Evaluating risk and reward potential in the execution of motivated behaviors is important in decision-making. Positive valence systems in the brain encode positive stimuli and play a key role in motivation, reward expectance, and appetitive behavior. Negative valence systems, on the other hand, encode negative stimuli such as fear and anxiety, and drive avoidance. Critically, an imbalance in these valence systems is thought to underlie many core symptoms in Major Depressive Disorder (MDD). Recent studies have shown that the brain regions responsible for encoding these divergent valence systems have anatomical and functional overlap. This raises the hypothesis that differences in network-level activity involving these overlapping areas may discriminate information of positive and negative valence. Here, I propose to employ in vivo recordings of electrical activity across multiple brain regions concurrently as mice perform a behavioral task designed to probe both reward and aversion. This task, modeled after the classic elevated plus maze and sucrose preference tasks, will directly quantify the impact of anxiogenic stimuli on reward-motivated behavior. Using machine-learning techniques, I will then generate neural models that reflect the network-level activity engaged during the performance of this task. I anticipate that this strategy with discover an independent network that corresponds with the positive valence system, and another independent network that corresponds with the negative valence system. I also anticipate that I will discover a network that directly integrates network-level activity in these two systems to drive decisions making. Lastly, the structure of these networks will be validated in a cohort of mice that will be subjected to chronic social defeat stress. A validated model of MDD, chronic social defeat stress induces increased anxiety-like phenotypes and decreased reward drive in a subset of mice (stress-susceptible mice) while only increasing anxiety-like phenotypes in other animals (stress-resilient mice). Thus, successful completion of the proposed work will lead to a network-level understanding of positive and negative valence systems. Furthermore, the framework discovered through this study has the potential to facilitate the development of new revolutionary approaches for diagnosis and treatment of MDD.
Network Dynamics of Negative and Positive Valence Systems in Decision Making Valence systems in the brain are responsible for encoding the response to positive and negative experiences. Altered interaction between positive and negative valence systems may play a role in the maladaptive behaviors observed in Major Depressive Disorder (MDD). Using in vivo recordings of electrical activity across key brain regions, we will uncover how the brain encodes reward and punishment at the network level. Findings from this work will provide insight into the diagnosis of MDD characterized by valence imbalance.
