A couple of months ago I blogged about a paper showing that 'filtering' of EEG data can create spurious effects. Now, we read about another form of bias that filters can introduce, this time for fMRI: Filtering induces correlation in fMRI resting state data.
Australian neuroscientists Catherine Davey and colleagues consider temporal filtering of fMRI data in studies looking at correlation (brain functional connectivity). Because both very high frequency and very slow changes in the fMRI signal are probably caused by artefacts, rather than interesting brain signals, it's common to use a filter to try and extract the medium-frequency changes that are of most interest (e.g. approximately 0.01 to 0.1 Hz). However, while this filtering is very useful, Davey et al show that it can - ironically - create artefacts of its own: here's the data from one volunteer scanned during a simple task and then analyzed in 4 different ways:
Without filtering (A) there's a huge amount of 'connectivity' - too much to be realistic. This is why filtering is important. But filtering, without correcting for the effects of the filter, actually makes things worse (B). It solves one problem but at the cost of creating another. The problem is those pesky autocorrelations. The authors say, however, that they've calculated a way to correct for filter-induced correlations (D) and that this gives more realistic results. They recommend that this should be used in future connectivity studies, but don't go into much detail regarding the question of what this means for the existing literature. Perhaps data 'filtering' is a misleading term. It implies that all you're doing is removing the unwanted noise, leaving pristine, crystal clear data, a bit like a water filter. Mmm. What could go wrong? In fact mathematical 'filters' can put stuff into the data as well as take it out, so should we stop using that word and just call them what they are: modifications?
Davey CE, Grayden DB, Egan GF, and Johnston LA (2012). Filtering induces correlation in fMRI resting state data. NeuroImage PMID: 22939874