, including vascular effects, in rs-fMRI are still not fully understood and the exact interpretation of measures linked to cerebrovascular characteristics is therefore more difficult.
The other common rs-fMRI metric is that of regional homogeneity, or ReHo. ReHo is a voxel-based measure of regional brain activity, based on the similarity of the time-series of a given voxel and its nearest neighbours. It quantifies the homogeneity of adjacent regions, to provide information about the coherence of neural activity of a specific spatial region.
Thus, both ALFF and ReHo give information about regional neural activity and have been shown to have high values in, for example, the default mode network regions during rest, indicating that they can point to the regions that play central roles in resting state networks. Because they provide information about regional neural activity at rest, both ALFF and ReHo can be used to determine an ROI for SCA.
Functional network metrics:
However, ALFF and ReHo are metrics of local neural activity, and are thus limited in their ability to provide information about large resting state networks. Network analyses therefore tend to focus on functional connectivity measures.
SCA and ICA, discussed above, both offer measures of functional connectivity within the brain. Both calculate the correlation of time series between voxels in the brain to produce spatial maps of Z-scores for each voxel. These scores reflect how well the time series of each voxel is correlated with the time series of other voxels and are a measure of functional connectivity. In SCA, the Z-scores reflect the correlation of each voxel with the average time course of the seed voxel, while in ICA the Z-scores reflect the correlation of each voxel with the average time series of the respective IC. Dual regression can be run with both SCA and ICA to enable the investigation of individual and group level differences of functional connectivity.
A good overview of the metrics described above is provided in Lv et al. (2018).
A more recent metric derived from rs-fMRI data is that of functional homotopy. Functional homotopy shows the synchrony of spontaneous neural activity between geometrically corresponding, i.e. homotopic, regions in the two hemispheres. It provides a measure of connectivity between corresponding interhemispheric regions, and can be used to determine regional versus hemispheric information processing.
Chao-Gan Yan asks whether these different measures of resting state functional connectivity show unique variance, and discusses the concordance among some of these metrics and also global connectivity (a graph theory measure, please see the next section), by drawing on work from his research group.
It is important to remember that most measures of resting state functional connectivity are based on correlational analyses and thus do not tell us anything about how regions of the brain influence the activity of other regions. It is possible to model the relationships between observed patterns of functional connectivity to be able to draw inferences about such neural influences, in an approach called effective connectivity, which is determined with Dynamic Causal Modelling. In his video, Karl Friston describes how we can use effective connectivity to infer causality from observed connectivity (minutes 0:57 to 23:07).
How can graph theory be applied to resting state data?
More advanced metrics can be derived from rs-fMRI data using graph theoretical analysis approaches. Graph theory is a mathematical method for mapping all the brain’s connections by depicting them as a graph consisting of nodes and edges. When graph theory is applied to rs-fMRI data, the nodes are often large-scale brain regions, and the edges represent the functional connectivity between them. The great advantage of graph theory over other measures of functional connectivity is that it offers a way to quantify the properties of large, complex networks.
Alex Fornito gives an excellent introduction to graph theory in his video. He discusses the rationale for using graph theory (minutes 0:55 to 3:39), before going on to give a history of graph theory (minutes 3:39 - 11:54). Then, Alex describes how network models can be created and shown as graphs (minutes 11:54 to 16:53), with a focus on defining nodes and edges. He describes how edges can be defined using fMRI data, including the potential problem of relying on the time series correlations that underpin functional connectivity (minutes 19:16 - 24:43). Finally, the construction of the graph is described (minutes 24:43 - 28:55).
Alex Fornito discusses several approaches to defining the nodes of a network. One of these is parcellation of the brain. The brain can be parcellated from rs-fMRI data through either SCA or ICA, as described by Carl Hacker.
Once a functional connectivity matrix has been created, either from brain parcellation or the components obtained from ICA, there are two options for deriving metrics. The first is to simply compare the functional connectivity matrices between two or more groups of participants. This approach can provide useful information about how the variable of interest, such as a disease, affects the connectivity between or within resting state networks, and has been used to characterise functional connectivity in diseases such as schizophrenia and autism. The other option is to create a graph from the functional connectivity matrix and study it with graph theory.
However, because functional connectivity matrices show correlations between the time series of defined brain regions, either approach is potentially susceptible to spurious or weak connections, for instance due to noise. One way to address this is to apply a threshold that removes the connections that fall below that threshold. Andrew Zalesky gives an introduction to network thresholding and an overview of how it is performed between 0:00 and 16:40 minutes of his video. He also provides an overview of the type of measures that can be extracted from brain graphs, with a focus on comparisons of edge strength (minutes 16:40 to 19:36).
Some regions of the brain are more strongly connected with others, and tend to be considered network hubs. Metrics related to network hubs are among the most commonly used in graph theoretical analysis. Martijn van den Heuvel discusses network hubs and the metrics associated with them (from about 1:30 minutes).
An extensive list of graph theory metrics and what they tell us about neural networks can be found in Rubinov and Sporns (2010).
For those interested, there is a small collection of videos on graph theory from last year’s presentations at the OHBM conference, including those discussed in this post.
What do the resting state networks actually show?
How do you interpret findings from your resting state analysis? Well, first, it is important to consider the biological function of the correlated temporal patterns. Unfortunately, it is not as simple as defining it as ‘activity during rest.’ RSNs are collections of brain regions that have synchronous BOLD fluctuations, but the source of the signal has not been unequivocally established. While there is strong evidence to suggest that the signal is neural, there is still ongoing debate about the extent to which it may be influenced by non-neuronal noise, such as respiratory and cardiac oscillations. However, the fact that rs-fMRI analysis results have been reproduced even when applying conservative physiological corrections across both individual subjects and groups points to a largely neural basis of the rs-fMRI signal.
So what does the functional connectivity mean? In purely methodological terms it is the statistical correlation of two time series. It has been suggested that such correlations have arisen as a result of neural populations that are active together to perform a task and have therefore ‘wired’ together. The rs-fMRI signal reflects their spontaneous neural activity in the absence of a specific task. There may be direct anatomic connections between networks derived from rs-fMRI analyses, or another joint source of the signal. This is currently not well understood, and rs-fMRI findings should be interpreted with caution.
A short, but good, outline of the origin of the rsfMRI signal is provided in van den Heuvel et al. (2010).
The Annual Event of Chinese Young Scholars for Human Brain Mapping was held on June 19th, during the 2018 OHBM Annual Meeting in Singapore. This was the second annual event, and continued the success from the inaugural meeting in Vancouver. The theme for this year’s event was “The Road to Independence”. Around 200 young scholars from universities around the world participated.
The annual event is committed to bringing together young Chinese researchers from a wide variety of backgrounds to share and discuss their professional expertise and career experiences, as well as any challenges they may have faced. This offered a platform for young researchers to build collaborations on cutting-edge neuroscience topics and methods, and also to learn from senior researchers on the route to a successful scientific career.
This year’s schedule commenced with a brief review of the annual event by one of the organisers: Professor Chaogan Yan. Then, Professor Yan introduced the two guest speakers: Professor Jiahong Gao (Director of the MRI Research Center of Peking University, Chair Elect of OHBM), and Professor Xinian Zuo (Director of the MRI Research Center, Institute of Psychology, Chinese Academy of Sciences, Program Committee Chair elect of OHBM).
Professor Jiahong Gao gave the first talk, entitled “ Brain Imaging in China: Opportunities and Challenges”. He summarized the fast development of human brain mapping research in China, and shared his vision on future directions of this field in a humorous way. Taking the latest advances on Magnetoencephalography development in his lab for instance, Professor Gao discussed the challenges and opportunities we face in brain imaging, and encouraged young scientists to seize the opportunities and bravely climb to the scientific peak.
The second speaker was Professor Xinian Zuo from the Institute of Psychology at the Chinese Academy of Sciences. In his talk titled “From Mathematics to Brain Sciences”, Professor Zuo shared his own career experiences, from a PhD in mathematics to becoming an outstanding independent researcher in human brain science. He particularly emphasized the importance of reliability and reproducibility in brain imaging studies, and briefly introduced several ongoing projects by his team, including the Chinese Color Nest Project and the Traveler Project.
After the two keynote talks, Professor Juan (Hellen) Zhou from Duke-NUS Medical School, and Professor Ning Liu from the Institute of Biophysics at the Chinese Academy of Sciences joined the guest speakers for a panel session. Professor Chaogan Yan moderated the discussion, and introduced several topics under this year’s focus “The Road to Independence”, including relationships with tutors, necessity of career planning and recovery from failures. Each senior researcher shared their insights on these questions.
Professor Jiahong Gao provided advice on these topics based on his own experiences. He pointed out that the extent of independence of a young scholar largely depends on the mentors’ style. Professor Gao encouraged young scholars to develop their skills with support from mentors, and to prepare themselves to become independent researchers. Young scholars should set spiritual goals, make plans to achieve them, and learn lessons from their consistent efforts.
Professor Xinian Zuo shared his insights based on his personal experiences of switching from mathematics to neuroimaging, and echoed Professor Gao that young scholars would better seek support from their mentors and develop the ability for independent research in projects with their mentors. He also shared his “failure” stories about manuscript writing and paper submission during his very early projects. He summarized that failure is not terrible, and that one should learn lessons from what he/she had experienced, and aim to improve from them.
Professor Juan (Hellen) Zhou shared her personal study experiences, and emphasized the importance of independence, as well as hard work and persistence in order to become a successful researcher. She provided the example of her public speaking training during her PhD, emphasizing the critical role of hard work for acquiring professional skills. She also advised that one could obtain power and motivation from setbacks, and should move forward towards one’s ultimate goal.
Professor Ning Liu provided her thoughts based on how she got along with her own students. She pointed out that unstructured ‘light-touch’ supervision would not be suitable for all students, and she suggested to supervise each student with specific proper strategies. She also discussed the special difficulties associated with animal studies, and encouraged young scholars to actively adapt to any difficulties or potential failures, and keep being positive towards their goals.
Professor Chaogan Yan talked about his personal “failure” when attempting to switch from neuroimaging studies using fMRI to animal studies, and how he subsequently adjusted his research direction back to human neuroimaging. He pointed out that it could be a big challenge to move to completely new fields for a PhD or postdoc. But he believed that it may still be worth trying, especially if you are keen on the new questions and are still young. Even if there was a high chance of failure, one could learn valuable lessons from these unforgettable experiences.
Towards the end of the panel session, Professor Jiahong Gao provided his answers to the questions from audience on how to get international impact as local scholars in mainland China and how to publish papers in high-impact journals. He encouraged young scholars to perform high-level studies in the field, and to actively communicate research results with international researchers and journal editors. He mentioned that “the point is not that we cannot publish high-impact papers, instead it’s that we have not yet achieved high-impact research results.” He continued, “we should cherish our time, and work hard, to pursue critical questions in the field. Only in this way, can we achieve influential results, and publish papers in high-impact journals, which will lead others to recognize our research capability.”
At the end, the audience thanked the speakers for their informative presentations and discussions with hearty rounds of applause. We took group pictures to conclude this inspiring and memorable event. After the meeting, we enjoyed a group dinner and more informal discussions on both science and life as a scientist.
Organizing Committee of the Annual Event of Chinese Young Scholars for Human Brain Mapping:
Chao-Gan Yan, Institute of Psychology, Chinese Academy of Sciences
Yuan Zhou, Institute of Psychology, Chinese Academy of Sciences
Rui-Bin Zhang, Department of Psychology, The University of Hong Kong
Xiang-Zhen Kong, MPI für Psycholinguistik
Chun-Yu Liu, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Xiao Chen, Institute of Psychology, Chinese Academy of Sciences