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Disruptions in segregation mechanisms in fMRI-based brain functional network predict the major depressive disorder condition

Author

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  • Khorev, Vladimir S.
  • Kurkin, Semen A.
  • Zlateva, Gabriella
  • Paunova, Rositsa
  • Kandilarova, Sevdalina
  • Maes, Michael
  • Stoyanov, Drozdstoy
  • Hramov, Alexander E.

Abstract

This study investigates the functional brain network in major depressive disorder using network theory and a consensus network approach. At the macroscopic level, we found significant differences in connectivity measures such as node strength and clustering coefficient, with healthy controls exhibiting higher values. This is consistent with disruptions in functional brain network segregation in patients with major depressive disorder. Consensus network analysis revealed that the central executive and salience networks were predominant in healthy controls, whereas depressed patients showed greater overlap with the default mode network. No differences were found in network efficiency measures, indicating comparable brain network integration between healthy controls and major depressive disorder groups. Importantly, the clustering coefficient emerged as an effective diagnostic biomarker for depression, achieving high sensitivity (90%), specificity (92%), and overall precision (90%). Further analysis at the mesoscale level uncovered unique functional connections distinguishing healthy controls and major depressive disorder groups. Our findings underscore the utility of analyzing functional networks from the macroscale to the mesoscale, and provide insight into overcoming the challenges associated with intersubject variability and the multiple comparisons problem in network analysis.

Suggested Citation

  • Khorev, Vladimir S. & Kurkin, Semen A. & Zlateva, Gabriella & Paunova, Rositsa & Kandilarova, Sevdalina & Maes, Michael & Stoyanov, Drozdstoy & Hramov, Alexander E., 2024. "Disruptions in segregation mechanisms in fMRI-based brain functional network predict the major depressive disorder condition," Chaos, Solitons & Fractals, Elsevier, vol. 188(C).
  • Handle: RePEc:eee:chsofr:v:188:y:2024:i:c:s0960077924011184
    DOI: 10.1016/j.chaos.2024.115566
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    References listed on IDEAS

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    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. Anton Lord & Dorothea Horn & Michael Breakspear & Martin Walter, 2012. "Changes in Community Structure of Resting State Functional Connectivity in Unipolar Depression," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-15, August.
    3. Holger Mohr & Uta Wolfensteller & Richard F. Betzel & Bratislav Mišić & Olaf Sporns & Jonas Richiardi & Hannes Ruge, 2016. "Integration and segregation of large-scale brain networks during short-term task automatization," Nature Communications, Nature, vol. 7(1), pages 1-12, December.
    4. 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.
    5. Gabriele Lohmann & Daniel S Margulies & Annette Horstmann & Burkhard Pleger & Joeran Lepsien & Dirk Goldhahn & Haiko Schloegl & Michael Stumvoll & Arno Villringer & Robert Turner, 2010. "Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain," PLOS ONE, Public Library of Science, vol. 5(4), pages 1-8, April.
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