Research Article Open Access

Weighted Kernel Density Estimation of the Prepulse Inhibition Test

Hongbo Zhou1, Qiang Cheng1, Hong-Ju Yang1 and Haiyun Xu1
  • 1 , Afganistan
Journal of Computer Science
Volume 7 No. 5, 2011, 611-618

DOI: https://doi.org/10.3844/jcssp.2011.611.618

Submitted On: 7 April 2011 Published On: 7 May 2011

How to Cite: Zhou, H., Cheng, Q., Yang, H. & Xu, H. (2011). Weighted Kernel Density Estimation of the Prepulse Inhibition Test. Journal of Computer Science, 7(5), 611-618. https://doi.org/10.3844/jcssp.2011.611.618

Abstract

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 simple-averaging 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.

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Keywords

  • Kernel density estimation
  • prepulse inhibitation test
  • startle response
  • Clozapine (CLZ)
  • Quetiapine (QTP)
  • non-parametric
  • random variables
  • Cuprizone (CPZ)
  • dopamine hyperactivity