Study of the brain functional network using synthetic data
Study of the brain functional network using synthetic data
复制标题
使用合成数据研究大脑功能网络
DOI：
10.1109/allerton.2014.7028476
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
J. Doyle
中科院分区：
文献类型：
--
作者：
S. Sojoudi;J. Doyle
大脑功能连接通常通过某些信号的相关系数来评估。偏相关矩阵可以揭示大脑区域之间的直接相互作用。然而，由于样本数量有限，计算该矩阵通常具有挑战性。作为替代方案，对样本相关矩阵进行阈值化是识别直接相互作用的常用技术。在这项工作中，除了一些其他众所周知的技术（即图形套索和 Chow-Liu 算法）之外，我们还研究了该方法的性能。我们的分析是对具有某些结构特性的电路模型产生的一些合成数据进行的。我们证明，对相关矩阵进行阈值化的简单方法和图形套索算法都会产生误报和误报，从而错误地暗示某些网络属性，例如小世界性。我们还将这些技术应用于一些静息态功能 MRI (fMRI) 数据，并表明可以进行类似的观察。
The brain functional connectivity is usually assessed with the correlation coefficients of certain signals. The partial correlation matrix can reveal direct interactions between brain regions. However, computing this matrix is usually challenging due to the availability of only a limited number of samples. As an alternative, thresholding the sample correlation matrix is a common technique for the identification of the direct interactions. In this work, we investigate the performance of this method in addition to some other well-known techniques, namely graphical lasso and Chow-Liu algorithm. Our analysis is performed on some synthetic data produced by an electrical circuit model with certain structural properties. We show that the simple method of thresholding the correlation matrix and the graphical lasso algorithm would both create false positives and negatives that wrongly imply some network properties such as small-worldness. We also apply these techniques to some resting-state functional MRI (fMRI) data and show that similar observations can be made.