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Spectral measures of linear Granger causality have been widely applied to study the causal connectivity between time series data in neuroscience, biology, and economics. Traditional Granger causality measures are based on linear autoregressive with exogenous (ARX) inputs models of time series data, which cannot truly reveal nonlinear effects in the data especially in the frequency domain. In this...
The self-connected group lasso is used to estimate sparse multivariable autoregressive with exogenous (MVARX) input models of the cortical interactions excited by direct current stimulation of the cortex. The group lasso criterion introduces a direct network connection between two sites only if the presence of the connection significantly reduces the mean-squared error of the model. This method is...
We demonstrate a method to build an autoregressive model for the whole brain without carrying out any aggregation of the fMRI data. The model gives biologically meaningful results and has several desirable properties. We show that the model gives significantly improved prediction on unseen data as compared to baseline methods. The voxels with better prediction are distributed throughout the brain,...
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