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Objective hearing threshold identification from auditory brainstem response measurements using supervised and self-supervised approaches

Hearing loss is a major health problem and psychological burden in humans. Mouse models offer a possibility to elucidate genes involved in the underlying developmental and pathophysiological mechanisms of hearing impairment. To this end, large-scale mouse phenotyping programs include auditory phenot...

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Autores principales: Thalmeier, Dominik, Miller, Gregor, Schneltzer, Elida, Hurt, Anja, Hrabě deAngelis, Martin, Becker, Lore, Müller, Christian L., Maier, Holger
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795643/
https://www.ncbi.nlm.nih.gov/pubmed/36575380
http://dx.doi.org/10.1186/s12868-022-00758-0
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author Thalmeier, Dominik
Miller, Gregor
Schneltzer, Elida
Hurt, Anja
Hrabě deAngelis, Martin
Becker, Lore
Müller, Christian L.
Maier, Holger
author_facet Thalmeier, Dominik
Miller, Gregor
Schneltzer, Elida
Hurt, Anja
Hrabě deAngelis, Martin
Becker, Lore
Müller, Christian L.
Maier, Holger
author_sort Thalmeier, Dominik
collection PubMed
description Hearing loss is a major health problem and psychological burden in humans. Mouse models offer a possibility to elucidate genes involved in the underlying developmental and pathophysiological mechanisms of hearing impairment. To this end, large-scale mouse phenotyping programs include auditory phenotyping of single-gene knockout mouse lines. Using the auditory brainstem response (ABR) procedure, the German Mouse Clinic and similar facilities worldwide have produced large, uniform data sets of averaged ABR raw data of mutant and wildtype mice. In the course of standard ABR analysis, hearing thresholds are assessed visually by trained staff from series of signal curves of increasing sound pressure level. This is time-consuming and prone to be biased by the reader as well as the graphical display quality and scale.In an attempt to reduce workload and improve quality and reproducibility, we developed and compared two methods for automated hearing threshold identification from averaged ABR raw data: a supervised approach involving two combined neural networks trained on human-generated labels and a self-supervised approach, which exploits the signal power spectrum and combines random forest sound level estimation with a piece-wise curve fitting algorithm for threshold finding.We show that both models work well and are suitable for fast, reliable, and unbiased hearing threshold detection and quality control. In a high-throughput mouse phenotyping environment, both methods perform well as part of an automated end-to-end screening pipeline to detect candidate genes for hearing involvement. Code for both models as well as data used for this work are freely available. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12868-022-00758-0.
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spelling pubmed-97956432022-12-29 Objective hearing threshold identification from auditory brainstem response measurements using supervised and self-supervised approaches Thalmeier, Dominik Miller, Gregor Schneltzer, Elida Hurt, Anja Hrabě deAngelis, Martin Becker, Lore Müller, Christian L. Maier, Holger BMC Neurosci Research Hearing loss is a major health problem and psychological burden in humans. Mouse models offer a possibility to elucidate genes involved in the underlying developmental and pathophysiological mechanisms of hearing impairment. To this end, large-scale mouse phenotyping programs include auditory phenotyping of single-gene knockout mouse lines. Using the auditory brainstem response (ABR) procedure, the German Mouse Clinic and similar facilities worldwide have produced large, uniform data sets of averaged ABR raw data of mutant and wildtype mice. In the course of standard ABR analysis, hearing thresholds are assessed visually by trained staff from series of signal curves of increasing sound pressure level. This is time-consuming and prone to be biased by the reader as well as the graphical display quality and scale.In an attempt to reduce workload and improve quality and reproducibility, we developed and compared two methods for automated hearing threshold identification from averaged ABR raw data: a supervised approach involving two combined neural networks trained on human-generated labels and a self-supervised approach, which exploits the signal power spectrum and combines random forest sound level estimation with a piece-wise curve fitting algorithm for threshold finding.We show that both models work well and are suitable for fast, reliable, and unbiased hearing threshold detection and quality control. In a high-throughput mouse phenotyping environment, both methods perform well as part of an automated end-to-end screening pipeline to detect candidate genes for hearing involvement. Code for both models as well as data used for this work are freely available. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12868-022-00758-0. BioMed Central 2022-12-27 /pmc/articles/PMC9795643/ /pubmed/36575380 http://dx.doi.org/10.1186/s12868-022-00758-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Thalmeier, Dominik
Miller, Gregor
Schneltzer, Elida
Hurt, Anja
Hrabě deAngelis, Martin
Becker, Lore
Müller, Christian L.
Maier, Holger
Objective hearing threshold identification from auditory brainstem response measurements using supervised and self-supervised approaches
title Objective hearing threshold identification from auditory brainstem response measurements using supervised and self-supervised approaches
title_full Objective hearing threshold identification from auditory brainstem response measurements using supervised and self-supervised approaches
title_fullStr Objective hearing threshold identification from auditory brainstem response measurements using supervised and self-supervised approaches
title_full_unstemmed Objective hearing threshold identification from auditory brainstem response measurements using supervised and self-supervised approaches
title_short Objective hearing threshold identification from auditory brainstem response measurements using supervised and self-supervised approaches
title_sort objective hearing threshold identification from auditory brainstem response measurements using supervised and self-supervised approaches
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795643/
https://www.ncbi.nlm.nih.gov/pubmed/36575380
http://dx.doi.org/10.1186/s12868-022-00758-0
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