Skip to main content
Log in

Electroencephalogram Coherence and Peripheral Markers of Nervous Tissue Damage in Depressive Disorders

  • Published:
Neuroscience and Behavioral Physiology Aims and scope Submit manuscript

Objectives. To assess electroencephalogram coherence parameters and the levels of peripheral markers of nerve tissue damage in patients with depressive disorders. Materials and methods. The study included 30 patients with diagnoses from the mood disorders cluster: affective disorder as a single depressive episode and recurrent depressive disorder. The control group consisted of 30 healthy subjects of comparable sex and age composition. Brain bioelectrical activity was recorded and analyzed with calculation of mean intra- and interhemisphere coherence coefficients. Serum calcium-binding protein S100b, myelin basic protein (MBP), and glial fibrillary acidic protein (GFAP) concentrations were determined by enzyme-linked immunosorbent assay. Results. Patients with depressive disorders showed statistically significantly lower coefficients of interhemisphere coherence in the α (p = 0.003), β (p = 0.042), and θ (p = 0.041) rhythms and intrahemisphere coherence of the α rhythm in the left (p = 0.016) and right (p = 0.026) hemispheres and β rhythm in the right hemisphere (p = 0.034), as compared with the healthy group. Higher MBP concentrations were found in the depressive disorders group than the control group (p = 0.008). Statistically significant correlations were identified between EEG coherence coefficients and serum markers in patients with depressive disorders. Conclusions. These data clearly confirm the presence of inflammatory changes in the brain in patients with depression, which is reflected in structural and functional changes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Wang, J., Wu, X., Lai, W., et al., “Prevalence of depression and depressive symptoms among outpatients: a systematic review and meta-analysis,” BMJ Open, 7, No. 8, e017173 (2017), https://doi.org/https://doi.org/10.1136/bmjopen-2017-017173.

    Article  Google Scholar 

  2. Colizzi, M., Lasalvia, A., and Ruggeri, M., “Prevention and early intervention in youth mental health: is it time for a multidisciplinary and trans-diagnostic model for care?” Int. J. Ment. Health Syst., 14, 23 (2020), https://doi.org/https://doi.org/10.1186/s13033-020-00356-9.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Tuychiev, Sh., Abdullaeva, V., and Matveeva, A., “Prevalence of anxiety and depression among COVID-19 patients: systematic review and meta-analysis,” Sci. Eur., 89, No. 1, 12–20 (2022).

    Google Scholar 

  4. Dorozhenok, I. Yu., “Depression during the COVID-19 pandemic (analysis of clinical cases),” Nevrol. Neuropsikhiatr. Psikhosomat., 13, No. 1, 81–86 (2021), https://doi.org/10.14412/2074-2711-2021-1-81-86.

  5. Cuijpers, P., Stringaris, A., and Wolpert, M., “Treatment outcomes for depression: challenges and opportunities,” Lancet Psychiatry, 7, No. 11, 925–927 (2020), https://doi.org/https://doi.org/10.1016/S2215-0366(20)30036-5.

    Article  PubMed  Google Scholar 

  6. Galkin, S. A., Vasil’eva, S. N., Ivanova, S. A., and Bokhan, N. A., “Electroencephalographic markers of the stability of depressive disorders to pharmacotherapy and determination of a possible approach to individual prognosis of treatment efficacy,” Psikhiatriya, 19, No. 2, 39–45 (2021), https://doi.org/10.30629/2618-6667-2021-19-2-39-45.

  7. Losenkov, I. S., Mulder, N., Levchuk, L. A., et al., “Association between BDNF gene variant Rs6265 and the severity of depression in antidepressant treatment-free depressed patients,” Front. Psychiatry, 11, 38 (2020), https://doi.org/https://doi.org/10.3389/fpsyt.2020.00038.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Levchuk, L. A., Roshchina, O. V., and Simutkin, G. G., et al., “Peripheral markers of nerve tissue damage in addictive and affective disorders,” Neirokhimiya, 1, 77–82 (2021), https://doi.org/10.31857/S1027813321010076.

  9. Loonen A. and Ivanova, S. A., “Circuits regulating pleasure and happiness: evolution and role in mental disorders,” Acta Neuropsychiatr., 30, No. 1, 29–42 (2018), https://doi.org/https://doi.org/10.1017/neu.2017.8.

    Article  PubMed  Google Scholar 

  10. Troubat, R., Barone, P., Leman, S., et al., “Neuroinflammation and depression: A review,” Eur. J. Neurosci., 53, No. 1, 151–171 (2021), https://doi.org/https://doi.org/10.1111/ejn.14720.

    Article  PubMed  CAS  Google Scholar 

  11. Gumenyuk, L. N., Belous, V. V., and Blinova, E. V., “Current concepts about the role of pro-inflammatory mediators in the pathogenesis of depression,” Tavrich. Zh. Psikhiatr., 81, No. 4, 5–10 (2017).

    Google Scholar 

  12. Iznak, A. F., Iznak, E. V., and Mel’nikova, T. S., “EEG coherence parameters as a reflection of brain neuroplasticity in mental pathology,” Psikhiatriya, 78, No. 2, 127–137 (2018), https://doi.org/10.30629/2618-6667-2018-78-127-137.

  13. Galkin, S. A., Ivanova, S. A., and Bokhan, N. A., “Current methods for predicting therapeutic response in patients with depressive disorders,” Zh. Nevrol. Psikhiatr., 122, No. 2, 15–21 (2022), https://doi.org/10.17116/jnevro202212202115.

  14. Lapin, I. A. and Rogacheva, T. A., “The potential of EEG coherence analysis for assessment of suicide risk in depression,” Sots. Klin. Psikhiatr., 28, No. 2, 30–38 (2018).

    Google Scholar 

  15. Assenza, G. and Di Lazzaro, V., “A useful electroencephalography (EEG) marker of brain plasticity: delta waves,” Neural Regen. Res., 10, No. 8, 1216–1217 (2015), https://doi.org/https://doi.org/10.4103/1673-5374.162698.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Duman, R. S. and Aghajanian, G. K., “Synaptic dysfunction in depression: potential therapeutic targets,” Science, 338, No. 6103, 68–72 (2012), https://doi.org/https://doi.org/10.1126/science.1222939.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Galkin, S. A. and Bokhan, N. A., “The role of functional brain activity in the impairment of inhibitory control in alcohol dependence,” Zh. Nevrol. Psikhiatr., 121, No. 11, 67–72 (2021), https://doi.org/10.17116/jnevro202112111167.

  18. Meyers, J. L., Zhang, J., Chorlian, D. B., et al., “A genome-wide association study of interhemispheric theta EEG coherence: implications for neural connectivity and alcohol use behavior,” Mol. Psychiatry, 26, No. 9, 5040–5052 (2021), https://doi.org/https://doi.org/10.1038/s41380-020-0777-6.

    Article  PubMed  CAS  Google Scholar 

  19. Kam, J. W., Bolbecker, A. R., O’Donnell, B. F., et al., “Resting state EEG power and coherence abnormalities in bipolar disorder and schizophrenia,” J. Psychiatr. Res., 47, No. 12, 1893–1901 (2013), https://doi.org/https://doi.org/10.1016/j.jpsychires.2013.09.009.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Galkin, S. A., Roshchina, O. V., and Kisel’, N. I., et al., “Parameters of coherence of bioelectric activity of the brain and the serum phosphorylated neurofilaments level in the comorbid course of alcohol dependence and affective disorders,” Patolog. Fiziol. Eksperim. Ter., 65, No. 1, 5–11 (2021), https://doi.org/10.25557/0031-2991.2021.01.5-11.

  21. Mel’nikova, T. S., Lapin, I. A., and Sarkisyan, V. V., “Review of the use of coherence EEG analysis in psychiatry,” Sots. Klin. Psikhiatr., 19, No. 1, 90–94 (2009).

  22. Levchuk, L., Roschina, O., Simutkin, G., et al., “S-100, MBP and glial fibrillary acidic protein (GFAP) in patients with mood disorders and alcohol use disorder,” Eur. Neuropsychopharmacol., 2, 133–134 (2020), https://doi.org/https://doi.org/10.1016/j.euroneuro.2020.09.176.

    Article  Google Scholar 

  23. Lotrich, F. E., “Inflammatory cytokine-associated depression,” Brain Res., 1617, 113–125 (2015), https://doi.org/https://doi.org/10.1016/j.brainres.2014.06.032.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. A. Galkin.

Additional information

Translated from Zhurnal Nevrologii i Psikhiatrii imeni S. S. Korsakova, Vol. 123, No. 3, pp. 82–87, March, 2023.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Galkin, S.A., Levchuk, L.A., Simutkin, G.G. et al. Electroencephalogram Coherence and Peripheral Markers of Nervous Tissue Damage in Depressive Disorders. Neurosci Behav Physi 53, 1355–1359 (2023). https://doi.org/10.1007/s11055-023-01525-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11055-023-01525-2

Keywords

Navigation