Penny W D, Stephan K E, Mechelli A, Friston K J
Wellcome Department of Imaging Neuroscience, University College London, London, United Kingdom.
Neuroimage. 2004;23 Suppl 1:S264-74. doi: 10.1016/j.neuroimage.2004.07.041.
The brain appears to adhere to two fundamental principles of functional organisation, functional integration and functional specialisation, where the integration within and among specialised areas is mediated by effective connectivity. In this paper, we review two different approaches to modelling effective connectivity from fMRI data, structural equation models (SEMs) and dynamic causal models (DCMs). In common to both approaches are model comparison frameworks in which inferences can be made about effective connectivity per se and about how that connectivity can be changed by perceptual or cognitive set. Underlying the two approaches, however, are two very different generative models. In DCM, a distinction is made between the 'neuronal level' and the 'hemodynamic level'. Experimental inputs cause changes in effective connectivity expressed at the level of neurodynamics, which in turn cause changes in the observed hemodynamics. In SEM, changes in effective connectivity lead directly to changes in the covariance structure of the observed hemodynamics. Because changes in effective connectivity in the brain occur at a neuronal level DCM is the preferred model for fMRI data. This review focuses on the underlying assumptions and limitations of each model and demonstrates their application to data from a study of attention to visual motion.
大脑似乎遵循功能组织的两个基本原则，即功能整合和功能特化，其中特化区域内部和之间的整合是由有效连接介导的。在本文中，我们回顾了两种从功能磁共振成像数据建模有效连接的不同方法，即结构方程模型（SEM）和动态因果模型（DCM）。这两种方法的共同点是模型比较框架，在该框架中，可以对有效连接本身以及该连接如何因感知或认知定势而改变进行推断。然而，这两种方法的基础是两个非常不同的生成模型。在DCM中，区分了“神经元水平”和“血液动力学水平”。实验输入会导致在神经动力学水平上表达的有效连接发生变化，进而导致观察到的血液动力学发生变化。在SEM中，有效连接的变化直接导致观察到的血液动力学协方差结构的变化。由于大脑中有效连接的变化发生在神经元水平，DCM是功能磁共振成像数据的首选模型。本综述重点关注每个模型的潜在假设和局限性，并展示它们在一项视觉运动注意力研究数据中的应用。
