CRCNS: Acetylcholine and state-dependent neural network reorganization

CRCNS：乙酰胆碱和状态依赖的神经网络重组

• 批准号: 10830050
• 负责人: SARA J ATON
• 金额: $ 31.9万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-13 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10830050
• 关键词: Acetylcholine; Affect; Anatomy; Area; Brain; Cells; Cognition; Cognition Disorders; Cognitive; Computer Models; Dementia; Equilibrium; Generations; Goals; Hippocampus; Hour; Impaired cognition; Individual; Information Storage; Interneurons; Intervention; Learning; Maps; Measures; Memory; Mental Depression; Mental disorders; Modeling; Mus; Muscarinic Acetylcholine Receptor; National Institute of Mental Health; Neural Network Simulation; Neurons; Neurosciences; Nicotinic Receptors; Pathway interactions; Pattern; Physiology; Play; Population; Post-Traumatic Stress Disorders; Process; Property; REM Sleep; Research; Role; Schizophrenia; Signal Transduction; Silicon Dioxide; Sleep; Sleep Architecture; Sleep Deprivation; Sleep disturbances; Somatostatin; Strategic Planning; Structure; Synapses; Synaptic plasticity; Testing; Training; Work; analytical method; analytical tool; autism spectrum disorder; biophysical model; cognitive function; computational network modeling; data modeling; density; experience; experimental study; in silico; in vivo; insight; long term memory; memory consolidation; memory encoding; memory process; network models; neural circuit; neural network; neuronal excitability; neuroregulation; neurotransmission; non rapid eye movement; novel; optogenetics; phenomenological models; recruit; response; segregation; sensory input; synaptic function; tool

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.

睡眠中断是认知和影响无序的主要预测指标，但有关睡眠在 大脑功能仍未得到解答。例如，为什么睡眠对于记忆巩固至关重要？为什么在那里 无处不在（大概是进化保守的）唤醒 - 非拉比眼运动（NREM） - REM睡眠状态 跨物种订购，两个睡眠状态的差异作用是什么？大脑电路范围如何 这些状态期间的动态变化，这些过渡如何影响记忆巩固的过程？ 唤醒，NREM和REM状态会产生功能连接的不同模式，这可能有助于重组 大脑网络在存储器存储的背景下。但是，多种机制可以在此过程中发挥作用， 包括国家驱动的结构（突触）变化，神经调节过程，尖峰时序或输入改变。 该提议提出了新的假设，即脑网络中的顺序乙酰胆碱（ACH）信号传导和 输入属性，与Wake-> nrem-> rem状态转换相关的输入属性，对于此中的内存存储至关重要 框架，每个州都起着独特的作用，与特定于州特定的网络兴奋/抑制平衡有关 神经元的输入响应属性。一起，这导致了差分电路激活和动态特性 在唤醒期间，NREM和REM。我们的初步网络建模数据表明NREM的特定属性 REM允许将神经元种群募集到单个ENGRAM（NREM）中，并生成不同的， 隔离的engram表示（REM）。这些特征在合并一个或多个时变得至关重要 回忆分别。在这里，我们建议应用计算建模，体内实验和分析 识别NREM（低ACH）和REM（高ACH）相关的动态状态的工具，以及 这些状态可以在神经回路中进行信息存储。具体而言，我们将：1）测量与状态相关的ACH效应 在硅神经网络模型中高度降低的功能网络连接和动态上，2） 在巩固期间，以国家针对海马的ACH输入和兴奋性抑制平衡的操作 一个或多个与睡眠有关的记忆，以及3）在海马电路的硅模型中发展一个预测性 记忆编码和随后的唤醒 - > nRem-> rem转换期间的重组。这些研究也将 阐明大脑中存储器存储的特定状态机制，以及如何唤醒-NREM-REM顺序 这些状态的排序（跨脊椎动物的普遍存在）有助于记忆巩固的过程。 这些研究将解决目标1.1。通过识别大脑的NIMH战略研究计划 依赖状态的神经回路机制在促进健康认知和记忆中的作用 贮存。