Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data

• URL: http://arxiv.org/abs/2307.09770v1
• Date: Wed, 19 Jul 2023 06:14:54 GMT
• Title: Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data
• Authors: Peizhen Yang, Xinke Shen, Zongsheng Li, Zixiang Luo, Kexin Lou, Quanying Liu
• Abstract summary: We trained neural networks to predict future EEG signals according to historical data and perturbed the networks' input to obtain effective connectivity. CNN and Transformer obtained the best performance on both 3-channel and 90-channel synthetic EEG data, outperforming the classical Granger causality method.
• Score: 0.7829352305480285
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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