Cognitive Neurodynamics

, Volume 9, Issue 4, pp 371–387 | Cite as

Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes

  • S. I. DimitriadisEmail author
  • N. A. Laskaris
  • S. Micheloyannis
Research Article


We studied how maturation influences the organization of functional brain networks engaged during mental calculations and in resting state. Surface EEG measurements from 20 children (8–12 years) and 25 students (21–26 years) were analyzed. Interregional synchronization of brain activity was quantified by means of Phase Lag Index and for various frequency bands. Based on these pairwise estimates of functional connectivity, we formed graphs which were then characterized in terms of local structure [local efficiency (LE)] and overall integration (global efficiency). The overall data analytic scheme was applied twice, in a static and time-varying mode. Our results showed a characteristic trend: functional segregation dominates the network organization of younger brains. Moreover, in childhood, the overall functional network possesses more prominent small-world network characteristics than in early acorrect in xmldulthood in accordance with the Neural Efficiency Hypothesis. The above trends were intensified by the time-varying approach and identified for the whole set of tested frequency bands (from δ to low γ). By mapping the time-indexed connectivity patterns to multivariate timeseries of nodal LE measurements, we carried out an elaborate study of the functional segregation dynamics and demonstrated that the underlying network undergoes transitions between a restricted number of stable states, that can be thought of as “network-level microstates”. The rate of these transitions provided a robust marker of developmental and task-induced alterations, that was found to be insensitive to reference montage and independent component analysis denoising.


EEG Resting state Numerical cognition Developmental Network connectivity microstates Symbolic dynamics 



This research was partially funded by AUTH research committee (Grant 50141).

Supplementary material

11571_2015_9330_MOESM1_ESM.doc (3.3 mb)
Supplementary material 1 (DOC 3372 kb)


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© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • S. I. Dimitriadis
    • 1
    • 2
    Email author
  • N. A. Laskaris
    • 1
    • 2
  • S. Micheloyannis
    • 3
  1. 1.Artificial Intelligence and Information Analysis Laboratory, Department of InformaticsAristotle UniversityThessalonikiGreece
  2. 2.NeuroInformatics GroupAUTHThessalonikiGreece
  3. 3.Medical Division (Laboratory L.Widen)University of CreteIraklion, CreteGreece

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