Weighted Kernel Density Estimation of the Prepulse Inhibition Test
Zhou · Journal of Computer Science · 2011
Problem statement: The goal of this study was to devise a more reliable and sensitive method for analysis of experimental data of the Prepulse Inhibition (PPI), the reduction in startle reaction towards a startle-eliciting "pulse" stimulus when it is shortly preceded by a sub-threshold "prepulse" stimulus.Approach: Different from the conventional simple averaging-based method, we proposed a probabilistic approach to modeling the PPI data.With this probabilistic description, we reconstructed complete response signals from the PPI data and devised a nonparametric weighted Kernel Density Estimation (KDE) method to tackle two important issues in PPI data related density estimation: instability and limited number of samples.We designed two sets of animal experiments using different medicines and compared the KDE based method with the conventional simpleaveraging based method.Results: Our results showed that the KDE method performed better than the conventional method and offered some advantages over the conventional method.Conclusion: The new method provided a more reliable and sensitive approach to the post-session analysis of PPI data.