Identifying causal relationships among distinct brain areas, known as effective connectivity, holds key insights into the brain's information processing and cognitive functions. Electroencephalogram (EEG) signals exhibit intricate dynamics and inter-areal interactions within the brain. However, methods for characterizing nonlinear causal interactions among multiple brain regions remain relatively underdeveloped. In this study, we proposed a data-driven framework to infer effective connectivity by perturbing the trained neural networks. Specifically, we trained neural networks (i.e., CNN, vanilla RNN, GRU, LSTM, and Transformer) to predict future EEG signals according to historical data and perturbed the networks' input to obtain effective connectivity (EC) between the perturbed EEG channel and the rest of the channels. The EC reflects the causal impact of perturbing one node on others. The performance was tested on the synthetic EEG generated by a biological-plausible Jansen-Rit model. CNN and Transformer obtained the best performance on both 3-channel and 90-channel synthetic EEG data, outperforming the classical Granger causality method. Our work demonstrated the potential of perturbing an artificial neural network, learned to predict future system dynamics, to uncover the underlying causal structure.

翻译：识别不同脑区之间的因果关系（即有效连接）对理解大脑的信息处理及认知功能至关重要。脑电图（EEG）信号展现出大脑内部复杂的动态特性及脑区间相互作用。然而，表征多个脑区之间非线性因果交互的方法仍相对不完善。本研究提出一种数据驱动框架，通过扰动训练好的神经网络来推断有效连接。具体而言，我们训练神经网络（包括CNN、原始RNN、GRU、LSTM及Transformer）基于历史数据预测未来EEG信号，并通过扰动网络输入以获取被扰动EEG通道与其他通道之间的有效连接（EC）。该有效连接反映了扰动一个节点对其他节点的因果影响。我们利用基于生物学合理的Jansen-Rit模型生成的合成EEG数据测试了该方法性能。在3通道与90通道的合成EEG数据上，CNN与Transformer均取得了最优结果，优于经典的格兰杰因果方法。本研究证明了通过扰动已学会预测未来系统动力学的人工神经网络来揭示潜在因果结构的可行性。