AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, Alabama, USA.
Univ. Grenoble Alpes, F-38000, Grenoble, France.
Magn Reson Med. 2017 Nov;78(5):2003-2010. doi: 10.1002/mrm.26583. Epub 2017 Jan 16.
Investigating directional interactions between brain regions plays a critical role in fully understanding brain function. Consequently, multiple methods have been developed for noninvasively inferring directional connectivity in human brain networks using functional MRI (fMRI). Recent simulations by Smith et al. showed that Patel's τ, a method based on higher-order statistics, was the best approach for inferring directional interactions from fMRI. Because simulations make restrictive assumptions about reality, we set out to verify this finding using experimental fMRI data obtained from a three-region network in a rat model with electrophysiological validation.
Previous studies have shown that dynamic causal modeling can correctly estimate the directionality of this three-region network; Granger causality can also work after the deconvolution of the hemodynamic response. Therefore, we set out to test the hypothesis that Patel's τ obtained from either raw or deconvolved fMRI data should correctly estimate the directionality of neuronal influences obtained from intracerebral electroencephalogram in this network.
Our results indicate that the accuracy of network directionality estimated using Patel's τ was not better than chance.
First, our results highlight the necessity of experimental validation of simulation results. Second, it is unclear which brain mechanism is modeled by a directionality inferred from Patel's τ. Third, this study shows that a directional connection ascertained by different methods may mean different things and more experimental studies are needed for investigating the neuronal mechanisms underlying the direction of a connection in the brain ascertained by fMRI using different methods. M Magn Reson Med 78:2003-2010, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
研究大脑区域之间的定向相互作用对于全面理解大脑功能起着至关重要的作用。因此，已经开发出多种方法来使用功能磁共振成像（fMRI）无创地推断人类大脑网络中的定向连接。Smith 等人的最近模拟表明，基于高阶统计的 Patel τ 是从 fMRI 推断定向相互作用的最佳方法。由于模拟对现实做出了限制性假设，因此我们着手使用从具有电生理验证的大鼠模型的三个区域网络获得的实验 fMRI 数据来验证这一发现。
先前的研究表明，动态因果建模可以正确估计这个三区域网络的方向性；在对血流动力学响应进行反卷积后，Granger 因果关系也可以工作。因此，我们着手检验以下假设：从原始或反卷积 fMRI 数据中获得的 Patel τ 应该可以正确估计该网络中脑内脑电图获得的神经元影响的方向性。
我们的结果表明，使用 Patel τ 估计的网络方向性的准确性并不优于偶然。
首先，我们的结果强调了模拟结果需要实验验证的必要性。其次，尚不清楚 Patel τ 推断的大脑机制是什么。第三，这项研究表明，不同方法确定的定向连接可能意味着不同的事情，需要更多的实验研究来研究使用不同方法通过 fMRI 确定的大脑中连接方向的神经元机制。磁共振医学 78:2003-2010, 2017. © 2017 国际磁共振学会。
