There's been a lot of buzz and some
scepticism about the
New brain scan to diagnose autism
Here's a quick overview. Autism is believed to be a disorder of brain development. If so, it should be possible to diagnose it based on a brain scan. Unfortunately, it's not. You can't tell, from a scan, whether someone has autism or not. Not even if you're a world expert.
There are
reports of various differences between autistic and non-autistic brains - a bit smaller here, a bit bigger there - but there's a lot of overlap. So at present, diagnosis of autism is purely based on symptoms.
Ecker et al, a team based at the Institute of Psychiatry in South London, made use of a mathematical technique called a Support Vector Machine (
SVM) to try to spot differences that the naked eye can't. An SVM is a learning algorithm: you "teach" it to spot differences by showing it lots of examples. In this case, they showed it 20 autistic brains, and the brains of 20 healthy controls matched for age, gender, and IQ.
How does an SVM work? Imagine that there are two kinds of, say, fruit. Both are kind of round but A's are more spherical than B's. So you could draw a plot of sphere-ness, and find a line separating A and B:
An SVM is an automatic method of finding that line. How? It's
complex, but fortunately you don't need to know (I don't). Of course, that's easy, but imagine that things got more tricky. As well as the variable of roundness, there's colour. Fruit B can be either spherical and dark, or non-spherical and light (maybe it's two different stages of ripeness).
An SVM could do this easily too:
Now suppose that there's 1000 different variables, and you want to find the "line" - actually a 1000-dimensional "
hyperplane" (a line is 2D, a plane is 3D, anything with 4D or more is a hyper-plane) - dividing the "space" of possibilities into two.
For a human that's impossible, but not for an SVM. This is essentially what Ecker et al did. Each
dimension of their "space" was the amount of grey matter at a particular point in the brain. So, they were training the SVM to distinguish between autistic brains and non-autistic brains, based on their shape, but in a much more complex way than a human could.
Did it work? Surprisingly well. Here's the end result (the multi-dimensional space has been helpfully compressed into 2D by the SVM):
It wasn't perfect, but the best approach, based on the cortical thickness in the left hemisphere, managed 90% accuracy, which is pretty awesome. Focussing on the headline 90% result is cherry-picking a bit, because using other variables, like cortical curvature, wasn't as good, but even the worst ones managed 70-85%, much better than chance (50%). Importantly, they also tried the system on 20 adults with
ADHD, and it classified them as non-autistic. This shows that it's not just measuring "normality".
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Now the question everyone's asking: is this going to be used for diagnosis in the real world any time soon? The first thing to remember is that this is a scientific paper, and this result is first and foremost of research interest: it provides clues towards the biology, and ultimately the causes, of autism.
But let's suppose you're a clinician and you have someone who you suspect may have autism, but you're not sure. They're a tricky one, a borderline case. You use this system on their brain and it says they
are autistic. Should that factor into your decision? It depends. The fact is that rather than an either-or result, the SVM returns a
distance from the hyperplane for each brain. You can see this clearly in the plot above.
In my opinion, if you have a borderline case, and the machine says he's borderline, then that's not much help, and it doesn't matter if he's just over the line, or not quite over it.
You already knew he was borderline.
But if the machine says that he's deep into the autism space, then I think that
is something. It tells you that his brain is very typical of people with autism. Interestingly, Ecker et al found that distance from the hyperplane correlated with symptom severity for "social" and "communication" symptoms (though not "repetitive behaviours"). That's a pretty cool result because the SVM wasn't trained to do that, it was trained to decide on an either-or basis.
What needs to happen next? As it stands, this system only works for adults: it would fail for children or teenagers, because their brains are a very different size and shape. Exactly the same SVM approach could be used in younger age groups, though, so long as the patients and the controls were the same age.
We also need to make sure that the SVM can tell the difference between autism and other conditions; Ecker et al showed that it could distinguish autism from ADHD, but that's only one comparison and it might not be the hardest one: I would want to see it tested against things like epilepsy, mental retardation, and dyslexia as well.
Overall though, this is very exciting work, and certainly a cut above most "Brain Scans To Diagnose Mental Illness" studies that make it into the headlines.
Full Discloser: I know some of the researchers involved in this work.
Links: The same team had a paper out a few months back, using a slightly less sophisticated SVM approach, which managed 80% accuracy. I wrote about another application of SVMs previously: How To Read Minds. This study has been blogged about at The New Republic and Dormivigilia.
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. Journal of Neuroscience, 30 (32), 10612-23 PMID: 20702694
They designed a script to Google the names of several antidepressants in the context of someone who's taking them, and checks to see if they describe any side-effects.
