This is the final paper in a Comments and Controversies series dedicated to “The
identification of interacting networks in the brain using fMRI: Model selection, causality and …

Current high-throughput data acquisition technologies probe dynamical systems with
different imaging modalities, generating massive data sets at different spatial and temporal …

Current analysis of event‐related potentials (ERP) data is usually based on the a priori
selection of channels and time windows of interest for studying the differences between …

We propose a novel technique to assess functional brain connectivity in
electroencephalographic (EEG)/magnetoencephalographic (MEG) signals. Our method …

The causal, directed interactions between brain regions at rest (brain–brain networks) and
between resting-state brain activity and autonomic nervous system (ANS) outflow (brain …

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in
Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and …

Functional brain network studies using the Blood Oxygen-Level Dependent (BOLD) signal
from functional Magnetic Resonance Imaging (fMRI) are becoming increasingly prevalent in …

We consider the problem of identifying large-scale effective connectivity of brain networks
from fMRI data. Standard vector autoregressive (VAR) models fail to estimate reliably …

The evaluation of time and frequency domain measures of coupling and causality relies on
the parametric representation of linear multivariate processes. The study of temporal …

The multidimensional nature of neuroscience data has made the use of multi-way statistical
analysis suitable in this field. Parallel Factor Analysis (PARAFAC) is a multidimensional …