Skip to main content
SpringerLink
Log in

Features of EEG Spectral Parameters in Depressive Patients with Different Efficiencies of Decision-making

  • Published:
Human Physiology Aims and scope Submit manuscript

Abstract

We analyzed the relationships between the EEG spectral parameters in depressed patients reflecting the functional state of the brain, and the efficiency of their decision-making based on both logic and emotional learning in order to clarify the neurophysiological mechanisms underlying decision-making impairments. As a result of multivariate cluster analysis based on 96 EEG spectral power parameters, two subgroups of depressive patients were identified. The groups differ in the EEG amplitude-frequency and spatial characteristics, but do not differ either in age or in severity of depression. The subgroup of depressive patients who formed cluster 2 and had higher spectral powers of the EEG ∆ (2–4 Hz), θ1 (4–6 Hz), θ2 (6–8 Hz), α1 (8–9 Hz) and α2 (9–11 Hz) subbands, as compared with the patients who formed cluster 1, had a higher level of cognitive test performance that requires decision-making: the Wisconsin Card Sorting Test (WCST), which evaluates logic-based decision-making, and the Iowa Gambling Task (IGT), which evaluates decision-making based on emotional learning. Cluster 2 patients also exhibited a better long-term memorization (in the ten-word long-term memory test) compared with those who formed cluster 1. Judging by the EEG parameters, the neurophysiological mechanisms of inhibition are more intact in patients forming cluster 2 than in cluster 1 patients who have lower spectral power values of the EEG ∆ (2–4 Hz), θ1 (4–6 Hz), θ2 (6–8 Hz), α1 (8–9 Hz), and α2 (9–11 Hz) subbands. The impairment of decision-making functions in depressive patients may, at least partially, be due to the deficit of the brain inhibition mechanisms that ensure normal integrative brain activity, including such higher mental functions as memory, attention, and decision-making.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.

Similar content being viewed by others

REFERENCES

  1. Depression: WHO Statistics, Geneva: World Health Org., 011.

  2. Reichenberg, A., Harvey, P.D., Bowie, C.R., et al., Neuropsychological function and dysfunction in schizophrenia and psychotic affective disorders, Schizophr. Bull., 2009, vol. 35, no. 5, p. 1022.

    Article  PubMed  Google Scholar 

  3. Austin, M.P., Mitchell, P., and Goodwin, G.M., Cognitive deficits in depression: possible implications for functional neuropathology, Br. J. Psychiatry, 2001, vol. 178, p. 200.

    Article  CAS  PubMed  Google Scholar 

  4. Cohen, R., Lohr, I., Paul, R., and Boland, R., Impairments of attention and effort among patients with major affective disorders, J. Neuropsychiatry Clin. Neurosci., 2001, vol. 13, no. 3, p. 385.

    Article  CAS  PubMed  Google Scholar 

  5. Ellwart, T., Rinck, M., and Becker, E.S., Selective memory and memory deficits in depressed inpatients, Depression Anxiety, 2003, vol. 17, no. 4, p. 197.

    Article  PubMed  Google Scholar 

  6. Farrin, L., Hull, L., Unwin, C., et al., Effects of depressed mood on objective and subjective measures of attention, J. Neuropsychiatry Clin. Neurosci., 2003, vol. 15, no. 1, p. 98.

    Article  PubMed  Google Scholar 

  7. Stordal, K.I., Lundervold, A.J., Egeland, J., et al., Impairment across executive functions in recurrent major depression, Nord. J. Psychiatry, 2004, vol. 58, no. 1, p. 41.

    Article  PubMed  Google Scholar 

  8. Castaneda, A.E., Tuulio-Henriksson, A., Marttunen, M., et al., A review on cognitive impairments in depressive and anxiety disorders with a focus on young adults, J. Affective Disord., 2008, vol. 106, nos. 1–2, p. 1.

    Article  Google Scholar 

  9. Adida, M., Jollant, F., Clark, L., et al., Trait-related decision-making impairment in the three phases of bipolar disorder, Biol. Psychiatry, 2011, vol. 70, no. 4, p. 357.

    Article  PubMed  Google Scholar 

  10. Must, A., Horvath, S., Nemeth, V.L., and Janka, Z., The Iowa Gambling Task in depression—what have we learned about sub-optimal decision-making strategies? Front. Psychol., 2013, vol. 4, p. 732.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Iznak, A.F., Medvedeva, T.I., Iznak, E.V., et al., Disruption of neurocognitive decision-making mechanisms in depression, Hum. Physiol., 2016, vol. 42, no. 6, p. 598.

    Article  Google Scholar 

  12. Galderisi, S., Maj, M., Mucci, A., et al., Historical, psychopathological, neurological, and neuropsychological aspects of deficit schizophrenia: a multicenter study, Am. J. Psychiatry, 2002, vol. 159, p. 983.

    Article  PubMed  Google Scholar 

  13. Cohen, A.S., Saperstein, A.M., Gold, J.M., et al., Neuropsychology of the deficit syndrome: new data and meta-analysis of findings to date, Schizophr. Bull., 2006, vol. 8, p. 608.

    Google Scholar 

  14. Medvedeva, T.I., Vorontsova, O.Yu., Barkhato-va, A.N., et al., Specifics of emotional biases in decision making in juvenile endogenous attack-like schizophrenia, Zh. Nevrol. Psikhiatr. im. S.S. Korsakova, 2014, vol. 114, no. 9, p. 53.

    Article  CAS  PubMed  Google Scholar 

  15. Iznak, A.F., Iznak, E.V., Kornilov, V.V., and Kontsevoi, V.A., Neuropsychophysiological correlates of reactive depression, Hum. Physiol., 2011, vol. 37, no. 6, p. 682.

    Article  Google Scholar 

  16. Iznak, A.F., Iznak, E.V., Yakovleva, O.B., et al., Neurophysiological measures of treatment efficacy in lateonset depression, Neurosci. Behav. Physiol., 2013, vol. 43, no. 9, p. 1113.

    Article  Google Scholar 

  17. Iznak, A.F., Iznak, E.V., and Sorokin, S.A., Changes in EEG and reaction times during the treatment of apathetic depression, Neurosci. Behav. Physiol., 2013, vol. 43, no. 1, p. 79.

    Article  Google Scholar 

  18. Strelets, V.B., Ivanitskii, A.M., Ivanitskii, G.A., et al., Disorganization of cortical processes in depression, Zh. Vyssh. Nervn. Deyat. im. I.P. Pavlova, 1996, vol. 46, no. 2, p. 241.

    Google Scholar 

  19. Thibodeau, R., Jorgensen, R.S., and Kim, S., Depression, anxiety, and resting frontal EEG asymmetry: a meta-analytic review, J. Abnorm. Psychol., 2006, vol. 115, no. 4, p. 715.

    Article  PubMed  Google Scholar 

  20. Iznak, A.F. and Nikishova, M.B., Electrophysiological correlates of psychogenic disorders, Hum. Physiol., 2007, vol. 33, no. 2, p. 250.

    Article  Google Scholar 

  21. Mathersul, D., Williams, L.M., Hopkinson, P.J., and Kemp, A.H., Investigating models of affect: relationships among EEG alpha asymmetry, depression and anxiety, J. Biol. Psychol., 2008, vol. 80, p. 560.

    Google Scholar 

  22. ICD-10: International Statistical Classification of Diseases and Related Health Problems 10th Revision. The ICD-10 Classification of Mental and Behavioral Disorders: Clinical Descriptions and Diagnostic Guidelines, Geneva: World Health Org., 1992.

  23. Hamilton, M.Y., Psychopathology of depressions: quantitative aspects, in Psychopathology of Depression, Helsinki, 1980, p. 201.

  24. Heaton, R.K., WCST: CV4 Wisconsin Card Sorting Test: Computer Version 4, Lutz: Psychol. Assess. Resour., 2003.

  25. Stuss, D.T., Levine, B., Alexander, M.P., et al., Wisconsin Card Sorting Test performance in patients with focal frontal and posterior brain damage: effects of lesion location and test structure on separable cognitive processes, Neuropsychologia, 2000, vol. 38, no. 4, p. 388.

    Article  CAS  PubMed  Google Scholar 

  26. Bechara, A., Damasio, A.R., Damasio, H., and Anderson, S.W., Insensitivity to future consequences following damage to human prefrontal cortex, Cognition, 1994, vol. 50, nos. 1–3, p. 7.

    Article  CAS  PubMed  Google Scholar 

  27. Medvedeva, T.I., Enikolopova, E.V., and Enikolo-pov, S.N., Damasio’s somatic marker hypothesis and Iowa Gambling Task (IGT): review, Psikhol. Issled., 2013, no. 6, p. 12.

  28. Luriya, A.R., Osnovy neiropsikhologii (Fundamentals of Neuropsychology), Moscow: Mosk. Gos. Univ., 2004, 3rd ed.

  29. Mitrofanov, A.A., Komp’yuternaya sistema analiza i topograficheskogo kartirovaniya elektricheskoi aktivnosti mozga s neirometricheskim bankom EEG-dannykh (opisanie i primenenie) (A Computer System for the Analysis and Topographical Mapping of Brain Electrical Activity Using the Neurometric EEG Data Bank: Description and Applications), Moscow, 2005.

  30. Gusel’nikov, V.I. and Iznak, A.F., Ritmicheskaya aktivnost’ v sensornykh sistemakh (Rhythmic Activity in Sensory Systems), Moscow: Mosk. Gos. Univ., 1983.

  31. Bucci, P. and Galderisi, S., Physiological basis of EEG signal, in Standard Electroencephalography in Clinical Psychiatry: A Practical Handbook, Boutros, N.N., Galderisi, S., Pogarell, O., and Riggio, S., Eds., New York: Wiley, 2011, ch. 2, p. 7.

Download references

Author information

Authors and Affiliations

  1. Mental Health Research Centre,, Moscow, Russia

    A. F. Iznak, E. V. Iznak, T. I. Medvedeva, I. V. Oleichik & E. R. Nikolaeva

Authors
  1. A. F. Iznak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. V. Iznak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. I. Medvedeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. I. V. Oleichik
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E. R. Nikolaeva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. F. Iznak.

Additional information

Translated by E. Babchenko

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Iznak, A.F., Iznak, E.V., Medvedeva, T.I. et al. Features of EEG Spectral Parameters in Depressive Patients with Different Efficiencies of Decision-making. Hum Physiol 44, 627–634 (2018). https://doi.org/10.1134/S0362119718060075

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0362119718060075

Keywords:

Search

Navigation