brain-based prediction of ADHD–now with 100% fewer brains!

UPDATE 10/13: a number of commenters left interesting comments below addressing some of the issues raised in this post. I expand on some of them here.

The ADHD-200 Global Competition, announced earlier this year, was designed to encourage researchers to develop better tools for diagnosing mental health disorders on the basis of neuroimaging data:

The competition invited participants to develop diagnostic classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging (MRI) of the brain. Applying their tools, participants provided diagnostic labels for previously unlabeled datasets. The competition assessed diagnostic accuracy of each submission and invited research papers describing novel, neuroscientific ideas related to ADHD diagnosis. Twenty-one international teams, from a mix of disciplines, including statistics, mathematics, and computer science, submitted diagnostic labels, with some trying their hand at imaging analysis and psychiatric diagnosis for the first time.

Data for the contest came from several research labs around the world, who donated brain scans from participants with ADHD (both inattentive and hyperactive subtypes) as well as healthy controls. The data were made openly available through the International Neuroimaging Data-sharing Initiative, and nicely illustrate the growing movement towards openly sharing large neuroimaging datasets and promoting their use in applied settings. It is, in virtually every respect, a commendable project.

Well, the results of the contest are now in–and they’re quite interesting. The winning team, from Johns Hopkins, came up with a method that performed substantially above chance and showed particularly high specificity (i.e., it made few false diagnoses, though it missed a lot of true ADHD cases). And all but one team performed above chance, demonstrating that the imaging data has at least some (though currently not a huge amount) of utility in diagnosing ADHD and ADHD subtype. There are some other interesting results on the page worth checking out.

But here’s hands-down the most entertaining part of the results, culled from the “Interesting Observations” section:

The team from the University of Alberta did not use imaging data for their prediction model. This was not consistent with intent of the competition. Instead they used only age, sex, handedness, and IQ. However, in doing so they obtained the most points, outscoring the team from Johns Hopkins University by 5 points, as well as obtaining the highest prediction accuracy (62.52%).

…or to put it differently, if you want to predict ADHD status using the ADHD-200 data, your best bet is to not really use the ADHD-200 data! At least, not the brain part of it.

I say this with tongue embedded firmly in cheek, of course; the fact that the Alberta team didn’t use the imaging data doesn’t mean imaging data won’t ultimately be useful for diagnosing mental health disorders. It remains quite plausible that ten or twenty years from now, structural or functional MRI scans (or some successor technology) will be the primary modality used to make such diagnoses. And the way we get from here to there is precisely by releasing these kinds of datasets and promoting this type of competition. So on the whole, I think this should actually be seen as a success story for the field of human neuroimaging–especially since virtually all of the teams performed above chance using the imaging data.

That said, there’s no question this result also serves as an important and timely reminder that we’re still in the very early days of brain-based prediction. Right now anyone who claims they can predict complex real-world behaviors better using brain imaging data than using (much cheaper) behavioral data has a lot of ‘splainin to do. And there’s a good chance that they’re trying to sell you something (like, cough, neuromarketing ‘technology’).