Time delay estimates of fMRI signals: efficient algorithm and estimate variance

Functional magnetic resonance imaging is an imaging technique that results in time courses reflecting localized neuronal activation in the human brain. Determination of activation delays at various brain areas can be used to advantage in efficient mapping of cortical function or studying the hierarchy of neuronal processing. Here, the authors present a computationally efficient algorithm for obtaining high resolution activation delay estimates. In addition, the authors empirically determined the variance of the delay estimate in the presence of fMRI noise and white noise. They found that the algorithm, using linear interpolation, detected time delays up to 1/1000 of the sampling period. In the presence of fMRI noise, delay estimate variance was significantly larger than in the presence of white noise