Authors:

Kaija Salwasser

Date Created:

Not specified

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Professor:

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About Paper:

Opioid use is growing public health crisis, causing almost 80,000 deaths annually (NIDA 2024). Although many individuals seek treatment, relapse remains a serious barrier to recovery. An often overlooked risk factor of relapse is the negative social effects experienced during both immediate and prolonged withdrawal. Some aspects of social behavior deficits are also present in mice, providing a potential animal model to understand the etiology of these symptoms. Traditional human-annotated behavioral recording limits both 311 UNIVERSITY OF OREGON • 2026 UNDERGRADUATE RESEARCH SYMPOSIUM TABLE OF CONTENTS the types of behaviors identified and the temporal precision of the analysis. My project aims to use machine learning to analyze a richer array of behaviors affected by opioid withdrawal. To do this, mice underwent chronic opioid withdrawal and a social behavior assay. Social behaviors were monitored with a high-speed camera and used to train a machine learning model called SLEAP to track the movement and location of the mice in the social experiment videos. The tracking data from SLEAP was then used to make comparisons on changes in social interactions following opioid withdrawal.

Source:

University of Oregon / 2026

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No topics listed

Co-authors:

Kaija Salwasser