Phenotype network and brain structural covariance network of anxiety

Je Yeon Yun, Yong Ku Kim

    Research output: Chapter in Book/Report/Conference proceedingChapter

    3 Citations (Scopus)


    Network-based approach for psychological phenotypes assumes the dynamical interactions among the psychiatric symptoms, psychological characteristics, and neurocognitive performances arise, as they coexist, propagate, and inhibit other components within the network of mental phenomena. For differential types of dataset from which the phenotype network is to be estimated, a Gaussian graphical model, an Ising model, a directed acyclic graph, or an intraindividual covariance network could be used. Accordingly, these network-based approaches for anxiety-related psychological phenomena have been helpful in quantitative and pictorial understanding of qualitative dynamics among the diverse psychological phenomena as well as mind-environment interactions. Brain structural covariance refers to the correlative patterns of diverse brain morphological features among differential brain regions comprising the brain, as calculated per participant or across the participants. These covarying patterns of brain morphology partly overlap with longitudinal patterns of brain cortical maturation and also with propagating pattern of brain morphological changes such as cortical thinning and brain volume reduction in patients diagnosed with neurologic or psychiatric disorders along the trajectory of disease progression. Previous studies that used the brain structural covariance network could show neural correlates of specific anxiety disorder such as panic disorder and also elucidate the neural underpinning of anxiety symptom severity in diverse psychiatric and neurologic disorder patients.

    Original languageEnglish
    Title of host publicationAdvances in Experimental Medicine and Biology
    Number of pages14
    Publication statusPublished - 2020

    Publication series

    NameAdvances in Experimental Medicine and Biology
    ISSN (Print)0065-2598
    ISSN (Electronic)2214-8019

    Bibliographical note

    Funding Information:
    Acknowledgments This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03028464).

    Publisher Copyright:
    © Springer Nature Singapore Pte Ltd. 2020.


    • Anxiety disorder
    • Brain magnetic resonance imaging
    • Directed acyclic network
    • Gaussian graphical model
    • Ising model
    • Phenotype network
    • Structural covariance network

    ASJC Scopus subject areas

    • General Biochemistry,Genetics and Molecular Biology


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