Bootstrap resampling method to estimate confidence intervals of activation-induced CBF changes using laser Doppler imaging

Abstract

Laser Doppler imaging (LDI) signal and noise characteristics can vary significantly depending upon the underlying vascular caliber. Further, noise characteristics are not constant over time (non-stationary) and can vary during resting and activated conditions in a typical experiment. Since only a limited number of images can be acquired in a single run, concatenation of data from similar experimental trials becomes necessary which can induce further variation in temporal noise due to instrumental response. In conventional statistical analysis methods such as cross-correlation, a fixed significance threshold is generally used (for the entire image) to detect activation assuming constant noise over time and a normal distribution. As a consequence, statistical significance can become strong or weak due to temporal differences in baseline LD noise, which can possibly deviate from a normal distribution. The main emphasis of this study was the application of bootstrap resampling in conjunction with cross-correlation to estimate the confidence intervals on a pixel-by-pixel basis to avoid distributional specifications on the additive measurement error leading to reliable whisker activation-induced CBF changes. At a 95% confidence level, bootstrap resampling followed by confidence intervals for the correlation coefficient distribution increased the number of active pixels by almost 45% when compared to conventional cross-correlation. These pixels were mostly confined to areas with intermediate and large baseline LD flux with considerable deviation from normality. It is suggested that confidence intervals of the bootstrap estimates can lead to unbiased detection of CBF change in the cerebral cortex, particularly in regions with large temporal variation in noise and low CNR.