EEG-characteristics in rest and parameters of event related potentials when arythmetic problems solving in primary school children with different levels of mental arithmetic calculation skills

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The EEG/ERP study involved 8–9 year old schoolchildren (24 participants). The participants performed arithmetic tasks in a delayed response verification paradigm: first a problem was presented, then an answer, to which they had to react by pressing a button if it was correct and skip if the answer was incorrect. Differences in the parameters of event related potentials (ERP) were revealed between the group of schoolchildren who made more than 50% of errors (8 people) – children with mental calculation difficulties – and the group of schoolchildren who made less than 20% of errors (13 people) – children with normal formed mental calculation skills. In the group of well-counting schoolchildren (in comparison with poorly counting), when presented with a correct answer, a smaller latency of the P2 component (on the time interval of 140–200 ms) in the central and frontal cortical areas and a larger amplitude of the positive component related to P3 (292–616 ms) with a maximum of differences in the central, parietal, and temporal areas of the right hemisphere were revealed. When an incorrect answer was presented – in the group of well-counting schoolchildren there was a large amplitude of positive components in the wide time interval 272–1232 ms – in the central and parietal cortical areas with a maximum in the right hemisphere. The revealed differences in ERP amplitudes affect both earlier perceptual components (in particular, P2) and later semantic components of ERP, reflecting different stages of information processing and decision making. According to the data of the resting state EEG analysis, the spectral power in θ-, α1- and β1-bands of the EEG, as well as the value of the integral parameter of spatial connectivity calculated from the structural function of the multichannel EEG, was higher in students with mental calculation difficulties than in students with formed mental calculation skills. Taking into account the age dynamics of the analyzed EEG parameters, these differences may characterize a slower maturation of the brain regulatory mechanisms in children with mental calculation difficulties.

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Sobre autores

Zh. Nagornova

Sechenov Institute of Evolutionary Physiology and Biochemistry, RAS

Autor responsável pela correspondência
Email: nagornova_zh@mail.ru
Rússia, St. Petersburg

M. Trifonov

Sechenov Institute of Evolutionary Physiology and Biochemistry, RAS

Email: nagornova_zh@mail.ru
Rússia, St. Petersburg

E. Panasevich

Sechenov Institute of Evolutionary Physiology and Biochemistry, RAS

Email: nagornova_zh@mail.ru
Rússia, St. Petersburg

V. Rozhkov

Sechenov Institute of Evolutionary Physiology and Biochemistry, RAS

Email: nagornova_zh@mail.ru
Rússia, St. Petersburg

V. Galkin

Sechenov Institute of Evolutionary Physiology and Biochemistry, RAS

Email: nagornova_zh@mail.ru
Rússia, St. Petersburg

A. Grokhotova

Sechenov Institute of Evolutionary Physiology and Biochemistry, RAS

Email: nagornova_zh@mail.ru
Rússia, St. Petersburg

E. Zavodova

Sechenov Institute of Evolutionary Physiology and Biochemistry, RAS

Email: nagornova_zh@mail.ru
Rússia, St. Petersburg

S. Soroko

Sechenov Institute of Evolutionary Physiology and Biochemistry, RAS

Email: nagornova_zh@mail.ru
Rússia, St. Petersburg

N. Shemyakina

Sechenov Institute of Evolutionary Physiology and Biochemistry, RAS

Email: nagornova_zh@mail.ru
Rússia, St. Petersburg

Bibliografia

  1. Krutetsky V.A. [Psychology of mathematical skills of schoolchildren]. Ed. Chuprikova N.I. Moscow: Institute of Practical Psychology, 1998. 416 p.
  2. O'Boyle M.W., Cunnington R., Silk T.J. et al. Mathematically gifted male adolescents activate a unique brain network during mental rotation // Brain Res. Cogn. Brain Res. 2005. V. 25. № 2. P. 583.
  3. Leikin M., Waisman I., Leikin R. How brain research can contribute to the evaluation of mathematical giftedness // Psychological Test and Assessment Modeling. 2013. V. 55. № 4. P. 415.
  4. Zhang L., Gan J.Q., Wang H. Neurocognitive mechanisms of mathematical giftedness: A literature review // Appl. Neuropsychol. Child. 2017. V. 6. № 1. P. 79.
  5. Mikheev I., Steiner H., Martynova O. Detecting cognitive traits and occupational proficiency using EEG and statistical inference // Sci. Rep. 2024. V. 14. № 1. P. 5605.
  6. Gashaj V., Trninić D., Formaz C. et al. Bridging cognitive neuroscience and education: Insights from EEG recording during mathematical proof evaluation // Trends Neurosci. Educ. 2024. V. 35. P. 100226.
  7. Micev R., Rogelj P. Exploring mathematical decision-making through EEG analysis / Proc. of the 10th Student Computing Research Symposium (SCORES’24). Maribor. Slovenia, October 3, 2024. P. 69.
  8. Gebuis T., Herfs I.K., Kenemans J.L. The development of automated access to symbolic and non-symbolic number knowledge in children: An ERP study // Eur. J. Neurosci. 2009. V. 30. № 10. P. 1999.
  9. Chen C.C., Berteletti I., Hyde D.C. Neural evidence of core foundations and conceptual change in preschool numeracy // Dev. Sci. 2024. V. 27. № 6. P. e13556.
  10. Dehaene S., Piazza M., Pinel P., Cohen L. Three parietal circuits for number processing // Cogn. Neuropsychol. 2003. V. 20. № 3. P. 487.
  11. Vanbinst K., De Smedt B. Individual differences in children’s mathematics achievement: The roles of symbolic numerical magnitude processing and domain-general cognitive functions // Prog. Brain Res. 2016. V. 227. P. 105.
  12. Nagornova Z.V., Shemyakina N.V., Soroko S.I. Cognitive event-related potentials in solving arithmetic tasks by adolescents living in different regions of Northern Russia // Human Physiology. 2020. V. 46. № 3. P. 257.
  13. Grenier A.E., Dickson D.S., Sparks C.S., Wicha N.Y.Y. Meaning to multiply: Electrophysiological evidence that children and adults treat multiplication facts differently // Dev. Cogn. Neurosci. 2020. V. 46. P. 100873.
  14. Cárdenas S.Y., Silva-Pereyra J., Prieto-Corona B. et al. Arithmetic processing in children with dyscalculia: An event-related potential study // PeerJ. 2021. V. 9. P. e10489.
  15. Berteletti I., Kimbley S.E., Sullivan S.J. et al. Different language modalities yet similar cognitive processes in arithmetic fact retrieval // Brain Sci. 2022. V. 12. № 2. P. 145.
  16. Kovaleva G.S., Danilenko O.V., Ermakova I.V. et al. [About first-graders: Results of research on the readiness of first-graders to study at school] // Municipal Education: Innovation and Experiment. 2012. № 5. P. 30.
  17. Kardanova E.Y., Ivanova A.E., Sergomanov P.A. et al. [Generalized types of first-graders' development at the entrance to school. Based on the materials of the iPIPS study] // Educational Studies Moscow. 2018. № 1. P. 8.
  18. Bull R., Scerif G. Executive functioning as a predictor of children’s mathematics ability: inhibition, switching, and working memory // Dev. Neuropsychol. 2001. V. 19. № 3. P. 273.
  19. Bezrukikh M.M., Machinskaya R.I., Farber D.A. Structural and functional organization of a developing brain and formation of cognitive functions in child ontogeny // Human Physiology. 2009. V. 35. № 6. P. 658.
  20. Hillman C.H., Pontifex M.B., Motl R.W. From ERPs to academics // Dev. Cogn. Neurosci. 2012. V. 2. P. 90.
  21. Robinson K.M., Arbuthnott K.D., Rose D. et al. Stability and change in children's division strategies // J. Exp. Child. Psychol. 2006. V. 93. № 3. P. 224.
  22. Roussel J.-L., Fayol M., Barrouillet P. Procedural vs. direct retrieval strategies in arithmetic: A comparison between additive and multiplicative problem solving // Eur. J. Cogn. Psychol. 2002. V. 14. № 1. P. 61.
  23. Thevenot C., Barrouillet P., Castel C., Uittenhove K. Ten-year-old children strategies in mental addition: A counting model account // Cognition. 2016. V. 146. P. 48.
  24. Semenova O.A., Machinskaya R.I., Lomakin D.I. The influence of the functional state of brain regulatory systems on the programming, selective regulation and control of cognitive activity in children: I. Neuropsychological and EEG analysis of age-related changes in brain regulatory functions in children aged 9–12 years // Human Physiology. 2015. V. 41. № 4. P. 345.
  25. Vigário R.N. Extraction of ocular artefacts from EEG using independent component analysis // Electroencephalogr. Clin. Neurophysiol. 1997. V. 103. № 3. P. 395.
  26. Jung T.P., Makeig S., Westerfield M. et al. Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects // Clin. Neurophysiol. 2000. V. 111. № 10. P. 1745.
  27. Tereshchenko E.P., Ponomarev V.A., Kropotov Yu.D., Müller A. Comparative efficiencies of different methods for removing blink artifacts in analyzing quantitative electroencephalogram and event-related potentials // Human Physiology. 2009. V. 35. № 2. P. 241.
  28. Pernet C.R., Latinus M., Nichols T.E., Rousselet G.A. Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study // J. Neuosci. Methods. 2015. V. 250. P. 85.
  29. Nikishena I.S., Ponomarev V.A., Kropotov J.D. Event-related potentials of the human brain during the comparison of visual stimuli // Human Physiology. 2023. V. 49. № 3. P. 264.
  30. Handbook of electroencephalography and clinical neurophysiology: Methods of analysis of brain and magnetic signals. Eds. Gevins A.S., Remond A. Elsevier Science Ltd. Amsterdam, The Netherlands, 1987. 683 p.
  31. Trifonov M.I., Panasevich E.A. Prediction of successful personal cognitive performance based on integrated characteristics of multichannel EEG // Human Physiology. 2018. V. 44. № 2. P. 208.
  32. Rozhkov V.P., Trifonov M.I., Soroko S.I. Control the functional state of the brain based on the dynamics of integral parameters of multichannel EEG in human under acute hypoxia // Human Physiology. 2021. V. 47. № 1. P. 1.
  33. Huynh H., Feldt L.S. Estimation of the box correction for degrees of freedom from sample data in randomized block and split-plot designs // J. Educ. Stat. 1976. V. 1. P. 69.
  34. Dickson D.S., Cerda V.R., Beavers R.N. et al. When 2 × 4 is meaningful: The N400 and P300 reveal operand format effects in multiplication verification // Psychophysiology. 2018. V. 55. № 11. P. e13212.
  35. Dickson D.S., Wicha N.Y.Y. P300 amplitude and latency reflect arithmetic skill: An ERP study of the problem size effect // Biol. Psychol. 2019. V. 148. P. 107745.
  36. Jasinski E.C., Coch D. ERPs across arithmetic operations in a delayed answer verification task // Psychophysiology. 2012. V. 49. № 7. P. 943.
  37. Suárez-Pellicioni M., Núñez-Peña M.I., Colomé A. Mathematical anxiety effects on simple arithmetic processing efficiency: An event-related potential study // Biol. Psychol. 2013. V. 94. № 3. P. 517.
  38. Zhou X., Chen C., Dong Q. et al. Event-related potentials of single-digit addition, subtraction, and multiplication // Neuropsychologia. 2006. V. 44. № 12. P. 2500.
  39. Niedeggen M., Rösler F. N400 effects reflect activation spread during retrieval of arithmetic facts // Psychological Science. 1999. V. 10. № 3. P. 271.
  40. Niedeggen M., Rösler F., Jost K. Processing of incongruous mental calculation problems: Evidence for an arithmetic N400 effect // Psychophysiology. 1999. V. 36. № 3. P. 307.
  41. Van Beek L., Ghesquièr P., De Smedt B., Lagae L. The arithmetic problem size effect in children: An event-related potential study // Front. Hum. Neurosci. 2014. V. 25. № 8. P. 756.
  42. Kutas M., Federmeier K.D. Thirty years and counting: Finding meaning in the N400 component of the event-related brain potential (ERP) // Annu. Rev. Psychol. 2011. V. 62. P. 621.
  43. Hoyniak C. Changes in the NoGo N2 event-related potential component across childhood: A systematic review and meta-analysis // Dev. Neuropsychol. 2017. V. 42. № 1. P. 1.
  44. Friederici A.D. Neurophysiological aspects of language processing // Clin. Neurosci. 1997. V. 4. № 2. P. 64.
  45. Megías P., Macizo P. Simple arithmetic: Electrophy-siological evidence of coactivation and selection of arithmetic facts // Exp. Brain. Res. 2016. V. 234. № 11. P. 3305.
  46. Wongupparaj P., Sumich A., Wickens M. et al. Individual differences in working memory and general intelligence indexed by P200 and P300: A latent variable model // Biol. Psychol. 2018. V. 139. P. 96.
  47. Dickson D.S., Grenier A.E., Obinyan B.O. Wicha N.Y.Y. When multiplying is meaningful in memory: Electrophysiological signature of the problem size effect in children // J. Exp. Child. Psychol. 2022. V. 219. P. 105399.
  48. Duan X., Shi J., Wu J. et al. Electrophysiological correlates for response inhibition in intellectually gifted children: A Go/NoGo study // Neurosci. Lett. 2009. V. 457. № 1. P. 45.
  49. Sokolova L.S., Machinskaya R.I. Formation of the functional organization of the cerebral cortex at rest in young schoolchildren varying in the maturity of cerebral regulatory systems: I. Analysis of EEG spectral characteristics in the state of rest // Human Physiology. 2006. V. 32. № 5. P. 499.
  50. Niedermeyer E., Lopes da Silva F.H. Electroencepha-lography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins, 2005. 1309 p.
  51. Eeg-Olofsson O. The development of the electroencephalogram in normal children and adolescents from the age of 1 through 21 years // Acta Paediat. Scand. 1970. V. 208. P. 1.
  52. Freschl J., Azizi L.A., Balboa L. et al. The development of peak alpha frequency from infancy to adolescence and its role in visual temporal processing: A meta-analysis // Dev. Cogn. Neurosci. 2022. V. 57. P. 101146.
  53. Whitford T.J., Rennie C.J., Grieve S.M. et al. Brain maturation in adolescence: Concurrent changes in neuroanatomy and neurophysiology // Hum. Brain Mapp. 2007. V. 28. № 3. P. 228.
  54. Gasser T., Verleger R., Bacher P., Sroka L. Development of the EEG of school-age children and adolescents: I, analysis of band power // Electroenceph. Clin. Neurophysiol. 1988. V. 69. № 2. P. 91.
  55. Alferova V.V., Farber D.A. [Reflection of age-specific features of the functional organization of the brain in the resting electroencephalogram / Structural and functional organization of the developing brain] // Eds. Farber D.A. et al. L.: Nauka, 1990. P. 45.
  56. Gorbachevskaya N.L., Sorokin A.B., Danilina K.K. [Age-related changes of neurophysiological characteristics of children in health and mental retardation syndrome, linked to fragile X chromosome (FRAXA)] // Psychological Science and Education. 2014. V. 19. № 4. P. 36.
  57. Kozhushko N.Y., Ponomarev V.A., Matveev Y.K., Evdokimov S.A. 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 Physiology. 2011. V. 37. № 3. P. 271.
  58. 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. 2023. V. 59. P. 1303.
  59. Machinskaya R.I., Kurgansky A.V., Lomakin D.I. Age-related trends in functional organization of cortical parts of regulatory brain systems in adolescents: An analysis of resting-state networks in the EEG source space // Human Physiology. 2019. V. 45. № 5. P. 461.
  60. Machinskaya R.I., Krupskaya E.V. EEG Analysis of the functional state of deep regulatory structures of the brain in hyperactive seven- to eight-year-old children // Human Physiology. 2001. V. 27. № 3. P. 368.
  61. Trifonov M. The structure function as new integral measure of spatial and temporal properties of multi-channel EEG // Brain Inform. 2016. V. 3. № 4. P. 211.
  62. Tsitseroshin M.N., Shepovalnikov A.N. [Becoming an integrative function of the brain]. Ed. Acad. Bekhtereva N.P. SPb.: Nauka, 2009. 249 p.
  63. Livanov M.N. [Spatial organization of brain processes]. Moscow: Nauka, 1972. 181 p.
  64. Babiloni C., Barry R. J., Basar E. et al. International Federation of Clinical Neurophysiology (IFCN) – EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies // Clin. Neurophysiol. 2020. V. 131. № 1. P. 285.
  65. O’Neill G.C., Tewarie P., Vidaurre D. et al. Dynamics of large-scale electrophysiological networks: A technical review // Neuroimage. 2018. V. 180. P. 559.
  66. Rozhkov V.P., Trifonov M.I., Soroko S.I. [CNS Maturation Process in Children and Adolescents in the Northern Region of the Russian Federation and Its Reflection in the Dynamics of Integral EEG Parameters] // Neurosci. Behav. Physi. 2022. V. 52. P. 383.
  67. Soroko S.I., Bekshaev S.S., Rozhkov V.P. [EEG correlates of geno-phenotypical features of the brain development in children of the native and newcomers' population of the Russian North-East] // Ross. Fiziol. Zh. Im I.M. Sechenova. 2012. V. 98. № 1. P. 3.
  68. Machinskaya R.I., Sokolova L.S., Krupskaya E.V. Formation of the functional organization of the cerebral cortex at rest in young schoolchildren varying in the maturity of cerebral regulatory systems: II. Analysis of EEG α-rhythm coherence // Human Physiology. 2007. V. 33. № 2. P. 129.
  69. Gmehlin D., Thomas C., Weisbrod M. et al. Development of brain synchronization within school-age–individual analysis of resting (α) coherence in a longitudinal data set // Clin. Neurophysiol. 2011. V. 122. № 10. P. 1973.

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2. Fig. 1. Sample structure.

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3. Fig. 2. Response time and percentage of erroneous reactions in perception of correct and incorrect answers to arithmetic examples by students aged 8–9. The abscissa axis shows the relative number of erroneous answers, in %, the ordinate axis shows the reaction time, in ms. Each symbol (circle) corresponds to one of the students, the light circle shows the students who calculated satisfactorily, the dark gray circle shows the students who calculated unsatisfactorily.

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4. Fig. 3. Evoked potentials (EP) in perception of correct and incorrect solutions in the general group of primary school students aged 8–9. Each graph shows the EP from one electrode. On the graphs: the x-axis shows time (ms), the y-axis shows the EP amplitude (μV). The black line shows the EP when the correct solution is presented. The gray line shows the EP when the incorrect solution is presented. The line under each graph shows individual clusters and intervals of differences (I–IV). The vertical dotted lines show the start and end times of response presentation (presentation time is 200 ms).

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5. Fig. 4. Evoked potentials (EP) upon presentation of the correct answer in an arithmetic task. Each graph shows EP from one electrode (F3-T6). On the graphs: along the x-axis is time (ms), along the y-axis is EP amplitude (μV). The black line shows EP in the group of schoolchildren with a normal level of acquisition of oral arithmetic skills; the gray line shows EP in the group of schoolchildren with a low level of acquisition of oral arithmetic skills. The Fz graph shows the latencies of the peaks of averaged EP in the group with a normal (black arrows) and low (gray arrows) level of acquisition of oral arithmetic skills. The gray fill shows individual intervals (clusters I and II) of differences. The topograms represent the spatial distribution of the amplitude differences between the groups with normal and low levels of mental arithmetic skills acquisition in the corresponding clusters of amplitude maxima of differences (ms).

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6. Fig. 5. Evoked potentials (EP) upon presentation of an incorrect answer in an arithmetic task. Each graph is the EP from one electrode. On the graphs: along the x-axis is time (ms), along the y-axis is the EP amplitude (μV). The black line is the EP in the group of schoolchildren with normal levels of mental arithmetic skills acquisition; the gray line is the EP in the group of schoolchildren with low levels of mental arithmetic skills acquisition. The gray fill indicates the intervals of differences. The topograms represent the distribution of the amplitude differences between the groups with normal and low levels of mental arithmetic skills acquisition in the corresponding time points (ms).

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7. Fig. 6. Statistically significant differences in the electroencephalography (EEG) power spectra in the "eyes closed" state between the groups of participants with low and normal development of mental arithmetic skills by ranges. The upward-pointing triangle at the location of the corresponding lead is a higher EEG power value in the group with low mental arithmetic skills compared to the normal level, according to the post-hoc analysis (p < 0.05, using Fisher's LSD criterion).

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8. Fig. 7. Distribution of schoolchildren with different levels of success in completing an arithmetic task in the coordinate system of two integral EEG parameters. The abscissa axis is the pT value, relative units, the ordinate axis is the pS value, relative units. A is the resting state with eyes closed, B is the resting state with eyes open. Each symbol (circle) corresponds to one of the students, the number is the student's ordinal number; light circle – students with a normal level of acquisition of oral arithmetic skills, dark gray – students with a low level of acquisition of oral arithmetic skills.

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