Functional connectivity measures based on partial correlation provide an estimate of the linear conditional dependence between brain regions after removing the linear influence of other regions.
Functional connectivity measurements based on interregional correlations were acquired at rest and during continuous, sequential finger motion.
Most MEG/EEG source connectivity methods are based on functional connectivity measures such as coherence or phase synchronization [Gross et al., 2002; Hoechstetter et al., 2004; Jerbi et al., 2007; Lachaux et al., 1999; Lin et al., 2004; Pollok et al., 2004, 2005; Timmermann et al., 2003].
The effect of SQ abnormalities on the functional connectivity measures was tested using correlation analysis between the SQ score and MC-CRBL and SMA-CRBL.
We investigated how biological and scanning parameters affect PPI and compared these with functional connectivity measures obtained using correlation analysis.
In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier.
However, based on structural and functional connectivity measures, the authors found that the access to these representations by the IFG was impaired.
Measures based on cISI histograms are well-known in the literature, also in the functional connectivity investigation on simultaneous recorded spike trains [20].
A popular fMRI method to detect brain networks is functional connectivity, based on the temporal correlation between BOLD signals in distant brain regions.
We found no effect of diagnosis on the functional connectivity measures.
