The bewildering bathroom challenge

The tap is a simple but genius piece of design. You turn a handle one way and water flows. You turn it the other way, and the water stops. The bathplug is even simpler. You find a pliable, waterproof substance and cut it to fit exactly into the hole out of which the water flows, and you equip it with handle or chain on top that you can grasp to remove it.

Hotels the world over, however, are not satisfied with such simplicity. They conspire to make the task of producing water and containing it ever more baffling. I had wondered whether they were just more focused on appearance than function, but this website makes it clear that it's deliberate: It's worth quoting the blurb in full:

"A lot of attention in the design world is focused on creating products that are intuitive and easy to use, but sometimes a little ambiguity can be a good thing. Designed for use in restaurant and hotel bathrooms these taps embrace ambiguity to create a sense of intrigue to provide a more engaging interaction."

Hmm. Ambiguity is not really what I'm seeking in a bathroom. And engaging isn't the word I'd use for my interaction, as I try turning, pressing, pulling levers and dials and waving my hands around under taps. It usually ends in quite a bit of swearing. And that's in the cases where I can actually find something to push, pull or turn.

I wonder if there is some secret competition, known only to hoteliers, scored as follows:

• 1 point for each room where a pool of water in basin indicates they haven't mastered the plug
• 2 points for each guest who gets a wet head when trying to turn on the bath tap
• 3 points for each guest who has to get someone from reception to explain how to turn on the tap
• 4 points for each guest too shy to ask reception so doesn't wash during their stay

Hotels in the old Soviet Union had a simpler approach to frustrating their guests - they just didn't provide a bath plug.

The devaluation of low-cost psychological research

Psychology encompasses a wide range of subject areas, including social, clinical and developmental psychology, cognitive psychology and neuroscience. The costs of doing different types of psychology vary hugely. If you just want to see how people remember different types of material, for instance, or test children's understanding of numerosity, this can be done at very little cost. For most of the psychology I did as an undergraduate, data collection did not involve complex equipment, and data analysis was pretty straightforward - certainly well within the capabilities of a modern desktop computer. The main cost for a research proposal in this area would be for staff to do data collection and analysis. Neuroscience, however, is a different matter. Most kinds of brain imaging require not only expensive equipment, but also a building to house it and staff to maintain it, and all or part of these costs will be passed on to researchers. Furthermore, data analysis is usually highly technical and complex, and can take weeks, or even months, rather than hours. A project that involves neuroimaging will typically cost orders of magnitude more than other kinds of psychological research.

In academic research, money follows money. This is quite explicit in funding systems that reward an institution in proportion to their research income. This makes sense: an institution that is doing costly research needs funding to support the infrastructure for that research. The problem is that the money, rather than the research, can become the indicator of success. Hiring committees will scrutinise CVs for evidence of ability to bring in large grants. My guess is that, if choosing between one candidate with strong publications and modest grant income vs. another with less influential publications and large grant income, many would favour the latter. Universities, after all, have to survive in a tough financial climate, and so we are all exhorted to go after large grants to help shore up our institution's income. Some Universities have even taken to firing people who don't bring in the expected income. This means that cheap cost-effective research in traditional psychological areas will be devalued relative to more expensive neuroimaging.

I have no quarrel, in principle, with psychologists doing neuroimaging studies - some of my best friends are neuroimagers -  and it is important that if good science is to be done in this area that it should be properly funded. I am uneasy, though, about an unintended consequence of the enthusiasm for neuroimaging, which is that it has led to a devaluation of the other kinds of psychological research. I've been reading Thinking Fast and Slow, by Daniel Kahneman, a psychologist who has the rare distinction of being a Nobel Laureate. This is just one example of a psychologist who has made major advances without using brain scanners. I couldn't help thinking that Kahneman would not fare well in the current academic climate, because his experiments were simple, elegant ... and inexpensive.

I've suggested previously that systems of academic rewards need to be rejigged to take into account not just research income and publication outputs, but the relationship between the two. Of course, some kinds of research require big bucks, but large-scale grants are not always cost-effective. And on the other side of the coin, there are people who do excellent, influential work on a small budget.

I thought I'd see if it might be possible to get some hard data on how this works in practice. I used data for Psychology Departments from the last Research Assessment Exercise (RAE), from this website, and matched this up against citation counts for publications that came out in the same time period (2000-2007) from Web of Knowledge. The latter is a bit tricky, and I'm aware that figures may contain inaccuracies, as I had to search by address, using the name of the institution coupled with the words Psychology and UK. This will miss articles that don't have these words in the address. Also when double-checking the numbers, I  found that for a search by address, results can fluctuate from one occasion to the next. For these reasons, I'd urge readers to treat the results with caution, and I won't refer to institutions by name. Note too that though I restrict consideration to articles between 2000-2007, the citations extend beyond the period when the RAE was completed. Web of Knowledge helpfully gives you an H-index for the institution if you ask for a citation report, and this is what I report here, as it is more stable across repeated searches than the citation count. Figure 1 shows how research income for a department relates to its H-index, just for those institutions deemed research active, which I defined as having a research income of at least £500K over the reporting period. The overall RAE rating is colour-coded into bandings, and the symbol denotes whether or not the departmental submission mentions neuroimaging as an important part of its work.

Data from RAE and Web of Knowledge: treat with caution!

Several features are seen in these data, and most are unsurprising:

• Research income and H-index are positively correlated, r = .74 (95%CI .59-.84) as we would expect. Both variables are correlated with the number of staff entered in the RAE, but the correlation between them remains healthy when this factor is partialled out, r = .61 (95%CI .40-.76).
• Institutions coded as doing neuroimaging have bigger grants: after taking into account differences in number of staff, the mean income for departments with neuroimaging was £7,428K and for those without it was £3,889K (difference significant at p = .01).
• Both research income and H-index are predictive of RAE rankings: the correlations are .68 (95% CI .50-.80) for research income and .79 (95% CI .66-.87) for H-index, and together they account for 80% of the variance in rankings. We would not expect perfect prediction, given that the RAE committee went beyond metrics to assess aspects of research quality not reflected in citations or income. And in addition, it must be noted that the citations counted here are for all researchers at a departmental address, not just those entered in the RAE.

A point of concern to me in these data, though, is the wide spread in H-index seen for those institutions with the highest levels of grant income. If these numbers are accurate, some departments are using their substantial income to do influential work, while others seem to achieve no more than other departments with much less funding. There may be reasonable explanations for this - for instance, a large tranche of funding may have been awarded in the RAE period but not had time to percolate through to publications. But nevertheless, it adds to my concern that we may be rewarding those who chase big grants without paying sufficient attention to what they do with the funding when they get it.

What, if anything, should we do about this? I've toyed in the past with the idea of a cost-efficiency metric (e.g. citations divided by grant income), but this would not work as a basis for allocating funds, because some types of research are intrinsically more expensive than others. In addition, it is difficult to get research funding, and success in this arena is in itself an indicator that the researchers have impressed a tough committee of their peers. So, yes, it makes sense to treat level of research funding as one indicator of an institution's research excellence when rating departments to determine who gets funding. My argument is simply that we should be aware of the unintended consequences if we rely too heavily on this metric. It would be nice to see some kind of indicator of cost-effectiveness included in ratings of departments alongside the more traditional metrics. In times of financial stringency, it is particularly short-sighted to discount the contribution of researchers who are able to do influential work with relatively scant resources.

Communicating science in the age of the internet

© www.CartoonStock.com

Here's an interesting test for those on Twitter. You see a tweet giving a link to an interesting topic. You click on the link and see it's a YouTube piece. Do you (a) feel pleased that it's something you can watch or (b) immediately lose interest. The answer is likely to depend on content, but also on how long it is. Typically, if I see a video is longer than 3 minutes, I'll give up unless it looks super-interesting.

Test #2 is for those of you who are scientists. You have to give a presentation about a recent piece of work to a non-specialist audience. How long do you think you will need? (a) one hour; (b) 20 minutes; (c) 10 minutes; (d) 3 minutes.

If you're anything like me, there's a disconnect between your reactions to these different scenarios. The time you feel you need to communicate to an audience is much greater than the time you are willing to spend watching others. Obviously, it's not a totally fair comparison: I'm willing to spend up to an hour listening to a good lecture (but no more!); though to tell the truth, it's an unusual lecturer who can keep me interested for the whole duration.

Those who use the internet to communicate science have learned that the traditional modes of academic communication are hopelessly ill-suited for drawing in a wider audience. TED talks have been a remarkably successful phenomenon, and are a million miles from the normal academic lecture: the ones I've seen are typically no longer than 15 minutes and make minimal use of visual aids. The number of site visits for TED talks is astronomically higher than, for instance, Cambridge University's archive of Film Interviews With Leading Thinkers, where Aaron Klug has had around 300 hits in just over one year, and Fred Sanger a mere 148. The reason is easy to guess: many of these Cambridge interviews last two hours or more. They constitute priceless archive material, and a wealth of insights into the influences that shape great academic minds, but they aren't suited to the casual viewer.

For most academics, though, shorter pieces pose a dilemma: they don't allow you to present the evidence for what you are saying. I felt this keenly when viewing a TED talk by autism expert Ami Klin. At 22 minutes, this was rather longer than the usual TED talk, but Klin is an engaging speaker, and he held my attention for the whole time. As I listened, though, I became increasingly uneasy. He was making some pretty dramatic claims. Specifically, as the accompanying blurb stated: "Ami Klin describes a new early detection method that uses eye-tracking technologies to gauge babies' social engagement skills and reliably measure their risk of developing autism". I was very surprised at the claims made for eye-tracking, and the data shown in the presentation were unconvincing. More generally, Klin talked about universal screening for 6-month-olds, but I was not sure that he understood the requirements for an effective screening test. After the end of the talk I checked out Klin's publications on Web of Science and couldn't find any published papers that gave a fuller picture to back up this claim. I asked my colleagues who work in autism and none of them was aware of such evidence. I emailed Klin last week to ask if he can point me to relevant sources but so far I've not had a reply. (If I do, I'll add the information). At the time of writing, his talk has had over 132,000 views.

So we have a dilemma here. Nearly everyone agrees that scientists should engage with audiences beyond their traditional narrow academic confines. But the usual academic lecture, saturated with PowerPoint explaining and justifying every statement, is ill-suited to such an audience. However, if we reduce our communications to the bottom line, then the audience has to take a lot on trust. It may be impossible to judge whether the speaker is expressing an accepted mainstream view. If, as in the Klin case, the speaker is both famous and charismatic, then it's unlikely that a general audience will realise that many experts in his field would want to see a lot more hard evidence before accepting what he was saying.

I've been brooding about this issue because I've recently joined up with some colleagues in a web-based campaign to raise awareness of language impairments in children. My initial idea was that we'd post lectures by experts, attempting to explain what we know about the nature, causes, and impacts of language impairments. Fortunately, we were dissuaded from this idea by our friends in TeamSpirit, a public relations company who have come on board to help us get launched. With their assistance, we've posted several videos and worked out a clearer idea of what our YouTube channel should do. We will have professionally produced films that feature the experiences of young people with language impairments and their families, as well as the professionals working with them. But we also wanted to ensure that the material we put out was evidence-based, and to include some pieces on issues where there were relevant research findings. We were advised that any piece by a talking academic head should be no more than 3 minutes long. I could see the wisdom of that, given my own reactions to longer video pieces. But I was uncomfortable. In 3 minutes, it's impossible to do more than give a bottom line. I didn't want people to have to take what I said on trust: I wanted them to have access to the evidence behind it. Well, we're now experimenting with an approach that I think may work to keep everyone happy. Our academic-style talks will stick to the 3 minute limit, but will be associated with a link to a PowerPoint presentation which will give a fuller account. This is still shorter than the usual academic talk - we aim for around 15-20 slides, all of which should be self-explanatory without needing an oral narrative. And, crucially, the PowerPoint will include references to peer-reviewed research to support what is said, and will include a link to a reference list, including where possible a review article. I anticipate that most people who visit our YouTube site will only get as far as the 3 minute video. That's absolutely fine - after all, only a small proportion of potential visitors will be evidence geeks. But, importantly, the evidence will be there for those who want it. The PowerPoint will give the bare bones, and the references will allow people to track back to the original sources.

We live in exciting times, where it has become remarkably easy to harness the power of the internet to disseminate research. The challenge is to do so in a way that is effective while preserving academic rigour.

Schoolgirls' health put at risk by Catholic view on vaccination

Today's Time Newsfeed carries a remarkable story: parents of children attending Catholic schools in Calgary were sent a special letter to accompany details of a vaccination programme against human papillomavirus (HPV), which protects against cervical cancer. In it, local bishops wrote: "Although school-based immunization delivery systems generally result in high numbers of students completing immunization, a school-based approach to vaccination sends a message that early sexual intercourse is allowed.” I find this amazing for several reasons:

• There's a complete failure to understand what affects teenagers' behaviour. Do the bishops seriously think that teenaged girls who are thinking of having sex say to themselves "Oh, wait a minute. I might get HPV. Let's not do it." Potential consequences of sex include a host of sexually transmitted diseases, as well as pregnancy. If these don't put girls off, then why should a risk of HPV? 
• HPV is a sexually transmitted disease. You can get it if you are a virgin who marries someone with HPV. You can get it if you are raped (something which has been known to occur in Catholic schools). 
• The recommendation seems theologically dubious. I'm an atheist, but my understanding of Catholicism is that whether or not something is a sin is largely to do with motivation rather than action. So if you are tempted to sex but desist because it would upset God, then that's good. If you are tempted to sex but desist only because of a fear of disease, that's still a sin. The church should be teaching girls to love God so much that they won't do things that offend him, not to conform to standards of sexual behaviour out of fear. No doubt religious readers will put me right if I've misunderstood this distinction. 
• These girls are attending a Catholic school where I assume morality is drummed into them day and night. The bishops assume that their grasp of that morality is so weak that having a HPV vaccination will be sufficient to overturn everything they have been told about sexual ethics. Doesn't say much for the religious teaching in the schools, or for the intelligence of the pupils. 
• One thing Jesus really understood is that humans aren't perfect and frequently fall short of the moral standards they try to adhere to. There's a huge emphasis on forgiveness of sin in his teachings. The Bishops are in effect saying that God won't forgive you if you stray from the straight and narrow: he'll commit you to a life with an unpleasant disease, and increase your risk of dying from cancer. That's not the Christian God I was taught about.

Causal models of developmental disorders: the perils of correlational data

Experimental psychology depends heavily on statistics, but psychologists don’t always agree about the best ways of analyzing data. Take the following problem:

I have two groups each of 30 children, dyslexics and controls. I give them a test of auditory discrimination and find a significant difference between the groups, with the dyslexic mean being lower. I want to see whether reading ability is related to the auditory task. I compute the correlation between the auditory measure and reading, and find it is .42, which in a sample of 64 cases is significant at the .001 level.

I write up the results, concluding that poor auditory skill is a risk factor for poor reading. But reviewers are critical. So what’s wrong with this?

I’ll deal quickly with two obvious points. First, there is the well-worn phrase that correlation does not equal causation. The correlation could reflect a causal link from auditory deficit to poor reading, but we need also to consider other causal routes, as I’ll illustrate further below. This is an issue about interpretation rather than data analysis.

A second point concerns the need to look at the data rather than just computing the correlation statistic. Correlations can be sensitive to distributional properties of the data and can be heavily influenced by outliers. There are statistical ways of checking for such effects, but a good first step is just plotting a scatterplot to see whether the data look orderly. A tip for students: if your supervisor asks to see your project data, don’t just turn up with numerical output from the analysis: be ready to show some plots.

Figure 1: Fictitious data showing spurious correlation between height and reading ability

A less familiar point concerns the pooling of data across the dyslexic and control groups. Some people have strong views about this, yet, as far as I’m aware, it hasn’t been discussed much in the context of developmental disorders. I therefore felt it would be good to give it an airing on my blog and see what others think.

Let’s start with a fictitious example that illustrates the dangers of pooling data from two groups. Figure 1 is a scatterplot showing the correlation between height and reading ability in groups of 6-year-olds and 10-year-olds. If I pool across groups, I’m likely to see a strong correlation between height and reading ability, whereas within any one age group the correlation is negligible. This is a clear case of spurious correlation, as illustrated in Figure 2. Here the case against pooling is unambiguous, and it's clear that if you look at the correlation within either age band, there is no relationship between reading ability and height.

Figure 2: Model showing how a spurious correlation between height and reading arises because both are affected by age

Examples such as this have led some people to argue that you shouldn’t pool data in studies such as the dyslexic vs. control example. Or, to be more precise, the recommendation is usually that you should check the correlations within each group, and avoid pooling if they don’t look consistent with the pooled correlation. I’ve always been a bit uneasy about this logic and have been giving some thought as to why.

First, there is the simple issue of power. If you halve your sample size, then you increase the standard error of estimate for a correlation coefficient, making it more likely that it will be nonsignificant. Figure 3 shows the 95% confidence intervals around a correlation of .5 depending on sample size, and you can readily see that these are larger for small than big samples. There's a nice website by Stan Brown that gives relevant formulae in Excel.

Figure 3: 95% confidence interval around estimated correlation of .5, with different sample sizes

A less obvious point is that the data in Figure 1 look analogous to the dyslexic vs. control example, but there is an important difference. We know where we are with age: it is unambiguous to define and measure. But dyslexia is more tricky. Suppose we substitute dyslexia for age, and auditory processing for height, in the model of spurious correlation in Figure 2. We have a problem: there is no independent diagnostic test for dyslexia. It is actually defined in terms of one of our correlated variables, reading ability. Thus, the criterion used to allocate children to groups is not independent of the measures that are entered into the correlation. This creates distortions in within-group correlations, as follows.

If we define our groups in terms of their scores on one variable, we effectively restrict the range of values obtained by each group, and this lowers the correlation.  Furthermore, the restriction will be less for the controls than for the dyslexic group - who are typically selected as scoring below a low cutoff, such as one SD below the mean. Figure 4 shows simulated data for two groups selected from a population where the true correlation between variables A and B is .5. Thirty individuals (dyslexics) are selected as scoring more than 1 SD below average on variable A, and another 30 (controls) are selected as scoring above this level. 

Figure 4: Correlations obtained in samples of dyslexic (red) and controls (blue) for 20 runs of simulation with N = 30 per group.

The Figure shows correlations from twenty runs of this simulation. For both groups, the average correlation is less than the true value of .5, because of the restricted range of scores on variable A. However, because the range is more restricted for the dyslexic group, their average correlation is lower than that of the controls. A correlation of .42 corresponds to the .05 significance level for a sample of this size, and we can see that the controls are more likely to exceed this value than the dyslexic group. All these results are just artefacts of the way in which the groups were selected: both groups come from the same population where r = .5.

What can we conclude from all this? Well, the bottom line is that if we find non-significant within-group correlations this does not necessarily invalidate a causal model. The simulation shows that we may find that within-group correlations look quite different in dyslexic and control groups, even if they come from a common distribution.

So where does this leave us?! It would seem that in general, within-group data are unlikely to help us distinguish between causal and non-causal models: they may be compatible with both. So how should we proceed?

There’s no simple solution, but here are some suggestions:

1. If considering correlational data, always report the 95% confidence interval. Usually people (including me!) just report the correlation coefficient, degrees of freedom and p-value. It’s so uncommon to add confidence intervals that I suspect most psychologists don’t know how to compute it. Do not assume that because one correlation is significant and another is not that they are meaningfully different. This website can be used to test for the significance of the difference between correlations. I would, however, advise against interpreting such a comparison if your data are affected by the kinds of restriction of range discussed above.

2. Study the relationship between key variables in a large unselected sample covering a wide range of scores. This is a more tractable solution, but is seldom done. Typically, people recruit an equivalent number of cases and controls, with a sample size that is inadequate for getting a precise estimate of a correlation in either group. If your underlying model predicts a linear relationship between, say, auditory processing and phonological awareness, then with a sample of 200 cases, a fairly precise estimate can be obtained. With this approach, one can also identify whether the relationship is linear.

3. More generally, it’s important to be explicit about what models you are testing. For instance, I’ve identified four underlying models of the relationship between auditory deficit and language impairment, as shown in Figure 5. In general, correlational data on these two skills won’t distinguish between these models, but specifying the alternatives may help you think of other data that could be informative. 

Figure 5: Models of causal relationships underlying observed correlation between auditory deficit and language impairment

For instance:

• We found that, when studying heritable conditions, it is useful to include data on parents or siblings. Models differ in predictions about how measures of genetic risk - for instance, family history, or presence of specific genetic variants - relate to A (auditory deficit) and B (language impairment) in the child. This approach is illustrated in this paper. Interestingly, we found that the causal model that is often implicitly assumed, which we termed the Endophenotype model, did not fit the data, but nor did the spurious correlation model, which corresponds here to the Pleiotropy model.
• There may be other groups that can be informative: for instance, if you think auditory deficits are key in causing language problems, it may be worth including children with hearing loss in a study - see this paper for an example of this approach using converging evidence.
• Longitudinal data can help distinguish whether A causes B or B causes A.
• Training studies are particularly powerful, in allowing one to manipulate A and see if it changes B.

So what’s the bottom line? In general, correlational data from small samples of clinical and control groups are inadequate for testing causal models. They can lead to type I errors, where pooling data leads to a spurious association between variables, but also to type II errors, where a genuine association is discounted because it isn’t evident within subject groups. For the field to move forward, we need to go beyond correlational data.

P.S. 9th July 2012
I've written a little tutorial on simulating data using R to illustrate some of these points. No prior knowledge of R required. see: http://tinyurl.com/d2868cg

Bishop DV, Hardiman MJ, & Barry JG (2012). Auditory deficit as a consequence rather than endophenotype of specific language impairment: electrophysiological evidence. PloS one, 7 (5) PMID: 22662112

If you liked this post, you may also be interested in my other posts on statistical topics:
Getting genetic effect sizes in perspective
The joys of inventing data
A short nerdy post about the use of percentiles
The difference between p < .05 and a screening test

The ‘autism epidemic’ and diagnostic substitution

Based on: King & Bearman (2011) American Sociological Review, 76(2), 320-346; 

Data from birth and diagnostic records for all children born in California 1992-2000

Everyone agrees there has been a remarkable increase in autism diagnosis across the world. There is, however, considerable debate about the reasons for this. Three very different kinds of explanation exist.

• Explanation #1 maintains that something in our modern environment has come along to increase the risk of autism. There are numerous candidates, as indicated in this blogpost by Emily Willingham. 
• Explanation #2 sees the risks as largely biological or genetic, with changing patterns of reproduction altering prevalence rates, either because of assortative mating (not much evidence, in my view) or because of an increase in older parents (more plausible). 
• Explanation #3 is very different: it says the increase is not a real increase - it’s just a change in what we count as autism. This has been termed ‘diagnostic substitution’ - the basic idea is that children who would previously have received another diagnosis or no diagnosis are now being identified with autism spectrum disorder (ASD). This could be in part because of new conceptualisations of autism, but may also be fuelled by strategic considerations: resources for children with ASD tend to be much better than those for children with other related conditions, such as language impairment or intellectual handicaps, so this diagnosis may be preferred.

In 2008, my research group published a study that documented one kind of diagnostic substitution. We contacted people who had taken part in our studies of children with specific language impairment years ago. We carried out a standard diagnostic observation procedure for autism with the young adults themselves and, where possible, interviewed their parents about their early history. We found a number of individuals who had been regarded as cases of specific language impairment ten or twenty years ago but who would nowadays be diagnosed with ASD. Although it’s possible that some people develop autistic symptomatology as they get older, in our cases the autistic symptoms appeared to have been present from early childhood - as indicated by the parental interviews. Around half of the sample had been identified as having ‘semantic-pragmatic disorder’ in childhood, but autism had been excluded because at that time, prior to publication of DSM-IV diagnostic guidelines, it was regarded as a very rare condition in which there were severe social and behavioural impairments. How many children would have qualified for ASD diagnoses had they been seen today? Well, it depends. I suspect few people appreciate just how flexible the diagnostic criteria are for autism, even when lengthy standardized diagnostic instruments are used. Although we used the gold standard diagnostic procedures (ADOS-G and ADI-R) we found they seldom gave the same answer. If we diagnosed ASD only when both diagnostic instruments agreed, 21% of cases met criteria. If we included anyone who met criteria for autism or PDDNOS on either ADI-R or ADOS, the rate shot up to 66%.

Last year, a fascinating study by Brugha and colleagues attacked the same question from a different angle. They did an epidemiological survey of a representative sample of adults from the English population, using the ADOS-G, and found that the rates of ASD were similar to those recently reported in children. Within the adult population, rates of ASD did not change with age. Thus, provided we stick to the same diagnostic criteria, then the prevalence of autism is the same for those born several decades ago, as it is for the current generation of children. Importantly, none of these adults with ASD had received a formal diagnosis.

Recently, we conducted a study with another group: children with an additional sex chromosome (i.e. trisomy). We had not intended to study diagnostic substitution: the goal was rather to understand more about the language difficulties that had previously been described in children with sex chromosome trisomies. The effect of an extra sex chromosome is relatively mild: most of these children attend mainstream schools and they do not have any obvious physical abnormalities. Indeed, they can be hard to study because many individuals with trisomies will be unaware of their condition. We gathered information by parental report, and did not do any direct evaluation of the child, but we did ask about whether the child had had any kind of diagnosis by a medical or psychological expert. We confirmed that there was a strong association with language problems in all three kinds of trisomy (girls with XXX, and boys with XYY or XXY), many of whom had had speech-language therapy. But we also found that 2/19 (11%) of boys with XXY and 11/58 (19%) of those with XYY had received an ASD diagnosis.

It is important to emphasise that most children with a sex chromosome trisomy did not have an ASD diagnosis, and many were not giving any cause for concern. Nevertheless, although they are only a minority of cases, the proportion with ASD is much higher than in the general population. We were really surprised at this because before publishing our study we had done a systematic review of the literature on children with sex chromosome trisomies, focusing on studies that avoided ascertainment bias. In these studies, not a single case of autism had been mentioned when discussing outcomes. So was our study a fluke? We are confident this is not the case, because this year two further studies from the USA have been reported (Ross et al and Lee et al, in press), both of which got results very similar to ours, though using different methods.

This research provides further evidence that diagnostic substitution has occurred, suggesting that children who in the past would have been diagnosed with language impairment are now being diagnosed with ASD. The only other way to explain the increased diagnosis rate in children with a known chromosomal abnormality would be if the trisomy acted as a risk factor, making children more sensitive to environmental factors that could cause autism. That’s a possibility, but it seems more likely that cases of ASD were missed in the past because more stringent diagnostic criteria were used, just as was found in our follow-up of children with SLI and in the epidemiological study of adults by Brugha and colleagues.

It is becoming clear that changing diagnostic criteria, increased awareness of ASD, and strategic use of diagnosis to gain access to services, have had a massive effect on the numbers of children with ASD. When I started studies in this area, I thought diagnostic substitution had happened but I did not think it would be sufficient to explain the increase in numbers of ASD diagnoses. But now, on the basis of studies reviewed here, I think it could be the full story.

PS: a slightly extended version of this blogpost was featured on PLOS Blogs on 8th June 2012.

References

Bishop, D., Jacobs, P., Lachlan, K., Wellesley, D., Barnicoat, A., Boyd, P., Fryer, A., Middlemiss, P., Smithson, S., Metcalfe, K., Shears, D., Leggett, V., Nation, K., & Scerif, G. (2010). Autism, language and communication in children with sex chromosome trisomies Archives of Disease in Childhood, 96 (10), 954-959 DOI: 10.1136/adc.2009.179747
 
Bishop, D., Whitehouse, A., Watt, H., & Line, E. (2008). Autism and diagnostic substitution: evidence from a study of adults with a history of developmental language disorder Developmental Medicine & Child Neurology, 50 (5), 341-345 DOI: 10.1111/j.1469-8749.2008.02057.x  

Brugha, T. (2011). Epidemiology of Autism Spectrum Disorders in Adults in the Community in England Archives of General Psychiatry, 68 (5) DOI: 10.1001/archgenpsychiatry.2011.38

Lee, N. R., Wallace, G. L., Adeyemi, E. I., Lopez, K. C., Blumenthal, J. D., Clasen, L. S., & Giedd, J. N. (2012, in press). Dosage effects of X and Y chromosomes on language and social functioning in children with supernumerary sex chromosome aneuploidies: Implications for idiopathic language impairment and autism spectrum disorders. Journal of Child Psychology and Psychiatry. 

Ross, J. L.,et al (2012). Behavioral and social phenotypes in boys with 47, XYY syndrome or 47, XXY Klinefelter syndrome.  Pediatrics, 129(4), 769-778. doi: 10.1542/peds.2011-0719



 

Well, this should be easy….

 Life and times of an amateur video-maker

It’s been an exciting week. On Friday, a small group of us launched a campaign to raise awareness of children’s language learning impairments (RALLI). We’ve been fortunate to have had considerable help from TeamSpirit, an agency whose expertise in marketing and advertising has been invaluable. With their assistance, we’ve set up a YouTube channel, which has kicked off with some professionally-made video shorts to introduce the campaign. But we don’t have funds to continue with a lot of expensive professional services, and so our plan is to post a mix of content on the site, including some videos made by the RALLI team. We are four academics and a speech-and-language therapist, none of whom has any expertise in filming, but the TeamSpirit folks were reassuring. What we needed was a digital flipcam, which would allow us to film ourselves in high definition video, download to the computer, and upload to YouTube. Easy peasy. Or so I thought. Before I began this exercise, I was a straightforward atheist. Now I believe in supernatural forces, but they aren’t benign.

The camera arrived in the post and looked great - same size as a mobile phone. I studied the manual. There was a battery. There was a slot labelled ‘battery compartment’. But there was a problem. The battery did not fit in the battery compartment, whichever way I tried. I grumbled to my PA that we’d been sent the wrong battery. She discovered a bit of the manual that explained how to insert the battery - in a quite different place. I left her to play with the camera while I went off to prepare a lecture, as she was clearly more suited to this than me. She emailed me to say that the camera worked well, but there was a snag. It stored exactly 30 seconds of footage. Should you want more than this, you had to buy a memory card. This is what went in the ‘battery compartment’. So, my plans for starting filming were foiled.

Onto the Kodak website. Astounded by how much I’d have to pay for a memory card. Realised I’d also need some kind of tripod to stabilise the camera when filming myself. Registered on the website, put in an order, tried to pay with Paypal, password rejected. Having assumed various emails from Paypal were spam, I was now uncertain as to whether or not my recorded password was still valid. But I wasn’t going to get a chance anyhow, as my failed password had somehow aborted the whole operation. Too busy to start again, so decided I’d take a look in Currys to see if I could buy memory etc.

The Currys option was the only positive thing to happen. Found a dinky little cushion thing that you could screw your camera into that cost far less than a tripod and worked as well. Also found that, as I didn’t plan to record hours of footage, I could buy a small memory card much more cheaply. So I was ready to go except for one thing. I needed an external microphone.

We had had a clearout of our lab a few months ago, during which we’d found a huge cache of microphones. For years we did research on language disorders that involved making good quality tape-recordings of children, and we had clip-on microphones, boundary microphones, big microphones, small microphones, none of which had been used for years. However, they had all been carefully put away. Somewhere. I thought I was getting close when I found a box full of headphones, but no. Several boxes later, I gave up. I wonder if other people have boxes full of cables that connect together things that you have never used, have no idea what they’re for, but can’t bear to throw away.

Eventually, a savvy member of my team arrived and located a boundary microphone, which I took home with me to experiment with over the weekend. Well, I guess this microphone had once been good, but it had lived in a box for about 8 years. I assumed that the little round battery in it was now well dead, but there were a couple of spare batteries still in their original packaging. Like most contemporary packaging, this  was designed to give you the impression that if you attack it with fingernails, you might get in, when in fact this is not the case. The only result is a broken fingernail. What is needed is scissors, and so I now went on a scissor-hunt. Eventual success, though why scissors should be in the fruit bowl I do not know.  

No indication as to which way round the battery should go, and I’d made the mistake of removing the existing one without checking. Tried new battery one way up. Nothing. But now a problem. The battery sat happily in the battery hole and did not want to come out. Tried fingernails, tried prodding with nail-scissors. It wobbled, but it wouldn’t budge. Gave to husband. He tried fingernails, and tried scissors. Then he had a remarkable insight. “What we need,” he said, “is a magnet”. This seemed to me no more than a theoretical speculation of no practical relevance. But he went further, and demonstrated his true genius in lateral thinking. “We need the little red man.” The little red man is a fridge magnet that we’d been given for Christmas. Downstairs to the kitchen again. The red man’s magnetic feet proved to be the perfect size for extracting little round batteries from microphones. We removed the battery. We rotated the battery. We reinserted the battery and plugged the microphone into the flipcam. Made a recording. Couldn’t hear any sound. What we now needed, clearly, was headphones. Headphone-hunt ensued. Headphones eventually located in the bedroom. Plugged in. Well, there was sound, but it was very faint. I tried modifying the controls on the flipcam to improve the gain, but that had  minimal effect. Here my amateur knowledge of technology failed me. If it was faint, could it mean that the battery was running out of juice? Husband thought unlikely but we did have one more spare battery to try. Another assault on packaging with scissors and we were in. We had another go with red Pete’s feet, but the new battery didn’t work at all. At this point it was getting late and husband was impatient to watch another episode of Breaking Bad (highly recommended: we are on series 3), so I gave up for the night.

Next morning decided to look in the geological specimen cabinet to see if I could find an alternative battery. This is an amazing piece of furniture that we picked up in a country auction about 30 years ago. Its original function was to store bits of rock, but it is a godsend for a hoarder, as it allows you to hoard your useless objects in labelled mahogany drawers. One drawer is called Batteries. The problem is that the batteries that live in it tend to be very old, but I did find some that were small and round and labelled as “For use only in NHS hearing aids.” Husband, who has a hearing aid, denied all knowledge of them. Ever optimistic, I decided to try one in my microphone, feeling ever-so-slightly wicked at disobeying the stern injunction on the packet. The battery fitted in the microphone slot very snugly. I tried recording. I got a signal, but it was even weaker than before. Oh well, I thought, maybe I should just buy a new battery. But then I had a problem. The snugly fitting NHS battery was wedged in. Even the full force of red Pete’s feet would not budge it. I felt that God was punishing me for misappropriating NHS property and sadly decided that the boundary microphone would have to be ditched, and I should just get myself a clip-on microphone (which was what TeamSpirit had originally recommended….).

Off I trotted to Currys. “No”, they said, “We don’t do microphones. You could try Maplins on the Botley Road.” This entailed a trip in the car, but, after standing for 10 minutes in a queue while the extraordinarily helpful Maplin’s staff explained some complicated electronic device to a customer, I was armed with my microphone and ready to go. Quick test when I got home and it worked! Excellent clear signal. So I should be able to make the two short video clips that I had undertaken to do.

Now my only problem was to perfect a three-minute spiel and record myself saying it in front of the camera. Well, there was another problem, which is that my usual weekend appearance is scruffy. I do scruffy very well. It’s my natural state. But if I was going to be recorded for posterity, I needed to try and look professional. I realised that only my top half would be visible, so put on smart top, jewellery and make-up. Husband wandered in at some point: surprised to see me dressed up but clearly thought I had just forgotten to change from track-suit trousers, which says much about my usual level of absent mindedness. Arranged camera on a stepladder to capture head-and-shoulders region, checked light levels, sat in chair, breathed in ready to start spiel, and … the phone rang. Blood transfusion service, wanting me to make an appointment to give blood. Go downstairs, find diary, make appointment. Start again.

The thing about talking in front of a video for three minutes is that it’s quite easy to do it for about two minutes, but then you snarl up. I had two takes that were near-perfect but where I then descended into gibberish. There was also one take where the top of my head was chopped off, and another where I forgot to plug in the microphone. But eventually, I had a version with just a minor stumble in the middle which I decided I could live with. So now, I just had to import it into my computer. Quick hunt for the instruction manual, eventually located underneath a newspaper. Cunningly designed camera has USB connector that you can pull out of slide slot: neat! You put it in your computer, which allows you to download the software that you need to edit your video. This gives instructions for yet other software that you need to find on the web. You download that and restart the computer as instructed. You then get a cheerful message to tell you that there’s a new version of your software, and would you like to download an update now. “No I would not!” I say sternly to the screen, determined to press on now I’ve got started. I’m confused as to the distinction between the two bits of software, but eventually manage to download my video. It’s looking good. Except the audio starts about three seconds before the video. I try again. Same story. I look at video on the camera: audio and video perfectly synchronised.

Decide I need coffee, but we are out of coffee, so nip across the road. Weird look from shopkeeper reminds me that I am make-up and jewellery on top half and tracksuit on bottom half. Coffee in hand, I regroup. No advice on out-of-sync films in the manual or on the website of the camera-maker, which is complex, confusing and looks unlikely to resolve my problems.  Try Google. There seem to be only a tiny handful of people out there who’ve had the same problem, and the replies they’ve had are not encouraging. One man had shot 20 hours of film before realising the problem, so I reckoned I was lucky in comparison to him. One suggestion to him was to get into an editing program that would allow him to shuffle along the audio track.  I dimly remember using some video editing software in the past that allowed me to separate the audio and video stream on a file. Hunt through all my software, and locate Windows Moviemaker. This is encouraging, except it doesn’t seem able to read mp4 files.

In the back of my mind, there’s a concern that maybe the problem is due to the microphone. Now, this is what happens to me when I encounter a succession of obstacles: I start calm and logical, but I then start to think that there’s a malign force out there chuckling over my misfortunes, and I lose the plot and move over to magical thinking. If the problem was the microphone, then my logical brain tells me that the video should be out of sync when viewed on the camera. But a little voice in my head is telling me I should try with a different microphone, and so off I go on a futile and time-consuming exercise. I have another microphone that’s attached to a headset. So I unplug the recorder from the computer USB port. In response, computer gives me blue screen of death. Switch off computer. Reboot. Relieved to find it still works okay.

So I return upstairs to my living room to record two new brief segments, one with original microphone and one with headset microphone. I come downstairs, I plug camera into USB port. Blue screen of death returns. Reboot computer. It won’t start. Realise that this might be due to camera in USB port. Remove camera. Computer starts OK. Gingerly put camera in USB port. This time it’s okay, and I download my two trial clips. Both download okay. But when I play them, I realise there’s a fatal flaw to my test. I recorded clips with me talking, but did not record my face. So I have no idea whether or not the audio is in sync with the video.

Upstairs again to re-record. Ultimately, this futile test confirms that both microphones give an in-sync film on the camera, which mysteriously transforms into an out-of-sync version on my computer.

I have a faint memory of things called codecs, which determine how audio and video is converted into a digital form. Maybe I don’t have the right codecs. At this point, a more sensible test occurs to me. I should try downloading on to a different computer. Husband who is peacefully working in his office at top of the house is willing to lend me a laptop, which I carry to my office at bottom of house. It takes a very long time to boot up, and once it’s done that, I can’t get the mouse to work. Try pressing buttons etc. No joy. Further consultation with husband. Decide to replace battery in mouse. We have batteries, but they are defended by packaging. Further hunt for scissors. Get battery. Replace battery. Mouse now works. Plug in camera. Download software. Restart. Get message telling me to download updated software and decide this may be a good idea, so do that and again restart. This is a computer that takes a good 5 minutes to boot up and to shut down. Make a cup of tea while all this is going on. And, joy oh joy, when I have got software installed and downloaded the video, it works. It is in sync! I have to edit it to chop off the first and last bits, where I am walking from the camera to the chair and back, and so I find the manual which explains how to do that, but I’m in a hurry, as we are going out for the evening, and somehow, I manage to do the opposite of what I intended, so am left with just the end of the film, which is a bit I wanted to discard. Still, I think, we’re getting there. Tomorrow is another day.

A new day dawns. I download the film to husband’s computer one more time. This time I do succeed in selecting the right portion to save, and create a file that we’ll be able to download to YouTube. But I’d really like to back it up on my computer, and there’s a problem. It’s too big to email, too big for Dropbox, and won’t fit on a memory stick. I used to have several pocket drives, but I blew up a couple of them by using the wrong power supply, and the others are at work. Hunt of the house eventually yields a pocket driving belonging to husband (who is amassing marital points at an unprecedented rate during this exercise) and transfer the video to my computer. But when I play it, the audio is out of sync with the video.

Now, although this is disappointing, I’m not sure whether it’s good news or bad news. The good news is that the file is clearly fine when played on either the camera or my husband’s PC. So the problem is with my PC and how it is interpreting the file. So I feel I have to get to grip with codecs again. The software has actually told me which codecs were used with the file, and I make a note of them. Googling the IDs leads me to a website that has oodles of codecs that you can download. A bit more Googling allows me to find out how to see which codecs are already installed on my machine. But now I have a quandary. It’s not clear to me that the codec download site is safe, and a bit more Googling confirms my worries. It seems that you can end up far worse than you started if you download a dodgy codec. So I have a new idea. I’ll try the Microsoft site and see what it says about codecs. What it says is possibly the least helpful advice I have ever seen. It suggests you search on the internet for the codecs you need, but it then says that it can be really, really dangerous to download codecs from the internet, and warns you against it.

Well, I think, maybe if I download an up-to-date version of Moviemaker, it might come with useful codecs. On to the Microsoft site. Yes, there’s a more recent version of Moviemaker, and I initiate the download process. But then it demands verification via Microsoft Genuine Advantage. This rings faint bells as something I decided not to sign up for, having read reviews that suggested it could slow up your machine. I think that maybe I should give it a try, but when I try to do so I ultimately get to a website that explains that the page isn’t working and Microsoft is aware of the problem.

I decide that, rather than wasting time on a fruitless hunt for a safe codec, I will shoot one more bit of footage. Once again, make-up, nice top, pearls. Part of me wonders whether there’s any point to this, and whether I should not instead adopt the Mary Beard approach of appearing au naturel. It definitely works for Mary, who is widely adored for her robust attitude to those who think she should have a make-over for TV. But I decide that I can’t now change tack, as it would really look weird if one bit of view had me all glammed up and the next one had the normal scruffy Bishop. The first two takes are fluffed, but the third is perfect. Except that when I try to stop the recording, the device is frozen. No buttons at all work, even the off switch. I’m starting to get emotional but have to try not to cry as it would just make my makeup run (another good reason for adopting the au naturel approach). The manual is singularly unhelpful - its advice on problems is restricted to occurrences such as having one’s finger in front of the viewfinder. Googling doesn’t help either. All my experiences seem unique to me  - further evidence of the malign force. Only solution, I guess, is to remove the battery. That restores the camera to normal functioning, but the last, perfect, take is described as “file type unknown”.

Back upstairs for yet another session. Eventually manage a version that seems okay, which I download successfully. And which looks fine on husband’s laptop but out-of-sync on mine. Thankfully get back into tracksuit, remove makeup, and decide I will reward myself with a negroni and an episode of the Bridge.

If these videos do ever get onto the RALLI site, you may think that I look a bit stressed for someone who’s just doing a three-minute piece. But now you know the true story.

Sharing of MRI dyslexia datasets

One of the great things about blogging is that it allows for communication to proceed far more rapidly than would be possible through conventional academic publications. In previous posts I’ve made a plea for MRI researchers to share data so that claims about the neurobiology of conditions such as dyslexia and autism can be replicated. After my last blogpost, I was contacted by Mark Eckert from the Medical University of South Carolina, one of the pioneers of MRI studies of dyslexia (e.g. Eckert et al, 2005). He tells me that a data-sharing project on dyslexia is already underway and asked if I would be able to share this information with my followers. I am of course delighted to do so! Here is some background from Mark:

The structural neuroimaging literature on dyslexia and other complex disorders is filled with inconsistent results.  Meta-analysis provides a mechanism for identifying results that are common across studies, but direct analysis of the same datasets provides greater power, methodological consistency, and new analysis opportunities that include taking advantage of the behavioral and neural heterogeneity that is often problematic in small samples.  For those reasons, there is a growing interest in sharing data.  Prospective multi-site studies are ideal because the same data collection and quality control procedures can be used across sites.  These studies tend to be very expensive, however.  Retrospective studies take advantage of existing datasets that are housed in dusty hard drives, but are limited by methodological inconsistencies across sites.  A new NIH supported project, directed by Mark Eckert, uses dyslexia as a model to address the challenges facing retrospective multi-site studies.  Methods are being developed in this project to address subject privacy, behavioral heterogeneity in dyslexia and control samples, missing data, and the underestimation of the variance in datasets when pooling data across different research sites.  His research group is collecting existing neuroimaging datasets and aims to have more than 2000 pediatric and adult cases from reading disability studies.  One long term goal of this project is to make available much of the data collected for this study so that scientists can ask new questions, apply new methods to the data, and develop new collaborations with other scientists who have complementary expertise and interests in reading disability.  There are incentives for research groups to contribute data. For example, contributors will be included in a Dyslexia Data Consortium that will be included in the list of authors for manuscripts stemming from this project.  If you are interested in learning more about the study and/or would like to contribute data, please contact Mark Eckert at dyslexia @ musc.edu.

Eckert MA, Leonard CM, Wilke M, Eckert M, Richards T, Richards A, & Berninger V (2005). Anatomical signatures of dyslexia in children: unique information from manual and voxel based morphometry brain measures. Cortex; a journal devoted to the study of the nervous system and behavior, 41 (3), 304-15 PMID: 15871596

Neuronal migration in language learning impairments: a suggestion

Specific language impairment (SLI) and dyslexia are related developmental disorders in which a child has difficulty learning to talk (SLI) or to read (dyslexia). Many children have both problems, although they can occur separately (Bishop & Snowling, 2004), and they are sometimes grouped together as ‘language learning impairments’. There's good evidence that genes are implicated in causing these conditions (Bishop, 2009).

A popular account maintains that the genes implicated in language learning impairments affect a very early process in the developing brain known as neuronal migration (Galaburda et al., 2006). It’s an attractive theory that has the potential to provide a link from genes to behaviour. However, when I looked at the evidence, I found myself not entirely convinced. Here I’ll briefly review research on this topic, explain my reservations, and conclude by proposing a study that needs doing. I’m not an expert in neuroanatomy or neuroimaging, so I’ll be interested to see if others think this proposal is sensible.

Abnormalities found in 1979 case report. Solid circles show ectopias/dysplasias, and shaded area shows micropolygyria (based on Galaburda et al, 1985) .

Over thirty years ago, Galaburda and Kemper published a post mortem study of the brain of a man with developmental dyslexia who died from an accidental fall at the age of 20 years. He’d had delayed language development, and was diagnosed with dyslexia in the first grade. His Stanford-Binet IQ of 105 was well in advance of his reading attainments. He developed epilepsy at 16 years of age. His brain showed areas of displaced neurons (ectopias) in the left cerebral hemisphere, especially around the left planum temporale. There was also an area of polymicrogyria, i.e. excessive number of small convolutions, giving a lumpy appearance to the cortex. This raised the possibility that we might find the origins of dyslexia not in the gross features of brain structure, but at the microscopic level, in the organisation of neurons. However, as the authors noted: “It is not possible to tell from a single case whether or not the anatomical findings have any causative relationship to the clinical findings – much less whether the malformation is responsible for the seizure disorder, the learning disability, both, or neither” (p. 99). They also noted that the kinds of neuroanatomical abnormality that they found in their patient were probably too rare to explain dyslexia in general, which has a prevalence of around 5-10% in the population.

A subsequent report added further evidence for a link to dyslexia (Galaburda et al, 1985). Similar abnormalities were found in three further post-mortem cases, and in none of these was epilepsy described, though one had delayed speech and one had “notable language difficulties”. Three additional cases, this time of female dyslexics, were reported by Humphreys et al (1990), but these were less compelling: the evidence for migrational abnormalities was less strong, and other pathologies could have been implicated.

There’s a general problem with the methodology of these studies, which is that they were not conducted blind. The cellular abnormalities that were described require an expert eye and clinical judgement, and you wouldn’t necessarily see them unless you were looking for them. Could they just be spurious findings? Galaburda and colleagues noted that similar anomalies are sometimes reported as incidental findings in unselected autopsy brains, and so a key question was whether the findings in dyslexic brains were really unusual. Accordingly, Kaufman and Galaburda (1989) analysed ten control brains using identical procedures to those used for dyslexic brains. They found abnormal cells in three control brains, but the anomalies were far less numerous than those seen in the dyslexic brains. This provides useful context, but ideally, we need a study where the neuroanatomist is given both dyslexic and control brains and asked to analyse them without knowing which was which, to avoid the perceptual and cognitive biases that can affect even the most scrupulous of observers.

The anomalies described by Galaburda and colleagues reflect disruption at an early stage of brain development, when neurons are being formed and organised into coherent structures. This website from Pasco Rakic has some nice animations showing how a brain is formed when neurons are first generated in the foetus. Neurons formed in the ventricular zone travel out to the surface of the cortex along radial glial fibres, gradually building up six distinct layers of the cortex from the inside out. Studies with rodents, and evidence from humans with developmental disorders, indicate that this process can be disrupted in a range of ways. In some people, a proportion of cells fail to migrate at all, and can be seen as clusters of abnormal cells around the ventricles. This condition, known as periventricular heterotopia, does not normally impair cognitive function but does cause epilepsy. In other cases, there is partial migration followed by arrest, leading to lissencephaly, typically associated with epilepsy and severe intellectual impairment(Guerrini& Parrini, 2010). In mice, a naturally-occurring genetic mutation leads to the phenotype of the reeler mouse, which has severe motor co-ordination problems linked to disorganisation of the usual laminar structure of the cortex, because the migrating neurons fail to penetrate to the surface of the brain. The cases studied by Galaburda and colleagues had a range of anomalies, described as ectopias, dysplasias, heterotopias, ‘brain warts’ and polymicrogyria, associated with disruption affecting different stages of neuronal migration and postmigrational development (Barkovich et al, 2012).

What makes this work exciting is a potential link to genetic studies of dyslexia. There are replicated associations of dyslexia with several genes, including DYX1X1, KIAA0319, DCDC2 and ROBO1. As Galaburda et al (2006) noted in their review, mutations of these genes have been linked to migrational anomalies in rodents. It looks, therefore, as though the route from brain to behaviour could be neatly explained by postulating a genetic influence on neuronal migration that leads to a brain that is not optimally connected.

Some puzzles, however, remain. First, the genetic variants associated with dyslexia are not mutations. They are common in the general population. Associations with dyslexia are found in studies with very large samples, but they are not very strong. For instance, one can deduce from the published data on the KIAA0319 locus that there is a low-risk version of the gene that is found in 39% of normal readers and 25% dyslexics, and a high-risk version that is found in 30% of normal readers and 35% dyslexics. If the dyslexic risk variant causes anomalies of neuronal migration, then we should see lots of people with those anomalies, many (most) of whom will not be dyslexic. Of course, it is all a matter of degree; it is possible that each risk variant has only minor effects on neuronal migration, and causes problems only if it occurs in conjunction with other genetic or environmental risks. Neuronal migration can be affected by environmental factors, such as toxins, nutrition, and disease or trauma affecting the brain. So the ubiquity of these risk alleles does not rule out a causal route via neuronal migration mechanisms, but it does make the story more complicated.

What if we look at the association between neuronal migration disorders and dyslexia from the other direction, i.e. assessing reading ability in individuals with known migrational abnormalities? Chang et al (2005) did this in people with periventricular nodular heterotopia - a disorder in which a proportion of neurons fail to migrate from the ventricular zone. Most of their participants had normal range IQ. On the Wide Range Achievement tests of reading and spelling, their mean scores were average or above-average. Many of them did, however, do poorly on the Nelson-Denny reading test and on this basis, the authors concluded they were dyslexic. But this test, which stresses speed, was designed for college students, not for the general population. The fact that most participants were older than college students, and all were on anti-epileptic medication, makes the claim of dyslexia in these people far from convincing. Minimally, this study should have included a comparison group to control for age, background and medication status.

A final issue is why migrational abnormalities haven’t been noted in MRI studies of dyslexia. In studies of children with specific language impairments, a Brazilian group has reported remarkably high rates of polymicrogyria (De Vasconcelos Hage et al, 2006). However, this does not seem to be a general explanation for SLI. My colleagues tell me there were no cases of this in people with SLI who participated in a recent MRI study that we published, and none was mentioned in a series reported by Webster etal (2008). MRI studies of dyslexia have been considerably more numerous, yet, as far as I can establish, none has mentioned migrational anomalies. Of course, many MRI studies focus on averaged data, which would mask individual variations. So, a key question is whether the failure to report migrational abnormalities in MRI studies is because (a) no-one was looking for them, (b) they are too subtle to see on regular MRI scan, or (c) they aren’t involved in most cases of language learning impairments.

I was intrigued by this question, so I looked for literature on detectability of neuronal migration anomalies on MRI scan. My impression is that these wouldn’t necessarily be detected unless you were looking for them, and if you were, detectability depends on the type and location of anomalies. Wagner et al (2011) devised an automated method of MRI analysis that was successful in picking up 82% of Type IIA cortical dysplasias and 92% of Type IIB, compared to 65% and 91% detected by an expert neuroradiologist. Periventricular nodular heterotopia seems a more obvious pathology that is routinely detected on MRI scan.

On this basis, I’d say there’s a study out there crying out to be done. There are plenty of reports of MRI scans comparing dyslexic vs control brains. We could revisit those scans using the automated methods developed by Wagner et al to test the hypothesis that the rate of neuromigrational anomalies is higher in the dyslexic vs control samples. It’s clear that MRI scans won’t pick up everything, and subtle anomalies may be missed. However, if the neuronal migration account of language learning impairments is correct, we should nevertheless expect to see a measureable difference in the rates of anomalies between cases of dyslexia/SLI vs. controls. And if genetic information is available as well, then a comparison could be done between those with and without risk variants.

References

Barkovich, A. J., Guerrini, R., Kuzniecky, R. I., Jackson, G. D., & Dobyns, W. B. (2012). A developmental and genetic classification for malformations of cortical development: update 2012. Brain, 135(5), 1348-1369. doi: 10.1093/brain/aws019

Bishop, D. V. M. (2009). Genes, cognition and communication: insights from neurodevelopmental disorders. The Year in Cognitive Neuroscience: Annals of the New York Academy of Sciences, 1156, 1-18.

Bishop, D. V. M., & Snowling, M. J. (2004). Developmental dyslexia and Specific Language Impairment: Same or different? Psychological Bulletin, 130, 858-886.

Chang, B. S., Ly, J., Appignani, B., Bodell, A., Apse, K. A., Ravenscroft, R. S., . . . Walsh, C. A. (2005). Reading impairment in the neuronal migration disorder of periventricular nodular heterotopia. Neurology, 64(5), 799-803.

De Vasconcelos Hage, S. R., Cendes, F., Montenegro, M. A., Abramides, D. V., Guimarães, C. A., & Guerreiro, M. M. (2006). Specific language impairment: linguistic and neurobiological aspects. Arquivos de Neuro-Psiquiatria, 64, 173-180.

Galaburda, A. M., & Kemper, T. (1979). Cytoarchitectonic abnormalities in developmental dyslexia. Annals of Neurology, 6, 94-100.

Galaburda, A. M., Sherman, G. F., Rosen, G. D., Aboitiz, F., & Geschwind, N. (1985). Developmental dyslexia: four consecutive cases with cortical anomalies. Annals of Neurology, 18, 222-233.

Galaburda, A. M., LoTurco, J. J., Ramus, F., Fitch, R. H., & Rosen, G. D. (2006). From genes to behavior in developmental dyslexia. Nature Neuroscience, 9, 1213-1217.

Guerrini, R., & Parrini, E. (2010). Neuronal migration disorders. Neurobiology of Disease, 38, 154-166.

Wagner, J., Weber, B., Urbach, H., Elger, C., & Huppertz, H. (2011). Morphometric MRI analysis improves detection of focal cortical dysplasia type II Brain, 134 (10), 2844-2854 DOI: 10.1093/brain/awr204

Webster, R. I., Erdos, C., Evans, K., Majnemer, A., Saigal, G., Kehayia, E., . . . Shevell, M. I. (2008). Neurological and magnetic resonance Imaging findings in children with developmental language impairment. Journal of Child Neurology, 23(8), 870-877. doi: 10.1177/0883073808315620