Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis
Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis
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DOI：
10.1016/s0730-725x(99)00102-2
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发表时间：
2000-01-01
影响因子：
2.5
通讯作者：
Somorjai, R
中科院分区：
文献类型：
--
作者：
Baumgartner, R;Ryner, L;Somorjai, R
探索性的数据驱动的方法，如模糊聚类分析（FCA）和主成分分析（PCA）可以被认为是假设生成程序，是功能磁共振成像（fMRI）的假设为主导的统计推断方法的补充。在这里，FCA和PCA之间的比较是在一个系统的fMRI研究中提出的，在空条件下获得的MR数据，即，无激活，具有不同的噪声贡献和模拟的变化的“激活”。对比度噪声比（CNR）范围在1-10之间。我们发现，如果fMRI数据被破坏的扫描仪噪声，FCA和PCA表现出相当的性能。在存在其它信号变化源的情况下（例如，在fMRI中，FCA在整个感兴趣的CNR范围内优于PCA，特别是对于低CNR值。我们介绍的比较方法可用于评估其他探索性方法，如独立成分分析或基于神经网络的技术。版权所有（C）2000。出版社：Elsevier Science Inc.
Exploratory data-driven methods such as Fuzzy clustering analysis (FCA) and Principal component analysis (PCA) may be considered as hypothesis-generating procedures that are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). Here, a comparison between FCA and PCA is presented in a systematic fMRI study, with MR data acquired under the null condition, i.e., no activation, with different noise contributions and simulated, varying "activation." The contrast-to-noise (CNR) ratio ranged between 1-10. We found that if fMRI data are corrupted by scanner noise only, FCA and PCA show comparable performance. In the presence of other sources of signal variation (e.g., physiological noise), FCA outperforms PCA in the entire CNR range of interest in fMRI, particularly for low CNR values. The comparison method that we introduced may be used to assess other exploratory approaches such as independent component analysis or neural network-based techniques. Crown Copyright (C) 2000. Published by Elsevier Science Inc.