Event-related functional magnetic resonance imaging (ER-fMRI) refers to the blood oxygen level-dependent (BOLD) signal in response to a short stimulus followed by a long period of rest. These paradigms have become more popular in the last few years due to some advantages over standard block techniques. Most of the analysis of the time series generated in such exams is based on a model of specific hemodynamic response function. In this paper we propose a new method for the analysis of ER-fMRI based in a specific aspect of information theory: the entropy of a signal using the Shannon formulation, which makes no assumption about the shape of the response. The results show the ability to discriminate between activated and resting cerebral regions for motor and visual stimuli. Moreover, the results of simulated data show a more stable pattern of the method, if compared to typical algorithms, when the signal to noise ratio decreases.

译文

事件相关的功能磁共振成像 (ER-fMRI) 是指对短暂刺激,随后长时间休息的血氧水平依赖性 (BOLD) 信号。由于与标准块技术相比具有一些优势,这些范例在最近几年变得越来越流行。在此类检查中生成的时间序列的大多数分析都是基于特定血液动力学响应函数的模型。在本文中,我们提出了一种基于信息论的特定方面的ER-fMRI分析的新方法: 使用Shannon公式的信号熵,该方法不对响应的形状进行假设。结果表明,能够区分运动和视觉刺激的激活和静止大脑区域。此外,当信噪比降低时,如果与典型算法相比,模拟数据的结果显示出该方法的更稳定模式。

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