Skip to main content
SpringerLink
Log in
Book cover

International Conference on Image and Graphics

ICIG 2021: Image and Graphics pp 18–29Cite as

Brain Connectivity: Exploring from a High-Level Topological Perspective

  • Conference paper
  • First Online:

  • 1670 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12889))

Abstract

How invariant structural architecture of brain coupling with variant functionality is still unclear in neuroscience. The previous exploration of relationships between large-scale structural and functional brain networks mainly focused on whole or partial statistical correlation, ignoring network context information, such as network topology structure. Here we applied a network representation learning approach to create high-order representations of structural or functional networks while preserving network context information for studying the function-structure coupling of the brain at topological subnetwork levels. We found that the structural and functional network obtained from the network representation learning method was more stable and more tightly coupled than those from the conventional correlation method, primarily distributed in high-order cognitive networks. Application on schizophrenia patients showed decoupling on the default-mode network, dorsal attention network, executive control network, and salience network, as well as the over-coupling on the sensorimotor network, compared with healthy controls. Overall, network representation learning can more effectively capture the higher-order coupling between brain structure and function and provides a good technical means for us to study mental illness.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
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

Learn about institutional subscriptions

References

  1. Freemon, F.R.: Histology of the nervous system of man and vertebrates. JAMA J. Am. Med. Assoc. 275, 493 (1996)

    Google Scholar 

  2. Park, H.J., Friston, K.: Structural and functional brain networks: from connections to cognition. Science 342, 1289 (2013)

    Google Scholar 

  3. Bullmore, E., Sporns, O.: The economy of brain network organization. Nat. Rev. Neurosci. 13(5), 336–349 (2012)

    Article  Google Scholar 

  4. Honey, C.J., et al.: Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl Acad. Sci. USA 104 (2007). 6 pages

    Google Scholar 

  5. Edelman, G.M., Gally, J.A: Degeneracy and complexity in biological systems. Proc. Natl. Acad. Sci. USA 98(24), 13763–13768 (2001)

    Google Scholar 

  6. Friston, K.J., Price, C.J.: Degeneracy and redundancy in cognitive anatomy. Trend Cogn. 7, 151–152 (2003)

    Article  Google Scholar 

  7. Hermundstad, A.M., et al.: Structural foundations of resting-state and task-based functional connectivity in the human brain. Proc. Natl. Acad. Sci. 110(15), 6169–6174 (2013)

    Article  Google Scholar 

  8. Ekman, M., et al.: Predicting errors from reconfiguration patterns in human brain networks. Proc. Natl. Acad. Sci. 109(41), 16714–16719 (2012)

    Article  Google Scholar 

  9. Goñi, J., et al.: Resting-brain functional connectivity predicted by analytic measures of network communication. Proc. Natl. Acad. Sci. 111(2), 833–838 (2014)

    Article  Google Scholar 

  10. Kuceyeski, A., et al.: The application of a mathematical model linking structural and functional connectomes in severe brain injury. Neuroimage Clin. 11, 635–647 (2016)

    Article  Google Scholar 

  11. Abdelnour, F., Voss, H.U., Raj, A.J.N.: Network diffusion accurately models the relationship between structural and functional brain connectivity networks. Neuroimage 90, 335–347 (2014)

    Article  Google Scholar 

  12. Gilson, M., et al.: Framework based on communicability and flow to analyze complex network dynamics. Phys. Rev. E 97(5), 052301 (2018)

    Google Scholar 

  13. Robinson, P.A.J.P.R.E., Interrelating anatomical, effective, and functional brain connectivity using propagators and neural field theory. Phys. Rev. E 85(1), 011912 (2012)

    Google Scholar 

  14. Scarselli, F., et al.: Computational capabilities of graph neural networks. IEEE Trans. Neural Netw. 20(1), 81–102 (2008)

    Article  Google Scholar 

  15. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2014)

    Google Scholar 

  16. Zhang, D., et al.: Network representation learning: a survey. IEEE Trans. Big Data 6(1), 3–28 (2018)

    Article  Google Scholar 

  17. Chen, Y., Lu, H., Qiu, J., Wang, L.: A tutorial of graph representation. In: Sun, X., Pan, Z., Bertino, E. (eds.) ICAIS 2019. LNCS, vol. 11632, pp. 368–378. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24274-9_33

    Chapter  Google Scholar 

  18. Dutta, A., et al.: Hierarchical stochastic graphlet embedding for graph-based pattern recognition, pp. 1–18 (2019)

    Google Scholar 

  19. Adhikari, B., Zhang, Y., Ramakrishnan, N., Prakash, B.A.: Sub2vec: feature learning for subgraphs. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10938, pp. 170–182. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93037-4_14

    Chapter  Google Scholar 

  20. Zang, C., Cui, P., Faloutsos, C.: Beyond sigmoids: The nettide model for social network growth, and its applications. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)

    Google Scholar 

  21. Ying, R., et al.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2018)

    Google Scholar 

  22. Gottlieb, A., et al.: PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol. Syst. Biol. 7(1), 496 (2011)

    Google Scholar 

  23. Li, X., Dvornek, N.C., Zhou, Y., Zhuang, J., Ventola, P., Duncan, J.S.: Graph neural network for interpreting task-fmri biomarkers. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 485–493. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32254-0_54

    Chapter  Google Scholar 

  24. Cui, Q., et al.: Dynamic changes of amplitude of low-frequency fluctuations in patients with generalized anxiety disorder. Hum. Brain Map. 4(16), 1667–1676 (2019)

    Google Scholar 

  25. Han, S., Huang, W., Zhang, Y., Zhao, J., Chen, H.: Recognition of early-onset schizophrenia using deep-learning method. Appl. Inform. 4(1), 1–6 (2017). https://doi.org/10.1186/s40535-017-0044-3

    Article  Google Scholar 

  26. Fan, L., et al.: The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb. Cortex 26, 3508–3526 (2016)

    Article  Google Scholar 

  27. Shrout, P.E., Fleiss, J.L.: Intraclass correlations: uses in assessing rater reliability. Physcol. Bull. 86(2), 420 (1979)

    Google Scholar 

  28. Shehzad, Z., et al.: The resting brain: unconstrained yet reliable. Cereb. Cortex 19, 2209–2229 (2009)

    Article  Google Scholar 

  29. Zuo, X.N., et al.: Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach. NeuroImage 49, 2163–2177 (2010)

    Article  Google Scholar 

  30. Richiardi, J., et al.: Machine learning with brain graphs: predictive modeling approaches for functional imaging in systems neuroscience. IEEE Signal Process. Mag. 30(3), 58–70 (2013)

    Article  Google Scholar 

  31. Rosenthal, G., et al.: Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes. Nat. Commun. 9(1), 1–12 (2018)

    Article  Google Scholar 

  32. Amunts, K., et al.: Brodmann’s areas 17 and 18 brought into stereotaxic space—where and how variable? Neuroimage 11(1), 66–84 (2000)

    Google Scholar 

  33. Bürgel, U., et al.: White matter fiber tracts of the human brain: three-dimensional mapping at microscopic resolution, topography and intersubject variability. Neuroimage 29(4), 1092–1105 (2006)

    Article  Google Scholar 

  34. Rypma, B., D’Esposito, M.J.: The roles of prefrontal brain regions in components of working memory: effects of memory load and individual differences. Proc. Natl. Acad. Sci. 96(11), 6558–6563 (1999)

    Article  Google Scholar 

  35. Newman, S.D., et al.: Frontal and parietal participation in problem solving in the Tower of London: fMRI and computational modeling of planning and high-level perception. Proc. Natl. Acad. 41(12), 1668–1682 (2003)

    Google Scholar 

  36. Buckner, R.L., et al.: The organization of the human cerebellum estimated by intrinsic functional connectivity. J. Neurophysiol. 106(5), 2322–2345 (2011)

    Article  Google Scholar 

  37. Honey, C.J., et al.: Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl. Acad. Sci. 106, 2035–2040 (2009)

    Article  Google Scholar 

  38. Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009)

    Article  Google Scholar 

  39. Falkenberg, I., et al.: Failure to deactivate medial prefrontal cortex in people at high risk for psychosis. Eur. Physc. 30, 633–640 (2015)

    Google Scholar 

  40. Salgado-Pineda, P., et al.: Correlated structural and functional brain abnormalities in the default mode network in schizophrenia patients. Schizophrenia Res. 125(2–3), 101–109 (2011)

    Article  Google Scholar 

  41. Wang, C., et al.: Disrupted salience network functional connectivity and white-matter microstructure in persons at risk for psychosis: findings from the LYRIKS study. Physchol. Med. 46(13), 2771–2783 (2016)

    Google Scholar 

  42. Kai, W., et al.: Selective impairment of attentional networks of orienting and executive control in schizophrenia. Schizophrenia Res. 78(2–3), 235–241 (2005)

    Google Scholar 

  43. Kaufmann, T., et al.: Disintegration of sensorimotor brain networks in schizophrenia. ScienceDirect. 33 (2016)

    Google Scholar 

Download references

Acknowledgments

This study was supported by the Key Project of Research and Development of Ministry of Science and Technology (2018AAA0100705), and the Natural Science Foundation of China (61533006, U1808204, 62036003, 81771919).

Author information

Authors and Affiliations

  1. The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    Wei Sheng, Liang Li, Shaoqiang Han, Yunshuang Fan, Chong Wang, Qin Tang, Yuyan Chen & Huafu Chen

  2. MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China

    Wei Sheng, Liang Li, Shaoqiang Han, Yunshuang Fan, Chong Wang, Qin Tang, Yuyan Chen, Qian Cui & Huafu Chen

Authors
  1. Wei Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shaoqiang Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yunshuang Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qin Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yuyan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Qian Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Huafu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huafu Chen .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Tsinghua University, Beijing, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Beijing, China

    Kun Xu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sheng, W. et al. (2021). Brain Connectivity: Exploring from a High-Level Topological Perspective. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12889. Springer, Cham. https://doi.org/10.1007/978-3-030-87358-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87358-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87357-8

  • Online ISBN: 978-3-030-87358-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation