Monoaminergic Signaling in the Human Brain: New Insights

Abstract

Understanding how neuromodulatory systems cooperate to shape cognitive and behavioral processes remains a central challenge in neuroscience. The monoamines dopamine, serotonin and norepinephrine uniquely contribute to neural computations throughout the forebrain, influencing attention, learning and decision-making. However, resolving these monoaminergic signals at physiologically and behaviorally relevant spatiotemporal scales in the human brain have been constrained by limitations of available techniques. Within this dissertation, I employ a machine learning-enhanced voltammetry (MLEV) technique that can detect sub-second transients of dopamine, serotonin and norepinephrine in the human brain. First, we demonstrate the chemical selectivity of MLEV using optogenetically evoked monoamine release in transgenic mice. We then move away from model organisms and apply MLEV in awake humans while they performed behavioral tasks. In this work, we had subjects play an emotional Stroop task and identified there were differential modulation of monoamines during the presentation of valenced words in the thalamus and anterior cingulate cortex. In a separate experiment, patients with Parkinson's disease or essential tremors played a social reward task. We observed opponency between dopamine and serotonin to positive prediction errors in patients with essential tremor, but not in those with Parkinson's disease. Moreover, patterns of dopaminergic and serotonergic signaling predicted disease state. The work in this dissertation demonstrates that coordinated monoaminergic signaling underlies the computations linking valence and reward processes.