Sleep EEG-pattern alteration as a specific marker of neuroplasticity in intellectually gifted schoolchildren

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Abstract

BACKGROUND: At school, children constantly acquire new knowledge, which provokes changes in the brain structures, especially during intensive learning. Neuroplasticity enables a person to realize his potential, to show giftedness. However, data on electroencephalographic (EEG) features during the sleep in gifted schoolchildren are insufficient and contradictory.

AIM: To study the features of EEG sleep patterns according to neurophysiological research data in intellectually gifted schoolchildren and their correlation with verbal and non-verbal mental abilities.

MATERIALS AND METHODS: 48 lyceum students aged 14-15 years were examined. All participants were performed the Wechsler-Intelligence test (children’s version) to determine them intelligence quotient (IQ). Two groups of adolescents were identified: the main group (n = 20) — with a high IQ (137.0±12.7 points), the control group (n = 28) — with an average IQ (110.9±10.4 points). Neurophysiological examination during the sleep was performed using an ambulatory wireless system for registration of electroencephalograms and polysomnograms “Neuron-Spectrum-AM” (“Neurosoft”, Ivanovo, Russian Federation,). The identification and analysis of EEG sleep patterns: cyclic alternating patterns and sleep spindles were carried out according to standard methods. Differences between groups were considered statistically significant at p < 0.05.

RESULTS: In gifted schoolchildren, significant changes were revealed as an increase in the time and the rate of cyclic alternating patterns with a predominance of A1 subtype (p = 0.0001) and a decrease in the proportion of А2-A3 subtypes, as well as an increase of sleep spindles density (p = 0.01), which significantly correlated with the Wechsler-Intelligence Test’s scores. So, percentage of A1 subtype had correlation with the index of thinking flexibility, while percentage of A2 subtype correlated with general and non-verbal intelligence, non-verbal visual-spatial abilities, the index of working memory) and the index of verbal perception, as well as the sleep spindles index and sleep spindles frequency peak correlated with the index of verbal perception, the index of working memory, the index of information processing speed and general intelligence.

CONCLUSIONS: Thus, we have demonstrated significant changes in the sleep microstructure and different correlations depending on the IQ values in schoolchildren, which was not previously covered in the literature. Cyclic alternating patterns and sleep spindles are recommended to be considered as physiological indicators of intelligence and academic performance, as well as specific markers of neuroplasticity in the intensification of learning.

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About the authors

Olga N. Berdina

Scientific Centre for Family Health and Human Reproduction Problems

Author for correspondence.
Email: goodnight_84@mail.ru
ORCID iD: 0000-0003-0930-6543
SPIN-code: 4280-7010

MD, Cand. Sci. (Med.), Leading Researcher of the Laboratory of Somnology and Neurophysiology

Russian Federation, 16, Timiryazev Str., Irkutsk, Irkutsk region, 664003

Irina M. Madaeva

Scientific Centre for Family Health and Human Reproduction Problems

Email: nightchild@mail.ru
ORCID iD: 0000-0003-3423-7260
SPIN-code: 9869-7793

MD, Dr. Sci. (Med.), Head Researcher of the Laboratory of Somnology and Neurophysiology

Russian Federation, 16, Timiryazev Str., Irkutsk, Irkutsk region, 664003

Vladimir M. Polyakov

Scientific Centre for Family Health and Human Reproduction Problems

Email: polyakov@mail.ru
ORCID iD: 0000-0001-6243-9391
SPIN-code: 2704-7719

Dr. Sci. (Biol.), Leading Researcher of the Laboratory of Psychoneurosomatic Pathology of Childhood

Russian Federation, 16, Timiryazev Str., Irkutsk, Irkutsk region, 664003

Lyubov V. Rychkova

Scientific Centre for Family Health and Human Reproduction Problems

Email: iphr@sbamsr.irk.ru
ORCID iD: 0000-0001-5292-0907
SPIN-code: 1369-6575

MD, Dr. Sci. (Med.), Professor, RAS Corresponding Member, director

Russian Federation, 16, Timiryazev Str., Irkutsk, Irkutsk region, 664003

References

  1. Madaeva I, Berdina O, Rychkova L, Bugun O. Sleep spindle characteristics in overweight adolescents with obstructive sleep apnea syndrome. Sleep Biol Rhythms. 2017;15(3):251–257. doi: 10.1007/s41105-017-0104-z
  2. Raven F, van der Zee EA, Meerlo P, Havekes R. The role of sleep in regulating structural plasticity and synaptic strength: Implications for memory and cognitive function. Sleep Med Rev. 2018;39:3–11. doi: 10.1016/j.smrv.2017.05.002
  3. Hossain MS, Fujino T. Plasmalogens enhance spatial memory by increasing synaptic plasticity. Medical Academic Journal. 2019;19(1S):15. doi: 10.17816/MAJ191S115
  4. Kholodnaya MA. Psikhologiya intellekta. Paradoksy issledovaniya. Saint Petersburg: Piter; 2002. (In Russ.)
  5. Thatcher RW, North D, Biver C. EEG and intelligence: Relations between EEG coherence, EEG phase delay and power. Clin Neurophysiol. 2005;116(9):2129–2141. doi: 10.1016/j.clinph.2005.04.026
  6. Berdina ON, Rychkova LV, Madaeva IM. Characteristics of sleep structure in schoolchildren with high intellectual abilities. S.S. Korsakov Journal of Neurology and Psychiatry. 2018;118(7):78–81. (In Russ.). doi: 10.17116/jnevro20181187178
  7. Geiger A, Huber R, Kurth S, et al. The sleep EEG as a marker of intellectual ability in school age children. Sleep. 2011;34(2):181–189. doi: 10.1093/sleep/34.2.181
  8. Gorgoni M, D’Atri A, Scarpelli S, et al. Sleep electroencephalography and brain maturation: developmental trajectories and the relation with cognitive functioning. Sleep Med. 2020;66:33–50. doi: 10.1016/j.sleep.2019.06.025
  9. Bruni O, Kohler M, Novelli L, et al. The role of NREM sleep instability in child cognitive performance. Sleep. 2012;35(5):649–656. doi: 10.5665/sleep.1824
  10. Seibt J, Richard CJ, Sigl-Glöckner J, et al. Cortical dendritic activity correlates with spindle-rich oscillations during sleep in rodents. Nat Commun. 2017;8(1):684. doi: 10.1038/s41467-017-00735-w
  11. Hahn M, Joechner A-K, Roell J, et al. Developmental changes of sleep spindles and their impact on sleep-dependent memory consolidation and general cognitive abilities: A longitudinal approach. Dev Sci. 2019;22(1):e12706. doi: 10.1111/desc.12706
  12. Parrino L, Ferri R, Bruni O, Terzano MG. Cyclic alternating pattern (CAP): The marker of sleep instability. Sleep Med Rev. 2012;16(1):27–45. doi: 10.1016/j.smrv.2011.02.003
  13. Burlachuk LF. Psikhodiagnostika: uchebnik dlya vuzov. Saint Petersburg: Piter; 2006. (In Russ.)
  14. Styck KM, Watkins MW. Structural validity of the WISC-IV for students with learning disabilities. J Learn Disabil. 2016;49(2):216–224. doi: 10.1177/0022219414539565
  15. Strogova SE. Evaluation of memory and attention by the subscales of the Wechsler test (children’s version) for mental pathology. Psychological Diagnostics. 2017. Vol. 14, No. 2. P. 22–30. (In Russ.)
  16. Vladimirova SG. David Wechsler’s scale: present and future in solving the problem of intelligence measurement. Yaroslavl Pedagogical Bulletin. 2016;2:122–126. (In Russ.)
  17. Klem GH, Lüders HO, Jasper HH, Elger C. The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. Electroencephalogr Clin Neurophysiol Suppl. 1999;52:3–6.
  18. Berry RB, Brooks R, Gamaldo CE, et al. The AASM Manual for the scoring of sleep and associated events: rules, terminology and technical specifications. Version 2.2. Darien, Illinois: American Academy of Sleep Medicine; 2015.
  19. Terzano MG, Parrino L, Smerieri AR, et al. Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. Sleep Med. 2001;2(6):537–553. doi: 10.1016/s1389-9457(01)00149-6
  20. Bruni O, Novelli L, Finotti E, et al. All-night EEG power spectral analysis of the cyclic alternating pattern at different ages. Clin Neurophysiol. 2009;120(2):248–256. doi: 10.1016/j.clinph.2008.11.001
  21. Brockmann P, Damiani F, Pincheira E, et al. Sleep spindle activity in children with obstructive sleep apnea as a marker of neurocognitive performance: a pilot study. Eur J Paediatr Neurol. 2018;22(3):434–439. doi: 10.1016/j.ejpn.2018.02.003
  22. Prokhorova IS, Solov’eva IV. Investigation of the particular features of social-psychological adaptation of intellectual public students. The World of Science, Culture and Education. 2017;(3(64)):264–266. (In Russ.)
  23. Kolesnikova L, Dzyatkovskaya E, Rychkova L, Polyakov V. New approaches to identifying children of psychosomatic disorders risk group. Procedia – Social and Behavioral Sciences. 2015;214:882–889. doi: 10.1016/j.sbspro.2015.11.745
  24. Kac EB. Psihofiziologicheskie i psihologicheskie osobennosti uchashhihsja s priznakami odarennosti [dissertation]. Rostov-na-Donu; 2010. (In Russ.)
  25. Social determinants of health and well-being among young people. Health Behavior in School-aged Children (HBSC) study: international report from the 2009/2010 survey [Internet]. WHO. Health Policy for Children and Adolescents. No. 6. Available from: https://www.euro.who.int/__data/assets/pdf_file/0003/163857/Social-determinants-of-health-and-well-being-among-young-people.pdf. Accessed: Nov 16, 2021.

Supplementary files

Supplementary Files
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1. Fig. 1. Examples of visual identification of “sleep spindles” (SS) on the sleep EEG in schoolchildren with high (top) and average (bottom) levels of intelligence. Increased “sleep spindles” index in gifted schoolchildren was demonstrated (2 event/min versus 1 event/min in “ordinary” adolescents)

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2. Fig. 2. Examples of visual determination of the cyclic alternation pattern on the sleep EEG in schoolchildren with high (top) and average (lower track) levels of intelligence. 1 — slow wave activity; 2 — fast wave activity. Gifted adolescents have a cyclic alternation pattern A1 subtype (83% slow wave activity and 17% fast wave activity). The “ordinary” schoolchildren have a cyclic alternation pattern A2 subtype (60% slow wave activity and 40% fast wave activity)

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Copyright (c) 2021 Berdina O.N., Madaeva I.M., Polyakov V.M., Rychkova L.V.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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