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’).

aftermath of the NYT / Lindstrom debacle

Over the last few days the commotion over Martin Lindstrom’s terrible New York Times iPhone loving Op-Ed, which I wrote about in my last post, seems to have spread far and wide. Highlights include excellent posts by David Dobbs and the Neurocritic, but really there are too many to list at this point. And the verdict is overwhelmingly negative; I don’t think I’ve seen a single post in defense of Lindstrom, which is probably not a good sign (for him).

In the meantime, Russ Poldrack and over 40 other neuroscientists and psychologists (including me) wrote a letter to the NYT complaining about the Lindstrom Op-Ed, which the NYT has now published. As per usual, they edited down the letter till it almost disappeared. But the original, along with a list of signees, is on Russ’s blog.

Anyway, the fact that the Times published the rebuttal letter is all well and good, but as I mentioned in my last post, the bigger problem is that since the Times doesn’t include links to related content on their articles, people who stumble across the Op-Ed aren’t going to have any way of knowing that it’s been roundly discredited by pretty much the entire web. Lindstrom’s piece was the most emailed article on the Times website for a day or two, but only a tiny fraction of those readers will ever see (or even hear about) the critical response. As far as I know, the NYT hasn’t issued an explanation or apology for publishing the Op-Ed; they’ve simply published the letter and gone on about their business (I guess I can’t fault them for this–if they had to issue a formal apology for every mistake that gets published, they’d have no time for anything else; the trick is really to catch this type of screw-up at the front end). Adding links from each article to related content wouldn’t solve the problem entirely, of course, but it would be something. The fact that Times’ platform currently doesn’t have this capacity is kind of perplexing.

The other point worth mentioning is that, in the aftermath of the tsunami of criticism he received, Lindstrom left a comment on several blogs (Russ Poldrack and David Dobbs were lucky recipients; sadly, I wasn’t on the guest list). Here’s the full text of the comment:

My first foray into neuro-marketing research was for my New York Times bestseller Buyology: Truth and Lies about Why We Buy. For that book I teamed up with Neurosense, a leading independent neuro-marketing company that specializes in consumer research using functional magnetic resonance imaging (fMRI) headed by Oxford University trained Gemma Calvert, BSc DPhil CPsychol FRSA and Neuro-Insight, a market research company that uses unique brain-imaging technology, called Steady-State Topography (SST), to measure how the brain responds to communications which is lead by Dr. Richard Silberstein, PhD. This was the single largest neuro-marketing study ever conducted—25x larger than any such study to date and cost more than seven million dollars to run.

In the three-year effort scientists scanned the brains of over 2,000 people from all over the world as they were exposed to various marketing and advertising strategies including clever product placements, sneaky subliminal messages, iconic brand logos, shocking health and safety warnings, and provocative product packages. The purpose of all of this was to understand, quite successfully I may add, the key drivers behind why we make the purchasing decisions that we do.

For the research that my recent Op-Ed column in the New York Times was based on I turned to Dr. David Hubbard, a board-certified neurologist and his company MindSign Neuro Marketing, an independently owned fMRI neuro-marketing company. I asked Dr. Hubbard and his team a simple question, “Are we addicted to our iPhones?” After analyzing the brains of 8 men and 8 women between the ages of 18-25 using fMRI technology, MindSign answered my question using standardized answering methods and completely reproducible results. The conclusion was that we are not addicted to our iPhones, we are in love with them.

The thought provoking dialogue that has been generated from the article has been overwhelmingly positive and I look forward to the continued comments from professionals in the field, readers and fans.

Respectfully,

Martin Lindstrom

As evasive responses go, this is a masterpiece; at no point does Lindstrom ever actually address any of the substantive criticisms leveled at him. He spends most of his response name dropping (the list of credentials is almost long enough to make you forget that the rebuttal letter to his Op-Ed was signed by over 40 PhDs) and rambling about previous unrelated neuromarketing work (which may as well not exist, since none of it has ever been made public), and then closes by shifting the responsibility for the study to MindSign, the company he paid to run the iPhone study. The claim that MindSign “answered [his] question using standardized answering methods and completely reproducible results” is particularly ludicrous; as I explained in my last post, there currently aren’t any standardized methods for reading addiction or love off of brain images. And ‘completely reproducible results’ implies that one has, you know, successfully reproduced the results, which is simply false unless Lindstrom is suggesting that MindSign did the same experiment twice. It’s hard to see any “thought provoking dialogue” taking place here, and the neuroimaging community’s response to the Op-Ed column has been, virtually without exception, overwhelmingly negative, not positive (as Lindstrom claims).

That all said, I do think there’s one very positive aspect to this entire saga, and that’s the amazing speed and effectiveness of the response from scientists, science journalists, and other scientifically literate folks. Ten years ago, Lindstrom’s piece might have gone completely unchallenged–and even if someone like Russ Poldrack had written a response, it would probably have appeared much later, been signed by fewer scientists (because coordination would have been much more difficult), and received much less attention. But with 48 hours of Lindstrom’s Op-Ed being published, dozens of critical blog posts had appeared, and hundreds, if not thousands, of people all over the world had tweeted or posted links to these critiques (my last post alone received over 12,000 hits). Scientific discourse, which used to be confined largely to peer-reviewed print journals and annual conferences, now takes place at a remarkable pace online, and it’s fantastic to see social media used in this way. The hope is that as these technologies develop further and scientists take on a more active role in communicating with the public (something that platforms like Twitter and Google+ seem to be facilitating amazingly well), it’ll become increasingly difficult for people like Lindstrom to make crazy pseudoscientific claims without being immediately and visibly called out on it–even in those rare cases when the NYT makes the mistake of leaving one the biggest microphones on earth open and unmonitored.