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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
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Exploring Functional Connectivity in Attention Deficit/Hyperactivity Disorder: A Functional Near-Infrared

S Lim, S-Y Dong, R S McIntyre

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    PubMed
    Summary
    This summary is machine-generated.

    Functional near-infrared spectroscopy (fNIRS) reveals distinct brain connectivity patterns in adults with attention deficit/hyperactivity disorder (ADHD). These fNIRS-derived metrics show potential as biomarkers for diagnosing ADHD.

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    Area of Science:

    • Neuroscience
    • Medical Imaging

    Background:

    • Attention deficit/hyperactivity disorder (ADHD) is associated with cognitive challenges and altered brain activity.
    • Research on adult ADHD, especially under task conditions, is less extensive than in children.
    • Functional connectivity investigation offers insights into the neural characteristics of ADHD.

    Purpose of the Study:

    • To investigate functional connectivity in adult ADHD patients compared to healthy controls under task-state conditions.
    • To explore the potential of functional connectivity metrics as ADHD biomarkers.

    Main Methods:

    • Utilized a functional near-infrared spectroscopy (fNIRS) dataset of 75 healthy controls and 75 medication-naïve adults with ADHD.
    • Compared network characteristics (density, global clustering coefficient, efficiency, betweenness centrality) during a verbal fluency task.
    • Employed machine learning classifiers, including linear support vector machine, to assess diagnostic potential.

    Main Results:

    • Significant differences in functional connectivity density were observed between ADHD patients and controls (p<0.001).
    • Machine learning models, particularly linear SVM, demonstrated high classification performance (accuracy ~0.80).
    • Distinct functional connectivity patterns were identified in adults with ADHD.

    Conclusions:

    • fNIRS-derived functional connectivity metrics show promise as objective biomarkers for adult ADHD.
    • Task-state fNIRS analysis can differentiate between adults with and without ADHD.
    • Further research can validate fNIRS for ADHD diagnosis.