Abstract
The human connectome is constructed by translating neuron activities into a complex network with nodes and edges. Although the topology of such networks is well studied, the formation rules and dynamics are not fully understood. It is challenging to develop a generative model for large scale dynamic complex networks due to the computational obstacle and nontrivial network structure. To study the node-based and subject-based network dynamics in resting-state brain, we divide the brain scan session into several sliding windows and generate a network for each segment. Then, an action-based model generator, which generates edges according to a topology manipulating actions, is fitted to the network series. This model, presumably as the first applicable approach for synthesizing the brain network dynamics, is shown capable of synthesizing a network series of the action-based model. Also, the estimated parameters shows that the actions of brain regions are related to their functionality.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Achard, S., Salvador, R., Whitcher, B., Suckling, J., Bullmore, E.D.: A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J. Neurosci. 26(1), 63–72 (2006)
Alexander-Bloch, A., Lambiotte, R., Roberts, B., Giedd, J., Gogtay, N., Bullmore, E.: The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia. Neuroimage 59(4), 3889–3900 (2012)
Amico, E., Goñi, J.: Maximizing the individual fingerprints of human functional connectomes through decomposition into brain connectivity modes. arXiv preprint arXiv:1707.02365 (2017)
Arora, V., Ventresca, M.: Action-based modeling of complex networks. Sci. Rep. 7 (2017)
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. Proc. Natl. Acad. Sci. U.S.A. 108, 7641–7646 (2010)
Betzel, R.F., Avena-Koenigsberger, A., Goñi, J., He, Y., de Reus, M.A., Griffa, A., Vértes, P.E., Mišic, B., Thiran, J.P., Hagmann, P., van den Heuvel, M., Zuo, X.N., Bullmore, E.T., Sporns, O.: Generative models of the human connectome. NeuroImage 124, 1054–1064. https://doi.org/10.1016/j.neuroimage.2015.09.041 (2016)
Feinberg, D.A., Moeller, S., Smith, S.M., Auerbach, E., Ramanna, S., Glasser, M.F., Miller, K.L., Ugurbil, K., Yacoub, E.: Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PloS one 5(12), e15,710 (2010)
Fischl, B., Van Der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D.H., Busa, E., Seidman, L.J., Goldstein, J., Kennedy, D.: Others: Automatically parcellating the human cerebral cortex. Cereb. Cortex 14(1), 11–22 (2004)
Glasser, M.F., Coalson, T.S., Robinson, E.C., Hacker, C.D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C.F., Jenkinson, M.: Others: A multi-modal parcellation of human cerebral cortex. Nature 536(7615), 171–178 (2016)
Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., Coalson, T.S., Fischl, B., Andersson, J.L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J.R.: Others: The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013)
Guo, F., Hanneke, S., Fu, W., Xing, E.P.: Recovering temporally rewiring networks: A model-based approach. In: Proceedings of the 24th International Conference on Machine Learning, pp. 321–328. ACM (2007)
Hajnal, J.V., Myers, R., Oatridge, A., Schwieso, J.E., Young, I.R., Bydder, G.M.: Artifacts due to stimulus correlated motion in functional imaging of the brain. Magn. Reson. Med. 31(3), 283–291 (1994)
Henson, R.N.A., Buechel, C., Josephs, O., Friston, K.J.: The slice-timing problem in event-related fMRI. NeuroImage 9, 125 (1999)
Holden, M.: A review of geometric transformations for nonrigid body registration. IEEE Trans. Med. Imaging 27(1), 111–128 (2008)
Jenkinson, M., Bannister, P., Brady, M., Smith, S.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17(2), 825–841 (2002)
Jung, T.P., Makeig, S., McKeown, M.J., Bell, A.J., Lee, T.W., Sejnowski, T.J.: Imaging brain dynamics using independent component analysis. Proc. IEEE 89(7), 1107–1122 (2001)
Leonardi, N., Richiardi, J., Gschwind, M., Simioni, S., Annoni, J.M., Schluep, M., Vuilleumier, P., Van De Ville, D.: Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest. NeuroImage 83, 937–950. https://doi.org/10.1016/j.neuroimage.2013.07.019 (2013)
Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., Oeltermann, A.: Neurophysiological investigation of the basis of the fMRI signal. Nature 412(6843), 150 (2001)
Micheloyannis, S., Pachou, E., Stam, C.J., Breakspear, M., Bitsios, P., Vourkas, M., Erimaki, S., Zervakis, M.: Small-world networks and disturbed functional connectivity in schizophrenia. Schizophr. Res. 87(1), 60–66 (2006)
Moeller, S., Yacoub, E., Olman, C.A., Auerbach, E., Strupp, J., Harel, N., Ugurbil, K.: Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn. Reson. Med. 63(5), 1144–1153 (2010)
Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)
Nieto-Castanon, A., Ghosh, S.S., Tourville, J.A., Guenther, F.H.: Region of interest based analysis of functional imaging data. Neuroimage 19(4), 1303–1316 (2003)
Poldrack, R.A.: Region of interest analysis for fMRI. Soc. Cogn. Affect. Neurosci. 2(1), 67–70 (2007)
Power, J.D., Mitra, A., Laumann, T.O., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E.: Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84, 320–341 (2014)
Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003 (2010)
Rubinov, M., Ypma, R.J.F., Watson, C., Bullmore, E.T.: Wiring cost and topological participation of the mouse brain connectome. Proc. Natl. Acad. Sci. 112(32), 10032–10037 (2015)
Serrano, M.Á., Boguná, M., Vespignani, A.: Extracting the multiscale backbone of complex weighted networks. Proc. Natl. Acad. Sci. 106(16), 6483–6488 (2009)
Setsompop, K., Gagoski, B.A., Polimeni, J.R., Witzel, T., Wedeen, V.J., Wald, L.L.: Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn. Reson. Med. 67(5), 1210–1224 (2012)
Simpson, S.L., Hayasaka, S., Laurienti, P.J.: Exponential random graph modeling for complex brain networks. PLoS ONE 6(5). https://doi.org/10.1371/journal.pone.0020039 (2011)
Smith, S.M., Miller, K.L., Salimi-Khorshidi, G., Webster, M., Beckmann, C.F., Nichols, T.E., Ramsey, J.D., Woolrich, M.W.: Network modelling methods for FMRI. Neuroimage 54(2), 875–891 (2011)
Sporns, O., Tononi, G., Edelman, G.M.: Connectivity and complexity: the relationship between neuroanatomy and brain dynamics. Neural Networks 13(8), 909–922 (2000)
Tononi, G., Sporns, O., Edelman, G.M.: A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc. Natl. Acad. Sci. 91(11), 5033–5037 (1994)
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E.J., Yacoub, E., Ugurbil, K., Consortium, W.M.H.C.P.: Others: The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)
Vértes, P.E., Alexander-Bloch, A.F., Gogtay, N., Giedd, J.N., Rapoport, J.L., Bullmore, E.T.: Simple models of human brain functional networks. Proc. Natl. Acad. Sci. 109(15), 5868–5873 (2012)
Xu, J., Moeller, S., Strupp, J., Auerbach, E.J., Chen, L., Feinberg, D.A., Ugurbil, K., Yacoub, E.: Highly accelerated whole brain imaging using aligned-blipped-controlled-aliasing multiband EPI. In: Proceedings of the 20th Annual Meeting of ISMRM, vol. 2306 (2012)
Yeo, B.T.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., Roffman, J.L., Smoller, J.W., Zöllei, L., Polimeni, J.R.: Others: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106(3), 1125–1165 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Guo, D., Arora, V., Amico, E., Goñi, J., Ventresca, M. (2018). Dynamic Generative Model of the Human Brain in Resting-State. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_103
Download citation
DOI: https://doi.org/10.1007/978-3-319-72150-7_103
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72149-1
Online ISBN: 978-3-319-72150-7
eBook Packages: EngineeringEngineering (R0)