Functional connectivity during autonomic stimulation estimated using spectral coherence of fMRI signals

Several studies in the last years have explored the potential of using a frequency domain approach in the analysis of functional brain connectivity. However, few studies have integrated the advantages of such approach with the brain network model given by graph theory. Furthermore, the use of such methods has rarely been explored with fixed frequency stimulation resting-state protocols. In this paper, we propose a method to model functional connectivity using both a frequency domain approach and a network model. By estimating coherence with the Welch method, it is possible to represent the brain activity using binary connectivity matrices, and to characterize such networks in terms of global and local measures. We tested our method with experimental data from an autonomic stimulation protocol and compared the results with the more common correlation analysis. We were able to see significant differences between the two sessions in the frequency range of the stimulation. Such differences involved brain areas associated with the central autonomic network and didn't show up in the time domain approach.