Abstract
Network science has widely studied the properties of brain networks. Recent work has observed a global back-to-front pattern of information flow for higher frequency bands in magnetoencephalography data. However, the effective connectivity at a local level remains yet to be analyzed. On a local level, the building blocks of all networks are motifs. In this study, we exploit the measure of dPTE to analyze motifs of the estimated effective connectivity networks. We find that some 3- and 4-motifs, the bidirectional two-hop path and its extended 4-node versions, are significantly overexpressed in the analyzed networks in comparison with random networks. With a recently developed motif-based clustering algorithm we separate the effective connectivity network in two main clusters which reveal its higher-order organization with a strong information flow between posterior hubs and anterior regions.
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References
Aertsen, A., Gerstein, G., Habib, M., Palm, G.: Dynamics of neuronal firing correlation: modulation of” effective connectivity”. Journal of Neurophysiology 61(5), 900-917 (1989)
Battaglia, D., Witt, A., Wolf, F., Geisel, T.: Dynamic effective connectivity of inter-areal brain circuits. PLoS Comput Biol 8(3), e1002,438 (2012)
Battiston, F., Nicosia, V., Chavez, M., Latora, V.: Multilayer motif analysis of brain networks. arXiv preprint arXiv:1606.09115 (2016)
Benson, A.R., Gleich, D.F., Leskovec, J.: Higher-order organization of complex networks. Science 353(6295), 163-166 (2016)
Deng, B., Deng, Y., Yu, H., Guo, X., Wang, J.: Dependence of inter-neuronal effective connectivity on synchrony dynamics in neuronal network motifs. Chaos, Solitons & Fractals 82, 48-59 (2016)
Friedman, E.J., Young, K., Tremper, G., Liang, J., Landsberg, A.S., Schuff, N., Initiative, A.D.N., et al.: Directed network motifs in alzheimer’s disease and mild cognitive impairment. PloS One 10(4), e0124,453 (2015)
Friston, K.J.: Functional and effective connectivity in neuroimaging: a synthesis. Human Brain Mapping 2(1-2), 56-78 (1994)
Gong, G., He, Y., Concha, L., Lebel, C., Gross, D.W., Evans, A.C., Beaulieu, C.: Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cerebral Cortex 19(3), 524-536 (2009)
Hillebrand, A., Barnes, G.R., Bosboom, J.L., Berendse, H.W., Stam, C.J.: Frequency- dependent functional connectivity within resting-state networks: an atlas-based meg beam- former solution. Neurolmage 59(4), 3909-3921 (2012)
Hillebrand, A., Tewarie, P., van Dellen, E., Yu, M., Carbo, E.W., Douw, L., Gouw, A.A., van Straaten, E.C., Stam, C.J.: Direction of information flow in large-scale resting-state networks is frequency-dependent. Proceedings of the National Academy of Sciences 113(14), 3867-3872 (2016)
Honey, C.J., Kotter, R., Breakspear, M., Sporns, O.: Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences 104(24), 10,240-10,245 (2007)
Jensen, P., Morini, M., Marton, K., Venturini, T., Vespignani, A., Jacomy, M., Cointet, J.P., Merckle, P., Fleury, E.: Detecting global bridges in networks. Journal of Complex Networks 4,319-329 (2016)
Kashtan, N., Itzkovitz, S., Milo, R., Alon, U.: Mfinder tool guide. Department of Molecular Cell Biology and Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot Israel, Tech Rep (2002)
Leskovec, J., Sosič, R.: Snap: A general-purpose network analysis and graph-mining library. ACM Transactions on Intelligent Systems and Technology (TIST) 8(1), 1 (2016)
Lobier, M., Siebenhuhner, F., Palva, S., Palva, J.M.: Phase transfer entropy: a novel phase- based measure for directed connectivity in networks coupled by oscillatory interactions. NeuroImage 85, 853-872 (2014)
Milo, R., Kashtan, N., Itzkovitz, S., Newman, M.E., Alon, U.: Uniform generation of random graphs with arbitrary degree sequences. arXiv preprint cond-mat/0312028 106, 1-4 (2003)
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824-827 (2002)
Rosenblum, M., Pikovsky, A., Kurths, J., Schäfer, C., Tass, P.A.: Phase synchronization: from theory to data analysis. Handbook of Biological Physics 4, 279-321 (2001)
Schreiber, T.: Measuring information transfer. Physical Review Letters 85(2), 461 (2000)
Sporns, O., Chialvo, D.R., Kaiser, M., Hilgetag, C.C.: Organization, development and function of complex brain networks. Trends in Cognitive Sciences 8(9), 418-425 (2004)
Sporns, O., Kötter, R.: Motifs in brain networks. PLoS Biol 2(11), e369 (2004)
Stam, C.J., Van Straaten, E.: The organization of physiological brain networks. Clinical Neurophysiology 123(6), 1067-1087 (2012)
Tononi, G., Edelman, G.M., Sporns, O.: Complexity and coherency: integrating information in the brain. Trends in Cognitive Sciences 2(12), 474-484 (1998)
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the mni mri single-subject brain. NeuroImage 15(1), 273-289 (2002)
Van Mieghem, P.: Graph Spectra for Complex Networks. Cambridge University Press (2011)
Zhigulin, V.P.: Dynamical motifs: building blocks of complex dynamics in sparsely connected random networks. Physical Review Letters 92(23), 238,701 (2004)
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Meier, J., Märtens, M., Hillebrand, A., Tewarie, P., Van Mieghem, P. (2017). Motif-Based Analysis of Effective Connectivity in Brain Networks. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_54
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DOI: https://doi.org/10.1007/978-3-319-50901-3_54
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