Analysing behavioral data from IntelliCage system in Python
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1
Nencki Institute of Experimental Biology, Poland
IntelliCage is a computerized cage for automated recording of mice behavior [1]. A group of up to 16 mice can be monitored simultaneously, each animal carries a subcutaneously implanted radio transponder for identification. A computer records precise data (time, duration, etc.) about visits of animals to the corners (conditioning units), in which reinforcements (such as sweetened water) or punishments (air puffs) can be administered, possibly with a different protocol for each animal. For each visit also the nosepokes to the sides of the corner and the licks from bottles with liquids are recorded. The system gives a unique opportunity for long-term studies of behavior of animals living in social groups.
The size and complexity of the data obtained from IntelliCages call for development of suitable data analysis methods and software. The software bundled with the cages, called Analyzer, allows for basic data processing; however, more advanced analysis has to be performed elsewhere. Typically this has been done using spreadsheets, which is both time-consuming and error-prone.
To address the growing need of data analysis we have developed a Python toolbox for processing and analysis of data from IntelliCages. The toolbox is organized in a modular way, with separate modules responsible for 1) loading the data, 2) verification of data integrity, 3) data analysis, plotting, and exporting results.
The data loading module allows for loading and merging both the raw IntelliCage data and the data preprocessed with Analyzer. The loaded data are stored in a database and presented via a Python interface, allowing for easy selection of relevant quantities for chosen groups of mice, with optional advanced filtering. Distinct phases of experiment (such as for example adaptaion, learning and extinction phases, or perhaps consecutive days) may be defined in a text file to facilitate the analysis.
Next both the behavioral data and the hardware logs can be tested to ensure that the number of errors is below set thresholds. Due to modular structure it is easy to adapt or extend the various tests, so that only the relevant aspects are taken into account.
Finally, the data are passed into data analysis modules. Currently implemented modules allow for analysis of 1) place preference learning (the relative number of visits, nospokes and licks in different corners), 2) social interaction between animals, defined as the percentage of visits to a corner which fall in a defined short time window after a visit of different mouse to the same corner.
Together, the three steps form a complete workflow for analysis of IntelliCage data.
References
[1] http://www.newbehavior.com/products/ic
Keywords:
Behavior, Animal,
mouse models,
learning and memory,
data analysis,
python language,
workflows
Conference:
Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.
Presentation Type:
Poster
Topic:
General neuroinformatics
Citation:
Kowalski
JM,
Puścian
A,
Mijakowska
Z,
Radwańska
K and
Łęski
S
(2013). Analysing behavioral data from IntelliCage system in Python.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2013.
doi: 10.3389/conf.fninf.2013.09.00081
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Received:
03 Apr 2013;
Published Online:
11 Jul 2013.
*
Correspondence:
Dr. Szymon Łęski, Nencki Institute of Experimental Biology, Warszawa, Poland, s.leski@nencki.gov.pl