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Resha** the Cortical Connectivity Gradient by Long-Term Cognitive Training During Development

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Abstract

The organization of the brain follows a topological hierarchy that changes dynamically during development. However, it remains unknown whether and how cognitive training administered over multiple years during development can modify this hierarchical topology. By measuring the brain and behavior of school children who had carried out abacus-based mental calculation (AMC) training for five years (starting from 7 years to 12 years old) in pre-training and post-training, we revealed the resha** effect of long-term AMC intervention during development on the brain hierarchical topology. We observed the development-induced emergence of the default network, AMC training-promoted shifting, and regional changes in cortical gradients. Moreover, the training-induced gradient changes were located in visual and somatomotor areas in association with the visuospatial/motor-imagery strategy. We found that gradient-based features can predict the math ability within groups. Our findings provide novel insights into the dynamic nature of network recruitment impacted by long-term cognitive training during development.

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Data and Code Availability

The gradient analysis codes are adapted from the open-access toolbox BrainSpace developed by Vos de Wael [70], which is available at https://github.com/MICA-MNI/BrainSpace. The datasets that support the findings of this study are only available on reasonable request to chenfy@zju.edu.cn.

References

  1. Mesulam MM. From sensation to cognition. Brain 1998, 121(Pt 6): 1013–1052.

    Article  PubMed  Google Scholar 

  2. Huntenburg JM, Bazin PL, Margulies DS. Large-scale gradients in human cortical organization. Trends Cogn Sci 2018, 22: 21–31.

    Article  PubMed  Google Scholar 

  3. Bernhardt BC, Smallwood J, Keilholz S, Margulies DS. Gradients in brain organization. Neuroimage 2022, 251: 118987.

    Article  PubMed  Google Scholar 

  4. Chen CH, Gutierrez ED, Thompson W, Panizzon MS, Jernigan TL, Eyler LT. Hierarchical genetic organization of human cortical surface area. Science 2012, 335: 1634–1636.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Burt JB, Demirtaş M, Eckner WJ, Navejar NM, Ji JL, Martin WJ, et al. Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography. Nat Neurosci 2018, 21: 1251–1259.

    Article  CAS  PubMed Central  Google Scholar 

  6. Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 2012, 489: 391–399.

    Article  CAS  PubMed Central  Google Scholar 

  7. Wagstyl K, Ronan L, Goodyer IM, Fletcher PC. Cortical thickness gradients in structural hierarchies. Neuroimage 2015, 111: 241–250.

    Article  Google Scholar 

  8. Margulies DS, Ghosh SS, Goulas A, Falkiewicz M, Huntenburg JM, Langs G, et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci U S A 2016, 113: 12574–12579.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hong SJ, Vos de Wael R, Bethlehem RAI, Lariviere S, Paquola C, Valk SL, et al. Atypical functional connectome hierarchy in autism. Nat Commun 2019, 10: 1022.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Huth AG, de Heer WA, Griffiths TL, Theunissen FE, Gallant JL. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 2016, 532: 453–458.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Baldassano C, Chen J, Zadbood A, Pillow JW, Hasson U, Norman KA. Discovering event structure in continuous narrative perception and memory. Neuron 2017, 95: 709-721.e5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Coifman RR, Lafon S. Diffusion maps. Appl Comput Harmon Anal 2006, 21: 5–30.

    Article  Google Scholar 

  13. Dong HM, Margulies DS, Zuo XN, Holmes AJ. Shifting gradients of macroscale cortical organization mark the transition from childhood to adolescence. Proc Natl Acad Sci U S A 2021, 118: e2024448118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Bethlehem RAI, Paquola C, Seidlitz J, Ronan L, Bernhardt B, Consortium CC, et al. Dispersion of functional gradients across the adult lifespan. Neuroimage 2020, 222: 117299.

    Article  PubMed  Google Scholar 

  15. Meng Y, Yang S, Chen H, Li J, Xu Q, Zhang Q, et al. Systematically disrupted functional gradient of the cortical connectome in generalized epilepsy: Initial discovery and independent sample replication. Neuroimage 2021, 230: 117831.

    Article  Google Scholar 

  16. **a M, Liu J, Mechelli A, Sun X, Ma Q, Wang X, et al. Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes. Mol Psychiatry 2022, 27: 1384–1393.

    Article  CAS  Google Scholar 

  17. Dong D, Yao D, Wang Y, Hong SJ, Genon S, **n F, et al. Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia. Psychol Med 2023, 53: 771–784.

    Article  Google Scholar 

  18. Bayrak Ş, Khalil AA, Villringer K, Fiebach JB, Villringer A, Margulies DS, et al. The impact of ischemic stroke on connectivity gradients. Neuroimage Clin 2019, 24: 101947.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Bassett DS, Yang M, Wymbs NF, Grafton ST. Learning-induced autonomy of sensorimotor systems. Nat Neurosci 2015, 18: 744–751.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Finc K, Bonna K, He X, Lydon-Staley DM, Kühn S, Duch W, et al. Dynamic reconfiguration of functional brain networks during working memory training. Nat Commun 2020, 11: 1–15.

    Google Scholar 

  21. Zhang Y, Wang C, Yao Y, Zhou C, Chen F. Adaptive reconfiguration of intrinsic community structure in children with 5-year abacus training. Cereb Cortex 2021, 31: 3122–3135.

    Article  PubMed  Google Scholar 

  22. **e Y, Weng J, Wang C, Xu T, Peng X, Chen F. The impact of long-term abacus training on modular properties of functional brain network. Neuroimage 2018, 183: 811–817.

    Article  Google Scholar 

  23. Barner D, Alvarez G, Sullivan J, Brooks N, Srinivasan M, Frank MC. Learning mathematics in a visuospatial format: A randomized, controlled trial of mental abacus instruction. Child Dev 2016, 87: 1146–1158.

    Article  Google Scholar 

  24. Kraus N, Chandrasekaran B. Music training for the development of auditory skills. Nat Rev Neurosci 2010, 11: 599–605.

    Article  CAS  PubMed  Google Scholar 

  25. Lutz A, Slagter HA, Dunne JD, Davidson RJ. Attention regulation and monitoring in meditation. Trends Cogn Sci 2008, 12: 163–169.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Tang YY, Posner MI. Attention training and attention state training. Trends Cogn Sci 2009, 13: 222–227.

    Article  PubMed  Google Scholar 

  27. Klingberg T. Training and plasticity of working memory. Trends Cogn Sci 2010, 14: 317–324.

    Article  PubMed  Google Scholar 

  28. Bavelier D, Green CS, Pouget A, Schrater p. Brain plasticity through the life span: Learning to learn and action video games. Annu Rev Neurosci 2012, 35: 391–416.

    Article  CAS  PubMed  Google Scholar 

  29. Jaeggi SM, Buschkuehl M, Jonides J, Perrig WJ. Improving fluid intelligence with training on working memory. Proc Natl Acad Sci U S A 2008, 105: 6829–6833.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jaeggi SM, Buschkuehl M, Jonides J, Shah p. Short- and long-term benefits of cognitive training. Proc Natl Acad Sci U S A 2011, 108: 10081–10086.

    Article  CAS  PubMed Central  Google Scholar 

  31. Taatgen NA, Strobach T, Karbach J (2016) Cognitive training: An Overview of Features and Applications, 1st edn Springer, Berlin.

    Google Scholar 

  32. McCormick EM, Peters S, Crone EA, Telzer EH. Longitudinal network re-organization across learning and development. Neuroimage 2021, 229: 117784.

    Article  PubMed  Google Scholar 

  33. Mohr H, Wolfensteller U, Betzel RF, Mišić B, Sporns O, Richiardi J, et al. Integration and segregation of large-scale brain networks during short-term task automatization. Nat Commun 2016, 7: 13217.

    Article  CAS  PubMed Central  Google Scholar 

  34. Stigler JW. “Mental abacus”: The effect of abacus training on Chinese children’s mental calculation. Cogn Psychol 1984, 16: 145–176.

    Article  Google Scholar 

  35. Frank MC, Barner D. Representing exact number visually using mental abacus. J Exp Psychol Gen 2012, 141: 134–149.

    Article  PubMed  Google Scholar 

  36. Li Y, Yuzheng H, Zhao M, Wang Y, Huang J, Chen F. The neural pathway underlying a numerical working memory task in abacus-trained children and associated functional connectivity in the resting brain. Brain Res 2013, 1539: 24–33.

    Article  CAS  PubMed  Google Scholar 

  37. Dong S, Wang C, **e Y, Hu Y, Weng J, Chen F. The impact of abacus training on working memory and underlying neural correlates in young adults. Neuroscience 2016, 332: 181–190.

    Article  CAS  PubMed  Google Scholar 

  38. Wang C, Xu T, Geng F, Hu Y, Wang Y, Liu H, et al. Training on abacus-based mental calculation enhances visuospatial working memory in children. J Neurosci 2019, 39: 6439–6448.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Wang C, Weng J, Yao Y, Dong S, Liu Y, Chen F. Effect of abacus training on executive function development and underlying neural correlates in Chinese children. Hum Brain Mapp 2017, 38: 5234–5249.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wang C, Hu Y, Weng J, Chen F, Liu H. Modular segregation of task-dependent brain networks contributes to the development of executive function in children. Neuroimage 2020, 206: 116334.

    Article  Google Scholar 

  41. Wang Y, Geng F, Hu Y, Du F, Chen F. Numerical processing efficiency improved in experienced mental abacus children. Cognition 2013, 127: 149–158.

    Article  PubMed  Google Scholar 

  42. Yao Y, Du F, Wang C, Liu Y, Weng J, Chen F. Numerical processing efficiency improved in children using mental abacus: ERP evidence utilizing a numerical Stroop task. Front Hum Neurosci 2015, 9: 245.

    Article  PubMed Central  Google Scholar 

  43. Hu Y, Geng F, Tao L, Hu N, Du F, Fu K, et al. Enhanced white matter tracts integrity in children with abacus training. Hum Brain Mapp 2011, 32: 10–21.

    Article  PubMed  Google Scholar 

  44. Li Y, Wang Y, Hu Y, Liang Y, Chen F. Structural changes in left fusiform areas and associated fiber connections in children with abacus training: Evidence from morphometry and tractography. Front Hum Neurosci 2013, 7: 335.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Zhou H, Geng F, Wang Y, Wang C, Hu Y, Chen F. Transfer effects of abacus training on transient and sustained brain activation in the frontal-parietal network. Neuroscience 2019, 408: 135–146.

    Article  CAS  PubMed  Google Scholar 

  46. Weng J, **e Y, Wang C, Chen F. The effects of long-term abacus training on topological properties of brain functional networks. Sci Rep 2017, 7: 8862.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Fair DA, Dosenbach NUF, Church JA, Cohen AL, Brahmbhatt S, Miezin FM, et al. Development of distinct control networks through segregation and integration. Proc Natl Acad Sci U S A 2007, 104: 13507–13512.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Fair DA, Cohen AL, Dosenbach NUF, Church JA, Miezin FM, Barch DM, et al. The maturing architecture of the brain’s default network. Proc Natl Acad Sci U S A 2008, 105: 4028–4032.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Fair DA, Cohen AL, Power JD, Dosenbach NU, Church JA, Miezin FM, et al. Functional brain networks develop from a “local to distributed” organization. PLoS Comput Biol 2009, 5: e1000381.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Ernst M, Torrisi S, Balderston N, Grillon C, Hale EA. fMRI functional connectivity applied to adolescent neurodevelopment. Annu Rev Clin Psychol 2015, 11: 361–377.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, et al. Dynamic map** of human cortical development during childhood through early adulthood. Proc Natl Acad Sci U S A 2004, 101: 8174–8179.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Casey BJ, Giedd JN, Thomas KM. Structural and functional brain development and its relation to cognitive development. Biol Psychol 2000, 54: 241–257.

    Article  CAS  PubMed  Google Scholar 

  53. Huttenlocher PR, Dabholkar AS. Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol 1997, 387: 167–178.

    Article  CAS  PubMed  Google Scholar 

  54. Larivière S, Vos de Wael R, Hong SJ, Paquola C, Tavakol S, Lowe AJ, et al. Multiscale structure-function gradients in the neonatal connectome. Cereb Cortex 2020, 30: 47–58.

    Article  PubMed  Google Scholar 

  55. **a Y, **a M, Liu J, Liao X, Lei T, Liang X, et al. Development of functional connectome gradients during childhood and adolescence. Sci Bull 2022, 67: 1049–1061.

    Article  Google Scholar 

  56. Case R, Okamoto Y, Griffin S, McKeough A, Bleiker C, Henderson B, et al. The role of central conceptual structures in the development of children’s thought. Monogr Soc Res Child Dev 1996, 61: i.

    Article  Google Scholar 

  57. Jean p. Part I: Cognitive development in children: Piaget development and learning. J Res Sci Teach 1964, 2: 176–186.

    Article  Google Scholar 

  58. Zilles K, Amunts K. Individual variability is not noise. Trends Cogn Sci 2013, 17: 153–155.

    Article  PubMed  Google Scholar 

  59. Dubois J, Adolphs R. Building a science of individual differences from fMRI. Trends Cogn Sci 2016, 20: 425–443.

    Article  PubMed Central  Google Scholar 

  60. Mueller S, Wang D, Fox MD, Yeo BT, Sepulcre J, Sabuncu MR, et al. Individual variability in functional connectivity architecture of the human brain. Neuron 2013, 77: 586–595.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Gordon EM, Laumann TO, Adeyemo B, Petersen SE. Individual variability of the system-level organization of the human brain. Cereb Cortex 2017, 27: 386–399.

    PubMed  Google Scholar 

  62. Laumann TO, Gordon EM, Adeyemo B, Snyder AZ, Joo SJ, Chen MY, et al. Functional system and areal organization of a highly sampled individual human brain. Neuron 2015, 87: 657–670.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Gordon EM, Laumann TO, Gilmore AW, Newbold DJ, Greene DJ, Berg JJ, et al. Precision functional map** of individual human brains. Neuron 2017, 95: 791.e7-807.e7.

    Article  Google Scholar 

  64. Kong R, Li J, Orban C, Sabuncu MR, Liu H, Schaefer A, et al. Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cereb Cortex 2019, 29: 2533–2551.

    Article  PubMed  Google Scholar 

  65. Li D, Chen G (1989) Combined Reven’s Test (CRT)-Chinese Revised Version. East China Normal University, Shanghai.

    Google Scholar 

  66. Haffner J, Baro K, Parzer p, Resch F. Heidelberger Rechentest: Erfassung Mathematischer Basiskompetenzen im Grundschulalter: Der Heidelberger Rechentest HRT. Diagnostik Math Neue Folge 2005, 4: 125–151.

    Google Scholar 

  67. Wu H, Li L. Development of Chinese rating scale of pupil’s mathematic abilities and study on its reliability and validity. Chinese Journal of Public Health 2005, 21(4): 473–475.

    Google Scholar 

  68. Zhao T, Liao X, Fonov VS, Wang Q, Men W, Wang Y, et al. Unbiased age-specific structural brain atlases for Chinese pediatric population. Neuroimage 2019, 189: 55–70.

    Article  PubMed  Google Scholar 

  69. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002, 15: 273–289.

    Article  CAS  PubMed  Google Scholar 

  70. Vos de Wael R, Benkarim O, Paquola C, Lariviere S, Royer J, Tavakol S, et al. BrainSpace: A toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun Biol 2020, 3: 103.

    Article  PubMed Central  Google Scholar 

  71. Langs G, Golland P, Ghosh SS. Predicting activation across individuals with resting-state functional connectivity based multi-atlas label fusion. Lecture Notes in Computer Science. Springer, Berlin, 2015, 313–320.

  72. Nenning KH, Xu T, Schwartz E, Arroyo J, Woehrer A, Franco AR, et al. Joint embedding: A scalable alignment to compare individuals in a connectivity space. Neuroimage 2020, 222: 117232.

    Article  Google Scholar 

  73. Langs G, Sweet A, Lashkari D, Tie Y, Rigolo L, Golby AJ, et al. Decoupling function and anatomy in atlases of functional connectivity patterns: Language map** in tumor patients. Neuroimage 2014, 103: 462–475.

    Article  PubMed  Google Scholar 

  74. Chen F, Hu Z, Zhao X, Wang R, Yang Z, Wang X, et al. Neural correlates of serial abacus mental calculation in children: A functional MRI study. Neurosci Lett 2006, 403: 46–51.

    Article  CAS  PubMed  Google Scholar 

  75. Hanakawa T, Honda M, Okada T, Fukuyama H, Shibasaki H. Neural correlates underlying mental calculation in abacus experts: A functional magnetic resonance imaging study. Neuroimage 2003, 19: 296–307.

    Article  PubMed  Google Scholar 

  76. Cho PS, So WC. A feel for numbers: The changing role of gesture in manipulating the mental representation of an abacus among children at different skill levels. Front Psychol 2018, 9: 1267.

    Article  PubMed Central  Google Scholar 

  77. Brooks NB, Barner D, Frank M, Goldin-Meadow S. The role of gesture in supporting mental representations: The case of mental abacus arithmetic. Cogn Sci 2018, 42: 554–575.

    Article  Google Scholar 

  78. Geers L, Pesenti M, Derosiere G, Duque J, Dricot L, Andres M. Role of the fronto-parietal cortex in prospective action judgments. Sci Rep 2021, 11: 7454.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Hanakawa T, Dimyan MA, Hallett M. Motor planning, imagery, and execution in the distributed motor network: A time-course study with functional MRI. Cereb Cortex 2008, 18: 2775–2788.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Du Boisgueheneuc F, Levy R, Volle E, Seassau M, Duffau H, Kinkingnehun S, et al. Functions of the left superior frontal gyrus in humans: A lesion study. Brain 2006, 129: 3315–3328.

    Article  PubMed  Google Scholar 

  81. Nachev p, Kennard C, Husain M. Functional role of the supplementary and pre-supplementary motor areas. Nat Rev Neurosci 2008, 9: 856–869.

    Article  CAS  Google Scholar 

  82. Schall JD. Visuomotor functions in the frontal lobe. Annu Rev Vis Sci 2015, 1: 469–498.

    Article  PubMed  Google Scholar 

  83. Euston DR, Gruber AJ, McNaughton BL. The role of medial prefrontal cortex in memory and decision making. Neuron 2012, 76: 1057–1070.

    Article  CAS  PubMed Central  Google Scholar 

  84. Kong J, Wang C, Kwong K, Vangel M, Chua E, Gollub R. The neural substrate of arithmetic operations and procedure complexity. Brain Res Cogn Brain Res 2005, 22: 397–405.

    Article  PubMed  Google Scholar 

  85. Rivera SM, Reiss AL, Eckert MA, Menon V. Developmental changes in mental arithmetic: Evidence for increased functional specialization in the left inferior parietal cortex. Cereb Cortex 2005, 15: 1779–1790.

    Article  CAS  PubMed  Google Scholar 

  86. Davis KD, Hutchison WD, Lozano AM, Tasker RR, Dostrovsky JO. Human anterior cingulate cortex neurons modulated by attention-demanding tasks. J Neurophysiol 2000, 83: 3575–3577.

    Article  CAS  Google Scholar 

  87. Davis KD, Taylor KS, Hutchison WD, Dostrovsky JO, McAndrews MP, Richter EO, et al. Human anterior cingulate cortex neurons encode cognitive and emotional demands. J Neurosci 2005, 25: 8402–8406.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Wang R, Lin p, Liu M, Wu Y, Zhou T, Zhou C. Hierarchical connectome modes and critical state jointly maximize human brain functional diversity. Phys Rev Lett 2019, 123: 038301.

    Article  CAS  PubMed  Google Scholar 

  89. Wang R, Liu M, Cheng X, Wu Y, Hildebrandt A, Zhou C. Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities. Proc Natl Acad Sci U S A 2021, 118: e2022288118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Hao L, Li L, Chen M, Xu J, Jiang M, Wang Y, et al. Map** domain- and age-specific functional brain activity for children’s cognitive and affective development. Neurosci Bull 2021, 37: 763–776.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo XN, Holmes AJ, et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb Cortex 2018, 28: 3095–3114.

    Article  PubMed  Google Scholar 

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Acknowledgments

We are grateful to the Chinese Abacus and Mental Arithmetic Association and the Heilongjiang Abacus Association for their kind support, as well as to the children, parents, and teachers of Qiqihar for their participation in the study. This work was supported by the National Natural Science Foundation of China (32071096 and 31270026); the National Social Science Foundation (17ZDA323); the STI 2030—Major Projects (2021ZD0200500); the Hong Kong Baptist University Research Committee Interdisciplinary Research Matching Scheme 2018/19 (IRMS/18-19/SCI01); the Recruitment Program of Global Experts of Zhejiang Province; and the Start-up Funds for Leading Talents at Bei**g Normal University and the National Basic Science Data Center “Chinese Data-sharing Warehouse for In-vivo Imaging Brain” (NBSDC-DB-15).

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Authors and Affiliations

  1. Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China

    Tianyong Xu, Yi Zhang, Feiyan Chen & Changsong Zhou

  2. Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China

    Yunying Wu

  3. State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China

    Yi Zhang

  4. State Key Laboratory of Cognitive Neuroscience and Learning, Bei**g Normal University, Bei**g, 100875, China

    **-Nian Zuo

  5. Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Bei**g Normal University, Bei**g, 100875, China

    **-Nian Zuo

  6. Zhejiang Province Key Laboratory of Quantum Technology and Devices, Zhejiang University, Hangzhou, 310027, China

    Feiyan Chen & Changsong Zhou

  7. Department of Physics, Centre for Nonlinear Studies and Bei**g-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong, 999077, China

    Changsong Zhou

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Xu, T., Wu, Y., Zhang, Y. et al. Resha** the Cortical Connectivity Gradient by Long-Term Cognitive Training During Development. Neurosci. Bull. 40, 50–64 (2024). https://doi.org/10.1007/s12264-023-01108-8

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