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Revealing patterns in major depressive disorder with machine learning and networks

Author

Listed:
  • Sallum, Loriz Francisco
  • Alves, Caroline L.
  • de O. Toutain, Thaise G.L.
  • Porto, Joel Augusto Moura
  • Thielemann, Christiane
  • Rodrigues, Francisco A.

Abstract

Major depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks.

Suggested Citation

  • Sallum, Loriz Francisco & Alves, Caroline L. & de O. Toutain, Thaise G.L. & Porto, Joel Augusto Moura & Thielemann, Christiane & Rodrigues, Francisco A., 2025. "Revealing patterns in major depressive disorder with machine learning and networks," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:chsofr:v:194:y:2025:i:c:s0960077925001766
    DOI: 10.1016/j.chaos.2025.116163
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    References listed on IDEAS

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    1. Hauke Jan & Kossowski Tomasz, 2011. "Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data," Quaestiones Geographicae, Sciendo, vol. 30(2), pages 87-93, June.
    2. Vaishnav Krishnan & Eric J. Nestler, 2008. "The molecular neurobiology of depression," Nature, Nature, vol. 455(7215), pages 894-902, October.
    3. Réka Albert & Hawoong Jeong & Albert-László Barabási, 1999. "Diameter of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 130-131, September.
    4. Caroline L Alves & Rubens Gisbert Cury & Kirstin Roster & Aruane M Pineda & Francisco A Rodrigues & Christiane Thielemann & Manuel Ciba, 2022. "Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-26, December.
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