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 的总体目标包含在中心目标 3 中：开发迭代 建模和实证研究之间的相互作用，以整合跨数据规模的知识。要做的事 因此，项目 4 将开发新颖的神经电路动力学计算模型，并将其应用于拟合特征 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 慢脑网络波动（SBNF）背后的动力学和生物物理机制及其影响 感觉信息的局部电路处理。目标 1 将拟合动态系统模型来捕获 通过 LFP、EEG 和 iEEG 测量的皮质区域光谱状态动态。此外，这些 区域模型将在大规模网络模型中互连，以模拟全脑动态 通过 fMRI 测量的现象，例如功能连接和 CAP 状态。我们将具体测试 假设光谱状态的缓慢（~0.1-1 Hz）波动可以通过双稳态捕获 由噪声和适应引起的转变，甚至更慢的波动（例如，在唤醒或内部与外部） 外部注意力）可以通过双稳态和单稳态动态机制之间的转变来捕获，并且 这些动态可以解释功能磁共振成像中观察到的时空效应。目标2将发展生物物理学 具有层流分辨率的新皮质回路的详细模型，专门设计用于带来宏观人类 iEEG/EEG 到微观细胞和电路级现象（细胞尖峰、LFP/CSD）。详细型号将 应用于研究少数关键电路中缓慢波动的机制，这些电路是研究的目标 项目 3 中的 NHP。我们将系统地探索细胞类型特异性特性和层 特定的丘脑皮质和皮质连接必须结合起来才能复制多尺度动力学 NHP 研究揭示。我们将检验外源驱动模式与 通道电导的细胞类型特异性神经调节可以引起电路活动的缓慢波动， 涵盖从细胞活动到脑电图的电生理尺度和物种。我们还将 描述持续的缓慢波动如何影响电路对自下而上的感官诱发信号的响应， 将缓慢的神经动力学与任务表现联系起来。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 的生物物理详细微电路模型作为大规模网络中的不同节点，其中 其他节点被模拟为目标 1 中的唯象动力系统。总的来说，目标 项目 4 将综合项目 1-3 的多模态记录，以开发多尺度机械 皮质动力学和 SBNF 的计算模型。