Tracking Anxiety: A Simple Software for Analyzing Mouse Behavior in Open Field Test
Code: G-1700
Authors: Mehrdad Nourizadeh * ℗, Mobina Hoseinzadeh, Saeed Mohammadzadeh Mounesyar, Yalda Jalali, Mahdi Amirhooshangi, Zeynab Rasouli, Mehrdad Neshat Gharamaleki
Schedule: Not Scheduled!
Tag: Biomedical Signal Processing
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Abstract:
Abstract
Background and Aims: Anxiety-like behavior is widely studied in rodent models, with the Open Field Test (OFT) being a standard method for evaluation. Typically, reduced center zone exploration and increased thigmotaxis indicate heightened anxiety. Manual scoring is time-consuming and prone to bias, while commercial automated tools are often expensive. This study introduces RASAD, a lightweight, low-cost software developed to automatically assess two anxiety-related parameters from OFT videos: center zone time and total distance traveled. Methods: Eighteen adult male mice (8–10 weeks, 25–30 g) were divided into three groups (n = 6): (1) Control, (2) Citicoline-treated (100 mg/kg, i.p., daily for 21 days), and (3) Ketamine-treated (10 mg/kg, i.p., single dose, 30 min before test). Each mouse was tested in a 50 × 50 cm open field for 5 minutes. Behavior was recorded using an overhead camera (480×640 px, 15 fps), resulting in 18 videos. Using Python and OpenCV, RASAD tracked mouse position, calculated time spent in the 25 × 25 cm center zone, and measured total distance. Manual scoring by trained observers validated the automated data. Results: Compared to Ethovision, RASAD demonstrated approximately 75% agreement with manual scoring. In the control group, mice traveled an average of 1100 ± 150 cm and spent 50 ± 10 seconds in the center zone. Citicoline-treated mice exhibited slightly increased locomotion (1150 ± 130 cm) and center zone time (70 ± 12 seconds). In contrast, Ketamine-treated mice spent significantly more time in the center (110 ± 18 seconds) with a reduced total distance traveled (980 ± 110 cm), indicating a strong anxiolytic effect with minimal impact on overall activity. Conclusion: RASAD provides an efficient and affordable solution for OFT analysis, especially for small labs. With further development, incorporating machine learning algorithms could enhance its accuracy and expand its capabilities, making it a powerful, low-cost alternative to expensive commercial tools like Ethovision.
Keywords
Python,Artificial Intelligence,Open Field Test, Anxiety, Mouse
