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

• Published in: Journal of neuroscience methods Vol. 229; pp. 53 - 67
• Main Authors: Khadem, Ali, Hossein-Zadeh, Gholam-Ali
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 30.05.2014
• Subjects: Adult; Algorithms; Alpha Rhythm; Brain - physiology; Computer Simulation; EEG; Electroencephalography - methods; Granger Causality; Humans; Information Theory; Linear Models; Male; Multilayer perceptron; Multivariate Analysis; Neural Networks (Computer); Nonlinear Dynamics; Nonlinear effective connectivity; Regression Analysis; Regressor selection; Rest - physiology; Sensitivity and Specificity; Young Adult; Multilayer perceptron; Granger Causality; EEG; Nonlinear effective connectivity; Regressor selection
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2014.04.008

•The direct nonlinear effective connectivity of high-dimensional datasets is estimated.•A combination of regressor selection, MLP modeling and Granger Causality is proposed.•βmRMR-MLP-GC can deal with highly nonlinear, high-dimensional datasets.•In simulations, βmRMR-MLP-GC yields both high sensitivity and specificity.•βmRMR-MLP-GC detects Back-to-Front alpha information flows in resting brain. 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. In this paper, a novel approach (called βmRMR-MLP-GC) is proposed to estimate direct nonlinear effective connectivity of high-dimensional datasets. βmRMR 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. Applying βmRMR-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. βmRMR-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, βmRMR-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. βmRMR-MLP-GC provides a novel tool to estimate the direct nonlinear causal networks of high-dimensional datasets.