is then ran through a connectivity analysis of coherence (multivariate grange-causality) and graph theory analysis of connectivity; using MVGC MATLAB toolbox, which is based on advanced vector auto-regression theory. Granger- Causality is a statistical notion of causality applied to time-series data, whereby, cause precedes, and helps predict effect. Defined in both time and frequency domains and allows for conditioning out of common influences. Otherwise, a test for determining whether one time-series is useful in predicting another. Modern neuroscience takes a network-centric approach to describing brain function and cognition. In which information, flows between multi-leveled, evolving network structures. Using the dipoles produced by ICA as vectors in a network model, multivariate granger-causality provides information about the level of functional communication between regions; the flow of information, its effect size, and the strength of communication between vectors.
Means of measuring brain connectivity is graph theory analysis. When applied to EEG we can better understand the directional connectivity of brain networks. We define the nodes of the graph as the dipoles and the edges as the functional connectivity between these dipoles. The graph describes the direction of communication between nodes, and weights are applied to the edges to represent the strength of the links between the nodes. Furthermore, network measures such as cluster coefficient, and path length are extracted from the graph theory for further interpretation. All of these data and images better allow us to produce a specialized protocol for your clients. Interpretations of these data and images are presented and reviewed during the online video consolation.