fMRI and False Positives: A Basic Flaw?

Over the past few years I've covered (1,2,3) the work of Anders Eklund, a Swedish researcher who has discovered a potentially serious flaw in software commonly used to analyse fMRI data. Eklund has shown that popular parametric statistical analysis tools for fMRI are prone to false positives - they often 'find' brain activation even where it doesn't exist. The issue affects the leading software packages such as FSL and SPM. One main root of the problem is spatial autocorrelation - the fact that the fMRI signal tends to be similar (correlated) across nearby regions. Spatial autocorrelation is a well known phenomenon and fMRI software tools have systems for dealing with it, but Eklund and his colleagues says that these fixes don't work properly. Specifically, the problem is that the software assumes that the spatial autocorrelation function has a Gaussian shape but in fact it has 'long tails', with more long-range correlations than expected. Ultimately this leads to false positives.