IEEE Trans Biomed Eng. 2018 Sep;65(9):1953-1963. doi: 10.1109/TBME.2018.2842769. Epub 2018 Jun 1.
Computer-aided estimation of brain connectivity aims to reveal information propagation in brain automatically, which has great potential in clinical applications, e.g., epilepsy foci diagnosis. Granger causality is an effective tool for directional connection analysis in multivariate time series. However, most existing methods based on Granger causality assume fixed time lags in information transmission, while the propagation delay between brain signals is usually changing constantly.
We propose a Granger causality estimator based on the recurrent neural network, called RNN-GC, to deal with the multivariate brain connectivity detection problem. Our model takes input of time-series signals with arbitrary length of transmission time lags and learns the information flow from the data using the gated RNN model, i.e., long short-term memory (LSTM). The LSTM model can sequentially update the gates in memory cells to determine how many preceding points should be considered for prediction. Therefore, the LSTM-based RNN-GC estimator works well on varying-length time lags and shows effectiveness even on very long transmission delays.
Experiments are carried out in comparison with other methods using both simulation data and epileptic electroencephalography signals. The RNN-GC estimator achieves superior performance in brain connectivity estimation and shows robustness in modeling multivariate connections with varying-length time lags.
The RNN-GC method is capable of modeling nonlinear and varying-length lagged information transmission and effective in directional brain connectivity estimation.
The proposed method is promising to serve as a robust brain connection analysis tool in clinical applications.
计算机辅助脑连接估计旨在自动揭示脑内信息传播，在临床应用中具有巨大潜力，例如癫痫灶诊断。格兰杰因果关系是分析多变量时间序列中方向连接的有效工具。然而，大多数基于格兰杰因果关系的现有方法都假设信息传输的固定时滞，而脑信号之间的传播延迟通常是不断变化的。
我们提出了一种基于递归神经网络的格兰杰因果关系估计器，称为 RNN-GC，用于处理多变量脑连接检测问题。我们的模型以具有任意传输时滞长度的时间序列信号作为输入，并使用门控 RNN 模型（即长短期记忆（LSTM））从数据中学习信息流。LSTM 模型可以顺序更新记忆单元中的门，以确定应该考虑多少个前置点进行预测。因此，基于 LSTM 的 RNN-GC 估计器在变化的时滞上表现良好，即使在非常长的传输延迟下也能有效工作。
使用模拟数据和癫痫脑电信号与其他方法进行了实验比较。RNN-GC 估计器在脑连接估计方面表现出优越的性能，并在建模具有变化长度时滞的多变量连接方面表现出稳健性。
RNN-GC 方法能够对非线性和时滞变化的信息传输进行建模，并且在方向脑连接估计方面非常有效。
该方法有望成为临床应用中一种稳健的脑连接分析工具。
