The frustrated brain: From dynamics on motifs to communities and networks

Leonardo L. Gollo, Michael Breakspear

Research output: Contribution to journalArticleResearchpeer-review

58 Citations (Scopus)


Cognitive function depends on an adaptive balance between flexible dynamics and integrative processes in distributed cortical networks. Patterns of zero-lag synchrony likely underpin numerous perceptual and cognitive functions. Synchronization fulfils integration by reducing entropy, while adaptive function mandates that a broad variety of stable states be readily accessible. Here, we elucidate two complementary influences on patterns of zero-lag synchrony that derive from basic properties of brain networks. First, mutually coupled pairs of neuronal subsystems-resonance pairs-promote stable zero-lag synchronyamong the small motifs in which they are embedded, andwhose effects can propagate along connected chains. Second, frustrated closed-loop motifs disrupt synchronous dynamics, enabling metastable configurations of zerolag synchrony to coexist. We document these two complementary influences in small motifs and illustrate how these effects underpin stable versus metastable phase-synchronization patterns in prototypical modular networks and in large-scale cortical networks of the macaque (CoCoMac). We find that the variability of synchronization patterns depends on the inter-node time delay, increases with the network size and is maximized for intermediate coupling strengths. We hypothesize that the dialectic influences of resonance versus frustration may form a dynamic substrate for flexible neuronal integration, an essential platform across diverse cognitive processes.

Original languageEnglish
Article number20130532
Number of pages11
JournalPhilosophical Transactions of the Royal Society B: Biological Sciences
Issue number1653
Publication statusPublished - 5 Oct 2014
Externally publishedYes


  • Anti-phase synchronization
  • Dynamic functional connectivity
  • Functional network
  • Macaque cortical network
  • Neural mass model

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