Unveiling Depression-Related Network Changes through EEG Coherence Analysis with Volume Conduction Mitigation

Abstract

Recently, a model has been proposed that defines depression as the disconnection of extensive neural networks affecting the functioning of cognitive and emotion-related systems. The present study conducted a secondary analysis on inter- and intrahemispheric coherence measures in individuals belonging to a high-depression group, defined via the Beck Depression Inventory (BDI), and compared them against those of the healthy subjects, using 121 individuals with high-depression BDI scores (n = 46) and normal (control) individuals (n = 75). Due to the use of threshold-based BDI score cut-off rather than a structured diagnosis of MDD, the conclusions regarding network dysconnectivity in depressed patients would be regarded as relating to depression symptoms alone. To increase spatial resolution and minimize the impact of volume conduction, a surface Laplacian filter was applied before estimating the coherence magnitude across five frequency bands. Coherence analysis showed significant coherence reductions in the high-depression group at several inter- (frontal – F3-F4, F7-F8, fronto-temporal – FT7-FT8, central – C3-C4, parietal – P3-P4, occipital - O1-O2) and intra-hemispheric electrode pairs (fronto-temporal – Fp1-T7, Fp2-T8; temporo-occipital – T7-O1, T8-O2), except the interhemispheric alpha coherence measures, which did not reach statistical significance. Both delta and theta bands showed inter-hemispheric coherence reduction, while gamma coherence decreased in fronto-temporal and frontal networks; the latter findings should be interpreted with caution due to possible contamination by high-frequency muscle artifacts in the EEG. Moderate machine learning performance was achieved, with accuracies ranging from 70% to 74% and an area under the curve between 0.60 and 0.76. These findings indicate large-scale impairments in neural synchronization across multiple frequency bands in MDD and support the hypothesis that depressive pathology involves disrupted integration within cognitive-emotional regulatory networks. Overall, the results highlight EEG coherence as a promising objective biomarker for characterizing functional dysconnectivity in MDD.

Keywords

• Coherence
• Spatial filtering
• Electroencephalography
• Machine learning

Project Number

1930460

Ethical Statement

All participants provided written informed consent in the original study. The original experimental procedures for the Cavanagh et al. cohort were approved by the University of Arizona Institutional Review Board. The present study used publicly available, de-identified OpenNeuro data (EEG: Depression rest, ds003478, version 1.1.0, DOI: 10.18112/openneuro.ds003478.v1.1.0; CC0 reuse framework); no additional data collection involving human participants was conducted (Cavanagh, 2021).

Thanks

The author would like to thank Prof. Dr. Oğuz Tan for his valuable contributions and clinical insights during the interpretation of the study results.

References

1. Berk, M., Köhler‐Forsberg, O., Turner, M., Penninx, B. W., Wrobel, A., Firth, J., ... & Marx, W. (2023). Comorbidity between major depressive disorder and physical diseases: a comprehensive review of epidemiology, mechanisms and management. World psychiatry, 22(3), 366-387.
2. Carter, G., Knapp, C., & Nuttall, A. (1973). Estimation of the magnitude-squared coherence function via overlapped fast Fourier transform processing. IEEE Transactions on Audio and Electroacoustics, 21(4), 337–344. https://doi.org/10.1109/TAU.1973.1162496
3. Carvalhaes, C., & de Barros, J. A. (2015). The surface Laplacian technique in EEG: Theory and methods. International Journal of Psychophysiology, 97(3), 174–188. https://doi.org/10.1016/j.ijpsycho.2015.04.023
4. Cavanagh, J. F. (2021). EEG: Depression rest (Version 1.1.0) [Data set]. OpenNeuro. https://doi.org/10.18112/openneuro.ds003478.v1.1.0
5. Cavanagh, J. F., Bismark, A. W., Frank, M. J., & Allen, J. J. B. (2019). Multiple dissociations between comorbid depression and anxiety on reward and punishment processing: Evidence from computationally informed EEG. Computational Psychiatry, 3, 1–17. https://doi.org/10.1162/CPSY_a_00024
6. Chen, H., Lei, Y., Li, R., Xia, X., Cui, N., Chen, X., Liu, J., Tang, H., Zhou, J., Huang, Y., Tian, Y., Wang, X., & Zhou, J. (2024). Resting-state EEG dynamic functional connectivity distinguishes non-psychotic major depression, psychotic major depression and schizophrenia. Molecular Psychiatry, 29(4), 1088–1098. https://doi.org/10.1038/s41380-023-02395-3
7. Cohen, M. X. (2014). Analyzing neural time series data: Theory and practice. MIT Press.
8. Dean, J., & Keshavan, M. (2017). The neurobiology of depression: An integrated view. Asian Journal of Psychiatry, 27, 101–111. https://doi.org/10.1016/j.ajp.2017.01.025

9. Depression, and W. H. O. (2017). Other common mental disorders: Global health estimates. World Health Organization.
10. Elkholy, M. M. (2023). Disruption of EEG resting state functional connectivity in patients with focal epilepsy. The Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 59(1), 122.
11. Elnaggar, K., El-Gayar, M. M., & Elmogy, M. (2025). Depression detection and diagnosis based on electroencephalogram (EEG) analysis: A comprehensive review. Diagnostics, 15(2), Article 210. https://doi.org/10.3390/diagnostics15020210
12. He, B., Astolfi, L., Valdes-Sosa, P. A., Marinazzo, D., Palva, S. O., Benar, C.-G., Michel, C. M., & Koenig, T. (2019). Electrophysiological brain connectivity: Theory and implementation. IEEE Transactions on Biomedical Engineering, 66(7), 2115–2137. https://doi.org/10.1109/TBME.2019.2913928
13. Jothi, N., Rashid, N. A. A., & Husain, W. (2015). Data mining in healthcare–a review. Procedia computer science, 72, 306-313.
14. Kayser, J., & Tenke, C. E. (2015). Issues and considerations for using the scalp surface Laplacian in EEG/ERP research: A tutorial review. International Journal of Psychophysiology, 97(3), 189–209. https://doi.org/10.1016/j.ijpsycho.2015.04.012
15. Kou, Y., Si, Y., Liu, L., Li, J., Zhang, Y., Li, W., Zhang, J., Wang, C., & Zhang, H. (2025). Exploring the functional connectivity of resting-state EEG in adolescent major depressive disorder. Journal of Integrative Neuroscience, 24(10), Article 42821. https://doi.org/10.31083/JIN42821
16. Krstajic, D., Buturovic, L. J., Leahy, D. E., & Thomas, S. (2014). Cross-validation pitfalls when selecting and assessing regression and classification models. Journal of Cheminformatics, 6(1), Article 10. https://doi.org/10.1186/1758-2946-6-10
17. Lei, L., Liu, Z., Zhang, Y., Guo, M., Liu, P., Hu, X., Yang, C., Zhang, A., Sun, N., Wang, Y., & Zhang, K. (2022). EEG microstates as markers of major depressive disorder and predictors of response to SSRIs therapy. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 116Trace, Article 110514. https://doi.org/10.1016/j.pnpbp.2022.110514
18. Lewine, J. D., Plis, S., Ulloa, A., Williams, C., Spitz, M., Foley, J., Paulson, K., Davis, J., Bangera, N., Snyder, T., & Weaver, L. (2019). Quantitative EEG biomarkers for mild traumatic brain injury. Journal of Clinical Neurophysiology, 36(4), 298–305. https://doi.org/10.1097/WNP.0000000000000588
19. Liu, X., Zhang, H., Cui, Y., Zhao, T., Wang, B., Xie, X., Liang, S., Sha, S., Yan, Y., Zhao, X., & Zhang, L. (2024). EEG-based major depressive disorder recognition by neural oscillation and asymmetry. Frontiers in Neuroscience, 18, Article 1362111. https://doi.org/10.3389/fnins.2024.1362111
20. Luo, X., Xu, Y., Yan, Z., Liu, W., Zhou, B., Li, G., & Zhu, Y. (2026). Multidimensional EEG features integration with feature selection strategy for precision diagnosis of depressive disorders. Frontiers in Psychiatry, 16, Article 1624997. https://doi.org/10.3389/fpsyt.2025.1624997
21. Malekpour, S., Gubner, J. A., & Sethares, W. A. (2018). Measures of generalized magnitude-squared coherence: Differences and similarities. Journal of the Franklin Institute, 355(5), 2932–2950. https://doi.org/10.1016/j.jfranklin.2018.01.014
22. Markovska-Simoska, S., Pop-Jordanova, N., & Pop-Jordanov, J. (2018). Inter- and intra-hemispheric EEG coherence study in adults with neuropsychiatric disorders. PRILOZI, 39(2–3), 5–19. https://doi.org/10.2478/prilozi-2018-0037
23. Miljevic, A., Murphy, O. W., Fitzgerald, P. B., & Bailey, N. W. (2025). Estimating sensor-space EEG connectivity: Identifying best performing methods for functional connectivity in simulated data. Clinical Neurophysiology, 174, 73–83. https://doi.org/10.1016/j.clinph.2025.03.043
24. Nassehi, F., Zupan, A., Eken, A., Yetkin, S., & Erogul, O. (2026). Toward multi-dimensional depression assessment: EEG-based machine learning and neurophysiological interpretation for diagnosis, severity, and cognitive decline. Brain Sciences, 16(2), Article 139. https://doi.org/10.3390/brainsci16020139
25. Olbrich, S., Jaworska, N., de la Salle, S., Knott, V., Blier, P., Brunovsky, M., Welt, T., de Bardeci, M., & Teng-Ip, C. (2026). Deep learning using electroencephalogram (EEG) data for diagnosing and predicting SSRI response in major depressive disorder. Communications Medicine, 6Trace, Article 159. https://doi.org/10.1038/s43856-026-01394-z
26. Olbrich, S., Tränkner, A., Chittka, T., Hegerl, U., & Schönknecht, P. (2014). Functional connectivity in major depression: Increased phase synchronization between frontal cortical EEG-source estimates. Psychiatry Research, 222(1–2), 91–99. https://doi.org/10.1016/j.pscychresns.2014.02.010
27. Otte, C., Gold, S. M., Penninx, B. W., Pariante, C. M., Etkin, A., Fava, M., Mohr, D. C., & Schatzberg, A. F. (2016). Major depressive disorder. Nature Reviews Disease Primers, 2(1), Article 16065. https://doi.org/10.1038/nrdp.2016.65
28. Perrin, F., Pernier, J., Bertrand, O., & Echallier, J. F. (1989). Spherical splines for scalp potential and current density mapping. Electroencephalography and Clinical Neurophysiology, 72(2), 184–187. https://doi.org/10.1016/0013-4694(89)90180-6
29. Perrin, F., Pernier, J., Bertrand, O., Giard, M. H., & Echallier, J. F. (1987). Mapping of scalp potentials by surface spline interpolation. Electroencephalography and Clinical Neurophysiology, 66(1), 75–81. https://doi.org/10.1016/0013-4694(87)90141-6
30. Ray, K. L., Griffin, N. R., Shumake, J., Alario, A., Allen, J. J. B., Beevers, C. G., & Schnyer, D. M. (2023). Altered electroencephalography resting state network coherence in remitted MDD. Brain Research, 1806, Article 148282. https://doi.org/10.1016/j.brainres.2023.148282
31. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
32. Shim, M., Im, C. H., Kim, Y. W., & Lee, S. H. (2018). Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study. NeuroImage: Clinical, 19, 1000-1007.
33. Srinivasan, R., Winter, W. R., Ding, J., & Nunez, P. L. (2007). EEG and MEG coherence: Measures of functional connectivity at distinct spatial scales of neocortical dynamics. Journal of Neuroscience Methods, 166(1), 41–52. https://doi.org/10.1016/j.jneumeth.2007.06.026
34. Stam, C. J., Van Der Made, Y., Pijnenburg, Y. A. L., & Scheltens, P. (2003). EEG synchronization in mild cognitive impairment and Alzheimer’s disease. Acta Neurologica Scandinavica, 108(2), 90–96. https://doi.org/10.1034/j.1600-0404.2003.02067.x
35. Suhhova, A., Bachmann, M., Aadamsoo, K., Võhma, Ü., Lass, J., & Hinrikus, H. (2009). EEG coherence as measure of depressive disorder. In J. Vander Sloten, P. Verdonck, M. Nyssen, & J. Haueisen (Eds.), 4th European Conference of the International Federation for Medical and Biological Engineering (pp. 353–355). Springer. https://doi.org/10.1007/978-3-540-89208-3_83
36. Tang, Z., Du, L., Tan, X., & Song, S. (2026). Research on depression recognition based on EEG microstate functional connectivity. Journal of Integrative Neuroscience, 25(2), Article 48264. https://doi.org/10.31083/JIN48264
37. Tas, C., Cebi, M., Tan, O., Hızlı-Sayar, G., Tarhan, N., & Brown, E. C. (2015). EEG power, cordance and coherence differences between unipolar and bipolar depression. Journal of Affective Disorders, 172, 184–190. https://doi.org/10.1016/j.jad.2014.10.001
38. Van de Steen, F., Faes, L., Karahan, E., Songsiri, J., Valdes-Sosa, P. A., & Marinazzo, D. (2019). Critical comments on EEG sensor space dynamical connectivity analysis. Brain Topography, 32(4), 643–654. https://doi.org/10.1007/s10548-016-0538-7
39. Varoquaux, G., Raamana, P. R., Engemann, D. A., Hoyos-Idrobo, A., Schwartz, Y., & Thirion, B. (2017). Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. NeuroImage, 145(Pt B), 166–179. https://doi.org/10.1016/j.neuroimage.2016.10.038
40. Velikova, S., Locatelli, M., Insacco, C., Smeraldi, E., Comi, G., & Leocani, L. (2010). Dysfunctional brain circuitry in obsessive–compulsive disorder: Source and coherence analysis of EEG rhythms. NeuroImage, 49(1), 977–983. https://doi.org/10.1016/j.neuroimage.2009.08.015
41. Wang, A., & Sun, J. (2025). Personalized EEG-guided brain stimulation targeting in major depression via network controllability and multi-objective optimization. BMC Psychiatry, 25, Article 723. https://doi.org/10.1186/s12888-025-07171-x
42. Winter, W. R., Nunez, P. L., Ding, J., & Srinivasan, R. (2007). Comparison of the effect of volume conduction on EEG coherence with the effect of field spread on MEG coherence. Statistics in Medicine, 26(21), 3946–3957. https://doi.org/10.1002/sim.2978
43. Yuvaraj, R., Murugappan, M., Omar, M. I., Ibrahim, N. M., Sundaraj, K., Mohamad, K., & Satiyan, M. (2014). Emotion processing in Parkinson’s disease: An EEG spectral power study. International Journal of Neuroscience, 124(7), 491–502. https://doi.org/10.3109/00207454.2013.860527
44. Zhai, L., Zhao, M., Zhang, J., Jamil, M., Naz, R., & Li, C. (2026). A systematic review of EEG-based biomarkers for depression, anxiety, and bipolar disorder: Trends in explainable artificial intelligence (XAI). BMC Psychiatry, 26Trace, Article 90. https://doi.org/10.1186/s12888-025-07589-3
45. Zheng, H., Xiao, H., Zhang, Y., Jia, H., Ma, X., & Gan, Y. (2025). Time-Frequency functional connectivity alterations in Alzheimer’s disease and frontotemporal dementia: An EEG analysis using machine learning. Clinical Neurophysiology, 170, 110-119.