Institut de Neurosciences des Systèmes, INSERM UMR 1106, Aix-Marseille Université, Marseille, France.
Systems Neuroscience Group, Queensland Institute of Medical Research Berghofer, Brisbane, Australia.
Neuroimage. 2015 May 1;111:385-430. doi: 10.1016/j.neuroimage.2015.01.002. Epub 2015 Jan 13.
In this article, we describe the mathematical framework of the computational model at the core of the tool The Virtual Brain (TVB), designed to simulate collective whole brain dynamics by virtualizing brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. Here, a consistent notation for the generalized BNM is given, so that in this form the equations represent a direct link between the mathematical description of BNMs and the components of the numerical implementation in TVB. Finally, we made a summary of the forward models implemented for mapping simulated neural activity (EEG, MEG, sterotactic electroencephalogram (sEEG), fMRI), identifying their advantages and limitations.
在本文中，我们描述了计算模型的数学框架，该模型是工具“虚拟大脑”（TVB）的核心，旨在通过虚拟大脑结构和功能来模拟集体全脑动力学，允许同时输出电和磁共振成像（EEG、MEG）和功能磁共振成像（fMRI）等多种实验模态。该实现允许对大规模脑网络模型（BNM）的每个基础组件进行系统的探索和操作，例如，控制局部动力学的神经质量模型或约束网络耦合时空结构的结构连接。在这里，给出了广义 BNM 的一致符号表示法，以便在这种形式下，方程代表了 BNMs 的数学描述与 TVB 中数值实现的组件之间的直接联系。最后，我们总结了为映射模拟神经活动（EEG、MEG、立体定向脑电图（sEEG）、fMRI）而实现的正向模型，确定了它们的优缺点。
