Dynamic Connectivity Mapping of Electrocorticographic Data using Bayesian Differential Structural Equation Modeling
Advances in the sophistication of imaging techniques necessitate the development of techniques to model the neural and cognitive phenomena they represent. Using electrocorticographic (ECoG) data, we propose a data driven approach using ordinary differential equations and Bayesian differential structural equation modeling (BdSEM) to model effective connectivity related to sensorimotor integration. First, we tested the region based covariance structure across subjects by each experimental condition to evaluate the tenability of pooling subject data to perform group level versus single subject analyses. Second, we applied a differential equation approach to model dynamic change originating from regional neuronal states across the data acquisition period. Finally, we employed an information theoretic search strategy to identify the optimal connectivity model within each experimental condition for a single subject. Results of subject specific (intra individual) relationship maps include effective, contemporaneous and delayed effective connections of across different brain regions.