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Bayesian Estimation of the Hemodynamic Response Function in Functional MRI

机译：功能性MRI中血流动力学响应函数的贝叶斯估计。

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Functional MRI (fMRI) is a recent, non-invasive technique allowing for the evolution of brain processes to be dynamically followed in various cognitive or behavioral tasks. In BOLD fMRI, what is actually measured is only indirectly related to neuronal activity through a process that is still under investigation. A convenient way to analyze BOLD fMRI data consists of considering the whole brain as a system characterized by a transfer response function, called the Hemodynamic Response Function (HRF). Precise and robust estimation of the HRF has not been achieved yet: parametric methods tend to be robust but require too strong constraints on the shape of the HRF, whereas non-parametric models are not reliable since the problem is badly conditioned. We therefore propose a full Bayesian, non-parametric method that makes use of basic but relevant a priori knowledge about the underlying physiological process to make robust inference about the HRF. We show that this model is very robust to decreasing signal-to-noise ratio and to the actual noise sampling distribution. We finally apply the method to real data, revealing a wide variety of HRF shapes.
机译：功能性MRI（fMRI）是一种最新的非侵入性技术，可在各种认知或行为任务中动态跟踪大脑过程的演变。在BOLD fMRI中，实际测量的值仅通过仍在研究中的过程与神经元活动间接相关。分析BOLD fMRI数据的便捷方法包括将整个大脑视为一个以传递响应函数为特征的系统，称为血液动力学响应函数（HRF）。尚未实现对HRF的精确和鲁棒的估计：参数方法趋于鲁棒，但对HRF的形状要求过强的约束，而非参数模型则不可靠，因为问题条件不佳。因此，我们提出了一种完整的贝叶斯非参数方法，该方法利用有关基础生理过程的基本但相关的先验知识来对HRF做出可靠的推断。我们表明，该模型对于降低信噪比和实际噪声采样分布非常鲁棒。最后，我们将该方法应用于实际数据，揭示了各种HRF形状。

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