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Related Experiment Video

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Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
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Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

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Time-resolved resting-state brain networks.

Andrew Zalesky1, Alex Fornito2, Luca Cocchi3

  • 1Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC 3010, Australia;Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia; azalesky@unimelb.edu.au.

Proceedings of the National Academy of Sciences of the United States of America
|July 2, 2014
PubMed
Summary
This summary is machine-generated.

Brain connectivity fluctuates rapidly, with dynamic links between brain systems enhancing information transfer temporarily. These brain dynamics balance efficient processing and energy use.

Keywords:
dynamic connectivitynetwork efficiencytime-dependent network

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Neuronal dynamics exhibit complex spatiotemporal coordination across distributed brain regions.
  • Functional magnetic resonance imaging (fMRI) reveals brain interaction topology but has limitations in temporal analysis.
  • Understanding the temporal evolution of brain functional connectivity is crucial for comprehending brain function.

Purpose of the Study:

  • To map time-resolved functional connectivity across the entire brain at subsecond resolution.
  • To investigate how nonstationary fluctuations in pairwise interactions relate to large-scale brain topology.
  • To understand the dynamic interplay between local and global brain network properties.

Main Methods:

  • Utilized high-resolution resting-state fMRI data from the Human Connectome Project.
  • Developed methods for mapping time-resolved functional connectivity at subsecond resolution.
  • Analyzed fluctuations in pairwise interactions and their relation to network topology.

Main Results:

  • Identified consistent functional connections with significant temporal strength fluctuations.
  • Found that the most dynamic connections are intermodular, linking distinct brain subsystems.
  • Observed spontaneous, brief increases in information transfer efficiency, creating temporary globally efficient network states.
  • Dynamic connections localized to hubs within default mode and fronto-parietal systems.

Conclusions:

  • Brain dynamics exhibit significant temporal variations in complex network properties.
  • These dynamic fluctuations may represent a mechanism for balancing efficient information processing and metabolic cost.
  • Subsecond temporal analysis of functional connectivity provides novel insights into brain network adaptability.