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的总体目标隶属于中心目标3：开发迭代