Stephan Klaas Enno, Mattout Jeremie, David Olivier, Friston Karl J
The Wellcome Dept. of Cognitive Neurology, University College London Queen Square, London, UK WC1N 3BG.
Curr Med Imaging Rev. 2006 Feb;2(1):15-34. doi: 10.2174/157340506775541659.
Inferences about brain function, using functional neuroimaging data, require models of how the data were caused. A variety of models are used in practice that range from conceptual models of functional anatomy to nonlinear mathematical models of hemodynamic responses (e.g. as measured by functional magnetic resonance imaging, fMRI) and neuronal responses. In this review, we discuss the most important models used to analyse functional imaging data and demonstrate how they are interrelated. Initially, we briefly review the anatomical foundations of current theories of brain function on which all mathematical models rest. We then introduce some basic statistical models (e.g. the general linear model) used for making classical (i.e. frequentist) and Bayesian inferences about where neuronal responses are expressed. The more challenging question, how these responses are caused, is addressed by models that incorporate biophysical constraints (e.g. forward models from the neural to the hemodynamic level) and/or consider causal interactions between several regions, i.e. models of effective connectivity. Some of the most refined models to date are neuronal mass models of electroencephalographic (EEG) responses. These models enable mechanistic inferences about how evoked responses are caused, at the level of neuronal subpopulations and the coupling among them.
利用功能神经影像学数据推断大脑功能，需要有关数据产生方式的模型。在实际应用中会用到各种各样的模型，从功能解剖学的概念模型到血流动力学反应（如通过功能磁共振成像，即fMRI测量）和神经元反应的非线性数学模型。在这篇综述中，我们讨论用于分析功能成像数据的最重要模型，并展示它们之间的相互关系。首先，我们简要回顾当前大脑功能理论的解剖学基础，所有数学模型都建立在这些基础之上。然后，我们介绍一些用于对神经元反应发生位置进行经典（即频率主义）和贝叶斯推断的基本统计模型（如一般线性模型）。更具挑战性的问题，即这些反应是如何产生的，则由纳入生物物理约束的模型（如从神经水平到血流动力学水平的正向模型）和/或考虑多个区域之间因果相互作用的模型，即有效连接模型来解决。迄今为止，一些最精细的模型是脑电图（EEG）反应的神经元群体模型。这些模型能够在神经元亚群及其之间的耦合水平上，对诱发反应的产生方式进行机制推断。
