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Related Experiment Video

Updated: Sep 21, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Predicting time-resolved electrophysiological brain networks from structural eigenmodes.

Prejaas Tewarie1, Bastian Prasse2, Jil Meier3

  • 1Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK.

Human Brain Mapping
|June 1, 2022
PubMed
Summary
This summary is machine-generated.

Structural eigenmodes, not direct connections, shape dynamic functional brain networks. These modes influence information integration and segregation, impacting cognitive performance.

Keywords:
dynamic functional connectivityeigenmodesmagnetoencephalography

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Network Science

Background:

  • Understanding the relationship between brain structure and function is crucial.
  • Temporal dynamics of functional brain networks are not fully explained by static anatomical connections.

Purpose of the Study:

  • To investigate the role of structural eigenmodes in shaping temporally evolving functional brain networks.
  • To explore the link between structural eigenmodes, functional connectivity dynamics, and cognitive performance.

Main Methods:

  • Analysis of resting-state magnetoencephalography (MEG) and diffusion magnetic resonance imaging (dMRI) data.
  • Individual subject-level analysis of structural eigenmodes and time-resolved functional connectivity (phase and amplitude).

Main Results:

  • Structural eigenmodes, rather than direct connections, partly explain short-timescale functional connectivity.
  • A stronger relationship exists between structural eigenmodes and time-resolved amplitude connectivity.
  • Dynamic network states, characterized by varying eigenmode expressions, were identified and linked to cognitive performance and network community structure.

Conclusions:

  • Time-resolved resting-state networks can be understood through the activation/deactivation of structural eigenmodes.
  • Structural eigenmodes play a key role in the dynamic integration and segregation of cortical information.
  • These dynamics are relevant for subserving cognitive functions.