Function and Modulation of Striatal Patch Compartments

Abstract

Animals must constantly evaluate both their internal state and the dynamic, unpredictable environment to select contextually appropriate behaviors. My graduate work has focused on investigating the neural circuits underlying reinforcement learning and action selection through two distinct efforts: first, an analysis of how opioids modulate action selection circuits; and second, an effort to uncover how these opioid sensitive circuits are involved in generating behavior optimal for maximizing reward. Opioid neuropeptides and their receptors are evolutionarily conserved neuromodulatory systems that profoundly influence behavior. In dorsal striatum, which expresses the endogenous opioid enkephalin, patches (or striosomes) are limbic-associated subcompartments enriched in mu opioid receptors. The functional implications of opioid signaling in dorsal striatum and the circuit elements in patches regulated by enkephalin are unclear. We examined how patch output is modulated by enkephalin and identified the underlying circuit mechanisms. We found that patches are relatively devoid of parvalbumin-expressing interneurons and exist as self-contained inhibitory microcircuits. Enkephalin suppresses inhibition onto striatal projection neurons selectively in patches, thereby disinhibiting their firing in response to cortical input. The majority of this neuromodulation is mediated by delta, not mu opioid receptors, acting specifically on intra-striatal collateral axons of striatopallidal neurons. These results suggest that enkephalin gates limbic information flow in dorsal striatum, acting via a patch-specific function for delta opioid receptors. How patch activity contributes to learning and generating behavior is unknown. One clue may come from the limbic synaptic inputs to patches: both the anterior cingulate cortex and prelimbic cortex are involved in appropriately balancing explorative and exploitative behavior. To investigate whether patch activity functions to optimally trade-off exploitation and exploration, we implemented a ‘two-armed bandit’ task for mice while imaging calcium activity of genetically defined striatal patch neurons. We modeled the behavior based on the theoretical optimal player: online inference in a hidden-Markov model (HMM). When trained on the animals’ behavior, the HMM outperforms logistic regression at predicting when the mouse will switch between exploitive and explorative behavior. Future analyses of the calcium activity with the behavior will reveal how striatal patches are involved in mediating this important decision process.