Measuring Granger Causality between Cortical Regions from Voxelwise fMRI BOLD Signals with LASSO
Measuring Granger Causality between Cortical Regions from Voxelwise fMRI BOLD Signals with LASSO
复制标题
使用 LASSO 从 Voxelwise fMRI BOLD 信号测量皮质区域之间的格兰杰因果关系
DOI：
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
M. Corbetta
中科院分区：
文献类型：
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作者：
Wei;S. Bressler;C. Sylvester;G. Shulman;M. Corbetta
利用功能磁共振成像（fMRI）的血氧水平依赖（BOLD）信号进行脑功能网络研究，在人类认知的神经基础研究中日益流行。脑功能网络分析的一个重要问题是了解认知过程中脑区域之间的直接功能相互作用。这一问题对于理解从额顶控制区到枕叶视觉皮层对视觉空间注意力自上而下的影响具有重要意义，这也是本研究的目标。测量两个大脑区域之间定向功能相互作用的一种常用方法是首先通过平均每个区域中所有体素的BOLD信号来创建节点信号，然后测量节点信号之间的定向功能相互作用。另一种避免平均的方法是测量两个区域中所有成对体素组合之间的定向功能相互作用。在这里，我们采用一种替代方法，避免了平均和成对体素测量的缺点。在这种方法中，我们首先使用最小绝对收缩选择算子（LASSO）预先选择用于分析的体素，然后从所选体素的时间序列中计算多元向量自回归（MVAR）模型，最后从模型中计算汇总格兰杰因果关系（GC）统计数据来表示直接的区域间相互作用。我们在模拟和经验fMRI数据上证明了这种方法的有效性。我们还表明，平均区域BOLD活动来创建节点信号可能导致有偏的GC估计定向区域间相互作用。本文提出的方法使计算脑区之间的GC变得可行，而不需要平均。我们的研究结果表明，在分析功能性脑网络时，必须仔细考虑网络节点和边缘的定义方式，因为这些定义可能对分析的有效性有重要影响。
Functional brain network studies using the Blood Oxygen-Level Dependent (BOLD) signal from functional Magnetic Resonance Imaging (fMRI) are becoming increasingly prevalent in research on the neural basis of human cognition. An important problem in functional brain network analysis is to understand directed functional interactions between brain regions during cognitive performance. This problem has important implications for understanding top-down influences from frontal and parietal control regions to visual occipital cortex in visuospatial attention, the goal motivating the present study. A common approach to measuring directed functional interactions between two brain regions is to first create nodal signals by averaging the BOLD signals of all the voxels in each region, and to then measure directed functional interactions between the nodal signals. Another approach, that avoids averaging, is to measure directed functional interactions between all pairwise combinations of voxels in the two regions. Here we employ an alternative approach that avoids the drawbacks of both averaging and pairwise voxel measures. In this approach, we first use the Least Absolute Shrinkage Selection Operator (LASSO) to pre-select voxels for analysis, then compute a Multivariate Vector AutoRegressive (MVAR) model from the time series of the selected voxels, and finally compute summary Granger Causality (GC) statistics from the model to represent directed interregional interactions. We demonstrate the effectiveness of this approach on both simulated and empirical fMRI data. We also show that averaging regional BOLD activity to create a nodal signal may lead to biased GC estimation of directed interregional interactions. The approach presented here makes it feasible to compute GC between brain regions without the need for averaging. Our results suggest that in the analysis of functional brain networks, careful consideration must be given to the way that network nodes and edges are defined because those definitions may have important implications for the validity of the analysis.
DOI：
10.4236/jbise.2010.39115
发表时间：
2010-09-01
期刊：
Journal of biomedical science and engineering
影响因子：
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作者：
Zhang L;Zhong G;Wu Y;Vangel MG;Jiang B;Kong J
通讯作者：
Kong J
影响因子：
3.7
作者：
He, Yong;Chen, Zhang J.;Evans, Alan C.
通讯作者：
Evans, Alan C.
影响因子：
3.7
作者：
Gong, Gaolang;He, Yong;Beaulieu, Christian
通讯作者：
Beaulieu, Christian