Another day, another autism diagnosis media storm. Last week, it was speech patterns. Before that it was urine. Today's story is that researchers in London have found a way to diagnose autism by performing multi-dimesional analyses of magnetic resonance imaging (MRI) brain scans.
So what did they actually do? And how close really are we to being able to use brain scanning to diagnose autism?
Christine Ecker and her colleagues at the Institute of Psychiatry tested a group of 20 high functioning adults with autism, who were assessed the traditional way using either a structured interview with the parent (the ADI-R) or by video-recording a structured interaction between the person with autism and the experimenter and then coding their behaviour (the ADOS). The authors note that many of the people with autism could be considered to have Asperger syndrome but they didn't attempt to explore this issue further due to the small sample size.
The 20 adults with autism, together with 20 control adults, all underwent a 15-minute MRI scan. The set-up would have been something like the picture below.
From the scans, the authors extracted five different dimensions for each hemisphere of the brain that they thought might differentiate between the two groups.
One of these factors, left hemisphere cortical thickness, did a pretty good job. If the thresholds were set optimally, the algorithm could achieve a 90% accuracy. In other words, based on cortical thickness, 18 of the 20 people with autism were correctly classified as having autism and only 2 of the controls were incorrectly classified as being autistic. Other measures fared slightly less well and in general the right hemisphere measures were much worse at differentiating between the two groups. They also looked at combining all the different factors, but this didn't actually improve the discrimination performance.
The authors conclude their paper by stating that this is a "proof of concept". It shows that it is feasible to use these kinds of analytic techniques to investigate differences in the autistic brain.
So what are the challenges to be overcome in order to go from this to an MRI-based diagnostic protocol? Here are a few off the top of my head:
Does it generalise to new populations? In the current study, the choice of measures and weights were optimised to provide the best discrimination between the two groups of people in the study, whose diagnoses were already known. But we need to know that the MRI algorithm works just as well when it's given data from new people it hasn't seen before. To address this issue, the authors removed one person from each group, and optimised the MRI algorithm using data only from the remaining 38 people, so the two people removed were effectively new subjects. They then looked to see whether the two 'new' people were still correctly 'diagnosed'. They did this 20 times, removing a different pair each time and showed that they were statistically above chance at classifying the 'new' people. However, it's not clear (to me at least) how much better than chance.
Does it work with kids? The participants with autism in this study were aged between 20 and 68. A diagnostic tool for autism that only works with adults is obviously of limited use. The authors are confident that the MRI strategy will work as well if not better with children, but this needs to be demonstrated. It's also worth bearing in mind that brain scanning young children (especially autistic children) is far from straightforward and often has to be done when the child is asleep or sedated. The quick and easy 15 minute scan suddenly becomes less of a reality.
Can it discriminate between autism and similar disorders? Here is the crux. The 90% accuracy headline figure is in comparison to typically developing brains. I don't think the authors are really suggesting that we should screen the entire population for autism using MRI. Apart from the cost and ethical issues, it wouldn't be at all effective. Given that 10% of non-autistic people are classified as having 'autistic brains' and that only 1% of the population have autism, this means that in a random sample, most of the people identified as having 'autistic' brains wouldn't actually have autism.
More useful, perhaps, would be if people who were already suspected of having autism could be given a brain scan to confirm or deny the diagnosis. People tend to think of autism as being a clear cut condition, but in truth it's a messy affair, with much overlap between different disorders and fuzzy boundaries. A 'brain' test could be really useful, but it would have to pass a much more stringent evaluation process. This would involve comparing two groups of people suspected of having autism - those who go on to have their diagnoses confirmed and those who are not ultimately diagnosed with autism. If these two groups differed consistently in their brain structures then MRI might prove to be a useful diagnostic tool.
Ecker and colleagues make a step in this direction by applying their algorithm to adults with ADHD. It does OK, but still mis-diagnoses 21% of them as having autism. And there's no suggestion that anyone ever thought these people might have had autism.
So why bother looking at brains?
Overall, I have to question whether there really is any value in trying to use brain scans to diagnose autism. Ecker et al.'s paper begins by acknowledging the fact that autism is a highly heterogeneous disorder and should perhaps be referred to as 'the autisms' rather than a single condition. As I've mentioned before, two people can get the same diagnosis by ticking completely different boxes and recent research only goes to strengthen the impression that there are many different causes of autism at the genetic level. So does it then make sense to try and find a brain 'fingerprint' for autism? Probably not.
Where I think this kind of approach might well come in useful in the future is in identifying subgroups within autism. There are already a huge number of autism brain scans that have been collected over the years and it might be possible to run similar analyses and identify clusters in multi-dimensional 'brain space' that correspond to meaningful subgroups.
Perhaps one day, it will be possible for a child newly diagnosed with autism to undergo a brain scan which will indicate what subtype of autism they have. The outcome of this could help determine what intervention strategies and support are likely to be the most beneficial. If and when that day arrives, the media excitement will truly be justified.
Ecker C, Marquand A, Mourão-Miranda J, Johnston P, Daly EM, Brammer MJ, Maltezos S, Murphy CM, Robertson D, Williams SC, & Murphy DG (2010). Describing the brain in autism in five dimensions--magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. The Journal of neuroscience : the official journal of the Society for Neuroscience, 30 (32), 10612-23 PMID: 20702694
Download a free copy of the original article from the Journal of Neuroscience
- Bishop Blog
- Trust the evidence
- NHS Choices
- Ian Wacogne
- The Mouse Trap
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- Autist's Corner
Journal of Neuroscience have just published a 'Journal Club' response from Jennifer Stevenson and Kristina Kellett - two members of Morton Gernsbacher's lab. It covers many of the points raised in my post and the other blogs linked to above, but also raises some further salient issues. The authors note that other similar studies of autism have found different patterns of group differences in the regions identified by Ecker et al as discriminating between autistic and non-autistic brains. For example, whereas Ecker et al found reduced cortical thickness in the parahippocampal gyrus, Jiao et al 2010 reported increased cortical thickness in the same region. And to further complicate matters, a cross-sectional study by Raznahan et al (2010) suggests that other regions show developmental changes in the direction of 'abnormality' in autistic brains.
In another recent development, Nicholas Lange and colleagues claimed that they could differentiate between autistic and non-autistic brains using DTI (another form of MRI). I've only had a very quick look at the paper, but it seems as though many of the concerns raised about the Ecker paper are also relevant here. (22/12/10: There's a write-up of the Lange study at Biology Files, which does indeed raise similar concerns about the prospects for MRI-based diagnoses).