Patel Rajan S, Bowman F Dubois, Rilling James K
Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, USA.
Hum Brain Mapp. 2006 Mar;27(3):267-76. doi: 10.1002/hbm.20182.
Recent work regarding the analysis of brain imaging data has focused on examining functional and effective connectivity of the brain. We develop a novel descriptive and inferential method to analyze the connectivity of the human brain using functional MRI (fMRI). We assess the relationship between pairs of distinct brain regions by comparing expected joint and marginal probabilities of elevated activity of voxel pairs through a Bayesian paradigm, which allows for the incorporation of previously known anatomical and functional information. We define the relationship between two distinct brain regions by measures of functional connectivity and ascendancy. After assessing the relationship between all pairs of brain voxels, we are able to construct hierarchical functional networks from any given brain region and assess significant functional connectivity and ascendancy in these networks. We illustrate the use of our connectivity analysis using data from an fMRI study of social cooperation among women who played an iterated "Prisoner's Dilemma" game. Our analysis reveals a functional network that includes the amygdala, anterior insula cortex, and anterior cingulate cortex, and another network that includes the ventral striatum, orbitofrontal cortex, and anterior insula. Our method can be used to develop causal brain networks for use with structural equation modeling and dynamic causal models.
近期有关脑成像数据分析的工作主要集中在研究大脑的功能连接和有效连接。我们开发了一种新颖的描述性和推断性方法，利用功能磁共振成像（fMRI）来分析人类大脑的连接性。我们通过贝叶斯范式比较体素对活动增强的预期联合概率和边缘概率，评估不同脑区之间的关系，该范式允许纳入先前已知的解剖学和功能信息。我们通过功能连接性和优势度的测量来定义两个不同脑区之间的关系。在评估所有脑体素对之间的关系后，我们能够从任何给定的脑区构建层次化功能网络，并评估这些网络中显著的功能连接性和优势度。我们使用一项针对参与重复“囚徒困境”游戏的女性进行的fMRI研究数据，说明了我们的连接性分析的应用。我们的分析揭示了一个包括杏仁核、前岛叶皮质和前扣带回皮质的功能网络，以及另一个包括腹侧纹状体、眶额皮质和前岛叶的网络。我们的方法可用于开发用于结构方程建模和动态因果模型的因果脑网络。
