Thomas Insell wants to re-orient NIMH research away from using DSM categories as the ‘gold standard’. This makes a lot of sense, as the DSM identifies clusters of symptoms, and symptoms are not reliable markers of discrete disease states. As we search for bio-markers of mental illness, then, it will make sense that results will not always align with the DSM.

On the other hand, it is surpising to see Insell state flatly that “Mental disorders are biological disorders”. The NIMH is betting big that this is still likely to be true, organizing massive efforts to collect imaging, genetic, and biological data in the hopes that this will clearly delineate sets of biological disorders. Perhaps this will work. Or, perhaps looking for the biological mechanism of depression is a bit like looking for the atomic mechanisms of a bad spark-plug. Perhaps mental illnesses really are *mental*, in the sense of sharing commonality only in patterns of thought, emotion, and behavior, and can be instantiated in a near infinite-pattern of biological substrates. Guess we’ll see….

Catching up on comments for recent papers. Back on 4/26 a *bombshell* paper from Ioannidis and colleagues came out on power and research quality [cite source=’pubmed’]23571845[/cite].  First, the paper summarizes some statistical points which *should* be well known by now: small samples sizes lead to poor power and extreme levels of sampling error in estimating the real effects of a treatment. With small sample sizes, the occasional over-estimate of effect size will lead to publication, but this will be misleading about the true nature of the effect. Moreover, if the overall level of true effects is low, then the published literature can end up dominated by false positives because they will continue to be found at 0.05, whereas true positives will be found only at their base rate * power.

What’s even more electrifying about Button et al. is a set of meta-anlaysis to determine the degree to which low power is a problem in neuroscience. In imaging studies, power was typically about 8%; in a large set of animals studies between 18-31%, and in papers being meta-analyzed in neuroscience 21%. That’s astonishing! This data suggests that most neuroscience studies are not at all adequate for accurate study of the effects of interest. Moreover, there were more poisitive findings reported in these studies than is statistically plausible given their low power.

Overall, Button et al., is a must-read for serious neuroscienctists and should become required reading for graduate programs.