Establishment of an Infrared-Camera-Based Home-Cage Tracking System Goblotrop

Area of Science:

• Animal behavior research
• Neuroscience
• Pharmacology
• Toxicology

Background:

• Locomotor activity tracking is vital for understanding animal physiology, behavior, and disease.
• Existing methods may lack precision or comprehensive behavioral analysis.

Purpose of the Study:

• To develop and validate Goblotrop, an artificial intelligence (AI)-based system for precise rodent localization and activity tracking.
• To assess Goblotrop's efficacy in capturing behavioral changes, including circadian rhythms.

Main Methods:

• Utilized two infrared cameras and a neural network for real-time rodent position tracking within home cages.
• Employed a starvation-induced hyperactivity model in female mice, a model for anorexia nervosa.
• Compared Goblotrop's measurements with traditional running wheel data.

Main Results:

• Goblotrop accurately tracked rodent position, speed, mobility, and climbing activity.
• Both Goblotrop and running wheels confirmed starvation increases food-anticipatory activity and decreases nocturnal activity.
• High consistency was observed between Goblotrop and running wheel measurements.

Conclusions:

• Goblotrop is a reliable and valuable tool for studying locomotor activity and circadian rhythms in animal models.
• The system offers detailed behavioral insights across various cage areas.
• Goblotrop has broad applicability in neuroscience, pharmacology, toxicology, and behavioral research.