For a given connectivity method, what effectively matters, is its capacity to properly detect a connection, so that mainly TPs (a connection properly detected) and FPs (a connection wrongly detected) have to be taken into account.
Further, PPC proved to be a reliable technique for selecting the threshold value for maximizing the performances of a specific connectivity method.
GCM is an effective connectivity method, which is based on the Granger causality concept to measure the existence and predominant direction of influence from information in time series [5].
We focus on (1) psycho-physiological interaction (PPI) analysis, which is a simple brain connectivity method that characterizes the activity in one brain region by the interaction between another region's activity and a psychological factor, and (2) interregional correlation analysis.
In addition, for both this patch-clamp method and our effective connectivity method, connection probability increased almost linearly with the number of common neighbors.
In this study, we apply a recently developed functional connectivity method and relate individual estimates of connectivity in multiple cortical networks to performance in multiple cognitive domains.
For each pair of neurons, the connectivity method provides an estimation of the connection strength (one for each direction).
Furthermore, these values indicate TE as the best connectivity method and show a slight improvement of the TE performances on EI models.
The approach we adopted, by considering the connection strength proportional to the value of the connectivity method, does not allowed us to identify inhibitory connections.
This exploratory connectivity method explains responses of a cortical area by means of an interaction term between the influence of another area and some experimental or psychological parameter (Friston et al., 1997; Stephan, 2004).
