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.
@article{arxiv.2310.07262,
title = {Dynamic Brain Networks with Prescribed Functional Connectivity},
author = {Umberto Casti and Giacomo Baggio and Danilo Benozzo and Sandro Zampieri and Alessandra Bertoldo and Alessandro Chiuso},
journal= {arXiv preprint arXiv:2310.07262},
year = {2023}
}
Comments
6 pages, 5 figures, to appear in the proceedings of IEEE CDC 2023