Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Exploring Connectivity of the Brain's White Matter with Dynamic Queries
IEEE Transactions on Visualization and Computer Graphics
Distinguishing Causal Interactions in Neural Populations
Neural Computation
Dynamic autoregressive neuromagnetic causality imaging (DANCI)
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
A comparison of multivariate causality based measures of effective connectivity
Computers in Biology and Medicine
A comparison of multivariate causality based measures of effective connectivity
Computers in Biology and Medicine
Statistical pitfalls in the comparison of multivariate causality measures for effective causality
Computers in Biology and Medicine
Computers in Biology and Medicine
Predicting cardiac autonomic neuropathy category for diabetic data with missing values
Computers in Biology and Medicine
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The most common method for calculating Granger causality requires the fitting of a system of autoregressive equations to multiple interrelated signals. Historically, the Levinson, Wiggins, Robinson (LWR) algorithm and the least-squares linear regression (LSLR) approach are the most widely used methods for fitting these autoregressive equations. In this manuscript we compare these algorithms head-to-head. LSLR, as implemented using the Dynamic Autoregressive Neuromagnetic Causal Imaging (DANCI) method, was faster, and produced better residual error, normality, independence, and autocorrelation functions when analyzing real magnetoencephalography signals. Simulations demonstrated that the accuracy of LSLR was much higher than the LWR method and that the LSLR method, at least as implemented by DANCI, could accurately resolve the causal connectivity of 50 interrelated signals. We conclude that the multichannel LSLR method, as implemented by DANCI, can accurately calculate the interdependencies among multiple signals and has the potential to accurately calculate Granger causality for large-scale neurophysiological networks.