Meeting Banner
Abstract #5536

In-variance causality: a novel layer of information in directed connectivity

Andrea Duggento1, Luca Passamonti2,3, Maria Guerrisi1, and Nicola Toschi1,4

1Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Roma, Italy, 2Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom, 3Istituto di Bioimmagini e Fisiologia Molecolare, Consiglio Nazionale delle Ricerche, Milano, Italy, 4Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States

Multivariate Granger Causality (MVGC) approaches have recently been employed to estimate the directionality of brain connectivity. While BOLD fluctuations also contain information about neurovascular coupling, so far all MVGC estimation frameworks have focused on central tendencies, hence disregarding directed coupling between volatilities (i.e. in-variance causality). In this paper, we develop a framework for simultaneous estimation of both in-mean and in-variance causality in complex networks. We validate our approach using synthetic data from complex ensembles of coupled nonlinear oscillators, and successively employ HCP data to provide the very first estimate of the in-variance connectome of the human brain.

This abstract and the presentation materials are available to members only; a login is required.

Join Here