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Related Experiment Video

Updated: Feb 2, 2026

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

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Quantifying Differences Between Passive and Task-Evoked Intrinsic Functional Connectivity in a Large-Scale Brain

Antonio Ulloa^1,2, Barry Horwitz¹

¹Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, Maryland.

Brain Connectivity

|November 16, 2018

Summary

This summary is machine-generated.

Computational models quantify how cognitive tasks alter intrinsic brain connectivity. This research links intrinsic and task-evoked brain activity, aiding in mapping brain regions non-invasively.

Keywords:

connectome fMRI functional connectivity graph theory neural modeling visual short-term memory

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Published on: December 5, 2014

Area of Science:

Neuroscience
Computational modeling
Graph theory

Background:

Understanding the relationship between intrinsic brain activity and task-evoked responses is crucial for mapping brain regions, especially in non-compliant patients.
Cognitive tasks may influence brain regions not directly involved in the task, a phenomenon that computational models can help elucidate.

Purpose of the Study:

To quantify changes in intrinsic functional brain connectivity (FC) during task execution using computational modeling and graph theoretical metrics.
To investigate the extent to which cognitive task execution affects intrinsic activity in non-task-related brain regions.

Main Methods:

Developed a large-scale neural model integrating a visual short-term memory model with an empirical connectome.
Simulated a neuroimaging study with 10 subjects performing passive fixation (PF), passive viewing (PV), and delayed match-to-sample (DMS) tasks.
Calculated functional connectivity (FC) matrices from simulated BOLD fMRI time series and computed graph theoretical measures.

Main Results:

Simulated graph theoretical measures showed consistency with experimental findings.
Quantified differences in graph metrics between the PF condition and the PV and DMS conditions.
Demonstrated the utility of graph theoretical methods on simulated brain networks for quantifying changes in intrinsic brain FC during task execution.

Conclusions:

Computational modeling combined with graph theory can effectively quantify alterations in intrinsic functional brain connectivity during cognitive tasks.
This approach provides a method to bridge the gap between intrinsic and task-evoked brain activity, advancing neuroimaging analysis.