HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity

  title={HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity},
  author={Guiomar Niso and Ricardo Bru{\~n}a and Ernesto Pereda and Ricardo Guti{\'e}rrez and Ricardo Bajo and Fernando Maest{\'u} and Francisco del Pozo},
The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ‘traditional’ set of linear methods… 

Comparison of connectivity analyses for resting state EEG data

It is shown that the target of information flow, in particular the frontal region, is an area of greater brain synchronization and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

Inferring correlations associated to causal interactions in brain signals using autoregressive models

This work introduces an extension of the well-known GC that analyses the correlation associated to the specific influence that a transmitter node has over the receiver, and indicates the G-causal link has a positive or negative effect if the predicted activity follows directly or inversely the dynamics of the sender.

Synaptic Granger Causality: A New Approach to Untangling Excitatory and Inhibitory Connectivities

The concept of synaptic Granger causality (sGC), an extension of the well-known Granger causability approach that quantifies the ratio of excitatory/inhibitory projections involved in a functional link on the basis of the analysis of time series constructed using autoregressive models, is introduced.

Quantifying Synchronization in a Biologically Inspired Neural Network

A collated set of algorithms to obtain objective measures of synchronisation in brain time-series data are presented as SyncBox, to understand the underlying dynamics in an existing population neural network that mimic Local Field Potentials of the visual thalamic tissue.

On the role of the entorhinal cortex in the effective connectivity of the hippocampal formation.

The results suggest that the combination of causality measures with neuronal modeling based on precise neuroanatomical tracing may provide a powerful framework to disambiguate causal interactions in the brain.

A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls

This tutorial will review and summarize current analysis methods used in the field of invasive and non-invasive electrophysiology to study the dynamic connections between neuronal populations, and highlights a number of interpretational caveats and common pitfalls that can arise when performing functional connectivity analysis.

Efficient Computation of Functional Brain Networks: toward Real-Time Functional Connectivity

This work presents a set of open-source tools that significantly increase computational efficiency of some well-known connectivity indices and Graph-Theory measures, even enabling whole-head real-time network analysis of brain function.

Dyconnmap: Dynamic connectome mapping—A neuroimaging python module

The word 'chronnectome' is adopted to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way and how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which the authors' brain evolved.



Measuring phase synchrony in brain signals

It is argued that whereas long‐scale effects do reflect cognitive processing, short‐scale synchronies are likely to be due to volume conduction, and ways to separate such conduction effects from true signal synchrony are discussed.

Transfer entropy—a model-free measure of effective connectivity for the neurosciences

Transfer entropy (TE) improved the detectability of effective connectivity for non-linear interactions, and for sensor level MEG signals where linear methods are hampered by signal-cross-talk due to volume conduction.

The elusive concept of brain connectivity

Synchronization likelihood with explicit time-frequency priors

A MATLAB toolbox for Granger causal connectivity analysis

  • A. Seth
  • Computer Science
    Journal of Neuroscience Methods
  • 2010

Nonlinear multivariate analysis of neurophysiological signals

A critical assessment of connectivity measures for EEG data: A simulation study