fMRI分析中血流动力学的生理高斯过程先验
Physiological Gaussian Process Priors for the Hemodynamics in fMRI Analysis
摘要：
背景资料：从功能磁共振成像数据推断面临的挑战是，血液动力学系统的神经活动所观察到的BOLD功能磁共振成像信号是未知的。新方法：我们提出了一个新的贝叶斯模型的任务功能磁共振成像数据具有以下特点：（i）联合估计的大脑活动和潜在的血流动力学，（ii）的血流动力学建模非参数与高斯过程（GP）的生理信息的指导下事先和（iii）预测的BOLD不一定是由线性时不变（LTI）系统产生。我们直接在预测的BOLD响应上放置GP先验，而不是像以前的文献中那样在血流动力学响应函数上放置GP先验。这使我们能够通过GP先验均值以灵活的方式结合生理信息，同时为我们提供了GP的非参数灵活性。结果如下：模拟数据的结果表明，该模型是能够区分活跃和非活跃的体素也当GP先验偏离真实的血液动力学。我们的模型发现时变动态时，应用到真正的功能磁共振成像数据。与现有方法的比较：所提出的模型在检测模拟数据中的活性方面优于标准模型，而不会增加假阳性率。当应用于真实的fMRI数据时，我们的GP模型在几种情况下发现了以前提出的LTI模型没有的大脑活动。结论：我们提出了一个新的非线性模型的血流动力学在任务功能磁共振成像，这是能够检测活动体素，并提供了机会，问新的类型的问题有关的血流动力学。
英文摘要：
Background: Inference from fMRI data faces the challenge that the hemodynamic system that relates neural activity to the observed BOLD fMRI signal is unknown. New Method: We propose a new Bayesian model for task fMRI data with the following features: (i) joint estimation of brain activity and the underlying hemodynamics, (ii) the hemodynamics is modeled nonparametrically with a Gaussian process (GP) prior guided by physiological information and (iii) the predicted BOLD is not necessarily generated by a linear time-invariant (LTI) system. We place a GP prior directly on the predicted BOLD response, rather than on the hemodynamic response function as in previous literature. This allows us to incorporate physiological information via the GP prior mean in a flexible way, and simultaneously gives us the nonparametric flexibility of the GP. Results: Results on simulated data show that the proposed model is able to discriminate between active and non-active voxels also when the GP prior deviates from the true hemodynamics. Our model finds time varying dynamics when applied to real fMRI data. Comparison with Existing Method(s): The proposed model is better at detecting activity in simulated data than standard models, without inflating the false positive rate. When applied to real fMRI data, our GP model in several cases finds brain activity where previously proposed LTI models does not. Conclusions: We have proposed a new non-linear model for the hemodynamics in task fMRI, that is able to detect active voxels, and gives the opportunity to ask new kinds of questions related to hemodynamics.
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