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Neuroinformatics

, Volume 11, Issue 4, pp 405–434 | Cite as

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

  • Guiomar NisoEmail author
  • Ricardo Bruña
  • Ernesto Pereda
  • Ricardo Gutiérrez
  • Ricardo Bajo
  • Fernando Maestú
  • Francisco del-Pozo
SOFTWARE ORIGINAL ARTICLE

Abstract

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, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality.

This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox.

Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

Keywords

Functional connectivity Effective connectivity Matlab toolbox Electroencephalography Magnetoencephalography Multiple comparisons problem 

Notes

Acknowledgments

We are very grateful to the generosity of many researchers who have made their code publicly available thereby contributing the initial seeds for HERMES. Among them, we are especially thankful to Dr. German Gómez-Herrero. We also acknowledge the role of Mr. Fernando Sanz, a former researcher of the Centre of Biomedical Technology, in the initial development of the toolbox.

We are also very grateful to the reviewers who perusal the original version of the manuscript and served as high quality beta testers of the toolbox, because they provided very useful feedback and comments that helped improving the quality of the work.

Role of the funding source

The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness through grants TEC2012-38453-CO4-01 and -03 and grant PSI2012-38375-C03-01 and the support of the Spanish Ministry of Science through grant PSI2009-14415-C03-01. Guiomar Niso has received the financial support of the Spanish Ministry of Education and Science through the FPU grant AP2008-02383. These funding sources have played no role in study design, the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Guiomar Niso
    • 1
    • 3
    Email author
  • Ricardo Bruña
    • 1
  • Ernesto Pereda
    • 2
  • Ricardo Gutiérrez
    • 1
  • Ricardo Bajo
    • 1
  • Fernando Maestú
    • 1
  • Francisco del-Pozo
    • 1
  1. 1.Centre for Biomedical TechnologyTechnical University of MadridMadridSpain
  2. 2.Electrical Engineering and Bioengineering Group, Department of Basic PhysicsUniversity of La LagunaTenerifeSpain
  3. 3.MadridSpain

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