Behavioral encoding across timescales by region-specific dopamine dynamics

Abstract

AbstractThe dorsal (DS) and ventral striatum (VS) receive dopaminergic projections that control motor functions and reward-related behavior. It remains poorly understood how dopamine release dynamics across different temporal scales in these regions are coupled to behavioral outcomes. Here, we employ the dopamine sensor dLight1.3b together with multi-region fiber photometry and machine learning-based analysis to decode dopamine dynamics across striatum during self-paced exploratory behavior in mice. Our data show a striking coordination of rapidly fluctuating signal in the DS, carrying information across dopamine levels, with a slower signal in the VS, consisting mainly of slow-paced transients. Importantly, these release dynamics correlated with discrete behavioral motifs, such as turns, running and grooming on a subsecond-to-minutes time scale. Disruption of dopamine dynamics with cocaine caused randomization of action selection sequencing and disturbance of DS-VS coordination. The data suggest that distinct dopamine dynamics of DS and VS jointly encode behavioral sequences during unconstrained activity with DS modulating the stringing together of actions and VS the signal to initiate and sustain the selected action.Significance StatementNew genetically encoded dopamine sensors offer unprecedented temporal resolution for measurement of dopamine release dynamics across different brain regions over extended periods. In this study, we use the dopamine sensor dLight1.3b to decipher the role of dopamine release dynamics in the dorsal (DS) and ventral striatum (VS) of mice during simple, self-paced exploratory behavior. By AI-based splitting of behavioral kinematics into individual motifs, we link differential but highly cooperative dopamine release dynamics of DS and VS with movements on a subsecond-to-minutes time scales. In addition to coupling region-specific dopamine dynamics to behavioral sequences, our study demonstrates the strength of a machine learning-based data analysis pipeline that can be readily applied to other neurotransmitters for which genetically encoded biosensors are available.