The Effect of ELS on mice movement and memory

Supervisors: Dr. Alex Dranovsky, Columbia University Department of Psychiatry, Dr. Maria Bompolaki, New York State Psychiatric Institute 

In the United States, depression and anxiety disorders are the most common and debilitating mental health issues. Early life stress (ELS) is one of the largest predictive risk factors for the diagnosis of mood and anxiety disorders in adults. However, the processes behind ELS’ influence on brain development and behavior are poorly understood. As ELS is implicated in psychiatric and memory disorders, it is critical to understand its basic mechanisms for better diagnosis and treatment for people suffering with depression and anxiety.

Methodology

One way to quantify the effects of ELS is by studying how it manifests itself in animal models—mice in particular. To induce ELS in mice, I will use a cohort bred in the lab through the limited bedding and nesting model, a standard and IACUC-approved model. These mice experience ELS because the mother lacks sufficient resources to build a nest, leading to maternal anxiety and unstable caregiving toward her pups. At the end of the ELS intervention, I will transfer them to an adult cage for experimentation.

To measure the impact of ELS on rodent behavior and memory, I will compare the performance of ELS mice to a healthy control group with four tasks: the open field, elevated plus, spontaneous alternation mazes, and sociability mazes. In the open field and elevated plus maze, I will be able to measure the fear and anxiety response of the mice based on their exploratory movement and arm preferences. As for the spontaneous alternation maze, I will assess memory and learning by measuring the rodent’s ability to choose the novel arm out of the four arms in the maze.  In the sociability tasks, I will measure how ELS mice interact with other mice.

To quantitatively analyze the ELS and control mice, I will then use DeepLabCut (DLC), a pose estimation system, to automatically track the mice as they move across the three behavioral tasks. In my senior year of high school, I trained a similar pose-estimation software to track and quantify the movement and behavior of octopuses, so I will apply these same technical skills to study mouse movement with DLC. By improving my lab’s current DLC model, I will be able to ensure that the lab’s machine learning system is robust for future behavioral analyses.

Objectives

At the conclusion of my study, I hope to have quantitative measures that show the impact of ELS on mice brain development and behavior based on their performance in the four tasks. From there, I plan to build upon these results to study how ELS impacts mouse behavior for more complex tasks, such as social learning and location. I hope that this research can be a starting point in connecting ELS’s effect on movement to its effect on neural circuits and neurotransmitter signaling for memory and locomotion. Through increasing our understanding of ELS influence on brain development and behavior, my research can contribute to the improved treatment of depression and anxiety in the future.