  In this paper, we consider stable stochastic linear systems modeling
whole-brain resting-state dynamics. We parametrize the state matrix of the
system (effective connectivity) in terms of its steady-state covariance matrix
(functional connectivity) and a skew-symmetric matrix $S$. We examine how the
matrix $S$ influences some relevant dynamic properties of the system.
Specifically, we show that a large $S$ enhances the degree of stability and
excitability of the system, and makes the latter more responsive to
high-frequency inputs.

Systems and Control;Systems and Control;Neurons and Cognition