Harrison L, Penny W D, Friston K
Wellcome Department of Imaging Neuroscience, University College London, 12 Queen Square, London WC1N 3BG, UK.
Neuroimage. 2003 Aug;19(4):1477-91. doi: 10.1016/s1053-8119(03)00160-5.
We propose the use of multivariate autoregressive (MAR) models of functional magnetic resonance imaging time series to make inferences about functional integration within the human brain. The method is demonstrated with synthetic and real data showing how such models are able to characterize interregional dependence. We extend linear MAR models to accommodate nonlinear interactions to model top-down modulatory processes with bilinear terms. MAR models are time series models and thereby model temporal order within measured brain activity. A further benefit of the MAR approach is that connectivity maps may contain loops, yet exact inference can proceed within a linear framework. Model order selection and parameter estimation are implemented by using Bayesian methods.
我们建议使用功能磁共振成像时间序列的多元自回归（MAR）模型来推断人类大脑内的功能整合。通过合成数据和真实数据对该方法进行了演示，展示了此类模型如何能够表征区域间的依赖性。我们将线性MAR模型进行扩展，以纳入非线性相互作用，用双线性项对自上而下的调节过程进行建模。MAR模型是时间序列模型，从而对测量到的大脑活动中的时间顺序进行建模。MAR方法的另一个优点是，连接图可能包含回路，但精确推断可以在一个线性框架内进行。通过使用贝叶斯方法来实现模型阶数选择和参数估计。
