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基于动态可传播性的全脑 fMRI 动力学网络分析:一种新框架

Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability.

机构信息

Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer de Ramon Trias Fargas 25-27, Barcelona, 08005, Spain.

Namur Institute for Complex Systems (naXys), Department of Mathematics, University of Namur, Rempart de la Vierge 8, B 5000, Namur, Belgium; DRIBIA Data Research S.L., Barcelona, Spain.

出版信息

Neuroimage. 2019 Nov 1;201:116007. doi: 10.1016/j.neuroimage.2019.116007. Epub 2019 Jul 12.

Abstract

Neuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often "static" despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time, moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.

摘要

神经影像学技术,如 MRI,已被广泛用于探索大脑区域之间的关联。结构连接(SC)捕捉大脑中的解剖路径,功能连接(FC)测量大脑区域活动之间的相关性。这些连接测量方法已经通过网络理论进行了大量研究,以揭示大脑结构的分布式组织,特别是用于特定任务的大脑通信的 FC。然而,尽管从 fMRI 获得的时间序列具有动态性质,但网络理论在研究 FC 矩阵中的应用通常是“静态的”。本研究旨在通过引入一种面向网络的分析方法来克服这一限制,该方法应用于整个大脑的有效连接(EC),有助于解释大脑动态。从技术上讲,我们调整多元 Ornstein-Uhlenbeck(MOU)过程来复制整个大脑静息状态 fMRI 信号的统计数据,这为 MOU-EC 以及输入特性(类似于局部兴奋性)提供了估计。然后,网络分析基于 Green 函数(或网络脉冲响应),该函数描述了在估计的动力学中节点之间随时间的相互作用。这种基于模型的方法提供了时变的图状描述符,称为可通信性,它描述了节点或连接在网络内活动传播中的作用。它们可用于全局和局部水平,还可以比较真实数据与替代物(例如随机网络或环形晶格)的估计值。与研究 SC 或 FC 的经典图方法相比,我们的框架强调了考虑 fMRI 信号的时间方面的重要性。我们的结果显示,功能社区随着时间的推移融合在一起,网络活动从隔离到全局整合。我们的形式主义为 fMRI 数据分析,包括任务诱发活动的分析和解释奠定了坚实的基础。

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