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

A novel naïve Bayes approach to identifying grooming behaviors in the force-plate actometric platform

BACKGROUND

Self-grooming behavior in rodents serves as a valuable behavioral index for investigating stereotyped and perseverative responses. Most current grooming analyses rely on video observation, which lacks standardization, efficiency, and quantitative information about force. To address these limitations, we developed an automated paradigm to analyze grooming using a force-plate actometer.

NEW METHOD

Grooming behavior is quantified by calculating ratios of relevant movement power spectral bands. These ratios are input into a naïve Bayes classifier, trained with manual video observations. The effectiveness of this method was tested using CIN-d mice, an animal model developed through early-life depletion of striatal cholinergic interneurons (CIN-d) and featuring prolonged grooming responses to acute stressors. Behavioral monitoring was simultaneously conducted on the force-place actometer and by video recording.

RESULTS

The naïve Bayes approach achieved 93.7% accurate classification and an area under the receiver operating characteristic curve of 0.894. We confirmed that male CIN-d mice displayed significantly longer grooming durations than controls. However, this elevation was not correlated with increases in grooming force. Notably, the dopaminergic antagonist haloperidol reduced grooming force and duration.

COMPARISON WITH EXISTING METHODS

In contrast to observation-based approaches, our method affords rapid, unbiased, and automated assessment of grooming duration, frequency, and force.

CONCLUSIONS

Our novel approach enables fast and accurate automated detection of grooming behaviors. This method holds promise for high-throughput assessments of grooming stereotypies in animal models of neuropsychiatric disorders.

A novel naïve Bayes approach to identifying grooming behaviors in the force-plate actometric platform

Background

Self-grooming behavior in rodents serves as a valuable model for investigating stereotyped and perseverative responses. Most current grooming analyses primarily rely on video observation, which lacks standardization, efficiency, and quantitative information about force. To address these limitations, we developed an automated paradigm to analyze grooming using a force-plate actometer.

New Method

Grooming behavior is quantified by calculating ratios of relevant movement power spectral bands. These ratios are then input into a naïve Bayes classifier, trained with manual video observations. To validate the effectiveness of this method, we applied it to the behavioral analysis of the early-life striatal cholinergic interneuron depletion (CIN-d) mouse, a model of tic pathophysiology recently developed in our laboratory, which exhibits prolonged grooming responses to acute stressors. Behavioral monitoring was simultaneously conducted on the force-place actometer and by video recording.

Results

The naïve Bayes approach achieved 93.7% accurate classification and an area under the receiver operating characteristic curve of 0.894. We confirmed that male CIN-d mice displayed significantly longer grooming durations compared to controls. However, this elevation was not correlated with increases in grooming force. Notably, haloperidol, a benchmark therapy for tic disorders, reduced both grooming force and duration.

Comparison with Existing Methods

In contrast to observation-based approaches, our method affords rapid, unbiased, and automated assessment of grooming duration, frequency, and force.

Conclusions

Our novel approach enables fast and accurate automated detection of grooming behaviors. This method holds promise for high-throughput assessments of grooming stereotypies in animal models of tic disorders and other psychiatric conditions.