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Related Experiment Video

Updated: Jun 12, 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

Predicting response speed and age from task-evoked effective connectivity.

Shufei Zhang^1,2, Kyesam Jung^1,2, Robert Langner^1,2

¹Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.

Network Neuroscience (Cambridge, Mass.)

|June 9, 2025

Summary

This summary is machine-generated.

Task-evoked effective connectivity (EC) better predicts reaction time (RT) than functional connectivity (FC). Dynamic causal modeling (DCM) designs influence prediction accuracy, with event-related models outperforming block-based ones.

Keywords:

Analytic flexibility Brain-based prediction Dynamic causal modeling Functional connectivity Machine learning Stimulus-response compatibility Task fMRI

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Area of Science:

Neuroimaging
Cognitive Neuroscience
Computational Neuroscience

Background:

Task-evoked functional connectivity (FC) shows promise in predicting individual traits.
The predictive power of task-evoked effective connectivity (EC) for individual differences remains largely unexplored.

Purpose of the Study:

To investigate the predictive capacity of intrinsic EC (I-EC) and task-modulated EC (M-EC) for individual reaction time (RT) and age.
To compare the performance of EC against task-evoked FC in predicting these traits.
To evaluate the impact of different data processing and modeling choices on prediction accuracy.

Main Methods:

Dynamic causal modeling (DCM) was used to calculate I-EC and M-EC from fMRI data during a stimulus-response compatibility task.
General linear model (GLM) designs (event-related vs. block-based), Bayesian model reduction, and cross-validation schemes were varied.
Machine learning models were employed for prediction of RT and age.

Main Results:

M-EC demonstrated superior prediction of RT compared to I-EC and task-evoked FC.
All connectivity types performed similarly in predicting age.
Event-related GLM and DCM designs yielded better predictions than block-based designs.
Significant differences were observed in predicting RT and age between I-EC and M-EC.

Conclusions:

Task-evoked EC, particularly M-EC, holds significant potential for predicting behavioral traits like RT.
The choice of GLM and DCM design critically influences the accuracy of EC-based predictions.
Findings contribute to understanding how neuroimaging analysis choices impact predictive modeling of individual differences.