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.

中文翻译：

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功能磁共振成像分析中血液动力学的生理高斯过程先验。

背景

从fMRI数据推断面临挑战，即将神经活动与观察到的BOLD fMRI信号相关的血液动力学系统尚不清楚。

新方法

我们为任务功能性MRI数据提出了一种新的贝叶斯模型，该模型具有以下特征：（i）大脑活动和潜在血液动力学的联合估计，（ii）先用生理信息指导的高斯过程（GP）对血液动力学进行非参数建模，并且（ iii）预测的BOLD不一定由线性时不变（LTI）系统生成。我们将GP先验直接放在预测的BOLD反应上，而不是像先前文献中那样将其放在血流动力学反应函数上。这使我们能够灵活地通过GP先验平均值合并生理信息，同时为我们提供GP的非参数灵活性。

结果

模拟数据的结果表明，当GP先验偏离真实的血液动力学时，提出的模型也能够区分主动和非主动体素。当将模型应用于真实的fMRI数据时，我们的模型会发现时变动力学。

与现有方法的比较

所提出的模型比标准模型在检测模拟数据中的活动方面更胜一筹，而不会虚假率很高。当应用于真实的fMRI数据时，在某些情况下，我们的GP模型会发现大脑活动，而先前提出的LTI模型则找不到。

结论

我们为任务功能磁共振成像中的血液动力学提出了一种新的非线性模型，该模型能够检测活动体素，并有机会提出与血液动力学有关的新问题。