Classification of multiple emotional states from facial expressions in head-fixed mice using a deep learning-based image analysis

Tanaka, Yudai; Nakata, Takuto; Hibino, Hiroshi et al.

PLoS ONE, 2023, 18(7), e0288930

Number of Access:661（2025-12-03 13:09 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/92476

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Title
Classification of multiple emotional states from facial expressions in head-fixed mice using a deep learning-based image analysis
Creator
CreatorName
Tanaka, Yudai
CreatorName
Nakata, Takuto
NRID
1000070314317 1000070314317
researchmap permalink
hiroshi_hibino hiroshi_hibino
Scopus Author ID
7007020846 7007020846
CreatorName
Hibino, Hiroshi
NRID
1000050423562 1000050423562
CreatorName
Nishiyama, Masaaki
NRID
1000040721981 1000040721981
ORCID
0000-0002-8112-0746 0000-0002-8112-0746
researchmap permalink
daisukeino daisukeino
CreatorName
Ino, Daisuke
Description
Tanaka Y., Nakata T., Hibino H., et al. Classification of multiple emotional states from facial expressions in head-fixed mice using a deep learning-based image analysis. PLoS ONE 18, e0288930 (2023); https://doi.org/10.1371/journal.pone.0288930.
Abstract
Facial expressions are widely recognized as universal indicators of underlying internal states in most species of animals, thereby presenting as a non-invasive measure for assessing physical and mental conditions. Despite the advancement of artificial intelligence-assisted tools for automated analysis of voluminous facial expression data in human subjects, the corresponding tools for mice still remain limited so far. Considering that mice are the most prevalent model animals for studying human health and diseases, a comprehensive characterization of emotion-dependent patterns of facial expressions in mice could extend our knowledge on the basis of emotions and the related disorders. Here, we present a framework for the development of a deep learning-powered tool for classifying facial expressions in head-fixed mouse. We demonstrate that our machine vision was capable of accurately classifying three different emotional states from lateral facial images in head-fixed mouse. Moreover, we objectively determined how our classifier characterized the differences among the facial images through the use of an interpretation technique called Gradient-weighted Class Activation Mapping. Importantly, our machine vision presumably discerned the data by leveraging multiple facial features. Our approach is likely to facilitate the non-invasive decoding of a variety of emotions from facial images in head-fixed mice.
Publisher
Public Library of Science
Source Title
PLoS ONE
Volume (Issue)
18(7)
Page
e0288930
Date of Issued
2023-07-20
Language
English
Handle URL
https://hdl.handle.net/11094/92476
eISSN
19326203
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1371/journal.pone.0288930
Access Rights
open access
CopyRight
© 2023 Tanaka et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article