CRCNS: Acetylcholine and state-dependent neural network reorganization

睡眠障碍是认知和情感障碍的主要预测因素，然而关于睡眠在 大脑功能仍未得到回答。例如，为什么睡眠对巩固记忆至关重要？为什么会有 无处不在的(可能是进化上保守的)觉醒-非快速眼动(NREM)-REM睡眠状态 跨物种排序，这两种睡眠状态的不同作用是什么？全脑回路是如何 在这些状态下，动态变化，以及这些转换如何影响记忆巩固的过程？ WAKE、NREM和REM状态会生成不同的功能连接模式，这可能有助于重组 在记忆存储的背景下，大脑网络。然而，多个机制可以在这一过程中发挥作用， 包括状态驱动的结构(突触)变化、神经调节过程、棘波计时或输入改变。 这一建议提出了一种新的假设，即大脑网络中的乙酰胆碱(ACh)信号和 与唤醒->NREM-&>REM状态转换相关联的输入属性对于此中的内存存储是必不可少的 框架中，每个状态都扮演着不同的角色，与状态特定的网络兴奋/抑制平衡和 神经元的输入-响应特性。总而言之，这会导致差动电路激活和动态特性 在觉醒、NREM和REM期间。我们的初步网络建模数据表明，NREM的特定性质 和REM允许将神经元群体招募到个体印迹(NREM)中，并产生独特的、 分离印记表示法(REM)。这些功能在整合一个或多个时变得至关重要 分别是记忆。在这里，我们建议应用计算建模、活体实验和分析 用于识别NREM(低ACh)和REM(高ACh)相关动力学状态的工具，以及 这些状态与神经回路中的信息存储有关。具体地说，我们将：1)测量与状态相关的ACH效应 关于功能网络连通性和动力学在高度约简的电子神经网络模型中的作用，2)测试效果 状态靶向操作对大鼠海马区ACh传入和兴奋性-抑制性平衡的影响 一个或多个睡眠相关的记忆，以及3)建立海马体电路的预测性电子模型 在内存编码和随后的唤醒-&>NREM-&>REM转换期间进行重组。这些研究还将 阐明大脑中记忆储存的特定状态机制，以及觉醒-NREM-REM序列 这些状态的顺序(在脊椎动物物种中普遍存在)有助于记忆巩固的过程。 这些研究将针对目标1.1。NIMH研究战略计划，通过识别大脑 睡眠促进健康认知和记忆的状态依赖神经回路机制 储藏室。

Disrupted sleep is a major predictor of disordered cognition and affect, yet many questions regarding sleep's role in brain function remain unanswered. For example, why is sleep critical for memory consolidation? Why is there ubiquitous (presumably, evolutionarily conserved) wake-non-rapid eye movement (NREM)-REM sleep state ordering across species, and what are the differential roles of the two sleep states? How do brain circuit-wide dynamics change during these states, and how do those transitions affect the process of memory consolidation? Wake, NREM, and REM states generate distinct patterns of functional connectivity, which may help to reorganize brain networks in the context of memory storage. However, multiple mechanisms could play a role in this process, including state-driven structural (synaptic) changes, neuromodulatory processes, spike timing, or input alterations. This proposal advances the novel hypothesis that sequential in brain networks' acetylcholine (ACh) signaling and input properties, associated with wake->NREM->REM state transitions, are essential for memory storage In this framework, each state plays a distinct role, associated with state-specific network excitatory/inhibitory balance and neurons' input-response properties. Together, this leads to differential circuit activation and dynamic properties during wake, NREM, and REM. Our preliminary network modeling data suggest that the specific properties of NREM and REM allow for recruitment of neuronal populations into individual engrams (NREM), and generation of distinct, segregated engram representations (REM). These features become critical during consolidation of one or multiple memories, respectively. Here, we propose to apply computational modeling, in vivo experimentation and analytical tools to identify NREM (low ACh) and REM (high ACh)-associated dynamical states, and the specific contribution of these states to information storage in neural circuits. Specifically we will: 1) measure state-associated ACh effects on functional network connectivity and dynamics in highly reduced in silico neural network models, 2) test effects of state-targeted manipulations to hippocampal ACh inputs and excitatory-inhibitory balance during consolidation of one, or multiple, sleep-dependent memories, and 3) develop a predictive in silico model of the hippocampal circuit's reorganization during memory encoding and subsequent wake->NREM->REM transitions. These studies will also clarify state-specific mechanisms of memory storage in the brain, and how the wake-NREM-REM sequential ordering of these states (ubiquitous across vertebrate species) contributes to the process of memory consolidation. These studies will address Objective 1.1. of the NIMH Strategic Plan for Research, by identifying brain state-dependent neural circuit mechanisms underlying sleep's role in promoting healthy cognition and memory storage.