Integrated brain network and cell-circuit models of slow network fluctuations

慢网络波动的集成脑网络和细胞电路模型

• 批准号: 10639547
• 负责人: STEPHANIE Ruggiano JONES
• 金额: $ 33.91万
• 依托单位: NATHAN S. KLINE INSTITUTE FOR PSYCH RES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-15 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10639547
• 关键词: Anatomy; Area; Arousal; Attention; Biological Models; Biophysical Process; Biophysics; Brain; Brain region; Calcium; Cells; Cognitive; Computer Models; Coupling; Data; Electrodes; Electroencephalography; Electrophysiology (science); Exhibits; Functional Magnetic Resonance Imaging; Funding; Goals; Grain; Human; Insula of Reil; Link; Measures; Modality; Modeling; Neocortex; Neurobiology; Noise; Nonlinear Dynamics; Pattern; Process; Property; Resolution; Role; Sensory; Shapes; Signal Transduction; Software Tools; Standardization; Stimulus; Structure; Task Performances; Testing; Thalamic structure; Translating; behavioral outcome; cell type; data format; design; dynamic system; functional magnetic resonance imaging/electroencephalography; insight; knowledge integration; multi-scale modeling; multimodality; neocortical; network models; neural; neural circuit; neural model; neuromechanism; neuroregulation; novel; phenomenological models; predictive modeling; response; spatiotemporal; statistics; tractography

ABSTRACT: The overarching goal of Project 4 is subsumed under Center Aim 3: Develop iterative interactions between modeling and empirical studies to integrate knowledge across data scales. To do so, Project 4 will develop novel computational models of neural circuit dynamics and apply them to fit features of multi-modal neural recordings in Projects 1-3. Models will be used to test hypotheses and gain insight into dynamical and biophysical mechanisms underlying slow brain network fluctuations (SBNFs) and their impact on local circuit processing of sensory information. Aim 1 will fit a dynamical systems model to capture the dynamics of spectral states in a cortical region, as measured by LFP, EEG, and iEEG. In addition, these regional models will be interconnected in a large-scale network model to simulate brain wide dynamical phenomena as measured by fMRI, such as functional connectivity and CAP states. We will test the specific hypotheses that slow (~0.1-1 Hz) fluctuations in spectral state can be captured through bistability with transitions induced by noise and adaptation, that even slower fluctuations (e.g, in arousal, or internal vs. external attention) can be captured by shifts between bistable and monostable dynamical regimes, and that these dynamics can account for spatiotemporal effects observed in fMRI. Aim 2 will develop biophysically detailed models of neocortical circuits with laminar resolution specifically designed to bring macroscale human iEEG/EEG to microscale cellular and circuit-level phenomena (cell spiking, LFP/CSD). Detailed models will be applied to study mechanisms of slow fluctuations in a small number key circuits that are the target of study in NHP in Project 3. We will systematically explore the manner in which cell type-specific properties, and layer specific thalamocortical and cortical connectivity, must be combined to replicate the multiscale dynamics revealed by NHP studies. We will test the specific hypothesis that patterns of exogenous drive together with cell-type-specific neuromodulation of channel conductances can induce slow fluctuations in circuit activity that translates across electrophysiological scales and species from cell activity up to EEG. We will also characterize how ongoing slow fluctuations impact circuit responses to bottom-up sensory evoked signals, linking slow neural dynamics to task performance. Exploratory Aim 3 will develop a multi-scale model to explore the interplay between microcircuit and large-scale network dynamics. Specifically, we will embed the biophysically detailed microcircuit models from Aim 2 as distinct nodes in a large-scale network in which the other nodes are simulated as phenomenological dynamical systems from Aim 1. Collectively, the aims of Project 4 will synthesize multi-modal recordings from Project 1-3 to develop multi-scale mechanistic computational models of cortical dynamics and SBNFs.

摘要：项目4的总体目标是在中心目标下包含的：开发迭代 建模与经验研究之间的相互作用以整合跨数据量表的知识。做 因此，项目4将开发神经电路动力学的新型计算模型，并将其应用于拟合功能 项目1-3中的多模式神经记录。模型将用于检验假设并深入了解 慢速大脑网络波动（SBNF）及其影响的动力学和生物物理机制 关于感官信息的本地电路处理。 AIM 1将适合动态系统模型以捕获 通过LFP，EEG和IEEG测量的皮质区域的光谱状态的动力学。另外，这些 区域模型将在大型网络模型中互连，以模拟大脑广泛的动力学 通过fMRI测量的现象，例如功能连通性和CAP状态。我们将测试特定的 可以通过与光谱状态下缓慢波动缓慢（〜0.1-1 Hz）的假设通过与 由噪声和适应引起的过渡，甚至较慢的波动（例如，唤醒或内部Vs。 外部注意力）可以通过双态和单稳态动力学之间的变化来捕获 这些动力学可以解释fMRI中观察到的时空效应。 AIM 2将在生物物理上发展 具有层流分辨率的新皮质电路的详细模型，专门设计用于带宏观的人 IEEG/EEG到显微镜细胞和电路级现象（细胞尖峰，LFP/CSD）。详细的模型将是 应用于少量关键电路中缓慢波动的研究机制，这是研究的目标 项目3中的NHP。我们将系统地探索特定于小区类型的属性和层的方式 必须组合特定的丘脑皮质和皮质连通性以复制多尺度动力学 由NHP研究揭示。我们将测试以下特定假设，即外源性驱动模式与 通道电导的细胞类型特异性神经调节可以引起电路活性的缓慢波动 从电生理量表和物种从细胞活性到脑电图翻译。我们也会 表征正在进行的缓慢波动如何影响对自下而上感觉诱发信号的电路响应， 将缓慢的神经动力与任务性能联系起来。探索性目标3将开发一个多尺度模型 探索微电路和大规模网络动力学之间的相互作用。具体来说，我们将嵌入 来自AIM 2的生物物理详细的微电路模型作为大型网络中的不同节点，其中 其他节点被模拟为AIM 1的现象学动力学系统。 项目4将综合项目1-3的多模式录音以开发多尺度机械 皮质动力学和SBNF的计算模型。