On the virtues and limitations of Granger-causal brain connectivity estimate: Critical analysis using neural mass models

Estimation of brain connectivity from neuroelectric data is a fundamental problem in modern neuroscience, and it is used to assess the network properties of brain function. In the present work, we critically assess the virtues and limitations of temporal Granger causality (using both conditional and unconditional formulations) for the estimation of functional brain connectivity, using a neural mass model as the ground truth. The model simulates transmission among different brain rhythms (in the <i>θ</i>, <i>α</i>, <i>β</i>, and <i>γ</i> bands) via excitatory and inhibitory synapses. The results show that Granger causality is able to detect relative changes in connectivity, but the estimated values are influenced by the operative conditions (sampling frequency, signal length, delay). Moreover, the absolute value of Granger causality depends on the particular rhythm transmitted and is affected by nonlinear phenomena, especially the activity level in the connected regions. In the case of complex connectivity networks, conditional Granger causality overwhelms the unconditional one, since the latter often discovers spurious connections. Finally, inhibitory connections can be revealed more easily by Granger causality than similar excitatory connections, a result generally neglected in brain network studies. The present results can drive the correct interpretation of Granger-causality-based connectivity networks derived from neuroelectric signals.