Computationally assisted multi-neurotransmitter detection for intracranial research

About This Grant

SUMMARY: Dopamine, serotonin, and norepinephrine neurotransmitters are known to be critically involved in process underlying substance use disorder and psychiatric illness, as well as healthy motivated behavior, decision-making, and learning. However, little is known about how these signals coordinate and modulate subjective feeling and motivate behavior as mammals (including humans) navigate the world. Progress has been hindered by a lack of technology that permits fast, real-time, measurements that can discriminate and track dopamine, serotonin, and norepinephrine release simultaneously in areas of the brain where two or more of these neurotransmitters are co-released. A major challenge to current methods (e.g., fast scan cyclic voltammetry) is that the calibration models use to interpret in vivo data are trained in vitro and it is unclear how the background signal changes between these environments and how this affects the measured responses. This proposal capitalizes on (and seeks to radically improve) a technological innovation developed by the principal investigator, which resulted in the first successful colocalized measurements of dopamine and serotonin release with sub-second temporal resolution from the brains of consciously behaving humans. Here, we pursue two specific aims, which seek to develop a computational approach to extend these kinds of measurements to include simultaneous detection of norepinephrine and make these methods available for a larger area of preclinical animal model research and human clinical neuroscience research. In both aims we will be testing the overarching hypotheses that 1) the ‘background’ signal present in fast scan cyclic voltammetry measurements can be quantitatively characterized, mathematically modeled, and therefore subtracted using a model-based approach in in vivo research paradigms; and 2) that the “in vitro bias” in the mathematical models used in model-based electrochemistry can be corrected for if we can obtain a better characterization of the background signals in each of the in vivo, ex vivo, and in vitro conditions. The experiments and analyses proposed will begin to provide much needed clarity on the impact biological ‘interferents’ have on interpreting in vivo fast scan cyclic voltammetry data – currently the only approach amenable to sub-second multi-neurotransmitter detection in humans. We expect to develop mathematical models and calibration methods that can be used to predict and control for unwanted interfering signals while significantly improving detection methods for multi-neurotransmitter detection. Notably, these advances – to be shared via open-source online repositories – would accelerate ongoing efforts in the field aimed at understanding how dopaminergic, serotonergic, and noradrenergic systems coordinate to motivate behavior in humans and pre-clinical model organisms, and thereby provide insight into mechanisms underlying human mental health.