Key points are not available for this paper at this time.
Abstract Data obtained from functional magnetic resonance imaging are often limtted by a low signal‐to‐noise ratio. The time‐course data obtained from activated regions contain both system noise and physiological noise, primarily linked to the heart and respiratory rates, that are superimposed on task induced signals. Time averaging of a practical number of data sets is not very effective in improving the signal‐to‐noise ratio because netther system nor physiological noise is truly random. In this paper, a method is presented for fittering un‐wanted physiological fluctuations, including aliased signals that are formed as a result of long repetition time ( TR ) values. A pulse oximeter was used to obtain cardiac and respiratory Information during the scanning period. Fintte impulse response band‐reject digital fitters were designed to remove the physiological fluctuations. For comparison, cross‐correlation analyses were performed at the same tevel of statistical significance on both fittered and unfiltered data. We demonstrate that this method can improve the detection of weak signals without increasing the probability of false positives.
Biswal et al. (Mon,) studied this question.