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1

New measures of social interactions for ecoHAB cage

100%
Maka J., Puscian A., Knapska E., Wojcik D. K.
Acta Neurobiologiae Experimentalis
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2017
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tom 77
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nr Suppl.1
INTRODUCTION: Eco-HAB is an open source system for automated measurements and analysis of social preferences and in-cohort sociability in mice. It requires no contact between a human experimenter and tested animals. In Eco-HAB, group-housed mice live in a spacious, four-compartment, resembling natural burrows. It allows an assessment of the tendency of mice to voluntarily spend time together in ethologically relevant mouse group sizes. Results are obtained faster, with less manpower needed and without confounding factors. AIM(S): The aim of the of this study is to develop measures for the EcoHAB system, which could well describe social relations in a group of mice. We test the proposed measures in experiments with four FX WT and three FX KO groups. We expected that FX KO mice would have disturbed social skills comparing to FX WT. METHOD(S): We developed a dedicated workflow for analysis of social interactions based on analysis of the decision patterns. For each pair of mice, one mouse is a leader, the other is a follower. After the leader changes the room, the follower’s reaction in a 3-second window is analysed. If the follower acts on the leader’s movement and follows it, the pattern is classified as “following”; otherwise it is “evasion”. Lack of follower’s reaction is ignored. The numbers of interactions for each pair and distribution of the patterns were obtained. To characterize the relations between the mice in selected time windows we used binomial model. We also studied changes of these relation in time and their distribution in mice groups. RESULTS: Our study proved that FX KO mice have significantly less interactions within a pair than FX WT. What’s more, FX WT are following each other more often and the character of interaction is more stable. CONCLUSIONS: EcoHAB is a good environment for conducting advanced analysis of mice social interactions. Proposed measures show significant difference between WT and KO group and are a promising tool to study social interactions.
2

PyEcoHAB: a Python library for analysis of rodent behavioral data recorded with Eco-HAB

76%
Jedrzejewska-Szmek J., Maka J., Leski S., Winiarski M., Wojcik D. K., Knapska E.
Acta Neurobiologiae Experimentalis
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2019
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tom 79
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nr Suppl.1
INTRODUCTION: How can we make mouse studies more reproducible? The obvious answer is the standardization of experimental conditions, minimization of human interference, automation of behavioral tests and data analysis, and introduction of data analysis pipelines to automate the process. Eco‑HAB, a system for automated measurement of social preference and in-cohort sociability in mice, provides a solution for the first two issues. Eco‑HAB closely follows murine ethology, providing a 4‑compartment apparatus with narrow tunnels, and minimizes contact between the experimenter and tested animals. Introduction of pyEcoHAB, a Python library for analysis of EcoHAB murine behavioral data. AIM(S): pyEcoHAB, a Python package, has been developed to automate and facilitate data analysis. METHOD(S): Combining data access and initial interpretation, pyEcoHAB removes the need to do this manually, and allows the researcher to build data analysis pipelines and automation of behavioral tests facilitating data interpretation. pyEcoHAB provides an object‑oriented application programming interface (API) and a data abstraction layer. Auxiliary utilities supporting development of analysis workflows are integrated with pyEcoHAB, including data validation and workflow configuration tools. Moreover, pyEcoHAB provides methods for assessment of mice social behavior, such as approach to social odor, total time spent by each pair of mice together in each compartment (in-cohort sociability), number of times each mouse follows other mice in narrow tunnels (following), and also, the number of times each mouse pushes other mice out of a narrow tunnel. The latter behaviorissimilarto tube dominance tests and is an example of how traditional behavioral tests can be automated. CONCLUSIONS: pyEcoHAB is a computational framework facilitating automatic analysis of behavioral data from EcoHAB system. FINANCIAL SUPPORT: This work was supported by the Polish National Science Centre grant 2017/27/B/ NZ4/02025.
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Zagospodarowanie odpadów z papieru i tektury

50%
Maka J.
Opakowanie
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2014
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nr 03
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