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Cross-species behavior analysis with attention-based domain-adversarial deep neural networks

Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and temporal scales of animal locomotion vary widely amon...

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Autores principales: Maekawa, Takuya, Higashide, Daiki, Hara, Takahiro, Matsumura, Kentarou, Ide, Kaoru, Miyatake, Takahisa, Kimura, Koutarou D., Takahashi, Susumu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8448872/
https://www.ncbi.nlm.nih.gov/pubmed/34535659
http://dx.doi.org/10.1038/s41467-021-25636-x
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author Maekawa, Takuya
Higashide, Daiki
Hara, Takahiro
Matsumura, Kentarou
Ide, Kaoru
Miyatake, Takahisa
Kimura, Koutarou D.
Takahashi, Susumu
author_facet Maekawa, Takuya
Higashide, Daiki
Hara, Takahiro
Matsumura, Kentarou
Ide, Kaoru
Miyatake, Takahisa
Kimura, Koutarou D.
Takahashi, Susumu
author_sort Maekawa, Takuya
collection PubMed
description Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and temporal scales of animal locomotion vary widely among species, conventional statistical analyses cannot be used to discover knowledge from the locomotion data. We propose a procedure to automatically discover locomotion features shared among animal species by means of domain-adversarial deep neural networks. Our neural network is equipped with a function which explains the meaning of segments of locomotion where the cross-species features are hidden by incorporating an attention mechanism into the neural network, regarded as a black box. It enables us to formulate a human-interpretable rule about the cross-species locomotion feature and validate it using statistical tests. We demonstrate the versatility of this procedure by identifying locomotion features shared across different species with dopamine deficiency, namely humans, mice, and worms, despite their evolutionary differences.
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spelling pubmed-84488722021-10-05 Cross-species behavior analysis with attention-based domain-adversarial deep neural networks Maekawa, Takuya Higashide, Daiki Hara, Takahiro Matsumura, Kentarou Ide, Kaoru Miyatake, Takahisa Kimura, Koutarou D. Takahashi, Susumu Nat Commun Article Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and temporal scales of animal locomotion vary widely among species, conventional statistical analyses cannot be used to discover knowledge from the locomotion data. We propose a procedure to automatically discover locomotion features shared among animal species by means of domain-adversarial deep neural networks. Our neural network is equipped with a function which explains the meaning of segments of locomotion where the cross-species features are hidden by incorporating an attention mechanism into the neural network, regarded as a black box. It enables us to formulate a human-interpretable rule about the cross-species locomotion feature and validate it using statistical tests. We demonstrate the versatility of this procedure by identifying locomotion features shared across different species with dopamine deficiency, namely humans, mice, and worms, despite their evolutionary differences. Nature Publishing Group UK 2021-09-17 /pmc/articles/PMC8448872/ /pubmed/34535659 http://dx.doi.org/10.1038/s41467-021-25636-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Maekawa, Takuya
Higashide, Daiki
Hara, Takahiro
Matsumura, Kentarou
Ide, Kaoru
Miyatake, Takahisa
Kimura, Koutarou D.
Takahashi, Susumu
Cross-species behavior analysis with attention-based domain-adversarial deep neural networks
title Cross-species behavior analysis with attention-based domain-adversarial deep neural networks
title_full Cross-species behavior analysis with attention-based domain-adversarial deep neural networks
title_fullStr Cross-species behavior analysis with attention-based domain-adversarial deep neural networks
title_full_unstemmed Cross-species behavior analysis with attention-based domain-adversarial deep neural networks
title_short Cross-species behavior analysis with attention-based domain-adversarial deep neural networks
title_sort cross-species behavior analysis with attention-based domain-adversarial deep neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8448872/
https://www.ncbi.nlm.nih.gov/pubmed/34535659
http://dx.doi.org/10.1038/s41467-021-25636-x
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