Measures of Coupling between Neural Populations Based on Granger Causality Principle
Maciej Kaminski , Aneta Brzezicka , Jan Kamiński , Katarzyna J. Blinowska
AbstractThis 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.
|Journal series||Frontiers in Computational Neuroscience, ISSN 1662-5188, (A 30 pkt)|
|Publication size in sheets||5.7|
|Keywords in Polish||neuralsynchronization, Grangercausality, Directed Transfer Function, effective connectivity, causal coupling|
|Publication indicators||: 2016 = 0.791; : 2016 = 1.821 (2) - 2016=2.388 (5)|
|Citation count*||11 (2020-09-23)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.