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动态因果模型(Dynamic causal modeling,DCM)是一种时空上可再生的网络模型,用来研究功能核磁共振中功能整合的因果关系,是效应连通性的分析方法,该方法是将实验设计中得到的激活区域时间序列加入到DCM模型中,实验任务的刺激响应作为对模型的扰动,利用DCM和贝叶斯估计计算出各神经元或者神经系统之间前后影响的因果关系,以及大范围的内在连接,然后利用贝叶斯因子对所设计各模型的参数做最优化选择,从中选择出符合生理的最佳模型。本文主要研究心算借位减法任务激活的左侧大脑区域,左侧顶上小叶、左侧顶下小叶和左侧额中回之间的效应连接,并得到符合生理意义的连接网络。
Dynamic causal modeling (DCM) is a kind of spatiotemporal and renewable network model, which is used to study the causality of functional integration in functional MRI. It is an analytical method of effectivity connectivity, which is obtained from the experimental design Time domain of activation region was added to the DCM model. The stimulus response of the experimental task was used as a perturbation to the model. The causal relationship between the antecedent and the posterior effects among neurons or nervous systems was calculated using DCM and Bayesian estimation, and a large range of internal And then use Bayesian factor to optimize the parameters of each model designed, and select the best model that meets the physiological. This paper mainly studies the effect connection between left brain area, left top lobule, left parietal lobule and left frontal gyrus activated by mental arithmetic borrow subtraction task, and obtains the physiological connection network.