Multi-scale analysis and computational modeling of intrinsic coupling modes in the ferret brain

雪貂大脑内在耦合模式的多尺度分析和计算建模

• 批准号: 347572142
• 负责人: Professor Dr. Andreas K. Engel
• 金额: --
• 依托单位: Institut für Computational Neuroscience
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/347572142?language=en
• 关键词: Multi; scale; analysis; computational; modeling

Intrinsically generated dynamic coupling constitutes a key feature of brain activity. Available evidence suggests the existence of two types of intrinsic coupling modes (ICMs): Phase ICMs arise from phase coupling of oscillatory signals, whereas envelope ICMs result from coupled fluctuations of signal envelopes. Our overall goal is to systematically analyze and computationally model these types of ICMs in the ferret brain. Phase ICMs are widely studied neurophysiologically, but are not yet well understood in terms of modeling their relation to the structural connectome. Envelope ICMs have been extensively explored in connectomic models but are not well investigated with neurophysiological approaches. The interaction between these ICMs is almost completely unexplored and they have not yet been analyzed systematically in the same datasets. No computational modeling results have been reported for ICMs in the ferret brain. Using structural and functional connectomic datasets including large-scale electrocorticographic (ECoG-) recordings that we have previously acquired in the ferret, we will analyze both phase and envelope ICMs and provide a coherent computational modeling approach based on the structural connectome. The project will pursue the following key aims: (1) We will investigate phase ICMs by combining analysis of neurophysiological data and computational modeling; (2) we will analyze and model envelope ICMs; (3) we will investigate which interactions occur between phase and envelope ICMs. These aims map onto our work programme. Workpackage 1 will focus on the relation of phase ICMs to the structural connectome, on the spatiotemporal variability of phase coupling, on changes in phase ICMs associated with state changes, and on whether phase ICMs determine spreading waves and predict sensory stimulus processing. Workpackage 2 will study whether envelope ICMs differ in their relation to the structural connectome from phase ICMs, analyze the variability of envelope ICMs and their modulation by state changes, and test whether envelope ICMs in pre-stimulus activity predict sensory processing and form spreading waves. Workpackage 3 will investigate the relation between both types of ICMs and test whether they mutually predict each other in the context of state changes or network perturbation by stimuli. We will use the relation between signal phases and envelopes for quantifying large-scale connectivity, and investigate the relation of ICMs to dynamic phenomena such as criticality. We will provide an open-access repository of data and models used in this project (The Virtual Ferret) as part of TheVirtualBrain platform, which will facilitate data sharing and collaboration in the priority area of computational connectomics. The proposal addresses key themes of SPP 2041 by undertaking systematic analyses of complex network connectivity and developing computational models for explaining how network structure gives rise to neural dynamics.

内在产生的动态耦合构成了大脑活动的一个关键特征。现有证据表明存在两种类型的本征耦合模式：相位本征耦合是由振荡信号的相位耦合引起的，而包络本征耦合是由信号包络的耦合波动引起的。我们的总体目标是系统地分析和计算雪貂大脑中这些类型的icm。相ICMs在神经生理学上得到了广泛的研究，但在建立它们与结构连接体的关系方面尚未得到很好的理解。包膜ICMs已经在连接组模型中得到了广泛的探索，但还没有很好地研究神经生理学方法。这些icm之间的相互作用几乎完全没有被探索过，它们还没有在相同的数据集中被系统地分析过。目前还没有关于雪貂脑内ICMs的计算建模结果的报道。利用结构和功能连接组数据集，包括我们之前在雪貂中获得的大规模皮质电图（ECoG-）记录，我们将分析相位和包络icm，并提供基于结构连接组的连贯计算建模方法。该项目将追求以下主要目标：(1)我们将通过结合神经生理学数据分析和计算建模来研究阶段ICMs；(2)对包络icm进行分析和建模；(3)我们将研究相icm和包络icm之间发生了哪些相互作用。这些目标符合我们的工作计划。工作包1将重点关注相icm与结构连接体的关系，相耦合的时空变异性，与状态变化相关的相icm的变化，以及相icm是否决定传播波和预测感觉刺激处理。工作包2将研究包膜icm与结构连接体的关系是否与相位icm不同，分析包膜icm的可变性及其受状态变化的调节，并测试刺激前活动中的包膜icm是否预测感觉加工并形成传播波。工作包3将研究两种类型的icm之间的关系，并测试它们是否在状态变化或刺激网络扰动的背景下相互预测。我们将使用信号相位和包络之间的关系来量化大规模连通性，并研究icm与临界等动态现象的关系。作为TheVirtualBrain平台的一部分，我们将提供该项目（The Virtual Ferret）中使用的数据和模型的开放访问存储库，这将促进计算连接学优先领域的数据共享和协作。该提案通过对复杂网络连通性进行系统分析并开发计算模型来解释网络结构如何产生神经动力学，从而解决了SPP 2041的关键主题。