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Neural Plasticity
Volume 2016, Article ID 1213723, 9 pages
http://dx.doi.org/10.1155/2016/1213723
Review Article

Neural Plasticity following Abacus Training in Humans: A Review and Future Directions

1Institute of Clinical Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
2Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China

Received 3 August 2015; Revised 24 September 2015; Accepted 28 September 2015

Academic Editor: Preston E. Garraghty

Copyright © 2016 Yongxin Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. May, “Experience-dependent structural plasticity in the adult human brain,” Trends in Cognitive Sciences, vol. 15, no. 10, pp. 475–482, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. R. J. Zatorre, R. D. Fields, and H. Johansen-Berg, “Plasticity in gray and white: neuroimaging changes in brain structure during learning,” Nature Neuroscience, vol. 15, no. 4, pp. 528–536, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. F. Chen, Z. Hu, X. Zhao et al., “Neural correlates of serial abacus mental calculation in children: a functional MRI study,” Neuroscience Letters, vol. 403, no. 1-2, pp. 46–51, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. T. Hanakawa, M. Honda, T. Okada, H. Fukuyama, and H. Shibasaki, “Neural correlates underlying mental calculation in abacus experts: a functional magnetic resonance imaging study,” NeuroImage, vol. 19, no. 2, pp. 296–307, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Hu, F. Geng, L. Tao et al., “Enhanced white matter tracts integrity in children with abacus training,” Human Brain Mapping, vol. 32, no. 1, pp. 10–21, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Li, Y. Hu, M. Zhao, Y. Wang, J. Huang, and F. Chen, “The neural pathway underlying a numerical working memory task in abacus-trained children and associated functional connectivity in the resting brain,” Brain Research, vol. 1539, pp. 24–33, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Tanaka, C. Michimata, T. Kaminaga, M. Honda, and N. Sadato, “Superior digit memory of abacus experts: an event-related functional MRI study,” NeuroReport, vol. 13, no. 17, pp. 2187–2191, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Q. Wang, F. J. Geng, Y. Z. Hu, F. L. Du, and F. Y. Chen, “Numerical processing efficiency improved in experienced mental abacus children,” Cognition, vol. 127, no. 2, pp. 149–158, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Bhaskaran, A. Sengottaiyan, S. Madhu, and V. Ranganathan, “Evaluation of memory in abacus learners,” Indian Journal of Physiology and Pharmacology, vol. 50, no. 3, pp. 225–233, 2006. View at Google Scholar · View at Scopus
  10. M. C. Frank and D. Barner, “Representing exact number visually using mental abacus,” Journal of Experimental Psychology: General, vol. 141, no. 1, pp. 134–149, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. J. W. Stigler, “‘Mental abacus’: the effect of abacus training on Chinese children's mental calculation,” Cognitive Psychology, vol. 16, no. 2, pp. 145–176, 1984. View at Publisher · View at Google Scholar · View at Scopus
  12. J. W. Stigler, L. Chalip, and K. F. Miller, “Consequences of skill: the case of abacus training in Taiwan,” American Journal of Education, vol. 94, no. 4, pp. 447–479, 1986. View at Publisher · View at Google Scholar
  13. G. Hatano, Y. Miyake, and M. G. Binks, “Performance of expert abacus operators,” Cognition, vol. 5, no. 1, pp. 47–55, 1977. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Hishitani, “Imagery experts: how do expert abacus operators process imagery?” Applied Cognitive Psychology, vol. 4, no. 1, pp. 33–46, 1990. View at Publisher · View at Google Scholar
  15. K. F. Miller and J. W. Stigler, “Meanings of skill: effects of abacus expertise on number representation,” Cognition and Instruction, vol. 8, no. 1, pp. 29–67, 1991. View at Publisher · View at Google Scholar
  16. G. Hatano and K. Osawa, “Digit memory of grand experts in abacus-derived mental calculation,” Cognition, vol. 15, no. 1–3, pp. 95–110, 1983. View at Publisher · View at Google Scholar · View at Scopus
  17. T. Hatta and M. Miyazaki, “Visual imagery processing in Japanese abacus experts,” Imagination, Cognition and Personality, vol. 9, no. 2, pp. 91–102, 1990. View at Publisher · View at Google Scholar
  18. S. Dehaene, E. Spelke, P. Pinel, R. Stanescu, and S. Tsivkin, “Sources of mathematical thinking: behavioral and brain-imaging evidence,” Science, vol. 284, no. 5416, pp. 970–974, 1999. View at Publisher · View at Google Scholar · View at Scopus
  19. M. C. Frank, D. L. Everett, E. Fedorenko, and E. Gibson, “Number as a cognitive technology: evidence from Pirahã language and cognition,” Cognition, vol. 108, no. 3, pp. 819–824, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. G. Hatano, S. Amaiwa, and K. Shimizu, “Formation of a mental abacus for computation and its use as a memory device for digits: a developmental study,” Developmental Psychology, vol. 23, no. 6, pp. 832–838, 1987. View at Publisher · View at Google Scholar · View at Scopus
  21. T. Hatta and K. Ikeda, “Hemispheric specialization of abacus experts in mental calculation: evidence from the results of time-sharing tasks,” Neuropsychologia, vol. 26, no. 6, pp. 877–893, 1988. View at Publisher · View at Google Scholar · View at Scopus
  22. S. M. Jaeggi, M. Buschkuehl, J. Jonides, and P. Shah, “Short-and long-term benefits of cognitive training,” Proceedings of the National Academy of Sciences of the United States of America, vol. 108, no. 25, pp. 10081–10086, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. T. Klingberg, “Training and plasticity of working memory,” Trends in Cognitive Sciences, vol. 14, no. 7, pp. 317–324, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. P. Irwing, A. Hamza, O. Khaleefa, and R. Lynn, “Effects of Abacus training on the intelligence of Sudanese children,” Personality and Individual Differences, vol. 45, no. 7, pp. 694–696, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. H. Shen, “Teaching mental abacus calculation to students with mental retardation,” Journal of the International Association of Special Education, vol. 7, no. 1, pp. 56–66, 2006. View at Google Scholar
  26. K.-S. Na, S. I. Lee, J.-H. Park, H.-Y. Jung, and J.-H. Ryu, “Association between abacus training and improvement in response inhibition: a case-control study,” Clinical Psychopharmacology and Neuroscience, vol. 13, no. 2, pp. 163–167, 2015. View at Publisher · View at Google Scholar
  27. C. Lustig, P. Shah, R. Seidler, and P. A. Reuter-Lorenz, “Aging, training, and the brain: a review and future directions,” Neuropsychology Review, vol. 19, no. 4, pp. 504–522, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. P. A. Bandettini, “Twenty years of functional MRI: the science and the stories,” NeuroImage, vol. 62, no. 2, pp. 575–588, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. D. A. Leopold, “Neuroscience: fMRI under the spotlight,” Nature, vol. 465, no. 7299, pp. 700–701, 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Durston and B. J. Casey, “What have we learned about cognitive development from neuroimaging?” Neuropsychologia, vol. 44, no. 11, pp. 2149–2157, 2006. View at Publisher · View at Google Scholar · View at Scopus
  31. T.-H. Wu, C.-L. Chen, Y.-H. Huang, R.-S. Liu, J.-C. Hsieh, and J. J. S. Lee, “Effects of long-term practice and task complexity on brain activities when performing abacus-based mental calculations: a PET study,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 36, no. 3, pp. 436–445, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. T. H. Wu, C. L. Chen, R. S. Liu, Y. H. Huang, and J. S. Lee, “The computing brain: abacus-based mental calculation correlation between abacus experts and normal subjects in PET study,” in Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering, pp. 564–567, Capri Island, Italy, March 2003. View at Publisher · View at Google Scholar
  33. T. Hatta, T. Hirose, K. Ikeda, and H. Fukuhara, “Digit memory of soroban experts: evidence of utilization of mental imagery,” Applied Cognitive Psychology, vol. 3, no. 1, pp. 23–33, 1989. View at Publisher · View at Google Scholar
  34. C. L. Chen, T. H. Wu, M. C. Cheng et al., “Prospective demonstration of brain plasticity after intensive abacus-based mental calculation training: an fMRI study,” Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 569, no. 2, pp. 567–571, 2006. View at Publisher · View at Google Scholar · View at Scopus
  35. S. Tanaka, K. Seki, T. Hanakawa et al., “Abacus in the brain: a longitudinal functional MRI study of a skilled abacus user with a right hemispheric lesion,” Frontiers in Psychology, vol. 3, article 315, 2012. View at Publisher · View at Google Scholar · View at Scopus
  36. F. Du, F. Chen, Y. Li, Y. Hu, M. Tian, and H. Zhang, “Abacus training modulates the neural correlates of exact and approximate calculations in chinese children: an FMRI study,” BioMed Research International, vol. 2013, Article ID 694075, 12 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  37. Y. Ku, B. Hong, W. Zhou, M. Bodner, and Y.-D. Zhou, “Sequential neural processes in abacus mental addition: an EEG and fMRI case study,” PLoS ONE, vol. 7, no. 5, Article ID e36410, 2012. View at Publisher · View at Google Scholar · View at Scopus
  38. L. Bezzola, S. Mérillat, C. Gaser, and L. Jäncke, “Training-induced neural plasticity in golf novices,” The Journal of Neuroscience, vol. 31, no. 35, pp. 12444–12448, 2011. View at Publisher · View at Google Scholar · View at Scopus
  39. X. Duan, S. He, W. Liao et al., “Reduced caudate volume and enhanced striatal-DMN integration in chess experts,” NeuroImage, vol. 60, no. 2, pp. 1280–1286, 2012. View at Publisher · View at Google Scholar · View at Scopus
  40. K. L. Hyde, J. Lerch, A. Norton et al., “Musical training shapes structural brain development,” The Journal of Neuroscience, vol. 29, no. 10, pp. 3019–3025, 2009. View at Publisher · View at Google Scholar · View at Scopus
  41. U. Ott, B. K. Hölzel, and D. Vaitl, “Brain structure and meditation: how spiritual practice shapes the brain,” in Neuroscience, Consciousness and Spirituality, vol. 1 of Studies in Neuroscience, Consciousness and Spirituality, pp. 119–128, Springer, Dordrecht, The Netherlands, 2011. View at Publisher · View at Google Scholar
  42. Y.-Y. Tang, R. Tang, and M. I. Posner, “Brief meditation training induces smoking reduction,” Proceedings of the National Academy of Sciences of the United States of America, vol. 110, no. 34, pp. 13971–13975, 2013. View at Publisher · View at Google Scholar · View at Scopus
  43. H. Johansen-Berg, “The future of functionally-related structural change assessment,” NeuroImage, vol. 62, no. 2, pp. 1293–1298, 2012. View at Publisher · View at Google Scholar · View at Scopus
  44. J. Ashburner and K. J. Friston, “Voxel-based morphometry—the methods,” NeuroImage, vol. 11, no. 6, pp. 805–821, 2000. View at Publisher · View at Google Scholar · View at Scopus
  45. C. D. Good, I. S. Johnsrude, J. Ashburner, R. N. A. Henson, K. J. Friston, and R. S. J. Frackowiak, “A voxel-based morphometric study of ageing in 465 normal adult human brains,” NeuroImage, vol. 14, no. 1, pp. 21–36, 2001. View at Publisher · View at Google Scholar · View at Scopus
  46. Y. Li, Y. Wang, Y. Hu, Y. Liang, and F. Chen, “Structural changes in left fusiform areas and associated fiber connections in children with abacus training: evidence from morphometry and tractography,” Frontiers in Human Neuroscience, vol. 7, article 335, 2013. View at Publisher · View at Google Scholar · View at Scopus
  47. S. Jbabdi and H. Johansen-Berg, “Tractography: where do we go from here?” Brain Connectivity, vol. 1, no. 3, pp. 169–183, 2011. View at Publisher · View at Google Scholar
  48. H. Johansen-Berg and M. F. S. Rushworth, “Using diffusion imaging to study human connectional anatomy,” Annual Review of Neuroscience, vol. 32, pp. 75–94, 2009. View at Publisher · View at Google Scholar · View at Scopus
  49. D. Le Bihan, “Looking into the functional architecture of the brain with diffusion MRI,” Nature Reviews Neuroscience, vol. 4, no. 6, pp. 469–480, 2003. View at Publisher · View at Google Scholar · View at Scopus
  50. D. Le Bihan and H. Johansen-Berg, “Diffusion MRI at 25: exploring brain tissue structure and function,” NeuroImage, vol. 61, no. 2, pp. 324–341, 2012. View at Publisher · View at Google Scholar · View at Scopus
  51. P. J. Basser and C. Pierpaoli, “Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI,” Journal of Magnetic Resonance—Series B, vol. 111, no. 3, pp. 209–219, 1996. View at Publisher · View at Google Scholar · View at Scopus
  52. J. Scholz, M. C. Klein, T. E. J. Behrens, and H. Johansen-Berg, “Training induces changes in white-matter architecture,” Nature Neuroscience, vol. 12, no. 11, pp. 1370–1371, 2009. View at Publisher · View at Google Scholar · View at Scopus
  53. M. Skup, “Longitudinal fMRI analysis: a review of methods,” Statistics and Its Interface, vol. 3, no. 2, pp. 235–252, 2010. View at Publisher · View at Google Scholar
  54. A. Engvig, A. M. Fjell, L. T. Westlye et al., “Memory training impacts short-term changes in aging white matter: a Longitudinal Diffusion Tensor Imaging Study,” Human Brain Mapping, vol. 33, no. 10, pp. 2390–2406, 2012. View at Publisher · View at Google Scholar · View at Scopus
  55. A. Giorgio, K. E. Watkins, M. Chadwick et al., “Longitudinal changes in grey and white matter during adolescence,” NeuroImage, vol. 49, no. 1, pp. 94–103, 2010. View at Publisher · View at Google Scholar · View at Scopus
  56. J. Jiang, P. Sachdev, D. M. Lipnicki et al., “A longitudinal study of brain atrophy over two years in community-dwelling older individuals,” NeuroImage, vol. 86, pp. 203–211, 2014. View at Publisher · View at Google Scholar · View at Scopus
  57. J. P. Szaflarski, V. J. Schmithorst, M. Altaye et al., “A longitudinal functional magnetic resonance imaging study of language development in children 5 to 11 years old,” Annals of Neurology, vol. 59, no. 5, pp. 796–807, 2006. View at Publisher · View at Google Scholar · View at Scopus
  58. J. S. Damoiseaux and M. D. Greicius, “Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity,” Brain Structure and Function, vol. 213, no. 6, pp. 525–533, 2009. View at Publisher · View at Google Scholar · View at Scopus
  59. E. Rykhlevskaia, L. Q. Uddin, L. Kondos, and V. Menon, “Neuroanatomical correlates of developmental dyscalculia: combined evidence from morphometry and tractography,” Frontiers in Human Neuroscience, vol. 3, article 51, 2009. View at Publisher · View at Google Scholar · View at Scopus
  60. J. Sui, T. Adali, Q. Yu, J. Chen, and V. D. Calhoun, “A review of multivariate methods for multimodal fusion of brain imaging data,” Journal of Neuroscience Methods, vol. 204, no. 1, pp. 68–81, 2012. View at Publisher · View at Google Scholar · View at Scopus
  61. J. Sui, R. Huster, Q. Yu, J. M. Segall, and V. D. Calhoun, “Function-structure associations of the brain: evidence from multimodal connectivity and covariance studies,” NeuroImage, vol. 102, part 1, pp. 11–23, 2014. View at Publisher · View at Google Scholar · View at Scopus
  62. C. M. Michel and M. M. Murray, “Towards the utilization of EEG as a brain imaging tool,” NeuroImage, vol. 61, no. 2, pp. 371–385, 2012. View at Publisher · View at Google Scholar · View at Scopus
  63. S. M. Smith, “The future of FMRI connectivity,” NeuroImage, vol. 62, no. 2, pp. 1257–1266, 2012. View at Publisher · View at Google Scholar · View at Scopus
  64. E. Dahlin, L. Bäckman, A. S. Neely, and L. Nyberg, “Training of the executive component of working memory: subcortical areas mediate transfer effects,” Restorative Neurology and Neuroscience, vol. 27, no. 5, pp. 405–419, 2009. View at Publisher · View at Google Scholar · View at Scopus
  65. U. Hasson and C. J. Honey, “Future trends in neuroimaging: neural processes as expressed within real-life contexts,” NeuroImage, vol. 62, no. 2, pp. 1272–1278, 2012. View at Publisher · View at Google Scholar · View at Scopus
  66. R. L. Buckner, F. M. Krienen, and B. T. T. Yeo, “Opportunities and limitations of intrinsic functional connectivity MRI,” Nature Neuroscience, vol. 16, no. 7, pp. 832–837, 2013. View at Publisher · View at Google Scholar · View at Scopus
  67. L. Q. Uddin, “Complex relationships between structural and functional brain connectivity,” Trends in Cognitive Sciences, vol. 17, no. 12, pp. 600–602, 2013. View at Publisher · View at Google Scholar · View at Scopus
  68. M. P. Van Den Heuvel and H. E. Hulshoff Pol, “Exploring the brain network: a review on resting-state fMRI functional connectivity,” European Neuropsychopharmacology, vol. 20, no. 8, pp. 519–534, 2010. View at Publisher · View at Google Scholar · View at Scopus