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Related Concept Videos

Brain Imaging01:14

Brain Imaging

272
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...
272

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Related Experiment Video

Updated: Aug 4, 2025

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
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Aberrant Static and Dynamic Functional Brain Network in Depression Based on EEG Source Localization.

Xiangbin Lin, Weizhuang Kong, Jianxiu Li

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |April 4, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Depression alters functional brain networks, showing increased local connectivity but decreased global efficiency. Patients exhibit less flexible brain networks and spend more time in sparse states, offering potential diagnostic biomarkers.

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    Area of Science:

    • Neuroscience
    • Computational Psychiatry
    • Brain Network Analysis

    Background:

    • Depression is associated with disruptions in large-scale functional brain networks.
    • Understanding the topological abnormalities and dynamic changes in functional connectivity networks (FCNs) is crucial for depression research.

    Purpose of the Study:

    • To analyze the abnormal topology and dynamic changes of functional connectivity networks (FCNs) in depression using both static and dynamic methods.
    • To identify potential neurophysiological mechanisms and biomarkers for the clinical diagnosis of depression.

    Main Methods:

    • Collected resting-state electroencephalography (EEG) data from 27 depressed patients and 28 healthy controls.
    • Constructed functional connectivity networks (FCNs) using 68 regions of interest (ROIs) as nodes and correlations as edges.
    • Applied graph theory for static network analysis and a sliding window approach with k-means clustering for dynamic connectivity and state analyses.

    Main Results:

    • Depression was characterized by increased clustering coefficient and local efficiency, alongside decreased characteristic path length and global efficiency.
    • Altered connectivity patterns were observed across resting state networks (RSNs), with reduced connectivity in most RSNs but increased connectivity in the default mode network.
    • Depressed individuals exhibited less FCN flexibility, spent more time in sparsely connected states, and showed opposite trends in clustering coefficient over time compared to controls.

    Conclusions:

    • The findings reveal significant alterations in both static and dynamic aspects of functional brain networks in depression.
    • These network abnormalities, including reduced flexibility and altered connectivity patterns, may serve as valuable neurophysiological biomarkers for depression diagnosis.