Hu Zhenghui, Shi Pengcheng
Medical Image Computing Group, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
Med Image Comput Comput Assist Interv. 2007;10(Pt 2):734-41. doi: 10.1007/978-3-540-75759-7_89.
There is an increasing interest in exploiting the biophysical plausible models to investigate the physiological mechanisms that underlie observed BOLD response. However, most existing studies do not produce reliable model parameter estimates, are not robust due to the linearization of the nonlinear model, and do not perform statistics test to detect functional activation. To overcome these limitations, we developed a general framework for the analysis of fMRI data based on nonlinear physiological models. It performs system dynamics analysis to gain meaningful insight, followed by global sensitivity analysis for model reduction which leads to better system identifiability. Subsequently, a nonlinear filter is used to simultaneously estimate the state and parameter of the dynamic system, and statistics test is performed to derive activation maps based on such model. Furthermore, we investigate the change of the activation maps of these hidden physiological variables with experimental paradigm through time as well.
利用生物物理合理模型来研究观察到的血氧水平依赖（BOLD）反应背后的生理机制，这一兴趣正在日益增加。然而，大多数现有研究并未产生可靠的模型参数估计值，由于非线性模型的线性化而不够稳健，并且未进行统计检验以检测功能激活。为了克服这些限制，我们基于非线性生理模型开发了一个用于功能磁共振成像（fMRI）数据分析的通用框架。它进行系统动力学分析以获得有意义的见解，随后进行全局敏感性分析以进行模型简化，从而实现更好的系统可识别性。随后，使用非线性滤波器同时估计动态系统的状态和参数，并基于该模型进行统计检验以得出激活图。此外，我们还研究了这些隐藏生理变量的激活图随实验范式随时间的变化情况。
