Novosibirsk State Pedagogical University Bulletin, 2016, vol. 6, no. 1, pp. 141–148
UDC: 
796+612.825.26

The influence of physical activity status on the theta rhythm distribution

Lalaeva G. S. 1 (Tomsk, Russian Federation), Zakharova A. N. 1 (Tomsk, Russian Federation), Kabachkova A. V. 1 (Tomsk, Russian Federation)
1 National Research Tomsk State University,Tomsk, Russian Federation
Abstract: 

The aim of this research is to analyze the theta rhythm distribution in volunteers with a different physical activity status. We examine forty healthy males divided into four groups ac-cording to degree with physical activity (low, moderate, high dynamic and high static). The electroencephalographic data are recorded during both eyes-closed and eyes-open resting conditions. Resting state recordings had no differences in terms of the amplitude of the theta rhythm in representatives of groups. In their turn, the changes during eyes-closed and eyes-open conditions are more pronounced in the groups with high physical activity. For example, theta activity in high dynamic group decreases in the temporal region during eyes-open and in-creases in eyes-closed conditions. This rhythm in high static group increases in the occipital re-gion during eyes-open and reduces in eyes-closed conditions. We assume that a physical activi-ty status influences the formation of theta waves.

Keywords: 

rhythmic activity, theta activity, theta band, hemispheric asymmetry, eyes-closed, eyes-open, dynamic exercise, static exercise, physical inactivity

https://www.scopus.com/record/display.uri?eid=2-s2.0-85017581509&origin=...

The influence of physical activity status on the theta rhythm distribution

For citation:
Lalaeva G. S., Zakharova A. N., Kabachkova A. V. The influence of physical activity status on the theta rhythm distribution. Novosibirsk State Pedagogical University Bulletin, 2016, vol. 6, no. 1, pp. 141–148. DOI: http://dx.doi.org/10.15293/2226-3365.1601.13
References: 
  1. Adrianov O.S. Brain integrative activity: principles of organization. Moscow: Medicine Publ., 1976, 280 p. (In Russian)
  2. Zacharova A. N., Kabachkova A. V., Lalaeva G. S., Kapilevich L. V. Distribution of EEG rhythms in athletes of cyclic and power sport. Modern Problems of System Regulation of Physiological Functions. IV International Interdisciplinary Conference (17–18 September 2015). Moscow, 2015, pp. 254–258. (In Russian) DOI: 10.12737/12352
  3. Kabachkova A. V., Fomchenko V. V., Frolova Yu. S. Student physical activity. Tomsk State University Journal. 2015, no. 392, pp. 175–178. (In Russian) DOI: 10.17223/15617793/392/29
  4. Trushina D. A., Vedyasova O. A., Paramonova M. A. The spatial distribution of EEG rhythms in right-hander students during the exam. Samara State University Journal. 2014, no. 3 (114),
    pp. 202–212. (In Russian)
  5. Cherapkina L. P., Tristan V. G. Features of the brain activity in athletes. South Ural State University Journal. Series: Education, Health, Physical Education. 2011, no. 39 (256), pp. 27–31. (In Russian)
  6. Babiloni C., Del Percio C., Vecchio F. et al. Alpha, beta and gamma electrocorticographic rhythms in somatosensory, motor, premotor and prefrontal cortical areas differ in movement execution and observation in humans. Clin. Neurophysiol. 2016, vol. 127, no. 1, pp. 641–654.
  7. Babiloni C., Marzano N., Iacoboni M. et al. Resting state cortical rhythms in athletes: a high-resolution EEG study. Brain Res. Bull. 2010, vol. 81, no. 1, pp. 149–156.
  8. Brokaw K., Tishler W., Manceor S. et al. Resting State EEG Correlates of Memory Consolidation. Neurobiol. Learn. Mem. 2016, vol. 130, pp. 17–25.
  9. Del Percio C., Infarinato F., Marzano N. et al. Reactivity of alpha rhythms to eyes opening is lower in athletes than non-athletes: a high-resolution EEG study.  Int. J. Psychophysiol. 2011, vol. 82,
    no. 3, pp. 240–247.
  10. Hall E. E., Ekkekakis P., Petruzzello S. J. Regional brain activity and strenuous exercise: predicting affective responses using EEG asymmetry. Biol. Psychol. 2007, vol. 75, no. 2, pp. 194–200.
  11. Krivoschekov S. G., Lushnikov O. N. Sport addiction EEG indices: Perspectives for neurofeedback treatment. Int. J. Psychophysiol. 2012, vol. 85, no. 3, p. 350.
  12. MacDonald D. B. International Encyclopedia of the Social & Behavioral Sciences. Elsevier, 2015.
  13. Millett D., Coutin-Churchman P., Stern J. M. Brain Mapping. Elsevier, 2015.
  14. Miraglia F., Vecchio F., Bramanti P., Rossini P. M. EEG characteristics in ‘eyes-open’ versus ‘eyes-closed’ conditions: Small-world network architecture in healthy aging and age-related brain degeneration. Clin. Neurophysiol. 2015, vol. 127, no. 2, pp. 1261–1268.
  15. Petsche H, Stumpf C, Gogolák G. The significance of the rabbit's septum as a relay station between the midbrain and the hippocampus. I. The control of hippocampus arousal activity by the septum cells. Electroencephalography and Clinical Neurophysiology. 1962, no. 14, pp. 202–211. DOI: 10.1016/0013-4694(62)90030-5. 
  16. Schneider S., Brümmer V., Abel T. et al. Changes in brain cortical activity measured by EEG are related to individual exercise preferences. Physiol. Behav. 2009, vol. 98, no. 4, pp. 447–452.
  17. Vecchio F., Miraglia F., Quaranta D. et al. Cortical connectivity and memory performance in cognitive decline: a study via graph theory from EEG data. Neuroscience. 2015, vol. 316, pp. 143–150.
Date of the publication 29.02.2016