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Machine Learning in Modeling of Mouse Behavior.

Marjan Gharagozloo¹, Abdelaziz Amrani², Kevin Wittingstall³

¹Department of Neurology, Johns Hopkins University, Baltimore, MD, United States.

Frontiers in Neuroscience

|October 1, 2021

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Automated analysis of mouse behavior in home cages reveals distinct physiological states. Advanced machine learning models, including 1DConvBiLSTM, accurately classify behavior from short video clips, aiding disease diagnostics.

Area of Science:

Neuroscience
Computational Biology
Animal Behavior

Background:

Mouse behavior is crucial for evaluating therapeutic efficacy in brain disease research.
Automated home cage analysis provides detailed, high-frequency behavioral data.
Dimensionality reduction techniques are essential for managing complex behavioral datasets.

Purpose of the Study:

To develop novel methods for analyzing high-frequency (33-Hz) mouse behavioral data.
To investigate if short behavioral patterns predict physiological states.
To establish computer models for classifying mouse behavior and aiding disease diagnostics.

Main Methods:

Proposed novel approaches for analyzing 33-Hz data from CleverSys software.
Developed a data preprocessing pipeline for unbiased feature analysis.

Keywords:

behavior circadian rythm computer modeling home-cage ethome machine learning

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Employed logistic regression (LG), support vector machines (SVM), random forest (RF), and 1D convolutional neural networks with long short-term memory (1DConvBiLSTM) for classification.

Main Results:

A 5-minute video clip was sufficient for accurate mouse behavior classification.
LG, SVM, and RF achieved 85% accuracy; an ensemble of these reached 90%.
The 1DConvBiLSTM model yielded the highest accuracy at 96% for behavior classification.

Conclusions:

Computer modeling of home-cage ethograms effectively defines mouse physiological states.
Continuous behavioral data analysis can be approached similarly to natural language processing.
Findings support the use of behavioral profiling for diagnosing complex pathophysiological changes.