Sep 25 2012

Dead Fish Wins Ig Nobel

The Ig Nobel awards are a humorous take on the real thing, highlighting scientific studies over the last year that make you laugh, then make you think. This year’s winner in the neuroscience category is bringing back around a news story from earlier in the year : Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: An argument for multiple comparisons correction.

Essentially the researchers used an functional MRI scanner (fMRI) to examine the brain activity of a dead salmon – and they found some. The point of this study was to generate an absurd false positive in order to demonstrate how fMRI studies might be plagued by false positives. It was a clever idea, and it garnered the attention to their point I suspect the researchers were after.

This strategy, of generating an absurd false positive to make a point, reminded me of the study showing that listening to music about old age made subjects actually younger. The point of the study was actually to demonstrate how exploiting researcher degrees of freedom can generate false positive data, even when the hypothesis is impossible.

Many people have asked me about the dead salmon fMRI study, wondering if the bottom line of this research is that fMRI studies are inherently unreliable and should be looked at with a high degree of suspicion. Well – yes and no.

The precise point of the study was this:

Can we conclude from this data that the salmon is engaging in the perspective-taking task? Certainly not. What we can determine is that random noise in the EPI timeseries may yield spurious results if multiple comparisons are not controlled for. Adaptive methods for controlling the FDR and FWER are excellent options and are widely available in all major fMRI analysis packages. We argue that relying on standard statistical thresholds (p < 0.001) and low minimum cluster sizes (k > 8) is an ineffective control for multiple comparisons. We further argue that the vast majority of fMRI studies should be utilizing multiple comparisons correction as standard practice in the computation of their statistics.

In other words – when doing an fMRI study researchers should make a statistical correction for multiple comparisons, something which can be done right in the fMRI analysis package, in order to avoid a false positive due to the failure to make such a correction. Let’s say a study compares the incidence of a symptom with 100 possible causes and uses a P value of 0.05 as the cut off for statistical significance. This essentially means that on average, assuming none of the possible causes are actually linked to the symptom, 5 of the possible causes will correlate with the symptom with statistical significance, just by chance alone. Researchers can correct for the fact that they made 100 comparisons to more properly reflect the probability that a correlation is real.

Failure to correct for multiple comparisons (and sometimes even disclose multiple comparisons) is common in published research, and is something to look out for. The problem is not unique to fMRI studies, but it is especially common in such studies.

fMRI is the technique of using MRI scanning to look at changes in blood flow in the brain and infer from that brain activity. This is potentially a very powerful tool – researchers can give subjects a task and then, in real time, see which parts of the brain light up. We can use this technique, therefore, to map the parts and connections of the brain and correlate them with specific functions.

The problem is that the brain is very complex and noisy. In a waking person there is likely to be all sorts of activity going on all the time. There is generally a low signal to noise ratio, and researchers have to pick out the signal they are looking for from this background noise. This is done through statistical analysis of the data. Inherent to this process, because there is so much data to sift through, are multiple comparisons, so much so that the process can pick out brain activity in a dead salmon just from statistical noise.

This does not mean that all fMRI research is worthless and should be ignored. What it means is that fMRI research is tricky, and while some of it is reliable, a lot of it is just noise that should be looked at with skepticism. No single fMRI study should be seen as definitive or reliable. Only the most rigorous studies are likely to be useful, and even then replication is necessary to see that the claimed signal is genuine.

For example, some acupuncture proponents have realized that fMRI studies are a way to make is seem as if acupuncture points are real and have a genuine physiological specificity. The position is contradicted by the rest of medical and biological research which essentially shows that acupuncture points do not exist. There are now many small fMRI studies looking at brain “activation” with acupuncture and finding that stuff happens in the brain when you stick people with needles. A recent study in Parkinson’s disease found activity in the basal ganglia, for example (the study is from the Department of Meridian and Acupoint in the College of Korean Medicine). These results are about as reliable as the brain activity in the dead salmon.

A systematic review and meta-analysis of fMRI studies in acupuncture found that the results were very heterogeneous – meaning they were all over the place, which is what we would expect if the results were due to false positives from sloppy design or statistical analysis. They also criticized the research for lack of transparency is methodology, something which is essential in general but particularly for a tricky technique like fMRI.


Studies using fMRI scanning may be highly useful and informative, but definitely need to be looked at with special care and skepticism. fMRI studies should be generally considered as if they were preliminary studies. The results may be interesting, but until they are replicated with rigorous design and a consistent result, the findings are dubious.

Unfortunately, fMRI studies give the false impression of high-tech precision, because of the pretty pictures of alleged brain activity that are generated and the sophisticated nature of the studies. They are often, however, little more than “statistical fishing expeditions,” to borrow a phrase from another criticism.

I would not throw the baby out with the bathwater, however. Careful researchers are making good use of fMRI and rigorous studies with legitimate statistical analysis (including correcting for multiple comparisons) are out there. When evaluating fMRI studies – just remember the dead salmon.

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