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Updated: Jun 6, 2026

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

Statistical parametric network analysis of functional connectivity dynamics during a working memory task.

Cedric E Ginestet¹, Andrew Simmons

¹King's College London, Institute of Psychiatry, Centre for Neuroimaging Sciences (CNS), London, UK. cedric.ginestet@kcl.ac.uk

|November 25, 2010

Summary

This summary is machine-generated.

Functional connectivity networks in the brain dynamically change with cognitive load during working memory tasks. Decreased network cost predicts better performance, suggesting increased specialization enhances cognitive function.

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Area of Science:

Neuroscience
Cognitive Neuroscience
Network Science

Background:

Functional and structural Magnetic Resonance Imaging (fMRI/sMRI) are key tools for studying brain networks.
Limited research exists on how functional connectivity dynamics change with varying cognitive load.
Blood oxygen level dependent (BOLD) signal fluctuations can map functional connectivity networks.

Purpose of the Study:

To investigate topological changes in brain functional connectivity networks under different cognitive loads.
To explore the relationship between network dynamics, cognitive load, and task performance.
To develop a method for extracting and summarizing whole-brain network changes.

Main Methods:

Constructed weighted graphs of 90 brain regions from fMRI data in 43 healthy adults.
Utilized a mass-univariate approach to create statistical parametric networks (SPNs) comparing N-back task conditions.
Analyzed changes in weighted cost and small-world topological measures, controlling for mean correlation.

Main Results:

Functional networks maintained small-world properties across cognitive loads, with preserved global and local efficiency.
Weighted cost (mean between-region correlation) significantly decreased as working memory load increased.
Subject-specific weighted costs predicted N-back task performance, particularly at low frequencies (0.01-0.03 Hz).

Conclusions:

Brain functional networks adapt under increasing cognitive load, potentially through pruning and increased modular specialization.
Weighted cost serves as a valid predictor of cognitive performance, highlighting its importance in functional connectivity.
Findings support the integration/specialization dichotomy in brain function related to cognitive effort.