通过阈值化相关性和奇异值分解比较功能连接性。
Comparing functional connectivity via thresholding correlations and singular value decomposition.
作者信息
Worsley Keith J, Chen Jen-I, Lerch Jason, Evans Alan C
机构信息
Department of Mathematics and Statistics, McGill University, Montreal, Canada.
出版信息
Philos Trans R Soc Lond B Biol Sci. 2005 May 29;360(1457):913-20. doi: 10.1098/rstb.2005.1637.
Abstract
We compare two common methods for detecting functional connectivity: thresholding correlations and singular value decomposition (SVD). We find that thresholding correlations are better at detecting focal regions of correlated voxels, whereas SVD is better at detecting extensive regions of correlated voxels. We apply these results to resting state networks in an fMRI dataset to look for connectivity in cortical thickness.
摘要
我们比较了两种检测功能连接性的常用方法
相关性阈值化和奇异值分解（SVD）。我们发现，相关性阈值化在检测相关体素的焦点区域方面表现更好，而奇异值分解在检测相关体素的广泛区域方面表现更好。我们将这些结果应用于功能磁共振成像（fMRI）数据集中的静息态网络，以寻找皮质厚度的连接性。
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