Enhanced Sensitivity to Hyperpolarizing Inhibition in Mesoaccumbal Relative to Nigrostriatal Dopamine Neuron Subpopulations

Abstract

Midbrain dopamine neurons recorded in vivo pause their firing in response to reward omission and aversive stimuli. While the initiation of pauses typically involves synaptic or modulatory input, intrinsic membrane properties may also enhance or limit hyperpolarization, raising the question of how intrinsic conductances shape pauses in dopamine neurons. Using retrograde labeling and electrophysiological techniques combined with computational modeling, we examined the intrinsic conductances that shape pauses evoked by current injections and synaptic stimulation in subpopulations of dopamine neurons grouped according to their axonal projections to the nucleus accumbens or dorsal striatum in mice. Testing across a range of conditions and pulse durations, we found that mesoaccumbal and nigrostriatal neurons differ substantially in rebound properties with mesoaccumbal neurons displaying significantly longer delays to spiking following hyperpolarization. The underlying mechanism involves an inactivating potassium (I A ) current with decay time constants of up to 225 ms, and small-amplitude hyperpolarization-activated currents (I H ), characteristics that were most often observed in mesoaccumbal neurons. Pharmacological block of I A completely abolished rebound delays and, importantly, shortened synaptically evoked inhibitory pauses, thereby demonstrating the involvement of A-type potassium channels in prolonging pauses evoked by GABAergic inhibition. Therefore, these results show that mesoaccumbal and nigrostriatal neurons display differential responses to hyperpolarizing inhibitory stimuli that favors a higher sensitivity to inhibition in mesoaccumbal neurons. These findings may explain, in part, observations from in vivo experiments that ventral tegmental area neurons tend to exhibit longer aversive pauses relative to SNc neurons.

Functional dissection of basal ganglia inhibitory input onto SNc dopaminergic neurons

“…The strong rebound characteristics of the striosome-inhibited subpopulation of neurons will contribute to a synchronous rebound by allowing fast return to firing after inhibition. However, this characteristic is not ubiquitous among dopamine neurons, as dopamine neurons in the VTA are more variable in their recovery time from inhibition due to the slow A-type potassium current kinetics (Tarfa et al, 2017). Mechanistically, the structure of the striosome-dendron bouquet may contribute to synchrony among dopamine neurons.…”

“…Functionally testing the strength and characteristics of inhibitory inputs onto SNc neurons will allow us to determine which specific inhibitory sources can induce rebound activity. Interestingly, we have previously shown that only certain subpopulations of dopamine neurons contain the intrinsic mechanisms necessary to rebound from inhibition, while other subpopulations lack this feature Tarfa et al, 2017). This suggests that the pause-rebound firing pattern would be limited to a distinct population of dopamine neurons.…”

In vivo functional diversity of midbrain dopamine neurons within identified axonal projections

“…Only some of these identified in vivo differences were predicted based on previous studies of diverging intrinsic in vitro properties between distinct DA subpopulations (e.g. rebound delays and pausing) Lammel et al, 2008;Tarfa et al, 2017), while other features, like differences in mean firing rates were surprising and indicated that certain neuronal network properties might override intrinsic differences in excitability. Overall, our study provides a refined functional in vivo landscape of the midbrain DA system embedded in an anatomical context of identified axonal projections.…”

“…The functional properties and the emerging diversity of the midbrain dopamine (DA) system have been extensively studied across many different biological scales (Schultz, 2015;Watabe-Uchida et al, 2017). These differences range from the single-cell level, including diverging gene expression profiles (Kramer et al, 2018;Nichterwitz et al, 2016;Poulin et al, 2018;Saunders et al, 2018a;Tiklova et al, 2019), cellular and biophysical properties Lammel et al, 2008;Tarfa et al, 2017), as well as neurotransmitter co-release (Chuhma et al, 2018;Kim et al, 2015b;Tritsch et al, 2012;Zhang et al, 2015), up to different neural circuit affiliations (Beier et al, 2019;Beier et al, 2015;Lammel et al, 2012;Lerner et al, 2015;Menegas et al, 2015;Ogawa et al, 2014;Tian et al, 2016;Watabe-Uchida et al, 2012) and selective task engagement of DA subpopulations in awake behaving rodents (Dautan et al, 2016;de Jong et al, 2019;Duvarci et al, 2018;Gunaydin et al, 2014;Howe and Dombeck, 2016;Keiflin et al, 2019;Lammel et al, 2012;Matthews et al, 2016;Menegas et al, 2018;Menegas et al, 2017;Parker et al, 2016;Patriarchi et al, 2018;Saunders et al, 2018b;Vander Weele et al, 2018;Yang et al, 2018) and non-human primates …”

“…Support for the concept that different DA neuron subtypes carry out different computational processes arose from the mapping of their in vitro cellular properties in combination with retrograde tracing. This showed that projectionidentified groups of DA neurons, including those that project to specific subregions of the striatum, have marked differences in intrinsic biophysical properties and inputoutput relationships (Lammel et al, 2008;Tarfa et al 2017).…”