# Neural circuit mechanisms of drug-context associations in the hippocampus > **NIH NIH K01** · STANFORD UNIVERSITY · 2023 · $151,297 ## Abstract PROJECT ABSTRACT Addictive drugs usurp the normal neural machinery for learning and memory to generate pathological cognition that can lead to compulsive drug usage. One prominent example is re-exposure to a drug-associated environmental context, which robustly induces drug relapse in both humans and animal models. The hippocampal formation, which is critical for spatial and contextual learning, is well positioned to support the encoding of this type of drug-context association. Despite decades of hippocampal studies on drug-evoked molecular and cellular adaptations and drug-seeking behaviors, we still lack a clear understanding of which hippocampal circuits are involved in acquiring and maintaining maladapted drug-context associations and how neural dynamics in the hippocampus are transformed to support drug-seeking behavior. Moreover, there are no interventions that specifically target the drug-associated memories to treat substance use disorders. Here, with the proposed training in computational modeling for neural dynamics and the development of advanced genetic and imaging tools, I aim to fill these knowledge gaps by elucidating the neural circuit mechanisms in the hippocampus for drug-context associations and probing whether we can reverse this association using a memory-based intervention. Preliminary data suggest opioid reward vs. withdrawal-mediated associative learning have distinct effects on representing different spatial variables in CA1 neurons and ketamine was able to reset the maladapted contextual representation to disrupt the retrieval of drug-associated memories. For Aim 1, I will investigate how drug-associated information alters the neural coding in the hippocampus for multiple spatial variables that are critical for the perception of a given context. Using miniscope imaging in morphine conditioned place preference/aversion, I will learn to build linear-nonlinear Poisson (LNP) models to reveal how drug-context associations under positive vs. negative reinforcement affect the neural coding of CA1 for position, head orientation, running speed and their conjunctions. For Aim 2, I will test the hypothesis that Ketamine disrupts learned drug-context associations by restoring the maladapted representations of functional cell types (e.g., place cells) to their normal state. I will acquire expertise on opioid withdrawal and investigate ketamine’s effect on withdrawal-context associations by targeting memory reconsolidation and reveal the corresponding change in neural dynamics of CA1. For Aim 3, I will elucidate neural circuit assembly and dynamics for coding drug-associated contextual information in the subiculum, a major downstream target of the hippocampal CA1. This study will leverage my training in Aim 1 and 2 to advance our understanding of the principles for processing drug-associated information in the brain. Together, the proposed training and studies will not only help me to establish an independent research program but als... ## Key facts - **NIH application ID:** 10723049 - **Project number:** 1K01DA058743-01 - **Recipient organization:** STANFORD UNIVERSITY - **Principal Investigator:** Yanjun Sun - **Activity code:** K01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $151,297 - **Award type:** 1 - **Project period:** 2023-07-15 → 2028-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10723049 ## Citation > US National Institutes of Health, RePORTER application 10723049, Neural circuit mechanisms of drug-context associations in the hippocampus (1K01DA058743-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10723049. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*