Skip to main content
Springer Nature Link
Log in

Route Adjustment of Functional Brain Network in Mental Arithmetic Using Task-Evoked FMRI

  • Conference paper
  • First Online:
Brain Informatics (BI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11976))

Included in the following conference series:

  • 885 Accesses

Abstract

A large number of studies on altered functional brain network tend to focus only on the alternation in topological metric of functional connectivity, rather than on the details of graph adjustment that cause topological metric changes, such as significant adjusted route and the nodes on it. In this paper, we first used the brain atlas of Dosenbach to generate the functional brain networks of the 21 participants recruited in the mental arithmetic experiment. Then, the nodal efficiency of each brain region in the network were calculated and statistically compared between mental arithmetic cognitive states. The brain regions with significant alternation in nodal efficiency were taken as seeds for searching adjusted routes. The brain regions that have significant changes in network efficiency with the seed nodes were considered as destined nodes of the relative seed nodes. Finally, the details of two adopted indicators on altered functional brain network by comparing the adjusted route between the two endpoints of the adjusted route were given and used as clues for the better understanding of the cognitive pattern of mental arithmetic. In this paper, the average number of adjusted routes contributed by brain region is used to indicate the degree of contribution of the brain region to the route adjustment, and the interaction degree within specific network is indicated by the density of adjusted routes. The results show that both indicators of fronto-parietal network is significantly higher than that of other networks, which indicates the brain regions and the routes within fronto-parietal network are the most active. In summary, the method proposed in this paper provides a new perspective to study the causes of functional brain network alternation in mental arithmetic. However, due to the participants’ variation of adjusted routes and the nodes on it, a better understanding of these functional brain network alternation for individual participant with the proposed method needs more in-depth research.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Wang, M., Wang, L.: Localization of the brain calculation function area with MRI. Chin. Sci. Bull. 46(22), 1889–1892 (2001)

    Google Scholar 

  2. Liu, J., Zhang, H., Chen, C., et al.: The neural circuits for arithmetic principles. Neuroimage 147, 432–446 (2016). (Complete)

    Article  Google Scholar 

  3. Yang, Y., Zhong, N., Friston, K., et al.: The functional architectures of addition and subtraction: network discovery using fMRI and DCM. Hum. Brain Mapp. 38(6), 3210–3325 (2017)

    Article  Google Scholar 

  4. Zhang, X., Yang, Y., Zhang, M.-H., Zhong, N.: Network analysis of brain functional connectivity in mental arithmetic using task-evoked fMRI. In: Wang, S., et al. (eds.) BI 2018. LNCS (LNAI), vol. 11309, pp. 141–152. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05587-5_14

    Chapter  Google Scholar 

  5. Arsalidou, M., Taylor, M.J.: Is 2 + 2 = 4? meta-analyses of brain areas needed for numbers and calculations. Neuroimage 54(3), 2382–2393 (2011)

    Article  Google Scholar 

  6. Dehaene, S., Cohen, L.: Cerebral pathways for calculation: double dissociation between rote verbal and quantitative knowledge of arithmetic. Cortex 33(2), 219–250 (1997)

    Article  Google Scholar 

  7. Klein, E., Moeller, K., Glauche, V., et al.: Processing pathways in mental arithmetic—evidence from probabilistic Fiber tracking. PLoS ONE 8(1), 1–14 (2013)

    Google Scholar 

  8. Klein, E., Suchan, J., Moeller, K., et al.: Considering structural connectivity in the triple code model of numerical cognition: differential connectivity for magnitude processing and arithmetic facts. Brain Struct. Funct. 221(2), 979–995 (2016)

    Article  Google Scholar 

  9. Shine, J.M., Bissett, P.G., Bell, P.T., et al.: The dynamics of functional brain networks: integrated network states during cognitive function. Neuron 92(2), 544–554 (2015)

    Article  Google Scholar 

  10. Telesford, Q.K., Lynall, M.E., Vettel, J., et al.: Detection of functional brain network reconfiguration during task-driven cognitive states. Neuroimage 142, 198–210 (2016)

    Article  Google Scholar 

  11. Bassett, D.S., Wymbs, N.F., Porter, M.A., et al.: Dynamic reconfiguration of human brain networks during learning. Proc. Nat. Acad. Sci. U. S. Am. 108(18), 7641–7646 (2011)

    Article  Google Scholar 

  12. Braun, U., Schäfer, A., Walter, H., et al.: Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc. Nat. Acad. Sci. U.S. Am. 112(37), 11678–11683 (2015)

    Article  Google Scholar 

  13. Tanaka, S., Kirino, E.: Dynamic reconfiguration of the supplementary motor area network during imagined music performance. Front. Hum. Neurosci. 11, 1–11 (2017)

    Article  Google Scholar 

  14. Dosenbach, N.U.F., Nardos, B., Cohen, A.L., et al.: Prediction of individual brain maturity using fMRI. Science 329(5997), 1358–1361 (2010)

    Article  Google Scholar 

  15. Latora, V., Marchiori, M.: Efficient behavior of small-world networks. Phys. Rev. Lett. 87(19), 198701–198704 (2001)

    Article  Google Scholar 

  16. Hilger, K., Ekman, M., Fiebach, C.J., et al.: Efficient hubs in the intelligent brain: nodal efficiency of hub regions in the salience network is associated with general intelligence. Intelligence 60, 10–25 (2016)

    Article  Google Scholar 

  17. Zhong, N., Yau, S.S., Ma, J., et al.: Brain informatics-based big data and the wisdom web of things. IEEE Intell. Syst. 30(5), 2–7 (2015)

    Article  Google Scholar 

  18. Zhong, N., Chen, J.: Constructing a new-style conceptual model of brain data for systematic brain informatics. IEEE Trans. Knowl. Data Eng. 24(12), 2127–2142 (2012)

    Article  Google Scholar 

  19. Zhong, N., Ma, J.H., Huang, R.H., et al.: Research challenges and perspectives on wisdom web of things (W2T). J. Supercomput. 64(3), 862–882 (2013)

    Article  Google Scholar 

  20. Chen, J., Ma, J., Zhong, N., et al.: Waas: wisdom as a service. IEEE Intell. Syst. 29(6), 40–47 (2014)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (61420106005), the Science and Technology Project of Beijing Municipal Commission of Education (KM201710005026), and the JSPS Grants-in-Aid for Scientific Research of Japan (19K12123).

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Xiaofei Zhang, Ruohao Liu & Ning Zhong

  2. School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212003, China

    Xiaofei Zhang

  3. Department of Psychology, Beijing Forestry University, Beijing, 100083, China

    Yang Yang

  4. Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, 100124, China

    Xiaofei Zhang, Yang Yang, Ruohao Liu & Ning Zhong

  5. Beijing Key Laboratory of MRI and Brain Informatics, Beijing, 100124, China

    Xiaofei Zhang, Yang Yang, Ruohao Liu & Ning Zhong

  6. Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Gunma, 371-0816, Japan

    Yang Yang & Ning Zhong

Authors
  1. Xiaofei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruohao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ning Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ning Zhong .

Editor information

Editors and Affiliations

  1. Capital Normal University, Beijing, China

    Peipeng Liang

  2. York University, Toronto, ON, Canada

    Vinod Goel

  3. Shanghai University of Traditional Chinese Medicine, Shanghai, China

    Chunlei Shan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, X., Yang, Y., Liu, R., Zhong, N. (2019). Route Adjustment of Functional Brain Network in Mental Arithmetic Using Task-Evoked FMRI. In: Liang, P., Goel, V., Shan, C. (eds) Brain Informatics. BI 2019. Lecture Notes in Computer Science(), vol 11976. Springer, Cham. https://doi.org/10.1007/978-3-030-37078-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37078-7_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37077-0

  • Online ISBN: 978-3-030-37078-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics