An Auxiliary System for Calculating Effective Connectivity from Co-Activation Network Data

Effective connectivity analysis has been extensively used in recent years by scientists studying brain functional networks because it can illuminate the brain's mechanisms and functional logic. Dynamic causal modeling is the most widely used method for effective connectivity analysis, and it is often performed with statistical parametric mapping, the most popular statistical analysis procedure for brain function data. To solve problems related to the complexity of statistical parametric mapping and the limitations of existing meta-analytic databases, we designed an auxiliary system for calculating effective brain connectivity based on co-activation networks. This system improves the efficiency of dynamic causal modeling calculations and simplifies the pre-processing steps of statistical parametric mapping. To improve the execution efficiency of dynamic causal modeling, we used connectivity information for the co-activation network as a priori knowledge to assist the selection of regions of interest based on our previously proposed co-activation network method. During dynamic causal modeling calculations, we decreased the dimensionality and runtime of the analysis by applying association rule algorithms to exclude some impossible connectivity relationships in the co-activation network. We automatically extracted information from tables to establish an a priori knowledge database on published literature for greater convenience in meta-analyses and co-activation network mining. We decreased the risk of human errors in statistical parametric mapping by optimizing its preprocessing steps to modularize its processes and by simplifying unnecessary parameter settings. We have verified that our system increases the efficiency and accuracy of dynamic causal modeling. This will greatly aid both new and experienced users.