Reverse engineering the prefrontal microcircuit

描述（由申请人提供） 摘要：精神分裂症是一种毁灭性的疾病，人们普遍认为前额叶皮层（PFC）的功能障碍是这种疾病的认知障碍和其他衰弱方面的基础。目前的治疗方法充其量是有限的，尽管我们已经确定了许多与精神分裂症相关的遗传、分子、细胞和突触改变，但我们还无法将这些发现转化为更有效的治疗方法。其核心原因是，我们根本不知道PFC（或大脑其他部位）的回路是如何“工作”的--我们不知道细胞的特性或它们的相互作用是如何产生活动模式的，这些活动模式使这些大脑区域能够执行功能，包括那些在精神疾病中受损的功能。在这里，我们提出了一种新的策略来逆向工程皮层中的循环回路，即以一种能够推断其整体功能以及单个组件（例如特定细胞类型）对该功能的贡献的方式来测量它们的活动。我们的方法是广泛适用的，我们将通过回答有关前额叶皮层中复发性V层网络的具体问题来证明其效用，因为该网络的多巴胺能调制在精神分裂症中起着关键作用。这个网络中的活动可以收敛到稳定状态，或者通过确定性的状态序列进行，以便将信息存储在工作记忆中。或者，这个网络可能会产生嘈杂的活动，使前额叶输出更可变，以促进行为适应。在病理条件下，这种“噪音”可能会导致前额叶功能障碍。我们的方法将确定当各种多巴胺受体被激活时，第V层前额网络执行哪些假设功能。在此过程中，我们将定义“前额噪声”及相关概念。我们将使用脑切片制备将第V层与外部输入隔离，并使用遗传编码的钙指示剂GCaMP 3记录PFC第V层中许多神经元的同时活动。我们提出了使用GCaMP 3记录网络活动的例子，并描述了隐马尔可夫模型等统计方法如何解码这种活动并推断网络的功能。我们还表明，层V包含多种亚型的锥体神经元，并说明我们如何可以区分这些亚型，同时记录活动与GCaMP 3。多巴胺D2受体仅在这些亚型中的一种中表达，我们将测试D2受体激活通过对这些神经元的特定作用产生前额噪声的假设。最后，我们描述了通过将光遗传学刺激与GCaMP 3成像相结合来扩展我们的方法的方法。这些实验将回答有关前额叶网络的长期问题，并展示可能解构整个皮层网络的方法。此外，通过对前额叶回路的“黑匣子“进行逆向工程，通常会将遗传或发育损伤与其行为后果联系起来，这项研究可能会从根本上开辟思考精神疾病的新方法，使设计针对回路功能障碍的新疗法成为可能。 公共卫生相关性：精神分裂症影响全球约1%的人口，给患者及其家人造成痛苦，在大多数情况下，终身残疾。精神分裂症的许多最致残的症状被认为与多巴胺和前额叶皮层功能障碍有关。在这里，我们建议研究多巴胺如何调节前额叶皮层的功能，以确定精神分裂症和相关疾病中可能被破坏的关键机制。

DESCRIPTION (Provided by the applicant) Abstract: Schizophrenia is a devastating illness, and dysfunction of the prefrontal cortex (PFC) is widely believed to underlie cognitive impairment and other debilitating aspects of this disorder. Current treatments are limited at best, and although we have identified numerous genetic, molecular, cellular, and synaptic alterations related to schizophrenia, we have not been able to translate these findings into more effective treatments. The central reason for this is tha we simply do not know how circuits in the PFC (or elsewhere in the brain) ""work"" - we do not know how the properties of cells or their interactions give rise to patterns of activity that enabl these brain regions to carry out functions, including the ones impaired in psychiatric illnesses. Here we propose a novel strategy to reverse engineer recurrent circuits in the cortex, i.e. to measure their activity in a way that makes it possible to infer both their overall function, and th contributions that individual components (e.g. specific cell types) make to this function. Our approach is widely applicable, and we will demonstrate its utility by answering specific questions about the recurrent layer V network in prefrontal cortex, because dopaminergic modulation of this network plays a critical role in schizophrenia. Activity in this network may converge to stabl states or progress through a deterministic sequence of states in order to store information in working memory. Alternatively, this network may generate noisy activity that makes prefrontal output more variable in order to facilitate behavioral adaptation. Such ""noise"" could cause prefrontal dysfunction under pathological conditions. Our approach will determine which of these hypothesized functions the layer V prefrontal network carries out when various dopamine receptors are activated. In the process, we will define ""prefrontal noise"" and related concepts. We will isolate layer V from external inputs using a brain slice preparation, and record simultaneous activity from many neurons in layer V of the PFC using the genetically encoded calcium indicator GCaMP3. We present examples of network activity recording using GCaMP3, and describe how statistical methods such as Hidden Markov Models can decode this activity and infer the function of the network. We also show that layer V contains multiple subtypes of pyramidal neurons, and illustrate how we can distinguish these subtypes while recording activity with GCaMP3. Dopamine D2 receptors are only expressed in one of these subtypes, and we will test the hypothesis that D2 receptor activation generates prefrontal noise through specific effects on these neurons. Finally, we describe ways to extend our approach by combining optogenetic stimulation with GCaMP3 imaging. These experiments will answer long-standing questions about prefrontal networks and demonstrate approaches that may deconstruct networks throughout the cortex. Furthermore, by reverse engineering the ""black box"" of prefrontal circuitry that is commonly invoked to connect genetic or developmental lesions with their behavioral consequences, this study may open up fundamentally new ways of thinking about psychiatric illness, making it possible to design novel therapies that target circuit dysfunction. Public Health Relevance: Schizophrenia affects approximately 1% of the population worldwide, causing distress for patients and their families, and in most cases, life-long disabilit. Many of the most disabling symptoms of schizophrenia are thought to involve dopamine and dysfunction of the prefrontal cortex. Here we propose to study how dopamine modulates the function of the prefrontal cortex in order to identify critical mechanisms that are likely to be disrupted in schizophrenia and related conditions.