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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.7 no.30 >A new post-processing method of applying Independent Component Analysis to fMRI data
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A new post-processing method of applying Independent Component Analysis to fMRI data

机译：将独立分量分析应用于fMRI数据的新后处理方法

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摘要

Independent component analysis (ICA) method can be used to separate fMRI data into some task-related independent components, including one consistently task-related (CTR) and several transiently task-related (TTR) components. However, the weights, with which the CTR and TTRs contribute to the final task component, are often unknown, but are important for finding its relevant spatial activation area. Here we propose a new ICA post-processing method alternative to combine not only these CTR and TTRs which sometimes are judged in a subjective manner, but also others in an effort to identify a comprehended and summed spatial pattern that is responsible for the behavior under investigation. This proposed procedure has been successfully used in principal component analysis (PCA) based scaled subprofile modeling (SSM). Adopting this newly proposed approach, we essentially refer the ICA exploratory findings to a hypothesized temporal brain response pattern (reference function). Basically, we will use linear regression method to seek the relationship between the reference function and time courses of multi components generated from the ICA procedure. The linear regression coefficients are then used as relative weights in generating the final summed spatial pattern. Moreover, this approach allows a researcher to use T-test to statistically infer the importance of each independent component in its contribution to the final pattern and consequently the contribution to the cognitive process. Experiment result also shows that the spatial activation of the final task component becomes more accurate.
机译：独立成分分析（ICA）方法可用于将fMRI数据分离为一些与任务相关的独立成分，包括一个始终如一的任务相关（CTR）和多个瞬时任务相关（TTR）成分。但是，CTR和TTR构成最终任务组成部分的权重通常是未知的，但是对于找到其相关的空间激活区域很重要。在这里，我们提出了一种新的ICA后处理方法，不仅可以结合有时以主观方式进行判断的这些CTR和TTR，还可以结合其他方法来识别对调查行为负责的理解和汇总的空间模式。该提议的过程已成功用于基于主成分分析（PCA）的可缩放子轮廓模型（SSM）。通过采用这种新提出的方法，我们基本上将ICA的探索性结果参考到假设的颞部脑反应模式（参考功能）。基本上，我们将使用线性回归方法来查找参考函数与ICA程序生成的多分量的时间过程之间的关系。然后，将线性回归系数用作生成最终的总空间模式的相对权重。此外，这种方法允许研究人员使用T检验从统计学上推断每个独立成分对最终模式的贡献以及对认知过程的贡献的重要性。实验结果还表明，最终任务组件的空间激活变得更加准确。

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