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Relation Between the Distributed Load and Shear

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Related Experiment Video

Updated: Jun 27, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Detection of Low Resilience Using Data-Driven Effective Connectivity Measures.

Ayman Siddiqui, Rumaisa Abu Hasan, Syed Saad Azhar Ali

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |September 20, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Data-driven methods like Global Cost Efficiency (GCE-abs) and Orthogonal Minimum Spanning Trees (OMSTs) offer unbiased brain network analysis for mental resilience. These techniques achieved high accuracy in detecting low stress resilience in healthy individuals.

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    Area of Science:

    • Neuroscience
    • Graph Theory
    • Computational Psychiatry

    Background:

    • Conventional thresholding in brain network analysis is prone to human bias.
    • Characterizing mental stress and resilience often relies on these potentially unreliable methods.

    Purpose of the Study:

    • To investigate data-driven thresholding techniques (GCE-abs, OMSTs) for unbiased brain network analysis.
    • To identify key features of low mental resilience in healthy individuals.
    • To compare the efficacy of GCE-abs and OMSTs against conventional methods.

    Main Methods:

    • Utilized Phase Slope Index (PSI) to compute effective brain connectivity.
    • Applied Global Cost Efficiency (GCE-abs) and Orthogonal Minimum Spanning Trees (OMSTs) for data-driven network thresholding.
    • Employed resting-state EEG data from 36 healthy participants.
    • Trained a Support Vector Machine (SVM) classifier on extracted network features.

    Main Results:

    • The SVM model achieved 80.6% accuracy in detecting low stress resilience using GCE-abs.
    • The SVM model achieved 75% accuracy using OMSTs.
    • Data-driven methods demonstrated effectiveness in identifying features of low resilience.

    Conclusions:

    • Data-driven thresholding techniques (GCE-abs, OMSTs) provide a reliable, unbiased alternative to conventional methods in brain network analysis.
    • These methods show promise for objective assessment of mental resilience and stress.
    • Further research can refine these techniques for clinical applications.