Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
Comput Biol Med. 2019 Jul;110:93-107. doi: 10.1016/j.compbiomed.2019.05.012. Epub 2019 May 15.
Effective connectivity is an important notion in neuroscience research, useful for detecting the interactions between regions of the brain.
Since we are dealing with a dynamic system, it seems that using a dynamic tool could effectively achieve better results. In this paper, a novel approach, called "Recurrent Neural Network - Neuron Growth Using Error Whiteness - Granger Causality" (RNN-NGUEW-GC) is proposed to estimate the effective connectivity. An RNN is used for predicting and modeling time series and multivariate signals. NGUEW is used to determine the optimum time lag with the help of an error whiteness criterion. When this criterion is not satisfied, the number of neurons in the network input is increased, producing an increase in the time lag. Accordingly, the network achieves a self-organized structure. Finally, causal effects are determined for linear and nonlinear models using the concept of Granger causality. Also, an indicator of the ''intensity of causality'' is defined to approximate the strength of the linear interactions based on the structure of the network and the weights of the connections.
RNN-NGUEW-GC had a major outcome in terms of both method accuracy on simulation data and prediction of epileptic seizures on the EEG dataset. The main advantages of this method in comparison with other methods of determining the effective connectivity are: 1) there is no need for physiological information; 2) it yields a self-organized network structure. In addition, the calculation of the appropriate time lag using NGUEW is another superiority of this method in comparison with multivariate auto-regressive models.
有效连通性是神经科学研究中的一个重要概念，可用于检测大脑区域之间的相互作用。
由于我们正在处理一个动态系统，因此使用动态工具似乎可以有效地获得更好的结果。在本文中，提出了一种新方法，称为“递归神经网络-神经元生长误差白化-格兰杰因果关系”（RNN-NGUEW-GC），用于估计有效连通性。递归神经网络用于预测和建模时间序列和多变量信号。NGUEW 用于在误差白化准则的帮助下确定最佳时滞。当该准则不满足时，增加网络输入中的神经元数量，从而增加时滞。因此，网络实现了自组织结构。最后，使用格兰杰因果关系的概念确定线性和非线性模型的因果效应。此外，定义了一个“因果强度”指标，以根据网络结构和连接权重近似线性相互作用的强度。
RNN-NGUEW-GC 在模拟数据的方法准确性和 EEG 数据集上的癫痫发作预测方面都取得了重大成果。与确定有效连通性的其他方法相比，该方法的主要优点是：1）不需要生理信息；2）它产生了自组织的网络结构。此外，使用 NGUEW 计算适当的时滞是该方法与多元自回归模型相比的另一个优势。
