Quantitative characteristics of the alpha-rhythm of the electroencephalogram in depressive disorders
Stanislav A. Galkin , Svetlana N. Vasilyeva , German G. Simutkin , Nikolay A. Bokhan
Neurology Bulletin ›› 2021, Vol. LIII ›› Issue (3) : 19 -25.
Quantitative characteristics of the alpha-rhythm of the electroencephalogram in depressive disorders
The aim of research was to study the quantitative characteristics of the alpha rhythm in patients with depressive disorders.
Material and methods. The study sample consisted of patients who were treated at the clinic of the Research Institute of Mental Health (department of affective states) Tomsk NIMC. A total of 84 patients (67 women, 17 men) aged 20 to 60 years with mood disorders in the framework of a depressive episode, recurrent depressive disorder and dysthymia were examined. An electroencephalogram was recorded at rest with closed and open eyes. The values of the absolute spectral power of the alpha rhythm, the parameters of the microstructure of the alpha spindle were analyzed and the reactivity index (the Berger effect) was calculated.
Results. With open eyes, the spectral power of the alpha rhythm was statistically significantly higher in patients with depressive disorders in the Fp1 (p=0.041), F4 (p=0.042), F7 (p=0.046) and T4 (p=0.047) leads compared to the control. Also, in patients with depressive disorders, a predominantly low-amplitude alpha rhythm was recorded (53.6% vs. 26.7%, p=0.006). The degree of alpha-rhythm depression in the posterior temporal leads T5 (p=0.012) and T6 (p=0.006) was statistically significantly less pronounced in patients with depressive disorders compared to the control group of healthy individuals.
Conclusion. The detected changes indirectly indicate a decrease in the oscillatory activity of brain processes in depressive disorders.
depression / electroencephalography / alpha rhythm / spectral power / alpha spindle / Berger effect
| [1] |
Krasnov V.N. Depression as a social and clinical problem of modern medicine. Rossijskij psihiatricheskij zhurnal. 2011; (6): 8–10. (In Russ.) |
| [2] |
Краснов В.Н. Депрессия как социальная и клиническая проблема современной медицины // Рос. психиатрич. ж. 2011. №6. С. 8–10. |
| [3] |
Uzbekov M.G., Gurovich I.Ya., Ivanova S.A. Potential biomarkers of mental diseases in the aspect of a systematic approach. Social’naya i klinicheskaya psihiatriya. 2016; (1): 77–94. (In Russ.) |
| [4] |
Узбеков М.Г., Гурович И.Я., Иванова С.А. Потенциальные биомаркёры психических заболеваний в аспекте системного подхода // Социал. и клин. психиатрия. 2016. №1. С. 77–94. |
| [5] |
Galkin S.A., Peshkovskaya A.G., Simutkin G.G. et al. Violations of the function of spatial working memory in mild depression and their neurophysiological correlates. Zhurnal nevrologii i psihiatrii im. S.S. Korsakova. 2019; (10): 56–61. (In Russ.) |
| [6] |
Галкин С.А., Пешковская А.Г., Симуткин Г.Г. и др. Нарушения функции пространственной рабочей памяти при депрессии лёгкой степени тяжести и их нейрофизиологические корреляты // Ж. неврол. и психиатрии им. С.С. Корсакова. 2019. №10. С. 56–61. |
| [7] |
Lapin I.A., Alfimova M.V. EEG markers of depressive states. Social’naya i klinicheskaya psihiatriya. 2014; (4): 81–89. (In Russ.) |
| [8] |
Лапин И.А., Алфимова М.В. ЭЭГ-маркёры депрессивных состояний // Социал. и клин. психиатрия. 2014. №4. С. 81–89. |
| [9] |
Iznak A.F., Iznak E.V., Medvedeva T.I. et al. Features of EEG spectral parameters in patients with depression with different decision-making efficiency. Fiziologiya cheloveka. 2018; (6): 27–35. (In Russ.) |
| [10] |
Изнак А.Ф., Изнак Е.В., Медведева Т.И. и др. Особенности спектральных параметров ЭЭГ у больных депрессией с разной эффективностью принятия решений // Физиол. человека. 2018. №6. С. 27–35. |
| [11] |
Galkin S.A., Vasilyeva S.N., Simutkin G.G. et al. The spectral power of the beta rhythm of the electroencephalogram as a marker of depressive disorder. Nevrologicheskij vestnik. 2020; (4): 33–38. (In Russ.) |
| [12] |
Галкин С.А., Васильева С.Н., Симуткин Г.Г. и др. Спектральная мощность бета-ритма электроэнцефалограммы как маркёр депрессивного расстройства // Неврологич. вестн. 2020. №4. С. 33–38. |
| [13] |
Roh S.C., Park E.J., Shim M., Lee S.H. EEG beta and low gamma power correlates with inattention in patients with major depressive disorder. J. Affect Disord. 2016; (204): 124–130. DOI: 10.1016/j.jad.2016.06.033. |
| [14] |
Roh S.C., Park E.J., Shim M., Lee S.H. EEG beta and low gamma power correlates with inattention in patients with major depressive disorder // J. Affect Disord. 2016. №204. С. 124–130. DOI: 10.1016/j.jad.2016.06.033. |
| [15] |
Bazanova O.M. Modern interpretation of the alpha activity of the electroencephalogram. Uspekhi fiziologicheskih nauk. 2009; (3): 32–53. (In Russ.) |
| [16] |
Базанова О.М. Современная интерпретация альфа-активности электроэнцефалограммы // Успехи физиол. наук. 2009. №3. С. 32–53. |
| [17] |
Galkin S.A., Vasilyeva S.N., Ivanova S.A., Bokhan N.A. Electroencephalographic markers of resistance of depressive disorders to pharmacotherapy and determination of a possible approach to individual prognosis of therapy effectiveness. Psihiatriya. 2021; (2): 39–45. (In Russ.) |
| [18] |
Галкин С.А., Васильева С.Н., Иванова С.А., Бохан Н.А. Электроэнцефалографические маркёры устойчивости депрессивных расстройств к фармакотерапии и определение возможного подхода к индивидуальному прогнозу эффективности терапии // Психиатрия. 2021. №2. С. 39–45. |
| [19] |
Zoon H.F., Veth C.P., Arns M. et al. EEG alpha power as an intermediate measure between brain-derived neurotrophic factor Val66Met and depression severity in patients with major depressive disorder. J. Clin. Neurophysiol. 2013; (3): 261–267. DOI: 10.1097/WNP.0b013e3182933d6e. |
| [20] |
Zoon H.F., Veth C.P., Arns M. et al. EEG alpha power as an intermediate measure between brain-derived neurotrophic factor Val66Met and depression severity in patients with major depressive disorder // J. Clin. Neurophysiol. 2013. N. 3. Р. 261–267. DOI: 10.1097/WNP.0b013e3182933d6e. |
| [21] |
Tement S., Pahor A., Jaušovec N. EEG alpha frequency correlates of burnout and depression: The role of gender. Biol. Psychol. 2016; (114): 1–12. DOI: 10.1016/j.biopsycho.2015.11.005. |
| [22] |
Tement S., Pahor A., Jaušovec N. EEG alpha frequency correlates of burnout and depression: The role of gender // Biol. Psychol. 2016. N. 114. Р. 1–12. DOI: 10.1016/j.biopsycho.2015.11.005. |
| [23] |
Kustubayeva A., Kamzanova A., Kudaibergenova S. et al. Major depression and brain asymmetry in a decision-making task with negative and positive feedback. Symmetry. 2020; (12): 2118. DOI: 10.3390/sym12122118. |
| [24] |
Kustubayeva A., Kamzanova A., Kudaibergenova S. et al. Major depression and brain asymmetry in a decision-making task with negative and positive feedback // Symmetry. 2020. N. 12. Р. 2118. DOI: 10.3390/sym12122118. |
| [25] |
Melnikov E.M. Activation reaction on the electroencephalogram of chemically dependent persons: connections with narcological and psychological variables and changes in the context of neurobiological management training. Byulleten’ sibirskoj mediciny. 2014; (4): 66–72. (In Russ.) |
| [26] |
Мельников Е.М. Реакция активации на электроэнцефалограмме химически зависимых лиц: связи с наркологичес-кими и психологическими переменными и изменения в контексте тренинга нейробиоуправления // Бюлл. сибирской мед. 2014. №4. С. 66–72. |
| [27] |
Moosmann M., Ritter P., Krastel I. et al. Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy. Neuroimage. 2003; (1): 145–158. DOI: 10.1016/s1053–8119(03)00344–6. |
| [28] |
Moosmann M., Ritter P., Krastel I. et al. Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy // Neuroimage. 2003. N. 1. P. 145–158. DOI: 10.1016/s1053–8119(03)00344–6. |
| [29] |
Becker R., Knock S., Ritter P., Jirsa V. Relating alpha power and phase to population firing and hemodynamic activity using a thalamo-cortical neural mass model. PLoS Comput. Biol. 2015; (9): e1004352. DOI: 10.1371/journal.pcbi.1004352. |
| [30] |
Becker R., Knock S., Ritter P., Jirsa V. Relating alpha power and phase to population firing and hemodynamic activity using a thalamo-cortical neural mass model // PLoS Comput. Biol. 2015. N. 9. P. e1004352. DOI: 10.1371/journal.pcbi.1004352. |
Galkin S.A., Vasilyeva S.N., Simutkin G.G., Bokhan N.A.
/
| 〈 |
|
〉 |