Skip to main content

Technologies of Nonlinear Stimulation: Role in the Treatment of Diseases of the Brain and the Potential Applications in Healthy Individuals

Human Physiology volume 44, pages 289–299 (2018)Cite this article

Abstract

In 2015, the theory was proposed that links the development and maintenance of the typical in norm complex structure of neural networks and the activity of the brain with the complexity of visual and other sensory environmental signals that affect the person during the life. Simplification of the temporal structure of environmental cues is associated with abnormal development and aging of the central nervous system. As well, the use of fractal optic stimulation and complex aperiodic stimuli of other modalities may enhance the effectiveness of strategies for a recovery in the structure and function of the retina and brain, including neurodegenerative pathology, by reactivation of neuroplasticity. In the spectrum of nonlinear stimulating therapy techniques, different variants of mono- and multimodal fractal stimulation should be used, as well as their combinations with white noise, music therapy, cognitive, and physical training. We believe that using of non-linear stimulation technologies in a healthy person may be important in a variety of situations that lead to the simplification of the networks and dynamics of the activity of the brain. Application of physiologically adequate nonlinear stimuli is promising to slow and prevent age-related cognitive impairment in the elderly, in rehabilitation and recovery programs for healthy individuals of certain professions associated with physical or psychological stress, and athletes.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

References

  1. Zueva, M.V., Fractality of sensations and the brain health: the theory linking neurodegenerative disorder with distortion of spatial and temporal scale-invariance and fractal complexity of the visible world, Front. Aging Neurosci., 2015, vol. 7, p. 135.

    PubMed  PubMed Central  Article  Google Scholar 

  2. Skrebitskii, V.G. and Shtark, M.B., The fundaments of neuronal plasticity, Vestn. Ross. Akad. Med. Nauk, 2012, no. 9, p. 39.

    Article  Google Scholar 

  3. Butz, M., Wörgötter, F., and van Ooyen, A., Activitydependent structural plasticity, Brain Res. Rev., 2009, vol. 60, p. 287.

    PubMed  Article  Google Scholar 

  4. Pascual-Leone, A., Freitas, C., Oberman, L., et al., Characterizing brain cortical plasticity and network dynamics across the age-span in health and disease with TMS-EEG and TMS-fMRI, Brain Topogr., 2011, vol. 24, p. 302.

    PubMed  PubMed Central  Article  Google Scholar 

  5. Wainwright, S.R. and Galea, L.A.M., The neural plasticity theory of depression: assessing the roles of adult neurogenesis and PSA-NCAM within the hippocampus, Neural Plast., 2013, 805497.

    Google Scholar 

  6. Katz, L.C. and Shatz, C.J., Synaptic activity and the construction of cortical circuits, Science, 1996, vol. 274, p. 1133.

    PubMed  Article  CAS  Google Scholar 

  7. Buonomano, D.V. and Merzenich, M.M., Cortical plasticity: from synapses to maps, Ann. Rev. Neurosci., 1998, vol. 21, p. 149.

    PubMed  Article  CAS  Google Scholar 

  8. Carcea, I. and Froemke, R.C., Cortical plasticity, excitatory-inhibitory balance, and sensory perception, Progr. Brain Res., 2013, vol. 207, p. 65.

    Article  Google Scholar 

  9. Hubel, D.H. and Wiesel, T.N., The period of susceptibility to the physiological effects of unilateral eye closure in kittens, J. Physiol., 1970, vol. 206, p. 419.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  10. Hensch, T., Critical period plasticity in local cortical circuitries, Nat. Rev. Neurosci., 2005, vol. 6, p. 877.

    PubMed  Article  CAS  Google Scholar 

  11. Merabet, L.B. and Pascual-Leone, A., Neural reorganization following sensory loss: the opportunity of change, Nat. Rev. Neurosci., 2010, vol. 11, p. 44.

    PubMed  Article  CAS  Google Scholar 

  12. Bengoetxea, H., Ortuzar, N., Bulnes, S., et al., Enriched and deprived sensory experience induces structural changes and rewires connectivity during the postnatal development of the brain, Neural Plast., 2012, vol. 2012, p. 305693.

    PubMed  PubMed Central  Article  Google Scholar 

  13. Stein, B.E., Stanford, T.R., and Rowland, D.A., Development of multisensory integration from the perspective of the individual neuron, Nat. Rev. Neurosci., 2014, vol. 15, p. 520.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  14. Gilbert, C.D. and Li, W., Adult visual cortical plasticity, Neuron, 2012, vol. 75, no. 2, p. 250.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  15. Hensch, T.K. and Fagiolini, M., Excitatory-inhibitory balance and critical period plasticity in developing visual cortex, Progr. Brain Res., 2005, vol. 147, p. 115.

    Article  CAS  Google Scholar 

  16. Nudo, R.J., Neural bases of recovery after brain injury, J. Commun. Disord., 2011, vol. 44, no. 5, p. 515.

    PubMed  PubMed Central  Article  Google Scholar 

  17. Castellanos, N.P., Paúl, N., Ordóñez, V.E., et al., Reorganization of functional connectivity as a correlate of cognitive recovery in acquired brain injury, Brain, 2010, vol. 133, p. 2365.

    PubMed  Article  Google Scholar 

  18. Lewis, D.A. and González-Burgos, G., Neuroplasticity of neocortical circuits in schizophrenia, Neuropsychopharmacology, 2008, vol. 33, no. 1, p. 141.

    PubMed  Article  Google Scholar 

  19. Zhuang, X., Mazzoni, P., and Kang, U.J., The role of neuroplasticity in dopaminergic therapy for Parkinson disease, Nat. Rev. Neurol., 2013, vol. 9, p. 248.

    PubMed  Article  CAS  Google Scholar 

  20. van Praag, H., Shubert, T., Zhao, C., and Gage, F.H., Exercise enhances learning and hippocampal neurogenesis in aged mice, J. Neurosci., 2005, vol. 25, no. 38, p. 8680.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  21. Nithianantharajah, J. and Hannan, A.J., The neurobiology of brain and cognitive reserve: mental and physical activity as modulators of brain disorders, Progr. Neurobiol., 2009, vol. 89, p. 369.

    Article  Google Scholar 

  22. Mahncke, H.W., Connor, B.B., Appelman, J., et al., Memory enhancement in healthy older adults using a brain plasticity-based training program: a randomized, controlled study, Proc. Natl. Acad. Sci. U.S.A., 2006, vol. 103, no. 33, p. 12523.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  23. Smith, G.E., Housen, P., Yaffe, K., et al., A cognitive training program based on principles of brain plasticity: results from the improvement in memory with plasticity-based adaptive cognitive training (IMPACT) study, J. Am. Geriatr. Soc., 2009, vol. 57, p. 594.

    PubMed  PubMed Central  Article  Google Scholar 

  24. Foster, P.P., Rosenblatt, K.P., and Kuljiš, R.O., Exercise-induced cognitive plasticity, implications for mild cognitive impairment and Alzheimer’s disease, Front. Neurol., 2011, vol. 2, p. 28.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. Merzenich, M., Soft-Wired: How the New Science of Brain Plasticity Can Change Your Life, San Francisco: Parnassus, 2013, 2nd ed.

    Google Scholar 

  26. Alwis, D.S. and Rajan, R., Environmental enrichment and the sensory brain: the role of enrichment in remediating brain injury, Front. Syst. Neurosci., 2014, vol. 8, p. 156.

    PubMed  PubMed Central  Article  Google Scholar 

  27. Shors, T.J., Olson, R.L., Bates, M.E., Selby, E.A., and Alderman, B.L., Mental and physical (MAP) training: a neurogenesis-inspired intervention that enhances health in humans, Neurobiol. Learn. Mem., 2014, vol. 115, p. 3.

    PubMed  PubMed Central  Article  Google Scholar 

  28. van Praag, H., Kempermann, G., and Gage, F.H., Neural consequences of environmental enrichment, Nat. Rev. Neurosci., 2000, vol. 1, p. 191.

    PubMed  Article  CAS  Google Scholar 

  29. Mora, F., Segovia, G., and del Arco, A., Aging, plasticity and environmental enrichment: structural changes and neurotransmitter dynamics in several areas of the brain, Brain Res. Rev., 2007, vol. 55, no. 1, p. 78.

    PubMed  Article  CAS  Google Scholar 

  30. Maya-Vetencourt, J.F., Tiraboschi, E., Spolidoro, M., et al., Serotonin triggers a transient epigenetic mechanism that reinstates adult visual cortex plasticity in rats, Eur. J. Neurosci., 2011, vol. 33, no. 1, p. 49.

    PubMed  Article  Google Scholar 

  31. Baroncelli, L., Braschi, C., Spolidoro, M., et al., Nurturing brain plasticity: impact of environmental enrichment, Cell Death Diff., 2010, vol. 17, p. 1092.

    Article  CAS  Google Scholar 

  32. Baroncelli, L., Bonaccorsi, J., Milanese, M., et al., Enriched experience and recovery from amblyopia in adult rats: impact of motor, social and sensory components, Neuropharmacology, 2012, vol. 62, p. 2388.

    PubMed  Article  CAS  Google Scholar 

  33. Serruyaa, M.D. and Kahana, M.J., Techniques and devices to restore cognition, Behav. Brain Res., 2008, vol. 192, vol. 2, p. 149.

    Article  Google Scholar 

  34. Green, C.S. and Bavelier, D., Action-video-game experience alters the spatial resolution of vision, Psychol. Sci., 2007, vol. 18, no. 1, p. 88.

    PubMed  Article  CAS  Google Scholar 

  35. Dockx, K., Bekkers, E.M.J., van den Bergh, V., et al., Virtual reality for rehabilitation in Parkinson’s disease, Cochrane Database Syst. Rev., 2016, vol. 12, art. ID CD010760.

  36. Mandelbrot, B., The Fractal Geometry of Nature, New York: Macmillan, 1983.

    Google Scholar 

  37. West, G.B., Brown, J.H., and Enquist, B.J., The fourth dimension of life: fractal geometry and allometric scaling of organisms, Science, 1999, vol. 284, p. 1677.

    PubMed  Article  CAS  Google Scholar 

  38. Iannaccone, P.M. and Khokha, M.K., Fractal Geometry in Biological Systems: An Analytical Approach, Boca Raton, FL: CRC Press, 1996.

    Google Scholar 

  39. Taylor, R.P., Spehar, B., Donkelaar, P.V., and Hagerhall, C.M., Perceptual and physiological responses to Jackson Pollock’s fractals, Front. Hum. Neurosci., 2011, vol. 5, p. 60.

    PubMed  PubMed Central  Article  Google Scholar 

  40. Zueva, M.V., Nonlinear fractals: applications in physiology and ophthalmology, Ophthalmology, 2014, vol. 11, no. 1, p. 4. http://www.ophthalmojournal. com/opht/article/viewFile/29/175.

    Google Scholar 

  41. Simonian, G.S. and Simonian, A.G., Fractality of biological systems. III. Fractality of organs and organisms, Int. J. Appl. Fundam. Res., Biol. Sci., 2016, vol. 30, p. 272.

    Google Scholar 

  42. Sejdic, E. and Lipsitz, L.A., Necessity of noise in physiology and medicine, Comput. Methods Progr. Biomed., 2013, vol. 111, no. 2, p. 459.

    Article  Google Scholar 

  43. Halley, J.M., Ecology, evolution and 1/f–noise, Trends Ecol. Evol., 1996, vol. 11, no. 1, p. 33.

    PubMed  Article  CAS  Google Scholar 

  44. Storch, D., Gaston, K.J., and Cepák, J., Pink landscapes: 1/f spectra of spatial environmental variability and bird community composition, Proc. R. Soc. London, Ser. B, 2002, vol. 269, p. 1791.

    Article  Google Scholar 

  45. Vasseur, D.A. and Yodzis, P., The color of environmental noise, Ecology, 2004, vol. 85, no. 4, p. 1146.

    Article  Google Scholar 

  46. Halley, J.M. and Inchausti, P., The increasing importance of 1/f noise as models of ecological variability, Fluctuation Noise Lett., 2004, vol. 4, no. 2, pp. R1–R26.

    Article  Google Scholar 

  47. Dey, S., Proulx, S.R., and Teotónio, H., Adaptation to temporally fluctuating environments by the evolution of maternal effects, PLoS Biol., 2016, vol. 14, no. 2, p. e1002388.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. Anishchenko, V.S., Dynamic systems, Soros. Obraz. Zh., 1997, no. 11, p. 77.

    Google Scholar 

  49. Goldberger, A.L., Amaral, L.A.N., Hausdor, L.M., et al., Fractal dynamics in physiology: Alterations with disease and aging, Proc. Natl. Acad. Sci. U.S.A., 2002, vol. 99, p. 2466.

    PubMed  PubMed Central  Article  Google Scholar 

  50. Tan, C.O., Cohen, M.A., Eckberg, D.L., and Taylor, J.A., Fractal properties of human heart period variability: physiological and methodological implications, J. Physiol., 2009, vol. 1, p. 3929.

    Article  CAS  Google Scholar 

  51. Manor, B. and Lipsitz, L.A., Physiologic complexity and aging: implications for physical function and rehabilitation, Prog. Neuro-Psychopharmacol. Biol. Psychiatry, 2013, vol. 45, p. 287.

    Article  Google Scholar 

  52. Peng, C.K., Mietus, J.E., Liu, Y., et al., Quantifying fractal dynamics of human respiration: age and gender effects, Ann. Biomed. Eng., 2002, vol. 30, p. 683.

    PubMed  Article  CAS  Google Scholar 

  53. Goldberger, A.L., Fractal variability versus pathologic periodicity: complexity loss and stereotypy in disease, Perspect. Biol. Med., 1997, vol. 40, p. 543. doi 10.1353/pbm.1997.0063

    PubMed  Article  CAS  Google Scholar 

  54. Hausdorff, J.M., Peng, C.K., Ladin, Z., et al., Is walking a random walk? Evidence for long-range correlations in stride interval of human gait, J. Appl. Physiol., 1995, vol. 78, p. 349.

    PubMed  Article  CAS  Google Scholar 

  55. Lipsitz, L.A. and Goldberger, A.L., Loss of “complexity” and aging, J. Am. Med. Assoc., 1992, vol. 267, no. 13, p. 1806.

    Article  CAS  Google Scholar 

  56. Hu, K., van Someren, E.J., Shea, S.A., and Scheer, F.A., Reduction of scale invariance of activity fluctuations with aging and Alzheimer’s disease: involvement of the circadian pacemaker, Proc. Natl. Acad. Sci. U.S.A., 2009, vol. 106, p. 2490.

    PubMed  PubMed Central  Article  Google Scholar 

  57. Balasubramanian, K. and Nagaraj, N., Aging and cardiovascular complexity: effect of the length of RRtachograms, PeerJ, 2016, vol. 4, p. e2755.

    PubMed  PubMed Central  Article  Google Scholar 

  58. Geula, C., Abnormalities of neural circuitry in Alzheimer’s disease: hippocampus and cortical cholinergic innervation, Neurology, 1998, vol. 51, suppl. 1, p. S18.

    PubMed  Article  CAS  Google Scholar 

  59. Li, Y., Tong, S., Liu, D., et al., Abnormal EEG complexity in patients with schizophrenia and depression, Clin. Neurophysiol., 2008, vol. 119, no. 6, p. 1232.

    PubMed  Article  Google Scholar 

  60. Wei, X., Day, A.G., Ouelette-Kuntz, H., and Hevland, D.K., The association between nutritional adequacy and long-term outcomes in critically ill patients requiring prolonged mechanical ventilation: a multicenter cohort study, Crit. Care Med., 2015, vol. 43, no. 8, p. 1569. doi 10.1097/CCM. 0000000000001000

    PubMed  Article  Google Scholar 

  61. Zhang, Y., Wang, C., Sun, C., et al., Neural complexity in patients with poststroke depression: a resting EEG study, J. Affective Disord., 2015, vol. 188, p. 310.

    Article  Google Scholar 

  62. Liu, X., Zhang, C., Ji, Z., et al., Multiple characteristics analysis of Alzheimer’s electroencephalogram by power spectral density and Lempel–Ziv complexity, Cognit. Neurodyn., 2016, vol. 10, no. 2, p. 121.

    Article  Google Scholar 

  63. Yuvaraj, R. and Murugappan, M., Hemispheric asymmetry non-linear analysis of EEG during emotional responses from idiopathic Parkinson’s disease patients, Cognit. Neurodyn., 2016, vol. 10, no. 3, p. 225.

    Article  CAS  Google Scholar 

  64. Jeong, J., EEG dynamics in patients with Alzheimer’s disease, Clin. Neurophysiol., 2004, vol. 115, no. 7, p. 1490.

    PubMed  Article  Google Scholar 

  65. Dauwels, J., Srinivasan, K., Reddy, M.R., et al., Slowing and loss of complexity in Alzheimer’s EEG: two sides of the same coin? Int. J. Alzheimer’s Dis., 2011, vol. 2011, art ID539621.

  66. Vecchio, F., Babiloni, C., Lizio, R., et al., Resting state cortical EEG rhythms in Alzheimer’s disease: toward EEG markers for clinical applications: a review, Suppl. Clin. Neurophysiol., 2013, vol. 62, p. 223.

    PubMed  Article  Google Scholar 

  67. Babiloni, C., Lizio, R., Marzano, N., et al., Brain neural synchronization and functional coupling in Alzheimer’s disease as revealed by resting state EEG rhythms, Int. J. Psychophysiol., 2016, vol. 103, p. 882.

    Article  Google Scholar 

  68. Kitzbichler, M.G., Smith, M.L., Christensen, S.R., and Bullmore, E., Broadband criticality of human brain network synchronization, PLoS Comput. Biol., 2009, vol. 5, no. 3, p. e1000314.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  69. Bak, P., Tang, C., and Wiesenfeld, K., Self-organized criticality: an explanation of the 1/f noise, Phys. Rev. Lett., 1987, vol. 59, no. 4, p. 381.

    PubMed  Article  CAS  Google Scholar 

  70. Bilder, R.M. and Knudsen, K.S., Creative cognition and systems biology on the edge of chaos, Front. Psychol., 2014, vol. 5, p. 1104.

    PubMed  PubMed Central  Article  Google Scholar 

  71. Timme, N.M., Marshall, N.J., Bennett, N., et al., Criticality maximizes complexity in neural tissue, Front. Psychol., 2016, vol. 7, p. 425.

    Google Scholar 

  72. Stam, C.J., Nonlinear dynamical analysis of EEG and MEG: review of an emerging field, Clin. Neurophysiol., 2005, vol. 116, no. 10, p. 2266.

    PubMed  Article  CAS  Google Scholar 

  73. Srinivasan, R., Bibi, F.A., and Nunez, P.L., Steadystate visual evoked potentials: distributed local sources and wave-like dynamics are sensitive to flicker frequency, Brain Topogr., 2006, vol. 18, no. 3, p. 167.

    PubMed  PubMed Central  Article  Google Scholar 

  74. Huang, T.L. and Charyton, C., A comprehensive review of the psychological effects of brainwave entrainment, Alt. Ther. Health Med., 2008, vol. 14, no. 5, p. 38.

    Google Scholar 

  75. Hmel, F.C. and Cohen, L.G., Drivers of brain plasticity, Curr. Opin. Neurol., 2005, vol., 18, p. 667. doi 10.1097/01.wco.0000189876.37475.42

    Article  Google Scholar 

  76. Cheng, W., Law, P.K., Kwan, H.C., and Cheng, R.S., Stimulation therapies and the relevance of fractal dynamics to the treatment of diseases, Open J. Regener. Med., 2014, vol. 3, p. 73.

    Article  Google Scholar 

  77. Antal, A. and Herrmann, C.S., Transcranial alternating current and random noise stimulation: possible mechanisms, Neural Plast., 2016, vol. 2016, art. ID 3616807.

  78. Söderlund, G.B., Björk, C., and Gustafsson, P., Comparing auditory noise treatment with stimulant medication on cognitive task performance in children with attention deficit hyperactivity disorder: results from a pilot study, Front. Psychol., 2016, vol. 7, p. 1331.

    PubMed  PubMed Central  Google Scholar 

  79. Zaehle, T., Rach, S., and Herrmann, C.S., Transcranial alternating current stimulation enhances individual alpha activity in human EEG, PLoS One, 2010, vol. 5, p. e13766.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  80. Thut, G., Veniero, D., Romei, V., et al., Rhythmic TMS causes local entrainment of natural oscillatory signatures, Curr. Biol., 2011, vol. 21, p. 1176.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  81. Vernet, M., Bashir, S., Yoo, W.-K., et al., Insights on the neural basis of motor plasticity induced by theta burst stimulation from TMS-EEG, Eur. J. Neurosci., 2013, vol. 37, p. 598.

    PubMed  Article  Google Scholar 

  82. Marshall, L., Kirov, R., Brade, J., et al., Transcranial electrical currents to probe EEG brain rhythms and memory consolidation during sleep in humans, PLoS One, 2011, 6, p. e16905.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  83. Sauseng, P., Gerloff, C., and Hummel, F.C., Two brakes are better than one: the neural bases of inhibitory control of motor memory traces, Neuroimage, 2013, vol. 65, p. 52.

    PubMed  Article  Google Scholar 

  84. Engel, A.K., Fries, P., and Singer, W., Dynamic predictions: oscillations and synchrony in top-down processing, Nat. Rev. Neurosci., 2001, vol. 2, p. 704.

    PubMed  Article  CAS  Google Scholar 

  85. Varela, F., Lachaux, J.P., Rodriguez, E., and Martinerie, J., The brainweb: phase synchronization and large-scale integration, Nat. Rev. Neurosci., 2001, vol. 2, p. 229.

    PubMed  Article  CAS  Google Scholar 

  86. Buzsáki, G. and Draguhn, A., Neuronal oscillations in cortical networks, Science, 2004, vol. 304, p. 1926.

    PubMed  Article  CAS  Google Scholar 

  87. Krawinkel, L.A., Engel, A.K., and Hummel, F.C., Modulating pathological oscillations by rhythmic non-invasive brain stimulation—a therapeutic concept? Front. Syst. Neurosci., 2015, vol. 9, art. ID 33.

  88. Huang, Y.Z., Edwards, M.J., Rounis, E., et al., Theta burst stimulation of the human motor cortex, Neuron, 2005, vol. 45, p. 201.

    PubMed  Article  CAS  Google Scholar 

  89. Pascual-Leone, A., Valls-Solé, J., Wassermann, E.M., and Hallett, M., Responses to rapid-rate transcranial magnetic stimulation of the human motor cortex, Brain, 1994, vol. 117, no. 4, p. 847.

    PubMed  Article  Google Scholar 

  90. Crowell, A.L., Ryapolova-Webb, E.S., Ostrem, J.L., et al., Oscillations in sensorimotor cortex in movement disorders: an electrocorticography study, Brain, 2012, vol. 135, no. 2, p. 615.

    PubMed  PubMed Central  Article  Google Scholar 

  91. Heinrichs-Graham, E., Wilson, T.W., Santamaria, P.M., et al., Neuromagnetic evidence of abnormal movement-related beta desynchronization in Parkinson’s disease, Cereb. Cortex, 2014, vol. 24, p. 2669.

    PubMed  Article  Google Scholar 

  92. Herz, D.M., Florin, E., Christensen, M.S., et al., Dopamine replacement modulates oscillatory coupling between premotor and motor cortical areas in Parkinson’s disease, Cerebral Cortex, 2014, vol. 24, p. 2873.

    PubMed  Article  Google Scholar 

  93. Sun, Y., Farzan, F., Barr, M.S., et al., γOscillations in schizophrenia: mechanisms and clinical significance, Brain Res., 2011, vol. 1413, p. 98.

    PubMed  Article  CAS  Google Scholar 

  94. Uhlhaas, P.J., Haenschel, C., Nikolic, D., and Singer, W., The role of oscillations and synchrony in cortical networks and their putative relevance for the pathophysiology of schizophrenia, Schizophr. Bull., 2008, vol. 34, p. 927.

    PubMed  PubMed Central  Article  Google Scholar 

  95. Andreou, C., Nolte, G., Leicht, G., et al., Increased resting-state gamma-band connectivity in first-episode schizophrenia, Schizophr. Bull., 2014, vol. 4, p. 930.

    Google Scholar 

  96. Westlake, K.P., Hinkley, L.B., Bucci, M., et al., Resting state a-band functional connectivity and recovery after stroke, Exp. Neurol., 2012, vol. 237, p. 160.

    PubMed  PubMed Central  Article  Google Scholar 

  97. Laaksonen, K., Helle, L., Parkkonen, L., et al., Alterations in spontaneous brain oscillations during stroke recovery, PLoS One, 2013, vol. 8, e61146.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  98. Siebner, H.R. and Ziemann, U., Rippling the cortex with high frequency (>100 Hz) alternating current stimulation, J. Physiol., 2010, vol. 588, no. 24, p. 4851.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  99. Pogosyan, A., Gaynor, L.D., Eusebio, A., and Brown, P. Boosting cortical activity at beta-band frequencies slows movement in humans, Curr. Biol., 2009, vol. 19, p. 1637.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  100. Lapenta, O.M., Minati, L., Fregni, F., and Boggio, P.S., Je pense donc je fais: transcranial direct current stimulation modulates brain oscillations associated with motor imagery and movement observation, Front. Hum. Neurosci., 2013, vol. 7, p. 256.

    PubMed  PubMed Central  Article  Google Scholar 

  101. Adrian, E.D. and Matthews, B.H., The Berger rhythm: potential changes from the occipital lobes of man, Brain, 1934, vol. 57, p. 355.

    Article  Google Scholar 

  102. Barlow, J.S., Rhythmic activity induced by photic stimulation in relation to intrinsical activity of the brain in man, Electroencephalogr. Clin. Neurophysiol., 1960, vol. 12, p. 317.

    PubMed  Article  CAS  Google Scholar 

  103. Williams, J.H., Frequency-specific effects of flicker on recognition memory, Neuroscience, 2001, vol. 104, p. 283.

    PubMed  Article  CAS  Google Scholar 

  104. Williams, J., Ramaswamy, D., and Oulhaj, A., 10 Hz flicker improves recognition memory in older people, BMC Neurosci., 2006, vol. 7, no. 5, p. 21.

    PubMed  PubMed Central  Article  Google Scholar 

  105. Priplata, A., Niemi, J.B., Harry, J.D., Lipsitz, L.A., and Collins, J.J., Vibrating insoles and balance control in elderly people, Lancet, 2007, vol. 362, p. 1123.

    Article  Google Scholar 

  106. Costa, M., Priplata, A.A., Lipsitz, L.A., et al., Noise and poise: enhancement of postural complexity in the elderly with a stochastic-resonance-based therapy, Europhys. Lett., 2007, vol. 77, no. 6, p. 68008.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  107. Yamamoto, Y., Struzik, Z.R., Soma, R., et al., Noisy vestibular stimulation improves autonomic and motor responsiveness in central neurodegenerative disorders, Ann. Neurol., 2005, vol. 58, p. 175.

    PubMed  Article  Google Scholar 

  108. Ross, S., Arnold, B., Blackburn, J.T., et al., Enhanced balance associated with coordination training with stochastic resonance stimulation in subjects with functional ankle instability: an experimental trial, J. Neuroeng. Rehabil., 2007, vol. 4, no. 7, p. 47.

    PubMed  PubMed Central  Article  Google Scholar 

  109. Soderlund, G., Sikstrom, S., Loftesnes, J., et al., The effects of background white noise on memory performance in inattentive school children, Behav. Brain Funct., 2010, vol. 6, no. 1, p. 55.

    PubMed  PubMed Central  Article  Google Scholar 

  110. Sikström, S. and Söderlund, G., Stimulus-dependent dopamine release in attention-deficit/hyperactivity disorder, Psychol. Rev., 2007, vol. 114, p. 1047.

    PubMed  Article  Google Scholar 

  111. Soderlund, G., Sikstrom, S., and Smart, A., Listen to the noise: noise is beneficial for cognitive performance in ADHD, J. Child Psychol. Psychiatry, 2007, vol. 48, no. 8, p. 840847.

    Article  Google Scholar 

  112. Zueva, M.V., Dynamic fractal flickering as a tool in research of non-linear dynamics of the evoked activity of a visual system and the possible basis for new diagnostics and treatment of neurodegenerative diseases of the retina and brain, World Appl. Sci. J., 2013, vol. 427, p. 462.

    Google Scholar 

  113. Hove, M.J., Suzuki, K., Uchitomi, H., et al., Interactive rhythmic auditory stimulation reinstates natural 1/f timing in gait of Parkinson’s patients, PLoS One, 2012, vol. 7, no. 3, p. e32600.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  114. Sejdic, E., Fu, Y., Pak, A., et al., The effects of rhythmic sensory cues on the temporal dynamics of human gait, PLoS One, 2012, vol. 7, no. 8, p. e43104.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  115. Hunt, N., McGrath, D., and Stergiou, N., The influence of auditory-motor coupling on fractal dynamics in human gait, Sci. Rep., 2014, vol. 4, p. 5879.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  116. Rhea, C.K., Kiefer, A.W., Wittstein, M.W., et al., Fractal gait patterns are retained after entrainment to a fractal stimulus, PLoS One, 2014, vol. 9, no. 9, p. e106755.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  117. Manjarrez, E., Rojas-Piloni, J., Méndez, I., et al., Internal stochastic resonance in the coherence between spinal and cortical neuronal ensembles in the cat, Neurosci. Lett., 2002, vol. 326, p. 93.

    PubMed  Article  CAS  Google Scholar 

  118. Hummel, F. and Gerloff, C., Larger interregional synchrony is associated with greater behavioral success in a complex sensory integration task in humans, Cereb. Cortex, 2005, vol. 15, p. 670.

    PubMed  Article  Google Scholar 

  119. Schoffelen, J.M., Oostenveld, R., and Fries, P., Neuronal coherence as a mechanism of effective corticospinal interaction, Science, 2005, vol. 308, p. 111.

    PubMed  Article  CAS  Google Scholar 

  120. Boccaletti, S., Kurths, J., Osipov, G., Valladares, D.L., and Zhou, C.S., The synchronization of chaotic systems, Phys. Rep., 2002, vol. 366, nos. 1–2, p. 1.

    Article  CAS  Google Scholar 

  121. Arenas, A., Díaz-Guilera, A., Kurths, J., Moreno, Y., and Zhou, C., Synchronization in complex networks, Phys. Rep., 2008, vol. 469, no. 3, p. 93.

    Article  Google Scholar 

  122. Bigerelle, M. and Iost, A., Fractal dimension and classification of music, Chaos, Solitons Fractals, 2000, vol. 11, no. 14, p. 2179.

    Article  Google Scholar 

  123. Hazard, C., Kimport, C., and Johnson, D., Fractal music, Research project, 1998-1999. http://www.tursiops. cc/fm.

    Google Scholar 

  124. Pyankova, S.D., Fractal analysis in psychology: perception of self-similar objects, Psikhol. Issled., 2016, vol. 9, no. 46, p. 12. http://psystudy.ru/ index.php/eng/v9n46e/1278-pyankova46.html.

    Google Scholar 

  125. Särkämö, T., Ripollés, P., Versäläinen, H., et al., Structural changes induced by daily music listening in the recovering brain after middle cerebral artery stroke: a voxel-based morphometry study, Front. Hum. Neurosci., 2014, vol. 8, art. ID 301. doi 10.3389/ fnhum.2014.00245

  126. Jaušovec, N., Jaušovec, K., and Gerlic, I., The influence of Mozart’s music on brain activity in the process of learning, Clin. Neurophysiol., 2006, vol. 117, no. 12, p. 2703. doi 10.1016/j.clinph.2006.08.010

    PubMed  Article  Google Scholar 

  127. Nozaradan, S., Exploring how musical rhythm entrains brain activity with electroencephalogram frequency- tagging, Philos. Trans. R. Soc., B, 2014, vol. 369, p. 20130393.

    Article  Google Scholar 

  128. Hsü, K.J. and Hsü, A., Self-similarity of the 1/f Noise called music, Proc. Natl. Acad. Sci. U.S.A., 1991, vol. 88, p. 3507.

    PubMed  PubMed Central  Article  Google Scholar 

  129. Jenkins, J.S., The Mozart effect, J. R. Soc. Med., 2001, vol. 94, no. 4, p. 170.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  130. Strait, D.L., Parbery-Clark, A., Hittner, E., and Kraus, N., Musical training during early childhood enhances the neural encoding of speech in noise, Brain Lang., 2012, vol. 123, p. 191.

    PubMed  PubMed Central  Article  Google Scholar 

  131. Kraus, N. and White-Schwoch, T., Music training: lifelong investment to protect the brain from aging and hearing loss, Acoust. Austral., 2014, vol. 42, no. 2, p. 117.

    Google Scholar 

  132. Herholz, S.C. and Zatorre, R.J., Musical training as a framework for brain plasticity: behavior, function, and structure, Neuron, 2012, vol. 76, p. 486.

    PubMed  Article  CAS  Google Scholar 

  133. Namazi, H., Kulish, V., and Akrami, A., The analysis of the influence of fractal structure of stimuli on fractal dynamics in fixational eye movements and EEG signal, Sci. Res., 2016, vol. 6, p. 26639.

    CAS  Google Scholar 

  134. Zueva, M., Semenova, N., Tsapenko, I., et al., Fractal photobiomodulation of evoked electrical potentials of the rabbit’s retina: the first experience of a new technology application, VIII Rossiiskii obshchenatsional’nyi oftal’mologicheskii forum (VIII Russian National Ophthalmic Forum), Moscow, 2015, p. 50.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Moscow Helmholtz Research Institute of Eye Diseases, Moscow, Russia

    M. V. Zueva

Authors
  1. M. V. Zueva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. V. Zueva.

Additional information

Published in Russian in Fiziologiya Cheloveka, 2018, Vol. 44, No. 3, pp. 62–73.

The article was translated by the authors.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zueva, M.V. Technologies of Nonlinear Stimulation: Role in the Treatment of Diseases of the Brain and the Potential Applications in Healthy Individuals. Hum Physiol 44, 289–299 (2018). https://doi.org/10.1134/S0362119718030180

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0362119718030180

Keywords

  • Nonlinear stimulation
  • dynamics of the brain activity
  • neurological disorders
  • neurorehabilitation
  • enhancement of cognitive performance