IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i21p14045-d955953.html
   My bibliography  Save this article

Resting-State Functional Connectivity Impairment in Patients with Major Depressive Episode

Author

Listed:
  • Drozdstoy Stoyanov

    (Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria)

  • Vladimir Khorev

    (Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia)

  • Rositsa Paunova

    (Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria)

  • Sevdalina Kandilarova

    (Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria)

  • Denitsa Simeonova

    (Department of Psychiatry and Medical Psychology, Research Institute, Medical University Plovdiv, 4002 Plovdiv, Bulgaria)

  • Artem Badarin

    (Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
    Neuroscience Research Institute, Samara State Medical University, 443001 Samara, Russia)

  • Alexander Hramov

    (Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
    Neuroscience Research Institute, Samara State Medical University, 443001 Samara, Russia)

  • Semen Kurkin

    (Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
    Neuroscience Research Institute, Samara State Medical University, 443001 Samara, Russia)

Abstract

Aim: This study aims to develop new approaches to characterize brain networks to potentially contribute to a better understanding of mechanisms involved in depression. Method and subjects: We recruited 90 subjects: 49 healthy controls (HC) and 41 patients with a major depressive episode (MDE). All subjects underwent clinical evaluation and functional resting-state MRI. The data were processed investigating functional connectivity network measures across the two groups using Brain Connectivity Toolbox. The statistical inferences were developed at a functional network level, using a false discovery rate method. Linear discriminant analysis was used to differentiate between the two groups. Results and discussion: Significant differences in functional connectivity (FC) between depressed patients vs. healthy controls was demonstrated, with brain regions including the lingual gyrus, cerebellum, midcingulate cortex and thalamus more prominent in healthy subjects as compared to depression where the orbitofrontal cortex emerged as a key node. Linear discriminant analysis demonstrated that full-connectivity matrices were the most precise in differentiating between depression vs. health subjects. Conclusion: The study provides supportive evidence for impaired functional connectivity networks in MDE patients.

Suggested Citation

  • Drozdstoy Stoyanov & Vladimir Khorev & Rositsa Paunova & Sevdalina Kandilarova & Denitsa Simeonova & Artem Badarin & Alexander Hramov & Semen Kurkin, 2022. "Resting-State Functional Connectivity Impairment in Patients with Major Depressive Episode," IJERPH, MDPI, vol. 19(21), pages 1-19, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14045-:d:955953
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/21/14045/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/21/14045/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pitsik, Elena N. & Maximenko, Vladimir A. & Kurkin, Semen A. & Sergeev, Alexander P. & Stoyanov, Drozdstoy & Paunova, Rositsa & Kandilarova, Sevdalina & Simeonova, Denitsa & Hramov, Alexander E., 2023. "The topology of fMRI-based networks defines the performance of a graph neural network for the classification of patients with major depressive disorder," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    2. Oleg E. Karpov & Elena N. Pitsik & Semen A. Kurkin & Vladimir A. Maksimenko & Alexander V. Gusev & Natali N. Shusharina & Alexander E. Hramov, 2023. "Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach," IJERPH, MDPI, vol. 20(7), pages 1-17, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14045-:d:955953. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.