Investigation of the relationship of individual characteristics of the brain bioelectrical activity with levels of mental stress, aggression, anxiety, and depression

Chizhikova A.A.; Gimbelevskaya A.P.; Bakoev S.Yu.; Keskinov A.A.

doi:https://doi.org/10.17116/jnevro2025125051108

OBJECTIVE

To identify relationships between the features of bioelectrical activity (BEA) of the brain and levels of mental stress, aggression, anxiety, and depression.

MATERIAL AND METHODS

The study included 50 apparently healthy volunteers (mean age 23—54 years (32.5±8.2), 62% females, 38% males). A set of validated psychological tests was used for clinical and psychological assessment: the neuropsychiatric stress questionnaire, the Spielberger Anxiety Scale for the overall study population (50 subjects); the Beck’s Depression Inventory, the Perceived Stress Scale, and the Buss Perry Aggression Questionnaire for the sample with similar sex and age (31 subjects). Electroencephalography (EEG) was used to record brain activity as an accessible and non-invasive method with high time resolution. The amplitude-frequency characteristics of the BEA obtained by spectral analysis of native EEG segments were compared with the test results.

RESULTS

Statistically significant correlations between the results of most psychological tests and BEA parameters were identified. Many scales showed a positive correlation with the amplitude of the dominant alpha activity: for mental stress, in symmetrical zones from the occipital-parietal to the anterior temporal and frontal leads; for stress-associated overexertion, in narrower anterior temporal and temporal zones; for hostility, extensively from the frontal and anterior temporal to the occipital and right parietal regions; for anger, locally in the left frontal and anterior temporal region; for trait anxiety, bilaterally in the parietal and occipital regions. The dominant frequency of alpha activity was negatively correlated with the level of state anxiety in the central and right occipital and temporal lead. In the theta range, there was an acceleration of oscillations in the right posterior and left parietal and temporal leads in subjects with high state anxiety, as well as bilateral enhancement in the posterior regions in subjects with mental stress. In the left hemisphere, the dominant frequency of beta-2 activity was directly related to the scale of depression and its cognitive-affective manifestations, as well as to the stress counteraction scale. A negative correlation was noted between the overall depression score and the frequency of beta-1 activity locally in the left frontal-central lead.

CONCLUSION

The identified signs need further research as possible markers of the cortex BEA to assess a mental state or personality traits.

Keywords:

electroencephalography (EEG)

spectral analysis

psychological testing

aggressiveness

depression

anxiety

stress

Authors:

Chizhikova A.A.

Centre for Strategic Planning and Management of Biomedical Health Risks of the Federal Medical Biological Agency

ORCID: 0009-0008-1752-3226

Gimbelevskaya A.P.

Centre for Strategic Planning and Management of Biomedical Health Risks of the Federal Medical Biological Agency

ORCID: 0009-0004-9403-5171

Bakoev S.Yu.

Centre for Strategic Planning and Management of Biomedical Health Risks of the Federal Medical Biological Agency

ORCID: 0000-0002-0324-3580

Keskinov A.A.

Centre for Strategic Planning and Management of Medical-Biological Health Risks Federal Medical-Biological Agency

ORCID: 0000-0001-7378-983X

Received:

09.07.2024

Accepted:

10.02.2025

Close metadata

References:

Sauseng P, Klimesch W. What does phase information of oscillatory brain activity tell us about cognitive processes? Neuroscience & Biobehavioral Reviews, 2008;32(5):1001-1013. https://doi.org/10.1016/j.neubiorev.2008.03.014
Yvert B, Bertrand O, Thévenet M, et al. A systematic evaluation of the spherical model accuracy in EEG dipole localization. Electroencephalography and Clinical Neurophysiology, 1997;102(5):452-459. https://doi.org/10.1016/S0921-884X(97)96611-X
Ivanitsky GA. Individual Stable Patterns of Human Brain Rhythms as a Reflection of Mental Processes. Sovrem Tehnol Med. 2019;11(1):116. (In Russ.). https://doi.org/10.17691/stm2019.11.1.14
Ambrosius U, Lietzenmaier S, Wehrle R. Heritability of Sleep Electroencephalogram. Biological Psychiatry, 2008;64(4):344-348. https://doi.org/10.1016/j.biopsych.2008.03.002
Rangaswamy M, Porjesz B, Chorlian DB, et al. Resting EEG in offspring of male alcoholics: beta frequencies. International Journal of Psychophysiology, 2004;51(3):239-251. https://doi.org/10.1016/j.ijpsycho.2003.09.003
Vogel F. Genetics and the Electroencephalogram. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. https://doi.org/10.1007/978-3-642-57040-7
Porjesz B, Rangaswamy M, Kamarajan C, et al. The utility of neurophysiological markers in the study of alcoholism. Clinical Neurophysiology, 2005;116(5):993-1018. https://doi.org/10.1016/j.clinph.2004.12.016
Clarke AR, Barry RJ, McCarthy R, et al. EEG-defined subtypes of children with attention-deficit/hyperactivity disorder. Clin Neurophysiol. 2001;112(11):2098-2105. https://doi.org/10.1016/s1388-2457(01)00668-x
Bresnahan SM, Barry RJ. Specificity of quantitative EEG analysis in adults with attention deficit hyperactivity disorder. Psychiatry Res. 2002;112(2):133-144. https://doi.org/10.1016/s0165-1781(02)00190-7
Quintana H, Snyder SM, Purnell W, et al. Comparison of a standard psychiatric evaluation to rating scales and EEG in the differential diagnosis of attention-deficit/hyperactivity disorder. Psychiatry Res. 2007;152(2-3):211-222. https://doi.org/10.1016/j.psychres.2006.04.015
Snyder SM, Quintana H, Sexson SB, et al. Blinded, multi-center validation of EEG and rating scales in identifying ADHD within a clinical sample. Psychiatry Res. 2008;159(3):346-358. https://doi.org/10.1016/j.psychres.2007.05.006
Bruder GE, Tenke CE, Warner V, et al. Electroencephalographic measures of regional hemispheric activity in offspring at risk for depressive disorders. Biol Psychiatry. 2005;57(4):328-335. https://doi.org/10.1016/j.biopsych.2004.11.015
Iznak AF, Smulevich AB. Modern ideas about the neurophysiological basis of depressive disorders. Depression and Comorbid Disorders. Moscow: Mental Health Research Center, 1997;166-179. (In Russ.).
Knott V, Mahoney C, Kennedy S, et al. EEG power, frequency, asymmetry and coherence in male depression. Psychiatry Res. 2001;106(2):123-140. https://doi.org/10.1016/s0925-4927(00)00080-9
Gotlib IH. EEG Alpha Asymmetry, Depression, and Cognitive Functioning. Cognition & Emotion. 1998;12(3):449-478. https://doi.org/10.1080/026999398379673
Javelle F, Löw A, Bloch W. Unraveling the Contribution of Serotonergic Polymorphisms, Prefrontal Alpha Asymmetry, and Individual Alpha Peak Frequency to the Emotion-Related Impulsivity Endophenotype. Mol Neurobiol. 2022;59(10):6062-6075. https://doi.org/10.1007/s12035-022-02957-6
Blackhart GC, Minnix JA, Kline JP. Can EEG asymmetry patterns predict future development of anxiety and depression? A preliminary study. Biol Psychol. 2006;72(1):46-50. https://doi.org/10.1016/j.biopsycho.2005.06.010
Papousek I, Schulter G. Covariations of EEG asymmetries and emotional states indicate that activity at frontopolar locations is particularly affected by state factors. Psychophysiology. 2002;39(3):350-360. https://doi.org/10.1017/s0048577201393083
Baving L, Laucht M, Schmidt MH. Frontal brain activation in anxious school children. J Child Psychol & Psychiat. 2002;43(2):265-274. https://doi.org/10.1111/1469-7610.00019
Kuznetsova IL, Ponomareva NV, Alemastseva EA. The Interactive Effect of Genetic and Epigenetic Variations in FKBP5 and ApoE Genes on Anxiety and Brain EEG Parameters. Genes. 2022;13(2):164. https://doi.org/10.3390/genes13020164
Dam H, Buch JOD, Nielsen AB, et al. Clinical association to FKBP5 rs1360780 in patients with depression. Psychiatric Genetics. 2019;29(6):220-225. https://doi.org/10.1097/YPG.0000000000000228
Im SY, Jin G, Jeong J, et al. Gender Differences in Aggression-related Responses on EEG and ECG. Exp Neurobiol. 2018;27(6):526-538. https://doi.org/10.5607/en.2018.27.6.526
Aggensteiner PM, Holz NE, Kaiser A, et al. Exploring psychophysiological indices of disruptive behavior disorders and their subtypes of aggression. International Journal of Psychophysiology. 2022;175:24-31. https://doi.org/10.1016/j.ijpsycho.2021.12.010
Fingelkurts AA, Fingelkurts AA, Rytsälä H, et al. Composition of brain oscillations in ongoing EEG during major depression disorder. Neurosci Res. 2006;56(2):133-144. https://doi.org/10.1016/j.neures.2006.06.006
Grin-Yatsenko VA, Baas I, Ponomarev VA, et al. EEG power spectra at early stages of depressive disorders. J Clin Neurophysiol. 2009;26(6):401-406. https://doi.org/10.1097/WNP.0b013e3181c298fe
Hinrikus H, Suhhova A, Bachmann M, et al. Spectral features of EEG in depression. Biomed Tech (Berl). 2010;55(3):155-161. https://doi.org/10.1515/BMT.2010.011
Strelec VB, Garah ZHV, Novotockij-Vlasov VYu. Comparative study of the gamma rhythm in normal conditions, during examination stress, and in patients with first depressive episode. ZHurnal vysshej nervnoj deyatel’nosti im. IP. Pavlova. 2006;56(2):219 (In Russ.).
Orekhov YV, Golikova ZHV, Strelec VB. Psychophysiologic parameters of reproducing emotions in healthy subjects and patients during the first episode of depression. ZHurnal vysshej nervnoj deyatel’nosti im. IP. Pavlova. 2004;54(5):612-619 (In Russ.).
Flores-Gutiérrez EO, Díaz J-L, Barrios FA, et al. Differential alpha coherence hemispheric patterns in men and women during pleasant and unpleasant musical emotions. Int J Psychophysiol. 2009;71(1):43-49. https://doi.org/10.1016/j.ijpsycho.2008.07.007
Passynkova N, Neubauer H, Scheich H. Spatial organization of EEG coherence during listening to consonant and dissonant chords. Neurosci Lett. 2007;412(1):6-11. https://doi.org/10.1016/j.neulet.2006.09.029
Andersen SB, Moore RA, Venables L, et al. Electrophysiological correlates of anxious rumination. Int J Psychophysiol. 2009;71(2):156-169. https://doi.org/10.1016/j.ijpsycho.2008.09.004
Chizhikova AA. Electroencephalography: features of the obtained data and its applicability in psychiatry. SS. Korsakov Journal of Neurology and Psychiatry. 2024;124(5):31-39. https://doi.org/10.17116/jnevro202412405131(InRuss.).
de Geus EJC. Introducing genetic psychophysiology. Biological Psychology. 2002;61(1-2):1-10. https://doi.org/10.1016/s0301-0511(02)00049-2
Gottesman II, Gould TD. The Endophenotype Concept in Psychiatry: Etymology and Strategic Intentions. AJP. 2003;160(4):636-645. https://doi.org/10.1176/appi.ajp.160.4.636
de Geus EJ. From genotype to EEG endophenotype: a route for post-genomic understanding of complex psychiatric disease? Genome Med. 2010;2(9):63. https://doi.org/10.1186/gm184
Nemchin TA. Metodika izmereniya nervno-psikhicheskogo napryazheniya pri pomoshchi oprosnika. 1981. (In Russ.).
Nemchin TA. Sostoyaniya nervno-psikhicheskogo napryazheniya. Leningrad: LSU,1983. (In Russ.).
Prokhorov AO. Metodiki diagnostiki, izmereniya psikhicheskikh sostoyanii lichnosti. Moscow: PER SE, 2004 (In Russ.).
Khanin YuL. Kratkoe rukovodstvo k primeneniyu shkaly reaktivnoi, lichnostnoi trevozhnosti Spilbergera. Leningrad: LNIIFK, 1976. (In Russ.).
Ababkov VA, Baryshnikova K, Vorontsova-Venger OV, et al. Validation of the Russian version of the questionnaire “Scale of perceived stress-10”. Vestnik SPbSU. 2016;16(2):6-15. (In Russ.).
Enikolopov SN, Tsibul’skii NP. Psikhometricheskii analiz russkoyazychnoi versii oprosnika diagnostiki agressii A. Bassa, M. Perri, Psikhologicheskii zhurnal. 2007;28(1):115-124. (In Russ.).
Tarabrina NV. Praktikum po psikhologii posttravmaticheskogo stressa. Saint-Petersburg: Piter, 2001:182-190. (In Russ.).
Lopes da Silva F. EEG and MEG: Relevance to Neuroscience. Neuron. 2013;80(5):1112-1128. https://doi.org/10.1016/j.neuron.2013.10.017
di Fronso S, Fiedler P, Tamburro G, et al. Dry EEG in Sports Sciences: A Fast and Reliable Tool to Assess Individual Alpha Peak Frequency Changes Induced by Physical Effort. Front. Neurosci. 2019;13:982. https://doi.org/10.3389/fnins.2019.00982
Ferree TC, Luu P, Russell GS, Tucker DM. Scalp electrode impedance, infection risk, and EEG data quality. Clinical Neurophysiology. 2001;112(3):536-544. https://doi.org/10.1016/S1388-2457(00)00533-2
Jackson AF, Bolger DJ. The neurophysiological bases of EEG and EEG measurement: A review for the rest of us. Psychophysiol. 2014;51(11):1061-1071. https://doi.org/10.1111/psyp.12283
CMI Brain Research — Prodolzhitel’nost’ zapisi EEG. Accessed November 29,2024. (In Russ.). https://cmi.to/%D0%BF%D1%80%D0%BE%D0%B4%D0%BE%D0%BB%D0%B6%D0%B8%D1%82%D0%B5%D0%BB%D1%8C%D0%BD%D0%BE%D1%81%D1%82%D1%8C-%D0%B7%D0%B0%D0%BF%D0%B8%D1%81%D0%B8-%D1%8D%D1%8D%D0%B3/
Maltez J, Hyllienmark L, Nikulin VV, Brismar T. Time course and variability of power in different frequency bands of EEG during resting conditions. Neurophysiologie Clinique/Clinical Neurophysiology. 2004;34(5):195-202. https://doi.org/10.1016/j.neucli.2004.09.003
Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews. 1999;29(2-3):169-195. https://doi.org/10.1016/s0165-0173(98)00056-3
Libenson MH. Practical approach to electroencephalography. Philadelphia, Pa: Saunders Elsevier, 2010.
Van Baal GCM, de Geus EJC, Boomsma DI. Genetic architecture of EEG power spectra in early life. Electroencephalography and Clinical Neurophysiology, 1996;98(6):502-514. https://doi.org/10.1016/0013-4694(96)95601-1
Chen ACN, Feng W, Zhao H, et al. EEG default mode network in the human brain: Spectral regional field powers. NeuroImage. 2008;41(2):561-574. https://doi.org/10.1016/j.neuroimage.2007.12.064
PyCharm: The Python IDE for professional developers by JetBrains. Accessed November 29,2024. https://www.jetbrains.com/pycharm/?ysclid=lt8h64da7c910300589
NumPy. Accessed November 29,2024. https://numpy.org/
pandas — Python Data Analysis Library. Accessed November 29,2024. https://pandas.pydata.org/
GitHub — JonasMoss/univariateML: An R package for maximum likelihood estimation of univariate densities. Accessed November 29,2024. https://github.com/JonasMoss/univariateML
Astashchenko AP, Gorbatenko NP, Dorokhov EV, et al. Vliyanie trevozhnosti, svyazannoi s ekzamenatsionnym stressom, na smeshchenie zritel’nogo vnimaniya, elektricheskuyu aktivnost’ frontal’nykh zon mozga. Ul’yanovskii mediko-biologicheskii zhurnal. 2020:2. (In Russ.).
Buss AH, Perry M. The Aggression Questionnaire. Journal of Personality and Social Psychology, 1992;63(3):452-459. https://doi.org/10.1037/0022-3514.63.3.452
Lapin IA, Alfimova MV. EEG-markers for depressive conditions. Social and Clinical Psychiatry. 2014;24(4):81-89 (In Russ.).
Davidson RJ. Anterior cerebral asymmetry and the nature of emotion, Brain and Cognition, 1992;20(1):125-151. https://doi.org/10.1016/0278-2626(92)90065-T
Klimesch W. Auditorily elicited EEG desynchronization and synchronization: A review of Christina M. Krause’s doctoral thesis. Scandinavian Journal of Psychology. 1999;40(4):329-331. https://doi.org/10.1111/1467-9450.00133
Niedermeyer E. Alpha rhythms as physiological and abnormal phenomena. International Journal of Psychophysiology. 1997;26(1-3):31-49. https://doi.org/10.1016/s0167-8760(97)00754-x
Gnezditskii VV. Obratnaya zadacha EEG i klinicheskaya elektroentsefalografiya. Moscow: MEDpress-inform, 2004 (In Russ.).
Davidson RJ. What does the prefrontal cortex “do” in affect: perspectives on frontal EEG asymmetry research. Biological Psychology. 2004;67(1-2):219-234. https://doi.org/10.1016/j.biopsycho.2004.03.008
Rolls ET. The functions of the orbitofrontal cortex. Neurocase. 1999;5(4):301-312. https://doi.org/10.1080/13554799908411984
Smith BD, Meyers M, Kline R, Bozman A. Hemispheric asymmetry and emotion: Lateralized parietal processing of affect and cognition. Biological Psychology. 1987;25(3):247-260. https://doi.org/10.1016/0301-0511(87)90050-0
Koles ZJ, Lind JC, Flor-Henry P. Spatial patterns in the background EEG underlying mental disease in man. Electroencephalography and Clinical Neurophysiology. 1994;91(5):319-328. https://doi.org/10.1016/0013-4694(94)90119-8
Rusinov VS, Boldyreva GN, Maiorchik VE, et al. Klinicheskaya elektroentsefalografiya. VS. Rusinov eds. Moskva: Meditsina, 1973. (In Russ.).
Saeed SMU, Anwar SM, Khalid H, et al. EEG Based Classification of Long-Term Stress Using Psychological Labeling. Sensors. 2020;20(7):1886. https://doi.org/10.3390/s20071886
Lapin IA. EEG coherence characteristics in depressive conditions with different prevalent affects. Social and Clinical Psychiatry. 2014;24(2):11-17 (In Russ.).
Ivanov LB, Strekalina NN, Chulkova IJ, Budkevich AV. Spatial distribution of alpha-activity depending on the form of affective frustration. Funktsional’naya diagnostika, 2009;1:41-49 (In Russ.).