Related Experiment Video
Updated: Mar 12, 2026
Habituation and Prepulse Inhibition of Acoustic Startle in Rodents
Published on: September 1, 2011
Improved assessment of sensorimotor gating in animal models relevant to ASD: A data modelling approach to quantify
Stéphanie Degroote1, Darel Hunting2, Larissa Takser1
1Département de Pédiatrie, Faculté de Médecine et Sciences de la Santé, Université de Sherbrooke, Québec, Canada.
Background:
The PrePulse Inhibition (PPI) of the acoustic startle reflex is a neurobehavioral test frequently used in neurodevelopmental studies. Most PPI studies have used rodent models of schizophrenia; however, the currently used data analysis method does not take into account the variability present in autistic preclinical models.
New Method:
We propose a new data modelling approach for PPI data obtained from animals exposed to valproic acid or endocrine disruptors, using mixed modeling; and a new calculation of inhibition of the acoustic startle, which takes into account the habituation phenomenon.
Results:
Habituation, or possibly exhaustion, occurred in all groups. The classic method of calculation of inhibition analysed with ANOVA indicated no group or sex effect for the overall inhibition of startle. In contrast, when analysed using mixed models, group and sex effects were observed. In addition, using the new method of calculation, both statistical analyses showed a sex effect, with females having decreased global inhibition but no group effect. ANOVA generated more false positive results for PPI in relation to prepulse intensities.
Comparison With Existing Method:
The current classic method of analysis of PPI test is a calculation of inhibition based on average startle amplitude throughout the test session and a statistical ANOVA analysis. This method does not take into account habituation/exhaustion and within-subject and -group variability.
Conclusions:
The results of this study demonstrate that use of ANOVA analysis leads to misinterpretation of PPI data in autistic preclinical models and we propose a new data analysis adapted to these models.
