Brain Topography

, Volume 26, Issue 3, pp 397–409

On the Quantization of Time-Varying Phase Synchrony Patterns into Distinct Functional Connectivity Microstates (FCμstates) in a Multi-trial Visual ERP Paradigm

Original Paper

DOI: 10.1007/s10548-013-0276-z

Cite this article as:
Dimitriadis, S.I., Laskaris, N.A. & Tzelepi, A. Brain Topogr (2013) 26: 397. doi:10.1007/s10548-013-0276-z

Abstract

The analysis of functional brain connectivity has been supported by various techniques encompassing spatiotemporal interactions between distinct areas and enabling the description of network organization. Different brain states are known to be associated with specific connectivity patterns. We introduce here the concept of functional connectivity microstates (FCμstates) as short lasting connectivity patterns resulting from the discretization of temporal variations in connectivity and mediating a parsimonious representation of coordinated activity in the brain. Modifying a well-established framework for mining brain dynamics, we show that a small sized repertoire of FCμstates can be derived so as to encapsulate both the inter-subject and inter-trial response variability and further provide novel insights into cognition. The main practical advantage of our approach lies in the fact that time-varying connectivity analysis can be simplified significantly by considering each FCμstate as prototypical connectivity pattern, and this is achieved without sacrificing the temporal aspects of dynamics. Multi-trial datasets from a visual ERP experiment were employed so as to provide a proof of concept, while phase synchrony was emphasized in the description of connectivity structure. The power of FCμstates in knowledge discovery is demonstrated through the application of network topology descriptors. Their time-evolution and association with event-related responses is explored.

Keywords

Mining brain dynamics Functional connectomics Microstates Symbolic dynamics 

Supplementary material

10548_2013_276_MOESM1_ESM.docx (3.2 mb)
Supplementary material 1 (DOCX 3228 kb)

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • S. I. Dimitriadis
    • 1
    • 2
  • N. A. Laskaris
    • 2
  • A. Tzelepi
    • 3
  1. 1.Electronics Laboratory, Department of PhysicsUniversity of PatrasPatrasGreece
  2. 2.Artificial Intelligence and Information Analysis Laboratory, Department of InformaticsAristotle UniversityThessalonikiGreece
  3. 3.Institute of Communication and Computer SystemsNational Technical University of AthensAthensGreece

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