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Related Experiment Video

Updated: Feb 18, 2026

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

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

Published on: March 21, 2019

Dynamic effective connectivity in resting state fMRI.

Hae-Jeong Park¹, Karl J Friston², Chongwon Pae³

¹Department of Nuclear Medicine, Radiology and Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea; BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea.

|November 22, 2017

Summary

This summary is machine-generated.

This study introduces a new method using spectral dynamic causal modeling (spDCM) to analyze dynamic effective connectivity in the brain. The approach reveals consistent baseline connectivity and dynamic fluctuations, improving our understanding of brain network function.

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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Brain connectivity fluctuates dynamically, influencing functional integration.
Resting-state functional magnetic resonance imaging (fMRI) research increasingly focuses on these dynamic connectivity patterns.
Understanding dynamic functional connectivity requires methods to infer directed effective connectivity.

Purpose of the Study:

To introduce a novel method for identifying dynamic effective connectivity using spectral dynamic causal modeling (spDCM).
To model fluctuations in directed brain connectivity over time using resting-state fMRI data.
To evaluate the consistency and dynamics of effective connectivity within the default mode network (DMN).

Main Methods:

Employed spectral dynamic causal modeling (spDCM) with parametric empirical Bayes (PEB) to analyze resting-state fMRI time series.
Utilized hierarchical PEB to model random effects on connectivity parameters across time windows.
Applied a discrete cosine transform basis set or eigenvariates to capture fluctuations in effective connectivity.

Main Results:

Demonstrated dynamic fluctuations in effective connectivity, explaining observed dynamic functional connectivity.
Found consistent baseline effective connectivity within the default mode network (DMN) across independent sessions.
Showcased that hierarchical modeling with spDCM enhances the characterization of effective connectivity dynamics.

Conclusions:

The developed spDCM approach effectively characterizes dynamic effective connectivity.
Hierarchical modeling provides a robust framework for analyzing dynamic brain network interactions.
This method offers improved consistency in estimating baseline effective connectivity compared to conventional models.