Deconvolution filtering: Temporal smoothing revisited

Results for the estimation of functional connectivity of simulated BOLD data demonstrated that analysis (via standard estimation methods) using deconvolution filtered BOLD data achieved superior performance to analysis performed using unfiltered BOLD data and was statistically similar to well-tuned band-pass filtered BOLD data. Contrary to band-pass filtering, however, deconvolution filtering is built upon physiological arguments and has the potential, at low TR, to match the performance of an optimal band-pass filter. The results from task estimation on real BOLD data suggest that deconvolution filtering provides superior or equivalent detection of task activations relative to comparable analyses on unfiltered signals and also provides decreased variance over the estimate. In turn, these results suggest that standard preprocessing of the BOLD signal ignores significant sources of noise that can be effectively removed without damaging the underlying signal.