Study of the brain functional network using synthetic data

The brain functional connectivity is usually assessed with the correlation coefficients of certain signals. The partial correlation matrix can reveal direct interactions between brain regions. However, computing this matrix is usually challenging due to the availability of only a limited number of samples. As an alternative, thresholding the sample correlation matrix is a common technique for the identification of the direct interactions. In this work, we investigate the performance of this method in addition to some other well-known techniques, namely graphical lasso and Chow-Liu algorithm. Our analysis is performed on some synthetic data produced by an electrical circuit model with certain structural properties. We show that the simple method of thresholding the correlation matrix and the graphical lasso algorithm would both create false positives and negatives that wrongly imply some network properties such as small-worldness. We also apply these techniques to some resting-state functional MRI (fMRI) data and show that similar observations can be made.