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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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Time-Varying Directed Interactions in Functional Brain Networks: Modeling and Validation.

Nan Xu^1,2, Xiaodi Zhang², Wen-Ju Pan²

¹Fischell Department of Bioengineering, Department of Electrical and Computer Engineering, Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD, United States.

|February 27, 2026

Summary

This summary is machine-generated.

We developed sliding-window prediction correlation (SWpC) to reveal time-varying directed brain connectivity, offering insights into information flow. SWpC improves upon traditional methods by capturing directional interactions for better understanding of brain function and disorders.

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Area of Science:

Neuroscience
Computational Neuroscience
Network Science

Background:

Understanding dynamic brain connectivity is crucial for neuroscience, but traditional methods like sliding-window correlation (SWC) fail to capture directional interactions.
Limitations in current methods hinder the inference of time-resolved information flow in brain networks.

Purpose of the Study:

To introduce sliding-window prediction correlation (SWpC), a novel method for estimating time-varying directed functional connectivity (FC).
To assess SWpC's ability to capture directional interactions and its performance compared to SWC across different neuroimaging modalities and applications.

Main Methods:

SWpC embeds a directional linear time-invariant (LTI) model within sliding windows to estimate directed FC.
The method yields measures of interaction strength (prediction correlation) and duration (information transfer duration).
Validation involved concurrent local field potential (LFP) and fMRI BOLD recordings in rats, Human Connectome Project (HCP) fMRI data, and post-concussion vestibular dysfunction (PCVD) patient data.

Main Results:

SWpC demonstrated stable directionality estimates in both LFP and BOLD signals.
It detected significant task-evoked changes in directed FC strength and duration in HCP data, outperforming SWC in sensitivity.
In PCVD, SWpC identified reproducible brain-state shifts and improved patient vs. control discrimination.

Conclusions:

SWpC provides biologically interpretable, time-resolved directed connectivity patterns.
The method is validated across multimodal neuroimaging and clinical settings, supporting basic and translational neuroscience.
SWpC offers a significant advancement for studying dynamic brain networks and their role in function and dysfunction.