Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
Magn Reson Imaging. 2010 Oct;28(8):1113-9. doi: 10.1016/j.mri.2010.03.028. Epub 2010 Apr 21.
We investigated the use and implementation of a nonlinear methodology for establishing which changes in neurophysiological signals cause changes in the blood oxygenation level-dependent (BOLD) contrast measured in functional magnetic resonance imaging. Unlike previous analytical approaches, which used linear correlation to establish covariations between neural activity and BOLD, we propose a directed information-theoretic measure, the transfer entropy, which can elucidate even highly nonlinear causal relationships between neural activity and BOLD signal. In this study we investigated the practicality of such an analysis given the limited data samples that can be collected experimentally due to the low temporal resolution of BOLD signals. We implemented several algorithms for the estimation of transfer entropy and we tested their effectiveness using simulated local field potentials (LFPs) and BOLD data constructed to match the main statistical properties of real LFP and BOLD signals measured simultaneously in monkey primary visual cortex. We found that using the advanced methods of entropy estimation implemented and described here, a transfer entropy analysis of neurovascular coupling based on experimentally attainable data sets is feasible.
我们研究了一种非线性方法的使用和实施情况，该方法用于确定神经生理信号中的哪些变化会导致功能磁共振成像中测量的血氧水平依赖（BOLD）对比的变化。与之前使用线性相关来确定神经活动和 BOLD 之间的协变的分析方法不同，我们提出了一种有向信息论度量，即转移熵，它可以阐明神经活动和 BOLD 信号之间甚至高度非线性的因果关系。在这项研究中，我们研究了由于 BOLD 信号的时间分辨率低，实验中可以收集到的有限数据样本的情况下，这种分析的实用性。我们实现了几种转移熵估计的算法，并使用模拟局部场电位（LFP）和 BOLD 数据来测试它们的有效性，这些数据是根据在猴子初级视觉皮层中同时测量的真实 LFP 和 BOLD 信号的主要统计特性构建的。我们发现，使用这里实现和描述的熵估计的高级方法，基于可实验获得的数据集的神经血管耦合的转移熵分析是可行的。
