DOI: https://doi.org/10.31071/kit2020.16.05

Inventory reference

ISSN 1812-7231 Klin.inform.telemed. Volume 15, Issue 16, 2020, Pages 108-120

Author(s)

O. Yu. Mayorov^{1, 2}, E. A. Mikhailova¹, A. B. Prognimak², T. D. Nessonova²

Institution(s)

¹SU "Institute of Health Protection of Children and Adolescents of the National Academy of Medical Sciences of Ukraine", Kharkiv

²Kharkiv Medical Academy of Postgraduate Education, Ministry of Health of Ukraine

Article title

Factor models of the Kolmogorov–Sinai entropy indicators of EEG in adolescents with depression

Abstract (resume)

Introduction. According to the WHO, there is currently an increase in the prevalence, incidence and rejuvenation of depression. This phenomenon is also observed in adolescents.

Purpose of the study. Search for sensitive and specific "markers" of depressive disorder in adolescents, which not only make it possible to distinguish between patients and healthy people, but will also be able to assess the effectiveness of different types of treatment.

The contingent of the surveyed. Research methods. Examined: 1. Group of adolescents with depression: 52 patients (35 girls and 17 boys). 2. Control group (healthy) — 40 adolescents (18 girls and 22 boys). 3. The EEG was recorded in a state of calm wakefulness and during mental stress. 4. EEG analysis — qEEG software complex — NeuroResearcher®InnovationSuite (MI&T Institute, Ukraine). The entropy of Kolmogorov–Sinai EEG was calculated — a nonlinear indicator of the state of neurodynamics in the studied EEG electrode placement. 5. Multivariate statistical analysis. Factor analysis was used to create the models (STATISTICA, 13.3).

Results. The search for objective quantitative "markers" of the depressive state of both sexes adolescents was carried out on the basis of nonlinear EEG analysis and the creation of factor models of the results obtained. The factorial models of the Kolmogorov–Sinai EEG entropy of the studied areas of the cerebral hemispheres of sick and healthy both sexes adolescents in a state of calm wakefulness and during mental test were obtained. A physiological interpretation of the identified main factors is given. Comparison of factor models made it possible to identify differences between depressed and healthy adolescents, as well as gender differences. Differences in the factor models of the EEG pacemaker parameters were also revealed in depressed adolescents in a state of calm wakefulness and during mental stress. Based on the obtained factor models, it is possible to calculate the individual values of the factors for each patient. This allows to determine the individual severity of the studied pathology. The revealed significant differences in factor models in adolescents of both sexes with depression in comparison with factor models of adolescents in the control group can be used to detect depressive disorder during EEG examination.

Keywords

Depression in adolescents, EEG, Nonlinear EEG analysis, Kolmogorov–Sinai entropy, Factor analysis

References

1. Depression and Other Common Mental Disorders. Global Health Estimates. WHO. 2017, 24 р.

2. Ito Y., Teicher M. H., Glod C. A., Ackerman E. Preliminary evidence for aberrant cortical development in abused children. A Quantitative EEG study. J. Neuropsychiatry Clin. Neurosci., 1998, vol. 10, рр. 298–307.
https://doi.org/10.1176/jnp.10.3.298

3. Handbook of Depression in Adolescents. Ed by Nolen-Hoeksema S. and Hilt L. M. Taylor & Francis Group, LLC., 2009, 711 p.

4. Siever L.J., Davis K.L. Towards a dysregulation hypothesis of depression. Am. J. Psychiatry, 1985, vol. 142, pp. 1017–1031.
https://doi.org/10.1176/ajp.142.9.1017

5. Mayorov O. Yu., Mikhailova E. A., Mikhalchuk O. Ya., et. al. Criteria ("markers") of depression in teenagers based on the estimation of the state of neurodynamics by methods of nonlinear analysis of EEG and correlation with the CDRS-R scale. Zn. Klin. inform. telemed. [J. Clin. inform. and Telemed ], 2019, vol. 14, iss. 15, pp. 35–45. (In Russ.).
https://doi.org/10.31071/kit2019.15.02

6. Mayorov O. Yu., Fenchenko V. N. Searching for "neuromarkkers" characteristic for pathologic changes in schizophrenia by using the scaling indices of the cerebral bioelectrical activity. Eur. J. Biomedical Informatics (EJBI), 2018, vol. 14, iss. 1, pp. 67–74.
https://doi.org/10.24105/ejbi.2018.14.1.11

7. Bente D. Psychophysiologische Hypothesen zur Genese depressiver Erkrankungen. Ärztl. Praxis, 1975, vol. 27, pp. 3641–3643.

8. Leuchter A. F., Cook I. A., Hunter A.M., Cai C., Horvath S. Resting-State Quantitative Electroencephalography Reveals Increased Neurophysiologic Connectivity in Depression. PLoS ONE, 2012, vol. 7, iss. 2, e32508.
https://doi.org/10.1371/journal.pone.0032508

9. Insel T., Cuthbert B., Garvey M., Heinssen R., Pine D.S., et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry, 2010, iss. 167, pр. 748–751.
https://doi.org/10.1176/appi.ajp.2010.09091379

10. Pizzagalli D. A., Sherwood R. J., Henriques J. B., Davidson R.J. Frontal brain asymmetry and reward responsiveness: a sourcelocalization study. J. Psychol. Sci., 2005, vol. 16, pp. 805–813.
https://doi.org/10.1111/j.1467-9280.2005.01618.x

11. Segrave R. A., Thomson R. H., Cooper N. R., et al. Upper alpha activity during working memory processing reflects abnormal inhibition in major depression. J. Affect. Disord., 2010, vol. 127, pр. 191–198.
https://doi.org/10.1016/j.jad.2010.05.022

12. Regier D. A., Narrow W. E., Kuhl E. A., Kupfer D. J. The conceptual development of DSM-V. Am. J. Psychiatry, 2009, vol. 66, pр. 645–650.
https://doi.org/10.1176/appi.ajp.2009.09020279

13. Ulrich G., Brand K. Dynamically rigid EEG and subtyping of depressive syndromes. Eur Psychiatry, 1993, iss. 8, pp. 25–34.
https://doi.org/10.1017/S0924933800001516

14. Olbrich S. & Arns M. EEG biomarkers in major depressive disorder: Discriminative power and prediction of treatment response. Intern. Review of Psychiatry, 2013; vol. 25, iss. 5, pp. 604–618.
https://doi.org/10.3109/09540261.2013.816269

15. Lapin I. A., Alfimova M. V. EEG markers of depressive states. Sotsial'naya i klinicheskaya psikhiatriya [Social and Clinical Psychiatry], 2014, vol. 24, no. 4, cc. 81–89. (In Russ.).

16. Newson J. J. and Thiagarajan T. C. EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies Front. Hum. Neurosci., 2019, vol. 12, article 521.
https://doi.org/10.3389/fnhum.2018.00521

17. Coburn K. L., Lauterbach E. C., Boutros N. N. et al. The value of A Report by the Committee on Research of the American Neuropsychiatric Association. J. Neuropsychiatry Clin. Neurosci., 2006, vol. 18, pp. 460–500.
https://doi.org/10.1176/jnp.2006.18.4.460

18. Mendlewicz J., & Kerkhofs M. Sleep electroencephalography in depressive illness: A collaborative study by the World Health Organization. The British Journal of Psychiatry, 1991, vol. 159, iss. 4, pp. 505–509.
https://doi.org/10.1192/bjp.159.4.505

19. Armitage R. Microarchitectural findings in sleep EEG in depression: diagnostic implications. Biol. Psychiatry, 1995, vol. 15, no. 2, pp. 72–84.
https://doi.org/10.1016/0006-3223(94)00082-E

20. Rao U., Hammen C. L., Poland R. E. Risk markers for depression in adolescents: sleep and HPA measures. Neuropsychopharmacology, 2009, vol. 34, no. 8, pp. 1936–1945
https://doi.org/10.1038/npp.2009.27

21. Kanda P. A. M., Anghinah R., Smidt M. T., Silva J. M. The clinical use of quantitative EEG in cognitive disorders. Dementia & Neuropsychologia, 2009, vol. 3, no. 3, pp. 195–203.
https://doi.org/10.1590/S1980-57642009DN30300004

22. Steiger A., Pawlowski M., Kimura M. Sleep electroencephalography as a biomarker in depression. 2015, vol. 5, no. 5, pp. 15–25.
https://doi.org/10.2147/CPT.S41760

23. Mikhailova E. S., Chakhava V. O. Changes in the circadian rhythm of some physiological functions in depression. Zh. Nevropatol. i psikhiatr. [J. Neuropathology and Psychiatry], 1992, no. 1, pp. 95–99. (In Russ.).

24. Itil T. M., Arikan M. K., Itil K., Le Bars P., Eralp E. Clinical CEEG/DBM Findings with A New Antidepressant: Dothiepin. Integrative Psychiatry, 1992, vol. 8, no. 3, pp. 241–251.

25. Iznak A. F., Monosova A. Zh., Chayanov N. V. Topographical mapping of EEG responses to emotionally-loaded olfactory stimulation in normal subjects and in depressive patients. 19th CINP Congr., Satellite Symp. on Quantitative EEG & Brain Mapping in Psychopharmacol. Washington: DC, 1994, p.16.

26. Grin-Yatsenko V. A., Baas I., Ponomarev V. A., Kropotov J. D. EEG power spectra at early stages of depressive disorders. J. Clin. Neurophysiol., 2009, vol. 26 (6). pp. 401–406.
https://doi.org/10.1097/WNP.0b013e3181c298fe

27. Hinrikus H., Suhhova A., Bachmann M. et al. Electroencephalographic spectral asymmetry index for detection of depression. Med. Biol. Eng. Comput., 2009, vol. 47, no. 12, pp. 1291–1299.
https://doi.org/10.1007/s11517-009-0554-9

28. Debener S., Beauducel A., Nessler D. et al. Is resting anterior EEG alpha asymmetry a trait marker for depression? Findings for healthy adults and clinically depressed patients. Neuropsychobiology, 2000, vol. 41, pp. 31–37.
https://doi.org/10.1159/000026630

29. Itil T. M., Le Bars P., Eralp E. Quantitative EEG as biological marker. Neuropsychopharmaology, 1994, vol. 10, p. 310.

30. Stewart J. L., Coan J. A., Towers D. N., and Allen John J. B. Resting and Task-Elicited Prefrontal EEG Alpha Asymmetry in Depression: Support for the Capability Model. Psychophysiology, 2014, vol. 51, iss. 5, pp. 446–455.
https://doi.org/10.1111/psyp.12191

31. Lowry A. Kirkby F. J., Luongo M. B. et al. An Amygdala-Hippocampus Subnetwork that Encodes Variation in Human Mood. J. Cell, 2018, vol. 175, iss. 6, pp. 1688–1700.
https://doi.org/10.1016/j.cell.2018.10.005

32. Mackey M. C. and Glass L. Oscillation and chaos in physiological control systems. Science, 1977, vol. 197, pp. 287–289.
https://doi.org/10.1126/science.267326

33. Glass L. and Mackey M. C. Pathological physiological conditions resulting from instabilities in physiological control systems. Ann. NY. Acad. Sci., 1979, iss. 316, pp. 214–235.
https://doi.org/10.1111/j.1749-6632.1979.tb29471.x

34. Nandrino J.-L., Pezard L., Martinerie J., et. al. Decrease of comp lexity in EEG as a symptom of depression. NeuroReport, 1994, vol. 5, pp. 528–530.
https://doi.org/10.1097/00001756-199401120-00042

35. Bélair J., Glass L., van der Heiden U. & Milton J. Dynamical disease: Mathematical analysis of human illness. American Institute of Physics, Woodbury, NY, 1995, 215 p.

36. Thomasson N., Laurent P., Boyer P., Renault B., and Martinerie J. Nonlinear EEG Changes in a 48-Hour Cyclic Manic-Depressive Patient Nonlinear Dynamics. Psychology and Life Sciences, 2002, vol. 6, no. 3, pp. 259–267.
https://doi.org/10.1023/A:1015082611626

37. Fritzsche M., Mayorov O. Yu., Glukhov A. and oth. Anandamide included model-psychosis assessed by nonlinear EEG analysis. J. BMC Psychiatry (e-Jornal), 2003, 14 p.

38. Mayorov O. Yu., Fritzsche M., Glukchov A. and oth. Disfunctional information processing during acute psychosis. 12th AEP Congr. Assoc. of Europ. Psychiatrists. Geneva. Switzerland. 2004, p. 78.

39. Hanshu Cai, Jiashuo Han, Yunfei Chen, … Jьrg Gutknecht, "A Pervasive Approach to EEG-Based Depression Detection", Complexity, vol. 2018, Article ID 5238028, 13 p.
https://doi.org/10.1155/2018/5238028

40. Fern R., Pettinalo S., Alicata F., Gracco S. D., Elia M. & Musumeci S. A. Correlation dimension of EEG slow wave activity during sleep in children and young adults. EEG & Clin. Neurophysiol., 1998, vol. 106, pp. 124-128.
https://doi.org/10.1016/S0013-4694(97)00163-6

41. Mekler A. A., Bolotova Ye. V. Features of calculating the value of the correlation dimension of the restored EEG attractor for children 4–6 years old. In the book: Slow oscillatory processes in the human body. Teoreticheskiye i prikladnyye aspekty nelineynoy dinamiki v fiziologii i meditsine [Theoretical and applied aspects of nonlinear dynamics in physiology and medicine]. Ed. A. N. Fleischman, Novokuznetsk, 2005, pp. 152–153. (In Russ.).

42. Red'ka I. V., Mayorov O. Yu. Nonlinear electroencephalographic correlates of auditory-motor integration in boys with acquired visual dysfunction. Fiziol. zhurnal [Physiol. Journal], 2015, vol. 61, no. 3, pp. 90–98. (In Ukr.)
https://doi.org/10.15407/fz61.03.090

43. Red'ka I. V., Mayorov O. Yu. Changes in nonlinear dynamics of girls' brain electrical activity in visual dysfunction. Visnyk Cherkas'koho universytetu [Bulletin of the Cherkasy Univer.], 2015, no. 2 (335), pp. 86–91. (In Ukr.).

44. Mayorov O. Yu., Mikhaylova E. A. Neurophysiological features of depression in children 7-11 years old. In the book: Depressiya u detey i podrostkov [Depression in children and adolescents]. Stil'-Izdat Publ., 2016, pp. 217–238. (In Russ.).

45. Mykhailova I., Mayorov O., Goloborodko N., Matkovska T., Mitelov D., Bagatskaya N. Clinical variants of depression in adolescents in the age aspect. Proc. of The 19th WPA World Congress of Psychiatry, Lisbon, Portugal, 21–24 August, 2019, pp. 2201–2202.

46. Mayorov O. Yu., Fritzsche M., Kosidubova S. M., Glukhov A. B., Prognimak A. B., Timschenko L. N. New neurodiagnostics technology for brain research on the basis of multivariate and nonlinear (deterministic chaos) analysis of EEG. Proc. of 2nd Eur. Congr. "Achievements in space medicine into health care practice and industry". Pabst Science Publ., Berlin, 2003, pp. 157–166.

47. Mayorov O. Yu., Glukhov A. B., Fenchenko V. N., Prognimak A. B. Implementation of the displacement method by estimating the dimensions of the attractor axes from a onedimensional realization of the dynamic system of the brain. Trudy Instituta kibernetiki NAN Ukrainy [Proc. of the Institute of Cybernetics of NAS of Ukraine]. 2007, no. 153, pp. 3–11. (In Russ.).

48. Mayorov O. Yu., Fenchenko V. N. On the identification of neurodynamic systems of the brain by the methods of multidimensional spectral analysis and deterministic chaos by EEG signals. Trudy Instituta kibernetiki NAN Ukrainy [Proc. of the Institute of Cybernetics of NAS of Ukraine], vol. 155, 2009, pp. 3–9. (In Russ.).

49. Mayorov O. Yu., Fenchenko V. N., Prognimak A. B., Fritzsche M., Fritzsche L. Application of EEG multidimensional spectral analysis and deterministic chaos to brain neurodynamic systems. Scharite, Berlin, 2010. Biosignal 2010: Intern. Biosignal Processing Conf. 2010, p. 75.

50. Mayorov O. Yu., Fenchenko V. N. Method of detection of schizo phrenic row disorders at early stages in patients from groups with "functional psychoses" basing on EEG scaling indicators Zh. Klinical inform. i telemed. [J. Clin. informatics and telemedicine], 2018, vol. 13, no. 14, pp. 37–46. (In Russ.).
https://doi.org/10.31071/kit2018.14.05

51. Mayorov O. Yu., Fenchenko V. N. Increase reliability of bioelectric activity (EEG, ECG and HRV) deterministic chaos researches by the nonlinear analysis methods. Zh. Klin. inform. i telemed. [J. Clin. informatics and telemedicine], 2009, vol. 5, no. 6, pp. 10–17. (In Russ.).

52. Mayorov O. Yu., Fenchenko V. N. Reliability of bioelectric activity (EEG, ECG and HRV) researches of the deterministic chaos by the nonlinear analysis methods. Book of Abstracts. 3rd Chaotic Modeling and Simulation. Intern.Conf., Chania Crete, Greece, 2010, p. 61.

53. Gubler E. V. Vychislitel'nyye metody analiza i raspoznavaniya patologicheskikh protsessov [Computational methods of analysis and recognition of pathological processes]. L., Medicine Publ., 1978, 296 p. (In Russ.).

54. Yunkerov V. I., Grigoriev S. G. Matematiko­statisticheskaya obrabotka dannykh meditsinskikh issledovaniy [Mathematical and statistical processing of medical research data]. VMedA Publ., 2002, 266 p. (In Russ.).

55. Uberla K. Faktorniy analiz [Factor analysis]. M., Statistics Publ., 1980, 398 p. (In Russ.).

56. Afifi A., Eisen S. Statisticheskiy analiz [Statistical analysis]. M., Mir Publ., 1982, 486 p. (In Russ.).

57. Grassberger P., Procaccia I. Measuring the strangeness of strange attractors. Physica D 9, 1983, pp. 189–208.
https://doi.org/10.1016/0167-2789(83)90298-1

58. Kantz H. and Schrieber T., Nonlinear Time Series Analysis. NY. Cambridge University Press, 2000, 304 р.

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