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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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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. |
format | Online Article Text |
id | pubmed-8448872 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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