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Related Experiment Video

Updated: May 16, 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

Continuous-time causal distribution learning with identifiability for brain dynamic effective connectivity inference.

Yiding Wang¹, Longyun Chen¹, Chen Qiao¹

¹School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China.

Medical Image Analysis

|May 14, 2026

Summary

This summary is machine-generated.

This study introduces a new continuous-time model for dynamic effective connectivity (dEC) analysis in the brain. The model improves causality learning and reveals organized brain network changes in tasks and Alzheimer's disease.

Keywords:

Continuous time causal learning Dynamic effective connectivity networks Free energy principle Identifiability

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Last Updated: May 16, 2026

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Published on: March 21, 2019

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Published on: July 1, 2014

Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Dynamic effective connectivity (dEC) analysis reveals brain information transmission mechanisms.
Existing discrete-time dEC models have limitations in frequency matching and data efficiency.
The free energy principle offers a more robust learning framework than maximum likelihood estimation.

Purpose of the Study:

To propose a novel continuous-time dEC distribution learning model.
To enhance parameter learning using the free energy principle.
To ensure theoretical identifiability and uniqueness of learned dEC.

Main Methods:

Developed a continuous-time dynamic causality distribution learning framework.
Employed the free energy principle for parameter learning.
Validated the model using synthetic datasets and task/resting-state fMRI data.

Main Results:

The proposed model outperforms existing methods in causality learning and time series reconstruction.
Revealed organized information flow around the left supramarginal gyrus during cognitive tasks.
Identified distinct alterations in brain networks associated with Alzheimer's disease, including executive control and visual integration networks.

Conclusions:

The continuous-time dEC model offers superior performance and theoretical guarantees.
The model effectively captures dynamic brain network changes during cognitive tasks and in neurological disorders.
dEC analysis provides insights into the distributed nature of Alzheimer's disease pathology beyond memory and emotion networks.