Poster Perfection

This is the latest in the blog series co-authored by myself and the wonderful Sue Fletcher-Watson. After a brief hiatus we are back with our latest post – how to put together a great poster. Designing an attractive and informative poster is an incredibly useful skill, especially for early-career researchers. For any non-academic readers – most academic conferences and often internal events at Universities will have a poster session.  These allow more people to share their research findings, in addition to scheduled speakers. So posters matter, but in our experience there is surprisingly little guidance given on how to do them well. So here are our dos and don’ts of putting together a killer poster.


  • Plan ahead

Putting together a great poster takes some time and you will need to factor in a few days to get it printed as well. So plan ahead. In our experience people leave posters to the absolute last minute (you know who you are). The result is usually something that looks haphazard and counterintuitive. There is also that mad panic and rush to the print shop…which is never open when you need it to be… and then you almost miss your flight… which is always best avoided.

Make sure you check whether it needs to be landscape or portrait, and what size the presentation boards will be.  You don’t want a poster that’s too big for your board! The conference organisers will be able to tell you this.

A great poster is something you can come back to many times – it can be easily shared online, re-used at multiple events, and it might end up being displayed on your department walls – so it is worth doing well. When done properly, a poster should be easy to present, easy on the eye and it may even win you a prize or two J. So plan it properly.

  • You can only tell one good story with a poster

In a paper you may have 2 or 3 ongoing stories, or main findings that the manuscript is constructed around. This doesn’t work for a poster. A poster needs a single key message that you want to get across. Make sure that this features in the title. We both prefer titles that convey the story or finding you’re reporting on, rather than emphasising the method or approach.

People will only be at your poster for a few minutes (at best). You might not be there or available to explain it to them. It will likely be crowded and noisy. All of this means that you need to boil down the message to a single narrative and explain it well in a few points. When Sue does a poster, she always tries to have two or three bullet points that set out the essential background and central research question.  These are matched with two or three bullets to summarise the conclusions.  It should be possible to understand the study by reading just these sections.  The viewer can quickly and easily decide if they want to linger by your poster board, to get more detail on methods or results. The first step in designing a great poster is choosing this central narrative and presenting it neatly and briefly.

  • Select the key pieces of information, and make them pop

Avoid large amounts of text. Graphics are a great way of conveying a lot of information in a small space. Don’t just paste in figures from your paper: these will probably need amending or focussing to fit the tight narrative that you have adopted for the poster.

Think about what will be central – literally, right in the middle – in your poster. This is the prime space for key graphics and findings. Construct the rest of the poster around this. You can also use colour to make sure important details are easy for the reader to spot. If you’re using, say, navy blue text on an off-white background, maybe you could reverse these colours for your conclusions so that they stand out from the main text?

  • Imagine you are there… what visual aids would help?

Trial run your poster. You would never imagine giving a talk that you hadn’t practiced (would you?!), and you need to apply the same logic to your posters. Show lab mates or departmental colleagues who are not familiar with your study. Can you use the poster to explain the study in about 3 minutes? Are there visual aids or diagrams that might help? Going through this process is vital to make sure that your poster actually works.

  • Don’t be constrained by the template

Many institutions have set templates which are approved by corporate people somewhere (I guess). Our advice is to not let this constrain how you display your poster. You are the person who will have to stand next to this thing and use it. As long as you have the right logos and basic palette, and it is otherwise a brilliant poster, you should get away with it. And this is far preferable to a poster structure that simply doesn’t work. A common poster template is a landscape organised into three columns.  If you add information to these in a traditional sequence of background – research question – methods – results – discussion, you can often end up with interesting graphs at the bottom or off to one side. Don’t be afraid of going a little off-piste if this helps you get your message across. We won’t tell corporate if you don’t…

  • If you don’t have an eye for colour then ask someone who does

Finally, if you have bad taste when it comes to colours (again… you know who you are) then ask someone to help you. Try and avoid clashing colour combos, needlessly vivid and flashy figures. Posters are an inherently visual medium, so get the colours right. Think about what logos you have to have on the poster, and pick a palette which compliments at least one of them.  An easy fix is to go with different shades of the same colour – blues and greens work well – rather than mixing up your colours too much.

To round this off, here is a poster – this one made by Sue which she is pretty pleased with.

And there you have it. Investing time to make the poster good is worth it. You will enjoy the conference much more, you might win a prize, and it will look great on the lab wall once you’re done. So plan ahead, focus the message, get some good graphics… and be a great scientist!


Don’t rule the lab with an iron fist: tips for effective lab management


This is the latest in our semi-regular (trying to be regular) blog series, with the indomitable Sue Fletcher-Watson. The practical skills of being a good scientist are rarely taught but are vital. This is the subject for our series of blogs, and this time we are turning our attention to managing the lab.

You have your University position, you have research funding of some kind (whoop!), and the lab – you, your students, researchers and technicians – is starting to take shape. Managing the team effectively will make for happy and productive group members. This can turn into a virtuous cycle – lab members who enjoy what they are doing are a joy to manage, and make your life so much easier.

What follows is stuff we have learnt over the past few years. The points are broadly organised into two sections – firstly we discuss issues of organisation and management style, then we move onto the non-science elements of a successful lab.

  • Balance lab identity with flexibility

It might seem obvious, but a group needs a shared identity that their projects can hang off. This could be a series of over-arching questions, a particular sample of interest, a set of new methods you are advancing, or a core set of principles. A clear group identity helps lab members understand the overall picture, how their science is contributing, and how/where expertise can be shared. When Sue joined the Patrick Wild Centre and was given a branded PhD mug it made her feel one of the team. This kind of thing makes a real difference for new staff and students.

But be careful that your lab identity doesn’t kill off flexibility. It’s important that individual lab members are able to take intellectual ownership of their work, even if they are junior. In Duncan’s lab he focuses cognitive and brain development in childhood. When a potential student approached him with an interest in socio-economic status he was initially very reluctant. He could see it could easily turn into an intractable mush – that literature can be a real mess. But he went with it, and together they put together a project they were both happy with. Turns out, the student was amazing, the project highly successful, and this line of research now forms a main arm of his current research programme. Giving lab members intellectual ownership and being flexible about projects means that you give your students and staff permission to innovate. In science this is the most valuable thing of all, and an essential component of how we make progress.

So have a clear identity and make sure this is explicit, and understood by all members – but don’t sacrifice flexibility.

  • Balance easy wins and long-term goals

When starting your lab you will have various potential projects. In the early days when the lab is small you need to be strategic. Investing all of your energies (and those of your lab) in a single demanding, high-risk project, which may take several years to come to fruition, is unwise. When Duncan started his lab in Cambridge a very wise mentor told him to balance easy wins and long-term goals – alongside a large project he needed simpler experiments. This advice was invaluable. That big project took Duncan’s embryonic team 18 months to collect the data alone, and another 18 months or so to analyse fully. All the lab members were trying to build their CVs and could not afford an empty patch for 3 years. So along the way they published several standalone experimental papers, which made everyone feel more relaxed and meant they could establish ourselves as a lab.

(In the end that big project came to fruition and generated multiple big papers, so our patience was rewarded!).

Meanwhile, during Sue’s postdoc she designed a novel intervention and then evaluated it in a small randomised controlled trial.  That’s a long time to wait for your big paper. To make matters worse, she took maternity leave twice during the project! It was a tough time, waiting for that research to get out into the world, though writing a few review papers and getting the remainder of her PhD studies into journals helped pass the time!

  • Don’t rule your lab with an iron fist

We have all heard about those labs where the staff are not so much managed as subjugated (maybe there will be a blog on that, if we are feeling brave enough). Lab members are micromanaged, their outgoing emails vetted and they are subjected to surprise inspections. If you are tempted to run your lab in this way, or if this is your natural management style, our advice: just don’t.  It results in very unhappy lab members, creates needless extra work for you, and is ultimately pointless. If your lab members are not enjoying working within your group then they will not be productive. Running them like a sweatshop will do nothing to improve that.

A better approach is to have a clear role-setting process when each member joins the lab. Explain your role as the PI and their role as the post-doc (or whatever). This role-setting includes outlining expectations – mandatory meetings, duties within the group, initial goals, the schedule for your individual meetings etc. If you feel this has not been respected, then of course, pull them up and gently tell them that you want to change things. But don’t micro-manage your group.

  • Have regular meetings with minutes

Regular group meetings (we both have them fortnightly) provide a space for sharing experience, best practice, technical expertise, troubleshooting and peer support. They’re also a quick and easy way to get a snapshot of where everyone is up to on their projects. Good lab meetings will save you time, and build a happy team. Make sure you reserve a timeslot and don’t let them run over. If you fall into the habit of having needlessly long lab meetings they can easily become onerous. Keep them to time. When you meet, get members of the lab to take turns writing minutes. Sue’s lab stores these in a shared folder which is also crammed with resources to help lab members work independently – model ethics documents and cover letters, useful logos and campus maps.

Sometimes subgroups within your lab may need to meet without you – let them. This could be a really valuable way of them tackling various problems without you needing to expend any time. Don’t insist on being there for every discussion or meeting.


So far we have focussed on elements of lab organisation, strategy and management style. For the next set of points we turn our attention to the more interpersonal elements of running a successful lab.

  • Encourage your lab to have a social life

Within Duncan’s lab there is a designated Social Secretary, with the job of orchestrating lab social events (checking availability, garnering opinion of what people would like to do, making bookings etc). Make sure that any social events are not enforced, and remember that alcohol is not an essential component of social occasions! It is important not to make anyone feel excluded. N.B. some social activities like life drawing classes might be an unwise choice (sorry Joe), so try to stick to simple events that allow everyone an opportunity to chat without feeling uncomfortable.

Things to keep in mind: what is your role within these social situations? Remember that you still have to be these people’s boss come Monday morning. Have fun but don’t be unprofessional. No-nos include gossiping about your colleagues or asking intrusive questions about people’s personal lives. While taking to the dancefloor to bust a groove is a big YES.

  • Be a good mentor and keep an eye on wellbeing

Running a successful lab is also about keeping an eye on the wellbeing of those you manage. It’s important to let them know that you care. Both Sue and Duncan make a point of asking, even though everyone seems pretty happy most of the time. This makes lab members mindful of their own wellbeing and flags the fact that you are a person they can speak to if / when they struggle. If you’re doing an annual review, ask them if they’re happy with their work-life balance. Remind your lab group to take time off – especially PhD students who can fall for the myth that they ought to be working 7-day weeks. It can also be a good idea for lab members to have mentors outside the group. With their mentor they can discuss matters like job opportunities, or difficulties they are having with their PI (i.e. you!). It can also provide a valuable alternate perspective on their work.

There is always a slight tension about how much of your own personal life you expose lab members to. Both Sue and Duncan think it is important to show your lab members that you too are a human being, with your own stresses, frustrations and problems. In our experience, when you choose to be yourself with your lab members, and be honest about how you are doing, you give lab members permission to do likewise. It creates a safe space in which everyone feels that they can be themselves, and this is important in creating a supportive work environment. Don’t overshare, but do be yourself.

So there we have it, our tips on running an effective lab. Be flexible, be a human being, be a good scientist.

Are early interventions effective?

Early years interventions can seem a particularly powerful way to forestall developmental difficulties. The wide-ranging evidence that early cognitive and behavioural difficulties can predict lifelong outcomes[i],[ii] makes it seem obvious that if a child is struggling, intervening earlier is better. This has led to considerable interest in intervening within the first few years of a child’s life, and the temptation to seek out earlier indicators of children’s cognitive development and wellbeing.

However, there are several challenges with choosing effective early interventions. First, reliably identifying which children will require support is a substantial challenge. This is particularly evident in the case of language development. Children’s language is a rich area for research on early intervention, for several reasons: it provides a window onto learning at school entry, and weak language at school entry is a risk factor for poor educational, social and emotional outcomes in subsequent years. There is also a clear socio-economic gradient in children’s language abilities, in which children from lower socio-economic backgrounds tend to have weaker language skills than their peers before they start school.


(But is the story really so simple?)

Despite this, children’s language abilities are highly variable and volatile before they are 4-5 years old, which makes the early identification of children who may be in need of intervention non-trivial. This is demonstrated by several studies showing that whilst children’s early vocabulary around 2 years of age can predict their later vocabulary and reading skills at school entry, this predictive power is very small. Only about 10-20%[iii],[iv] of the variation in children’s abilities at school is typically predicted by their early language skills, which indicates that there are multiple other factors that shape children’s outcomes. This also suggests that finding a sufficiently sensitive marker of early difficulties is challenging because of how much a child’s abilities can shift and develop over time.

This relates to the second challenge: early interventions can struggle to have sustained impacts unless intervention is ongoing. A good example of this comes from a recent study by McGillion and colleagues (2017). The researchers were interested in whether a caregiver intervention to promote talking with their 11-month old child would lead to changes in both parenting approach and children’s language development. Caregivers from a range of socio-economic backgrounds were randomly assigned to one of two conditions: watching a short video about talking with their child, or a control video about dental hygiene. One month later the caregivers in the language video condition talked more with their children than those in the control video condition. This fed into vocabulary improvement in the low socio-economic group: children in the intervention condition had higher vocabularies at 15 and 18 months compared to children in the control condition. However, at 24 months these benefits had disappeared, and there was no effect of the intervention on children’s vocabulary.

These findings demonstrate several important points about early interventions. Interventions can have a positive impact: in this case a brief, low-intensity video intervention was able to positively change caregiver behaviour, and this may have helped gains in children’s vocabulary a few months later. However, these effects were not sustained over time: they had faded by the time children were 2 years old. This shows how critical longer-term follow-ups are in intervention studies to understand whether benefits are long lasting. Moreover, this result reminds us that without ongoing support it is difficult for early gains in one area of cognition to offset other challenges children might face. The promise of early interventions means there can be an assumption that they will permanently shift a child’s trajectory. In reality this is often not the case, and initial gains may often require ongoing support to have real long-term effects for children.

It is important to therefore strike a balance between the promises and limitations of early interventions. Whilst endeavouring to find the right areas to target is undoubtedly valuable, the challenge of finding reliable indicators of difficulties early in a child’s life might mean searching for stable predictors at later ages (e.g. from school entry) may be a more fruitful approach[v]. In addition, long-term follow-ups to interventions may be key to understanding the extent to which they are effective. The challenge of early interventions is that development is complex and shaped by multiple factors. Working within these constraints may help us better identify and help children in need of ongoing support.

 This newest article was contributed by our beloved lab member Erin Hawkins. Erin’s research focuses on understanding the mechanisms and interventions of developmental difficulties in children.


[i] Caspi et al. (2016). Childhood forecast of a small segment of the population with large economic burden. Nature Human Behaviour, doi:10.1038/s41562-016-0005

[ii] The Allen Report (2011). Early Intervention: The Next Steps.

[iii] Duff, Reen, Plunkett, & Nation (2015). Do infant vocabulary skills predict school-age language and literacy outcomes? Journal of Child Psychology and Psychiatry, doi:10.1111/jcpp.12378.

[iv] McGillion, Pine, Herbert & Matthews. (2017). A randomised controlled trial to test the effect of promoting caregiver contingent talk on language development in infants from diverse socioeconomic status backgrounds. Journal of Child Psychology and Psychiatry, doi:10.1111/jcpp.12725

[v] Norbury, C. (2015). Editorial: Early intervention in response to language delays – is there a danger of putting too many eggs in the wrong basket? Journal of Child Psychology and Psychiatry, doi:10.1111/jcpp.12446

For further reading:


The Dos and Don’ts of PhD supervision

Myself and Sue Fletcher-Watson (you know, the fabulously clever one…) have been putting our minds to a series of blog posts, attempting to help the fledgling academic get to grips with some of their new professional duties.  This week it is a real classic – how to supervise a PhD student.


Being a good supervisor is not easy, and tricky relationships between students and supervisors are all too common (you may have direct experience yourself). Understanding and mutually agreeing upon the role of the student and the supervisor is a crucial starting point. Establishing these expectations is an important early step and will make navigating the PhD easier for all concerned. With a shared idea of where you are both starting from, and where you want to get to, together you can chart the path forward.  Hopefully these DOs and DON’Ts will help you get off on the right foot as a PhD supervisor.

  1. Managing your intellectual contribution

DO challenge their thinking…

A good PhD supervisor should question their student’s decision making – some part of your supervision meetings should be viva-like. Why are you doing this? How does this method answer this question? What do these data mean? Make sure your student understands what you’re doing and why – be explicit that you expect them to defend their interpretation of the literature / research plans NOT adhere to yours. It is important that they don’t feel that this is a test, with just one right answer.

When questioning your student, strike a balance between exploring alternatives, and making forward progress. Probing assumptions is important but don’t become a pedant; you need to recognise when is the time to question the fundamentals, and when is the time to move the debate on to the next decision.

DON’T make decisions for them…

Help students determine the next decision to be made (“now that we have selected our measures we need to consider what analysis framework we will use”) and also the parameters that constrain this decision… but remember that it is not your place to make the decisions for them. Flagging the consequences of the various choices is an excellent way to bring your experience to bear, without eclipsing the student. You may wish to highlight budget constraints, discuss the likely recruitment rate, or consider the consequences of a chosen data type in terms of analysis time and required skills. Help them see how each decision feeds back. Sue recently worked with a student to move from research question, to methods, to power calculation, to ideal sample size, and then – finding that the project budget was inadequate to support the sample target – back to RQ again. It’s tough for a student to have to re-visit earlier decisions but important to consider all the relevant factors before committing to data collection.

  1. Who’s in charge?

DO give them project management responsibility

Both Sue and Duncan run fortnightly lab group meetings with all their students and this is highly recommended as a way to check in and ensure no student gets left hanging. But don’t make the mistake of becoming your student’s project manager. Whether you prefer frequent informal check-ins or more formal supervisions that are spaced apart, your student should be in charge of monitoring actual progress against plans, and recording decisions. For formal meetings, they can provide an agenda, attachments and minutes, assigning action points to you and other supervisors, and chasing these if needed. They should monitor the budget, and develop excellent version control and data management skills.

This achieves a number of different goals simultaneously. First, it gives your student a chance to learn the generic project management skills which will be essential if they continue in an academic career, and useful if they leave academia too. Second, it helps to reinforce the sense that this is their project, since power and responsibility go hand in hand. Finally, it means that your role in the project is as an intellectual contributor, shaping and challenging the research, rather than wasting your skills and time on bureaucratic tasks.

DON’T make them a lackey to serve your professional goals

Graduate students are not cheap research assistants. They are highly talented early career researchers with independent funding (in the majority of cases) that has been awarded to them on merit. They have chosen to place their graduate research project in your lab. They are not there to conduct your research or write your papers. In any case, attempting this approach is self-defeating. Students soon realise that they are being taken advantage of, especially when they chat with friends in other labs. When students become unhappy and the trust in their supervisor breaks down, the whole process can become ineffective for everyone concerned. As the supervisor you are there to help and guide their project… not the other way around.

This can be really challenging when graduate projects are embedded within a larger funded project. How do you balance the commitment you’ve made to the funder alongside the need for students to have sufficient ownership? Firstly, think carefully about whether your project really lends itself to a graduate student. Highly specified and rigid projects need great research assistants rather than graduate students. Secondly, build in sufficient scope for individual projects and analyses, for example by collecting flexible data types (e.g. parent-child play samples) which invite novel analyses, and make sure that students are aware of any constraints before they start.


  1. What are they here to learn?


DO provide opportunities to learn skills which extend beyond the project goals

A successful graduate project is not just measured in terms of thesis or papers, but in the professional skills acquired and whether these help them launch a career in whichever direction they choose. This will mean allowing your students to learn skills necessary for their research, but also giving them broader opportunities: formal training courses, giving and hearing talks, visiting other labs or attending conferences. This is all to be encouraged, though be careful that it happens alongside research progress, rather than as a displacement activity. Towards the end of the PhD, as the student prepares to fly the nest, these activities can be an important way of building the connections that are necessary to be part of a scientific community and make the next step in their career.


DON’T expect them to achieve technical marvels without support

All too often supervisors see exciting new analyses in papers or in talks and want to bring those skills to their lab. But remember, if you cannot teach the students to do something, then who will? Duncan still tries to get his hands dirty with the code (and is humoured in this effort by his lab members), but often manages to underestimate how difficult some technical steps can be. Meanwhile, Sue is coming to terms with the fact that she will never fully understand R.

If you recommend new technical analyses, or development of innovative stimuli, then make sure you fully understand what you are asking of your student – allow sufficient time for this and provide appropriate support. When thinking of co-supervisors, don’t always go for the bigwig you are trying to cosy up to… often a more junior member of staff whose technical skills are up-to-date would be far more useful to your student. Also try and create a lab culture with good peer support. Just as ‘it takes a village to raise a child’, so “it takes a lab to supervise a PhD student”. Proper peer support mechanisms, shared problem solving and an open lab culture are important ingredients for giving students the proper support they need.

  1. Being reasonable

DO set clear expectations            

Step one of any PhD should be to create a project timeline.  Sue normally recommends a detailed 6-month plan alongside a sketched 3-year plan, both of which should be updated about quarterly. For a study which is broken down into a series of short experiments, a different model might be better. Whatever planning system you adopt, you should work with your student to set realistic deadlines for specific tasks, assuming a full time 9-5 working day and no more, and adhere to these. Model best practice by providing timely feedback at an appropriate level of detail – remember, you can give too much as well as too little feedback.

Think carefully about what sort of outputs to ask for – make them reasonable and appropriate to the task. You might want propose a lengthy piece of writing in the first six months, to demonstrate that your student has grasped the literature and can pull it together coherently to make a case for their chosen research.  But, depending on the project, it might also be a good idea to get them to contrast some differing theoretical models in a mini-presentation to your lab, create a table summarising key methodological details and findings from previous work, or publish a web page to prioritise community engagement with the project topic.

DON’T forget their mental health and work-life balance

PhD students might have recently moved from another country or city – they may be tackling cultural differences at work and socially. Simultaneous life changes often happen at the same time as a PhD – meeting a life partner, marriage, children. Your students might be living in rented and shared accommodation, with all the stresses that can bring.

Make sure your students go home at a reasonable hour.  If you must work outside office hours be clear you don’t expect the same from them. Remind them to take a holiday – especially if they have had a period of working hard (e.g. meeting a deadline, collecting data at weekends). Ensure that information about the University support services are available prominently in the lab.

Remember to take into account their personal lives when you are discussing project progress. Being honest about your own personal situation (without over-sharing) creates an environment where it is OK to talk about stuff happening outside the office. Looking after your students’ mental health doesn’t mean being soft, or turning a blind eye to missed deadlines or poor quality work – it means being proactive and taking action when you can see things aren’t going well.

What about the research??

We are planning another blog in the future about MSc and undergrad supervision which might address some other questions more closely tied to the research itself – how to design an encapsulated, realistic but also worthwhile piece of research, and how to structure this over time. But the success (or otherwise) of a PhD hangs on a lot more than just having a great idea and delivering it on time. We hope this post will help readers to reflect on their personal management style and think about how that impacts their students.

So, be humble. Be supportive. Be a good supervisor.

Data-driven subtyping of behavioural problems associated with ADHD in children

Over 20% of children will experience some problems with learning during their schooling that can have a negative impact on their academic attainment, wellbeing, and longer-term prospects. Traditionally difficulties are grouped in diagnostic categories like attention deficit hyperactivity disorder (ADHD), dyslexia, or conduct disorder. Each diagnostic category is associated with certain behaviours that need to be present for the label to be applied. However, children’s difficulties often span multiple domains. For instance, a child could have difficulties with paying attention in the classroom (ADHD) and could also have a deficit in reading (dyslexia). This makes it difficult to decide which diagnostic label is the most appropriate and, consequently, what support to provide. Further, there can be a mixture of difficulties within a diagnostic category. For instance, children with ADHD can have mostly deficits with inattention or hyperactivity, or both. This heterogeneity makes research on the mechanisms behind these disorders very difficult as each subtype may be associated with specific mechanisms. However, there is currently no agreement about the presence, number, or nature of subtypes in most developmental disorders.
In our latest study, we applied a new approach to group behavioural difficulties in a large sample of children who attended the Centre for Attention, Learning, and Memory (CALM). This sample is a mixed group of children who were referred because of any difficulty relating to attention, learning, and/or memory. The parents of the children filled in a questionnaire that is often part of the assessment that informs the advice of education specialists on the most appropriate support for a child. The questionnaire contained questions like “Forgets to turn in completed work”, “Does not understand what he/she reads”, or “Does not get invited to play or go out with others”. In our analysis, we grouped children by the similarity of ratings on this questionnaire using a data-driven algorithm. The algorithm had two aims: a) form groups that are maximally different b) groups should contain cases that are as similar as possible. The results suggested that there were three groups of children with: 1) primary difficulties with attention, hyperactivity, and self-management 2) primary difficulties with school learning, e.g. reading and maths deficits 3) primary difficulties with making and maintaining friendships.
Next, we investigated if the behavioural profiles identified through the algorithm also show differences in brain anatomy. We found that white matter connections of the prefrontal and anterior cingulate cortex mostly strongly distinguished the groups. These areas are implicated in cognitive control, decision making, and behavioural regulation. This indicates that the data-driven grouping aligns well with biological differences that we can investigate in more detail in future studies.
The preprint of the study can be found here:
The code used for the analysis can be found here:

Bringing home the bacon: how to budget a grant application

Economic_impactRather than attempt a single blog covering all aspects of grant-writing, the wonderfully talented Sue Fletcher-Watson and I are continuing our blog series of advice to newly-fledged researchers by focusing on the challenges of costing a new grant.  Costing a grant means working out exactly where money will be spent, and being realistic about what it is going to cost. While it might seem like a tiresome administrative job, setting the budget has a big influence on your chances of success – both in terms of winning the funding and administering the project successfully.  It’s also an area where junior PIs are offered little or no guidance.

Let’s start with a few definitions.

Grant budgets are often divided into direct and indirect costs.  In a nutshell, direct costs are for things that just wouldn’t happen without the grant – wages for dedicated staff in new posts, paying participants for their time, the cost of any questionnaires or other materials you’re using. Indirect costs are contributions to things which would be paid anyway – like a portion of the time of an existing member of staff. You might cost for 5% of the salary of a technician, or senior advisor, which should equate to them spending a couple of hours per week on your project.

Another key phrase is Full Economic Costing, or FEC (don’t smirk). This is when a funder pays for various indirect costs and estates costs linked to your grant.  For example, a University provides buildings with heating and electricity, furniture and IT support, kitchens and meeting rooms. Staff on your grant will have access to a library and various support services.  All of these cost money and FEC is a way to assign some of that cost to incoming grants.  Not all funders cover FEC (e.g. charities tend not to, but UK research councils do make a contribution). Warning: FEC is always way more than you think. It can as much as double the cost of a single member of staff. So if there is an upper limit on what you can apply for, and FEC is included in this, you need to rein in your plans for what you’ll spend on equipment or post-doc salaries. Seek advice early on from a grants officer (more on this below).


Budgeting basics

You’re going to want to start with a nice Excel spreadsheet or similar – ask a colleague if they have an old one you can use as a template. It might be that your department has a financial administrator that can send you an up-to-date one – Universities have a habit of changing the details of how costs are calculated. Once you have your template, it can be easiest at the start to break down costs by activity – so you list all the participant payment, questionnaires, and travel costs associated with Study 1, Study 2 etc. However, when you submit your budget to the funder, you will want to group them by category.  All the travel costs will get summed together, and all the ‘consumables’ (anything that gets used once and can’t be used again – like postage or IQ-test forms – as distinct from “equipment” which can be re-used, like a laptop) will be in a different total. So make sure you label each cost according to type and then you can easily re-order your file and get the totals you need.

Each funder asks for costs organised in subtly different ways too.  They might want them broken down by year of the project, or use particular headings. EU funding want salary costs organised into “person-hours” which are utterly fiendish. Some funders might define “equipment” as only things that cost £2000 or more.  Others might pay for salaries but only allow them to be a certain proportion of the total budget. Check these constraints at the start and make sure you label and organise your budget accordingly.


Getting support and approval

Another key thing to check up front is the relevant permissions you need to go through, in your host institution. At the University of Cambridge (and probably others), certain forms may need submitting weeks in advance (sometimes months), and in many cases these will need some details from your budget. Check this out well in advance to avoid stress. Salaries in particular will need to be aligned with your institution’s pay-scales.  You should have a conversation with someone – probably a grants officer in your department – about the right pay grade to cost for a research assistant with a Masters degree, versus a lab technician, versus a post-doc. Don’t cost at the bottom point of the grade for a new post that you are going to advertise – costing at the middle or top of a grade range will give you freedom to negotiate with the applicant and offer an attractive salary. Normally the amount you should put in your budget for, say, a two-year full time post-doc, will be provided by the grants officer. You don’t just google it like Sue did with her first ever grant! Get the ballpark figures early on so you know what you’ve got to play with in the total budget. And always remember that your grants officer is being constantly bombarded with requests for costs, often at very short notice.  Contact them early on, keep them in the loop, and agree a rough timeline for preparing the application as partners.

Some funders only cover partial costs for large items of equipment.  Others won’t pay salary contributions for existing staff but will pay for teaching “buy out”.  This is when a grant will pay the salary of someone (normally at a mid-level pay grade) to replace the time that a senior lecturer or professor might otherwise have spent teaching.  This frees-up the Prof to help with your grant, while costing the original funder a lot less. If you have these sorts of constraints on the funding, make sure you understand them and, crucially, try to get something in writing from your department to guarantee that they will comply with the grant terms and provide any top-up funding you will need.


Costing staff

Staff will normally be the biggest cost. If you’re applying for a Fellowship you’ll probably be requesting your full-time salary and maybe a %FTE (full-time equivalent: 20% FTE = one working day per week) for a senior colleague. A contribution to the time of a department administrator is also a great idea if there’s someone available, and you will have tasks for them – they could run expenses claims, book travel and monitor your budgets, saving you valuable time that you’d rather spend on science. Try not to ask for favours or trade authorship on articles for work – if you can, pay for things like specialist statistics and programming support, or for access to an assessment space. Otherwise, if the project gets funded, you are potentially beholden to others’ goodwill and availability to get things done.

You might also employ a research assistant or post-doc. Think carefully about the kind of skills you need, the level of independence you want from your staff, and the number of days per week you need them.  While it might seem like having someone more experienced is always a good thing, actually recruiting a research assistant with a good Masters degree is much better if you have a tightly-defined project and want someone to just get on with it. Post-docs, and PhD students, will have agendas that extend beyond the goals of your project.  While we don’t mean to suggest you shouldn’t also be offering an RA career-development opportunities and intellectual input to the work, their career stage means that just by recruiting participants, collecting and analysing data they are building and extending their skills. You need a good match between what you’re asking your employees to do, what they already know, and what they are ready to learn.


What else should I ask for?

Other big costs will be conferences, including travel, registration, accommodation – budget generously for these and try to specify the meetings so the reviewers can see you have considered carefully the appropriate place to share your findings.  Don’t just cost for you to attend – bring your team along too, or at least give each of them one chance to attend a scientific meeting. Another way to support your staff is to ensure you cost for some training expenses – obviously these need to be relevant to the project but it also means they will finish their time working with you with more skills than when they started.

You might want to cost for a study advisory board including community representatives – their travel to meetings, some good quality catering, maybe a stipend. If you think this is overkill or too expensive for your project, you can at least cost an “honorarium” (one-off payment) for one or two consultants to advise on your work. Sue pays autistic consultants on all new autism projects now, normally aiming for £500 per year for about ten “contact points”.  A contact point might be commenting on documents by email, attending a meeting to design the methods, or advising on recruitment pathways. Be generous in your funds for participant reimbursement too, normally estimated as an hourly rate with extra for their travel costs. For children, budget for a box of gifts that they can choose from at the end of an assessment session. If working with charities or schools you might want to include a one-off gift to the organisation. If doing an online survey you can offer a prize draw, which increases response rates without pushing your budget through the roof.

Don’t neglect the impact costs either.  Open access fees for journal articles are important – though some funders like the Wellcome Trust cover these for you under a separate budget. If you want to reach out to stakeholders with your results think about costing for a graphic designer, professional printing, or an animator to help you share your findings creatively. Budget generously for dissemination events – you don’t want your exciting ideas buried because of a lack of resources. Funders are becoming increasingly suspicious of researchers promising the world in terms of impact, but with no budget attached.


What about cost effectiveness?

A grant should be cost-effective.  Reviewers will be specifically asked whether they think your work represents good value for money. This doesn’t mean though that you should just cut cut cut. An under-funded project is a big risk and reviewers and funding committees will be reluctant to support something that doesn’t seem appropriately resourced. On the other hand, if you want multiple staff, or large items of new equipment you will need to properly justify these.  In the case of staff, you should be able to describe their responsibilities and these should clearly match to their proposed pay-grade and full or part-time hours. An easy way to save costs if you need to is to ask whether you need staff for the entire duration of the project – can you set it up yourself and just employ an RA when data collection begins?

In the case of equipment, if you are buying large and expensive items, reviewers might well ask themselves why your department doesn’t have these already, and whether you know how to use them properly. Tackle these concerns head on – maybe you have tons of EEG experience, but you need brand new kit because you’re going to be working with infants for the first time.

A successful grant application requires so much more than a good research idea. Your costs are a key way to show that you have thought about the practical aspects of your research.  You have a clear plan, appropriately resourced. By costing for community consultants, open access fees, or staff conference attendance, you show the reviewers your commitment to ethical science and good lab management. Budgeting your research should be an integral part of a funding application process so don’t leave it as a final afterthought.


Be thorough. Be practical. Be a good scientist.


Crushed by the ivory tower – a personal account of mental health in academia

The tone was set on my first day at university. After a tough year of sitting exams to get the grades required to be admitted to a competitive undergraduate science programme, I was sitting in my first lecture. The professor stood in front of around 300 excited undergraduate students and began their first lecture with the statement “A year from now around half of you will be gone”. Indeed, what followed were three gruelling years of constant pressure with exams every 6 weeks and term breaks filled with additional lab work, seminar presentations, or essays. By the end, only around a third obtained their degree.

Rather than protesting this brutal pressure that robbed us of our intellectual and social breathing space, we students were largely complicit. On the contrary, a culture developed that aggravated and justified the pressure. After all, this level of sacrifice is to be expected if you want to make it at a top university. Plus, the recession had hit hard and obtaining jobs was seen as a struggle for life at an institution that offered little perspective on employment prospects outside of academia. It seemed like there was no chance to find employment without top grades and extracurricular activities to make you stand out.

I observed a similar culture as an MSc and later PhD student at a different institution in a different country. Senior academics told us that the commitment necessary to obtain their position had taken a substantial toll on their private lives. Again, there were myths floating around that made the pressure balloon to even more crushing proportions. Apparently, getting a position as a university lecturer is impossible with a Nature or Science publication, students working less than 45 hours per week might as well give up, and if you really took it seriously you should consider emulating Professor X who gets through his enormous workload with caffeine pills and nicotine gum. I adapted to this culture by working extremely long hours, working on weekends, taking on additional projects, and neglecting my social life.

After bearing the pressure stoically for a few years, I had my first breakdown during the second year of my PhD. I had fallen into a vicious cycle of caffeine-fuelled work during the week and heavy drinking on weekends – the abundance of cafes and pubs around university might indicate that this is a common pattern. One Monday morning, I did not find the strength to get up. The isolation and constant pressure of my PhD had sucked the joy out of my life. By sacrificing almost everything for my work, I had eroded all the support that might have provided some resilience.  Fortunately, I could get professional help and managed to start building a healthier relationship with academic work, which is still very much work in progress.

I think a learned a few lessons about triggers that make academic work problematic for people with a predisposition for depression and/or anxiety like myself:

  • unclear or unrealistic expectations: We often don’t know what leads to success and if it is attainable
  • isolated work: In my experience, scientists often work by themselves and identify a lot with their work
  • high standards, abundant criticism: from anonymous peer review to seminar questions, scientists have to deal with a lot of negative feedback
  • comparisons: the golden boy from lab X just received a fellowship right after his PhD, the people I follow on social media publish papers in high impact journals all the time, X is not only a great postdoc but also an accomplished concert pianist, …

I wish I had a good solution for dealing with these triggers, but, unfortunately, I don’t. However, I think that there are things to all of us can do to change this culture of pressure and make life more enjoyable for all. First, we should acknowledge that longer working hours and higher workloads are not the route to success. In contrast, leisure time is essential for building creativity and resilience. Second, share your difficulties as well as your successes. Even the most accomplished scientists are not some sort of Übermensch and will have experienced some setbacks. It is important for junior scientists to hear these stories and learn how they can be overcome. Third, we should all monitor how we communicate with other people. You may disagree with another scientist’s work, but you should acknowledge that this scientist’s time, sweat, and blood went into this work. So, in reviews or Q&A sessions at least try to find something nice to say.

Please share your experiences and your suggestions in the comments below.





It’s a jungle out there: bagging a post-doc job


The incomparable Sue Fletcher-Watson and I continue our intermittent blog series on academic life today, this time focusing on the stressful post-PhD employment scene.  There are a number of options for those who want to stay in academia post-PhD, all highly competitive and with unique stressors. Today, we’re talking about one of the most common scenarios: applying for an advertised pot-doc research role in someone else’s lab. In this case, a PI has secured a grant already and is now advertising a clearly defined job, for someone to carry out the project. So how do you get your application to stand out?  How can you impress at interview?  Here’s a few tips, based on our recent experiences of being on the other side of the interview table.

Address ALL the job criteria, thoroughly but succinctly

Job adverts normally list essential and desirable criteria. In your application, compose a cover letter which specifically demonstrates your ability to meet these criteria. You’d be surprised how many people don’t do this, instead providing what amounts to an abstract for their PhD.  This is all well and good, but remember that the selection panel will use the job criteria to rate applicants.  They won’t give you the benefit of the doubt – you must demonstrate what you can do.

It is tricky to do this succinctly of course, and criteria like “must have excellent communication skills” can be hard to prove.  “I have excellent communication skills” isn’t exactly convincing. Focus on experiences you’ve had and what they taught you.  e.g. “My PhD was supervised by academics from two very different disciplines (informatics, psychology), enabling me to develop my excellent communication skills.  I ran three novel eye-tracking experiments, recruiting more than 80 autistic adults in 12 months, showing my innovative approach to science and time management abilities”. This tells the panel something about your specific skills in a range of areas: research methods, project management, interpersonal skills.

Don’t give the panel extra work

Very occasionally there is a really good reason to contact the panel before the interview. You might have a pressing question about the planned start date for example, or want to ask about potential to do the role part time. However, even in these cases you’d probably do better to apply and then negotiate on the details afterwards, if you’re offered the job.  If they’ve decided you are the right candidate, they will be much more willing to adapt to your situation.

More commonly, people get in touch before the interviews to ask questions like “Am I eligible for the job?” This infuriating query amounts to a request to vet your application prior to seeing other candidates, which is entirely unreasonable. The answer to this is always the same: please read the job description and if you think you are eligible then apply. The second type of advance question is along the lines of a proposal to adapt the study: e.g. have you thought about adding in an fMRI element to this? This also gets Sue’s back up. By this stage, the proposal for funding will have been tweaked and adapted in response to comments from multiple co-applicants, internal and external reviewers. It may have been turned down by one or two funders previously. This is not a good time to start to question fundamental components of the design!

That said, we should note that asking at the interview about where in the protocol there is room to innovate and make your mark on the project is to be encouraged. No-one wants to work with a cipher, just make sure you ask this with a recognition that there is a fixed topic, budget and timeline which you must operate within.

For goodness’ sake, proof-read

Another way to demonstrate your excellent abilities is to show them off in the quality of your application documents. Most scientists will value thoroughness, clarity and precision. Illustrate these qualities by submitting a clearly laid-out CV and cover letter, without typos or grammatical errors. Save it as a pdf so the formatting won’t be messed up by the recipient’s Word template. Don’t submit additional attachments such as reference letters or transcripts of your grades (unless the job advert specifies these). These are superfluous to requirements and just add to the difficulty of screening large numbers of applicants.  It will not work in your favour.

Aim to submit 24 hours before the deadline so you can check the upload works OK.  Sue once applied for a job very close to the deadline, and found at the last minute that her application file size was larger than that allowed on the system.  Cue frantic panic, adapting the document to get under the limit.

Preparation, preparation, preparation

So, you’ve been invited to interview?  Well done! Probably only 4 or 5 people have got this far. Most panels will ask you to prepare something – often a short presentation.  Whatever you do, practice it a lot and keep it to time. Remember that you might be a bit slower on the day if you are nervous (though Sue and Duncan always speed up when anxious…).  Pay close attention to what you’ve been asked to do and stick to the remit. Duncan’s experience is that candidates often overrun… and he has now taken to cutting people off mid-sentence. Everyone should have the same time, and managing the time limit is one of the points of asking interviewees to give a talk.

Prepare for some standard questions: why do you want this job?  What skills and experiences will you bring to the team?  What do you think are the major challenges for this project? How does it fit in with your personal career goals? Make sure you are familiar with the definitions (or debates over definitions) of key terms linked to the project so you can use these confidently and fluently.

It might be that the project has some aspects which are outside your expertise. Sue recently advertised a job on an autism & bilingualism study. This topic is so under-studied that none of the candidates had detailed expertise in both areas. If this happens to you, make sure you read up on a few of the latest findings in the literature – familiarise yourself with the current big debates so you can mention them if asked. Don’t worry about becoming an expert overnight though – it’s OK to say “I haven’t done work in this area myself…” so long as you follow up with “…but I’ve read a handful of papers to prepare and I was struck by…”.  Your panel will like to see that you care enough about the job to do some reading and won’t expect you to know every detail.

Focus on your transferable skills

In your statement and CV, and when preparing for the interview (well done if you get that far!) pull out what type of things you have learned which will be relevant to the project. Your panel are looking for aptitude and abilities that transcend the specific topic.  Maybe you’ve never worked with brain scans, but you have processed and managed large, complex data sets. Maybe you haven’t done research with children, but you did volunteer on a play scheme in the past. Maybe you haven’t done working memory research but you have designed novel tasks to measure attention. Show how what you have done relates to the project by pulling out the common threads – ability to innovate, attention to detail, sensitivity to participant needs.

If you’ve had challenges in the past it is fine to mention these – for example if you’re asked about the experiences you bring to the project. The panel will sympathise with issues recruiting hard to reach populations or making sense of complex patterns in your data.  Make sure though that you present these as problem-solving experiences: this was difficult and this is how we solved the issue. Whatever you think of your former supervisor or boss, don’t complain about them at the interview (yes, this has happened!).

Where the role requires some technical skills, you may find that these are put to the test. Duncan uses a simple ‘data handling’ exercise in his interview process for postdocs. This is undertaken on the day of the interview. Candidates can use any package of their choosing (R, Matlab, Python, Excel), and are typically given 30 minutes to complete the exercise. Be aware that if you have said that you have programming skills then you may have to prove it.

Show 360º thinking

A good researcher will always have multiple levels of a project in mind.  These include the theoretical level – what motivates this research?  What can it tell us theoretically or mechanistically about the way the world works? This should, in turn, relate to the methodological level: how are we going to address this question? Will we develop new methods or work with existing tools? Then there’s the practical level: what resources are needed to carry out this project?  How will they be managed? And finally you will want to consider the human level: who will I be working with – in the research team and as participants? What do they need from me and how can I deliver it?

Thinking about the job you’re applying for at all of these levels encourages you to consider deeply the pertinent issues that will make the study a success. If you take this approach, you won’t go far wrong.

Of course, it is possible that after all this you won’t be offered the job.  If that happens, don’t be down-hearted.  In academia, putting yourself out there by applying for jobs, writing papers, giving talks, submitting grants and speaking up in your workplace is essential.  Knock-backs are a core part of the process and one which we must all learn to embrace and grow from. By all means, email the panel and ask for their feedback. But remember, in 90% of cases the difference between you and the successful candidate will be about fit for the role (e.g. specific experience in a specific skill) rather than anything you could have done differently.

So, go for it. Be brave. Be thorough. Be a good scientist.

Catching up with the Internet Era: Online data collection for researchers

In the world we humans spend a great deal of time connected to the internet, this is especially true for younger people – who are growing up surrounded by this technology. You can see this huge change over time in graph from Our World in Data below!

Alex post pic1

Increasingly, researchers and companies are leveraging this remote access to behaviour to answer questions about how humans behave. Companies have been collecting ‘user data’ for years from online platforms, and using this inferred information about people to improve user experience, and in some cases sell more products to the correct people. The amount of data we are able to collect on behaviour is expanding exponentially, and at the same time so is the quality and modality of this data – as people connect different devices (like activity monitors, clocks, fridges). Wearable sensors are becoming particularly more frequent – often this data is stored using internet-based services.


Infographic: The Predicted Wearables Boom Is All About The Wrist | Statista
Taken from Statista

Psychology and cognitive science is starting to catch up on this trend, as it offers the ability to carry out controlled experiments on a much larger scale. This offers the opportunity to characterise subtle differences, that would be lost in the noise of small samples tested in a lab environment.

However, for many the task of running an online experiment is daunting; there are so many choices, and dealing with building, hosting and data processing can be tricky!

Web Browsers

A good starting point, and often the most straightforward, is building experiments to work in a web browser. The primary advantage of this is that you can run experiments on the vast majority of computers, and even mobile devices, with no installation overhead. There are some limitations though:


Internet Explorer - sigh

With multiple different web browsers, operating systems, and devices, the possible combinations number in the 1000s. This can lead to unexpected bugs and errors in your experiment. A workaround is to restrict access to a few devices (see below for tips on how to do this in JavaScript) – but this is traded off with how many participants you would like to access.


Web browsers were not designed to run reaction time experiments in, or present stimulus with millisecond precision. Despite this, some research has shown equivalent precision for Reaction Time, and Stimulus Presentation.

If you are very concerned still, you may utilise WebGL, a web graphics engine, which allows you to gain analogous presentation times to native programs, and even use a computer’s graphics card. Although this will be limited by the operating system and hardware of the user!

There are a number of tools that can help you with browser experiments. From fee-paying services like Gorilla, which deals with task building, hosting and data management for you, to fully open source projects like jsPsych, and PsychoPy’s PsychoJS – which deal with building experiments and data, but not hosting (although there are plans to develop a hosting and data storage solution). All of these offer a graphical user-interface, which allows experiments to be built without any prior knowledge of programming!



One intermediate tool – which we are currently using – is cross platform development environment called Unity. Whilst originally intended for creating video-games, Unity can be repurposed for creating experimental apps. The large advantage is an easy capability to build to a vast variety of operating systems and platforms with minimal effort: a Unity project can be built for a web browser, iOS app, Android app, Windows, OSX, Linux…. and so on. You can also gain access to sensor information on devices (hear rate monitors, step counting, microphone, camera), to start to access the richness of information contained in these devices.

The utility of this tool for experimental research is huge, and apparently appears to be under-utilised – it has an easy to learn interface, and requires minimal programming knowledge.


Whilst this post is largely non-instructional, hopefully it has shed some light on the potential tools you can use to start running research online (without employing an expensive web or app developer), or hopefully just piqued your interest a tiny bit.

If you would like to dive in to the murky (but exciting) world of web development, you can also check out a few tips for improving the quality of your online data here.



This exciting post was written by Alexander Irvine, one of the newest members of our lab. Alex previously worked on developing web-based study at Oxford before joining the lab and is experienced in an array of programming languages and tools. Check out his personal website if you want to read more in-depth about online data collection.

The weather and the brain – using new methods to understand developmental disorders


The latest article was written by our brilliant lab member Danyal Akarca. It describes some of his MPhil research which aims to explore transient brain networks in individuals with a particular type of genetic mutation. Dan finished his degree in Pre-Clinical Medicine before joining our lab and has since been fascinated by the intersection of genetic disorders and the dynamics of brain networks.

The brain is a complex dynamic system. It can be very difficult to understand how specific differences within that system can be associated with the cognitive and behavioural difficulties that some children experience. This is because even if we group children together on the basis that they all have a particular developmental disorder, that group of children will likely have a heterogeneous aetiology. That is, even though they all fall into the same category, there may be a wide variety of different underlying brain causes. This makes these disorders notoriously difficult to study.

Developmental disorders that have a known genetic cause can be very useful for understanding these brain-cognition relationships, because by definition they all have the same causal mechanism (i.e. the same gene is responsible for the difficulties that each child experiences). We have been studying a language disorder caused by a mutation to a gene called ZDHHC9. These children have broader cognitive difficulties, and more specific difficulties with speech production, alongside a form of childhood epilepsy called rolandic epilepsy.

In our lab, we have explored how brain structure is organised differently in individuals with this mutation, relative to typically developing controls. Since then our attention has turned to applying new analysis methods to explore differences in dynamic brain function. We have done this by directly recording magnetic fields generated by the activity of neurons, through a device known as a magnetoencephalography (MEG) scanner. The scanner uses magnetic fields generated by the brain to infer electrical activity.

The typical way that MEG data is interpreted, is by comparing how electrical activity within the brain changes in response to a stimulus. These changes can take many forms, including how well synchronised different brain areas are, or the how size of the magnetic response differs across individuals. However, in our current work, we are trying to explore how the brain configures itself within different networks, in a dynamic fashion. This is especially interesting to us, because we think that the ZDHHC9 gene has an impact on the excitability of neurons in particular parts of the brain, specifically in those areas that are associated with language. These changes in network dynamics might be linked to the kinds of cognitive difficulties that these individuals have.

We used an analysis method called “Group Level Exploratory Analysis of Networks” – or GLEAN for short – and has recently been developed at the Oxford centre for Human Brain Activity. The concept behind GLEAN is that the brain changes between different patterns of activation in a fashion that is probabilistic. This is much like the concept of the weather – just as the weather can change from day to day in some probabilistic way, so too may the brain change in its activation.


This analysis method not only allows us to observe what regions of the brain are active when the participants are in the MEG scanner. It also allows us to see the probabilistic way in which they can change between each other. For example, just as it is more likely to transition from rain one day to cloudiness the next day, relative to say rain to blistering sun, we find that brain activation patterns can be described in a very similar way over sub-second timescales. We can characterise those dynamic transitions in lots of different ways, such as how long you stay in a specific brain state or how long does it take to return to a state once you’ve transitioned away. (A more theoretical account of this can be found in another recent blog post in our Methods section – “The resting brain… that never rests”.) We have found that a number networks differ between individuals with the mutation and our control subjects.


(These are two brain networks that show the most differences in activation – largely in the parietal and frontotemporal regions of the brain.)

Interestingly, these networks strongly overlap with areas of the brain that are known to express the gene (we found this out by using data from the Allen Atlas). This is the first time that we know of that researchers have been able to link a particular gene, to differences dynamic electrical brain networks, to a particular pattern of cognitive difficulties. And we are really excited!