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Related Experiment Video

Updated: Jun 3, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman

Jin Kang1, Liang Wang, Chaogan Yan

  • 1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.

Neuroimage
|March 23, 2011
PubMed
Summary

Resting-state functional connectivity (RSFC) dynamics were investigated in healthy subjects. Findings reveal that brain network interactions are time-varying, offering new insights into brain function.

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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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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Functional Neuroimaging

Background:

  • Brain cognitive functions rely on interconnected brain regions forming functional networks.
  • Resting-state functional MRI (fMRI) analyzes temporal interactions (functional connectivity) of blood oxygen level-dependent (BOLD) signals.
  • Previous studies often assumed static functional connectivity, with limited understanding of its dynamic nature.

Purpose of the Study:

  • To investigate the dynamic characteristics of resting-state functional connectivity (RSFC) within and between multiple brain networks.
  • To explore the temporal variability of functional interactions in the human brain during rest.

Main Methods:

  • Identified major RSFC networks (default-mode, motor, attention, etc.) using conventional correlation analysis with predefined regions of interest (ROIs).
  • Employed a variable parameter regression model with Kalman filtering to detect dynamic interactions between ROIs and other brain voxels.
  • Analyzed connectivity patterns within and between identified functional brain networks.

Main Results:

  • Functional interactions within RSFC maps demonstrated significant time-varying properties.
  • Approximately 10-20% of voxels within each RSFC map exhibited significant functional connectivity to ROIs over time.
  • Dynamic connectivity patterns were also observed between different functional networks, with high spatial similarity between adjacent time points.

Conclusions:

  • Resting-state functional brain networks exhibit dynamic, time-varying connectivity.
  • This study provides crucial insights into the dynamic nature of brain functional networks during rest.
  • Understanding RSFC dynamics is essential for a comprehensive view of brain function.