Reliability of Automated Scoring of The Novel Object Recognition Task in a Mouse Model of Bipolar Disorder

Education Level

Undergraduate

Faculty Advisor(s)

Professor Victoria Heimer-McGinn.

Academic Department(s)

Psychology

Comments

This research was presented at the 2024 Rhode Island Summer Undergraduate Research Symposium, held on Friday, July 26, at the University of Rhode Island and supported by RI-INBRE.

Symposium Date

2024

Abstract

Bipolar disorder (BD) is a chronic mood disorder characterized by shifts in mood and energy. Several studies suggest the ClockΔ19 mouse may be a valid model of BD. We seek to further validate the ClockΔ19 model through our investigation of cognitive deficits that are present in human bipolar disorder using cognitive tasks such as novel object recognition. We currently use a manual scoring process to analyze behaviors in a series of cognitive tasks. Following this study, we aim to automate our behavioral scoring process using EthoVision XT to analyze behavior in ClockΔ19 mice. EthoVision XT is a video tracking software used for tracking and analyzing movement and behaviors in rodents. By using automated video tracking software such as EthoVision XT, we can analyze a wider range of behaviors at a much higher efficiency. While initial investments into video tracking software are costly, decreased labor hours involved with manual scoring offsets high software costs. Alongside these benefits of automated scoring, there is increased consistency and reliability in removing the possibility of differences between scorers in the manual scoring process. Several studies have suggested automated behavioral scoring can produce near identical results as manual scoring in rodents. In this study, we compare manual and automated behavioral scoring in the novel object recognition task (NOR) with a reliability test. We found no significant difference between manual and automating scoring using EthoVision XT.

This document is currently not available here.

Share

COinS