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

Takuya Maekawa (), Daiki Higashide, Takahiro Hara, Kentarou Matsumura, Kaoru Ide, Takahisa Miyatake, Koutarou D. Kimura and Susumu Takahashi
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Takuya Maekawa: Osaka University
Daiki Higashide: Osaka University
Takahiro Hara: Osaka University
Kentarou Matsumura: Kagawa University
Kaoru Ide: Doshisha University
Takahisa Miyatake: Okayama University
Koutarou D. Kimura: Nagoya City University
Susumu Takahashi: Doshisha University

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-25636-x

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