Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain.
Neuroimage. 2013 Oct 15;80:318-29. doi: 10.1016/j.neuroimage.2013.04.055. Epub 2013 Apr 26.
With the increasing availability of advanced imaging technologies, we are entering a new era of neuroscience. Detailed descriptions of the complex brain network enable us to map out a structural connectome, characterize it with graph theoretical methods, and compare it to the functional networks with increasing detail. To link these two aspects and understand how dynamics and structure interact to form functional brain networks in task and in the resting state, we use theoretical models. The advantage of using theoretical models is that by recreating functional connectivity and time series explicitly from structure and pre-defined dynamics, we can extract critical mechanisms by linking structure and function in ways not directly accessible in the real brain. Recently, resting-state models with varying local dynamics have reproduced empirical functional connectivity patterns, and given support to the view that the brain works at a critical point at the edge of a bifurcation of the system. Here, we present an overview of a modeling approach of the resting brain network and give an application of a neural mass model in the study of complexity changes in aging.
随着先进成像技术的日益普及，我们正进入神经科学的新时代。对复杂脑网络的详细描述使我们能够绘制出结构连接组，用图论方法对其进行特征描述，并越来越详细地将其与功能网络进行比较。为了将这两个方面联系起来，并理解动态和结构如何相互作用，以在任务和静息状态下形成功能脑网络，我们使用理论模型。使用理论模型的优势在于，通过从结构和预定义的动力学中明确重建功能连接和时间序列，我们可以通过以在真实大脑中无法直接访问的方式将结构和功能联系起来，提取关键机制。最近，具有不同局部动力学的静息状态模型再现了经验性功能连接模式，并支持了这样一种观点，即大脑在系统分岔的边缘的临界点上工作。在这里，我们介绍了一种静息态脑网络的建模方法，并应用神经质量模型研究了衰老过程中复杂性的变化。
