HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity (1305.2550v2)

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 for the analysis 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.

• The paper introduces HERMES as a unified Matlab toolbox that combines multiple metrics to assess both functional and effective brain connectivity.
• It employs a range of methods—from classical correlation and phase synchronization to Granger causality and information theory—augmented by statistical tests like FDR and CBPT.
• HERMES enhances neuroimaging research through interactive visualizations and scalable computing, simplifying complex connectivity analyses.

An Overview of HERMES: A Brain Connectivity Analysis Toolbox

The paper "HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity" presents the design and implementation of a comprehensive software toolbox aimed at the analysis of brain connectivity. The toolbox, named HERMES, has been developed for use in the Matlab environment to facilitate the paper of functional (FC) and effective connectivity (EC) in neurophysiological data. HERMES integrates a wide range of connectivity measures into a single analytical framework intended to simplify access to these methods for neuroscientific researchers.

Concept and Scope

HERMES addresses a critical need in the brain connectivity analysis domain by providing a unified platform that incorporates both established and specialized connectivity metrics. Functional connectivity is defined through statistical dependencies between neural signals, while effective connectivity adds the dimension of causal interactions. The paper outlines how HERMES incorporates methods suitable for various data types, such as EEG and MEG, to ascertain these dependencies and causal structures.

Connectivity Measures

The paper provides an extensive categorization of the connectivity measures supplied within HERMES:

1. Classical Measures: These encompass linear analysis tools like Pearson’s correlation, coherence, and cross-correlation functions, which are foundational in FC analysis.
2. Phase Synchronization Indexes: The toolbox utilizes phase-based measures like Phase Locking Value (PLV) and Phase-Lag Index (PLI), key for assessing FC via phase synchronization.
3. Generalized Synchronization Tools: Measures of generalized synchronization such as the S, H, N, M, L indexes, and Synchronization Likelihood (SL) are included for more complex signal interrelationships.
4. Granger Causality Measures: Offering Granger Causality along with Partial Directed Coherence (PDC) and the Direct Transfer Function (DTF), for EC assessment through linear autoregressive modeling.
5. Information Theoretic Measures: The inclusion of Mutual Information (MI) and Transfer Entropy (TE) provides model-free approaches for detecting both linear and non-linear dependencies with potentials for uncovering directional interactions.

Statistical Methods and Visualization

To address the multiple comparisons issue in statistical analysis, HERMES implements both False Discovery Rate (FDR) control and non-parametric cluster-based permutation tests (CBPT). The visualization capabilities in HERMES facilitate an accessible display of connectivity measures, enabling users to manipulate and explore data dimensions interactively.

Implications and Future Directions

The introduction of the HERMES toolbox holds substantial implications for the research community, particularly for researchers seeking an integrated solution to analyze complex neurophysiological datasets. Given the breadth of connectivity measures and the supporting statistical frameworks, HERMES enhances the accessibility and applicability of advanced connectivity analysis to broader neuroscientific inquiries.

The future trajectory for HERMES, as indicated in the paper, involves incorporating additional connectivity indices and optimizing computational efficiency, a necessary evolution to keep pace with the expanding methodological landscape in brain connectivity research. Emphasis on adaptation to multicore processing and the development of parallel computing capabilities reflects a commitment to scaling the toolbox for comprehensive datasets.

Conclusion

The paper lays foundational groundwork for the application of computational methods in neuroimaging analysis. By amalgamating a diverse array of tools and presenting them within an intuitive Matlab-based environment, HERMES is poised to become a significant utility for researchers investigating the structure and dynamics of brain connectivity. Through continual updates and expanded measure integrations, HERMES can evolve alongside scientific advances, sustaining its utility and relevance in the field of neuroscience.