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EEG Analysis of the Functional State of the Brain in 5- to 7-Years-Old Children

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Abstract

The study is aimed at assessing individual and age-related features of the functional state of various parts of the brain and the patterns of their ontogenetic changes based on the structural analysis of resting-state electroencephalogram (EEG) patterns in 5–7-years-old children. The study involved 266 children, who were divided into different age groups: Group 1—5-years-old (mean age 4.98 ± 0.33), Group 2—6-years-old (mean age 6.03 ± 0.35), and Group 3—7-years-old (mean age 6.85 ± 0.22). Alpha-rhythm parameters recorded mainly in the occipital areas may serve as an indicator for the functional maturation of the brain. Significant age-related changes in the alpha-rhythm parameters have been revealed. The presence of a regular alpha-rhythm with a frequency of 8–10 Hz increases from 5 to 7 years of age. The occurrence of the alpha-rhythm of low frequency significantly decreases by the age of 7 years, and the occurrence of the polyrhythmic alpha-rhythm—by the age of 6 years. These changes are caused both by complications of the structural and functional organization of the cerebral cortex at the cellular level, which occur throughout the studied age period, and the improvement of its relationships with subcortical structures. A decrease in the occurrence of high-amplitude alpha-band electrical activity (EA) with signs of hypersynchrony in the caudal regions may indicate the maturation of the system of nonspecific activation of the brainstem reticular formation from 5 to 7 years of age. Age dynamics is also manifested in a significant decrease in the EEG occurrence of theta-band EA, and in its zonal distribution in 5–7-years-old children aged. Such changes specify the process of progressive formation of functional connections between individual areas of the cortex, as well as the cortex and subcortical structures, in particular thalamo-cortical ones. The occurrence of alpha-band EA (less than 5.0%) and beta-band EA (about 13.0%) arranged topographically in the anterior cortex did not differ significantly with age. However, generalized EEG activity in the form of different frequency band waves, which characterizes the functional state of predominantly hypothalamic structures, occurs reliably more often in 7-years-old children rather than in 5-year-old children. Such dynamics is presumably associated with an increased reactivity of the hypothalamic-pituitary system in response to adaptive stresses caused by the transition to systematic learning and can be considered as a distinctive feature of this age period. Due to great restructuring of the brain functioning, all its structures become especially sensitive to high intellectual and emotional stress, which is characteristic of preschool children nowadays. The novelty of this study is highlighted by the identification of patterns, structure and nature of EA changes in 5- to 7-year-old normotypical children’s brain to assess the functional state of the cortex and regulatory brain systems. The research results based on a large sample of children, growing up in modern social and cultural conditions, would provide guidance for the formation of age standards.

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REFERENCES

  1. Long X, Benischek A, Dewey D, Lebel C (2017) Age-related functional brain changes in young children. NeuroImage 155: 322–330. https://doi.org/10.1016/j.neuroimage.2017.04.059

    Article  PubMed  Google Scholar 

  2. Steiner L, Federspiel A, Slavova N, Wiest R, Grunt S, Steinlin M, Everts R (2020) Functional topography of the thalamo-cortical system during development and its relation to cognition. Neuroimage 223: 117361. https://doi.org/10.1016/j.neuroimage.2020.117361

    Article  PubMed  Google Scholar 

  3. Whedon M, Perry NB, Bell MA (2020) Relations between frontal EEG maturation and inhibitory control in preschool in the prediction of children’s early academic skills. Brain and Cognition 146: 105636. https://doi.org/10.1016/j.bandc.2020.105636

    Article  PubMed  PubMed Central  Google Scholar 

  4. Perone S, Palanisamy J, Carlson SM (2018) Age-related change in brain rhythms from early to middle childhood: Links to executive function. Dev Sci 21(6): e12691. https://doi.org/10.1111/desc.12691

    Article  PubMed  Google Scholar 

  5. Machinskaya RI, Lukashevich IP, Fishman MN (1997) Dynamics of brain electrical activity in 5- to 8-year-old normal children and children with learning difficulties. Human Physiol 23(5): 517–522.

    Google Scholar 

  6. Paulino C, Flores A, Gomez C (2011) Developmental Changes in the EEG Rhythms of Children and Young Adults Analyzed by Means of Correlational, Brain Topography and Principal Component Analysis. J Psychophysiol 25: 143–158. https://doi.org/10.1027/0269-8803/a000052

    Article  Google Scholar 

  7. Miskovic V, Ma X, Chou CA, Fan M, Owens M, Sayama H, Gibb BE (2015) Developmental changes in spontaneous electrocortical activity and network organization from early to late childhood. Neuroimage 118: 237–247. https://doi.org/10.1016/j.neuroimage.2015.06.013

    Article  PubMed  Google Scholar 

  8. Cuevas K, Bell MA (2022) EEG frequency development across infancy and childhood. In: Gable PA, Miller MW, Bernat EM (eds) The Oxford handbook of human EEG frequency. Oxford. https://doi.org/10.1093/oxfordhb/9780192898340.013.13

    Chapter  Google Scholar 

  9. Feige B, Scheffler K, Esposito F, Di Salle F, Hennig J, Seifritz E (2005) Cortical and subcortical correlates of electroencephalographic alpha rhythm modulation. J Neurophysiol 93(5): 2864–2872. https://doi.org/10.1152/jn.00721.2004

    Article  PubMed  Google Scholar 

  10. Eeg-Olofsson O (1970) The development of the electroencephalogram in normal children and adolescents from the age of 1 through 21 years. Acta Paediatr Scand Suppl 208(Suppl 208): 1.

    Google Scholar 

  11. Cellier D, Riddle J, Petersen I, Hwang K (2021) The development of theta and alpha neural oscillations from ages 3 to 24 years. Dev Cogn Neurosci 50: 100969. https://doi.org/10.1016/j.dcn.2021.100969

    Article  PubMed  PubMed Central  Google Scholar 

  12. Clarke AR, Barry RJ, McCarthy R, Selikowitz M (2001) Age and sex effects in the EEG: development of the normal child. Clin Neurophysiol 112(5): 806–814. https://doi.org/10.1016/s1388-2457(01)00488

    Article  CAS  PubMed  Google Scholar 

  13. Marshall PJ, Bar-Haim Y, Fox NA (2002) Development of the EEG from 5 months to 4 years of age. Clin Neurophysiol 113(8): 1199–1208. https://doi.org/10.1016/s1388-2457(02)00163-3

    Article  PubMed  Google Scholar 

  14. Kozhushko NY, Ponomarev VA, Matveev YK, Evdokimov SA (2011) Developmental features of the formation of the brain’s bioelectrical activity in children with remote consequences of a perinatal lesion of the CNS: II. EEG typology in health and mental disorders. Human Physiol 37(3): 271–277. https://doi.org/10.1134/S0362119711020095

    Article  Google Scholar 

  15. Ucles P, Lorente S, Rosa F (1996) Neurophysiological methods testing the psychoneural basis of attention deficit hyperactivity disorder. Childs Nerv Syst 12(4): 215–217. https://doi.org/10.1007/BF00301253

    Article  CAS  PubMed  Google Scholar 

  16. Hofstee M, Huijding J, Cuevas K, Deković M (2022) Self-regulation and frontal EEG alpha activity during infancy and early childhood: A multilevel meta-analysis. Dev Sci 25(6): e13298. https://doi.org/10.1111/desc.13298

    Article  PubMed  Google Scholar 

  17. Clarke AR, Barry RJ, Dupuy FE, McCarthy R, Selikowitz M, Johnstone SJ (2013) Excess beta activity in the EEG of children with attention-deficit/hyperactivity disorder: a disorder of arousal? Int J Psychophysiol 89(3): 314-319. https://doi.org/10.1016/j.ijpsycho.2013.04.009

    Article  PubMed  Google Scholar 

  18. Lukashevich IP, Machinskaya RI, Fishman MN (1999) The EEG-expert automatic diagnostic system. Biomed Eng 33(6): 302–307. https://doi.org/10.1007/BF02385390

    Article  Google Scholar 

  19. Semenova OA, Machinskaya RI (2016) Assessing Regulatory Components of the Cognitive Performance in Children Aged 5-10 with EEG Patterns of the Limbic System Non-Optimal Functioning. Zh Vyssh Nerv Deiat Im IP Pavlova 66(4): 458–469. https://doi.org/10.7868/S0044467716040109

    Article  CAS  Google Scholar 

  20. Lukashevich IP, Machinskaja RI, Shklovskij VM (2004) Features of autonomic regulation and the nature of seizures in children with stuttering. Human Physiol 30(4): 50–53. (In Russ).

    Article  CAS  Google Scholar 

  21. Semenova OA, Machinskaya RI (2015) The influence of the functional state of brain regulatory systems on the efficiency of voluntary regulation of cognitive activity in children: ii. neuropsychological and eeg analysis of brain regulatory functions in 10–12-year-old children with learning difficulties. Human Physiol 41(5): 478–486. https://doi.org/10.1134/S0362119715050126

    Article  Google Scholar 

  22. Zhirmunskaya EA (1991) Clinical Electroencephalography. Literature Review and Prospects for Using the Method M. “MEJBI”. (In Russ).

    Google Scholar 

  23. Goldman-Rakic PS, Porrino LJ (1985) The primate mediodorsal (MD) nucleus and its projection to the frontal lobe. J Comp Neurol 242(4): 535–560. https://doi.org/10.1002/cne.902420406

    Article  CAS  PubMed  Google Scholar 

  24. Seeber M, Cantonas LM, Hoevels M, Sesia T, Visser-Vandewalle V, Michel CM (2019) Subcortical electrophysiological activity is detectable with high-density EEG source imaging. Nat Commun 10(1): 753. https://doi.org/10.1038/s41467-019-08725-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Gatev P, Wichmann T (2009) Interactions between cortical rhythms and spiking activity of single basal ganglia neurons in the normal and parkinsonian state. Cereb Cortex 19(6): 1330–1344. https://doi.org/10.1093/cercor/bhn171

    Article  PubMed  Google Scholar 

  26. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57 (1): 289–300. https://doi.org/10.2307/2346101

    Article  Google Scholar 

  27. Bezrukikh MM, Loginova ES, Partsalis EM (2015) Children with impaired cognitive development: complex assessment and Intervention. Human Physiol 41(4): 356–366. https://doi.org/10.1134/S0362119715040040

    Article  Google Scholar 

  28. Hughes JR (1994) EEG in Clinical Practice. Second edition. Butterworth-Heinemann, Boston.

    Google Scholar 

  29. Connemann BJ, Mann K, Lange-Asschenfeldt C, Ruchsow M, Schreckenberger M, Bartenstein P, Gründer G (2005) Anterior limbic alpha-like activity: a low-resolution electromagnetic tomography study with lorazepam challenge. Clin Neurophysiol 116(4): 886–894. https://doi.org/10.1016/j.clinph.2004.11.015

    Article  PubMed  Google Scholar 

  30. Boldyreva GN (2018) Atypical forms of cerebral α-activity in the case of lesions in regulatory structures of the human brain. Human Physiol 44(3): 246–256. https://doi.org/10.1134/S0362119718020032

    Article  Google Scholar 

  31. Lozano-Soldevilla D (2018) On the physiological modulation and potential mechanisms underlying parieto-occipital alpha oscillations. Front Comput Neurosci 12: 23. https://doi.org/10.3389/fncom.2018.00023

    Article  PubMed  PubMed Central  Google Scholar 

  32. Remer J, Croteau-Chonka E, Dean DC, D’Arpino S, Dirks H, Whiley D, Deoni SCL (2017) Quantifying cortical development in typically developing toddlers and young children, 1–6 years of age. Neuroimage 153: 246–261. https://doi.org/10.1016/j.neuroimage.2017.04.010

    Article  PubMed  Google Scholar 

  33. Cekhmistrenko TA, Vasileva VA, Humejko NS, Hernyh NA (2009) Structural changes of the human cerebral cortex and cerebellum in postnatal ontogenesis. In: Farber DA, Bezrukih MM (eds) Brain development and formation of cognitive activity. Publ House of the Moscow Psychol and Social Institute. Voronezh “Modek”, M. 9–63. (In Russ).

    Google Scholar 

  34. Tsekhmistrenko ТA, Chernykh NA (2013) Developmental Characteristics of The Microstructure of Layer V of the Frontal Cortex in Humans. Neurosci Behav Physiol 43: 582–586. https://doi.org/10.1007/s11055-013-9775-3

    Article  Google Scholar 

  35. Alferova VV (1990) Reflection of age-related features of the functional organization of the brain in the resting electroencephalogram. In: Farber DA, Semenova LK, Alferova VV (eds) Structural and functional organization of the developing brain. Nauka, L. 45–65. (In Russ).

    Google Scholar 

  36. Beteleva TG, Dubrovinskaya NV, Farber DA (1977) Sensory Mechanisms of the Developing Brain. Nauka, M. (In Russ).

    Google Scholar 

  37. Latash P (1968) Hypothalamus, Adaptive Activity and Electroencephalogram. Nauka, M. (In Russ).

    Google Scholar 

  38. Farber DA, Alferova VV (1972) Electroencephalogram in Children and Adolescents. Pedagogika, M. (In Russ).

    Google Scholar 

  39. Ahmadi M, Kazemi K, Kuc K, Cybulska-Klosowicz A, Zakrzewska M, Racicka-Pawlukiewicz E, Helfroush MS, Aarabi A (2020) Cortical source analysis of resting state EEG data in children with attention deficit hyperactivity disorder. Clin Neurophysiol 131(9): 2115–2130. https://doi.org/10.1016/j.clinph.2020.05.028

    Article  PubMed  Google Scholar 

  40. Brazhnik EC, Vinogradova OS, Karanov AM (1984) Regulation of the theta activity of septal neurons by cortical and brain stem structures. Zh Vyssh Nerv Deiat Im IP Pavlova 34(1): 71–80. (In Russ).

    CAS  Google Scholar 

  41. Orekhova EV, Stroganova TA, Posikera IN, Elam M (2006) EEG theta rhythm in infants and preschool children. Clin Neurophysiol 117(5): 1047–1062. https://doi.org/10.1016/j.clinph.2005.12.027

    Article  CAS  PubMed  Google Scholar 

  42. Peresleni LI, Rozhkova LA (1996) Neurophysiological Mechanisms of Prognostic Disturbances in Children with Learning Difficulties. Defektologiya 5: 15–22. (In Russ).

    Google Scholar 

  43. Kim J, Woo J, Park YG, Chae S, Jo S, Choi JW, Jun HY, Yeom YI, Park SH, Kim KH, Shin HS, Kim D (2011) Thalamic T-type Ca(2)+ channels mediate frontal lobe dysfunctions caused by a hypoxia-like damage in the prefrontal cortex. J Neurosci 31(11): 4063–4073. https://doi.org/10.1523/JNEUROSCI.4493-10.2011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Sarnthein J, Jeanmonod D (2007) High thalamocortical theta coherence in patients with Parkinson’s disease. J Neurosci 27(1): 124–131. https://doi.org/10.1523/JNEUROSCI.4493-10.2011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Melnikov ME (2021) A single phenomenon with a multitude of interpretations: eeg frontal alpha asymmetry in healthy people. Part I. Uspekhi fiziol nauk 52(3): 56–80. https://doi.org/10.31857/S0301179821030036

    Article  Google Scholar 

  46. Threadgill AH, Gable PA (2018) Resting beta activation and trait motivation: Neurophysiological markers of motivated motor-action preparation. Int J Psychophysiol 127: 46–51. https://doi.org/10.1016/j.ijpsycho.2018.03.002

    Article  PubMed  Google Scholar 

  47. Kropotov JD (2016) Functional Neuromarkers for Psychiatry. Acad Press. https://doi.org/10.1016/C2012-0-07144-X

    Book  Google Scholar 

  48. Chiang CT, Ouyang CS, Yang RC, Wu RC, Lin LC (2020) Increased Temporal Lobe Beta Activity in Boys With Attention-Deficit Hyperactivity Disorder by LORETA Analysis. Front Behav Neurosci 14: 85. https://doi.org/10.3389/fnbeh.2020.00085

    Article  PubMed  PubMed Central  Google Scholar 

  49. Rozhkova LA (2008) EEG spectral power of young schoolchildren with perinatal pathology of the cns. Human Physiol 34(1): 22–32. https://doi.org/10.1007/s10747-008-1003-0

    Article  Google Scholar 

  50. Loo SK, Hale TS, Macion J, Hanada G, McGough JJ, McCracken JT, Smalley SL (2009) Cortical activity patterns in ADHD during arousal, activation and sustained attention. Neuropsychologia 47(10): 2114–2119. https://doi.org/10.1016/j.neuropsychologia

    Article  PubMed  PubMed Central  Google Scholar 

  51. Ogawa T, Sonoda H, Ishiwa S, Goto K, Kojou M, Sawaguchi H, Wakayama K, Suzuki M (1989) Developmental characteristics of the beta waves of EEG in normal healthy children. No To Hattatsu 21(5): 424–429.

    CAS  PubMed  Google Scholar 

  52. Biau E, Kotz SA (2018) Lower Beta: A Central Coordinator of Temporal Prediction in Multimodal Speech. Front Hum Neurosci 12: 434. https://doi.org/10.3389/fnhum.2018.00434

    Article  PubMed  PubMed Central  Google Scholar 

  53. Weiss S, Mueller HM (2012) “Too Many betas do not Spoil the Broth”: The Role of Beta Brain Oscillations in Language Processing. Front Psychol 3: 201. https://doi.org/10.3389/fpsyg.2012.00201

    Article  PubMed  PubMed Central  Google Scholar 

  54. Williams D, Tijssen M, Van Bruggen G, Bosch A, Insola A, Di Lazzaro V, Mazzone P, Oliviero A, Quartarone A, Speelman H, Brown P (2002) Dopamine-dependent changes in the functional connectivity between basal ganglia and cerebral cortex in humans. Brain 125(Pt 7): 1558–1569. https://doi.org/10.1093/brain/awf156

    Article  PubMed  Google Scholar 

  55. Lofredi R, Okudzhava L, Irmen F, Brücke C, Huebl J, Krauss JK, Schneider GH, Faust K, Neumann WJ, Kühn AA (2023) Subthalamic beta bursts correlate with dopamine-dependent motor symptoms in 106 Parkinson’s patients. NPJ Parkinsons Dis 9(1): 2. https://doi.org/10.1038/s41531-022-00443-3

    Article  PubMed  PubMed Central  Google Scholar 

  56. Iskhakova L, Rappel P, Deffains M, Fonar G, Marmor O, Paz R, Israel Z, Eitan R, Bergman H (2021) Modulation of dopamine tone induces frequency shifts in cortico-basal ganglia beta oscillations. Nat Commun 12(1): 7026. https://doi.org/10.1038/s41467-021-27375-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Sharott A, Magill PJ, Harnack D, Kupsch A, Meissner W, Brown P (2005) Dopamine depletion increases the power and coherence of beta-oscillations in the cerebral cortex and subthalamic nucleus of the awake rat. Eur J Neurosci 21(5): 1413–1422. https://doi.org/10.1111/j.1460-9568.2005.03973.x

    Article  PubMed  Google Scholar 

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Funding

The present work was carried out at the expense of funds allocated for the fulfillment of the state task.

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Authors and Affiliations

  1. Institute of Developmental Physiology RAE, Moscow, Russia

    Yu. N. Komkova & M. M. Bezrukikh

  2. Penza State University, Penza, Russia

    G. A. Sugrobova

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  1. Yu. N. Komkova
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  2. G. A. Sugrobova
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Contributions

Yu.N.K.—concept of the study, editing, approval of the final version of the article, collection and processing of material, statistical processing, writing of the text, responsibility for the integrity of all parts of the article.

G.A.S.—concept and design of the study, collection and processing of material, preparation of illustrations and tables, writing of the text.

M.M.B.—Writing and editing the text of the article.

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Correspondence to Yu. N. Komkova.

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COMPLIANCE WITH ETHICAL STANDARDS

All studies were conducted in accordance with the principles of biomedical ethics formulated in the Helsinki Declaration of 1964 and its subsequent updates, and approved by the Ethics Commission of the Academic Council of the Institute of Age Physiology of the Russian Academy of Sciences (Moscow, protocol no. 1 of October 5, 2020).

Informed consent. Children 5–7-year-old with written parental consent participated in the study. Each participant of the study provided voluntary written informed consent, signed by them after explaining to them the potential risks and benefits, as well as the nature of the upcoming study.

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest.

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Translated by A. Dyomina

Russian Text © The Author(s), 2023, published in Rossiiskii Fiziologicheskii Zhurnal imeni I.M. Sechenova, 2023, Vol. 109, No. 7, pp. 954–974https://doi.org/10.31857/S0869813923070075.

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Komkova, Y.N., Sugrobova, G.A. & Bezrukikh, M.M. EEG Analysis of the Functional State of the Brain in 5- to 7-Years-Old Children. J Evol Biochem Phys 59, 1303–1319 (2023). https://doi.org/10.1134/S0022093023040233

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