The intention here is to explore the effects of temporal filtering and the spatial variation of the autocorrelations in real FMRI data. We could attempt to examine the autocorrelation, or equivalently the power spectral density, itself. However, this would give (number of scans or time points) data points for each voxel. Instead, we use:
An indicates white noise and indicates a time series with positive autocorrelation. We examined one rest/null dataset from a normal volunteer. Two hundred echo planar images (EPI) were acquired using a 3 Tesla system with time to echo (TE) = 30ms, TR=3 secs, in-plane resolution 4mm and slice thickness 7mm. The first 4 scans were discarded to leave scans and the data was motion corrected using AIR (Woods et al., 1993). To calculate at each voxel we arbitrarily used the nonparametric PAVA autocorrelation approach to estimate the autocorrelation.