Skip to main content

Practice and Task Experience Change the Gradient Organization in the Resting Brain

  • Conference paper
Brain Informatics and Health (BIH 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8609))

Included in the following conference series:

Abstract

A kind of metacognitive and cognitive activity patterns in the brain have been found in the task state, showing the gradient distribution from abstract to concrete processing in the frontal and parietal cortex especially. In our early study, it is observed that this kind of gradient organization is intrinsic and prepared in the resting state. Learning experience is a process from metacognitive to cognitive processing, which might change the spontaneous activity patterns in the resting brain. This study is to explore how the learning experience, including both long-term practice and short-term task experience, influences the intrinsic gradient organization in the human brain. Focused on the task-evoked metacognitive and cognitive pattern regions, by comparing four resting state data, before and after task performing in Day 1 before practice named pre-pre and pre-post respectively, and before and after task in Day 7 after 5-day’s practice, named post-pre and post-post respectively, we investigated the change of gradient organization in the human brain with the approach of functional connectivity (FC) analysis. The result showed that the gradient organization is quite stable across the four resting states, which is similar with our previous finding. Task performance enhanced the correlation between cognitive and mixed functional network, especially after long-time practice, suggesting the key role of cognitive network in the task execution. Moreover, after long practice, the internal connectivity within the metacognitive network and the connection between mixed and cognitive functional network were both weakened, which suggested a functional modulation and separation when task performance became more and more skilled and automatic.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, J.R., Betts, S., Ferris, J.L., Fincham, J.M.: Cognitive and metacognitive activity in mathematical problem solving: prefrontal and parietal patterns. Cognitive, Affective, Behavioral Neuroscience 11, 52–67 (2011)

    Article  Google Scholar 

  2. Asari, T., Konishi, S., Jimura, K., Miyashita, Y.: Multiple components of lateral posterior parietal activation associated with cognitive set shifting. NeuroImage 26, 694–702 (2005)

    Article  Google Scholar 

  3. Badre, D.: Cognitive control, hierachy, and the rostro-caudal organization of the frontal lobes. Trends in Cognitive Sciences 12(5), 193–200 (2008)

    Article  Google Scholar 

  4. Buckner, R.L.: Functional-anatomic correlates of control processes in memory. The Journal of Neuroscience 23(10), 3999–4004 (2003)

    Google Scholar 

  5. Christoff, K., Gabrieli, J.D.E.: The frontopolar cortex and human cognition: Evidence for a rostrocaudal hierarchical organization within the human prefrontal cortex. Psychology 28(2), 168–186 (2000)

    Google Scholar 

  6. Nee, D.E., Brown, J.W.: Rostral-caudal gradients of abstraction revealed by multi-variate pattern analysis of working memory. NeuroImage 63(3), 1285–1294 (2012)

    Article  Google Scholar 

  7. Zhou, H.Y., Wang, Z.J., Yang, J., Qin, Y.L., Li, K.C., Zhong, N.: The gradient cognitive and metacognitive organization in the resting brain (2013) (Preparation)

    Google Scholar 

  8. Kitchner, K.S.: Cognition, Metacognition, and Epistemic Cognition. Human Development 26(4), 222–232 (1983)

    Article  Google Scholar 

  9. Buckner, R.L., Andrews-Hanna, J.R., Schacter, D.L.: The brain’s default network. Annals of the New York Academy of Sciences 1124, 1–38 (2008)

    Article  Google Scholar 

  10. Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E.: The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences 102(27), 9673–9678 (2005)

    Article  Google Scholar 

  11. Qin, Y.L., Carter, C.S., Silk, E.M., Stenger, V.A., Fissell, K., Goode, A., Anderson, J.R.: The change of the brain activation patterns as children learn algebra equation solving. Proceedings of the National Academy of Sciences 101(15), 5686–5691 (2004)

    Article  Google Scholar 

  12. Bassett, D.S., Wymbs, N.F., Porter, M.A., Mucha, P.J., Carlson, J.M., Grafton, S.T.: Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences 108(18), 7641–7646 (2011)

    Article  Google Scholar 

  13. Wang, Z.J., Liu, J.M., Zhong, N., Qin, Y.L., Zhou, H.Y., Li, K.C.: Changes in the brain intrinsic organization in both on-task state and post-task resting state. NeuroImage 62, 394–407 (2012)

    Article  Google Scholar 

  14. Wintermute, S., Betts, S., Ferris, J.L., Fincham, J.M., Anderson, J.R.: Brain networks supporting execution of mathematical skills versus acquisition of new mathematical competence. PLoS ONE 7(12), 1–16 (2012)

    Article  Google Scholar 

  15. Rosenberg-Lee, M., Lovett, M.C., Anderson, J.R.: Neural correlates of arithmetic calculation strategies. Cognitive, Affective, Behavioral Neuroscience 9(3), 270–285 (2009)

    Article  Google Scholar 

  16. Qin, Y.L., Xiang, J., Wang, R.F., Zhou, H.Y., Li, H.C., Zhong, N.: Neural bases for basic processes in heuristic problem solving: Take solving Sudoku puzzles as an example. PsyCh Journal 1(2), 101–117 (2012)

    Article  Google Scholar 

  17. Yan, C.G., Zang, Y.F.: DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Neuroscience 4(13), 1–7 (2010)

    Google Scholar 

  18. Jenkins, G.M., Watts, D.G.: Spectral Analysis and Its Applications. Holden-Day Series in Time Series Analysis (1968)

    Google Scholar 

  19. Ramnani, N., Owen, A.M.: Anterior prefrontal cortex: Insights into function from anatomy and neuroimaging. Nature Reviews Neuroscience 5, 184–194 (2004)

    Article  Google Scholar 

  20. Chein, J.M., Schneider, W.: The Brain’s Learning and Control Architecture. Psychological Science 21(2), 78–84 (2012)

    Google Scholar 

  21. Pascual-Leone, A., Amedi, A., Fregni, F., Merabet, L.B.: The plastic human brain cortex. Annual Review of Neuroscience 28, 377–401 (2005)

    Article  Google Scholar 

  22. Hill, N.M., Schneider, W.: Brain Changes in the Development of Expertise: Neuroanatomical and Neurophysiological Evidence about Skill-Based Adaptations. In: The Cambridge handbook of expertise and expert performance, pp. 653–682. Cambridge University Press, NY (2006)

    Chapter  Google Scholar 

  23. Lewis, C.M., Baldassarre, A., Committeri, G., Romani, G.L., Corbetta, M.: Learning sculpts the spontaneous activity of the resting human brain. Proceedings of the National of Sciences 106(41), 17558–17563 (2009)

    Article  Google Scholar 

  24. Shiffrin, R.M., Schneider, W.: Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological Review 84(2), 127–190 (1977)

    Article  Google Scholar 

  25. Schneider, W., Shiffrin, R.M.: Controlled and automatic human information processing: I. Detection, search, and attention. Psychological Review 84(1), 1–66 (1977)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhou, J. et al. (2014). Practice and Task Experience Change the Gradient Organization in the Resting Brain. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-09891-3_45

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09890-6

  • Online ISBN: 978-3-319-09891-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics