Investigating the role of norepinephrine in learned cue-reward associations

University Of Michigan At Ann Arbor, Ann Arbor MI

Investigators

Abstract

Project Summary/Abstract Substance use disorder (SUD), characterized by compulsive or repeated use of a substance despite harmful consequences, is a growing epidemic in the USA. With a relapse rate of over 50%, understanding the neural mechanisms underlying SUD and relapse are critical for the development of effective treatment options. Individuals with SUD have an increased attentional bias to drug-associated cues and sensory cortices have enhanced responses to these cues. One candidate for the emergence and facilitation of these cue-reward associations is norepinephrine (NE). NE release via locus coeruleus (LC) is critical for maintaining arousal and attention states across the entire brain. In sensory cortices, NE has specific roles in learning and plasticity, task execution, and response bias. We hypothesize that NE release in visual association cortex will facilitate learning and enhanced responses to salient, rewarded cues and contribute to reinstatement caused by cue and stress exposure. The goal of this proposal is to establish the pattern of release and functional role of NE in forming and maintaining cue-reward associations in visual association cortices (VisCtx). First, I will use in vivo two-photon microscopy to characterize neural activity and NE release in VisCtx during acquisition of a cue-reward association task, as well as determine if inhibition of NE release during early or late learning is sufficient to prevent acquisition and performance. Next, I will characterize neural activity and NE release in VisCtx during extinction and cue and stress-induced reinstatement, as well as determine if inhibition of NE release during cue presentation or stress administration is sufficient to prevent reinstatement. I will use a combination of in vivo two-photon calcium imaging, fluorescent biosensors, optogenetics, and behavioral tracking to investigate these phenomena. Collectively, the proposed research will significantly improve our understanding of cue representations in VisCtx, including the role of NE in learning cue-reward associations and reinstatement. The proposed research is innovative as it combines traditional computational and behavioral analysis with novel machine learning techniques to dynamically model reinstatement. By using a multidisciplinary approach to elucidate the mechanisms underlying learned cue-reward associations, we will increase understanding of maladaptive cue- associations and help to identify predictive biomarkers and therapeutic targets for the treatment of SUD.