Useful Papers - Temporal Filtering
Summary: The major last defense of low-pass filtering (aka temporal smoothing) mounted by the SPM group before abandoning it for a different mathematical tack in estimation, more amenable to pre-whitening. The case made is that pre-whitening, while more efficient at removing variance from the timecourses, can be more sensitive to bias due to errors in estimating the autocorrelation, and so band-pass filtering has its place.
Summary: Clear and relatively concise look at exactly how noise autocorrelation can bias inferences in both directions, with specific references to existing models like the extended GLM of Friston et. al and the AR(1) model of Bullmore et. al. Proposes an AR + white noise model to model temporal autocorrelation (which ends up being largely adopted in SPM2)
Summary: Once again, Skudlarski et. al's look at single-subject simulated data, analyzing various preprocessing choices with receiver operating characteristic (ROC) curves, measuring both types of error at a given threshold. They show convincingly that low-pass filtering with real fMRI noise hurts sensitivity, while high-pass filtering can help - although only a precise cutoff threshold choice improved high-pass filtering above simple quadratic and linear trend removal.
Summary: Another look at this paper, which examines various preprocessing choices from simulated data with the twin measures of power and false-positive rate. They find high-pass filtering to be useful with a fixed ISI (but not particularly for variable ISIs). They find both low-pass filtering and pre-whitening to decrease power but only pre-whitening to be especially good at protecting against false-positive increases.
Summary: An oldie but a goodie, Holmes et. al shows the first published example of a design matrix including the high-pass filter that becomes a staple of later versions of SPM. The confounds are modeled in basic but effective fashion - a set of cosines of gradually increasing frequency, up to a certain cutoff point. They demonstrate that these regressors account for a good chunk of noise variance.