fMRI血液动力学响应模型的参数敏感性分析及优化

发布时间：2018-09-11 19:12

【摘要】：功能磁共振成像(f MRI)是近几年来研究大脑功能的主要手段,要探究f MRI数据与神经活动的关系,研究血液动力学响应函数模型是关键。由Cohen提出的gamma函数形式的血液动力学响应函数,可以描述相应的响应现象,然而由于在实验数据拟合时未考虑参数间的耦合作用,导致得到的函数模型在求解精度和拟合效果上都并非最优。针对该问题,本研究利用参数敏感度分析和遗传算法相结合的方法,对3参数gamma函数形式的血液动力学响应函数进行整体和全局的优化。首先通过拉丁超立方抽样和spearman秩相关分析理论结合的方法,对该模型中的材料参数进行参数敏感度分析,得出参数敏感度相当的合理取值范围。将该取值范围作为初始搜索区间,结合遗传算法理论对各个参数进行全局最优搜索,结果即为考虑各个参数相互耦合变化的优化值。优化后的模型能较好地描述和预测血液动力学响应函数。该结果较Cohen的模型在拟合效果和实验预测精度上有一定的提高。
[Abstract]:Functional magnetic resonance imaging (f MRI) is the main method to study brain function in recent years. It is crucial to study the relationship between f MRI data and neural activity and to study the hemodynamic response function model. The hemodynamic response function in the form of gamma function proposed by Cohen can describe the corresponding response phenomenon. However, the coupling between parameters is not considered in the fitting of experimental data. The resulting function model is not optimal in accuracy and fitting effect. In order to solve this problem, the method of parameter sensitivity analysis and genetic algorithm is used to optimize the hemodynamic response function in the form of 3-parameter gamma function. Firstly, by using the method of Latin hypercube sampling and spearman rank correlation analysis, the material parameters in the model are analyzed and the reasonable range of parameter sensitivity is obtained. Using this range as the initial search interval and combining the genetic algorithm theory, the global optimal search of each parameter is carried out. The result is the optimal value considering the coupling variation of each parameter. The optimized model can well describe and predict the hemodynamic response function. The result is better than that of Cohen model in fitting effect and experimental precision.
【作者单位】：四川大学工程力学系;
【基金】：国家自然科学基金资助项目(11272224)
【分类号】：R445.2

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