Zhang Yan, Chen Chunxiao, Lu Guangming, Zhang Zhiqiang, Yu Haiyan, Huang Wei, Chen Zhili, Zhong Yuan
Department of Medical Imaging, Nanjing General Hospital of Nanjing Military Command, Nanjing 210002, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2009 Oct;26(5):931-5, 940.
Dynamic causal modeling (DCM) is a spatio-temporal renewable network model. As an analytical method of causality of functional integration in fMRI, DCM is applied to study the effective connectivity. The neuro-imaging time series of activated regions were put into DCM, and the trial-bound inputs were used as perturbations to the designed model. DCM was used in combination with Bayesian estimation to evaluate the intrinsic connectivity among selected neurons. Bayes factors were used to compute different neuro-physiological models with intrinsic connectivity structures, and then were used to select the optimal model. The selected regions in this mental calculation task are the left superior parietal lobule (SPL), the left inferior parietal lobule (IPL) and the left middle frontal gyrus (MFG). Finally, the connected network in conformity to physiological significance was obtained.
动态因果模型（DCM）是一种时空可再生网络模型。作为功能磁共振成像（fMRI）中功能整合因果关系的一种分析方法，DCM被用于研究有效连接性。将激活区域的神经成像时间序列输入DCM，并将试验相关输入用作对设计模型的扰动。DCM与贝叶斯估计结合使用，以评估选定神经元之间的内在连接性。贝叶斯因子用于计算具有内在连接结构的不同神经生理模型，然后用于选择最优模型。该心理计算任务中选定的区域是左侧顶上小叶（SPL）、左侧顶下小叶（IPL）和左侧额中回（MFG）。最后，获得了符合生理意义的连接网络。
