Understanding how different areas of the human brain communicate with each other is a
crucial issue in neuroscience. The concepts of structural, functional and effective …

Multivariate time series analysis is extensively used in neurophysiology with the aim of
studying the relationship between simultaneously recorded signals. Recently, advances on …

We review the theory and algorithms of electrophysiological brain connectivity analysis. This
tutorial is aimed at providing an introduction to brain functional connectivity from …

Understanding causal relationships, or effective connectivity, between parts of the brain is of
utmost importance because a large part of the brain's activity is thought to be internally …

For decades, the main ways to study the effect of one part of the nervous system upon
another have been either to stimulate or lesion the first part and investigate the outcome in …

Assessing directed functional connectivity from time series data is a key challenge in
neuroscience. One approach to this problem leverages a combination of Granger causality …

We propose Granger causality map** (GCM) as an approach to explore directed
influences between neuronal populations (effective connectivity) in fMRI data. The method …

Today, neuroimaging techniques are frequently used to investigate the integration of
functionally specialized brain regions in a network. Functional connectivity, which quantifies …

Multielectrode neurophysiological recordings produce massive quantities of data.
Multivariate time series analysis provides the basic framework for analyzing the patterns of …

We present a framework aimed to reveal directed interactions of activated brain areas using
time-resolved fMRI and vector autoregressive (VAR) modeling in the context of Granger …