Causal Interactions Within the Default Mode Network as Revealed by Low-Frequency Brain Fluctuations and Information Transfer Entropy

  • Maksim Sharaev
  • Vadim Ushakov
  • Boris Velichkovsky
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 449)


The Default Mode Network (DMN) is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. The aim of the current work is to find a connectivity pattern between the four DMN key regions without any a priori assumptions on the underlying network architecture. For this purpose functional magnetic resonance imaging (fMRI) data from 30 healthy subjects (1000 time points from each one) was acquired and Transfer Entropy (TE) between fMRI time-series was calculated. The significant results at the group level were obtained by testing against the surrogate data. For initial 500, final 500 and total 1000 time points we found stable causal interactions between mPFC, PCC and LIPC. For some scanning intervals there are also connections from RIPC to mPFC and PCC. These results are in part conforming to earlier studies and models of effective connectivity within the DMN.


Effective connectivity Default mode network Resting-state fMRI Transfer entropy 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Maksim Sharaev
    • 1
    • 2
    • 3
  • Vadim Ushakov
    • 1
    • 4
  • Boris Velichkovsky
    • 1
    • 5
    • 6
  1. 1.National Research Centre “Kurchatov Institute”MoscowRussian Federation
  2. 2.Faculty of PhysicsM.V.Lomonosov Moscow State UniversityMoscowRussian Federation
  3. 3.Institute for Higher Nervous Activity and NeurophysiologyThe Russian Academy of SciencesMoscowRussian Federation
  4. 4.Department of CyberneticsNational Research Nuclear University “MEPhI”MoscowRussian Federation
  5. 5.NBICS-FacultyMoscow Institute of Physics and TechnologyDolgoprudnyRussian Federation
  6. 6.Applied Cognitive Research UnitTechnical University DresdenDresdenGermany

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