Direction of information flow in large-scale resting-state networks is frequency-dependent

Arjan Hillebrand*, Prejaas Tewarie, Edwin Van Dellen, Meichen Yu, Ellen W.S. Carbo, Linda Douw, Alida A. Gouw, Elisabeth C.W. Van Straaten, Cornelis J. Stam

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

235 Citations (Scopus)


Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-toposterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequencydependent reentry loops that are dominated by flow from parietooccipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

Original languageEnglish
Pages (from-to)3867-3872
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number14
Publication statusPublished - 5 Apr 2016
Externally publishedYes


  • Atlas-based beamforming
  • Information flow
  • Magnetoencephalography
  • Phase transfer entropy
  • Resting-state networks
  • n/a OA procedure


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