Spectral power of the beta rhythm of the electroencephalogram as a marker of depressive disorder

Abstract

The aim of this study was to identify the features of electroencephalograms in patients with depressive disorders and to evaluate correlations with clinical and dynamic parameters.

Material and methods. The study included 74 patients with depressive disorder. The severity of depressive disorder was assessed using the Hamilton depression scale and the General clinical impression scale. To assess the clinical features of the course of depressive disorder, the Hamilton anxiety scale, the Snaith–Hamilton anhedonia scale, and the social adaptation scale were used. Information about the age of depressive disorder was taken from patients’ medical records. In addition to the clinical data, the level of cognitive flexibility was assessed using the Stroop test. The electroencephalogram was recorded and analyzed using the international 10–20 system at rest with closed eyes. The values of the spectral power of rhythms were analyzed. Correlation analysis of clinical and electroencephalographic data of patients was performed.

Results. Analysis of the spectral power of rhythms revealed statistically significant differences between the group of patients with depressive disorders and the control only in the β-frequency range in the frontal (p=0.000001), central (p=0.00028) and parietal (p=0.017) cortex. Direct correlations were found between the level of beta — rhythm spectral power in the frontal cortex and the severity of depressive disorder (r=0.2856; p=0.015), the level of anxiety (r=0.2622; p=0.028), and the degree of cognitive rigidity (r=0.3728; p=0.007). Direct correlations were also found between the spectral power of the β-rhythm in the central cortex and the degree of cognitive rigidity (r=0.3332; p=0.017).

Conclusions. The predominance of high-frequency activity in patients with depressive disorders reflects an increase in cortical arousal in the brain, which is accompanied by a number of clinical features in the form of a more severe course of the disease, the presence of anxiety symptoms and cognitive rigidity. Thus, the results obtained allow us to use quantitative electroencephalogram data to clarify the severity of clinical symptoms of depressive disorder.

Keywords

depression / clinic / anxiety / cognitive rigidity / electroencephalography / beta rhythm / marker

References

[1] 1. Краснов В.Н. Депрессия как социальная и клиническая проблема современной медицины. Рос. психиатрич. ж. 2011; (6): 8–10. [Krasnov V.N. Depression as a social and clinical problem of modern medicine. Rossijskij psihiatricheskij zhurnal. 2011; (6): 8–10. (In Russ.)]

[2] 2. Kessler R.C., Bromet E.J. The epidemiology of depression across cultures. Annu. Rev. Public Health. 2013; (34): 119–138. DOI: 10.1146/annurev-publhealth-031912-114409.

[3] 3. Aboraya A., Rankin E., France C. et al. The reliability of psychiatric diagnosis revisited: The clinician's guide to improve the reliability of psychiatric diagnosis. Psychiatry (Edgmont). 2006; 3 (1): 41–50.

[4] 4. Mohammadi M., Al-Azab F., Raahemi B. et al. Data mining EEG signals in depression for their diagnostic value. BMC Med. Inform. Decis. Mak. 2015; (15): 108. DOI: 10.1186/s12911-015-0227-6.

[5] 5. Drevets W.C., Price J.L., Furey M.L. Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression. Brain Struct. Funct. 2008; (1–2): 93–118. DOI: 10.1007/s00429-008-0189-x.

[6] 6. Birur B., Kraguljac N.V., Shelton R.C., Lahti A.C. Brain structure, function, and neurochemistry in schizophrenia and bipolar disorder-a systematic review of the magnetic resonance neuroimaging literature. NPJ Schizophr. 2017; (3): 15. DOI: 10.1038/s41537-017-0013-9.

[7] 7. Изнак А.Ф., Изнак Е.В., Абрамова Л.И., Ложников М.А. Модели количественного прогноза терапевтического ответа больных депрессией по параметрам исходной ЭЭГ. Физиол. человека. 2019; (6): 36–43. [Iznak A.F., Iznak E.V., Abramova L.I., Lozhnikov M.A. Models for quantitative prediction of therapeutic response in patients with depression based on the parameters of the initial EEG. Fiziologija cheloveka. 2019; (6): 36–43. (In Russ.)]

[8] 8. Галкин С.А., Рощина О.В., Васильева С.Н. и др. Нейрокогнитивные изменения при депрессивных расстройствах. Социал. и клин. психиатрия. 2020; (3): 26–30. [Galkin S.A., Roshchina O.V., Vasilyeva S.N. et al. Neurocognitive changes in depressive disorders. Social'naya i klinicheskaya psihiatriya. 2020; (3): 26–30. (In Russ.)]

[9] 9. Segrave R.A., Cooper N.R., Thomson R.H. et al. Individualized alpha activity and frontal asymmetry in major depression. Clin. EEG Neurosci. 2011; (1): 45–52. DOI: 10.1177/155005941104200110.

[10] 10. Vinne N., Vollebregt M.A., Putten M., Arns M. Frontal alpha asymmetry as a diagnostic marker in depression: Fact or fiction? A meta-analysis. Neuroimage Clin. 2017; (16): 79–87. DOI: 10.1016/j.nicl.2017.07.006.

[11] 11. Галкин С.А., Ткачёва Г.Д., Симуткин Г.Г. и др. Изменения показателей биоэлектрической активности мозга при депрессивных расстройствах в процессе терапии СИОЗС. Психич. здоровье. 2020; (3): 3–8. [Galkin S.A., Tkacheva G.D., Simutkin G.G. et al. Changes in indicators of brain bioelectric activity in depressive disorders during SSRI therapy. Psihicheskoe zdorov'e. 2020; (3): 3–8. (In Russ.)]

[12] 12. Fingelkurts A.A., Fingelkurts A.A., Rytsala H. et al. Impaired functional connectivity at EEG alpha and theta frequency bands in major depression. Hum. Brain Mapp. 2007; (3): 247–261. DOI: 10.1002/hbm.20275.

[13] 13. Scarpina F., Tagini S. The stroop color and word test. Front. Psychol. 2017; (8): 557. DOI: 10.3389/fpsyg.2017.00557.

[14] 14. Newson J.J., Thiagarajan T.C. EEG frequency bands in psychiatric disorders: A review of resting state studies. Front. Hum. Neurosci. 2019; (12): 521. DOI: 10.3389/fnhum.2018.00521.

[15] 15. Лапин И.А. Нейрофизиологические маркёры суицидального риска при депрессиях. Социал. и клин. психиатрия. 2017; 2: 29–40. [Lapin I.A. Neurophysiological markers of suicidal risk in depression. Social'naya i klinicheskaya psihiatriya. 2017; 2: 29–40. (In Russ.)]

Funding

Работа выполнена в рамках темы новой медицинской технологии «Прогноз эффективности терапии пациентов с аффективными расстройствами на основе анализа спектральных характеристик ЭЭГ». Протокол ЛЭКа №136 от 16 ноября 2020 г.(Дело №136/2.2020.)

RIGHTS & PERMISSIONS

Galkin S.A., Vasilyeva S.N., Simutkin G.G., Ivanova S.A., Bokhan N.A.