Skip to main content
SpringerLink
Log in

Dynamics of Brain Electrical Activity Patterns in Maladaptive Disorders

  • Published:
Neuroscience and Behavioral Physiology Aims and scope Submit manuscript

This review addresses the application of wavelet, multifractal, and recurrence analysis methods to the study of changes occurring in the patterns of electrical activity of the human brain, recorded as electroencephalograms, in maladjustment disorders associated with anxious-phobic disorders, panic attacks, and moderate cognitive impairment. The possibility of using these methods to identify objective indicators of the correction of psychogenic pain in anxious-phobic states and to improve the functional state of the nervous system after stimulatory actions directed to activating the functional connections of the brain in people with panic attacks and moderate cognitive impairment is demonstrated .

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

Similar content being viewed by others

References

  1. E. A. Antipenko, “Antioxidant therapy in cerebral ischemia,” Zh. Nevrol. Psikhiatr., No. 110, 53 (2010).

  2. O. E. Dick and A. L. Glazov, “Application of joint recurrence analysis to the estimation of phase synchronizations of physiological signals,” Zh. Tekhnich. Fiz., 91, No. 12, 2045–2058 (2021).

    Google Scholar 

  3. O. E. Dick, I. A. Svyatogor, V. A. Ishinova, and A. D. Nozdrachev, “Fractal characteristics of the functional state of the brain in patients with anxious-phobic disorders,” Fiziol. Cheloveka, 38, 30–36 (2012).

    Google Scholar 

  4. O. E. Dick, “ Wavelet and multifractal analysis of human brain responses to rhythmic photostimulation in vascular pathologies,” in: Proc. 17th All-Russ. Sci. Techn. Conf. Neuroinformatics-2016, April 25–29, 2016, National Research Nuclear University MEPhI, Moscow (2016), Chpt. 3, pp. 12–20.

  5. O. E. Dick and A. D. Nozdrachev, “Dynamics of patterns of electrical activity of the brain in impairments to its functional state,” Usp. Fiziol. Nauk., 51, No. 2, 1–20 (2020).

    Google Scholar 

  6. O. E. Dick, I. A. Svyatogor, and O. E. Dick, et al., “Analysis of reactive EEG patterns in people with atrial fibrillation,” Fiziol. Cheloveka, 45, 49–63 (2019).

    Google Scholar 

  7. O. E. Dick and A. D. Nozdrachev, The Mechanisms of Changes in the Dynamic Complexity of Patterns of Physiological Signals: Monograph, St. Petersburg University Press, St. Petersburg (2019), ISBN: 9785288.

  8. O. E. Dick, I. A. Svyatogor, and T. N. Reznikova, et al., “ Analysis of EEG patterns in people with panic attacks,” Fiziol. Cheloveka, 46, No. 2, 63 (2020).

  9. O. E. Dick and A. L. Glazov, “Parameters of phase synchronization in electroencephalographic patterns as markers of cognitive impairments,” Zh. Tekh. Fiz., 91, No. 4, 678–688 (2021).

    Google Scholar 

  10. I. Dobeshi, Ten Lectures on Wavelets, Regular and Stochastic Dynamics Research Center, Izhevsk (2001).

    Google Scholar 

  11. I. M. Dremin, O. V. Ivanov, and V. A. Nechitailo, “Wavelets and their uses,” Usp. Fiz. Nauk., 171, 465–501 (2001).

    Article  Google Scholar 

  12. M. Yu. Drobizhev, E. A. Makukh, and A. I. Dzantieva, “Cognitive disorders in general medicine,” Lech. Vrach, No. 8, 35 (2007).

  13. V. A. Ishinova and I. A. Svyatogor, “Color reflection of pain in patients with phobic anxiety disorders,” Vest. St. Peterburg. Gos. Med. Akad. im. Mechnikova, 31, 198–199 (2009).

    Google Scholar 

  14. A. M. Lugovaya, A Means of Correcting Psychoemotional Status Using the A. M. Lugovaya Method, Patent RF N 2313282 (2007).

  15. I. A. Novikova, A. G. Solov’ev, and V. V. Mestechko, “Features of the emotional domain in elderly people with borderline personality disorders,” Med. Psikhol. Ross., No. 4, 27 (2014).

  16. A. N. Pavlov and V. S. Anishchenko, “Multifractal analysis of complex signals,” Usp. Fiz. Nauk., 177, 859 (2007).

    Google Scholar 

  17. V. A. Parfenov, “Treatment of poststroke cognitive impairments,” Russ. Med. Zh., No. 16, 986 (2010).

  18. T. N. Reznikova, A. A. Krasnov, N. A. Seliverstova, et al., “Studies of the ‘internal picture of disease’ in patients with organic and functional CNS pathology in the process of therapeutic activations by the method of artificial stable functional connections of the human brain,” Vestn. Klinich. Psikhol., 2, No. 1, 84 (2004).

  19. T. N. Reznikova, D. A. Fedoryaka, N. A. Seliverstova, and I. A. Mokhovikova, “Experience in the use of sensory impulse stimulation in patients with panic attacks,” Vestn. Psikhoter., No. 68, 47 (2018).

  20. T. N. Reznikova and N. A. “Seliverstova, “Correction of mental status by a non-medication-based method in multiple sclerosis,” Klin. Spets. Psikhol., 8, No. 1, 177 (2019).

  21. T. N. Reznikova, N. A. Seliverstova, O. E. Dick, et al., “Assessment of psychophysiological status in elderly people with moderate cognitive impairments with sensory impulse stimulation,” Psikhich. Zdorov., No. 9, 12–18 (2020).

  22. V. S. Rusinov, O. M. Grindel’, G. N. Boldyreva, and E. M. Vakar, Human Brain Biopotentials. Mathematical Analysis, Meditsina, Moscow (1987).

  23. I. A. Svyatogor, “Classification of EEG patterns and their morphological interpretation in maladaptation disorders,” Biol. Obratn. Svyaz., 3, 10–19 (2000).

    Google Scholar 

  24. I. A. Svyatogor, T. N. Reznikova, and N. V. Petukhova, “Dynamics of psychophysiological status in the process of treatment using the artificial stable functional connections method,” Biol. Obratn. Svyaz., No. 4, 24 (2001).

  25. I. A. Svyatogor and N. L. Guseva, “EEG rhythm assimilation reaction in health and impairments to the functional state of the central nervous system,” Vestn. Klin. Neirofiziol., 1, 13–19 (2014).

    Google Scholar 

  26. I. A. Svyatogor, O. E. Dick, A. D. Nozdrachev, et al., “Analysis of changes in EEG patterns in response to rhythmic photostimulation in different impairments to the functional state of the CNS,” Fiziol. Cheloveka, 41, 41–49 (2015).

    CAS  PubMed  Google Scholar 

  27. N. V. Serov, Light Therapy. The Meaning and Importance of Color. Information – Color – Intelligence, Rech, St. Petersburg (2002).

  28. V. M. Smirnov, T. N. Reznikova, and Yu. M. Gubachev, “Artificial stable functional connections and neuroregulatory correction of psychoautonomic disorders,” Fiziol. Cheloveka, 13, No. 5, 715–722 (1987).

    CAS  PubMed  Google Scholar 

  29. V. M. Smirnov, Yu. S. Borodkin, and T. N. Reznikova, “Properties of the human brain in conditions of activation of subcortical structures to form a set of stable intracerebral functional connections,” in: Scientific Discoveries. A Collection of Brief Descriptions, Moscow, St. Petersburg (1999), No. 1, pp. 23–26.

  30. Yu. A. Starchina, “Cognitive impairments and their treatment in patients with arterial hypertension,” Consilium Medicum, No. 1, 1 (2009).

  31. G. R. Tabeeva, “Stress-associated cognitive and noncognitive disorders in elderly patients,” Nevrol. Neiropsikh. Psikhosom., No. 1, 87 (2015).

  32. A. I. Fedotchev, A. T. Bondar’, and I. G. Akoev, “EEG resonance reactions in rhythmic light exposures of different intensities and frequencies,” Zh. Vyssh. Nerv. Deyat., 51, No. 1, 17–23 (2001).

  33. A. I. Fedotchev, A. T. Bondar’, S. G. Matrusov, et al., “Use of feedback signals from patients’ endogenous rhythms for non-medication-based correction of functional disorders,” Usp. Fiziol. Nauk., 37, No. 4, 82–92 (2006).

  34. A. Arneodo, E. Bacry, and J. F. Muzy, “The thermodynamics of fractals revisited with wavelets,” Physica, 213, 232–275 (1995).

    Article  CAS  Google Scholar 

  35. C. Bass, R. Pevele, and A. House, “Somatoform disorders: severe psychiatric illnesses neglected by psychiatrists,” Br. J. Psychiatry, 179, 11–14 (2001).

    Article  CAS  PubMed  Google Scholar 

  36. N. M. Batelaan, A. J. Van Balkom, and D. J. Stein, “Evidence -based pharmacotherapy of panic disorder: An update,” Int. J. Neuropsychopharmacol., 15, No. 3, 403–415 (2012).

    Article  CAS  PubMed  Google Scholar 

  37. E. J. M. Bierman, H. C. Comijs, and F. Rijmen, et al., “Anxiety symptoms and cognitive performance in later life: results from the longitudinal aging study,” Aging Ment. Health, 12, No. 4, 517 (2008).

  38. D. Bosnyakov, A. Gabova, G. Kuznetsova, et al., “Time-frequency analysis of spike-wave discharges using a modified wavelet transform,” J. Neurosci. Methods, 1654, 80–88 (2006).

    Article  Google Scholar 

  39. U. V. Borodina and R. R. Aliev, “Wavelet spectra of visual evoked potentials; time course of delta, theta, alpha and beta bands,” Neurocomputing, 121, 551–555 (2013).

    Article  Google Scholar 

  40. S. V. Bozhokin and N. B. Suvorov, “Wavelet analysis of transient processes in EEG photic,” Biomed. Electron., 3, 13–19 (2008).

    Google Scholar 

  41. F. Cosci, “The psychological development of panic disorder: implications for neurobiology and treatment,” Rev. Bras. Psiquiatr., 34, 9–31 (2012).

    Article  Google Scholar 

  42. O. E. Dick and I. A. Mochovikova, “Multifractal and wavelet analysis of epileptic seizures,” in: Chaos Theory: Modeling, Simulation and Applications, Selected Papers from the 3rd Chaotic Modeling and Simulation International Conference, C. H. Skiadas et al. (eds.), World Scientific Publishing (2011), pp. 159–166.

  43. O. E. Dick and I. A. Svyatogor, “Potentialities of the wavelet and multifractal techniques to evaluate changes in the functional state of the human brain,” Neurocomputing, 82, 207–215 (2012).

    Article  Google Scholar 

  44. O. E. Dick, “Wavelet analysis of the human brain lability to reproduce the external rhythm,” J. Chaot. Model. Simul., 3, 387–394 (2013).

    Google Scholar 

  45. O. E. Dick and I. A. Svyatogor, “Wavelet and multifractal estimation of the intermittent photic stimulation response in the electroencephalogram of patients with dyscirculatory encephalopathy,” Neurocomputing, 165, 361–374 (2015).

    Article  Google Scholar 

  46. O. E. Dick, “Nonlinear dynamics of reactive EEG patterns under cerebrovascular and cardiovascular distortions,” J. Chaot. Model. Simul., 4, 501–512 (2017).

    Google Scholar 

  47. O. E. Dick, “Wavelet and recurrence analysis of EEG patterns of subjects with panic attacks,” Stud. Computat. Intell., 856, 172 (2020).

    Article  Google Scholar 

  48. O. E. Dick and A. L. Glazov, “Estimation of the synchronization between intermittent photic stimulation and brain response in hypertension disease by the recurrence and synchrosqueezed wavelet transform,” Neurocomputing, 455, 163–177 (2021).

    Article  Google Scholar 

  49. I. Fedotchev, A. T. Bondar, and I. G. Akoev, “Dynamic characteristics of the human resonance EEG responses to rhythmic photostimulation,” Hum. Physiol., 26, No. 2, 64–72 (2000).

    Article  CAS  Google Scholar 

  50. A. M. Fraser and H. L. Swinney, “Independent coordinates for strange attractors from mutual information,” Phys. Rev., 33, No. 2, 1134–1140 (1986).

    Article  CAS  Google Scholar 

  51. A. E. Hramov, A. A. Koronovsky, V. A. Makarov, et al., “Wavelets in neuroscience,” in: Springer Series in Synergetics, Springer, Berlin (2015).

  52. R. Gallacher, “Early stage dementia,” Nurs. Stand., 28, No. 20, 61 (2014).

  53. A. W. Goddard, G. F. Mason, M. Appel, et al., “Impaired GABA neuronal response to acute benzodiazepine administration in panic disorder,” Am. J. Psychiatry, 161, No. 12, 2186–2193 (2004).

    Article  PubMed  Google Scholar 

  54. V. A. Ishinova, I. A. Svyatogor, and T. N. Reznikova, and J. “Features of color reflection in psychogenic pain in patients with somatoform disorders during psychotherapeutic treatment,” Span. J. Psychol., 12, 715–724 (2009).

    Article  PubMed  Google Scholar 

  55. M. B. Kennel, R. Brown, and H. D. Abarbanel, “Determining embedding dimension for phase-space reconstruction using a geometrical construction,” Phys. Rev. A, 45, No. 6, 3403 (1992).

  56. J. Kurths, M. C. Romano, M. Thiel, et al., “Synchronization analysis of coupled noncoherent oscillators,” Nonlin. Dynam., 44, 135 (2006).

    Article  Google Scholar 

  57. N. Marwan, N. Wessel, U. Meyerfeldt, and E., et al., “Recurrence plot based measures of complexity and its application to heart rate variability data,” Phys. Rev., 66, 26702–26710 (2002).

  58. N. Marwan and J. Kurths, and A. “Nonlinear analysis of bivariate data with cross recurrence plots,” Phys. Lett., 302, 299–307 (2002).

    Article  CAS  Google Scholar 

  59. N. Marwan, M. C. Romano, M. Thiel, et al., “Recurrence plots for the analysis of complex systems,” Phys. Rep., 438, 237–329 (2007).

    Article  Google Scholar 

  60. B. Milrod, F. Busch, C. LeonA, et al., “A pilot open trial of brief psychodynamic psychotherapy for panic disorder,” J. Psychother. Pract. Res., 10, 239–245 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. J. F. Muzy, E. Bacry, and A. Arneodo, “Multifractal formalism for fractal signals: the structure-function approach versus the wavelet-transform modulus-maxima method,” Phys. Rev. E, 47, 875–884 (1993).

    Article  CAS  Google Scholar 

  62. K. Natarajan, R. Acharya, F. Alias, et al., “Nonlinear analysis of EEG signals at different mental states,” Biomed. Eng., 3, 7–18 (2004).

    Google Scholar 

  63. D. Nutt, Anxiety Disorders Comorbid with Depression: Panic Disorder and Agoraphobia, D. Nutt et al. (eds.), Martin Dunitz Ltd. (2002).

  64. D. Popivanov, V. Stomonyakov, Z. Minchev, et al., “Multifractality of decomposed EEG during imaginary and real visual-motor tracking,” Biol. Cybern., 94, 149–156 (2006).

    Article  CAS  PubMed  Google Scholar 

  65. M. A. Qianli, N. Xinba, and W. Jun, et al., “A new measure to characterize multifractality of sleep electroencephalogram,” Chinese Sci. Bull., 51, 3059–3064 (2006).

    Article  Google Scholar 

  66. S. U. Qureshi, M. E. Long, and M. R. Bradshaw, et al., “Does PTSD impair cognition beyond the effect of trauma?” J. Neuropsychiatr. Clin. Neurosci., 23, No. 1, 16 (2011).

  67. D. Rangaprakash and N. Pradhan, “Study of phase synchronization in multichannel seizure EEG using nonlinear recurrence measure,” Biomed. Signal Process. Control, 11, 114–112 (2014).

    Article  Google Scholar 

  68. M. C. Romano, M. Thiel, J. Kurths, et al., “Detection of synchronization for non-phase-coherent and non-stationary data,” Europhys. Lett., 71, 466 (2005).

    Article  CAS  Google Scholar 

  69. L. Senhadji and F. Wendling, “Epileptic transient detection: wavelets and time-frequency approaches,” Neurophysiol. Clin., 32, 175–192 (2002).

    Article  PubMed  Google Scholar 

  70. G. M. Sullivan, J. D. Coplan, J. M. Kent, et al., “The noradrenergic system in pathological anxiety: a focus on panic with relevance to generalized anxiety and phobias,” Biol. Psychiatry, 46, No. 9, 1205–1218 (1999).

    Article  CAS  PubMed  Google Scholar 

  71. F. Takens, “Detecting strange attractors in turbulence,” in: Dynamical Systems and Turbulence, Lecture Notes in Mathematics, D. Rand and L. S. Young (eds.), Springer, Berlin (1981), Vol. 898, pp. 366–381.

    Google Scholar 

  72. V. E. Titov and O. E. Dick, “Computational evaluation of the effectiveness of therapy method with help recurrent analysis,” J. Phys. Conf. Ser., 1889, 42092–42098 (2021).

    Article  Google Scholar 

  73. H. G. Westenberg and M. R. Liebowitz, “Overview of panic and social anxiety disorders,” J. Clin. Psychiatry, 65, Suppl. 14, 22–26 (2004).

    CAS  PubMed  Google Scholar 

  74. K. A. Wilson and C. Hayward, “A prospective evaluation of agoraphobia and depression symptoms following panic attacks in a community sample of adolescents,” J. Anxiety Disord, 19, No. 1, 87–103 (2005).

    Article  PubMed  Google Scholar 

  75. A. M. Wink, E. Bullmore, A. Barnes, et al., “Monofractal and multifractal dynamics of low frequency endogenous brain oscillations in functional MRI,” Hum. Brain Mapp., 29, 791–801 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Y. Xu, Q. D. Y. Ma, D. T. Schmitt, et al., “Effects of coarse-graining on the scaling behavior of long-range correlated and anti-correlated signals,” Physica, 390, 4057–4072 (2011).

    Article  Google Scholar 

  77. M. Yamamoto, “Fluctuations observed in biological time series signals and their functional significance,” Front. Med. Biol. Eng., 3, 135–137 (1991).

    CAS  PubMed  Google Scholar 

  78. S. T. Yu, M. K. Kim, B. Kim, et al., “The effects of 5-HTR1A polymorphism on cingulum connectivity in patients with panic disorder,” Psychiatry Investig., 10, No. 4, 399–406 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Pavlov Institute of Physiology, Russian Academy of Sciences, St. Petersburg, Russia

    O. E. Dick

Authors
  1. O. E. Dick
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to O. E. Dick.

Additional information

Translated from Uspekhi Fiziologicheskikh Nauk, Vol. 53, No. 1, pp. 34–51, January–March, 2022.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dick, O.E. Dynamics of Brain Electrical Activity Patterns in Maladaptive Disorders. Neurosci Behav Physi 52, 1491–1505 (2022). https://doi.org/10.1007/s11055-023-01380-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11055-023-01380-1

Keywords

Search

Navigation