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Related Experiment Video

Updated: Jun 3, 2025

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Low-dimensional controllability of brain networks.

Remy Ben Messaoud^1,2, Vincent Le Du², Camile Bousfiha^1,2

¹Inria Paris, Paris, France.

Plos Computational Biology

|January 8, 2025

Summary

This summary is machine-generated.

This study introduces a new framework to accurately identify key driver nodes in complex networks. The method improves control accuracy in biological systems and reveals new insights into human brain connectivity.

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Last Updated: Jun 3, 2025

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Published on: May 7, 2014

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Published on: October 13, 2023

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

Area of Science:

Network science
Systems biology
Neuroscience

Background:

Identifying critical nodes (drivers) in biological networks is vital for understanding causal interactions and designing interventions.
Current network control methods struggle with accuracy when the number of drivers is small relative to network size.

Purpose of the Study:

To develop a novel framework to enhance the accuracy of identifying driver nodes in large-scale networks.
To improve the practical usability of network control theory in real-world applications, particularly in neuroscience.

Main Methods:

Integrated spectral graph theory and output controllability to project network states into a lower-dimensional topological space.
Developed a new low-dimensional controllability metric.
Validated the framework on synthetic networks and 6134 human connectomes from the UK Biobank.

Main Results:

Demonstrated significant improvement in control accuracy using a reduced number of projected components.
Identified previously unrecognized influential brain regions.
Mapped directed interactions between specialized cerebral systems and provided insights into hemispheric lateralization.

Conclusions:

The proposed framework offers a theoretically sound approach to network controllability.
Provides novel insights into the causal interactions and functional organization of the human brain.