Stephan Klaas E, Harrison Lee M, Penny Will D, Friston Karl J
The Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK.
Curr Opin Neurobiol. 2004 Oct;14(5):629-35. doi: 10.1016/j.conb.2004.08.006.
Functional magnetic resonance imaging (fMRI) is used to investigate where the neural implementation of specific cognitive processes occurs. The standard approach uses linear convolution models that relate experimentally designed inputs, through a haemodynamic response function, to observed blood oxygen level dependent (BOLD) signals. Such models are, however, blind to the causal mechanisms that underlie observed BOLD responses. Recent developments have focused on how BOLD responses are generated and include biophysical input-state-output models with neural and haemodynamic state equations and models of functional integration that explain local dynamics through interactions with remote areas. Forward models with parameters at the neural level, such as dynamic causal modelling, combine both approaches, modelling the whole causal chain from external stimuli, via induced neural dynamics, to observed BOLD responses.
功能磁共振成像（fMRI）用于研究特定认知过程的神经实现发生在何处。标准方法使用线性卷积模型，该模型通过血液动力学响应函数将实验设计的输入与观察到的血氧水平依赖（BOLD）信号相关联。然而，这种模型对观察到的BOLD反应背后的因果机制视而不见。最近的进展集中在BOLD反应是如何产生的，包括具有神经和血液动力学状态方程的生物物理输入-状态-输出模型以及通过与远程区域的相互作用来解释局部动态的功能整合模型。具有神经水平参数的正向模型，如动态因果模型，结合了这两种方法，对从外部刺激经由诱导神经动力学到观察到的BOLD反应的整个因果链进行建模。
