Volume- 4
Issue- 2
Year- 2016
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Galina V. Portnova , Mikhail S. Atanov
The aim of this study was to present new methods to explore the EEG dynamic changes during human development and aging and to assess its advantages. The electroencephalogram (EEG) was recorded from 19 scalp locations from 246 healthy subjects ranging from 3 to 75 years old. All participants were divided in to six groups: preschool childhood, middle childhood, adolescence, early adulthood, middle adulthood, late adulthood. We recorded EEG with closed eyes, open eyes and during fingernails scratching auditory stimulation. The comparative analysis included spectral analysis, peak alpha frequency, correlation dimension D2 and stability of rhythms. We found significant age differences in 6 age groups using the described methods. Moreover, we've found the edge between adolescence and adulthood using narrowband D2 at alpha-rhythm frequencies. Unpleasant auditory stimulation proved to be more sensitive to age differences in comparison with resting states. Our results support previous findings describing aging and developmental EEG changes, but also provide qualitatively new results.
[1] Thatcher, R.W , North, D.M. and Biver, C. Human Brain Mapping. (2007). Development of cortical connections as measured by EEG coherence and phase delays. Hum Brain Mapp, 29(12), 1400-1415.
[2] Petersen, I., & Eeg-Olofsson, O. (1971). The development of the electroencephalogram in normal children from the age of 1 through 15 years. Neuropaediatrie, 2, 247-304.
[3] Blume, W.T. (1982). Atlas of Pediatric Encephalography, Raven Press, New York.
[4] Sleimen-Malkoun, R., Temprado, J.J., Hong, S.L. (2014). Aging induced loss of complexity and dedifferentiation: consequences for coordination dynamics within and between brain, muscular and behavioral levels. Front Aging Neurosci, 6 (140). doi: 10.3389/fnagi.2014.00140
[5] Kales, A., Kales, J.D. (1984). Evaluation and treatment of insomnia. New York: Oxford University Press, London, 324.
[6] Portnova, G.V., Gladun, K.V., Ivanitskii, A.M. (2014). The EEG Analysis of Auditory Emotional Stimuli Perception in TBI Patients with Different SCG Score. Open Journal of Modern Neurosurgery, 4, 81-96.
[7] Olofsson JK, Nordin S, Sequeira H, Polich J. (2008). Affective picture processing: an integrative review of ERP findings. Biol Psychol, 77(3), 247-265.
[8] Ito, T.A., Larsen, J.T., Smith, N.K., Cacioppo, J.T. (1998). Negative information weighs more heavily on the brain: the negativity bias in evaluative categorizations. J Pers Soc Psychol, 75(4), 887-900.
[9] Isaacowitz, D. M., Allard, E. S., Murphy, N. A., & Schlangel, M. (2009). The Time Course of Age-Related Preferences Toward Positive and Negative Stimuli. J Gerontol B Psychol Sci Soc Sci, 64(2), 188-192.
[10] Pierce, T.W., Watson, T.D., King, J.S., Kelly, S.P., Pribram, K.H. Brain Topogr. (2003). Age differences in factor analysis of EEG. Brain Topogr, 16(1), 19-27.
[11] Portnova, G.V., Gladun, K.V., Sharova, E.V., Ivanitsky, A.M. (2013). Changes of EEG power spectrum in response to the emotional auditory stimuli in patients in acute and recovery stages of TBI. Zhurnal VyssheÄ NervnoÄ Deiatelnosti Imeni I P Pavlova, 63(6), 753-765.
[12] Delorme, A., Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of singletrial EEG dynamics including independent component analysis. J. Neurosci. Methods, 134, 9–21.
[13] Higuchi, T. (1988). Approach to an irregular time series on the basis of the fractal theory. Physica D: Nonlinear Phenomena, 31(2), 277-283.
[14] Ktonas, P. Y., & Papp, N. (1980). Instantaneous envelope and phase extraction from real signals: theory, implementation, and an application to EEG analysis.Signal Processing, 2(4), 373-385.
[15] Purdon, P.L., Pavone, K.J., Akeju, O., Smith, A.C., Sampson, A.L., Lee, J., Zhou, D.W., Solt, K., Brown, E.N. (2015). The Ageing Brain: Age-dependent changes in theelectroencephalogram. Br J Anaesth, 115 (1), i46-i57.
[16] Angelakis, E., Lubar, J.F., Stathopoulou, S. (2004). Electroencephalographic peak alpha frequency correlates of cognitive traits. Neurosci Lett, 371(1), 60-63.
[17] Angelakis, E., Lubar, J.F., Stathopoulou, S., Kounios, J. (2004). Peak alpha frequency: an electroencephalographic measure of cognitive preparedness. Electroencephalographic peak alpha frequency correlates of cognitive traits. Clin Neurophysiol, 115(4), 887-897.
[18] Klimesch, W. (1997 EEG-alpha rhythms and memory processes. Int J Psychophysiol, 26, 319–340.
[19] Posthuma, D., Neale, M.C., Boomsma, D.I., de Geus E.J.C. (2001). Are smarter brains running faster? Heritability of alpha peak frequency, IQ, and their interrelation. Behav Genet, 31(6), 567–579.
[20] Duffy, F.H., McAnulty, G.B. (1993). The pattern of age-related differences in electrophysiological activity of healthy males and females. Neurobiology of Aging, 14(1), 73–84.
[21] Dockreea, P.M., Kellyb, S.P., Rochea, R.A.P., Hoganc, M.J., Reillyb, R.B., Robertsona, I.H. (2004). Behavioural and physiological impairments of sustained attention after traumatic brain injury. Cognitive Brain Research, 20, 403– 414
Institute of Higher Nervous Activity and Neurophysiology of RAS (IHNA&NPh RAS), 5A Butlerova St., Moscow 117485, Russia.
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