An improved approach to separating startle data from noise

Abstract

BACKGROUND: The acoustic startle reflex (ASR) is a rapid, involuntary movement to sound, found in many species. The ASR can be modulated by external stimuli and internal state, making it a useful tool in many disciplines. ASR data collection and interpretation varies greatly across laboratories making comparisons a challenge. NEW METHOD: Here we investigate the animal movement associated with a startle in mouse (CBA/CaJ). Movements were simultaneously captured with high-speed video and a piezoelectric startle plate. We also use simple mathematical extrapolations to convert startle data (force) into center of mass displacement ("height"), which incorporates the animal's mass. RESULTS: Startle plate force data revealed a stereotype waveform associated with a startle that contained three distinct peaks. This waveform allowed researchers to separate trials into 'startles' and 'no-startles' (termed 'manual classification). Fleiss' kappa and Krippendorff"s alpha (0.865 for both) indicate very good levels of agreement between researchers. Further work uses this waveform to develop an automated startle classifier. The automated classifier compares favorably with manual classification. A two-way ANOVA reveals no significant difference in the magnitude of the 3 peaks as classified by the manual and automated methods (P1: p=0.526, N1: p=0.488, P2: p=0.529). COMPARISON WITH EXISTING METHOD(S): The ability of the automated classifier was compared with three other commonly used classification methods; the automated classifier far outperformed these methods. CONCLUSIONS: The improvements made allow researchers to automatically separate startle data from noise, and normalize for an individual animal's mass. These steps ease inter-animal and inter-laboratory comparisons of startle data.