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Related Experiment Video

Updated: Sep 11, 2025

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

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Published on: March 21, 2019

Random Effects Models for Understanding Variability and Association between Brain Functional and Structural

Lingyi Peng, Qiaochu Wang, Yaotian Wang

|August 13, 2025

Summary

This summary is machine-generated.

Network-level and edge-level analyses of functional connectivity (FC) and structural connectivity (SC) yield different insights into brain networks. New models reveal distinct sources of variability influencing these correlations.

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Last Updated: Sep 11, 2025

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Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Neuroscience
Network Science
Biostatistics

Background:

The human brain is a complex network with both functional connectivity (FC) and structural connectivity (SC).
Previous research primarily focused on network-level correlations between FC and SC, overlooking edge-level analyses.
Edge-level correlations, examining FC-SC relationships at individual connections across subjects, are underexplored.

Purpose of the Study:

To systematically analyze and compare network-level and edge-level FC-SC correlations.
To explain discrepancies observed between network-level and edge-level FC-SC association strengths.
To introduce novel statistical models for decomposing FC and SC variability.

Main Methods:

Systematic analysis of both network-level and edge-level functional connectivity (FC) and structural connectivity (SC) correlations.
Development and application of new random effects models.
Decomposition of FC and SC variability into subject effects, edge effects, and their interactions.

Main Results:

Network-level and edge-level FC-SC correlations lead to divergent conclusions regarding brain function-structure association strength.
The developed random effects models successfully disentangle sources of FC and SC variability.
Different effects significantly influence network-level versus edge-level correlations, contributing uniquely to total variability.

Conclusions:

The study highlights the importance of considering both network-level and edge-level analyses for a comprehensive understanding of brain connectivity.
Novel statistical modeling provides a quantitative framework for assessing sources of variability in functional and structural brain networks.
These findings offer new insights into the intricate relationship between functional and structural brain organization.