IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v194y2025ics0960077925001766.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077925001766
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2025.116163?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohd-Zaid, Fairul & Kabban, Christine M. Schubert & Deckro, Richard F. & White, Edward D., 2017. "Parameter specification for the degree distribution of simulated Barabási–Albert graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 141-152.
    2. Agumas Alamirew Mebratu, 2024. "Theoretical foundations of voluntary tax compliance: evidence from a developing country," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-8, December.
    3. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    4. Javier García López & Raffaele Sisto & Javier Benayas & Álvaro de Juanes & Julio Lumbreras & Carlos Mataix, 2021. "Assessment of the Results and Methodology of the Sustainable Development Index for Spanish Cities," Sustainability, MDPI, vol. 13(11), pages 1-29, June.
    5. Zheng, Mingbo & Zhang, Xinyu, 2025. "Digitalization and renewable energy development: Analysis based on cross-country panel data," Energy, Elsevier, vol. 319(C).
    6. Elias Carroni & Paolo Pin & Simone Righi, 2020. "Bring a Friend! Privately or Publicly?," Management Science, INFORMS, vol. 66(5), pages 2269-2290, May.
    7. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    8. Adriana Gómez-Cabrera & Amalia Sanz-Benlloch & Laura Montalban-Domingo & Jose Luis Ponz-Tienda & Eugenio Pellicer, 2020. "Identification of Factors Affecting the Performance of Rural Road Projects in Colombia," Sustainability, MDPI, vol. 12(18), pages 1-18, September.
    9. Baek, Seung Ki & Kim, Tae Young & Kim, Beom Jun, 2008. "Testing a priority-based queue model with Linux command histories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3660-3668.
    10. Freddy Hern�n Cepeda L�pez, 2008. "La topolog�a de redes como herramienta de Seguimiento en el sistema de Pagos de Alto Valor en Colombia," Borradores de Economia 4676, Banco de la Republica.
    11. Chung-Yuan Huang & Chuen-Tsai Sun & Hsun-Cheng Lin, 2005. "Influence of Local Information on Social Simulations in Small-World Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-8.
    12. Xue Guo & Hu Zhang & Tianhai Tian, 2019. "Multi-Likelihood Methods for Developing Stock Relationship Networks Using Financial Big Data," Papers 1906.08088, arXiv.org.
    13. Díaz-Vallejo, Mauricio & Peña-Peniche, Alexander & Mota-Vargas, Claudio & Piña-Torres, Javier & Valencia-Rodríguez, Daniel & Rangel-Rivera, Coral E. & Gaviria-Hernández, Juliana & Rojas-Soto, Octavio, 2024. "Analyses of the variable selection using correlation methods: An approach to the importance of statistical inferences in the modelling process," Ecological Modelling, Elsevier, vol. 498(C).
    14. Chang, Chia-ling & Chen, Shu-heng, 2011. "Interactions in DSGE models: The Boltzmann-Gibbs machine and social networks approach," Economics Discussion Papers 2011-25, Kiel Institute for the World Economy (IfW Kiel).
    15. Lin, Yi & Zhang, Jianwei & Yang, Bo & Liu, Hong & Zhao, Liping, 2019. "An optimal routing strategy for transport networks with minimal transmission cost and high network capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 551-561.
    16. Judit Bar-Ilan & Mark Levene, 2015. "The hw-rank: an h-index variant for ranking web pages," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2247-2253, March.
    17. Stefano Breschi & Lucia Cusmano, 2002. "Unveiling the Texture of a European Research Area: Emergence of Oligarchic Networks under EU Framework Programmes," KITeS Working Papers 130, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Jul 2002.
    18. Ma Zhong & Rong Xu & Xinyi Liao & Shuangli Zhang, 2019. "Do CSR Ratings Converge in China? A Comparison Between RKS and Hexun Scores," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    19. He, Xuan & Zhao, Hai & Cai, Wei & Li, Guang-Guang & Pei, Fan-Dong, 2015. "Analyzing the structure of earthquake network by k-core decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 34-43.
    20. Huang, Huilin, 2009. "The degree sequences of an asymmetrical growing network," Statistics & Probability Letters, Elsevier, vol. 79(4), pages 420-425, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:chsofr:v:194:y:2025:i:c:s0960077925001766. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.