Large-scale neural model validation of partial correlation analysis for effective connectivity investigation in functional MRI.

Abstract

Recent studies of functional connectivity based upon blood oxygen level dependent functional magnetic resonance imaging have shown that this technique allows one to investigate large-scale functional brain networks. In a previous study, we advocated that data-driven measures of effective connectivity should be developed to bridge the gap between functional and effective connectivity. To attain this goal, we proposed a novel approach based on the partial correlation matrix. In this study, we further validate the use of partial correlation analysis by employing a large-scale, neurobiologically realistic neural network model to generate simulated data that we analyze with both structural equation modeling (SEM) and the partial correlation approach. Unlike real experimental data, where the interregional anatomical links are not necessarily known, the links between the nodes of the network model are fully specified, and thus provide a standard against which to judge the results of SEM and partial correlation analyses. Our results show that partial correlation analysis from the data alone exhibits patterns of effective connectivity that are similar to those found using SEM, and both are in agreement with respect to the underlying neuroarchitecture. Our findings thus provide a strong validation for the partial correlation method.