Literature DB >> 32178942

The assessment of mouse spontaneous locomotor activity using motion picture.

Koji Kobayashi1, Naoyuki Shimizu1, Seiji Matsushita1, Takahisa Murata2.   

Abstract

Spontaneous locomotor activity (SLA) is a useful parameter reflecting physical and mental status of experimental animals. Here we aimed to establish a novel and simple method to assess mouse SLA using motion picture. Movement of C57BL/6 mice was continuously recorded by an infrared video camera connected with a single board computer. The geometric center of mouse outline in each frame was calculated using an image processing library, OpenCV in a programming language Python. Moving distance of the geometric center every second was utilized as an index of mouse SLA. Twenty-four hours assessment of SLA showed that mice repeated active and resting phase. Mice moved more actively during the dark period compared with the light period. Time-frequency analysis of SLA followed by unsupervised clustering classified their active and resting phase. Administration of a sedative, chlorpromazine (5 mg/kg) abolished mouse SLA for 8 h. In contrast, administration of a central nervous stimulant, caffeine (25 mg/kg) increased SLA for 3 h. In conclusion, we here established the automatic measurement system of mouse SLA using motion picture. This system is composed of common equipment and analysis software written in freely available programming language. We also confirmed that it is applicable for drug assessment.
Copyright © 2020 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Behavior; Caffeine; Chlorpromazine; Motion picture; Spontaneous locomotor activity

Year:  2020        PMID: 32178942     DOI: 10.1016/j.jphs.2020.02.003

Source DB:  PubMed          Journal:  J Pharmacol Sci        ISSN: 1347-8613            Impact factor:   3.337


  3 in total

1.  Peripheral NOD-like receptor deficient inflammatory macrophages trigger neutrophil infiltration into the brain disrupting daytime locomotion.

Authors:  Victoria Kwon; Peiwen Cai; Cameron T Dixon; Celia E Shiau; Victoria Hamlin; Caroline G Spencer; Alison M Rojas; Matthew Hamilton
Journal:  Commun Biol       Date:  2022-05-16

2.  Automated detection of mouse scratching behaviour using convolutional recurrent neural network.

Authors:  Koji Kobayashi; Seiji Matsushita; Naoyuki Shimizu; Sakura Masuko; Masahito Yamamoto; Takahisa Murata
Journal:  Sci Rep       Date:  2021-01-12       Impact factor: 4.379

3.  Automated Grooming Detection of Mouse by Three-Dimensional Convolutional Neural Network.

Authors:  Naoaki Sakamoto; Koji Kobayashi; Teruko Yamamoto; Sakura Masuko; Masahito Yamamoto; Takahisa Murata
Journal:  Front Behav Neurosci       Date:  2022-02-02       Impact factor: 3.558

  3 in total

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