Measures of Coupling between Neural Populations Based on Granger Causality Principle.
This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality) principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a "weak node." Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF) supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures.
Department of Biomedical Physics, Faculty of Physics, University of Warsaw Warsaw, Poland.
Maciej
M
Kaminski
Department of Psychology, University of Social Sciences and Humanities Warsaw, Poland.
Aneta
A
Brzezicka
Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University Torun, Poland.
Jan
J
Kaminski
Department of Biomedical Physics, Faculty of Physics, University of WarsawWarsaw, Poland; Institute of Biocybernetics and Biomedical Engineering of Polish Academy of SciencesWarsaw, Poland.
Katarzyna J
KJ
Blinowska
Directed Transfer Function; Granger causality; causal coupling; effective connectivity; neural synchronization