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.
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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).
Wang, M., Wang, L.: Localization of the brain calculation function area with MRI. Chin. Sci. Bull. 46(22), 1889–1892 (2001)Google Scholar
Liu, J., Zhang, H., Chen, C., et al.: The neural circuits for arithmetic principles. Neuroimage 147, 432–446 (2016). (Complete)CrossRefGoogle Scholar
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)CrossRefGoogle Scholar
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)CrossRefGoogle Scholar
Dehaene, S., Cohen, L.: Cerebral pathways for calculation: double dissociation between rote verbal and quantitative knowledge of arithmetic. Cortex 33(2), 219–250 (1997)CrossRefGoogle Scholar
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
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)CrossRefGoogle Scholar
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)CrossRefGoogle Scholar
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)CrossRefGoogle Scholar
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)CrossRefGoogle Scholar
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)CrossRefGoogle Scholar
Tanaka, S., Kirino, E.: Dynamic reconfiguration of the supplementary motor area network during imagined music performance. Front. Hum. Neurosci. 11, 1–11 (2017)CrossRefGoogle Scholar
Dosenbach, N.U.F., Nardos, B., Cohen, A.L., et al.: Prediction of individual brain maturity using fMRI. Science 329(5997), 1358–1361 (2010)CrossRefGoogle Scholar
Latora, V., Marchiori, M.: Efficient behavior of small-world networks. Phys. Rev. Lett. 87(19), 198701–198704 (2001)CrossRefGoogle Scholar
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)CrossRefGoogle Scholar
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)CrossRefGoogle Scholar
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)CrossRefGoogle Scholar
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)CrossRefGoogle Scholar
Chen, J., Ma, J., Zhong, N., et al.: Waas: wisdom as a service. IEEE Intell. Syst. 29(6), 40–47 (2014)CrossRefGoogle Scholar