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Related Experiment Video

Updated: Aug 14, 2025

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

Activity flow mapping over probabilistic functional connectivity.

Hengcheng Zhu¹, Ziyi Huang¹, Yifeixue Yang¹

¹Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China.

Human Brain Mapping

|January 17, 2023

Summary

This summary is machine-generated.

Dynamic functional connectivity (FC) improves predicting brain activity flow. Between-network connections and specific task contrasts are key for cognitive control systems, unlike sensorimotor areas.

Keywords:

activity flow mapping cognitive control systems dynamic framework functional MRI probabilistic functional connectivity sensorimotor systems

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Last Updated: Aug 14, 2025

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

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Published on: July 1, 2014

Area of Science:

Neuroscience
Cognitive Neuroscience
Network Science

Background:

Resting-state network topology can predict task-evoked brain activity.
Previous studies used static, linear functional connectivity (FC) for activity flow routes.
The dynamic nature of FC and its impact on predicting task activations remain underexplored.

Purpose of the Study:

To introduce a novel probabilistic FC estimation from a dynamic framework as an activity flow route.
To investigate the predictive power of between- versus within-network connections on task-evoked activity.
To examine the influence of task contrasts on activity flow prediction across brain systems.

Main Methods:

Proposed a probabilistic FC estimation derived from a dynamic framework.
Applied activity flow mapping using between- and within-network connections.
Tested the effects of tight task contrasts on prediction accuracy.

Main Results:

Probabilistic FC routes significantly enhanced individual-level activity flow prediction.
Between-network connections demonstrated higher prediction performance in cognitive control than sensorimotor systems.
Tight task contrasts improved prediction accuracy for cognitive control but decreased it for sensorimotor systems.

Conclusions:

Probabilistic FC estimates are effective for activity flow mapping.
Network topology and task contrasts differentially influence activity flow prediction across brain systems.
Findings highlight the importance of dynamic FC and network interactions in understanding brain function.