A Comparative Study of the Robustness of Frequency-Domain Connectivity Measures to Finite Data Length

  • Sara Sommariva
  • Alberto Sorrentino
  • Michele Piana
  • Vittorio Pizzella
  • Laura Marzetti
Original Paper
  • 107 Downloads

Abstract

In this work we use numerical simulation to investigate how the temporal length of the data affects the reliability of the estimates of brain connectivity from EEG time-series. We assume that the neural sources follow a stable MultiVariate AutoRegressive model, and consider three connectivity metrics: imaginary part of coherency (IC), generalized partial directed coherence (gPDC) and frequency-domain granger causality (fGC). In order to assess the statistical significance of the estimated values, we use the surrogate data test by generating phase-randomized and autoregressive surrogate data. We first consider the ideal case where we know the source time courses exactly. Here we show how, expectedly, even exact knowledge of the source time courses is not sufficient to provide reliable estimates of the connectivity when the number of samples gets small; however, while gPDC and fGC tend to provide a larger number of false positives, the IC becomes less sensitive to the presence of connectivity. Then we proceed with more realistic simulations, where the source time courses are estimated using eLORETA, and the EEG signal is affected by biological noise of increasing intensity. Using the ideal case as a reference, we show that the impact of biological noise on IC estimates is qualitatively different from the impact on gPDC and fGC.

Keywords

Dynamic functional connectivity Imaginary part of coherency Generalized partial directed coherence Frequency-domain granger causality Surrogate data EEG 

Notes

Acknowledgements

The authors thank the anonymous reviewers for their valuable comments and suggestions. L.M. and V.P. have been supported in part by the grant Functional connectivity and neuroplasticity in physiological and pathological aging, PRIN 20102011 n. 2010SH7H3F_006, and by the grant Breaking the Nonuniqueness Barrier in Electromagnetic Neuroimaging (BREAKBEN), H2020-FETOPEN-2014-2015/H2020-FETOPEN-2014-2015-RIA, Project reference: 686865.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Dipartimento di MatematicaUniversità di GenovaGenoaItaly
  2. 2.Department of Neuroscience, Imaging and Clinical SciencesG. d’Annunzio University of Chieti-PescaraChietiItaly
  3. 3.Institute for Advanced Biomedical TechnologiesG. d’Annunzio University of Chieti-PescaraChietiItaly

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