Stimulus-independent data analysis for fMRI

被引：0
|
作者：
Dodel, S [1 ]
Herrmann, JM [1 ]
Geisel, T [1 ]
机构：
[1] Max Planck Inst Stromungsforsch, D-37073 Gottingen, Germany
来源：
EMERGENT NEURAL COMPUTATIONAL ARCHITECTURES BASED ON NEUROSCIENCE: TOWARDS NEUROSCIENCE-INSPIRED COMPUTING | 2001年 / 2036卷
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D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We discuss methods for analyzing fMRI data, stimulus-based such as baseline substraction and correlation analysis versus stimulus-independent methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) with respect to their capabilities of separating noise sources from functional activity. The methods are applied to a finger tapping fMRI experiment and it is shown that the stimulus-independent methods in addition to the extraction of the stimulus can reveal several non-stimulus related influences such as head movements or breathing.
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页码：39 / 52
页数：14
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