Kappa Opioid Receptor Mediated Differential Regulation of Serotonin and Dopamine Transporters in Mood and Substance Use Disorder

Resolution of tonic concentrations of highly similar neurotransmitters using voltammetry and deep learning

With advances in our understanding regarding the neurochemical underpinnings of neurological and psychiatric diseases, there is an increased demand for advanced computational methods for neurochemical analysis. Despite having a variety of techniques for measuring tonic extracellular concentrations of neurotransmitters, including voltammetry, enzyme-based sensors, amperometry, and in vivo microdialysis, there is currently no means to resolve concentrations of structurally similar neurotransmitters from mixtures in the in vivo environment with high spatiotemporal resolution and limited tissue damage. Since a variety of research and clinical investigations involve brain regions containing electrochemically similar monoamines, such as dopamine and norepinephrine, developing a model to resolve the respective contributions of these neurotransmitters is of vital importance. Here we have developed a deep learning network, DiscrimNet, a convolutional autoencoder capable of accurately predicting individual tonic concentrations of dopamine, norepinephrine, and serotonin from both in vitro mixtures and the in vivo environment in anesthetized rats, measured using voltammetry. The architecture of DiscrimNet is described, and its ability to accurately predict in vitro and unseen in vivo concentrations is shown to vastly outperform a variety of shallow learning algorithms previously used for neurotransmitter discrimination. DiscrimNet is shown to generalize well to data captured from electrodes unseen during model training, eliminating the need to retrain the model for each new electrode. DiscrimNet is also shown to accurately predict the expected changes in dopamine and serotonin after cocaine and oxycodone administration in anesthetized rats in vivo. DiscrimNet therefore offers an exciting new method for real-time resolution of in vivo voltammetric signals into component neurotransmitters.

The role of enkephalinergic systems in substance use disorders

Enkephalin, an endogenous opioid peptide, is highly expressed in the reward pathway and may modulate neurotransmission to regulate reward-related behaviors, such as drug-taking and drug-seeking behaviors. Drugs of abuse also directly increase enkephalin in this pathway, yet it is unknown whether or not changes in the enkephalinergic system after drug administration mediate any specific behaviors. The use of animal models of substance use disorders (SUDs) concurrently with pharmacological, genetic, and molecular tools has allowed researchers to directly investigate the role of enkephalin in promoting these behaviors. In this review, we explore neurochemical mechanisms by which enkephalin levels and enkephalin-mediated signaling are altered by drug administration and interrogate the contribution of enkephalin systems to SUDs. Studies manipulating the receptors that enkephalin targets (e.g., mu and delta opioid receptors mainly) implicate the endogenous opioid peptide in drug-induced neuroadaptations and reward-related behaviors; however, further studies will need to confirm the role of enkephalin directly. Overall, these findings suggest that the enkephalinergic system is involved in multiple aspects of SUDs, such as the primary reinforcing properties of drugs, conditioned reinforcing effects, and sensitization. The idea of dopaminergic-opioidergic interactions in these behaviors remains relatively novel and warrants further research. Continuing work to elucidate the role of enkephalin in mediating neurotransmission in reward circuitry driving behaviors related to SUDs remains crucial.