Quantitative measures of cortical functional connectivity: A state-of-the-art brief survey

Abstract

The modern concepts of quantitative measurement of the strength of functional and effective cortical connectivity in neurocognitive networks (large-scale distributed brain systems of interacting neuronal populations that are believed to underlie cognitive processing) are reviewed. The two main classes of the methods of connectivity assessment (linear and nonlinear) are discussed. In the class of linear methods, in addition to coherence routinely used to measure the strength of functional links, the vector autoregressive modeling of multichannel EEG is discussed in detail. The latter technique allows the estimation of both functional and effective connectivity, i.e., the measures such as directed transfer function (DTF) and direct partial coherence (PDC) extensively used in cognitive neuroscience. The impact of volume conduction on different estimates of connectivity is considered and the imaginary part of complex-valued coherence as a way to reduce the artificial influence of volume conduction is discussed. Independent component analysis (ICA) and transfer entropy as a method of estimating direct influence are reviewed in the class of nonlinear methods.