Estimation of direct nonlinear effective connectivity using information theory and multilayer perceptron

Background: Despite the variety of effective connectivity measures, few methods can quantify direct nonlinear causal couplings and most of them are not applicable to high-dimensional datasets. New method: In this paper, a novel approach (called beta mRMR-MLP-GC) is proposed to estimate direct nonlinear effective connectivity of high-dimensional datasets. beta mRMR is used to select a suitable subset of candidate regressors for approximating each neural (here EEG) signal. The multilayer perceptron (MLP) is used for multivariate characterization of EEG signals while the optimum MLP structure is selected using an iterative cross-validation scheme. Finally a causality measure is defined based on Granger Causality (GC) concept to quantify the casual relations among EEG channels. Results: Applying beta mRMR-MLP-GC to high-dimensional simulated datasets with different linear and nonlinear structures yields sensitivity and specificity values higher than 95%. Also, applying it to eyes-closed resting state EEG of six normal subjects in the alpha frequency band yields significant net activity propagations from the posterior to anterior brain regions. This is in accordance with the most previous studies in this field. Comparison with existing method(s): beta mRMR-MLP-GC is compared with Granger Causality Index, Conditional Granger Causality Index, and Transfer Entropy. It outperforms these methods in terms of sensitivity and specificity in simulated datasets. Also, beta mRMR-MLP-GC detects the most number of significant and reproducible Back-to-Front net information flows among the specified brain regions and highlights the posterior brain regions as dominant source of alpha activity propagation. Conclusions: beta mRMR-MLP-GC provides a novel tool to estimate the direct nonlinear causal networks of high-dimensional datasets. (C) 2014 Elsevier B.V. All rights reserved.