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

TL;DR

This paper introduces SWpC, a novel method for estimating time-varying directed functional connectivity in brain networks, validated across animal, human, and clinical data, outperforming traditional undirected approaches.

Contribution

The paper presents SWpC, a new approach embedding a directional LTI model within sliding windows to measure directed brain connectivity dynamics.

Findings

SWpC provides stable directionality estimates in LFP and BOLD signals.

SWpC detects significant task-evoked changes in directed FC.

SWpC improves discrimination between healthy controls and patients in clinical settings.

Abstract

Understanding the dynamic nature of brain connectivity is critical for elucidating neural processing, behavior, and brain disorders. Traditional approaches such as sliding-window correlation (SWC) characterize time-varying undirected associations but do not resolve directional interactions, limiting inference about time-resolved information flow in brain networks. We introduce sliding-window prediction correlation (SWpC), which embeds a directional linear time-invariant (LTI) model within each sliding window to estimate time-varying directed functional connectivity (FC). SWpC yields two complementary descriptors of directed interactions: a strength measure (prediction correlation) and a duration measure (window-wise duration of information transfer). Using concurrent local field potential (LFP) and fMRI BOLD recordings from rat somatosensory cortices, we demonstrate stable directionality estimates in both LFP band-limited power and BOLD. Using Human Connectome Project (HCP) motor task fMRI, SWpC detects significant task-evoked changes in directed FC strength and duration and shows higher sensitivity than SWC for identifying task-evoked connectivity differences. Finally, in post-concussion vestibular dysfunction (PCVD), SWpC reveals reproducible vestibular-multisensory brain-state shifts and improves healthy-control vs subacute patient (HC-ST) discrimination using state-derived features. Together, these results show that SWpC provides biologically interpretable, time-resolved directed connectivity patterns across multimodal validation and clinical application settings, supporting both basic and translational neuroscience.