Malavika Murugan is a postdoctoral fellow working in the lab of Dr. Ilana Witten at Princeton University. She received her Ph.D. in neurobiology under the supervision of Professor Richard Mooney at Duke University in 2013, where she was supported by a fellowship from the Ruth Broad Foundation. Dr. Murugan’s research uses a combination of in vivo cellular-resolution calcium imaging, optogenetics and viral targeting strategies in rodents with advanced statistical methods to understand the neural circuitry that supports social behavior.
“Dissecting neural circuits that support social behavior.”
A central goal of modern neuroscience is to link brain activity to behavior. Because of the complexity of that relationship, most behaviors studied to date have been relatively simple and highly controlled—such as a mouse making a simple choice between two food-dispensing levers in a box or a monkey moving its eyes to a visual target. However, these simple experiments do not address arguably one of the most essential sets of behaviors of all mammals, including humans: social interactions. It may seem premature to study this more complicated behavioral repertoire. After all, the challenges are immense. Social interaction is inherently complex and variable, and is driven by an extraordinarily intricate network of brain circuits. However, new experimental and statistical tools are poised to overcome these challenges. We propose to take advantage of and further develop these tools to dissect social behavior in mice. In our task, a mouse will explore an environment with two separate chambers. In one chamber is another mouse, a stranger, and in the other is an object the mouse has never seen. In addition, we will test the mouse in its home chamber while introducing either a stranger mouse or a novel object. In this setup, we can observe the social behaviors of the mouse, such as sniffing or grooming, compared to exploring nonsocial but still novel objects. Brain activity observed during the presence of the stranger mouse may be specific to social behavior. To measure brain activity, we will record the electrical signals of many neurons in two key brain areas: the medial prefrontal cortex (mPFC) and the nucleus accumbens (nAC), which I’ve recently identified as being involved in social behavior. We will overcome the analytical challenges of this data by applying cutting-edge Bayesian statistical techniques that are well suited to transforming large data sets into variables tractable for more traditional analysis. To further dissect the complex anatomy underlying this brain activity, we will employ a technique in which inactivated (and so harmless) viruses are used as a way to trace out highly specific neural circuits. By combining advanced statistical methodology with the modern neural circuit dissection tool kit, we can make unprecedented progress on understanding this most fundamental question of how and why we interact with each other.