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Updated: Feb 7, 2026
16:33
Functional Calcium Imaging in Developing Cortical Networks
Published on: October 22, 2011
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Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks
Diego Vidaurre1,2,3,4, Laurence T Hunt5,6,7, Andrew J Quinn5,7
1Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, OX37XJ, UK. diego.vidaurre@ohba.ox.ac.uk.
Nature Communications
|August 1, 2018
Summary
Brain networks dynamically shift between transient states, coordinating activity through frequency-specific oscillations. This study reveals distinct patterns in resting-state networks, highlighting functional specialization in brain communication.
Area of Science:
- Neuroscience
- Cognitive Neuroscience
- Computational Neuroscience
Background:
- Neuronal oscillations and phase-coupling are crucial for brain coordination during cognitive tasks.
- Functional brain networks likely reorganize and coordinate activity using similar mechanisms.
Purpose of the Study:
- To develop a novel method for identifying large-scale phase-coupled network dynamics.
- To characterize resting-state brain networks using transient brain states.
Main Methods:
- Developed a novel method to identify large-scale phase-coupled network dynamics.
- Analyzed magnetoencephalography (MEG) data to identify transient brain states.
- Characterized oscillatory power and coherence patterns within specific frequency bands.
Main Results:
- Resting brain networks are characterized by short-lived, transient states.
- These states exhibit distinct spatial patterns of oscillatory power and coherence.
- Identified specific frequency bands (alpha, delta/theta) associated with cognitive networks, including default mode network subdivisions.
Conclusions:
- Large-scale cortical phase-coupling networks show characteristic signatures in specific frequency bands.
- These signatures may reflect functional specialization at different intrinsic timescales.
- Transient brain states provide a framework for understanding large-scale network coordination.
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