Description
The cerebellum is recognized for its role in motor movement, balance, and more recently, social behavior. Cerebellar injury at birth and during critical periods reduces social preference in animal models and increases the risk of autism in humans. Social behavior is commonly assessed with the three-chamber test, where a mouse travels between chambers that contain a conspecific and an object confined under a wire cup. However, this test is unable to quantify interactive behaviors between pairs of mice, which could not be tracked until the recent development of machine learning programs that track animal behavior. In this study, both the three-chamber test and a novel freely-moving social interaction test assessed social behavior in untreated male and female mice, as well as in male mice injected with hM3Dq (excitatory) DREADDs. In the three-chamber test, significant differences were found in the time spent (female: p < 0.05, male: p < 0.001) and distance traveled (female: p < 0.05, male: p < 0.001) in the chamber with the familiar conspecific, compared to the chamber with the object, for untreated male, untreated female, and mice with activated hM3Dq DREADDs. A social memory test was added, where the object was replaced with a novel mouse. Untreated male mice spent significantly more time (p < 0.05) and traveled a greater distance (p < 0.05) in the chamber with the novel mouse, while male mice with activated hM3Dq DREADDs spent more time (p<0.05) in the chamber with the familiar conspecific. Data from the freely-moving social interaction test was used to calculate freely-moving interactive behaviors between pairs of mice and interactions with an object. No sex differences were found, but mice with excited hM3Dq DREADDs engaged in significantly more anogenital sniffing (p < 0.05) and side-side contact (p < 0.05) behaviors. All these results indicate how machine learning allows for nuanced insights into how both sex and chemogenetic excitation impact social behavior in freely-moving mice.
Details
Title
- Utilization of Deep Neural Networks to Investigate Sex-Dependent and Cerebellar Modulation Impacts on Social Behavior in Mice
Contributors
- Nelson, Megan (Author)
- Verpeut, Jessica (Thesis director)
- Bimonte-Nelson, Heather (Committee member)
- Barrett, The Honors College (Contributor)
- Department of Psychology (Contributor)
- School of Life Sciences (Contributor)
- School of Mathematical and Statistical Sciences (Contributor)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2024-05
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