Advertisement

Human Physiology

, Volume 45, Issue 6, pp 614–620 | Cite as

Models for the Quantitative Prediction of Therapeutic Responses Based on the Baseline EEG Parameters in Depressive Patients

  • A. F. IznakEmail author
  • E. V. Iznak
  • L. I. Abramova
  • M. A. Lozhnikov
Article
  • 3 Downloads

Abstract

To determine the possibility for individualized quantitative prediction of therapeutic responses in depressive patients from the baseline EEG parameters, we conducted a correlational analysis of relationships between the baseline EEG spectral power values (80 variations in total) recorded in 42 depressive patients before the start of the therapeutic course and clinical quantitative assessments of the post-treatment mental conditions of these patients. Based on these data, regression models were built for individualized quantitative prediction of therapeutic response, including no more than three pre-treatment EEG parameters and describing up to 75% of the variance in clinical post-treatment assessment values. On the one hand, our results confirm the possibility of designing fairly accurate mathematical models for the individualized quantitative prediction of therapeutic responses in depressive patients by a small number of baseline neurophysiological parameters. On the other hand, the employed mathematical approaches make it possible to clarify the neurophysiological mechanisms underlying depressive disorders.

Keywords:

depression therapy EEG mathematical models therapeutic response prediction 

Notes

FUNDING

The study was supported by the Russian Foundation for Basic Research (grant no. 18-01-00029а).

COMPLIANCE WITH ETHICAL STANDARDS

Conflict of interest. The authors declare that they have no obvious or potential conflict of interest related to the publication of this article.

Statement of compliance with standards of research involving humans as subjects. All investigations were conducted in the correspondence with the principles of biomedical ethics stipulated under the Helsinki Declaration 1964 and its subsequent amendments and approved by the local bioethics committee of the Mental Health Research Center (Moscow, Russia). Each participant of the study gave his/her voluntary informed consent in writing, signed by him/her after the information about potential risks and advantages, as well as about the character of the planned study.

REFERENCES

  1. 1.
    Kessler, R.C., Berglund, P., Demler, O., et al., The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R), JAMA,J. Am. Med. Assoc., 2003, vol. 289, no. 23, p. 3095.CrossRefGoogle Scholar
  2. 2.
    Preventing Suicide: A Global Imperative, Geneva: W.H.O. Press, 2014.Google Scholar
  3. 3.
    Tiganov, A.S., Endogenous affective disorders: problems of systematics and typology, Vestn. Ross. Akad. Med. Nauk, 2011, no. 4, p. 4.Google Scholar
  4. 4.
    Krasnov, V.N., Rasstroistva affektivnogo spektra (Affective Spectrum Disorders), Moscow: Prakticheskaya Meditsina, 2011.Google Scholar
  5. 5.
    Fava, M., Diagnosis and definition of treatment-resistant depression, Biol. Psychiatry, 2003, vol. 53, no. 8, p. 649.CrossRefGoogle Scholar
  6. 6.
    Bykov, Yu.V., Bekker, R.A., and Reznikov, M.K., Depressii i rezistentnost’ (Depressions and Resistance), Moscow: Infra-M, 2013.Google Scholar
  7. 7.
    Mosolov, S.N., Biologicheskie metody terapii psikhicheskikh rasstroistv (dokazatel’naya meditsina-klinicheskoi praktike) (Biological Methods of Therapy of Mental Disorders: From Evidence-Based Medicine to Clinical Practice), Moscow: Sotsial’no-Proliticheskaya Mysl’, 2012.Google Scholar
  8. 8.
    Cook, L.A., Biomarkers in psychiatry: potentials, pitfalls, and pragmatics, Prim. Psychiatry, 2008, vol. 15, no. 3, p. 54.Google Scholar
  9. 9.
    Leuchter, A.F., Cook, I.A., Marangell, L.B., et al., Comparative effectiveness of biomarkers and clinical indicators for predicting outcomes of SSRI treatment in major depressive disorder: results of the BRITE-MD study, Psychiatry Res., 2009, vol. 169, no. 2, p. 124.CrossRefGoogle Scholar
  10. 10.
    Iosifescu, D.V., Electroencephalography-derived biomarkers of antidepressant response, Harv. Rev. Psychiatry, 2011, vol. 19, no. 3, p. 144.CrossRefGoogle Scholar
  11. 11.
    Baskaran, A., Milev, R., and McIntyre, R.S., The neurobiology of the EEG biomarker as a predictor of treatment response in depression, Neuropharmacology, 2012, vol. 63, no. 4, p. 507.CrossRefGoogle Scholar
  12. 12.
    Hamilton, M.Y., A rating scale for depression, J. Neurol., Neurosurg., Psychiatry, 1960, vol. 23, no. 1, p. 56.CrossRefGoogle Scholar
  13. 13.
    Montgomery, S.A. and Asberg, M.A., A new depression scale designed to be sensitive to change, Br. J. Psychiatry, 1979, vol. 134, no. 4, p. 382.CrossRefGoogle Scholar
  14. 14.
    Iznak, A.F., Iznak, E.V., Klyushnik, T.P., et al., Regression models of the relationship of clinical and neurobiological parameters in the treatment of manic-delusional states in the context of paroxysmal schizophrenia, Zh. Nevropatol. Psikhiatr. im. S.S. Korsakova, 2016, vol. 116, no. 3, p. 24.Google Scholar
  15. 15.
    Iznak, A.F., Iznak, E.V., Klyushnik, T.P., et al., Neurobiological parameters in quantitative prediction of treatment outcome in schizophrenic patients, J. Integr. Neurosci., 2018, vol. 17, no. 3, p. 221.CrossRefGoogle Scholar
  16. 16.
    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.Google Scholar
  17. 17.
    Spearing, M.K., Robert, M.P., Leverich, G.S., et al., Modification of the clinical global impressions (CGI) scale for use in bipolar illness (BP): the CGI-BP, Psychiatry Res., 1997, vol. 73, no. 3, p. 159.CrossRefGoogle Scholar
  18. 18.
    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.Google Scholar
  19. 19.
    Iznak, A.F., Tiganov, A.S., Iznak, E.V., and Sorokin, S.A., EEG correlates and possible predictors of the efficacy of the treatment of endogenous depression, Hum. Physiol., 2013, vol. 39, no. 4, p. 378.CrossRefGoogle Scholar
  20. 20.
    Iznak, A.F., Iznak, E.V., Yakovleva, O.B., et al., Neurophysiological measures of treatment efficacy in late-onset depression, Neurosci. Behav. Physiol., 2013, vol. 43, no. 9, p. 1113.CrossRefGoogle Scholar
  21. 21.
    Iznak, A.F., Iznak, E.V., Oleichik, I.V., et al., EEG correlates of frontal dysfunction as predictors of relative pharmacoresistance in the treatment of endogenous affective disorders, Zh. Nevropatol. Psikhiatr. im. S.S. Korsakova, 2014, vol. 114, no. 12, p. 54.CrossRefGoogle Scholar
  22. 22.
    Bares, M., Novak, T., Brunovsky, M., et al., The change of QEEG prefrontal cordance as a response predictor to antidepressive intervention in bipolar depression: a pilot study, J. Psychiatric Res., 2012, vol. 46, no. 2, p. 219.CrossRefGoogle Scholar
  23. 23.
    Bruder, G.E., Sedoruk, J.P., Stewart, J.W., et al., Electroencephalographic alpha measures predict therapeutic response to a selective serotonin reuptake inhibitor antidepressant: pre- and post-treatment findings, Biol. Psychiatry, 2008, vol. 63, no. 12, p. 1171.CrossRefGoogle Scholar
  24. 24.
    Knott, V., Mahoney, C., Kennedy, S., and Evans, K., Pre-treatment EEG and its relationship to depression severity and paroxetine treatment outcome, Pharmacopsychiatry, 2000, vol. 33, no. 6, p. 201.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Inc. 2019

Authors and Affiliations

  • A. F. Iznak
    • 1
    Email author
  • E. V. Iznak
    • 1
  • L. I. Abramova
    • 1
  • M. A. Lozhnikov
    • 2
  1. 1.Mental Health Research CenterMoscowRussia
  2. 2.Lomonosov Moscow State UniversityMoscowRussia

Personalised recommendations