一种基于统计参数映射融合独立成分分析的新的激活区探测方法

A New Activation Detecting Method Based on Combination of Statistical Parametric Mapping with the Independent Component Analysis

统计参数映射在某种程度上依赖于广义线性模型和高斯场理论。广义线性模型的缺陷在于这些假设不能很好地表示fMRI数据,并且脑活动分布模式和血液动力学模型也不能由广义线性模型回归方程来恰如其分地模拟。而独立成分分析不能够提供每一独立成分激活区的显著性估计,这使得实验者不能够很好地解释所获得的结果。提出一种将SPM和ICA技术进行融合的方法,此方法可以将ICA自身的某些优势和GLM的假设检验方法结合起来,互相取长补短。实验结果证明了这种方法在探测由运动任务所产生的激活区方面是有效的。

Statistical parametric mapping (SPM) depends on the general linear model(GLM) and the theory of Gaussian fields to some extent. But the disadvantages of the GLM are related to the fact that these assumptions outlined do not fairly represent the fMRI data. Also, hemodynamics and distributed patterns of the brain activity may not be well modeled by the GLM regression framework. While, the independent component analysis(ICA) does not provide the investigator with a significance estimate for each component activation, which may discourage experimenters from attempting to interpret the results. The paper proposes a method which combines some of the benefits of ICA with the hypothesistesting approach of the SPM. Experimental results demonstrate that the proposed method is effective for detecting the activations resulting from a motor task.