A Dynamic Diversity of Dopamine Neurons

最近鉴定的中脑多巴胺（DA）神经元的不同亚群在以下方面表现出差异： 它们的两种主要的体内活动模式，紧张性（单尖峰）放电和相位爆发。这些射击拍- 突触和它们之间的转换对于驱动轴突和树突多巴胺的释放至关重要， 从而控制皮质-纹状体回路的信号处理和行为功能。我们将证明 这些图案背后的离子机制以及这些图案的信息内容是不同的 DA亚群之间，通过不同类型的多巴胺能信号传导的行为控制的强烈影响。目的1将揭示腹侧被盖中经典慢放电和最近发现的快放电DA神经元之间紧张性放电差异的生物物理机制 区域（VTA）。这些机制可能也有助于其不同的频率在体内的突发发射。目的 2将阐明在内侧黑质（SN）爆发的生物物理基础，使ATP敏感 K+（K-ATP）通道和必要的新奇诱导的探索，与爆发的外侧SN， 由Ca 2+激活的SK K+通道控制，并可能控制习惯性运动序列。目标3将定义 两个投射特异性DA亚群的行为功能具有最独特的体内放电模式：更快的爆发VTA DA神经元投射到内侧壳的延髓，这可能是 参与显著性或奖励信号，而中吻侧SN DA神经元投射到外侧 在新奇的探索过程中，它显示出连续和缓慢的爆裂。 不同DA的行为相关放电模式的差异生物物理控制机制 在许多已知的DA信号传导障碍中，包括成瘾、精神分裂症和帕金森病（PD），亚群可能被选择性地影响。例如，对SN DA神经元选择性的活性依赖性Ca 2+负载的减少是PD中有希望的神经保护方法。进一步剖析 提出了VTA和SN DA亚群的动力学和分子生物物理学差异， 在此有望导致甚至更有选择性地定制的治疗策略， 特定的DA亚群。这个项目是基于最近的，令人兴奋的进展建模的多样性， Canavier博士实验室中的DA神经元有助于解释Roeper博士开创的DA神经元的多样性。 理论和计算方法结合联合收割机非线性动力学和分叉分析， 形态逼真的多房室模型。罗珀博士的实验室使用最先进的技术 包括逆行追踪、成年小鼠切片电生理学、通道选择性药理学、光开关K-ATP阻断剂和动态钳，加上细胞外记录，并结合体内小鼠中具有确定轴突投射的单个DA神经元的细胞外标记，以量化多样性。 的DA人群的全面的方式，形成一个协同循环测试模型的预测。

Recently-identified, distinct subpopulations of midbrain dopamine (DA) neurons exhibit differences in their two primary in vivo activity patterns, tonic (single spike) firing and phasic bursting. These firing pat- terns, and transitions between them, are essential for driving axonal and dendritic dopamine release, and thus for controlling signal processing and behavioral functions of cortico-striatal circuits. We will show that the ionic mechanisms underlying these patterns, as well as the information content of these patterns, differ between DA subpopulations, with strong implications for behavioral control via distinct types of dopaminergic signaling. Aim 1 will uncover the biophysical mechanisms underlying the difference in tonic firing between the classic slow-firing and the more recently identified fast-firing DA neurons in the ventral tegmental area (VTA). These mechanisms likely also contribute to their distinct frequencies of burst firing in vivo. Aim 2 will elucidate the biophysical basis of bursts in the medial substantia nigra (SN), enabled by ATP-sensitive K+ (K-ATP) channels and necessary for novelty-induced exploration, versus bursts in the lateral SN that are controlled by Ca2+-activated SK K+ channels and may gate habitual motor sequences. Aim 3 will define the behavioral functions of two projection-specific DA subpopulations with the most distinctive in vivo firing patterns: the faster bursting VTA DA neurons projecting to the medial shell of the accumbens, which might be involved in salience or reward signaling, versus the medio-rostral SN DA neurons projecting to the lateral shell of the accumbens, which display continuous and slower bursting during novelty-mediated exploration. Differential biophysical control mechanisms of behaviorally-relevant firing patterns for distinct DA subpopulations may be selectively affected in the many known disorders of DA signaling, including addiction, schizophrenia and Parkinson's disease (PD). For example, a reduction in activity-dependent Ca2+ loading selective for SN DA neurons is a promising neuroprotective approach in PD. The further dissection of differences in the dynamics and molecular biophysics of both VTA and SN DA subpopulations proposed herein promises to lead to even more selectively tailored therapeutic strategies that tune the firing pattern in specific DA subpopulations. This project is based on recent, exciting advances in modeling the diversity of DA neurons in Dr. Canavier's lab that help explain the diversity of DA neurons pioneered by Dr. Roeper. The theoretical and computational approaches combine nonlinear dynamics and bifurcation analyses with morphologically realistic multi-compartmental modeling. Dr. Roeper's lab uses state of the art techniques, including retrograde tracing, adult mouse slice electrophysiology, channel-selective pharmacology, photoswitchable K-ATP blockers, and Dynamic Clamp, plus extracellular recordings and combined with juxtacellular labeling of single DA neurons with identified axonal projections in mice in vivo, to quantify the diversity of the DA populations in a comprehensive fashion, forming a synergistic loop for testing model predictions.