Нейронные корреляты решения арифметических задач у взрослых

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Была проведена функциональная магнитно-резонансная томография (фМРТ) во время устного вычисления задач на арифметические операции с тремя уровнями сложности. Во время решения задач с использованием вычитания, умножения и деления на легком уровне была обнаружена активность мозга в левой нижней теменной дольке и левой угловой извилине, которая может отражать извлечение информации из долговременной памяти; также в левой нижней лобной извилине – для задач с использованием деления, что является признаком применения процедурной стратегии. С повышением уровня сложности задачи активность мозга в левой нижней теменной дольке и левой угловой извилине становилась билатеральной и более интенсивной, дополнительно стали вовлечены такие структуры, как верхняя лобная извилина, дополнительная двигательная зона, нижняя средняя и височная извилины, а также мозжечок, что свидетельствует о необходимости увеличения нейронных ресурсов для решения более сложных задач. При решении задач на трехзначное деление выявлена билатеральная активность в островковой коре, которую связывают со всеми видами аффективных и когнитивных процессов. Выявленные области активаций мозга подчеркивают различные системы, задействованные в решении арифметических задач разной степени сложности. Несмотря на сходства в паттернах активации мозга, поведенческие результаты показали статистически значимые различия между арифметическими операциями. Данное исследование способствовало нейрокартированию математических процессов у здоровых взрослых в стереотаксических координатах.

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Об авторах

А. В. Истомина

Национальный исследовательский университет «Высшая школа экономики»

Автор, ответственный за переписку.
Email: avistomona@hse.ru
Россия, Москва

А. Ю. Фабер

Национальный исследовательский университет «Высшая школа экономики»

Email: avistomona@hse.ru
Россия, Москва

А. В. Манжурцев

НИИ неотложной детской хирургии и травматологии

Email: avistomona@hse.ru
Россия, Москва

М. В. Ублинский

НИИ неотложной детской хирургии и травматологии

Email: avistomona@hse.ru
Россия, Москва

M. Арсалиду

Йоркский университет; NeuroPsyLab

Email: avistomona@hse.ru
Канада, Торонто; Торонто

Список литературы

  1. Arsalidou M., Pascual-Leone J., Johnson J., Kotova T. The constructive operators of the working mind: a developmental account of mental-attentional capacity. Russ. J. Cogn. Sci. 2019. 6 (44–55): 3061–3079.
  2. Arsalidou M., Taylor M.J. Is 2+ 2 = 4? Meta-analyses of brain areas needed for numbers and calculations. Neuroimage. 2011. 54 (3): 2382–2393. https://doi.org/10.1016/j.neuroimage.2010.10.009
  3. Artemenko C. Developmental fronto-parietal shift of brain activation during mental arithmetic across the lifespan: A registered report protocol. Plos One. 2021. 16 (8):e0256232. https://doi.org/10.1371/journal.pone.0256232
  4. Baumann O., Mattingley J.B. Scaling of neural responses to visual and auditory motion in the human cerebellum. Journal of Neuroscience. 2010. 30(12):4489–95. https://doi.org/10.1523/JNEUROSCI.5661-09.2010
  5. Bloechle J., Huber S., Bahnmueller J., Rennig J., Willmes K., Cavdaroglu S., Moeller K., Klein E. Fact learning in complex arithmetic – the role of the angular gyrus revisited. Human Brain Mapping. 2016. 37 (9):3061–79. https://doi.org/10.1002/hbm.23226
  6. Blumenfeld H.K., Booth J.R., Burman D.D. Differential prefrontal-temporal neural correlates of semantic processing in children. Brain and language. 2006. 99 (3):226–35. https://doi.org/10.1016/j.bandl.2005.07.004
  7. Brown A.A., Upton S., Craig S., Froeliger B. Associations between right inferior frontal gyrus morphometry and inhibitory control in individuals with nicotine dependence. Drug and alcohol dependence. 2023. 244:109766. https://doi.org/10.1016/j.drugalcdep.2023.10976
  8. Burns M. About teaching mathematics: A K-8 resource. Math Solutions Publications, Marilyn Burns Education Associates. 2000.
  9. Byers W. How mathematicians think: Using ambiguity, contradiction, and paradox to create mathematics. Princeton University Press. 2010.
  10. Caballero-Gaudes C., Reynolds R.C. Methods for cleaning the BOLD fMRI signal. Neuroimage. 2017. 154:128–49. https://doi.org/10.1016/j.neuroimage.2016.12.018
  11. Cañas A., Juncadella M., Lau R, Gabarrós A., Hernández M. Working memory deficits after lesions involving the supplementary motor area. Frontiers in psychology. 2018. 9:765. https://doi.org/10.3389/fpsyg.2018.00765
  12. Caviola S., Mammarella I.C., Cornoldi C., Lucangeli D. The involvement of working memory in children’s exact and approximate mental addition. Journal of experimental child psychology. 2012. 112 (2):141–60. https://doi.org/10.1016/j.jecp.2012.02.005
  13. Chen G., Saad Z.S., Nath A.R., Beauchamp M.S., Cox R.W. FMRI group analysis combining effect estimates and their variances. Neuroimage. 2012. 60(1):747–65. https://doi.org/10.1016/j.neuroimage.2011.12.060
  14. Cheng D., Li M., Cui J., Wang L., Wang N., Ouyang L., Wang X., Bai X., Zhou X. Algebra dissociates from arithmetic in the brain semantic network. Behavioral and Brain Functions. 2022. 18(1): 1. https://doi.org/10.21203/rs.3.rs-806057/v1
  15. Chin K.E., Pierce R. University students’ conceptions of mathematical symbols and expressions. EURASIA Journal of Mathematics, Science and Technology Education. 2019. 15 (9). https://doi.org/10.29333/ejmste/103736
  16. Chou T.L., Booth J.R., Bitan T., Burman D.D., Bigio J.D., Cone N.E., Lu D., Cao F. Developmental and skill effects on the neural correlates of semantic processing to visually presented words. Human brain mapping. 2006. 27 (11):915–24. doi: 10.1002/hbm.20231
  17. Cox R.W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical research. 1996. 29 (3):162–73. https://doi.org/10.1006/cbmr.1996.0014
  18. De Smedt B., Boets B. Phonological processing and arithmetic fact retrieval: Evidence from developmental dyslexia. Neuropsychologia. 2010. 48 (14):3973–81. https://doi.org/10.1016/j.neuropsychologia.2010.10.018
  19. De Visscher A., Vogel S.E., Reishofer G., Hassler E., Koschutnig K., De Smedt B., Grabner R.H. Interference and problem size effect in multiplication fact solving: Individual differences in brain activations and arithmetic performance. NeuroImage. 2018. 172:718–27. https://doi.org/10.1016/j.neuroimage.2018.01.060
  20. Dehaene S., Tzourio N., Frak V., Raynaud L., Cohen L., Mehler J., Mazoyer B. Cerebral activations during number multiplication and comparison: a PET study. Neuropsychologia. 1996. 34 (11):1097–106. https://doi.org/10.1016/0028-3932(96)00027-9
  21. Delazer M., Domahs F., Lochy A., Karner E., Benke T., Poewe W. Number processing and basal ganglia dysfunction: a single case study. Neuropsychologia. 2004. 42(8):1050–62. https://doi.org/10.1016/j.neuropsychologia.2003.12.009
  22. Deschuyteneer M., de Rammelaere S., Fias W. The addition of two-digit numbers: Exploring carry versus no-carry problems. Psychology Science. 2005. 47(1):74–83.
  23. Dibbets P., Evers E.A., Hurks P.P., Bakker K., Jolles J. Differential brain activation patterns in adult attention-deficit hyperactivity disorder (ADHD) associated with task switching. Neuropsychology. 2010. 24 (4):732–9. https://doi.org/10.1037/a0018997
  24. Doya K. Complementary roles of basal ganglia and cerebellum in learning and motor control. Current opinion in neurobiology. 2000. 10 (6):732–9. https://doi.org/10.1016/S0959-4388(00)00153-7
  25. Elmers J., Yu S., Talebi N., Prochnow A., Beste C. Neurophysiological effective network connectivity supports a threshold-dependent management of dynamic working memory gating. Iscience. 2024. 27 (4). https://doi.org/10.1016/j.isci.2024.109521
  26. Emch M., von Bastian C.C., Koch K. Neural correlates of verbal working memory: An fMRI meta-analysis. Frontiers in human neuroscience. 2019. 13:180. https://doi.org/10.3389/fnhum.2019.00180
  27. Fagginger Auer M.F., Hickendorff M., Putten C.M. V. Training can increase students’ choices for written solution strategies and performance in solving multi-digit division problems. Frontiers in Psychology. 2018. 9: 1644. https://doi.org/10.3389/fpsyg.2018.01644
  28. Fehr T., Code C., Herrmann M. Common brain regions underlying different arithmetic operations as revealed by conjunct fMRI-BOLD activation. Brain research. 2007. 1172:93–102. https://doi.org/10.1016/j.brainres.2007.07.043
  29. Fias W., Lammertyn J., Reynvoet B., Dupont P., Orban G.A. Parietal representation of symbolic and nonsymbolic magnitude. Journal of cognitive neuroscience. 2003. 15 (1):906–13. https://doi.org/10.1162/089892903321107819
  30. Froeling M. QMRTools: a Mathematica toolbox for quantitative MRI analysis. Journal of Open Source Software. 2019. 4 (38):1204. doi: 10.21105/JOSS.01204
  31. Gabrieli J.D., Poldrack R.A., Desmond J.E. The role of left prefrontal cortex in language and memory. Proceedings of the national Academy of Sciences. 1998. 95 (3):906–13. https://doi.org/10.1073/pnas.95.3.906
  32. Göbel S.M., Terry R., Klein E., Hymers M., Kaufmann L. Impaired arithmetic fact retrieval in an adult with developmental dyscalculia: evidence from behavioral and functional brain imaging data. Brain Sciences. 2022. 12 (6). https://doi.org/10.3390/brainsci12060735
  33. Göbel S.M., Watson S.E., Lervag A., Hulme C. Children’s arithmetic development: It is number knowledge, not the approximate number sense, that counts. Psychological science. 2014. 25 (3):789–98. https://doi.org/10.1177/0956797613516471
  34. Glen D.R., Taylor P.A., Buchsbaum B.R., Cox R.W., Reynolds R.C. Beware (surprisingly common) left-right flips in your MRI data: an efficient and robust method to check MRI dataset consistency using AFNI. Frontiers in neuroinformatics. 2020. 505994. https://doi.org/10.3389/fninf.2020.00018
  35. Gliksman Y., Berebbi S., Henik A. Math fluency during primary school. Brain Sciences. 2022. 12 (3). https://doi.org/10.3390/brainsci12030371
  36. Glover G.H. Overview of functional magnetic resonance imaging. Neurosurgery Clinics. 2011. 22 (2):133–9. https://doi.org/10.1016/j.nec.2010.11.001
  37. Grabner R.H., Ansari D., Koschutnig K., Reishofer G., Ebner F. The function of the left angular gyrus in mental arithmetic: evidence from the associative confusion effect. Human brain mapping. 2013. 34 (5):1013–24. https://doi.org/10.1002/hbm.21489
  38. Gruber O., Indefrey P., Steinmetz H., Kleinschmidt A. Dissociating neural correlates of cognitive components in mental calculation. Cerebral cortex. 2001. 11 (4):350–9. https://doi.org/10.1093/cercor/11.4.350
  39. Harada T., Bridge D.J., Chiao J.Y. Dynamic social power modulates neural basis of math calculation. Frontiers in Human Neuroscience. 2013. 6:350. https://doi.org/10.3389/fnhum.2012.00350
  40. Hawes Z., Sokolowski H.M., Ononye C.B., Ansari D. Neural underpinnings of numerical and spatial cognition: An fMRI meta-analysis of brain regions associated with symbolic number, arithmetic, and mental rotation. Neuroscience. Biobehavioral Reviews. 2019. 103:316–36. https://doi.org/10.1016/j.neubiorev.2019.05.007
  41. Henschen S.E. Über sprach-, musik- und rechenmechanismen und ihre lokalisationen im großhirn. Zeitschrift für die gesamte Neurologie und Psychiatrie. 1919. 52:273–98. https://doi.org/10.1007/BF02872428
  42. Holloway I.D., Price G.R., Ansari D. Common and segregated neural pathways for the processing of symbolic and nonsymbolic numerical magnitude: An fMRI study. Neuroimage. 2010. 49 (1):1006–17. https://doi.org/10.1016/j.neuroimage.2009.07.071
  43. Huber S., Fischer U., Moeller K., Nuerk H.C. On the interrelation of multiplication and division in secondary school children. Frontiers in psychology. 2013. 4:740. https://doi.org/10.3389/fpsyg.2013.00740
  44. Imbo I., LeFevre J. A. The role of phonological and visual working memory in complex arithmetic for Chinese and Canadian educated adults. Memory, Cognition. 2010. 38(2):176–85. https://doi.org/10.3758/MC.38.2.176
  45. Imbo I., Vandierendonck A., Vergauwe E. The role of working memory in carrying and borrowing. Psychological research. 2007. 71 (4):467–83. https://doi.org/10.1007/s00426-006-0044-8
  46. Ischebeck A., Zamarian L., Schocke M., Delazer M. Flexible transfer of knowledge in mental arithmeticÑAn fMRI study. Neuroimage. 2009. 44 (3):1103–12. https://doi.org/10.1016/j.neuroimage.2008.10.025
  47. Istomina A., Arsalidou M. Add, subtract and multiply: Meta-analyses of brain correlates of arithmetic operations in children and adults. Developmental Cognitive Neuroscience. 2024. 101419. https://doi.org/10.1016/j.dcn.2024.101419
  48. Ivanitskiĭ A.M., Portnova G.V., Martynova O.V., Maĭorova L.A., Fedina O.N., Petrushevskiĭ A.G. Ivanitski' A.M., Portnova G.V., Martynova O.V., Ma'orova L.A., Fedina O.N., Petrushevski A.G. Brain mapping in verbal and spatial thinking. Zhurnal Vysshei Nervnoi Deiatelnosti Imeni I.P. Pavlova. 2013. 63 (6):677–86. https://doi.org/10.7868/s0044467713060075
  49. Ivry R.B., Baldo J.V. Is the cerebellum involved in learning and cognition? Current opinion in neurobiology. 1992. 2 (2):212–6. https://doi.org/10.1016/0959-4388(92)90015-D
  50. Kadosh R.C., Walsh V. Numerical representation in the parietal lobes: Abstract or not abstract? Behavioral and brain sciences. 2009. 32 (3–4):313–28. https://doi.org/10.1017/S0140525X09990938
  51. Kim S.G., Uğurbil K., Strick P.L. Activation of a cerebellar output nucleus during cognitive processing. Science. 1994. 265 (5174):949–51. doi: 10.1126/science.8052851
  52. King M., Hernandez-Castillo C. R., Poldrack R.A., Ivry R.B., Diedrichsen J. Functional boundaries in the human cerebellum revealed by a multi-domain task battery. Nature neuroscience. 2019. 22 (8):1371–8. https://doi.org/10.1038/s41593-019-0436-x
  53. Klaus J., Schutter D.J. Functional topography of anger and aggression in the human cerebellum. NeuroImage. 2021. 226. 117582. https://doi.org/10.1016/j.neuroimage.2020.117582
  54. Knowlton B.J., Mangels J.A., Squire L.R. A neostriatal habit learning system in humans. Science. 1996. 273(5280):1399–402. doi: 10.1126/science.273.5280.1399
  55. Konopkina K., Arsalidou M. Brain areas associated with basic mathematical operations. Organization for Human Brain Mapping, Annual Conference. 2019.
  56. Leiner H.C., Leiner A.L., Dow R.S. Cognitive and language functions of the human cerebellum. Trends in neurosciences. 1993. 16 (11):444–7. https://doi.org/10.1016/0166-2236(93)90072-T
  57. Lemaire P., Arnaud L. Young and older adults’ strategies in complex arithmetic. The American journal of psychology. 2008. 121 (1):1–6. https://doi.org/10.2307/20445440
  58. Lemaire P. How Distracting Events Influence Young and Older adults’ Arithmetic Performance? Experimental Aging Research. 2023. 1–20. https://doi.org/10.1080/0361073X.2023.2250224
  59. Li M., Lu Y., Zhou X. The involvement of the semantic neural network in rule identification of mathematical processing. Cortex. 2023. 164:11–20. https://doi.org/10.1016/j.cortex.2023.03.010
  60. Liu J., Yuan L., Chen C., Cui J., Zhang H., Zhou X. The semantic system supports the processing of mathematical principles. Neuroscience. 2019. 404:491–501. 10.1016/j.neuroscience.2019.01.043
  61. Lotze M., Montoya P., Erb M., Hülsmann E., Flor H., Klose U., Birbaumer N., Grodd W. Activation of cortical and cerebellar motor areas during executed and imagined hand movements: an fMRI study. Journal of cognitive neuroscience. 1999. 11(5):491–501. https://doi.org/10.1162/089892999563553
  62. Mannarelli D., Pauletti C., Missori P., Trompetto C., Cotellessa F., Fattapposta F., Currà A. Cerebellum’s Contribution to Attention, Executive Functions and Timing: Psychophysiological Evidence from Event-Related Potentials. Brain Sciences. 2023. 13 (12):1683. https://doi.org/10.3390/brainsci13121683
  63. Matejko A.A., Ansari D. The neural association between arithmetic and basic numerical processing depends on arithmetic problem size and not chronological age. Developmental Cognitive Neuroscience. 2019. 37:100653. https://doi.org/10.1016/j.dcn.2019.100653
  64. Mauro D.G., Le Fevre J.A., Morris J. Effects of problem format on division and multiplication performance: division facts are mediated via multiplication-based representations. Journal of Experimental Psychology: Learning, Memory, and Cognition. 2003. 29 (2):163. https://doi.org/10.1037/0278-7393.29.2.163
  65. McCloskey M., Caramazza A., Basili A. Cognitive mechanisms in number processing and calculation: Evidence from dyscalculia. Brain and cognition. 1985. 4(2):171–196. https://doi.org/10.1016/0278-2626(85)90069-7
  66. Metcalfe A.W., Campbell J.I. Adults’ strategies for simple addition and multiplication: Verbal self-reports and the operand recognition paradigm. Journal of Experimental Psychology: Learning, Memory, and Cognition. 2011. 37(3):661.
  67. Middleton F.A., Strick P.L. Anatomical evidence for cerebellar and basal ganglia involvement in higher cognitive function. Science. 1994. 266 (5184):458–61. doi: 10.1126/science.7939688
  68. Middleton F.A., Strick P.L. Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain research reviews. 2000. 31 (2–3):236–50. https://doi.org/10.1016/S0165-0173(99)00040-5
  69. Moore A.M., Rudig N.O., Ashcraft M.H. Affect, motivation, working memory, and mathematics. 2014. https://doi.org/10.1093/oxfordhb/9780199642342.013.004
  70. Narayanan S., Thirumalai V. Contributions of the cerebellum for predictive and instructional control of movement. Current opinion in physiology. 2019. 8:146–151. https://doi.org/10.1016/j.cophys.2019.01.011
  71. Nuerk H.C., Willmes K., Fischer M.H. Multi-digit number processing. Zeitschrift für Psychologie. 2015. https://doi.org/10.1027/2151-2604/a000040
  72. Mikheev I., Steiner H., Martynova O. Detecting cognitive traits and occupational proficiency using EEG and statistical inference. Scientific Reports. 2024. 14 (1):5605. https://doi.org/10.1038/s41598-024-55163-w
  73. Mikl M., Mareček R., Hluštík P., Pavlicová M., Drastich A., Chlebus P., Brázdil M., Krupa P. Effects of spatial smoothing on fMRI group inferences. Magnetic resonance imaging. 2008. 26 (4):490–503. https://doi.org/10.1016/j.mri.2007.08.006
  74. Molina del Río J., Guevara M.A., Hernández González M., Hidalgo Aguirre R.M., Cruz Aguilar M.A. EEG correlation during the solving of simple and complex logical-mathematical problems. Cognitive, Affective, Behavioral Neuroscience. 2019. 19:1036– 46. https://doi.org/10.3758/s13415-019-00703-5
  75. Moustafa A.A., Tindle R., Ansari Z., Doyle M.J., Hewedi D.H., Eissa A. Mathematics, anxiety, and the brain. Reviews in the Neurosciences. 2017. 28 (4):417–29. https://doi.org/10.1515/revneuro-2016-0065
  76. Obayashi S. Cognitive and linguistic dysfunction after thalamic stroke and recovery process: possible mechanism. AIMS neuroscience. 2022. 9 (1). 10.3934/Neuroscience.2022001
  77. Owen A.M., McMillan K. M., Laird A.R., Bullmore E. N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Human brain mapping. 2005. 25 (1):46–59.
  78. Peters G., De Smedt B., Torbeyns J., Ghesquire P., Verschaffel L. Adults’ use of subtraction by addition. Acta Psychologica. 2010. 135 (3):323–9. https://doi.org/10.1016/j.actpsy.2010.08.007
  79. Pierce J.E., Thomasson M., Voruz P., Selosse G., Peron J. Explicit and implicit emotion processing in the cerebellum: a meta-analysis and systematic review. The Cerebellum. 2023. 22 (5):852–64. https://doi.org/10.1007/s12311-022-01459-4
  80. Pletzer B. Sex differences in number processing: differential systems for subtraction and multiplication were confirmed in men, but not in women. Scientific reports. 2016. 6 (1):39064. https://doi.org/10.1038/srep39064
  81. Pollack C., Ashby N.C. Where arithmetic and phonology meet: the meta-analytic convergence of arithmetic and phonological processing in the brain. Developmental cognitive neuroscience. 2018. 30:251–64. https://doi.org/10.1016/j.dcn.2017.05.003
  82. Power J.D., Plitt M., Laumann T.O., Martin A. Sources and implications of whole-brain fMRI signals in humans. Neuroimage. 2017. 146:136–208. https://doi.org/10.1016/j.neuroimage.2016.09.038
  83. Prati J.M., Pontes-Silva A., Gianlorenço A.C. The cerebellum and its connections to other brain structures involved in motor and non-motor functions: a comprehensive review. Behavioural Brain Research. 2024. 114933. https://doi.org/10.1016/j.bbr.2024.114933
  84. Rempel S., Colzato L., Zhang W., Wolff N., Mückschel M., Beste C. Distinguishing multiple coding levels in theta band activity during working memory gating processes. Neuroscience. 2021. 478:11–23. https://doi.org/10.1016/j.neuroscience.2021.09.025
  85. Ritchie S.J., Bates T.C. Enduring links from childhood mathematics and reading achievement to adult socioeconomic status. Psychological science. 2013. 24 (7):1301–8. https://doi.org/10.1016/j.neuroscience.2021.09.025
  86. Rodríguez-Nieto G., Seer C., Sidlauskaite J., Vleugels L., Van Roy A., Hardwick R., Swinnen S. Inhibition, shifting and updating: Inter and intra-domain commonalities and differences from an executive functions activation likelihood estimation meta-analysis. NeuroImage. 2022. 264. 119665. https://doi.org/10.1016/j.neuroimage.2022.119665
  87. Rosenberg-Lee M., Barth M., Menon V. What difference does a year of schooling make? Maturation of brain response and connectivity between 2nd and 3rd grades during arithmetic problem solving. Neuroimage. 2011. 57 (3):796–808. https://doi.org/10.1016/j.neuroimage.2011.05.013
  88. Rottschy C., Langner R., Dogan I., Reetz K., Laird A.R., Schulz J.B., Eickhoff S.B. Modelling neural correlates of working memory: A coordinate based meta-analysis. NeuroImage. 2012. 60. 830–846. https://doi.org/10.1016/j.neuroimage.2011.05.013
  89. Ruan J., Bludau S., Palomero-Gallagher N., Caspers S., Mohlberg H., Eickhoff S.B., Seitz R.J., Amunts K. Cytoarchitecture, probability maps, and functions of the human supplementary and pre-supplementary motor areas. Brain structure, function. 2018. 223 (9): 4169–4186. https://doi.org/10.1007/s00429-018-1738-6
  90. Saad Z.S., Chen G., Reynolds R.C., Christidis P.P., Hammett K.R., Bellgowan P.S., Cox R.W. Functional imaging analysis contest (FIAC) analysis according to AFNI and SUMA. Human brain mapping. 2006. 27 (5): 417–424. doi: 10.1002/hbm.20247
  91. Saarikivi K., Chan T.M., Huotilainen M., Tervaniemi M., Putkinen V. Enhanced neural mechanisms of set shifting in musically trained adolescents and young adults: converging fMRI, EEG, and behavioral evidence. Cerebral Cortex. 2023. 33 (11):7237–49. https://doi.org/10.1093/cercor/bhad034
  92. Safiati O.A., Prastiti T.D. On division operation of any numbers: introducing a new technique. In Journal of Physics: Conference Series (Vol. 1836. № 1. Р. 012055). IOP Publishing. 2011. 7237–49. doi: 10.1088/1742-6596/1836/1/012055
  93. Seghier M.L. The angular gyrus: multiple functions and multiple subdivisions. The Neuroscientist. 2013. 19 (1):43–61. https://doi.org/10.1177/1073858412440596
  94. Sekeris E., Verschaffel L., Luwel K. Exact arithmetic, computational estimation and approximate arithmetic are different skills: Evidence from a study with 5 year olds. Infant and Child Development. 2021. 30 (5). https://doi.org/10.1002/icd.2248
  95. Shipman M.L., Green J.T. Cerebellum and cognition: does the rodent cerebellum participate in cognitive functions? Neurobiology of learning and memory. 2020. 170:106996. https://doi.org/10.1016/j.nlm.2019.02.006
  96. Sitnikova M., Marakshina J.A., Adamovich T.V., Pronin G.O., Asadullaev R.G. The neural correlates of exact calculation in word and numerical formats in low and high math performers: a fNIRS study. International Journal of Cognitive Research in Science, Engineering and Education: (IJCRSEE). 2023. 11 (1):93–114.
  97. Skagenholt M., Träff U., Västfjäll D., Skagerlund K. Examining the Triple Code Model in numerical cognition: An fMRI study. PLoS One. 2018. 13 (6):e0199247. https://doi.org/10.1371/journal.pone.0199247
  98. Sokolowski H.M., Fias W., Mousa A., Ansari D. Common and distinct brain regions in both parietal and frontal cortex support symbolic and nonsymbolic number processing in humans: A functional neuroimaging meta-analysis. Neuroimage. 2017. 146:376–94. https://doi.org/10.1016/j.neuroimage.2016.10.028
  99. Sokolowski H.M., Hawes Z., Ansari D. The neural correlates of retrieval and procedural strategies in mental arithmetic: A functional neuroimaging meta-analysis. Human Brain Mapping. 2023. 44 (1):222–44. https://doi.org/10.1002/hbm.26082
  100. Sokolowski H.M., Hawes Z., Peters L., Ansari D. Symbols are special: An fMRI adaptation study of symbolic, nonsymbolic, and non-numerical magnitude processing in the human brain. Cerebral Cortex Communications. 2021. 2 (3). https://doi.org/10.1093/texcom/tgab048
  101. Soltanlou M., Dresler T., Artemenko C., Rosenbaum D., Ehlis A.C., Nuerk H.C. Training causes activation increase in temporo-parietal and parietal regions in children with mathematical disabilities. Brain Structure and Function. 2022. 227 (5):1757–71. https://doi.org/10.1007/s00429-022-02470-5
  102. Stoodley C.J., Schmahmann J.D. Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. Neuroimage. 2009. 44 (2):12574–83. https://doi.org/10.1016/j.neuroimage.2008.08.039
  103. Soylu F., Raymond D., Gutierrez A., Newman S.D. The differential relationship between finger gnosis, and addition and subtraction: An fMRI study. Journal of Numerical Cognition. 2018. 3 (3). https://doi.org/10.5964/jnc.v3i3.102
  104. Sundby K.K., Jana S., Aron A.R. Double-blind disruption of right inferior frontal cortex with TMS reduces right frontal beta power for action stopping. Journal of Neurophysiology. 2021. 125 (1):140–53. ttps://doi.org/10.1152/jn.00459.2020
  105. Szkudlarek E., Zhang H., de Wind N.K., Brannon E.M. Young children intuitively divide before they recognize the division symbol. Frontiers in Human Neuroscience. 2022. 16:752190. https://doi.org/10.3389/fnhum.2022.752190
  106. Threlfall J. Strategies and flexibility in mental calculation. ZDM. 2009. 41:541–55. https://doi.org/10.1007/s11858-009-0195-3
  107. Uddin L.Q., Nomi J.S., HŽbert-Seropian B., Ghaziri J., Boucher O. Structure and function of the human insula. Journal of Clinical Neurophysiology. 2017. 34 (4):300–6. doi: 10.1097/WNP.0000000000000377
  108. Van der Auwera S., de Smedt B., Torbeyns J., Verguts G., Verschaffel L. Subtraction by addition in young multi-digit subtraction learners: A choice/no-choice study. Journal of Experimental Child Psychology. 2023. 226 (105544):1–16. https://doi.org/10.1016/j.jecp.2022.105544
  109. Van Overwalle F., Ma Q., Haihambo N., Bylemans T., Catoira B., Firouzi M., Li M., Pu M., Heleven E., Baeken C., Baetens K. A functional atlas of the cerebellum based on neurosynth task coordinates. The Cerebellum. 2024. 23 (3):993–1012. https://doi.org/10.1007/s12311-023-01596-4
  110. Verguts T., Fias W. Interacting neighbors: A connectionist model of retrieval in single-digit multiplication. Memory, cognition. 2005. 33:1–6. https://doi.org/10.3758/BF03195293
  111. Vincent J.L., Kahn I., Snyder A.Z., Raichle M.E., Buckner R.L. Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. Journal of neurophysiology. 2008. 100 (6):3328–42. https://doi.org/10.1152/jn.90355.2008
  112. Wang L., Li M., Yang T., Wang L., Zhou X. Mathematics meets science in the brain. Cerebral Cortex. 2022. 32 (1):123–36. https://doi.org/10.1093/cercor/bhab198
  113. Wood G., Nuerk H.C., Moeller K., Geppert B., Schnitker R., Weber J., Willmes K. All for one but not one for all: How multiple number representations are recruited in one numerical task. Brain research. 2008. 1187:154–66. https://doi.org/10.1016/j.brainres.2007.09.094
  114. Yang Y., Zhong N., Friston K., Imamura K., Lu S., Li M., Zhou H., Wang H., Li K., Hu B. The functional architectures of addition and subtraction: Network discovery using fMRI and DCM. Human Brain Mapping. 2017. 38 (6):3210–25. https://doi.org/10.1002/hbm.23585
  115. Yaple Z.A., Tolomeo S., Yu R. Mapping working memory-specific dysfunction using a transdiagnostic approach. NeuroImage: Clinical. 2021. 31:102747. https://doi.org/10.1016/j.nicl.2021.102747
  116. Zamarian L., Ischebeck A., Delazer M. Neuroscience of learning arithmetic. Evidence from brain imaging studies. Neuroscience, Biobehavioral Reviews. 2009. 33 (6):909–25. https://doi.org/10.1016/j.neubiorev.2009.03.005
  117. Zarnhofer S., Braunstein V., Ebner F., Koschutnig K., Neuper C., Reishofer G., Ischebeck A. The influence of verbalization on the pattern of cortical activation during mental arithmetic. Behavioral and Brain Functions. 2012. 8:1–15. https://doi.org/10.1186/1744-9081-8-13
  118. Zhang R., Deng H., Xiao X. The Insular Cortex: An Interface between Sensation, Emotion and Cognition. Neuroscience Bulletin. 2024. 1–11. https://doi.org/10.1007/s12264-024-01211-4
  119. Zweegman S., Wildes T.M. Addition by subtraction. Blood, The Journal of the American Society of Hematology. 2021. 137 (22):3005–6. https://doi.org/10.1182/blood.2021011144

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2. Рис. 1. (a) – типы задач и уровни сложности в параметрической математической задаче (ПМЗ); (б) – последовательность событий и их длительность в параметрической математической задаче (ПМЗ).

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3. Рис. 2. (а) – графики точности при решении задач на сложение, вычитание, умножение и деление, а также при решении контрольного задания в трех уровнях сложности (1, 2, 3). Статистически значимые различия между заданиями внутри каждого из трех уровней: * – p < 0.05; (б) – графики времени реакции при решении задач на сложение, вычитание, умножение и деление, а также при решении контрольного задания в трех уровнях сложности (1, 2, 3). Статистически значимые различия между заданиями внутри каждого из трех уровней: * – p < 0.05.

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4. Рис. 3. Выбранные срезы в координатах (X, Y, Z) в пространстве MNI иллюстрируют активность при расчете контрастов: (a) – УРОВЕНЬ 1: ДЕЛЕНИЕ > КОНТРОЛЬНОЕ ЗАДАНИЕ: левой нижней теменной дольки и угловой извилины (пик кластера на пересечении зеленых линий), дополнительной двигательной зоны слева и мозжечка справа; (б) – УРОВЕНЬ 2: ДЕЛЕНИЕ > КОНТРОЛЬНОЕ ЗАДАНИЕ: левая (91 вокселей – пик кластера на пересечении зеленых линий), средняя и нижняя височные извилины; (в) – УРОВЕНЬ 3: ДЕЛЕНИЕ > КОНТРОЛЬНОЕ ЗАДАНИЕ: левая (125 вокселей – пик кластера на пересечении зеленых линий) и правая (115 вокселей) островковая кора и нижняя лобная извилина; p < 0.005 (с поправкой на множественные сравнения). Минимальный размер кластера составляет 50 вокселей при p < 0.005 (с поправкой на множественные сравнения). Примечание: L – левое, R – правое, А – переднее, Р – заднее.

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