Estimation of Directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome
Fig 6
Information conveyed by spatio-temporal FC and hemodynamic response function (HRF).
A Schematic representation of the properties of the two dynamic cortical model: ND network whose activity directly models the BOLD signals; dynamic mean-field (DMF) model whose synaptic activity is processed by the HRF to obtain the BOLD signal. B Two types of neural connectivity used with the DMF: random and SC matrix obtained from dwMRI. In the matrix, darker pixels indicate a higher probability of existing fibers between cortical areas. C Autocovariances of the two models for 50 different simulations. The y-axis has a log-scale. For each simulation, the curves are centered vertically with respect of the mean over all nodes to focus on the slope. The black line represents the exponential decay fitted on the mean experimental BOLD with time constant 5.3 s. D Left: Similarity between neural and BOLD covariances (excluding variances) for the two considered DMF models, as measured by the Pearson correlation coefficient. Right: Performance of EC estimation from BOLD FC for the DMF models. The performance is measured by the Pearson correlation coefficient. For each simulation, the objective is the average BOLD FC taken from 50 simulations of the same network. E Example of a typical mapping between neural and BOLD covariances for a DMF/SC network. The covariances have been rescaled. F Variability of the EC estimated from individual FCs. The grey dots represent the match of four EC each estimated for a single simulation of 300 s. The red dots correspond to the average over 50 estimated EC for 50 simulations of the same DMF/SC network. G Uncertainty of the estimated EC as a function of the estimated weight for each neural connection. The y-axis is the standard deviation over the 50 optimizations in F divided by the mean.