1. Department of Chemistry, Wayne State University, , Detroit, Michigan, USA;2. Department of Biology, Duke University, , Durham, North Carolina, USA;3. Department of Mathematics, Duke University, , Durham, North Carolina, USA;4. Department of Mathematics, The Ohio State University, , Columbus, Ohio, USA
Abstract:
The neurotransmitter serotonin underlies many of the brain's functions. Understanding serotonin neurochemistry is important for improving treatments for neuropsychiatric disorders such as depression. Antidepressants commonly target serotonin clearance via serotonin transporters and have variable clinical effects. Adjunctive therapies, targeting other systems including serotonin autoreceptors, also vary clinically and carry adverse consequences. Fast scan cyclic voltammetry is particularly well suited for studying antidepressant effects on serotonin clearance and autoreceptors by providing real‐time chemical information on serotonin kinetics in vivo. However, the complex nature of in vivo serotonin responses makes it difficult to interpret experimental data with established kinetic models. Here, we electrically stimulated the mouse medial forebrain bundle to provoke and detect terminal serotonin in the substantia nigra reticulata. In response to medial forebrain bundle stimulation we found three dynamically distinct serotonin signals. To interpret these signals we developed a computational model that supports two independent serotonin reuptake mechanisms (high affinity, low efficiency reuptake mechanism, and low affinity, high efficiency reuptake system) and bolsters an important inhibitory role for the serotonin autoreceptors. Our data and analysis, afforded by the powerful combination of voltammetric and theoretical methods, gives new understanding of the chemical heterogeneity of serotonin dynamics in the brain. This diverse serotonergic matrix likely contributes to clinical variability of antidepressants.
