All posts by Forging Connections

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!


Why should we take a dimensional approach to studying developmental disorders?

Developmental disorders like attention deficit disorder, attention deficit hyperactivity disorder (ADD/ADHD), autism spectrum disorder (ASD), language, learning and movement disorders are relatively common, more common then we might think. Furthermore, these disorders have a considerable impact upon the daily lives of those who struggle with them. Because some of these disorders are more apparent in some contexts than others, and their severity is highly variable, they may go unrecognised for some time. Indeed, in many cases these disorders are only formally recognised when a child has already progressed through years of formal schooling. This means that they may already have had a largely negative experience of learning, may have lost motivation, and may already have fallen far behind their peers.

When trying to study these disorders, researchers normally use a case-control approach. It’s an observational study in which two groups differing in outcome are identified and compared on the basis of some presumed causal attribute. Researchers use this method to identify factors that may contribute to a medical condition with the help of comparing subjects who have that condition (“cases”) with patients who do not have the condition but are otherwise similar (“controls”).

Case-control studies are relatively cheap and they are a frequently used type of epidemiological study that can be carried out by small teams or individual researchers in single facilities in a way that more complex experimental studies often cannot be. This design is often used in the study of rare diseases or as an exploratory study where little is known about the connection between the risk factor and the disease. In several cases they have bigger statistical power than cohort studies. This approach has largely been translated from the clinical sphere to study developmental disorders.

Well-designed observational studies, like case-control designs, can provide valuable evidence. It is however worth noting that they are quasi-experimental in nature and thus do not bring the same level of evidence as randomized controlled experiments. That said, case-control designs can sit well alongside complementary randomized controlled experiments. There are however other problematic features of case-control designs, which are particularly highlighted when studying developmental disorders. Selecting an outcome of choice, indeed the basis for choosing one particular group, may produce unintended biases that can have a strong effect in overall findings.

One such example is the exclusion of children with any comorbid symptoms – this is routine practice when using a case-control design to study developmental disorders. The most important disadvantage in case-control studies relates to the problem of acquiring reliable information about an individual’s status over time, and then using this as a basis for choosing some children whilst excluding others. The children actually included may be atypical of those with a particular disorder. This exemplifies why this design does not always translate well into the study of developmental disorders; in developmental disorder comorbidity is more the rule than the exception. This approach can also give a false impression of the nature of these disorders, which can be graded rather than discrete.

However, it also is possible to use a dimensional approach instead of the case-control approach. A dimensional approach puts focus on the kind of problem a person is experiencing and on the extent to which that aspect of cognition is impaired. It doesn’t place people into diagnostic categories but along dimensions. Diagnosis then becomes not a process of deciding the presence or absence of a symptom or disorder, but the degree to which particular characteristic is present. This is entirely the approach taken by the Centre for Attention Learning & Memory (CALM; Children are referred not on the basis of any discrete disorder or diagnosis, but because they are experiencing problems in the areas of attention, learning and memory. The researchers are taking a dimensional approach to exploring the nature of these impairments.

Instead of making judgements of “present or not?” the dimensional approach asks the question “how much?”. It ranks disorder on a continuum based upon multiple domains of cognition, assessed using standardised materials. A dimension is viewed as a cluster of related psychological/behavioural characteristics that occur together. This approach generates profiles, rather than discrete diagnostic categories. Of course, one could argue that this approach is far ‘messier’ than a simple case-control approach. However, one might also argue that this unique profiling is far more informative about the nature and extent of the impairments themselves, and provides a far clearer picture about the pattern of deficits actually present in a population of children with problems of learning.

Whilst working at CALM I have been trying to understand how children attend, listen and remember and how these skills impact on learning. These include difficulties in language, literacy and maths. By improving our understanding of the cognitive and brain processes involved in learning, we hope to develop ways of identifying and overcoming problems that might appear during childhood. We also hope to provide an information hub for researchers and professionals in children’s services, and to run regular workshops.

A child visiting the CALM clinic is profiled by grading the severity of symptoms from a number of dimensions using standardised tests. For example these dimensions include working memory, attentional control, short-term memory, phonological skills, the ability to inhibit and control responses, to initiate, plan, organise and set goals, inattention, hyperactivity, aggression, conduct problems, emotional symptoms, peer relations, prosocial behaviour, as well as aspects of communication (speech, syntax, semantics, coherence, initiation, use of context and non-verbal communication). This information can be fed back to the referrer and can then be used to help guide the support that the child receives. In parallel to this we are building a large and rich dataset. A dimensional approach is better able to capture the complexities that a categorical approach may miss. Of course, this approach is not without its challenges also. How does one define a dimension? What statistical approaches ought we to use? And what kind of scores would warrant some form of intervention?

Despite these challenges, we think that the dimensional approach will provide a way of capturing the rich complexities of these data. Whilst other disciplines may strongly favour an approach with rigid categorical boundaries, this approach is not always appropriate for studying developmental disorders. Whilst strict case-control studies can be valuable, reliance on these designs alone can provide a biased and unrealistic view of the children with problems of learning.


Gelder M, Harrison P, Cowen P. Classification and diagnosis. In: Shorter oxford textbook of psychiatry. 5 th ed. Oxford: Oxford University Press; 2006. p. 21-34.

Helzer, J. E., Kraemer, H. C., & Krueger, R. F. (2006). The feasibility and need for dimensional psychiatric diagnoses. Psychological Medicine(36), 1671–1680.

Kendell RE. Five criteria for an improved taxonomy of mental. In: Helzer JE, Hudziak J, editors. Defining psychopathology in the 21 st century: DSM-V and beyond. Washington DC: American Psychiatric Publishing; 2002. p. 3-18.

Lewallen, S., & Courtright, P. (1998). Epidemiology in Practice: Case-Control Studies. Community Eye Health(11(28)), 57–58.

Does working memory training change neurophysiology in childhood?

The short answer to that question is ‘yes’.

We have known for some time that training particular cognitive skills, like working memory, can produce improvements in cognition. These improvements transfer to other untrained tasks, provided that they are similarly structured. However, we know very little about how these kinds of intensive cognitive training programmes change children’s performance.

The study has been under embargo whilst it awaits publication in the Journal of Neuroscience, but the embargo has just been lifted, and we can now tell you all about it (the paper itself should be published very soon – open access, naturally). We used magnetoencephalography – a technique for measuring electrical brain activity – to explore patterns of brain activity as children rested in the scanner with their eyes closed. We repeated this procedure before and after the children underwent working memory training. Importantly, only half of the children underwent an intensive version of the training, with the other half doing a low intensity version. This latter group acted as a control, and the children were randomly allocated to the high or low intensity conditions.

After the training, children’s working memory performance improved. We used standardised assessments of working memory to show this. Importantly, these improvements were specific to the group of children who had undertaken the intensive training programme, with the control group showing little or no improvement. This pattern could not have resulted from us expecting that the children in the intensive group ought to show bigger improvements than the controls, because the researcher doing the assessments did not know which group the children were assigned to.

The magnetoencephalography data allowed us to explore whether there were any significant changes in children’s brains, and whether these changes mirrored the improvements in performance that we observed. We used the spontaneous electrical activity in the children’s brains to explore network connectivity – that is, how different brain areas are coordinated. After the training, connectivity within networks involved in attentional control were significantly enhanced. Furthermore, the bigger the change in connectivity, the bigger the improvement in the child’s working memory.

We have a lot more data on these children, which we are slowly crunching our way through. So there will be more to come!

Coder’s Little Time Saver

We all know the problem; it’s getting late in the office, all your colleagues left hours ago, and your eyes are watering from staring at the analysis output of a script that should have finished running ages ago. Yet for some inexplicable reason, it’s still not done. Wouldn’t it be great if you could just nip out to get some fresh air and be informed when the script is finally done? Well actually, you can! Here’s a handy little tip explaining how to embed email alerts in MATLAB and Python scripts:

MATLAB comes with a handy function that supports sending emails within scripts. But before we can actually get to the email sending, we need to configure some server information. Here is an example for a gmail account:

mail = ''; %Your GMail email address
password = 'secret'; %Your GMail password

props = java.lang.System.getProperties;
props.setProperty('mail.smtp.socketFactory.class', '');

Now, we are ready to send an email:

sendmail(‘’,’Hello there!’);

The second argument in the sendmail function corresponds to the subject line of the email. If you are keen to let MATLAB send a more elaborate email, you can also include a text body:

sendmail('','Hello there!','Have you seen that great post on the Forging Connections Blog?');

It is even possible to send attachments:

sendmail('','Hello there!','Have you seen that great post on the Forging Connections Blog?',{'/Users/Fred/image.jpeg’});

By including these few lines of code in your time-consuming MATLAB script, you can now get notified when it is time to go back to the office for the results.

Python offers a simple solution to send emails from within scripts via the smtplib module. Here is a function that provides the configuration for gmail:
def send_email():
import smtplib

gmail_user = ""
gmail_pwd = "Password"
FROM = ''
TO = ['']
SUBJECT = "Meeting at 3pm"
TEXT = "Are you coming to the meeting?"

# Prepare actual message
message = """\From: %s\nTo: %s\nSubject: %s\n\n%s
""" % (FROM, ", ".join(TO), SUBJECT, TEXT)
server = smtplib.SMTP("", 587)
server.login(gmail_user, gmail_pwd)
server.sendmail(FROM, TO, message)
print 'successfully sent the mail'
print "failed to send mail"

For more information see and