Frequency-dependent functional connectivity in resting state networks

Jessica Samogin, Marco Marino, Camillo Porcaro, Nicole Wenderoth, Patrick Dupont, Stephan P. Swinnen, Dante Mantini

Research output: Contribution to journalArticlepeer-review

Abstract

Functional magnetic resonance imaging studies have documented the resting human brain to be functionally organized in multiple large-scale networks, called resting-state networks (RSNs). Other brain imaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), have been used for investigating the electrophysiological basis of RSNs. To date, it is largely unclear how neural oscillations measured with EEG and MEG are related to functional connectivity in the resting state. In addition, it remains to be elucidated whether and how the observed neural oscillations are related to the spatial distribution of the network nodes over the cortex. To address these questions, we examined frequency-dependent functional connectivity between the main nodes of several RSNs, spanning large part of the cortex. We estimated connectivity using band-limited power correlations from high-density EEG data collected in healthy participants. We observed that functional interactions within RSNs are characterized by a specific combination of neuronal oscillations in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–80 Hz) bands, which highly depend on the position of the network nodes. This finding may contribute to a better understanding of the mechanisms through which neural oscillations support functional connectivity in the brain.

Original languageEnglish
Pages (from-to)5187-5198
Number of pages12
JournalHuman Brain Mapping
Volume41
Issue number18
DOIs
Publication statusPublished - Dec 15 2020

Keywords

  • functional connectivity
  • high-density electroencephalography
  • neuronal communication
  • resting state
  • time-frequency analysis

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

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