Development of a computer-aided system for an effective brain connectivity network

Abstract

Currently, dynamic causal modeling (DCM) is one of the most widely used models for an effective brain connectivity network, but it also has some disadvantages (e.g., researchers' selection of cerebral regions of interest [ROIs] is subjective, a substantial time is required for computation, etc.). Statistical Parametric Mapping (SPM) is the most popular statistical data analysis software for brain function, but its settings cumbersome, especially the data preprocessing section. In response to these disadvantages of DCM and SPM, we designed and created a computer-aided system for an effective brain connectivity network, modularized the data preprocessing section of SPM, and we explored the cerebral ROIs and possible co-activation network based on our proposed approach. The co-activation network has as a prior interconnection relationship, and it is used to assist in the selection of ROIs in similar cognitive experiments; thus, the testing of meaningless noise connection modes by the DCM is prevented, the number of models DMC is decreased, and the accuracy of the conclusions and computational efficiency of the DCM are improved.