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Related Concept Videos

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Focal Seizures
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Updated: Jun 23, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Classifying Post-COVID "Brain Fog" Patients and Identifying Key ROIs via Graph Neural Network Model.

Yuzhe Li, Yubo Zhang, Zhaomin Dong

    IEEE Journal of Biomedical and Health Informatics
    |April 23, 2026
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    Summary
    This summary is machine-generated.

    Brain fog after COVID-19 impacts memory and concentration. An interpretable graph neural network model using resting-state fMRI shows promise for identifying brain fog and its neural markers.

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    Area of Science:

    • Neuroscience
    • Artificial Intelligence
    • Medical Imaging

    Background:

    • Brain fog is a common neurocognitive impairment post-COVID-19, affecting memory, concentration, and language.
    • Neural mechanisms of brain fog remain unclear, lacking objective diagnostic tools.
    • Resting-state functional magnetic resonance imaging (rs-fMRI) offers potential for objective assessment.

    Purpose of the Study:

    • To develop and validate an interpretable machine learning model for identifying brain fog using rs-fMRI data.
    • To uncover potential neurobiological markers associated with brain fog in post-COVID-19 individuals.
    • To investigate the relationship between brain activity in specific regions and cognitive symptoms.

    Main Methods:

    • Recruited 72 post-COVID-19 patients with brain fog and 68 controls without brain fog.
    • Utilized resting-state functional magnetic resonance imaging (rs-fMRI) data.
    • Employed an interpretable graph neural network (BrainGNN) model on functional connectivity graphs.

    Main Results:

    • BrainGNN achieved 75.71% accuracy in cross-validation and 82.14% on an independent test set.
    • The model identified bilateral insula, Heschl's gyri, and left superior temporal gyrus as potential neurobiological markers.
    • Left insula activity correlated with language and memory symptom severity.

    Conclusions:

    • Interpretable graph neural networks effectively identify functional markers of brain fog using rs-fMRI.
    • The study provides insights into the neurobiological underpinnings of COVID-19-related cognitive impairment.
    • This approach holds potential for objective diagnosis and understanding of brain fog mechanisms.