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Universal automated classification of the acoustic startle reflex using machine learning

Fawcett, Timothy J.; Longenecker, Ryan J.; Brunelle, Dimitri L.; Berger, Joel I.; Wallace, Mark N.; Galazyuk, Alex V.; Rosen, Merri J.; Salvi, Richard J.; Walton, Joseph P.

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Authors

Timothy J. Fawcett

Ryan J. Longenecker

Dimitri L. Brunelle

Joel I. Berger

Mark N. Wallace

Alex V. Galazyuk

Merri J. Rosen

Richard J. Salvi

Joseph P. Walton



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. [Abstract copyright: Copyright © 2022. Published by Elsevier B.V.]

Journal Article Type Article
Acceptance Date Dec 12, 2022
Online Publication Date Dec 15, 2022
Publication Date 2023-02
Deposit Date Jan 13, 2023
Publicly Available Date Dec 16, 2023
Journal Hearing Research
Electronic ISSN 1878-5891
Peer Reviewed Peer Reviewed
Volume 428
Article Number 108667
DOI https://doi.org/10.1016/j.heares.2022.108667
Keywords Ensemble models, Waveform classification, Machine learning, Pre-pulse inhibition, Acoustic startle response
Public URL https://nottingham-repository.worktribe.com/output/15924093
Publisher URL https://www.sciencedirect.com/science/article/pii/S0378595522002350?via%3Dihub

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