Estimating Causality among Cortical Areas of the Human Brain: A Study on the Application of Directed Transfer Function and Structural Equation Modeling to High Resolution EEG

The concept of brain connectivity plays a central role in the neuroscience and different methodologies for the estimation of such connectivity have been adopted in literature. This paper presents advanced methods for the estimation of cortical connectivity by applying Structural Equation Modeling (SEM) and Directed Transfer Function (DTF) on the cortical signals estimated from high resolution EEG recordings. Before the application of SEM and DTF methodology to the cortical waveforms estimated from high resolution EEG data in human, we performed a simulation study, in which different main factors (signal to noise ratio, SNR, and simulated cortical activity duration, LENGTH) were systematically manipulated in the generation of test signals, and the errors in the estimated connectivity were evaluated by the Analysis of Variance (ANOVA). The statistical analysis returned that during simulations, both SEM and DTF estimators were able to correctly estimate the imposed connectivity patterns under reasonable operative conditions, i.e. when data exhibit a SNR of at least 3 and a LENGTH of at least 75 seconds of non-consecutive EEG recordings at 64 Hz of sampling rate. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed fMRI. These methods were evaluated in human experimental EEG and fMRI data recorded in separate sessions. K eywords: Structural Equation Modeling, Directed Transfer Function, High resolution EEG, fMRI, connectivity.