Evidence map›Paper›PMID 41757288›Full record

ArticleArXiv2026

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

Nan Xu, Xiaodi Zhang, Wen-Ju Pan, Jeremy L Smith, Eric H Schumacher, Jason W Allen, Vince D Calhoun, Shella D Keilholz

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PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Nan XuFischell Department of Bioengineering, Department of Electrical and Computer Engineering, Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD, United States.
Xiaodi ZhangWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.
Wen-Ju PanWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.
Jeremy L SmithDepartment of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States.
Eric H SchumacherSchool of Psychology, Georgia Institute of Technology, Atlanta, GA, United States.
Jason W AllenDepartment of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States.
Vince D CalhounTri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University and Georgia Institute of Technology and Emory University, Atlanta, GA, United States.
Shella D KeilholzWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.

Funding

Contribution of Ultra Low Frequency LFPs to Functional MRIR01NS078095 · NINDS · EMORY UNIVERSITY · PI KEILHOLZ, SHELLA D · 2012 to 2022
$3.4M
Time-varying spatiotemporal causal interactions in the functional brain networksR00NS123113 · NINDS · UNIV OF MARYLAND, COLLEGE PARK · PI Nan Xu · 2025 to 2026
$531k
NINDS NIH HHS R00 NS123113NINDS NIH HHS R01 NS078095
6 · The paper itself

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.

Identifiers

PMID41757288
PMCPMC12935017

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.