Citation

Fawcett TJ, Longenecker RJ, Brunelle DL, Berger JI, Wallace MN, Galazyuk AV, Rosen MJ, Salvi RJ, Walton JP. Hear. Res. 2022; 428: e108667.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.heares.2022.108667

PMID

36566642

Abstract

The startle reflex (SR), a robust, motor response elicited by an intense auditory, visual, or somatosensory stimulus has been widely used as a tool to assess psychophysiology in humans and animals for almost a century in diverse fields such as schizophrenia, bipolar disorder, hearing loss, and tinnitus. Previously, SR waveforms have been ignored, or assessed with basic statistical techniques and/or simple template matching paradigms. This has led to considerable variability in SR studies from different laboratories, and species. In an effort to standardize SR assessment methods, we developed a machine learning algorithm and workflow to automatically classify SR waveforms in virtually any animal model including mice, rats, guinea pigs, and gerbils obtained with various paradigms and modalities from several laboratories. The universal features common to SR waveforms of various species and paradigms are examined and discussed in the context of each animal model. The procedure describes common results using the SR across species and how to fully implement the open-source R implementation. Since SR is widely used to investigate toxicological or pharmaceutical efficacy, a detailed and universal SR waveform classification protocol should be developed to aid in standardizing SR assessment procedures across different laboratories and species. This machine learning-based method will improve data reliability and translatability between labs that use the startle reflex paradigm.

Language: en

Keywords

Machine learning; Acoustic startle response; Ensemble models; Pre-pulse inhibition; Waveform classification