使用主成分分析（PCA）、典型相关分析（CCA）和向量自回归模型比较功能磁共振成像（fMRI）数据的因果关系度量。
Comparing causality measures of fMRI data using PCA, CCA and vector autoregressive modelling.
作者信息
Shah Adnan, Khalid Muhammad Usman, Seghouane Abd-Krim
机构信息
National ICT Australia, Canberra Research Laboratory, The Australian National University, College of Engineering and Computer Science, Canberra, Australia.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:6184-7. doi: 10.1109/EMBC.2012.6347406.
Extracting the directional interaction between activated brain areas from functional magnetic resonance imaging (fMRI) time series measurements of their activity is a significant step in understanding the process of brain functions. In this paper, the directional interaction between fMRI time series characterizing the activity of two neuronal sites is quantified using two measures; one derived based on univariate autoregressive and autoregressive exogenous (AR/ARX) and other derived based on multivariate vector autoregressive and vector autoregressive exogenous (VAR/VARX) models. The significance and effectiveness of these measures is illustrated on both simulated and real fMRI data sets. It has been revealed that VAR modelling of the regions of interest is robust in inferring true causality compared to principal component analysis (PCA) and canonical correlation analysis (CCA) based causality methods.
从功能磁共振成像（fMRI）对大脑活动区域的时间序列测量中提取激活脑区之间的定向相互作用，是理解脑功能过程的重要一步。本文使用两种方法对表征两个神经元位点活动的fMRI时间序列之间的定向相互作用进行量化；一种基于单变量自回归和自回归外生（AR/ARX）模型推导得出，另一种基于多变量向量自回归和向量自回归外生（VAR/VARX）模型推导得出。这些方法的有效性和重要性在模拟和真实的fMRI数据集上均得到了验证。结果表明，与基于主成分分析（PCA）和典型相关分析（CCA）的因果关系方法相比，感兴趣区域的VAR建模在推断真实因果关系方面具有更强的稳健性。
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