Weighted Kernel Density Estimation of the Prepulse Inhibition Test

Prepulse inhibition (PPI) refers to the reduction in startle reaction towards a startle-eliciting “pulse” stimulus when it is shortly preceded by a sub-threshold “prepulse” stimulus. PPI deficits have been seen in patients with schizophrenia and animal models of this mental disorder. The goal of this study was to provide an alternative method for the analysis of PPI data. The new method is expected to be more reliable and sensitive than the existing conventional method. We applied the Kernel density estimation (KDE) in the analysis of PPI data. KDE is a non-parametric method of estimating the probability density function of a random variable and is widely used in inferring population statistics based on limited, noisy samples of continuous random variables. Our results showed that the KDE method performed better than the conventional method and offered some advantages which are of significant in the post-session analysis of PPI data and in performing animal experiments.