Using Conditional Mutual Information to Approximate Causality for Multivariate Physiological Time Series

Causality analytic techniques based on conditional mutual information are described. Causality analysis may be used to infer linear and nonlinear causal relations between selected brain regions, and can account for identified non-causal confounds. The analysis results in a directed graph whose nodes are brain regions, and whose edges represent information flow. This causal information measure in principle should handle arbitrary nonlinear interactions without presupposing particular models of interaction. Keywords—Functional connectivity, causality, multivariate time series analysis