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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: Jul 12, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

When Can Brain Connectivity Track the Working Mind? A Large-Scale Benchmark of Dynamic Functional Connectivity Across

Mohammad Torabi, Jean-Baptiste Poline, Georgios D Mitsis

    Biorxiv : the Preprint Server for Biology
    |July 10, 2026
    PubMed
    Summary

    Dynamic functional connectivity (dFC) reliably tracks cognitive engagement when experimental designs feature longer, regular task blocks. However, performance varies with data quality and method choice, highlighting limitations for brain network analysis.

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    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

    Published on: June 30, 2018

    Area of Science:

    • Neuroscience
    • Cognitive Science
    • Data Science

    Background:

    • Dynamic functional connectivity (dFC) analyzes time-varying brain network interactions to understand cognitive processes.
    • A key unresolved question is whether dFC can reliably indicate cognitive engagement.

    Purpose of the Study:

    • To benchmark seven dFC methods for their ability to predict task presence across diverse fMRI datasets.
    • To identify factors influencing the reliability of dFC in tracking cognitive states.

    Main Methods:

    • Evaluated seven dFC methods on 16 fMRI datasets (>1,500 participants, 28 settings).
    • Utilized simulated data to complement experimental findings.
    • Analyzed the impact of experimental design, data quality, and dFC method choice on decoding performance.

    Main Results:

    • dFC-based tracking of cognitive engagement was often unreliable, with performance near chance for most method-experiment combinations.
    • No single dFC method succeeded across all contexts.
    • Decoding performance systematically varied with experimental design, data quality, and dFC method, not dFC features alone.
    • Longer, regular task blocks and fewer task-rest transitions improved dFC-based decoding.

    Conclusions:

    • dFC's reliability in tracking cognitive engagement is context-dependent.
    • Actionable principles for optimal dFC application in cognitive neuroscience research were identified.
    • Experimental design and data quality are critical factors for successful dFC analysis.